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NAVAL WEAPONS STATIONCONCORD EXPORT CAPABILITY
A SIMULATION MODEL.
David C. Fountain
7
NAVAL POSTGRADUATE SCHOOL
Monterey, California
THESISNAVAL WEAPONS STATION, CONCORD
EXPORT CAPABILITY:A SIMULATION MODEL
by
David C. Fountain
June 1977
Thesis Advisor Robert W. Sagehorn
Approved for public release; distribution unlimited
T 179878
SECURITY CLASSIFICATION OF THIS PAGE (Whit Dmlm Entmrad)
REPORT DOCUMENTATION PAGE READ INSTRUCTIONSBEFORE COMPLETING FORM
1. REPORT NUMBER 2. GOVT ACCESSION NO 3. RECIPIENT'S CATALOG NUMBER
4. T\TLE (Td Submit)
NAVAL WEAPONS STATION, CONCORD EXPORTCAPABILITY: A SIMULATION MODEL
5. TYPE OF REPORT & PERIOD COVERED
Master's Thesislimp 1977
6. PERFORMING ORG. REPORT NUMBER
7. AUTHORf«;
David C. Fountain
8. CONTRACT OR GRANT NUMBERS.)
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Naval Postgraduate SchoolMonterey, California 93 9M-0
10. PROGRAM ELEMENT. PROJECT, TASKAREA a WORK UNIT NUMBERS
II. CONTROLLING OFFICE NAME AND ADDRESS
Naval Postgraduate SchoolMonterey, California 93940
12. REPORT DATE
June 197713. NUMBER OF PAGES
14314. MONITORING AGENCY NAME b. AOORESSff/ dlltatanx (torn Controlling OHIcm)
Naval Postgraduate SchoolMonterey, California 93940
IS. SECURITY CLASS, (ol ihla rmpon)
Unclassified
(Si. DECLASSIFI CATION/ DOWNGRADINGSCHEDULE
16. DISTRIBUTION ST ATEMEN T (ol thla Rapott)
Approved for public release; distribution unlimited
17. DISTRIBUTION STATEMENT (ol tha mbatrmct anfrad In Block 30, II dlllmranl /ram Rapott)
18. SUPPLEMENTARY NOTES
19. KEY WORDS Conrinut on rmrmram aid* It nmcmmamry and Idantlty by block numbar)
SLmulation, Water ports, Transportation, Export capability,Ammunition, TRANSIM
20. ABSTRACT (Conllnxim on ravaraa aid* II nacaaamrr and Identity by block mmtbat)
This paper represents an analysis of the ocean ammunitionexport function of the Naval Weapons Station at Concord,California. The analysis, utilizing the general purposetransportation simulator TRANSIM, defines the system and adaptsit to TRANSIM notation. The objective of the project was toestablish a workable model and use it to predict the maximumexport capability of NWS, Concord. Five simulations were
do ,;FORMAN 73 1473 EDITION OF 1 NOV 68 IS OBSOLETE
S/N 0102-014-6601I
SECURITY CLASSIFICATION OF THIS PAGe (Whmn Dmlm Kntmrmd)
fctuWTv CL A»SI*IC A Tiow o* TmiS B»Gtc*,>i" r»»r« Ent«Md
completed; three replicated the historical data to establishthe model's workability and the other two indicated increasedcapabilities possible.
DD Form 14731 Jan 73 —
5/ N 0102-014-6601 security CLASiiricATio* o' THI * **Gerw>»«« o««« «"(•<••<*>
Approved for public release; distribution unlimited
NAVAL WEAPONS STATION, CONCORD EXPORT CAPABILITYA SIMULATION MODEL
by
David C. FountainCaptain, United States Army
3. S. , university of Washington, 1967
Submitted in partial fulfillment of therequirements for the degree of
MASTER 0? SCIENCE IN MANAGEMENT
from the
NAVAL POSTGRADUATE SCHOOL
June 1977
WtttYWOX LIBRARY
NAVAl POSTGRADUATE SCHOOL
ABSTRACT
This paper represents an analysis of the ocean
ammunition export function of the Naval Weapons
Station at Concord, California. The analysis,
utilizing the general purpose transportation simulator
rSANSIM, defines the system and adapts it to TRANSIM
notation. Tha objective of the project *as to
establish a workable model and ase it to predict the
maximum export capability of MWS, Concord. Five
simulations were completed; three replicated the
historical data to establish the model's workability
and the other two indicated increased capabilities
possible.
flMM ! KM'
TABLE OF CONTENTS
I. BACK GROUND 11
II. INTRODUCTION 13
III. INTENT OF THE PROJECT 17
IV. ASSUMPTIONS 19
V. i::E NAVAL WEAPONS STATION, CONCORD TIDAL AREA.... 21
A. PLANNING CONSPIRATIONS 21
3. VAN AND RAILCAR MOVEMENT PROCEDURES 22
C. AREA CAPABILITIES 23
D. OPERATING TIMES 26
71. THE T3ANSIM VIS* OF NNS, CONCDRD 23
A. SYSTEM BOUNDARIES 28
B. cASIC ELEMENTS OF THE MODEL 29
C. ALTERNATIVE APPROACHES 31
D. CAIA ELEMENTS 3 3
1. Capacity Elsaants 33
2. Time Elements 3 4
E. SYSTEM SUMMARY 37
711. THE DERIVATION OF THE PARAMETERS 3 9
A. PARAMETERS HELD CONSTANT 39
E. PARAMETERS TO 3S VARIED IN SIMULATIONS 39
1. Distribution of Vessel Classes 41
2. 7essel Class Berth Times 4 3
3. Vans and Railcars per Vessel Class.. 47
4. Unloaling Times of Vans and Railcars 48
5. Int erarri va 1 Time for Trains 49
6. Interarrival Time of Vessels 49
7. In tec arrival limes for Trucks 5
3. Number of Railcars per Trains 51
VIII. RESULTS 52
IX. CONCLUSIONS 57
X. RECOHMENDATION 59
Appendix A: OPERATING RULES OF THE MODEL 60
Appendix E: SAMPLE OPERATING RULES b4
Appendix C: INITIAL SITUATION IN THE MODEL 72
Appendix D: ITERATIVE PROCESS OF TBAMSIfl 74
Appeniix E: RESULTS OF SIMULATION RUN 1 32
Appendix F: RESULTS OF SIMULATION RUN II 93
Appendix G: RESULTS OF SIMULATION RUN III 104
Appendix H: RESULTS OF SIMULATION RUN IV 115
Appendix I: RESULTS OF SIMULATION RUN V 126
Appendix J: VESSEL EXPORT TONNAGES IN CLASS GROUPINGS
FOR JANUARY THROUGH JUNE 196 3 137
Appendix X: THE TRIANGULAR DISTRIBUTION 139
LIST OF REFERENCES 140
BIBLIOGRAPHY 141
INITIAL DISTRIBUTION LIST 143
LIST OF FIGURESy
LIST 0? FIGURES
1. Generalized Systeoi 14
2. Hap of N'rfS, Concord Tidal Area 25
3. Detailed Schematic 30
4. Vessel Class Distribution... 40
5. Berth Ii;nes by Vessel Class 42
6. Actual vs. Simulated Tons per Vessel Class 44
7. Actual and Theoretical Numbers a: Vans and Railcurs
per Vessel Class 4 6
8. Composite Results 55
9. Actual Numbers of Vessels in Each Class for Each
Simulation 56
10. Sample Operating Pules Input 65
11. Sample Operating Rules Input (cont.) 56
12. Sample Operating Rules Input (cont .
)
67
13. Sample Operating Rules Input (cont .) 68
14. Sample Operating Rules Input (cont.) 69
15. Sample Operating Rules Input (cont.) 70
16. Sample Operating Rules Input (cont.) 71
17. Initial Data in the Model 73
13. Sample IRANSIH Iterations 76
19. Sample IRAN SIM Iterations (cont.) 77
20. Sample TRANSIT Iterations (com.) 73
21. Sample 1RANSIM Iterations (cor.t.) 79
22. Sample TRANSI.1 Iterations (cor.t.) 80
23. Sample TRANSIT Iterations (cor.t.) 51
24. Occupancy of All Berths (Sim. I) 3'J
25. Total Truck Cargo Exported (Sim. I) 85
26. Total Rail Cargo Exported (Sim. I) ^6
27. Ship Time at All Berths (Sim. I) 37
23. Ship Delay Times (Sim. I) 38
29. Number of Vans in Storage Ar^as (Sim. I) 39
30. Storage Times of Vans (Sim. I) 90
31. Number cf Trains at Storage Area (Sim. I) 91
32. Storage Times of Ruilcars (Sim. I) 92
33. Occupancy of All Berths (Sim. II) 35
34. Total Truck Cargo Exported (Sim. II) 36
35. Total Rail Cargo Exported (Sim. II) 97
3o. Ship TiTe at All Berths (Sim. II) 98
37. Ship Delay Times (Sim. II) 99
33. Number cf Vans in Storage Areas (Sim. II) 100
39. Storage Times of Vans (Sim. II) 10 1
uo. Numo^r cf Trains at Storage Area (Sim. IT) 102
41. Storage Times of Railcars (Sim. II) .....103
42. Occapancy of All Berths (Sim. Ill) 106
43. Total Truck Bargo Exported (Sim. Ill) 107
44. Total Rail Cargo Exported (Sim. Ill) 108
45. Ship Tine at All Berths (Sim. Ill) 109
46. Ship Delay Times (Sin. Ill) 110
47. Number cf Vans in Storage Areas (Sim. Ill) 111
48. Storage Times of Vans (Sim. Ill) 112
49. Number cf Trains at Storage Area (Sim. Ill) 113
50. Storage Times of Railcars (Sim. Ill) 114
51. Occupancy of All Berths (Sim. IV) -117
52. Total Track: Cargo Exported (Sim. IV) 118
53. Total Rail Cargo Exported (Sim. IV) 119
54. Ship Tine at All Berths (Sim. IV) 120
55. Ship Delay Times (Sim. IV) ...121
56. Number cf Vans in Storage Areas (Sim. IV) 122
57. Storage Times of Vans (Sim. IV) 123
53. Number cf Trains at Storage Area (Sim. IV) 124
59. Storage Times of Railcars (Sim. IV) 125
60. Occupancy of All 3erths (Sim. V) 128
61. Total Truck Cargo Exported (Sim. V) 129
62. Total Rail Cargo Exported (Sim. V) 130
63. Ship Tiiie at All Bertns (Sim. V) 131
64. Ship Delay Times (Sim. V) 132
o5. Number cf Vans in Storage Areas (Sim. V) 133
66. Storage Times of Vans (Sim. V) 134
67. Number cf Trains at Storage Area (Sim. V) 135
68. Storage Times of Railcars (Sim. V) 136
69. Vessel Export Tonnages in Class Groupings for
January through June 1963 133
70. A Triangular Distribution 139
10
I. BACKGROUND
The Naval Weapons Station (NWS), Concord is located in
California about thirty-five miles northeast of San
Francisco and seventy miles southwest of Sacramento.
Although the station comprises three activities at sites
near Concord, Pittsburg, and Vallejo, this study concerned
itself with only the Concord activity.
Of the -many missions assigned to NWS, Concord, only two
have relevance to this study. The two missions that related
directly to the sxport capability are the following:
1.andoperat
and ior d nanordnan
terainin sue
2.ser vieShios
Ccert cansing f
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.
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al fap crt
es i(AEs)
at esniporceR eca ai
itemteri
oeci 1 iof tid en shorn
ames :
eimusalratyne
upeo
munitions, storage,nt f aciiit v to
maintenancesupport the
vs. renovate, maintain, store,aition explosives, expendableand/or weapons and technical
•
te and maintain an oceanto transship ordnance materialArmed Forces. .
.
logistics au i port terminalcort of Pacific Fleet Ammunitionorted at the Station... "1-1].
Although NWS, Concord is a Wavy owned an i operated
installation, by its rather unique position as the only
major West Coast ammunition ocean terminal, it is required
to support the other services and is thereoy critical to the
Department of Defense. The facility is strictly Naval but
it does report for administration and accounting purposes to
the Oakland Terminal of the Military Traffic Management
Command, the Department of Defense agency responsible for
worldwide traffic management. NWS, Concord is the
ammunition counterpart of Oakland, the primary West Coast
1 1
general cargo port. (The Oakland facility is about
twenty-five miles southwest of Concord.)
The facility at Concord is divided into the Inland Ar ?a
and the Tidal Area. The Inland Area is concerned with
research, administration, military housing, and similar
activities, while the Tidal Area is concerned with the
operational aspects of the transshipment of ammunition.
