Collaborative Slot Allocation Sea Rail Multimodal

8/10/2019 Collaborative Slot Allocation Sea Rail Multimodal

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A collaborative slot allocation model for the sea-rail

multimodal transport service providers based on

revenue management

Yasanur Kayikci ([email protected])

Montanuniversitt Leoben, Industrial Logistics

Abstract

This paper concerns the operational integration of liner shipping and railway freightservice providers and proposes a collaborative slot allocation model for the sea-rail

multimodal transport with the revenue management. The methodology is based on a

linear programming model, which maximizes the freight contribution and provides an

optimal slot allocation data according to different fare classes. The model obtains also

the multimodal transport service providers with this solid background to support their

strategic and operational decisions.

Keywords: Multimodal transport, Revenue management, Slot allocation

IntroductionSince the deregulation of the aviation industry in the late 1970s there have been

numerous advances in the field of revenue management (RM). Although this field is

now well-established in the airline (Belobaba, 1987; Smith et al., 1992; Vinod, 1995)

and hotel industries, academic researchers have neglected to investigate the

opportunities and challenges in other industries such as in multimodal transport industry

(McGill and van Ryzin, 1999). The multimodal transport (also known as combined

transport, intermodal transport or co-modality) becomes a major revenue contributor to

increase the income and it may offer similar opportunities to those found in the airline

industry, but this industry has seen relatively little attention to RM problems. Transport

service providers in this industry have difficulty making a reasonable profit from the

multimodal transport operations and even they may run a deficit. Multimodal transport

concept unites multiple modes of transport to facilitate an efficient, effective and

sustainable transport network. It performs at least with two different means of transport

among sea, rail, road, inland waterway and air in transport chain. The association of

transport modes for multimodal transport takes place across different combinations such

as: road-rail, river-road, sea-road, sea-rail, and so on. Nevertheless, multimodal

transport suffers from some constraints. Multimodal transport can be generally

competitive at transported distances above 300 km (longer than one day of trucking)

(Lowe, 2005; Tavasszy and van Meijeren, 2011). The industry is marked by a wide

variety of bilateral relationships and contract negotiations. These lead to a high degree

of non transparency in the market. This industry is a capital-intensive industry whichrequires high investments in vessels, containers, wagons, port/terminal equipment and

infrastructure; therefore it is characterized with high fixed costs and economies of scale.


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Additionally, multimodal transport service providers (MTPs) may often operate with

unutilized capacity due to the circumstances like market fluctuations, trade imbalances

or demand changes. In this respect, an efficient capacity management and slot allocation

in multimodal freight transport are of critical importance in ensuring that MTPs operate

collaboratively. In the competitive freight market, MTPs may employ RM in order tomaximize profits by using slot allocation and pricing.

In this research, a collaborative slot allocation model is applied by using linear

programming in order to maximize freight utilization. This model is developed for the

sea-rail multimodal freight transport where two MTPs establish transport collaboration.

MTPs use RoRo vessels and RoLa trains for sea-rail connections and they carry semi-

trailers. The model is illustrated with a case study of a liner shipping provider and a

railway freight provider which operate together. The result of this study gives the

applicability of proposed model in practice.

Research Focus

The nature of the transport modes is inherently competitive and has tended to produce atransport system that is segmented and un-integrated. Each transport mode, particularly

that operate them, has sought to exploit its own advantages in terms of cost, service,

reliability and safety (Reis et al., 2013). But the lack of modal interconnectivity and

non-collaborative behavior among modes might cause transport inefficiencies like

insufficient freight revenue realization. Multimodal transport represents a major effort

to integrate separate transport systems. In this respect, creating the collaborative

synergies among the number of MTPs from the different modes of transport and

development collaborative business models is extremely important in order to drive

those inefficiencies out of supply chain and to operate the freight flow more efficiently,

effectively, and sustainably (Kayikci and Zsifkovits, 2012). Sea-rail multimodal

transport is still in the early stages; it hasnt reached the required transport volume yet in

comparison with other types of multimodal transport (Rodrigue, 2013). But due to

recent policy changes and significant investments in infrastructure and other measures,

the current condition of sea-rail multimodal transport may have a large room for

attaining noticeable improvement in coming future (Reis et al., 2013). On the one hand,

the development of the sea-rail multimodal transport relies on the construction of

networked comprehensive cargo hub system. These cargo hubs provide the multimodal

transport with the services of transport mode transfer. They usually have stockyard for

stacking transport units, as well as dispatching and configuration of freight trains,

vessels or vehicles. Meanwhile, they have good highway connections, railway facilities,

seaport and well-tuned information systems, which are essential for the freight transportservices and helpful for tracking, managing and controlling the freight flow (Lowe,

