Inventory Theory - Group 11 - Technical Paper (1)

8/13/2019 Inventory Theory - Group 11 - Technical Paper (1)

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HIERARCHICAL FACILITYNETWORK PLANNING MODEL

FOR GLOBAL LOGISTICSNETWORK CONFIGURATIONS

FRANS HENDRY DAVID - BRIAN

INVENTORY THEORY

FALL 2013NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY

THE BEST SUPPLY CHAINS AREN'T JUST FAST AND COST-EFFECTIVE.THEY ARE ALSO AGILE ANDADAPTABLE. - HAU L. LEE


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OUTLINE

Introduction

System Specification

Hierarchical Cluster Analysis

Facility Network Configuration

Model Development

Experimental Design & Data Collection

Setting Parameter Analysis of Numerical Study

Numerical Study

Conclusions

INVENTORY THEORY - FALL 2013NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY

GROUP 11TECHNICAL PAPER


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INTRODUCTION




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Network configurations are critical issues in the area of global logistics (GLs) as theydetermine the performance of GLs operational strategies.

Elaborate via integration and classification of corresponding facilities, such as internationalhubs and depots.

Performance of GLs strategies and their functional integration should rely on elaboratenetwork configurations to accomplish the goals of GLs management.

Implementation: DHL, UPS, FedEx and TNT

Challenges for GLs network design:

1. Efficiently coordinating activities of all transnational facilities, is difficult due to thedifferent functional relationships in both the spatial and temporal domains.

GLs operational networks are typically hierarchical, containing different nodes located indifferent network layers, where each node has its own operational goals and problems.

2. Existing models that are suitable for GLs hierarchical network planning are scarce.

Most of previous literature is likely to address the issue of GLs network configurations directlyby mathematical programming, thus solving the induced facility location problems all in onephase without considering the hierarchical and geographic relationships



PROBLEM FORMULATION


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Paper Study that were used in this paper are divided intothree sections:

Pioneering Work

Algorithm Paper

Technique Paper



LITERATURE REVIEW (1)


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General logistic network configuration have been addressed in some pioneeringworks:

1. T. Miller, D. Wise, L. Clair, Transport network design and mode choice modeling forautomobile distribution: a case study

Determine best transport mode and rail network location strategy using a mixed integer programmingmodel

2. T.G. Crainic, Service network design in freight transportation

Used Mathematical programming approach for inter-modal service network design to seek for a set ofinterrelated decisions that ensure an optimal allocation and utilization of resources to achieve theeconomic and customer service goals of the company.

3. S. Melkote, M.S. Daskin, Capacitated facility location/network design problems

Formulated a combined facility location/network design problem using a mixed integer programmingapproach, where service capacities of facilities are considered.

4. A. Cakravastia, I.S. Toha, N. Nakamura, A two-stage model for the design of supply chainnetworks

Developed an analytical model of the supplier selection process in designing a supply chain network,where the capacity constraints associated with potential suppliers are considered in the supplierselection process





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5. V. Jayaraman, A. Ross, A simulated annealing methodology to distribution networkdesign and management.

Proposed the Production, Logistics, Outbound, Transportation (PLOT) distribution networkdesign system, which was characterized by functions of multiple distribution channelmembers and their corresponding locations

6. Z. Drezner, G.O. Wesolowsky, Network design: selection and design of links and

facility location

Introduced a novel network design problem which determines the links and facilitylocations, using several heuristic solution tools such as a descent algorithm, simulatedannealing, tabu search, and a genetic algorithm.

7. D. Ambrosino, M.G. Scutella, Distribution network design: new problems andrelated models

Solved some complex distribution network design problems, which involve facility location,warehousing, transportation and inventory decisions





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Few hierarchical network design studies have used algorithm:

8. J.R. Current, C.S. Revelle, J.L. Cohon, The hierarchical design problem

Formulated a hierarchical network design problem (HNDP) for identifying the shortestpaths among facilities in a proposed two-level hierarchical network.

9. N.G.F. Sancho, The hierarchical network design problem with multiple primary

paths Developed a dynamic programming model to find a suboptimal solution for the HNDP with

multiple primary paths. For all hierarchical network characteristic, the model still stressesthe algorithm improvement to search better optimal solution in the proposed model

10. C.C. Lin, S.H. Chen, The hierarchical network design problem for time-definiteexpress common carriers

Utilized a time-constrained hierarchical hub-and-spoke network to determine fleet size andschedules on primary and secondary routes to minimize total operating cost while meetingthe desired service level.