12
INTRODUCTION
The analysis of NWS, Coacor.l's capabilities was
accomplished by using as a basis the simulation model
TRANSIT. TRANSIrt is a jeneral purpose? transportation system
simulator u2veloped originally in 1366 by the Department of
Engineering, University of California at Los Angeles (UCLA)
for the Undersecretary of Transportation in the U.S.
Department of Commerce. The continuing effort of the
Project THANSIH office at UCLA is to update and simplify the
program, most recently published as TR AM SIM IV.
TRANSIH is a modular approach to a transportation system
model which can be utilized to model transportation
networks, transfer facilities, and intermodal relationships
including a Tarine/port complex. It is capable of utilizing
either deterministic or pro baoi listic input data. Specific
elements of TRANSIH as it applies to NWS, Concori are
discussed in Chapter Six.
The input of ammunition was by two modes, truck and
rail, and is recognized when the truck or the rail car
"passes through the gate" and becomes available for handling
by NWS, Concord. The ammunition is inspected, catalogued,
and placed in storage until required for loading aboard a
vessel. Empty vessels (partially loaded vessels from other
ports were considered empty vessels throughout tne analysis)
enter the system through a harbor, and exit to the harbor
after loading. A jeneral schematic 3f the system elements
is shown in Figure 1.
13
1 / I I I t I 1 I I I i
c/^
IX
i
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OJ v"—
u
U
u
g X
go:
N£
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2n
/n LU
-«—u QC
Oto
swEhCO
in
a&q
IS3
H<03
CJ3
D(-1
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14
The study considered only the export of ammunition
aboard merchant marine type cargo vessels and did not
provide for the handling of any waterborne imported cargo.
This specifically excluded ship discharge operations and
operations of the rJ. S. Navy vessels commonly referred to
as AEs f which wer« found to be low productive factors in the
historical data. Since the AEs are homeported at Concord,
this restriction may not have been completely realistic.
Therefor?, toward the end of the analysis consideration was
given to the probable presence and needs of AEs, and these
vessels are discussed as a part of the system in Chapter
Eight. However, to achieve the maximum export capability of
NWS, Concori, the AEs were considered a negative influence,
as will be explained later, and were excluded in the
building of the model.
The study was restricted in one notable area. All
computer simulations were processed with the computer at
UCLA, where the most current version of TRANSIT is stored on
tape. The complexity of the program did not allow for the
program to be transferred to another computer site without a
significant IBANSIM training program involved in addition to
possible logistical support problems. Using the computer at
UCLA involved using research funis that could only provile
for approximately fifteen runs on the computer with very
limited storage and time allocations.
The general approach of the study progressed
consecutively through the following steps:
1. Becarre familiar with NWS, Concord operation and its
facilities.
2. Gathered raw (lata from the activity.
3. Drafted a rough TRANSIT model to represent the NWS,
Concord operation.
15
4. Organized the raw data and transformed it into
usable parameters.
5. Tested the THANSIS model on the computer for
accuracy, realism, and computer efficiency, and subsequently
reworked the model and parameters until the original
situation was replicated.
6. Varied the parameters to determine limits of NWS,
Concord capability.
16
III. INTENT OF THE PROJECT
The objective of the thesis was to create a simulation
model that could predict the maximum export capability of
the NWS, Concord. One way to hypothesize the ultimate
capability of an activity ./as to find the historical maximum
output the activity and let that be th2 maximum
capability. This analysis used the historical maximum
output of 387,113 long tons in the first half of 1968
(Vietnam War period) as a basis for a simulation model,
emphasizirg high productive factors and de-emphasizing low
productive factors in the activity, to predict greater
capability. The system considered for analysis is defined,
restrictions and assumptions stated, and then the analysis
proceeds. As the study progressed some of the system
boundaries and assumptions were changed or modified and are
noted
.
The Ccncord facility loads ammunition for many ports in
tne Pacific Ocean area, often on the same vessel. Of
course, stowage of the cargo aboard a vessel requires
ordering of the cargo being loaded to insure direct access
by the unloading ports. This model has not provided
specifically for such ordering of cargo, having thought that
this function more properly belongs in the planning phases
of transportation and not the operational loading phase.
The longshoremen load what is sent to them and it is this
aspect of the Conccrd export mission that is examined.
The research indicated that often many records of data
were not available. The intent of the analysis was tc ta'<e
the best available data, both official and unofficial, ani,
17
if appropriate, apply it to the model. Host of the 1968
data was kept at Concord for historical reasons only. Not
all divisions in the Concord facility require or desire such
inf or nation . Consequently some data was from 1968, while-
other data was from 1976 and 1577, and still other lata has
never been gathered.
The TFANSIM program has a capability to analyze a system
in extensive detail. In the instance of a marine port
complex, to model movements of pallets, forklifts, dunnage,
railcars and many other small but identifiable units is
conceivable, but the expense is jreat and the time involved
is in terms of months and years. This simulation of tf*T 3,
Concord was an attempt to model the most significant
elements in the determination of export tonnige moved.
Every effort was extended to keep the model as real as
possible. As the study progressed many decisions regarding
the system boundaries and data elements centered on the
issue of realism versus model scope and efficiency. Tc keep
the scope and efficiency manageaol?, assumptions and
approximations were necessary, but they rfere always
juxtaposed against the iesire for realism to insure the most
reasomblt picduct.
This paper progresses oy defining the operational
process at N*5, Concord, then defining the TRANSIT model in
its final fcrms, followed with a derivation of the specific
data. Pinally the results are discussed ind some
conclusions offered.
1 }
IV. ASSUMPTIONS
To build the model, assumptions had to be male. First
there were broad, general assumptions which held fcr the
entire model and all systems considered. Second, there were
specific assumptions which were lirectly applicable to a
portion of the system, in element of data, or decision
rule in the model. However, specific assumptions were not
necessarily void of any influence on any other system
element cr model decision rule. They could indirectly
effect another element or rule and could thereby influence
results in Tore than one way. The specific assumptions are
listed in Chapter Six while the general assumptions are as
fellows:
1. An adequate labor force was available to maintain
maximum strengta of all possible working elements in the
port simultaneously.
2. All necessary equipment and maintenance facilities
were available to provide sufficient cargo handling
capabilities for ail possible working elements in the port
simultaneously with a nominal deadline rite allowed. The
nominal deadline rate was presumed to be whatever was
acceptable by the activity during 1963 operations.
3. .Sufficient administrative and logistic support was
available to supply the needs of operational forces.
4. No container operations were allowed except for the
occasion il deck sr.owel container whicn must be loaded with
ship's qear.
5. Ihe year 1963 was representative of a balanced
system at NWS, Concord while handling a record amount of
ammunition. A balanced system was defined as one where the
19
inputs equaled outputs, that is, no cargo was created or
destroyed within the system.
20
V. THE NAVAL WEAPONS STATION, CONCORD TIDAL A32A
Before N*"S, Concord can begin the planning for and the
processing of export cargo, higher authorities outside the
facility coordinate land movements of cargo to meet vessels
in various pcrts. Prom taese authorities, notices of vessel
arrivals, cargo loads, and destinations are received as well
as information on cargo arrival by highway and rail. Upon
receipt of this information, NWS, Concord begins planning
for the positioning of the cargo in the storage areas and
for the hindling of the cargo to provide orderly and safe
stowage of the cargo aboard the vessel.
A. PLANNING CONSIDERATIONS
The positioning of the vans is essentially void of any
restrictions since the parking area is centrally located
with respect to the berths and except: for the occasional
trailer-lead of dynamite, the van requires no bunkers or
other protection. On the other hand, the positioning of
railcars considers the separation of dynamite into specific
bunkers and the storage location with reference to the
expected vessel's berth. Specific bunkers are assignei for
dynamite only, as the need arises. The inflexibility of the
routing of railcars due to the layout of the track in the
storage area demands extensive planning for the railcar
storage location. Therefore, proper positioning of the
railcars can prove more efficient when the berth is
accessible directly from its storage location instead of
indirectly through numerous switches. Although a railcar
21
can be moved to any berth from any storage location, seme of
the track is directly accessible to only twe or four of the
berths.
Vessel berth assignments are dependent upon projected
available berths and upon cargo storage location since all
the berths have essentially the same physical
characteristics. Even though planning is done, the berth
assignment, is often changed due to a change in priorities or
delays of cargo or vessels. These changes can cause
previously developed plans to be discarded and inefficient
situations to arise.
B. VAN AND RAILCAR MOVEMENT PROCEDURES
When a truck arrives with cargo it is inspected,
received (a paperwork accounting process) , and sent to a
specific numbered location in the storage area. Often the
prime highway mover transfers the trailer to a U. S. Navy
owned and operated tractor at the receiving point, so that
the trailer is parked in the storage area by NWS, Ccncord
personnel. At other times local drayage firms, familiar
with NWS, Ccncord procedures and facilities, are allowed to
park the trailer. Once parked, the trailer remains
stationary, without tractor, until called for loading aboard
a vessel. (Since most trailers are vans as opposed to
flatbeds, van and trailer will be used interchangably
throughout this paper. Tractor will refer to the prime
mover, ana truck will indicate a combination of tractor and
van or trailer.)
When a train arrives, NWS, Concord railroad personnel
are advised, and arrange for the inspection and receiving of
the rulcars. Then the yarimaster directs switching
22
operations to segregate the cars into specific numbered
bunkers in accordance with the aforementioned plans where
they remain in storage until called for a vessel.
When notification of vessel arrival is obtained,
operations personnel mobilize the labor and equipment to
accomplish the plan-ned loading of the vessel. As specific
cargo is required for loading, notification is provided
through a central locator to either the railroad or tractor
operators tc move a particular railcar or trailer from its
storage location to a specific location on a designated
berth. Once the conveyances have been unloaded, the
railroad and tractor operators are again notified of the
movement requirements. Later, notification is given to the
owner of the trailer or raiicar that it has been unloaded
and is available again for his use. Once the vessel is
loaded with its intended cargo, the vessel is secured, and
provided aith sailing instructions.
C. AREA CAPABILITIES
The following physical characteristics and operational
limitiations of the SWS, Concord facility are noted. The
Tidal Area has three quays with two oerths each for a total
of six cerths. It is possible to simultaneously have a C-5
type vessel at each berth while retaining an ability tc work
all hatches rrcm the quay side of the vessel. Each quay has
at least two parallel bracks entering and one track exiting
the berth areas expanding to four parallel tracks in the
actual vessel berthing area. For safety, no more than
twenty-five loaded railcars are allowed on a quay at any on^
time, with no more than fifteen loaded railcars allowed at a
berth at one time.
23
There are 38 bunkers with a total capacity of 244
railcars. The bunkers are in three sizes, capable of
storing either four, eight, or twelve fifty-foot railcars.
This number of variously sized bunkers allows many storage
configurations, providing compatibility for all classes of
ammunition. The distinctions in ammunition compatibility
which could effect the rail storage capacity are minimal due
to the number of bunkers and their varying size. In
addition to the bunkers, there are two classification yards
that can be used for storage of 287 railcars providing that
the safety requirements, such as the area net explosive
weight liiitat ion and ammunition compatibility require meats,
are met. Therefore the actual railcar capacity is
approximately 531.
The van storage areas consist of two paved and marked
parking lets with aa approximate total capacity of 200 vans.
The ability tc store a surge of vans, possibly 150 more, for
a short period of time is possible when consideration is
given to using unpaved and unmarked open areas and even
roadway shoulders. The only limitations for continued'
efficient operations are localized congestion in a storage
area ana the distance of the storage area from the berths.
Additionally, van cargo is sometimes transferred to U. 5.
Navy owned railcars on station. This provides an added
flexibility if the van storage becomes congested. This
analysis considered the van storage capacity at 200
trailers.
In the instances just mentioned, the implication is uot
that HAS, Concord can store an unlimited number of railcars
or trailers. However, it must be emphasized that the stated
restrictions on the storage capabilities are not the strict
limitations for the entire facility. In fact, in addition
to the areas mentioned ibove, there are additional storage
areas in the Inland Area. These other storage areas have
24
11
*1 i
PP., .Life Uq(
Ias 1 » z <j 3
i ;
i:
25
been used in the past, specifically in 1968. It is
important to realize that once the normal railcar and
trailer storage capacities, 531 and 200, respectively, have
been achieved, in all probability the efficient movement of
conveyances will decrease. This paper did not analyze how
that efficiency varies but assumed maximum efficency can be
achieved at the normal capacities stated.
The station also has a barge pier and a large number of
barges tor use in the port area. The barges can be loaded
by mobile pier-based cranes at a separate pier that is
accessible to botn vans and railcars. This allows carjo to
be transferred to a vessel at anchor in the stream or to a
berthed vessel from the water side, or simply placed in
temporary storage until required for leading aboard a
vessel.