2005). On the other hand, the capacity management including route planning and

vessel/train scheduling is likely to be a crucial success factor for the sustainability of

sea-rail multimodal transport. Inadequate capacity utilization may cause dramatic losses

for MTPs. Therefore a high level of collaboration and seamless integration among

MTPs is significant. The capacity of freight trains as well as vessels is being utilized

generally at a rate of over 70% per trip. In this respect, RM strategies and technologies

may help MTPs to increase capacity utilization rate (load factor) and margins of their

services by applying slot allocation and pricing.

Revenue management (RM) can be defined as an integrated management of price

and inventory to maximize the profitability of a company (Kasilingam, 1998).Multimodal transport chain has the advantage that shippers (customers) only buy that

part of a transport service (space) needed to meet their individual requirements.


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Generally in multimodal industry it has aim to sell the right service to the right shipper

at the right price for the right duration. The determination of right entails achieving

both the most revenue possible for MTPs and also delivering the greatest commercial

utility or maximum value to the shippers (Cross, 1995). Here the slot allocation

according to different fare / shipper classes is very important. A few studies havealready treated slot allocation with the concept of RM adopted in industries such as

airline (Curry, 1990), hotel (Koide and Ishii, 2005), railway (Campbell, 1996;

Ciancimino et al. 1999) or shipping industry (Ha, 1994; Ting and Tzeng, 2004; Lee et

al., 2007), but there are still not any collaborative slot allocation solution exist in sea-

rail multimodal transport industry. Figure 1 depicts the collaborative slot allocation for

sea-rail RM systems. The sea-rail multimodal transport chain is constituted with multi-

node origin and destination (O-D) connectivity, where a number of liner shipping

provider(s) (seaway freight) and railway freight provider(s) collaborate together and

provide a weekly service for every calling node. The service travels along some route,

where route is made up of at least one leg, where a leg is define as two adjacent nodes

(railway terminal and seaport) (Armstrong and Meissner, 2010). System offers aseamless reservation; whenever shipper makes a reservation; collaborative system

allocates a space simultaneously on vessels deck as well as trains wagon. The goal of

RM is to find the optimal maximum of freight travelling along each leg in order to

maximize the overall revenue. Each MTP in multimodal transport chain determines its

available slot capacity, which is distributed to trip(s) according to shipper classes. Here

the problem arises in terms of management of the slot capacities and how the available

slot capacity of vessel and train is allocated for every O-D node pair according to each

shipper type such that the capacity of every vessel and train is utilized and the total

revenue is maximized. The RM faces also with the trade-offs in decision-making which

are analyzed under different scenarios like lower capacity utilization vs. higher

revenue or higher capacity utilization vs. lower revenue, reservation cancel vs.

capacity enlargements and so on. These scenarios may lead the MTPs to take strategic

and operational decisions for their collaborative activities.

Figure 1- Collaborative slot allocation for sea-rail revenue management system

There are three shipper classes, which address the demand and pricing strategy (Liu

and Yang, 2012). (1) contractual (regular) shipper is characterized with a fixed-

commitment contract and negotiated market price; a certain slot allotment is reserved in

vessel and train, where the orders of major shippers and forwarders have priority to be

fulfilled. (2) ad-hoc shipperbuys the slot with the spot market price; this fare is offered

until one-two weeks before the departure date of vessel or train. (3) urgent shipper


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provides typically last-minute freight service and is willing to pay a higher fare. The

reservation horizon is until two-to-three days before the departure. The fares close to the

departure date might become higher or lower in order to utilize the capacity with the

trend of fares depends on the sudden demand changes. In addition, every O-D node pair

has different freight rates. Fares can differ in terms of not only the shipper classes butalso the import or export purpose of travel (the direction of O-D node pair). Currently in

sea-rail multimodal transport, the fares for empty and loaded freight units are rated the

same; only the size and shape of freight unit can be categorized with different fare

classes. The sea-rail multimodal transport uses different transport units (i.e. semi-

trailers, maritime containers) for the shipment. Although each service contract for

contractual shipper may carry a different rate for the same capacity, the difference in

rate between two different contractual shippers is relatively small when compared to the

difference between any contractual and ad-hoc shipper. Therefore, the rate of ad-hoc, as

well as urgent, shippers can cause a greater impact on revenue of the shipment (Lee et

al., 2007). Determination of allotments for different shippers requires careful

consideration of a lot of important factors (Kasilingam, 1998). It must be made sure thatspace is available on the vessel as well as train to accommodate the allotment request.