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Related technique paper:11. Y.W. Wan, R.K. Cheung, J. Liu, J.H. Tong, Warehouse location problems for

airfreight forwarders: a challenge created by the airport relocation

12. C.C. Lin, Y.J. Lin, D.Y. Lin, The economic effects of center-to-center directs on hub-and-spoke for air express common carriers

13. M. Wasner, G. Zapfel, An integrated multi-depot hub-location vehicle routingmodel for network planning of parcel service

14. A.A. Chaves, L. Lan, Hybrid algorithms with detection of promising areas for theprize collecting traveling salesman problem

15. J.-B. Sheu, A novel dynamic resource allocation model for demand-responsivecity logistics distribution operations

16. K.-M. Osei-Bryson, T.R. Inniss, A hybrid clustering algorithm17. A. Nagurney, D. Matsypura, Global supply chain network dynamics with

multicriteria decision-making under risk and uncertainty

18. B. Groothedde, C. Ruijgrik, L. Tavasszy, Towards collaborative, intermodal hubnetworks: a case study in the fast moving consumer goods market





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Main weakness for previous literature research: Studies regarding hierarchical GLs network configurations are rare

Limited to the scope of domestic logistics, and thus, issues of global logistics and influencingfactors characterizing uncertainties of transnational logistics activities remain unsolved

This paper presents a novel planning methodology for hierarchical GLs network

that integrates cluster analysis and integer programming to solve the GLs networkdesign problem.

In short, this study contains:

1. Hierarchical clustering to partition the demand dataset into a meaningful set of mutuallyexclusive hierarchical clusters.

2. GLs facility classification, where influencing factors associated with each type of facility are

considered.3. The integer programming methodology , to address the resulting network design issues,

where the corresponding facilities (hubs, dist. Center, depot) are hierarchically structured.

This study also considering multiple GLs channel members and related factors(e.g., customs accessibility, transnational transportation and inventory costs)





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SYSTEM SPECIFICATION


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System Component Definition

Conceptual Framework



SYSTEM SPECIFICATION


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Three node types are defined:

Hubs

Distribution

Center

Warehouse

Depot

Based on



SYSTEM COMPONENTDEFINITION


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Service component intensity () is composed of:

Transshipment amount ()

Storage value ()

Where= + Specifically is used to determine the type of candidate facilities

1Hub

2 < 1DistributionCenter

< 2Warehouse

1and 2are two thresholds

which can be determined by

averaging the values suggested by

GL enterprise managers in

practical application



SYSTEM COMPONENTDEFINITION


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PHASE 1

Identifying original demand spotlocation

Hierarchy clustering the demand spotlocation

PHASE 2 & 3

Determining the number and locationof facility nodes for each demandgroup using integer programming.

Considering several factors:

1. Investment costs and risks

2. Logistic operational costs

3. Potential benefits

4. Transnational logistic restriction

5. Regional demand variations



CONCEPTUAL FRAMEWORK


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Four assumptions used to facilitate the model formulation:

Only three facility types (hubs, distribution center, warehousedepots)

Differ in their express cargo capacities

The demand quantity associated with each given original demandspot is known.

The range of service-competence intensity associated with eachnode type in the proposed hierarchical GLs network is known.

The proposed hierarchy is composed of three layershubs,distribution centers, and warehouse depots

The facilities on a given layer are served by facilities of layer directly above.



CONCEPTUAL FRAMEWORK


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MODEL DEVELOPMENT


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SCHEME OFPROPOSED MODEL


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DEMAND-SPOT HIERARCHICALCLUSTER ANALYSIS



Hierarchicalcluster analysis

considers each

given object ias

a point in a

multi-

dimensionalspace

characterized

by two

attributes : the

amount of

inboundandoutbound

cargo

associated

with object i

Variable is an

important step in

hierarchical

cluster analysis,

since differences

in units and

magnitude of

variance betweenattributes

influence

computational

results of

distance metrics.


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HIERARCHICAL TREE




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FACILITY NETWORKCONFIGURATION



Given the aforementioned assumptions, an integrated and composite multi-

objective model is formulated to obtain optimal solutions with the goals of :

Minimizing hierarchical GLs network investment cost (1)

Maximizing profit from hierarchical GLs network operations (2), and

The aggregate satisfaction rate of customer demand (3).

However, these three goals may be in conflict during the corresponding

hierarchical GLs network configuration process. A typical example is the trade-off

between minimizing hierarchical GLs network investment cost and maximizing

operational profit.