D. OPERATING TI22S
N'ormal operating hours for the longshoremen labor force
at N*5, Concord are 0000 to 1700 hours with a one-hour break
for lunch. Additionally, fifteen-minute smoke breaKs are
prcvidad after two hours of each four- hour work period.
During periods of two shifts, the second shift is from 2000
to 0500 hours with the same breaks as the first shift.
Other employees that interface with or support the
longshoremen may start slightly earlier or later. Curing
periods of two shifts, all elements are staffed except for
the receiving and inspecting functions which work only
eight-hour days.
At the berths, because of safety requirements, there are
limitations on when switches of railcars and vans can be
made and the size of the switches. Standard operating
26
procedures at NWS, Ccncord provide for switches between the
hours of 1200 to 1245, 1700 to 1945, 0000 to 0045, and 0500
to 0745 when no cargo operations on the vessels occur.
Switches can also be male daring the fifteen-rain ate smoke
breaks. Also, safety restrictions limit the number of cars
in a single switch anywhere in the Tidal Area to twenty-two
railcars
.
Additionally, it is mandatory that all vans on the berth
be removed ficm the track area at the berth prior to any
movement of railcars, and only after the railcar movement is
complete at. the berth may vans be placed back in position at
the berth for unloading. This procedure must be followed
because for unloading, the vans are placed across the
railroad tracks and thereby restrict railcar movements.
Nominal time for the movement of trains is fort /-five
minutes in the berth area. This allows only for the
positioning of the cars at th'3 oerth and does not include
time for retrieving the desired cars from their various
storage locations. The retrieval time will depend on the
nutcber of cars involved, their relative location, and their
grouping. The nominal time for a van movement is twenty to
thirty minutes one-way. This provides time for locating the
van, connecting it to the tractor, raising the dolly,
positioning the van, lowering tae dolly, and disconnecting
the van.
27
VI. THE T3ANSIM VI£tf OF NSS, CCMCOH')
This chapter portrays the system just described frcm the
view of the simulator. The modifications of the system and
the specific assumptions are specified.
A. SYSTEM ECDNDARIES
Originally the boundaries coincided with the
geographical perimeter of the NWS, Concord Tidal Area. This
allowed thre- openings in the perimeter for the entry and
exit of cargo. Cargo entered the system by rail and truck
and left aboard vessels. The empty vessels originated at
the system boundary at the juays and after loading at the
berths departed the system. However, provision had to be
made for anchorage of vessels awaiting an empty berth.
Therefore, the system boundary was extended to include a
harbor or sufficient size in which to anchor all the vessels
that might require waiting.
Also it was intended that the trains and trucks would
enter at the boundary gate, the physical device the
conveyances actually passed through to enter the Tidal Area.
Due to the lack of data and in consideration of th Q
simulation objective, the function was deemed irrelevant.
The primary elements in the determination of export
capability were the number of vessels, the tonnage loaded,
and the capability of the storage areas to accumulate
sufficient cargo to keep the vessels supplied with the
required cargo while not exceeding the normal storage
23
capacity. As a result the system boundaries were drawn in
to the truck and railcar storage areas.
After the railcars and trucks were unloaded at the
berths, they left the system. It was assumed that there
existed enough storage space for empty conveyances both
within the perimeter of the Tidal Area and on adjacent roads
and tracks and/or that the local carriers would pickup th^ir
empty trailers and railcars so that the storage of empties
woull not impact on the tonnage exported.
BASIC ELEMENTS 0? I!HE MODEL
The rRANSIM program accounts for the location and
activity of traffic units. Traffic units are the smallest
identifiable elements in the system. In this model, the
traffic units originally were long tons, vans, railcars, and
vessels. The greatest problem in the exercise was the
organization of the raw data into a form that woull pnoviae
efficient use of the TRANSIT model within the time and
funding constraints dictated. The first computer run
providing detail in terms of tonnage (long tons) used an
inordinate amount of computer capability, and required
reconsideration of the validity of the four traffic units.
Dropping long tons from the primary elements was the
first modification of the original plan and resulted in
subsequent data approximations and assumptions. The
smallest traffic units became the conveyances themselves,
specifically the trucks, the vessels, and the railcars. It
seemed reasonable to assume an average cargo load per
conveyance from which gross tonnages could easily be
calculated when needed.
29
^VESSELSg -A
§ GO
UHHss—uin
Q
m
EH«Q
Maen
H
VESSELS OJQa:crX
SHIPS[
CDn
y\ 5en
30
Even though each van and each railcar had the same
average tonnage as any other van and railcar, respectively,
it was essential for the model to identify each van and
railcar to determine times each element spent at a
particular activity. Throughout the model, vans were
identified individually. However, for trains a further
consolidation was deemed necessary after the early test runs
continued tc show an inordinate demand on computer time and
memory uhile the model was maintaining individual
accountability of raiicars. Therefore, trains were composed
of groups of ten raiicars.
This one consolidation caused the most concern in the
entire model development. It meant the complete sacrifice
of any attempt to model the switching restrictions (2 2 cars
per switch) or berth railcar capacities (25 raiicars per two
vessels <<ith no more that 15 raiicars per vessel) and/or the
allowable switching times. To achieve both the most
efficient computer use and desired realism was not possible.
Efficient computer use required grouping the railcar units.
Realism required that nilcars oe individually identified to
facilitate the movement of raiicars in both the accounts
specified and at only the allowed switching times. Realism
had to yield to efficient (in terms of cost) computer use in
the model.
C. ALTERNATIVE APPROACHES
The objective of the project was achievable in two ways.
The model could be structured to generate cargo from the
storage areas at a prescribed rate and load the vessels to a
predetermined capacity at which time they would sail. Then
a ccmoarison of simulated berth times with actual oerth
31
times would render possible conclusions. Alternatively, the
vessels cculd be called in berth for a prescribed time to
load a predetermined amount of cargo. Then comparisons
could be made between the simulated accumulation of cargo in
the storage area with the physical ability of the storage
areas to efficiently handle the cargo. The latter approach
was chosen for the following reasons:
1. Berth time data was available while cargo arrival,
storage, and handling times could only be guessed, because
historical data was not available and current data was
insufficient
.
2. The second alternative seems more realistic in that
plans are usually made first for the vessel's cargo while
the handling and storage capabilities of the port are
secondary conditions, if considered at all.
Using total berth time as a driving force allowed the
model tc be less ietailed than the switching time
restrictions, switching size limitations, and the berth
railcar capacity limitation required. The ten-railcar
traffic unit appeared to b3 a plausible approximation. Two
of the consolidated units were 91 percent of maximum .switch
size. If only one vessel was at a juay, a consolidated unit
was 67 percent of the berth railcir capacity, and if two
vessels were in adjacent berths, a more likely situation
when trying to maximize tonnage output, two units were
eighty percent of capacity. If one consolidated unit of ten
raiicars was associated with a vessel, none of the safety
restrictions would be violated and, assuming that a
requirement for all switches and berth railcar storage
capacities to be maximized all the time is not realistic,
the number cf raiicars available for unloading on the berths
was tolerable.
32
Regarding the only other variable, switching time
restrictions, the allowance for the switching time had to be
realized in a reduction of the unloading rate. Inplicit in
this arrangement was the assumption that the units of: ten
railcars was neither too Large nor too small to nave a
significant effect on the tonnage loaded, and that in the
long run the cargo loaded during switching times would
balance the lack of cargo during cargo loading times.
D. DATA ELEMENTS
Raw ^ta was ootainable either from records or
interviews fcr most elements within the Tidal Area. The
data base consisted of either capacity elements or time
elements. Capacity elements included vessel loading space,
van and train storage capacities, and tonnages on vans,
railcars, and vessels. Time elements were vessel leading
and vessel total berth times, interar rival times of trucks,
trains and vessels, inspection and receiving times of vans
and railcars, movement times of vans and railcars both from
receiving tc storage and from storage to berths for
unloading, and unloading times of vans and railcars.
1 . Ca£acity_ Elements
Storage and berthing capabilities were taken from
existing facilities in April 1977. These were the same as
in 1963, except foe the first forty-five days of 1968, whan
two of the berths were still under construction. (All 1968
data in the rrodel has been prorated to provide an equivalent
full year's capability.) In the past, vessels at NSJS,
Concorl have occasionally loaded cargo, usually in very
small tonnages, while at anchor 'in the stream and have
33
loaded vessels from the harbor side of a vessel while at a
berth. The stream operation was not considered in the mod^l
but the barge operation on a berthed vessel, while not
specifically modeled is allowed in the way the model
evolved. Therefore six vessels, one per berth, were
considered loadable at any one time.
The mcdel assumed a nominal storage capacity of 530
railcars and that safe storage could be provided all types
of ammunition. For the model the van storage capacity was
200. Storage of :argo on barges was not included in the
model
.
In 1968 NWS, Concord exported record number of
long tons of ammunition and this was thought to be the most
appropriate year in which to base the model to determine the
port's ultimate capability. The official manifest tonnages
of 196d vessels were available and were used in the model.
The data indicated a broad spectrum of tonnages loaded
aboard vessels, from eight tons to over ten thousand tons.
In order tc categorize vessels, they iiere divided into ten
classes, each class with a range of one tnousand tons. This
allowed for a distrioution of tonnage by class and also a
distribution of vessel frequency by class.' For each class
an average tonnag? was calculated and used as the tcnnage
for that class of vessel.
Truck and rail tonnages were not available for 1963.
This data was taken from 1976 records and averaged to obtain
a nominal tcnnage per conveyance. The implicit assumption
is that the tonnage per conveyance would not change
significantly over time.
2. Time Elements
34
Much of the time data was not recorded; in fact,
only the total berth times of the vessels were recordel.
However, from the berth time data, vessel interarrival times
were calculated. Although nor recorded, the working time of
a vessel was the only other time data that had a possibility
of validation. Records did not indicate the precise working
and non-wcrking times on a vessel. Since only a guess,
based on normal working hours, could be made on vessel
loading tines, all vessel tonnage capacities and loading
rates were based on total berth time. To determine the port
capability two shifts were assumed.
Because of the adjustments of the system boundaries
to the storage araa, it was not necessary to represent
inspection times, receiving times, or movement times of vans
and railcars into their respective storage areas. The
critical issue was to show an arrival of vans and railcars
into a storage area where they could be available for
unloading to a vessel. But no records of truck or train
interarrival times were available. To model cargo arrivals,
NtfS, Concord employees w-^re interviewed to obtain their
'•best guess." For vans, the arrival process occured over an
eight-hour span in the day shift, while the railcars were
usually moved in a group at one time during the day or in
groups of railcars just a few times a day.
The simulation allowed trains to arrive only at 0800
hours, precluding multiple time arrivals during the lay.
Since the usual practice in ports is to have cargo on hand
prior to a vessel's arrival, this assumption was not
unrealistic. However, to simulate the arrival of iifferent
numbers of railcars during a day the number of train
arrivals was said to obey a distribution of one train
arrival 50 percent of the lays and either no train arrivals
or two train arrivals, each 25 percent of the days.
Depending on expected vessel berth occupancies, the number
35
of trains, with ten cars per train, could be
prooabilistically varied to satisfy the vessel cargo
requirements. The overriding influence was to balance
inputs with outputs and ensure sufficient railcars in
storage to allow the system to operate in apparent realism.
Truck arrivals were simulated differently. It
seemed appropriate to represent truck arrivals by a Pcisson
distribution. Except for a possible buildup early in the
receiving period, the Poisson distribution would represent a
random arrival pattern. However, the stipulation was
included that trucks could be received only between 0800 and
1600 hours tc simulate the real-life situation of trucks
being received for only one eight hour shift. The Poisson
distribution worked on a twenty-four hour basis but specific
rules were included in the program to cause trucks that
arrived between 1600 and 0300 hours to leave the system with
no ether action. The mean interarrival time was dictated by
balancing the output of trucks with the input of trucks for
the system. The mean interarrival time was calculated by
dividing the allowed eight hour receiving period by the
average number of trucks to be input daily into the system.
Again, the effort was directed to ensuring sufficient
arrivals in the period to match the demands of cargo by the
vessels.
As a check of this hypothesized model, the timing of
cargo input, was considered a function of tonnage, as already
inferred. Tc provide for cargo input, cargo coming into the
system was assumed to equal the cargo coming out of the
system ov^r a long period of time. Specifically the model
was structured to have the cargo entering by trucks and
trains equal the cargo loaded on vessels in i six month
period. with tee knowledge of the tonnage exported and the
tonnage per truck and train, only the arrival rates had to
be determined for the trucks and trains. However, the ratio
36
of vans to railcars was not available. A "best guess" by
some of the experienced employees at NWS, Concord was the
best information available. A ratio of three railcars to
every truck was determined to best represent the actual
situation.