After confirming space availability an evaluation must be made about the profitability

of the allotment. This evaluation will be based on the expected profitability of this

request versus the expected profitability from other requests (spot market sale demand).

Contractual shippers might request more allotments because the ad-hoc and urgent

shippers generate higher revenue. It is optimal to accept as many such shippers as

possible, hence it is better to set a certain slot allotment for contractual shippers. The

essential factor in determining the slot allocation is cargo demand which is not

deterministic, but its trend is reflected in past records and so can be estimated also for

the current trip (Ting and Tzeng, 2004). Different slot allocation scenarios are applied

into the sea-rail revenue management system in order to obtain an optimal slot

allocation data, whereby an advanced booking and final slot reservations can be made

by using this data.

Collaborative slot allocation model

The collaborative slot allocation problem is how MTPs allocate the available total slot

capacity for vessel space as well as train space according to shipper types from every

origin node (loading) to destination node (discharging) pair efficiently, effectively and

sustainably in order to maximize the total freight utilization from the whole trip. The

model deals with the collaborative slot allocation problem trip by trip. Here this model

is used to decide whether a transport unit from each shipper type should be accepted orrejected. If a contractual one is accepted, the model determines also whether it should be

postponed to the next shipment. The model based on linear programming is determined

as follows:

Assumptions:

Liner shipping and railway companies are agreed on the service route planning and

vessel shipping and train carriage scheduling.

All transhipments are loaded freights.

All loaded freights are shipped only by using semi-trailers.

The weight of the semi-trailer cannot exceed the required loaded tonnage.

For simplicity, assume initially that all shippers on each O-D node pair will consigntheir freight, that is, there are no cancellations and no-shows.

The average freight rate of each O-D node pair for each shipper class is estimated.


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There is no additional cargo demand (semi-trailer) available for loading from the cargo-

hub to vessel and train.Indices and parameters:

= the index of loading node (origin node) of the freight flow, 1, 2, , .= the index of discharging node (destination node) of the freight flow, 1, 2, , . = the index of shipper types ( 1 for contractual shipper, 2 ad-hoc shipper, 3urgent shipper), = the index of train trip, 1,2, , . Each trip is constrained by the maximumserviceable capacity

= the total slot capacity of directed leg in sea-rail multimodal transport line; .= the slot capacity of vessel= the slot capacity of train = the available slot capacity of thtrip in train.

= the freight contribution of each

-type of shippers semi-trailer truck shipped

from loading nodeto discharging node(equal to )= the freight revenue of each -type of shippers semi-trailer truck shipped fromloading nodeto discharging node.= the freight variable cost of each -type of shippers semi-trailer truck shippedfrom loading nodeto discharging node.= the maximum slot allotment of contractual shippers. = the minimum slot number of freight demand for -type of shipper from loadingnodeto discharging node. = the maximum slot number of freight demand for -type of shipper from loadingnodeto discharging node.

Decision variables:

= slot allocating numbers of each -type of shippers semi-trailer truck shipped fromloading nodeto discharging node. 1 if trip is part of routing 0 otherwise

Objective function:

The objective function of the model is to maximize the total freight contribution (freight

revenue minus variable cost) from the shipment. This is represented in equation (1).

1

Constraints:

(a) Vessel constraints

There are two restrictions: one represented in equation (2), the allocated slot for the

loaded freights cannot exceed the vessel operational capacity. The second represented in

equation (3), is that the total slots for contractual shipper in a vessel cannot exceed the

maximum slot allotment of contractual shippers.


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(b) Train constraints:

The slot allocation for the loaded freights cannot exceed the total train operational

capacity, which is obtained from equation (4). The other constraint is represented in

equation (5): the total slots for contractual shipper in train cannot exceed the maximum

slot allotment of contractual shippers.

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(c) Total slot capacity constrain for the sea-rail multimodal freight transport:

The total slot capacity for the sea-rail multimodal freight transport cannot exceed the

total vessel operational capacity and train operational capacity. This is represented in

equation (6).

