Empty container management for intermodal transportation networks

We present a computational analysis of the effect of planning horizon length on empty container management for intermodal transportation networks. The analysis is based on an integer program that seeks to minimize total costs related to moving empty containers, subject to meeting requirements for moving loaded containers. A case study of potential container-on-barge operations within the Mississippi River basin illustrates the effects of planning horizon length on mode selection. A longer planning horizon can encourage the use of inexpensive, slow transportation modes, such as barge. The impact depends on the number and location of container storage pools.     

A maritime container repositioning yield-based optimization model with uncertain upsurge demand

The role of container repositioning has become more important under the severe cargo shipping environment, affected by world trade growth, trade imbalance, slow steaming strategy and high container manufacturing cost. Low cost, better routing, and supplying equipment to higher yield cargo become the top criteria. A yield-based container repositioning framework is developed, followed by a constrained linear programming optimizing the container repositioning from surplus to deficit locations. The model incorporated change of destinations of empty containers and adjustment factors handling upsurge demand. The model is applied to optimize daily container repositioning operations with a better route, costs and equipment supply.     

Measuring the perceived container leasing prices in liner shipping network design with empty container repositioning

This paper aims to measure the perceived container leasing prices at different ports by presenting a two-stage optimization method. In stage I, we propose a practical liner shipping network design problem with empty container repositioning. The proposed problem further considers the use of foldable containers and allows the mutual substitution between empty containers to decrease the number of empty containers to be repositioned. In stage II, the inverse optimization technique is used to determine the perceived container leasing prices at different ports, based on the solution obtained in stage I. Based on a set of candidate liner shipping service routes, a mixed-integer nonlinear programming model is built for the proposed problem in stage I. The nonlinear terms are linearized by introducing the auxiliary variables. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of our two-stage optimization method.     

The effect of multi-scenario policies on empty container repositioning

This paper addresses a container maritime-repositioning problem where several parameters are uncertain and historical data are useless for decision-making processes. To address this problem, we propose a time-extended multi-scenario optimization model in which scenarios can be generated taking into account shipping company opinions. We then show that multi-scenario policies put shipping companies in the position of satisfying empty-container demands for different values that may be taken by uncertain parameters.     

Assessing policy measures for the stimulation of intermodal transport: a GIS-based policy analysis

Intermodal transport is the combination and integration of several transport modes (such as rail, inland waterways with road transport). In order to make the transhipment easy and efficient standard loading units are used, such as containers or swap-bodies. As for the main haul, more environmental friendly modes are used (rail and inland waterways) and a modal shift towards these modes can help in reducing the congestion. Therefore several policies are directed to stimulate the intermodal transport market.In this paper, a location analysis model for Belgian intermodal terminals (LAMBIT) is developed and used to assess different policy measures in Belgium. The simulations show that the different policy measures oriented towards the rail/road and inland waterways/road combinations should be incorporated in a coherent, integrated vision, in order to not create a modal shift between the different intermodal transport options. The methodology can easily be extended towards a European scale.     

Decomposition approach for integrated intermodal logistics network design

The integrated intermodal logistics network design problem consists of determining terminal locations and selecting regular routes and transportation modes for loads. This problem was formulated using a path-based formulation and a decomposition-based search algorithm has been proposed for its solution. Computational results show that this approach is able to obtain optimal solutions for non-trivial problem instances of up to 150 nodes in reasonable computational times. Previous studies have only been able to obtain approximate solutions for network problems of this size. A few general insights about the effects of design parameters on solution characteristics were also obtained.     

Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market

Collaborative hub networks can provide an answer to the need to decrease logistics cost and maintain logistics service levels by shifting consolidated flows to modes that are better suited for handling large volumes (rail, barge, coastal shipping), so economies of scale can be obtained. This necessity has been increased by the tendency of globalization of industries, smaller shipments sizes, high frequencies, and the fragmentation of flows. Through collaboration the necessary synchronization between expensive but fast and flexible means of transport and inexpensive, but slow and inflexible means can be combined in an intermodal hub network. This paper shows the rationale behind these collaborative hub networks, based on the literature on the design of many-to-many hub networks. The resulting methodology is explained through presenting the results of the design and implementation of collaborative hub network for the distribution of fast moving consumer goods using a combination of trucking and inland barges. This concept, first proposed by Vermunt [Vermunt, A.J.M., 1999. Multilognet, the intelligent multimodal logistics network, an important node in the worldwide logistics net, Vermunt Logistiek Advies v.o.f., working paper (in Dutch)], won the European Intermodal Award of the European Intermodal Association in 2003, and after extensive research was launched in The Netherlands as a commercial pilot by logistics service provider Vos Logistics and barge operator Riverhopper in January 2004.     

Ant colony optimization for disaster relief operations

This paper presents a meta-heuristic of ant colony optimization (ACO) for solving the logistics problem arising in disaster relief activities. The logistics planning involves dispatching commodities to distribution centers in the affected areas and evacuating the wounded people to medical centers. The proposed method decomposes the original emergency logistics problem into two phases of decision making, i.e., the vehicle route construction, and the multi-commodity dispatch. The sub-problems are solved in an iterative manner. The first phase builds stochastic vehicle paths under the guidance of pheromone trails while a network flow based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. The performance of the algorithm is tested on a number of randomly generated networks and the results indicate that this algorithm performs well in terms of solution quality and run time.     

Revenue management policies for the truck rental industry

In this paper, we consider the problem of managing a fleet of trucks with different capacity to serve the requests of different customers that arise randomly over time. The problem is formulated via dynamic programming. Linear programming approximations of the problem are presented and their solutions are exploited to develop partitioned booking limits and bid prices policies. The numerical experiments show that the proposed policies can be profitably used in supporting the decision maker.     

Estimating attributes importance for container shipping industry by closing the listening gap with maximum convergent validity

This paper estimates the relationship of attributes importance (AI) to prospect purchase intention by closing the listening gap between customers and managers of container shipping companies. A proposition of minimum cross entropy is proposed to find a solution to the problem with maximum convergent validity, and this proposition is used to estimate AI, in which both opinions about AI of customers and managers of container shipping companies are included. Results indicate that price, discount, personal selling, and word of mouth, are the most important attributes to prospect purchase intention, within the industry. In addition, managerial implications are also discussed.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

Models of crisis management: an evaluation of their value for strategic planning in the international travel industry

Tourism is particularly prone to external shocks, which by their nature are unpredictable and need to be addressed through effective crisis management processes. The paper reviews the literature relating to crisis management in tourism and identifies and briefly critiques several models that have been developed to help managers in their strategic planning for such contingencies. The terrorist attacks of ‘9/11’ are used as an exemplar of the type of external shock that can lead to crisis if travel industry managers fail to take immediate and decisive action. This paper discusses the reactions of leading UK based tour operators to the terrorist attacks and a case study is presented to examine the reaction of a particular company to ‘9/11’ and to review the ‘turnaround’ strategies used. The crisis management process model is compared and contrasted with the steps actually undertaken at the company. It is evident that there are wider lessons for the travel industry including the need to: integrate crisis management with strategic planning processes, prepare detailed contingency plans, define decisional roles and responsibilities, and to retain a degree of flexibility. Copyright © 2005 John Wiley & Sons, Ltd.