The cnly unresolved times were van and railc^r
movement times from storage to berth and the railcar and van
unloading times. Again no specific records were available
and the basis of the model was conversation with experienced
NWS, Concord employees. The best solution was an attempt to
represent the vessel berth time in terms of cargo leaded.
Assuming the ratio of railcars to trucks remained the same
as the receiving rate (three to one) it was possible to
calculate a vessel loading rate.
The model did not count the movement time from
storage to berth. In effect, this time is mad.^ a part of
the loaling time of the vessel. Without including the
switching limitations described earlier, the movement time
has no meaning in any case.
SYST"iv SUGARY
In suimary the model is set up with three independent
variables: vessels, vans, and railcars, and several
dependent variables, which cannot be statistically verified.
Therefore several pilot runs of the program were made,
relying on the author's judgement to recognize an improbable
result and correct it. Apparent errors in the functional
data relationships were found, and production runs were not
made until the model was able to replicate real situations
in the past. The actual operating rules of the model are
defined in Appendix A.
37
The specific assumptions stated above are summarized as
follows:
1. Lccal carriers PICK empty conveyances or
sufficient van and railcar storage space exists such that
empty vans and railcars do not impact on export tonnage.
2. Movement times of conveyances from storage to berth,
and switching limitations can be incorporated into an
average vessel loading time for all classes of vessels.
3. Vans and railcars consolidating pallets of cargo can
properly represent tonnage loaded aboard the vessel.
4. Railcars in groups of ten can be moved as a unit
throughout the system.
5. The historical relationship between vessel tcnnage
and berth tirce is valid for any time period.
6. The cargo mix of virious explosive categories can be
safely stored in the normal areas.
7. The randcrcness of the simulation's Y.onte Carlo
technique provides mean values over a six month period.
8. Hypothesized arrival distributions and interarrival
times are valid approximations of the real situation.
9. The average ratio of railcars to trucks in and out
of the system is three to one.
10. Loading operations with vessels in the stream are
not allowed.
11. The average van and railcar tonnages are constant
over time.
38
VII. THE DERIVATION OF THE PARAMETERS
Numerous assumptions have oeen made in ar. effort to
transform the real situation into a form acceptable for both
the TRANSIM program and efficient computer utilization. It
is only appropriate to be more specific in the methods used
to organize the raw data into parameters. Throughout all
the simulation runs the distribution of vessel classes, the
vessel class berth times, the number of vans and railcars
per vessel class, the unloading times of vans and railcars,
and the interarrivil time of trains were the parameters held
constant. These constant parameters ar? now described,
while the parameters that were changed to create different
effects in the simulation are described later.
A. ?AHA;1ETE3S HELD CONSTANT
1 • Distribution of Vessel C lasses
rcr each vessel that arrived and departed (aade a
turnaround) frcm January 1968 through June 1963, the raw
data furnished the name of the vessel and its manifested
tonnage in lcng tons. This iata was ordered and plotted in
histogram fashion (Appendix J) with ten equal intervals of
tonnage (the intervals were all equal to 1000 tons except
the interval at the highest range of tonnage which was
allowed to include all greater tonnages) plotted against the
number of vessels. The number of tonnage intervals and the
interval lenatn itself were arbitrarily designated. rhe
39
lowest tcnnage interval (0 to 1000 long tons) was assigned
class 1, and the classes progressed consecutively higher to
class 10 (9000 long tons and above). By dividing the number
of ships in each class by the total number of ships (76) , a
distribution of vessels by tonnage class was achieved
(Figure 4) . In the TRANSIM program this distribution was
called the "shiprule."
One qualification of the raw data stated above must
be clarified. Recall that the only data used was for ships
that completed a turnaround within the six month period.
This eliminated any vessels on berth at the beginning of the
period and also those that failed to complete operations by
the end of the period. Remember also that ASs are not
included and that vessels involving any discharge operation
are not included.
VESSEL CLASS PERCENT OF VESSELS
01
02
03
04
05
06
07
03
09
10
6.6
2.6
2.6
7.9
13.
a
30.3
21.1
7.9
1. 3
1.3
Figure 4 - VESSEL CLASS DISTRIBUTION
HO
2 . Vessel Class Berth Times
The same raw data above also contained the vessel's
arrival and departure times from its berth. From that data
total berth times, to the nearest quarter hour, were
calculated for each ship and plotted against the vessel
class in which that ship belonged. Within the vessel
classes, there appeared no correlation between vessel berth
tiers and tonnage loaded. However, there was a trend of
increasing berth time with corresponding increase of vessel
class, which was expected.
The berth times in each vessel class were simply
averaged to crtair a mean. To give the same randomness to
the simulation that the raw data for each class did reflect,
a ten percent spread on either side of the mean was
arbitrarily assigned to a triangular distribution (Appendix
K) . (The triangular distriDution is a device incorporated
in Tr?AMSIM that assigns probabilities). The variance was
strictly arbitrary and was adjusted for the large class
vessels, after test runs indicated invalid results in which
the higher class vessels were not able to achieve their
prescribed tonnage in the allotted time. Therefore, vessel
classes eight, nine, and ten had increased variances to
rectify this problem (see page 43 for discussion of leading
times). Figure 5 gives the distribution of berth times for
the various vessel classes.
41
VESSEL CLASS TIME ON 3ESTH
LEAST AVERAGE MOST
01
02
03
04
05
06
07
08
09
10
103 114.3 125
137 152.6 167
165 183.4 20 1
170 189.2 203
134 204. 1 224
186 2 6.1 226
190 211.4 232
192 213.9 240
198 220.0 23
221 245.5 340
Figure 5 - 3E?.TH TIMES 3Y VESSEL CLA35
42
3- Vans and Rail cars per Vessel Class
It was desirable to make the tonnage for each class
of vessel an even multiple of trie railcar tonnage to avoid
the necessity of accounting for partial loads in vans and
railcars, which would be another burden upon computer
storage and a complicating element in the model. Therefore
the vessel tonnages, although based on the average tonnage,
were approximations.
To determine the tonnage unit required, the average
tcnnages per van and railcar were found. The only
reasonable record cf van and railcar tonnages was from 1976.
It is logical to expect the average tonnage of vans and
railcars to be relatively constant over time. The one
possible cause for variance might be the nature of
ammunition required due to a particular type of conflict
(napalm bombs are much lighter than explosive bombs)
.
Var. and railcar tonnages were based on data
generated in the first six months of 1976. The average long
tens per van and the average long tons per railcar were
calculated to be 15.9 and 53.2 respectively. After many
trials at what might have been even multiple combinations
that seemed reasonable, 18 long tons was chosen as the
average weight of a van with 54 long tons the average weight
of a railcar. Consequently each average vessel tonnage was
rounded to the nearest multiple of 54 long tons.
Although these tonnages were included in the
computer incut data, they were not actually used in the
simulation due to the elimination of the long ton traffic
unit. Figure 6 snows the actual average long tons per
vessel class and the average used in the simulation.
43
VESSEL CLASS
01
02
03
04
05
06
07
03
09
10
LONG TONS
SIMULATED ACTUAL
540 540
1674 1670
2160 2154
3726 3733
4536 4551
5400 5409
6430 6462
7398 7422
3154 3163
10152 10132
Figure 6 - ACTUAL VS. SIMULATED TONS PEP VESSEL CLASS
44
The number of railcars and vans per vessel class was
an easy calculation once the ratio of railcars to vans was
established. Applying the 3:1 ratio assumed above, the
solution of two simultaneous equations provided the number
of vans and railcars. The equations were:
1.) CARGO TONNAGE ABOARD VESSEL =
(NUMBER 0? VANS) (18 LONG TONS PER VAN) +
(NUMBER OF RAILCARS) (54 LONG TONS PER RAILCAR)
2.) NUMBER OF RAILCARS = 3 (NUMBER OF VANS)
The general feeling of personnel at NWS, Concord was
that the tonnage received in railcars in relation to the
same for trucks had decreased in the past few years.
Without data this was impossible to verify and it is only
hoped that the data, if available, would prove relatively
consistent with what has been hypothesized. When it became
apparent that the grouping of railcars into units of ten was
a more efficient .vay to model the system, the result was a
necessary rounding of the number of railcars, or trains, per
vessel tc the nearest multiple of ten and consequently an
effect en the van/railcar ratio to achieve the desired
tonnage per vessel class. The results of this consideration
is summarized in figure 7.
45
VESSEL CLASS VANS BAIL-CARS
ACTUAL THEORETICAL ACTUAL THEORETICAL
01 3 3 10 9
02 3 9 30 2 3
03 9 40 37
04 27 13 60 6 3
05 12 27 30 75
06 30 ^Q 90 90
07 30 36 1 10 108
C9 51 42 120 123
09 33 4 5 140 136
10 54 57 170 169
Figure 7 - ACTUAL AND THEORETICAL NUMBERS OF VANS AN'D
RAILCARS PER VESSEL CLASS
46
Consideration was given to making the distribution
of railcars and vans per vessel random, but was not
implemented for three reasons. First there wo nil be no
assurance that the length of time or number of vessels
generated in the simulation would be sufficient to cause the
average tonnage in a vessel class to achieve its desired
level. Secondly, the vessel berth time was an independent
and overriding factor on tonnage loaded, that is, even
though the simulation ma/ have generated a higher than
average number of railcars and vans for a vessel, there was
no way they could be loaded on the designated vessel once
its berth tine nad elapsed. Finally, the distribution would
have been a guess, and that would only compound the
uncertainty already incorporated in this part of the model.
This determination of traffic units per vessel was
necessarily uncertain and, regrettably, the detail which was
initially desired sacrificed.
4 • Unloading Ti mj?s of Vans and Ra ilea r
s
As previously discussed, the actual time of loading
the cargo from vans and railcars onto the vessel was not
documented in records at NHS , Concord. In any instance,
this would have been difficult to correlate wita the total
berth tiie cf the vessel which also included significant
amounts of non-productive time. Therefore by trial and
error, considering the number of traffic units necessarv to
achieve a desired vessel tonnage class, the ratio of trucks
and vans unloaded, and the oerth time of the vessel,
unloading rates were obtained. The nominal times used were
ninety minutes for a van and two hours for a railcar. To an
experienced port operator these times may seem high. But
recall that this rate of unloading considers not only the
sixteen hours of available productive time in a twenty-four
47
hour period hut also, shift changes and breaks, meal times,
vessel rigging times, cargo compartment opening and closing
times, blccking and bracing times, vessel time between
docking and commencement of operations and time between
ceasing c: operations and sailing time, cargo delays,
eguipment breakdowns, and short hatches. The figures seem
more plausible when the average tons loaded on a vessel per
hour of berth time for the January 1968 through June 1968
period was 25.3 long tons. In the pilot runs of the
simulation these times were analyzed very closely to insure
that the cargo was loaded within reasonable times of the
vessel departure. The tines were adjusted somewhat, along
with vessel berth times (see page 41) to insure the
simulation was as realistic as possible.
Dependent upon the type of cargo in the vans or
railcars the unloading rate could vary. Based on interviews
with operating personnel at NWS, Concord a spread on either
side of the mean loading times of 45 percent for vans and 30
percent for railcars was assigned. In the pilot runs this
randomness vas monitored and provided reasonable realism to
the simulation.
5 • Interarri val T im e for Trains
The basic idea for rail arrivals was to have an
average cf one train a day arrive and to vary the number of
railcars in the train to make the number of railcars
variable. However, it was a simple task to also vary the
number af trains per day and achieve more realism.
Therefore, still basing the train input on a mean
interarrival time of one per day, the number of trains was
allowed to vary from through 2 per day on a stepwise
uniform listributicn.
48
B. PARA3ETZES TO BE VARIED IN SIMULATIONS
The variable elements cf data include the interarrival
time of vessels, the interarrival times of trucks, and the
number of railcars per train.
1 • Interarrival Time of Vessels
The average interval between ship arrivals was
calculated fcom the nunuoer of vessels in the six months
compared with the total time in hours. The original result,
based on seventy-six vessels, was a mean interarrival time
of fifty-seven hours. The distribution of arrivals was
specified as Poisson. The Poisson distribution was used
throughout the simulation runs but the mean interarrival
time was changed depending on the number of vessels desired
for berthing in each run.