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FACILITY NETWORKCONFIGURATION




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GLs NETWORK INVESMENTCOST



BC : Building Cost

LC : Land Cost

AIC : Asset Input Cost

RRC : Related Risk Cost

RMC : Raw Material Cost

LBC : Labor Cost

LC : Land Cost

MC : Machine Cost

EC : Equipment Cost

PR : Political Risk

NDR : Natural Disaster Risk


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PROFIT FROM HIERARCHICALGLs NETWORK OPERATIONS



r : revenue

oc : operational cost

tdc : transportation and distribution cost

rorc : operational risk-oriented cost

r : revenue

oc : operational cost

tdc : transportation and distribution cost

err : exchange rate risk

hr : human resource


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THE AGGREGATE SATISFACTIONRATE OF CONSUMER DEMAND



Accounts for the proportion (Z) of potential consumer demands (D) that

can be served by the logistics distribution amount (Y) planned within a

preset upper bound of delivery time (t)


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NUMERICAL STUDY


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TAIWAN CHINA USA



EXPERIMENTAL DESIGNAND DATA COLLECTION (1)


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Based on the predetermined thresholds and proposed facility identificationrules, 3 original demand spots in Taiwan (e.g. Taipei, Taichung, and Kaohsiung)were chosen as candidates for the consideration of locating hubs.

Similarly, there are 15 and 36 original demand spots chosen as candidates forlocating hubs in China and the USA, respectively.

Accordingly, the problem scope has 3429 decision variables subject to 1068constraints.

Taiwan - 15 original demand spots

China - 116 original demand spots

USA - 260 original demand spots

using local population data

Thresholds 1and

2for facility

service-competence intensity index ()

averaging the values suggested by GL

enterprise managers were used in the

case study





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First, this study collected data for local populations of these demand spotsand the corresponding gross domestic product (GDP) data from databases inTaiwan, China, and the USA.

International express delivery demand associated with each original demand spotwas then approximated using a proportion of GDP and the corresponding local

population.

The next step generated a four-tier GLs hierarchical network based ongeographical relationships

Step by step

the threemain regions

(first tier)

sub-regions(second tier)

the local area(third tier)

originaldemand spots(fourth tier)





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HIERARCHICAL CLUSTERRESULT OF PROPOSED MODEL


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Estimating cost-related parameters directly from reported statistical data isdifficult due to business confidentiality and security concerns.

Therefore, interviews with key staff in express operations and logistics-related sectors of DHL were conducted to collect real data. The interviews

utilized both open-ended and closed questions regarding existingstrategies in express air delivery and logistics management, aswell as potential limitations.

A questionnaire was designed based on the need to estimate cost-relatedparameters of the model. For example, given a cost item, the correspondingsurvey respondent was asked to measure unit cost within an acceptable range.

Model parameters : (1) cost-related parameters(2) risk-related parameters

(3) boundary conditions

Estimated from:

interview survey data and

corresponding statistics.



SETTING PARAMETERS (1)


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Corresponding parameters are classified into and associated with the

following activities:

Governmentstability

NaturalDisaster

Exchangerate

Personnelskills




Risk-related parameters estimated in this scenario aim at

the unit increments of money risks for environmental

risk cost and operational risk cost induced by the

hierarchical GLs network configuration.


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Among these risk-related parameters, Government Stability and Personnel

skills are associated with the corresponding artificial organization andbehavior; the others are influenced by natural disasters and operationalsituations in the resulting hierarchical GLs network. As mentioned, a unitincrease in risk-induced penalty refers to the monetary value of a particularpenalty caused by the unit of a given physical amount associated with aparticular activity.

Natural Disaster Risk

To estimate unit incremental risks for natural disasters, this study firstaveraged aggregate earthquake and flood damage costs of these three regionsover the last 30 years using historical data provided by central governments.

Second, aggregate damage costs caused by earthquakes and floods weremeasured using the averaged aggregate earthquake and flood damage costsmultiplied by the ratio of natural disaster frequency over the last 30 years.





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Exchange rate risk

Exchange rate risk may depend on foreign reserves, exchange law, and foreigndebt. Therefore, this study estimated the exchange-oriented risk by approximating

the corresponding comparative measures of exchange risk from BERI, which is

similar to the concept of political risk cost for the three regions.

Here, according to the proposed method, exchange risk can be expressed by the

amount of foreign debt divided by the amount of foreign reserves. In this case study,

statistics for foreign debt and foreign reserves for these three governments were used

to estimate the corresponding exchange risk.

Personnel Skill RiskSimilar to risks induced by the government stability, personnel skill risk can be

caused by either Democracy or Communism. Accordingly, was estimated usingcomparative measures of freedom developed by the Freedom House and BERI for

workers and society. Notably, parameter may vary with race; particularly, whites

currently have an advantageous position worldwide.