6(d) Freight demand constraint:

The allocated slots to each O-D leg must be set between the interval of the lower and

upper bound of freight demand which is represented in equation (7).This helps to keep

thecapacity utilization at certain rate.

, and 7

Case study and analysis

It is assumed that a railway freight provider and a liner shipping provider have a long-

term contractual agreement and act as MTPs in order to operate in a sea-rail multimodaltransport chain with multi-node O-D connectivity. They collaborate on the lateral plane;

in other words, this transport collaboration is combining vertical and horizontal

collaboration. They share available capacities for the joint operations. The transport

chain is arranged in several railway legs and sea shipping voyages as shown in Figure 2.

denotes the thseaport, is the thrailway inland terminal and demonstrates thecargo hub for sea-rail multimodal transport where both loading and discharging

operations of vessels and trains are carried out. A combination of one railway node and

one sea shipping node refers an O-D pair of sea-rail multimodal freight flow. Railway

freight provider operates RoLa (rolling road) services to/from the ports/terminals, which

are specially designed wagons to carry wheeled cargo by rail. Liner shipping provider

has a fleet of RoRo (roll-on/roll-off) vessels, which are specially types of ships designedto carry wheeled cargo. Their transport units can be trucks, semi-trailer trucks, trailers,


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automobiles, railroad cars, project cargo, and maritime containers on MAFIs or

cassettes.

Figure 2 - The sea-rail multimodal transport chain

The railway freight provider operates round-trip daily RoLa train service with six/leg

from cargo hub to two railway inland terminals ( . The train capacity ( ) is32 semi-trailers/trip. The liner shipping provider operates round-voyage daily RoRo

vessel service with one/line from seaport to cargo hub ( ). The vessel capacity(

) is 240 semi-trailers/voyage. Operationally, capacity depends on the density of

booked shipments and their shapes as well as the dead weight restriction. Also, the

transport unit mix in relation to movable decks, internal ramps, lane heights etc., can be

a limiting factor as to how much cargo in a vessel or train wagon can accommodated.

We assume that every semi-trailer is loaded under the maximum load tonnage. The

maximum slot capacity of this sea-rail multimodal transport chain is 192 semi-trailers

(in consignments) which is exactly equal with the total capacity of train. 240-192= 48

semi-trailers, which are the capacity difference between vessel and train, are not the part

of sea-rail multimodal transport. Maximum allotment of contractual shippers iskept around 30%. These shippers have a long term contractual agreement with both

MTPs to secure the reservation priority.

Table 1- Initial reservation plan according to price and demand

Node Pair Contractual shipper Ad-hoc shipper Urgent shipper

O-D Price Demand Price Demand Price Demand

1215 32 1350 43 1418 12 1125 7 1250 11 1313 8 1341 24 1490 35 1565 13 1251 18 1390 10 1460 8 1305 38 1450 52 1523 16 1215 5 1350 7 1418 10

1431 20 1590 25 1670 8

1341 2 1490 14 1565 5The transport unit price of semi-trailer is Euro.


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Table 2 - Scenario 1 contractual shipper reservations executed


O-D Price Slot Price Slot Price Slot

1215 32 1350 30 1418 12 1125 7 1250 7 1313 8 1341 24 1490 26 1565 13 1251 18 1390 7 1460 8 1305 38 1450 25 1523 16 1215 5 1350 2 1418 10 1431 20 1590 25 1670 8 1341 2 1490 14 1565 5

Revenue 188.424 224.130 119.238

Total 531.792,00Utilization of vessel capacity: 92%, 71%; train trip capacity: 100%, 100%, 100%, and 77%. Contractual shipper allotment is 40,3%.

Table 3 - Scenario 2 contractual shipper allotment kept at 30%.



1215 28 1350 32 1418 12 1125 6 1250 10 1313 8 1341 20 1490 32 1565 13 1251 13 1390 10 1460 8 1305 15 1450 45 1523 16 1215 5 1350 5 1418 10

1431 20 1590 25 1670 8

1341 2 1490 14 1565 5Revenue

152.235 264.040 119.238

Total 535.513,00Utilization of vessel capacity: 92%, 71%; train trip capacity: 100%, 100%, 100%, and 77%. Contractual shipper allotment is 30%.