With the available barth time for six berths over
18C days egual to 25,920 hours, and the historical data base
giving an av=rag° of 197.5 hours berth time per vessel, the
eguations used to calculate the mean interarrival time were:
1.) NUKBEE 0? VESSELS =
TOTAL BERTH OCCUPANCY TIME
AVERAGE BERTH TIME PER VESSEL
(OCCUPANCY RATE) (AVAILABLE BERTH TIMS)
AVERAGE BERTH TIME PER VESSEL
(OCCUPANCY RATS) (25920 HOURS)
19 7.5 HOURS PER VESSEL
49
2.) interarrival time =
total time
number of vessels
2« Interarri val Times for Trucks
The distribution for truck arrivals was also assumed
to be Poisson. For the original runs the mean interarrival
time was forty-five minutes. with this distribution the
number o: trucks entering, leaving, and stored in the
storage area seemed reasonable in the test runs. However,
when greater tonnage requirements existed due to increased
berth occupancy it was noted that the relationship between
truck arrivals anl vessel classes on berth was extrenely
critical. When vessels that required great numbers of vans
were in a berth, there were insufficient numbers of vans in
storage to satisfy the demand. Therefore, in later runs,
the mean arrival rate was arbitrarily increased and
decreased depending on the supply of trucks, resulting in a
non-hoaogen ecus Pcisson distribution. This gave added
realism to the model because the nature of truck
transportation anl truck detention rates are such that
trucks are used when the interface time with another mode is
imminent cr short. To calculate the mean time when the
vessel leading requirements were varied, the following
equations, with the historical data base giving an average
of 25.197 vans per vessel (assuming the 3:1 railcanvan
ratio, adjusted only fcr rounding due to the grouping of
railcars) , were used:
1.) SOMBER OF VANS PER DAY =
(25.197 VANS PER VESSEL) (NUMBER OF VESSELS)
NUMBER 0? DAYS PER PERIOD
50
2.) VAN INTERARRIVAL TIMS =
(3 RECEIVING HOURS PER DAY)
NDMBEH OF VANS PER DAY
3. ^iinber of Railcars £er Tra in
The number of railcars per train was a direct result
of balancing inputs with outputs. The average number of
trains (ten railcars per train) per vessel was 8. 5^3, from
the historical data base (assuming again the 3:1 railcar:van
ratio, adjusted only for rounding due to the grouping of
railcars). The following eguation provided the number of
trains required:
8UMBES 0? TRAINS =
(3.593 TRAINS PER VESS EL) ( NUMBER OF VESSELS)
NUHBEB OF DAYS PER PERIOD
If the result contained a fraction, for example 5.4, then
cars per train was given a distribution of 60 percent of the
trains with five groups of ten railcars and 40 percent of
the trains with six groups of ten railcars which provided a
mean of 5.4 groups of ten railcars per train.
51
VIII. RESULTS
Five simulation runs of 130 day periods were made. The
first taree runs, I r II, and III, were made in orler to
replicate the original situation. Simulation runs IV and V
were made with the parameters varied to attempt to find the
upper limit cf NWS, Concord's capability. The results of
the study are included as Appendices E through I and are
summarized in Figures 8 and 9.
Simulation runs I, II, and III were the runs that
attempted to validate the model's accurancy, comparing
percent of berth occupancy, total export tonnage, and number
of ships cf the historical lata with the simulation results.
The historical data indicated a total of 76 ships exported
approximately 337,000 long tons with a 62.5 percent berth
occupancy for the first six months of 1963. Although there
was a small variance in the three runs' results they
bracketed the original historical data very satisfactorily.
Simulation run II was almost identical with the historical
data
.
The remaining data generated in the simulation could not
be validated historically but could be compared with actual
facilities in an attempt to judge the result's plausibility.
Particular attention was given the vans and raiicars in
storage. Simulation run I generated a seemingly high number
of vans and raiicars in storage. To a degree, this was to
be expected in comparison with simulation runs II and III in
that the cargo was arriving at the same rate but fewer ships
were leaded.
52
However, further detailed analysis indicated an
adjustment in the van arrival rate might be necessary
because the number of vans was steadily increasing.
Therefore, simulation runs II through V had variable van
arrival rates applied to the model to reflect fewer arrivals
when vans in storage approached capacity and more arrivals
when vans in storage were low. Henceforth, on.the average,
the storage reguir ements were consistently within the van
storage capacity with the normal capacity being exceeded
only in cr.e run by 26 vans which was felt to be tolerable.
Railcars in storage did not indicate any specific
necessary adjustments, but proved somewhat inconsistent.
The inconsistency was noticeable in the trends of berth
occupancy, tcnnage exported, and the number of vessels, none
of which had any correlation with the numbers of railcars in
storage. For simulation runs I and V there are significant
differences from runs II, III, and IV in the average number
of railcars in storage.
There was another trenl that had to be considered in the
seeking of a maximum export tonnage. The percentage of
ships turned around without any delays waiting for berth
generally decreased as the berth occupancy and tonnage
increased. This appeared to be logical in that unless
vessels were following an extremely precise schedule, there
would undoubtedly be delay times somewhere in their voyage.
Every tine a vessel is delayed, that vessel is not producing
revenue or moving any cargo for the charter price cf the
vessel. An idle vessel can be extremely costly. The
results indicated that high berth occupancy, although having
produced large tonnages, also produced more vessel delays.
The results indicated that the greatest export tonnage
in a 180 iay period is about 610,000 long tons. This
reguired 127 vessels of the assumed distribution with 99.
R
53
percent b^rth occupancy. However, this operation could be
extremely costly due to tae number of vessels in a queue
awaitinq a berth. Such a high berth occupancy rate is not a
very probable operation because too small an allowance is
provided for vessel switching times and other delays that
are bound to occur in the real situation. It was notable
that such a high oerth occupancy put demands on the storage
facilities that were felt to be tolerable.
A more reasonable export tonnage mark to be sought is
about 500,000 long tons which required Q 7 vessels of the
assumed distribution with 77.5 percent berth occupancy.
The only unfavorable factor appears to be the ship delays.
The cost of ship delays may be significant, possibly
somewhere between the cost of the historical operation in
1968 and the hypothetical 610,000 export tons operation.
However, a 77.5 percent berth occupancy allows fcr the
possible discharge of vessels, the handling of A2s, and/or
the vacant time that would probably occur.
An element of concern could be expected when analyzing
storage requiremants for such a tonnage exported. Again,
th° data was not conclusive, out consideration had to be
given requirements posed by this simulation that showed the
normal raiicar storage capacity would be seriously
deficient, requiring 1.8 times the nominal capacity on the
average and with a peak of three times the nominal capacity.
54
STATISTIC
II
SIMULATION RUN
III IV
Berth Occupancy 52.7 60.3 69.3 99.8 77.5
Van Exports (No.) 1650 1929 2253 3132 2496
Van Exports (Tons) 29700 34722 40554 56376 44928
Rail Exports (No.) 547 597 659 1026 840
Rail Experts (Tons) 325080 322380 356860 554040 453600
Total Exports 354780 357102 397414 610416 498528
Number of Ships 69 77 90 127 97
% Ships W/0 Delay 87.9 64.9 66.3 0.0 44.3
Max. Vans Stored 345 226 185 150 196
Ave. Vans Stored 177 98 55 55 94
Max. Cars Stored 1420 500 65 520 1530
Ave. Cars Stored 722 114 136 105 900
Ave. Time Van Stored 36 6.0 204. 5 96 .0 72. 2 153.9
Ave. Time Car Stored 454. 3 78.5 75.0 42. 5 404. 9
Figure 8 - COMPOSITE RESULTS
55
VESSEL CLASS SIMULATION ?UM
II III rv
01
02
03
0'4
05
06
07
08
09
10
TOTAL
8 8 6 8 5
1 4 4 1
2 1 5 4
7 4 3 5 12
10 14 18 25 16
23 22 25 35 22
14 22 20 30 27
4 5 8 12 9
1 3 1
1
69 77 90 127 97
Figurs 9 - ACTUAL NUMBERS OF VESSELS IN EACH CLASS FOP
EACH SIMULATION
56
IX. CONCLUSIONS
This thesis has attempted to both define a simulationmodel for an ammunition water port, specifically NWS,
Concord, and to utilize that model to determine a potential
capability cf the port. The analysis has been limited in
scope but is useful in modeling a port in general terns.
Two conclusions can be drawn from the analysis. First, NWS,
Concorl can export a significant increase in tonnage, up to
approximately 610,000 long tons, and second, the impact on
th€ storage areas is net conclusive.
To increase the actual export tonnage loaded aboard
vessels is a distinct possibility but it has some very
definite ccsts. Such an operation excludes any discharge
operations and precludes the servicing of any AEs. In 1968,
AE's occupied a berth at NWS, Concord 7.5 percent of the
time, while vessels involved in discharge were present 11
percent of the time. If it is possible to perform the
responsibilities incurred with these vessels at another
port, the increased tonnage provided by merchant rcarine
cargo type vessels instead, could be significant. However,
to insure sufficient vessels are on hand to receive the
increased tonnage, would necessitate a great increase in the
vessel lelay times awaiting berth. \ cost-benefit analysis
would be appropriate to determine the break even point
between the increased capability and the increased cost from
non-productive vessels. An indication of the trend of delay
times and tennage capabilities can be obtained by comparing
simulation runs II, IV, and V.
The increased tonnage just mentioned is a realization of
57
the capability provided by the number of vessels, berth
space, and vessel leading rites. However, to conclule
anything about the ability of the storage areas to handle
such amounts of tonnage is not so easy. It is difficult to
determine with the results nerein what might happen in the
storage areas. There are no .definitive trends that would
indicate whether the storage areas are sufficient under the
conditiors analyzed.
The extreme variances in the results do indicate that
the vessel class :iix, the sequence of vessel class arrivals,
and even the railcar/van ratio loaded aboard a vessel are
extremely iipcrtant. That the model could not be detailed
in this area may oe the reason for the indecisive results.
The assumptions made regarding the distribution of vans and
railcars aboard a vessel have a direct effect on the
drawdown or buildup of conveyances in the storage area.
Any conclusions about the storage area need to havs more
runs made and possibly a better data base regarding cargo
types loaded aboard a vessel and the frequency of such cargo
types.
It is worthwhile to have the model developed as it does
at least provide a vehicle to analyze an ammunition port
with a rather standard operation. The model can be used for
comparative analysis between ports and, with more
replications, may provide more conclusive results. TRANSIM
is a tool that has proven relatively easy to use, at least
in this rather general view, and is worthy of more use in
this ar^a.
5d
X. RECOMMENDATIONS
Further study of this port could prove both interesting
and worthwhile. Future analysis of the sane system might
explore a number cf areas.
To increase the numoer oi runs, if funds were available,
for a broader statistical base would be considerable
value. Different s^eds to the random number generator could
be used and the variable parameters could be changed to
generate possibly different and/or more conclusive results.
Although it would require a greater expense, the system
could be redefined to include some of the more detailed
operations or those that had to be excluded. This might
provide more realism in that some of the assumptions could
be delete!. Such detail, however, would require a broader
raw data base on which to base the new operations.
Obviously one area that can be studied is the
relationship of tne storage requirements with cargo input
and vessel leading. It is suggested that this might be a
reasonable study, with limited funds, that involves
primarily an analysis of the constant parameters. A number
of runs changing these data elements might prove most
illuminating .
59
APPENDIX A
OPERATING RULES 0? THE MODEL
The operating rules of the model are defined in the
order cargo night flow through the system. Some duplication
is necessary to provile for the movement of cargo by th? two
modes of van and railcar. The index to tne left of the
discussion of each 9lement or rule refers to the sample
program in Appendix 3.
1. Trains (ten railcars each) are generated randomly
by a given interarrival time distribution. .
Example: 25 ? 50 P 1 DAY 25 P 2 DAY
Interpretation: 25 percent of the days no trains
will arrive; 50 percent of the days one train
will arrive; 25 percent of the days two
trains will arrive.
2. The multiples of ten railcars per train ire
generated randomly by a given distribution.
Example: SRR = 50 ? 3 50 P a
Interpretation: 50 percent of the train arrivals
have 30 railcars and 50 percent of the train
arrivals have 40 railcars.
3. At the storage area a train substitutes into a
number of railcars.
Example: rSAIN 5 RULE NRR RAILCAR
RAILCAR S CARGORRA
CARGORRA R RRGAT3
Interpretation: At the storage area a train
substitutes into a number of railcars
just determined (by the previous two
which substitute into rail cargo unit-
are routed from the storage area
checkpoint (RRGATE)
.
steps)
which
to a
4. Trucks are generated randomly by a given
interarrival time distribution.
Example: TABLE TA^TRCK POISSOU 45 BIN
Interpretation: A truck will arrive according to
a Poisson distribution with mean in terarrival
time of 45 minutes.
5. At a checkpoint trucks will be route! dependent on
the time cf day.