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Parameters in the hierarchical GLs network based on cost-minimum function

Parameters in the hierarchical GLs network based on profit maximum function

Parameters in the hierarchical GLs network based on other primary parameters

Cost and risk-related parameters estimation:

ANALYSIS OF


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The numerical studies consider 2 different test scenarios:



ANALYSIS OFNUMERICAL RESULTS (1)

1. Performance of proposed model VS Existing model

Express delivery enterprise case without coordination of threefacilities, including hubs, distribution centers, and warehouse depots.

2. Sensitivity Analysis

Used to realize what the most important parameters are in theenterprise and assist in enterprise resource planning.

All other parameters preset in Tables 24 remain the same in both scenarios

LINGO 9.0 software package is employed to search for optimal solutions

ANALYSIS OF


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First Scenario: Optimal solutions was generated using the proposedmodel, and then compared the resulting aggregate profit with existingoperational performance.




The existing GLs network ofDHL are primarily driven

by operational strategiesto maximize profit.

The weight (w2)associated with sub-

objective function of

profit-maximizationis setto 1

Proposed model is likely to outperform the

existing strategy

ANALYSIS OF


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Second Scenario: we conducted sensitivity analysis with respect tothe following four parameters

The combination of w1= w2= w3= 1/3 was chosen for all casesexcept for the sensitivity analysis with respect to these weights (No. 4)




1The threshold(1) associated

with the facilityservice-

competenceintensity index

for determinationof hub locations

2

Original demand(Dj)

3

Upper and lower

bounds of thesatisfaction rateof customer

demand ( ,

)

4

The weights

associated withthe three sub-

objectivefunctions (w1,w2, and w3)

ANALYSIS OF


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The threshold (1) Sensitivity Analysis

In this analysis, the authors tried to know the effect of a different companypolicy in terms of facility allocation threshold (1) on the number ofhubs that can be located in certain area.

Implication:

A GL enterprise is allowed to loosen the facility allocation threshold bychoosing a lower value of 1 for deciding hub locations if the enterprisehas enough capital and human resources allocated in GL networkconfigurations.




> = > >

> > > >

> > > >

ANALYSIS OF


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The Demand and Satisfaction rate Sensitivity Analysis

In this analysis, the authors tried to find out the effect of the change innumber of demand and customer satisfaction rate for he proposed model.

Implications:

The reduction of original demands may contribute significantly to the

aggregate improvement in system performance. Given the necessity of increasing the satisfactory rate of customer may also

improve the aggregate performance of proposed model.




We think that its more appropriate to show the improvement in terms of

profitinstead of in terms of cost

ANALYSIS OF


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The Weights Configuration Sensitivity Analysis

The authors want to find out the best configurations of the weightsassociated with the three sub-objective functions (w1, w2, and w3).

Implication:

Adjusting the corresponding weights associated with the three sub-objective functions have a significant effect on enhancing overallimprovement.





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This work has presented a novel approach that integrates hierarchical

cluster analysis and integer programming to formulate a

hierarchical GLs network model for dealing with the facility location

problem by minimizing total costs and maximizing operational

profit and the satisfaction rate of customers.



CONCLUSIONS (1)

Compared to previous literature, the proposedmethod has 2 unique features:

The corresponding integrated supply & demand sides of the three-layer

hierarchical GLs network are formulated using generalized mathematicalform.

Internal and external factors (e.g., fundamental investment cost requirements,basic requirements of operational costs, related operational and disaster risks,and the satisfaction rate of customers) are considered by the proposed model.


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CONCLUSIONS (2)Case Result for the most suitable site for locating a hub forinternational express delivery enterprises in each area.

Taiwan Taipei

China Shenyang, Beijing, Shanghai, Chongqing, and Hong Kong

USA Los Angeles, Phoenix, Dallas, Houston, Chicago, New York, and Boston

Managers of international express delivery enterprises can conveniently

employ the proposed model as a decision-making support tool to

assist in strategically determining precedence for locating thecorresponding facilities, according to operational goals and overseas

investment resources.


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FUTURE RESEARCH The proposed model can be extended to determine dynamic multi-

resource allocation based on hierarchical GLs network configurationsproblems.

in-depth identification of qualitative and quantitative factors, such asdemand variation, risk uncertainty, and the time difference betweendifferent zones, also warrant further research.

Utilization of elaborate measures for demand data aggregation issuggested in further research to demonstrate the applicability of theproposed method.


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THANK YOUFOR YOUR ATTENTIONINVENTORY THEORY

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Inventory Theory - Group 11 - Technical Paper (1)