There are three fare tariffs: the average freight rate of each O-D node pair for ad-hoc

shipper is given; the rate for contractual shipper and the urgent shipper is calculated

according to rate of ad-hoc shipper. Contractual rate is generated from 90% of ad-hoc

rate whereas urgent rate is 105%. Table 1 shows the initial reservation plan according to

price and demand. The demands of RoRo vessel are for 221 units and for

202 units. RoLa train demands are for ) 113; for 108; 128 and ) 74 units. In this initial plan, RoRo vessels can obtain 92% and 84% capacity utilization rate, whereas RoLa trains rate are118% ( , 125% , 133% ( ) and 77% ( . However, the RoLatrains have constraints with 3 trips daily, that means that some of shipper demand

should be postponed to next day(s). The proposed model will find out which shippers

demand should be accepted or rejected by considering revenue maximization.

Generating different allocations scenario help to design an optimal slot allocation. The

optimization software LINGO 14.0 is used to solve the model. Each O-D node pair

shows the seamless sea-rail connectivity and associated cargo demand. It is assumed

that there is no additional cargo demand available for loading at the cargo-hub torailway and seaway. The simulation program allows generating different slot allocationscenarios. Table 2 demonstrates scenario 1, where all reservations of contractual

shippers are executed and capacity constraints for vessels and trains are met. 29 ad-hoc


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shipper reservations (17 for and 12 for ) are postponed to the nextday and 32 ad-hoc reservations for ( are cancelled because of the capacityrestriction. Under this circumstance, total revenue is calculated as 531.792. Table 3

shows the scenario 2. Here the contractual shipper allotment is kept around 30% so that

the capacity cut is pursued not only in the reservation units of ad-hoc shippers, but alsoin contractual shippers. 14 reservations of contractual and 15 of ad-hoc shippers are

postponed to next day, whereas 23 reservations of contractual and 9 of ad-hoc shippers

are cancelled. Total revenue is calculated as 535.513. Scenario 3 in Table 4 represents

the slot allocation by cutting contractual shipper reservation. 29 unit reservations of

contractual shipper are postponed and 32 are cancelled. Total revenue is higher than

other scenario results with 537.710. This scenario increases the total revenue

considerably, because the MTPs can make higher profit from ad-hoc and urgent

shippers. Therefore, the obtained result in this case seems to be better in order to

maximize the revenue as 1% increases in profitability.

In this model, MTPs decide together on what shippers reservation can be executed/

postponed or cancelled by analyzing different slot allocation scenarios. For the acceptedshipper reservation, a slot (semi-trailers space) is arranged both on RoRo vessel and

RoLa train simultaneously. This represents the differentiated service based on different

guaranteed trip for each service class.

Table 4 - Scenario 3 capacity adjustment by reducing contractual shipper reservation



1215 18 1350 43 1418 12 1125 4 1250 11 1313 8

1341 20 1490 35 1565 13

1251 10 1390 10 1460 8 1305 11 1450 52 1523 16 1215 0 1350 7 1418 10 1431 20 1590 25 1670 8 1341 2 1490 14 1565 5

Revenue

135.162

283.310

119.238

Total 537.710,00Utilization of vessel capacity: 92%, 71%; train trip capacity: 100%, 100%, 100%, and 77%. Contractual shipper allotment is 23,4%.

Conclusion

Sea-rail multimodal transport offers the technical and economic advantages of longdistance, high safety and speed, large transport capacity and low tariffs. Therefore, it is

expected that large investments in this kind of multimodal transport will be made in the

near future. The success of sea-rail multimodal transport relies of a set of driving forces

linked with technology, infrastructure and excellent revenue management. MTPs

employ RM in order to maximize profits by using slot allocation and pricing. In this

paper, a collaborative slot allocation model is proposed for sea-rail multimodal

transport. This model provides MTPs revenue maximization for their joint operations.

According to shipper demands and capacity constraints, MTPs can make a decision

what shipper demands should be approved, postponed or canceled. In addition, this

model enables a seamless reservation system for the shippers; accepted slots are

reserved both in vessels and trains. The model is developed in a manner that aims to

ease understanding and utilization, but it could be extended by applying different


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logistics constraints such as, various transport units including maritime containers,

complete unit and long vehicle and fleet size including set of various wagon types,

vessels types, entire voyage duration, time restriction, and booking period and so on.

RM in the sea-rail multimodal transport is a promising research area with the high

potential for developing new models and procedures to improve revenue and providestrategic as well as operational decision support to the both liner shipping and railway

freight service providers. Pricing and slot control problems as well as planning service

routes, carriage and shipping schedules might provide opportunities for future.

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Collaborative Slot Allocation Sea Rail Multimodal