Example: ELEMENT TRORIG
TRUCK2N 3 TRUCKGBN + CARGOTRA
CARGOTRA B 30 3 AM
160 p:i STORAGSTR
2 40 PM
Interpretation: At the point TRORIG, trucks will
be routed to the truck storage area if
between 0300 and 1600 hours on the day
clock; otherwise they will leave the system.
5 . At the storage area van cargo units are routed to
THGATE.
Example: ELEMENT STORAGSTR
CARGOTRA R TRGATE
Interpretation: Vans enter the storage area and
will flow out through a "gate" as vans of
cargo (CARGOTRA)
.
7. Vessels are generated randomly by a given time
distribution.
Example: TABLE TARVSH POISSON 57 HR
Interpretation: A vessel will arrive according to
a Poisson distribution with a me^n
interarrival time of 57 hours.
3. The vessels that arrive enter the system as cne of
61
ten classes with a given distribution.
Example: SHIPRULS = 6.6 P VSSSEL01
7.9 p VESSEL04
21.1 P VSSSEL07
Interpretation: 6.6 percent of vessel arrivals
will be class 1; 7.9 percent of the vessel
arrivals will be class 4, and 21.1 percent of
vessel arrivals will be class 7.
9. The vessels of a particular class are given a
random total berth time according to a given distribution.
Example: TABLE BERTH05 TRIANGLE ? 184 HR
204.1 HR 22 4 HR
Interpretation: A vessel of class 5 will have a
berth time according to a triangular
distribution where zero percent of th= time
it will oe on berth 134 hours or less, 50
percent of the time it will be on berth 204.1
hours, and zero percent of the time it will
be on berth 224 hours or more.
10. The vessel is assigned a berth.
Example: ELEMENT HARBOR
SHI? S RULE SHIPROLS
VESSEL** 8 BERTH1 IF VESSEL** EVESSEL**
AT BERTH1 E^ OvJ
VESSEL** R 3ERTH2 IF VESSEL-* EVESSEL**
AT BSRTH2 EQ OS
ETC.
VESSEL** R 3SRTH6 IF VESSEL** EVESSEL**
AT BERTH6 EQ :W
WAIT
Interpretation: The arriving vessel goes to berth
1 if there is no vessel of any class at berth
one; otherwise it screens berth two, three,
four, etc., until it finds an empty berth.
If all berths ace full it waits until a berth
empties and then occupies it.
62
This page intentionally left blank,
63
APPENDIX B
SAMPLE OPERATING ROLES
This appendix contains a sample of the operating rules
used by TRANSIM for the N»S, Concord aiodel. The indexed
portions of the figures are explained in Appendix A.
64
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APPENDIX C
INITIAL SITUATION IN TH3 MODEL
rhe model was provided an initial situation in order to
have seme operations in progress at the start cf the
simulation. Figure 15 is i sample of the initial situation
provided to TRANSIM. The railcar and van storage areas had
500 raiicazs (that is, 50 x 10) and 150 vans respectively-
Each berth had an empty vessel prepared to load. The vessel
classes chosen were representative of those classes most
often i o 3 i e i a t the port
.
72
DA Y HR a MTN 3 =t. STroAKFRR TYP- CARGQRRA UN IT S 55_D AYDAYDAYday
HR 3 MIN EL STORAGE'R T YPE CA^GOTRA UNITS 1 50HR 5HR 6
MINMINMIN
1 EL 5ERTH23 EL 3£P T H3JL_Ii 53 ^TH5
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DAY HR 3 MIN EL HA!=3 OR
TY=>E VES5EL05 UNITSTYPE VESSEL.?* UNITSTY PE VES SELDc UNITS
UNITSUNITSUNI ts
TY^E VSSSEL07TYPE VESSELC3TYPE VESSELTlTY°E TRUCKGENTYPE SHIP
DAY m=> 3 MIN 3 EL STDR*GE=?R TYPE TRAINTARVSH
J TAPCAIL
Figure 17 - INITIAL DATA IN THE MODEL
73
APPENDIX D
'ERATIVE PROCESS OF TRANSIT
This appendix provides a lemonstration of the pcocasses
th€ computer followed during the simulation. The output
shown has been extracted fro.?, one of the test runs used to
verify ths logic jf the model. There were some errors in
this un an 1 the reader, therefore, is cautioned to p<
attention only to those portions iescribed, which are
accurate images of the modal.
Items of interest are provided at the following svent
1 1 j e s :
15-14-51 A ship originates in the harbor and becomes a
vessel (#60635) delayed in the harbor awaiting a
berth because all berths are full.
15-13-11 A truck enters and immediately leaves the system
because of its arrival between 1600 an i Q,00
hours.
15-13-33 .a unit of railcars (#63187) completes
consolidation with the current carorder (#59134).
Tiis completes the loading function of this unit
it berth 4 and generates a completed order (NRRR
$60625) at berth 4. it the same time th-: next
caiorder (59124) moves to the loading element
element (MOVE 4) at berth 4 where it matches up
with the current railcar cargo (CARGORRA 463137)
:rc;, the 3RGAT2. Also the next railcar carjo
(CARGORRA 460934) moves :nn storage to the 3RGATE
74
awaiting the next carorder. (Author's note: in a
later program "MOVE" was changed to "LOAD" tc more
accurately reflect the actual loading function
being performed. The words have the sane meaning
throughout this portion of the extracted output.)
16- 3- A train cf 10 cars arrives, and becomes r^ilcar
cargo (CA5G0RRA) in storage.
16- 8-30 A truck arrives luring receiving hours and becomes
van carjo (CARGOTRA) which passes through the
TRGATE to the loading (MOVE 3) function at berth
3 where it begins consolidation with the current
vancrder. (In this case the truck storage area
wis empty of vans explaining why the van went
immediately to a berth. If the van storage area
was not depleted, the van would have been delayed
in storage similar to the railcars arriving at
16- 8- 0.)
17- 0-2 1 riie one- empty class 3 vessel (#55903) at berth
1 consolidates all completed van (RRTR) and
railcar (ORRR) shipments into a loaded vessel
(=57 153) and leaves tae system. Then the class 1
vessel (#60635) moves from the harbor to berth 1
where it becomes an ampty vessel with requirements
for 3 vanorders and 1 carorder ready for matching
with van and r.iilcar cargo at the respective
. i t^s.
75
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APPENDIX E
RESULTS OF SIiirjLATION RUN I
Simulation ru^ r wis run attempting to achieve 62.5
percent berth occupancy. The vessels arrivals were Pcisson
distributed with i mean interarrival time or 57 hours in
orier to jof. -rate about 7o vessels .a 130 lay period.
Train arrivals were liven a distribution of no trains
arriving 25 percent of the lays, one train 50 percent of the
days, a n 1 two train trains arriving 25 percent c f the days.
Arriving trains ha i 30 cars 50 percent of the ti;n a and 40
cars the remainder of +..--r time. Therefore, on the average,
35 raiicars arrived ^-ic':. lay.
Truck arrivals were Poisson listributed with a mean
interarrival time cf 45 minutes. H'^^ average number of
truck arrivals per sight hour receiving period wis expected
to be 10.6.
: ..i results for Simulation run I art shown in the
accompanying figures and are summarized is follows:
1) ecu nancy rate was 3.16/6 = 52.7 percent.
2) Van cargo exported was (1650) (18) = 29700 long tons.
3) Sail cargo exported was (547) (54 0) = 32 5080 long
tens.
a) Total cargo ^xoort?! was 325080 +29700 = 354,780 long
tons.
5) Namber of vessels turned around was 69.
6) Percent of vessels ielayed wis 100 - 57.9 = 12.1
82
percent.
7) Average/maximum number of vans in the storage area
was 177.0/345.
8) Average van storage time was about 366.0 hours.
9) Average/maximum number of railcars in the storage
area was 722/1420.
10) Average railcar storage time was ^bout 454.3 hours.
3 3
N*£ 1NCGRD EXPORT CAPABILITY SIX MONTH 1 3MA Y77
SUMMARY 1
XtPAiNCY OF ALL -i£R7hS (T3 T AL NUMBER OF 5HIP5 IN P:' T)
NUM3ER IN 5 ERVI CE
T^TAL NUN JMAX IMJM
£R IN ,^£p:«6 ( CN DAY
- O7 3 £ P. I C D
U OTHER TIMES*)A V ERA G ;
M I N I MJ A
3,16( UN DAYAND 5 ~Th=P TIMES )
NUM5ER
l th^j1 THRU
PERCENT
1
2
GF REFCRT2. ?
15.3
PERI ZL
3 7HKU* T riFL5 T Hh U6 TH.^U
3
c
11.72^.21 3. 3LL..3
3 VcR
igure 24 - OCCUPANCY OF ALL BERTHS (SIM. I)
5 4
NWS Ca.NCCaO ZXPCkT capability SIX MCN'H
SUMMARY 13T.TAL -AIL CARGO EXPORTED (UNJT5 OF 54.: LCNG'GNS)
: TAL NUM3ER IN SFRVI Zz , C ~L i.YEC .1. _QE __ICLI_
~E" VL NUMBER IN REPOR"v: X T MUM 4-3 ( ON DAY
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7 %-r 1- A6 ,
2 HTHFR TIMES)
2 Tr H^R T IMES
)
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"
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9P . -.
PER ICO
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'
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O -1 'MCU 7
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c '1 THRU ° "
9" 1 THPUl
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."*
Figure 25 - TOTAL TRUCK CARGO EXPORTED (SIM
NaS concord export capaeility SIX -10NTH
SUMMARY 12TOTAL TRUCK CARGO EXPORTED (UNITS OF 13 LONG""" 3N )
-:T4 L NUMBER IN SERVICE. CEL^YED* 0<5 IDL^
T,~~-L NUM3E D IN cEP n S'
VAX IMUM 1 - " (CM CiA
Y
,1V- F AG - 71 • 1 5MN IMUM ( IN C * Y
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5 OTHER T IMES )
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"
j THRU 1'
'
7C .31
*1 Thc-u 2 *
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2 1 THRU 3 " f f #f
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:
'- 1 THCij 5 * r l
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e i •h^u gi« »*
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"
r •/C VFP 1 •"
' »_ .„ .. -.
figure 26 - TOTAL RAIL CARGO EXPORTED (SIM. I)
NaS cncokd ^xpc^t cap4gil: t y
SUMMARY X5Sn I P
SIX ^ONT H 1 3MAY7
IME AT ALL 3;STH5 (ALL SHIPS)
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38
NWS CONCORD EXPORT CAPAEILITY SIX MONTH
SUMMARY i
NUMBER OF VANS IN STORAGE *
_T_3_TAL NU'^^c IN SERVICE. CELAY^D, CP ICL~
TOTAL NUMBER in Rfprp T PERIODMAXIMUM 3^-5 (CN CAY 177 V<iV^~iG- l""T .'
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N*S CONCORD EXPORT CAPABILITY SIX MON'H 13".'AY77
SUMMARY 2NUMBER IF TRAINS (1* CAPS d£r TRAIN) AT STORAGE AREA
TOTAL NUMBER IN SERVICE, C FLAYED, nc IDLE
TO'AL NUMBERVAXlMU^ 14-2iVE c AG r 72
IN R]
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Figure 31 - NUA3ER 0? TRAINS AT STORAGE AREA (SIM. I)
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APPENDIX F
3 2SULTS OF SIMULATION R rJ v: II
Simulation rur II was a run attempting to achieve 62.5
percent berth occupancy. The vessel arrivals were Poisson
distributed with a mean interarrivai time of 57 hours in
order to jenerata about 7b vessels in the 130 day period.
Train arrivals remained the same as for simulation run i
with no trains arriving 25 percent or the days, one train
arriving 50 percent of the days and two trains arriving 25
percent of the days. Arriving trains had 30 cars 50 percent
of the time and 4 cars the remainder of, the tine.
Therefore, on the iverage, 35 riilcars arrived each day.
Truck arrivals were Poisson distribute! with .n e a n
interarrivai time of 4 5 minu' during normal operations.
When the number of vans in the storage area w a: les than
10, the mean interarrivai time was 15 minutes and when the
numoer or vans in tne storage area was gr a t e r than 150, the
mean interarrivai time was 75 minutes. This was the first
of tne five simulation runs to use the non-homogeneous
arrival distribution. The average number of truck arrivals
ner eight hour receiving period was expected to oe 10.6.
Th results of simulation run II are shown in the
accompanying figures and a: j summarized as follows:
1) Occupancy rate was 3.62/6 = 6 0.3 percent.
2) Van cargo exported was (1929) (13) = 34722 long tors.
3) "ail cjll:i exported .'as (597) (540) = 322,380 long
tons.
4) T otal cargo exported was 322380 + 34722 = 357,102
long tons.
5) Number: of vessels turned around was 77.
6) Percent of vessels delayed was 100 - 64.9 = 35.1
percent.
7) Average/max imuni number of vans in the storage area
was 98.0/226.
3) Average van storage time was about 2C4.5 hours.
9) Average/maximum number of railcars in the storage
area was 114.3/500.
10) Average rail car storage time was about 78.5 hours.
34
NwS CCNCORD EXPORT CAPAElLITy SIX MONTHS 17MAY77SUMMARY IC
OCCUPANCY OF ALL 5E=~hS (t O t AL NUMBER OF SHIPS IN PO- T
)
-jmJAi^_JsJUVSEP_ll^«ERViC^A_n£LAYLED_i_.CR_XDLE.
TOTAL NUMBER IN REPOP"r PERIOD 77VAX I ^UM 5 (CN DAY "AT 9 0.
A NO 3 6 QTHFO T I ME S )
AVERAGE 3.62VINIMUM C (ON DAY r AT C«
AND 2 OTHER TIMES)
NUMEER PERCENT OF Q=POR" PERIOD
1 THFU I 10.
1
? THCU ? a^LiT3 THRU 3 2 2.0A THRU A C.35 THRU 5 7.06 THRU 6 28*0
over e o.o
igure 33 - OCCUPANCY OF ALL 3ERTH5 (SIM. II)
95
NaS CCNCORO EXPORT CAPAEILITY SIX MONTHS 17MAY77
SUMMARY UTOTAL TRUCK CARGO EXFCR'ED (UNITS OF 19 LONGTON)
T TA. L_ NU M BE R _ IN £E P V I CE. DELAYED. OR IDLE
TOT*L NUMBER IN oerocPT °ERICD 1929MAXIMUM 2 rv
<» (ON DAY 92 AT 330)*VE RAC C 9 3. • A A.
MNIMUM (ON DAY "5 A T C«AND 2 T HER T IMES )
NUMBER PERCENT OF RE°OP"r PERIOD1 .0
1THCU 10* 56,1
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J^gurs 34 - TOTAL TRUCK CARGO EXPORTED (SIM. II)
9o
N.vs CCNCCRD -.KPCRT CAPABILITY 51* MONTHS 17V1Y77
SUMMARY 13TOTAL nAlL CARGO EXPORTED (UNIT5 GF 54C LONGTONS)
JIXLTAJ NLLM 5LER_I N. 5 FRVTCF. _ _QELA^ °D. CC IDLE
TCT4L NUMEHR IN PEPORT PERIOD 597MAXIMUM 5f (CN DAY 1^3 a,t 123)WgPAGg Ii^a9 9VINIMU^ f! (ON DAY * A T 0)
NUMBE" PERCENT OF REPORT PERIODC . .6
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igure 35 - TOTAL RAIL CARGO EXPORTED (SIM. II)
97
NrtS CuNCORD lXPCRT CAPABILITY SIX MONTHS 1 7va Y7'
SJVMASY 15SHIP TIME AT ALL 3EPTHS (ALL SHIPS)
5LAP: XJ_MF__LI_n CLUDJ N_G_JD c LA Y_5 )
"2TAL TIME 15195 HPS 26 MIN 22 SECMAXlMJM T I M£ 233 MPS 22 VI N 10 StC
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igure 36 - SHIP TIME AT ALL BERTHS (SIM. II)
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SUMMARY 1
NUMoEP OK VANS IN STCRAGE A^EA
TOT A L NUMBER IN S ERVl CE, _ C EL AYEP. OP IDLE
TOTAL NUMBER IN REPORT PERIOD A071MAXIMUM 225 (ON CAY 153 AT 1522)AVERAGE 9J3 i* 1_M N I viU M (ON CAY D AT
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100
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NaS concord export capaeilty SIX MONTHS 1 7VAY7'
SUMMARY 2NUMBER CF TRAINS (1C CA^S PER TRAIN) AT STORAGE A<?EA
_L TAL NUMBEJS IN SERVICE* _B£L A Y ED. OR I_DU E__
TCT &L N'U'^BE 5 IN REPORT PERIOD 1251VAX I MUM El ( CN DAY "> AT 8 0)
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102
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10 3
APPENDIX G
RESULTS Or SlrllJLATTON RON III
Simulation run i*as run attempting to achieve t2.5
percent berth occupancy. The vessel arrivals were Poisson
distributed with i mean interarrival of 57 hours in order to
c^r^ratj id out 7 6 vessels in t 1 3 d ay pi 10- Thi run
was given a n e w
results.
to increase the randomness cf the
Train arrivals were liven a distribution of no trains
arriving 25 percent oc the days, org train 50 percent cf tae
days, and two trains arriving 25 percent of the days.
Arriving trains had 30 cars 50 percent of the time ana 40
cars th2 remainder of the time. Therefore, on the average,
3 5 rails a es arrived each day.
rruck arrivals were Poisson listributed with a mean
interarrival tine of 45 ninutes during normal operations.
ihen the number of vans in the storage area was less than
10, the j •? an interarrival time was 15 minutes and w > 2 n tne
number or vans in tne storage area was greater than 150, the
mean interarrival time was 75 minutes. The average number
of track arrivals per sight hour receiving period was
expected to he 10,6.
Th? results for simulation run ETI are s:iown in tne
•a cccmpa n l i n i figures aid are summarized as follows:
1) Occupancy rate was 4.16/5 = 69.3 percent.
2) Van cargo exported »as (2253) (13) = 40554 long tons.
104
3) Rail cargo exported was (659) (540) = 35686 long
tons.
4) Total cargo exported was 356860 40554 = 397,414
long tons.
5) Number or vessels turned around was 90.
6) Percent of vessels delayed was 100 - 66.3 = 33.7
percent
.
7) Average/maximum number of vans in the storage area
was 54.9/165.
8) Average van storaje ri^e was about 96.0 hours.
Q) average/maximum number of railcars in the storage
area was 136/650.
10) Average railcar storage ti :ue was about 75.0 hoars.
105
NaS CCNCCRD =XFCaT :apa3ilit SI X MONTHS 13MAY7'SLVMACy 1
CCCJ^ANCY OF ALL BERTHS (TOTAL NLM-Jch OF SHIPS IN PCRT)
. I Ai NUMRER _I b__3^PVICE. Of Jf-£LL»
TOTAL NUMBER IN REPORT PERIOD 90MAXIMUM 6 (ON DAY C AT SO.
4XD a-1 OTH ER TIME S_JLAVERAGEMINIMUM
<* . 1
(ON DAYAND
AT C.2 OTHER TIMES)
NUMBER PERCENT OF REPORT PERIOD1.6
1 THRl 1 5.32 THQU 2 11.63 THRU4 THRU5 THRJfc Th^U
over"
15. 1
2C. 1
12.2_3 2_,_6_
0.0
igars 42 - OCCUPANCY 0? ALL BERTHS (SIM. Ill)
1 )6
Na:
S L V M A h Y 12
CCNCQRD EXPORT CAPABILITY SIX MONTHS 1PM/
TOTAL T ^UCK- CARGO ^XPC-Hi: (UNITS OF IS LONGTQM
-TCTjU- MiMfi?5 Lfci. SERVICE! OELAY.EC_.__G2 LQLiE
TOTAL NUMBER IN fiEPCRT PEP IOC 2253VAX I Mo M 225 (ON CAY 13 AT 2 04 2)VitE£AC£ SL__ £JviNIvtUV (ON CAY : AT 0»
AND 2 OTHER TIMES)
NUMQER PERCENT OF REPC £LT__E BJL Q_Q_
ITHPUiGO 5 1.81C1 ThQU _CC 44.5
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_ 6 C_L T_HRJUL ______ C •
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0JtSR_Li2£ £jl5
figure U3 - TOTAL TROCK CARGO EXPORTED (SIM. Ill)
107
NWS CCNCCRC EXPC^ T CAPABILITY 5 I X MONTHS 16MA Y77
CARGO EXPORTEC (UNITS OF 540 LONGTCNS)
T PIT 41 NUM.E£L3_ _LQ1_£_
TCTAL NUMBER IN CEPOR'MAXIMUM 4 6 (ON DAYAVr RAilE 1 7 . ?
MINIMUM C (ON DAY
PER IOC16 AT
6591 73 0)
AT )
NUMBER3
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T HPU20 1 THRU-10 1
T H1'40 1 THRU5C1 THRU601 THRU701 THRU flfln
30 1T HPU 900
90 1 ThRUl COOOVER100G
PERIOD
Figure U4 - TOTAL RAIL CARGO EXPORTED (SIM. Ill)
103
\ .<. S C C NC C R EXPORT CAPABILITY SIX MONTHS 18VAY7
SLVMAKY 15SHIP TIME AT ALL 3E^ThS ( ILL SHIPS)
f: L >PS=Q T I V = L_IA£UJ£LU AYS)
TCTAL TIve 17Se£ r^S 2 ^ MIN 2 5 SECMAXIMUM TIMET £33 mkS 15 MIN 5^ SEC (CCCURREO ON DAY 51.
3_d ( - T \ N r NJ - AT Try. 1 r -* S )
JVr^Aot TIMEVIMMUV T I ME
ItS HRS 3 MI\ 15 SEC10e HRS 51 Al\ 7 SEC (OCCURRED ON DAY 124,
3EGINMNG AT TIME 1024)
I-C5 MIN S E
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Figure 45 - SHIP TIME AT ALL BERTHS (SIM. Ill)
109
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NUMBER OF VANS IN STORAGE ARE
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1 14
APPENDIX H
RESULTS 7 SI.'DILATION RUl] IV
Simulation run IV was run attempting to achieve 90
percent berth occupancy. The vessel arrivals were Poisson
distributed with interarrival time of 3 7 hours in
order to generate about 117 vessels in the 130 day period.
rrain arrivals were given a distribution of no trains
arriving 25 percent of the days, one train 50 percent of the
days, i a ; two trains arriving 25 percent of the lays.
Arriving trains had 50 cars UO percent of the time and o0
cars the remainder of the time. Therefore, on the average,
56 raiicars arrived each day.
Trici arrivals wer^ Poisson listributed with a mean
interarrival tine of 30 minutes during normal operations.
'.ihsn the number of vans in the storage area was l^ss than
10, the .map. interarrival time was 10 minutes and when the
numoer of vans in the storage area was greater than 150, the
aean intararrival time was 50 minutes. The average number
of truck arrivals per sight
expect el to be 16. 6
.
hour receiving period *is
The results for simulation run TV are shown in the
accompanying fijures and are summarized as follows:
1) Occupancy rate was 5.9 9/6 = 9 9.6 percent.
2) Van cargo exported was (3 132) (18) = 5o37b lona tors.
3) Rail cargo exported was (1026) (540) = 55404G long
tons.
1 15
4) Total cargo exported was 554040 + 56376 = 10,416
long tons.
5) Number of vessels turned around was 127.
6) Percent of vessels delayed was 100 - 100.0 = 0.0
percent
.
7) h verage/maxiiuum number of vans in the storage ir^a
was 5 5.2/150.
8) average van storage time was about 72.2 hours.
}) average/maximum number of railcars in the storage
area was 105/520.
10) Average railcar storage time us about 42.5 hours.
1 16
NWS CCNCCRC EXPORT CAPABILITY SIX MONTHS 19MAY77
V M A R Y 1 C
CCCUPANCY CF ALL ci^ThS (TCTAL NL^EEF OF SHIPS IN FCRT)
-IO-EAJ auyfJ r3 TM._£=av.LC=_«- I1E1 _LQ1_
TCTAL NUMBER IN REPORT FERIOC 127MAXIMUM 6 (ON DAY _AT 8 0.
AVERAGE E»<59VINIMLV C (ON CAY AT CO)
NUV6E3
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PERCENT GF REPCPT PERIOD0.2
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Figure 51 - OCCUPANCY OF ALL BERTHS (SIM. IV)
1 17
NWS CCNCCRC EXPCRT CARAdlLlTY SIX MONTHS 1 9MA
SLWAPY 12TOTAL TRUCK CARGO iXPC3T-C (UNITS OF 13 LGNGTCN)
TOTAI NiiM - C R r \, c-w\/TrFf p-i ay-^ t re
im -
TCTAL NUNieEPi IN wEPCPT RE^IGC 3152MAXIMUM 211 (GN DAY 146 AT 1253)-WER * G5 13 6 .4 ?M IN I MUV C (GN DAY AT )
NUVbER PER' :nt CF REPORT PEP I
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1 13
N*S CCNCCRC EXFCRT CAPABILITY SIX MONTHS 1=
SLNN1 A rY 12TOTAL KAIL CARGO EXFCRTEC (UMTS OF 54C LONGTCNS)
tct;l NJ^E^ IN serv IJ_E__CELAYEO. Cw IDL 2
TCTAL MJM8ER IN f^EPCST FERICC 102 6.lAXHUM 45 (ON DAY 7 AT 2:52.
ANC _r. TK_L TIMES)WE RAGEMINIMUM
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119
NaS CCNCCRC EXFCPT CAPABILITY SIX MONTHS 19MA Y
TC T a L time 2 5 1 = HR S 4 M I
N
VAXIMUV TIME 2.2 1 HRS 2 M IN 2 c
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TOTAL NUMBER 27
Figure 54 - SHIP TIME AT ALL BERTHS (SIM. IV)
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S L V V A F Y 1
NUMBER CF VANS IN S T J -! A G H AREA
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122
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APPtNOIX I
RESULTS 0? SIMULATION 2UN V
Simulation run V was run attempting to achieve SO
percent berth occupancy. The vessel Arrivals were Poisson
distributed with a mean in terarrival time of 4 2 hours in
order to generate about 104 vessels in the 130 day period.
Train arrivals were given a distribution of no trains
arriving 25 percent of tha days, one train 50 percent cf the
lays, and two trains arriving 25 percent of the days.
Arriving "-.rains had 50 cars 100 percent of the tine.
Therefore, on the average, 50 railcars arrived each lay.
Truck arrivals were Poisson distributed with a mean
interarrival time of 33 minutes during normal operations.
When the number of vans in the storage area was less than
10, the lean interarrival time was 13 minutes and when the
number of vans in the storage area was greater than 150, the
mean Interarrival time was 53 minutes. The average number
of truck arrivals per eight hour receiving period was
expected to be 14.5.
z results for simulation run V are shown in the
accompanying figures and are summarized as follows:
1) Occupancy rate was 4.65/6 = 77.5 percent.
2) /ir. cargo exported was (2496) (18) = 44928 long tons.
3) Bail cargo exported was (340) (540) = 453600 lona
tons.
4) Total cargo exported was 453600 + 44928 = 458,528
126
long tons.
5) Number of vessels turned around was Q 7.
6) Percert of vessels lelayed was 100 - 44.3 = 55.7
percent
.
7) Average/maximum number or vans in the storage area
was 9 4 . u / 1 9 6
.
-3) Average van storage time was about 153.1 hours.
9) Average/maximum number of railcars in the storage
urea was 500/1530.
10) Average railcar storage time was about 404.9 hours.
127
NWS CCNCORO EXPORT CAPABILITY SIX MONTHS 20MAY77ViAKY 10 '
OCCUPANCY OF ALL BERTH-S (TOTAL NUMBER OF SHIPS IN PCRT )
T G TA I NUMBER—IN S-R-VICE.—OEL-AYEH. OR IDLE
TOTAL NUMBER IN REPORT PERIOD 97MAXIMUM 6 (ON DAY AT 8 0.
-AUO 6-5—&-T4HER T-H4E S->-AVERAGE 4,65MINIMUM (ON DAY OAT 00)
NUMBER PERCENT OF REPORT PERIOD0. 2
1 THRU 1 1.92 THR U 2 _-2-*-0 ______3 THRU 3 16»84 THRU 4 7.65 THRU 5 10.76 THRU 6 50.9
OVER 6 0.0
Figure 60 - OCCUPANCY OF ALL BERTHS (SIM. V)
123
NWS CGNCCRC EXPORT CAPABILITY SIX MONTHS 20MAYSUMMARY 12
TOTAL TRUCK CARGO EXPORTED (UMTS OF 13 LONGTON)
_T_OXAi— NU.VHFR TN SERVICE,. DELAYED. OR IDLE
TOTAL NUMBER IN REPORT PERIOD 2496MAXIMUM 205 (ON DAY 26 AT 16 9)A^ERAG-£MINIMUM C (ON DAY AT 0)
NUMBER PERCENT OF REPORT PERIOD
1 THRU 100 42.7101 THRU 200 57.0201 THRU 30C 0.1—3^>4_T_><py—4 3^ 0^-Q401 THRU 500 0.0501 THRU 600 0.0601 THRU 700 0.07 1 THRU 30 -0 0^-0-S01 THRU 900 0.0901 THRU103C 0.0
OVER1 300 0.0
Figure 6 1 - TOTAL TRUCK CARGO EXPORTED (SIM. V)
129
nN*S CCNCCRC EXPORT CAPABILITY SI X MONTHS 2CMAY77
SLVMAPY 13TOTAL RAIL CARGO EXPCRTEC (UNITS OF 54 LONGTONS)
^N S E RVICE*—T.ELAYED.—OR IDLE
TOTAL NUMBER IN REPORT PERIOD 840MAXIMUM 47 (ON DAY 23 AT 1227)
. AA/-ERAGE 2 V+&ZMINIMUM ( N D A Y AT 0)
NUMBER PERCENT OF REPORT PERIODQ-,-6
1 THRU 100101 THRU 200201 THRU 300-3^-^-THRU—A&G-401 THRU 50C501 THRU 60C601 THRU 700701 THRU 300
59.4O.C0,0
0,00,0CO
8C1 THRU 9009C1 THRU1000
OVER1 00C0.00.0
Figure 62 - TOTAL RAIL CARGO EXPORTED (SIM. V)
1 30
N-S CCNCORC EXPORT CAPABILITY SIX MONTHS 2CMAY77
SLVMAPY 15SHIP TIME AT ALL BERTHS (ALL SHIPS)
._I_J___3_L. !i_—C_lJVCL.UDXN.C_D.tl_A YS L
TOTAL TIME 19522VAXIMUM TIME 237
AVERAGEVIM MU M
TIMET I ME
2C1108
HRSHRS
HRSHRS
2553
Ts44
M INM IN
MINM IN
g1 1
4427
SECSEC (OCCURRED
Q-EGXMvlNGSECSEC (OCCURRED
BEGINNING
ONAX-
ONAT
DAYJLXME-
DA YTI ME
17,—7- ax
160.610)
HRS MI N SEC HRS1^IN SEC FREQUENCY PER CENT
96 c Cfi LE ss 0. C96 c THRL 103 0.
1 03 r. THRU 1 20 Q c 5« 212C C THRL 1 32 C C 0.132 C THRL 1 44 c 0.144 C THRL 156 1
1 1 .
1 =;* r> n Th-RU 168 a o o 0»163 c a THRU 130 4 4, 1
iec g j THRU 192 o U 9 9. 3192 c C THRU 204 c 27 27.8PP4. r n THRL ? 1 6 28 29.Q216 o THRL 228 c 21 21 .6228 c THRU 240 2 2. 1
2a? c THRL 252 0. C5 = ? n o THRL 264 l-> r> t n
264 c THRL 276 0.276 c THRL 238 Q c 0. C
MCRE THAN 283 O.C
TOTAL NUMBER 97
Figure 63 - SHIP TIME AT ALL BERTHS (SIM. V)
131
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N*S CONCORD EXPORT CAPABILITY SIX MONTHS 20MAY77SUVMARY 1
NUMBER CF VANS IN STORAGE AREA
T C TAJ— NLLMBER IN SER-V.LCE-, DELA.YE.O. OR- IDLE
TOTAL NUMBER IN REPORT PERIOD 5299MAXIMUM 196 (ON DAY 104 AT 1559)_—A-VE-RA<i£ 94^42-MINIMUM (ON OAY AT 8 0.
AND 72 OTHER TIMES)
-NU-MaS.fi P-&R-CSNT OF REPORT PERIOD1.6
1 THRU 25 15.326 THRU 50 12,2
—5-1 -T--HRU 75 1-3-, 4-
76 THRU ICO 10.3101 THRU 125 12.5126 THRU 150 8.7
.I 54__T-HRU— 1-7-5 1-8.-7-
176 THRU 200 7.32C 1 THRU 225 0.0226 THRU 250 0.02SI- THRU- 2 75 0.-0276 THRU 300 0.0301 THRU 325 0.0326 THRU 350 0.0-35-1 THRU 375 0-.-0-
376 THRU 400 0.0OVER 400 0.0
Figure 65 - NUilBER OF VANS IN STORAGE AREAS (SIM. V)
13}
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SUV MARY 2NUMBER CF TRAINS (10 CA^S PER TRAIN) AT STORAGE AREA
T_QIAi__NUMaER_ L-N—SE-BV-I C £. aELAY£-CU—QR IPLE-
TOTAL NUMBER IN REPORT PERIOD 1747MAXIMUM 153 (ON DAY 140 AT 8 0)
_ AVERAGEMIN IMUM
Nil IMRPC
-S-O*Q (ON DAY AT 8 0»AND 1 OTHER TIMES)
'SRCr^T nr opdhot dpothh
1
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Figure 67 - NUMBER OF TfiAINS AT STORAGE AREA (SIM. V)
135
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136
appendix j
vessel export tonnages in class groupings yg1 january
through ju;;:^ 19cb
This appendix is a representation of all vessels in each
class, of the vessels that made complete turnarounds in the
first six months of 1363, snowing the relative distribution
of vessel cargo tonnages within each class and the number of
vessels in the class.
137
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1 3
APPENDIX K
132 TRIANGULAR DISTRIBUTION
Tha triangular distribution is a distribution made
availa-ola to the users of T3ANSIK. The upper and lower
ranges of the event possibilities ara defined to have zero
probability. The only other significant point on the
probability curve is it an arbitrary point between the upper
ind Lowar limits *ith an assigned probability. Connecting
tha threa points p orms a triangle, h>?nc? the name. In™
"curve" is just two connecting straight lines. The figure
belorf jives an example of the triangular density function
for tha berth time listribution o£ a class 01 vessel use-! in
this aod ?1
.
igure 70 - \ TRIANGULAR DISTRIBUTION
13)
LISP OF 23FSEE>I CES
1 • Iisj_tor Brief in gx Naval Weapons Station, Extracted from
the Haval Ordnance Capability Catalog.
1H )
3Ibliograp:iy
1 . Bre liar, , Loo, Statistics, With View Toward
10
1 1
Applications, Houghton Mifflin, 1973.
Cinlar, Erhan , Introduction to Stochastic Processes,
Prentice-Hall, 14 75.
Fishman, G. S., Concjjto _anj Methods Ln r)iocr-3te Event
Digit al Simulation, tfiley, 1973.
Frank, .
.
Transmission,
and Frisch, I. T. , Coinai unicat ion
ml Transportation Net wo rks,
Addison- Wesley, 13 71.
Gas s, S. I., Linear Pr oar 2a^J.nj , McGraw-Hill, 4 th
edition, 1975.
Hadley, G., Linear ? to^rd^jino , Addison-Wesley , 1962.
Larson, H . J., Introduction to Probab ility Theorv and
Statistical Inference, Wiley, 1969.
loder, J. J. and Phillips, C. R., Project /Jinajetont
±Ll- CF^ and ?_:JT , Reinhold, 1964.
raft, C. A., Management of ?avsical 2istrJ.buJ.ian 1R&
lE3.H£22Ii^-^ii2H' Irwin, 1972.
2ZKS£lL'- II General ZlL2'2^2. Il^lll Simulator, User ' s
Uanual, School of Engineering and Applied Science,
UCLA, 1971.
isitor Brief ing^ '^I'i^.L Weapons Station, Extracted from
the Naval Ordnance Capability Catalog.
U1
12. Whitehouse, G. E. , Systems Analysis and C^si^n Using
Network Techniques, Prentice-Hall, 1973.
1 4 2
INITIAL DISTRIBUTION LIST
No. Copies
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Library, Code 0142
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Department of Administrative Science
Nav\i Postgraduate School
Monterey, California 93 94
Command and General Stiff College
ATTN: Educational Advisor
2oom 123, Bell Hall
Fort Leavenworth, Kansas
LCD 3. Robert * . Sagehorn, USN Code 54 s
Administrative Sciences Department
Naval Pcstgradjate School
Monterey, California 93 940
LI. Don Bouchoux, USi.' Cole 553H
Operations Research Department
Naval postgraduate School
Monterey, California 93940
CPT. David C. Fountain
LHA , New Cumberland Army Depot
v Cumberland, Pennsylvania 17070
143
171622
I
Thesis
F6635 Fountain
c# l Naval Weapons Station/
Concord export capabil-
ity: a simulation mod-
el. .2667 6
17 CCT £8 3 2 8 8 1
JUN 9??V2B3862
ThesisF6635c.l
1^622Fountain
Naval Weapons Station,Concord export capabil-ity: a simulation mod-el.
thesF6635
Naval Weapons Station, Concord export ca
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