Discrete Applied Mathematics 123 (2002) 75-102. doi:10.1016/S0166-218X(01)00338-92016-03-04T18:46:49
A common problem faced by carriers in liner shipping is the design of their service network. Given a set of demand to be transported and a set of ports, a carrier wants to design service routes for his ships as efficiently as possible, using the underlying facilities. Furthermore, the profitability of the service routes designed depends on the paths chosen to ship the cargo. We present an integrated model, a mixed integer linear program, to solve the ship scheduling and the cargo routing problems simultaneously. The proposed model incorporates relevant constraints, such as the weekly frequency constraint on the operated routes, and emerging trends, such as the transshipment of cargo between two or more service routes. To solve the mixed integer program, we propose algorithms that exploit the separability of the problem. More specifically, a greedy heuristic, a column generation based algorithm and a two phase Benders decomposition based algorithm is developed and their computational efficiency in terms of the solution quality and the computational time taken is discussed. An efficient iterative search algorithm is proposed to generate good schedules for ships. Computational experiments are performed on randomly generated instances simulating real life with up to 20 ports and 100 ships. Our results indicate high percentage utilization of ships' capacities and a significant number of transshipments in the final solution.
Humanitarian supply chains involve many different entities, such as government, military, private, and non‐governmental organizations and individuals. Well‐coordinated interactions between entities can lead to synergies and improved humanitarian outcomes. Information technology (IT) tools can help facilitate collaboration, but cost and other barriers have limited their use. We document the use of an IT tool to improve last‐mile supply distribution and data management in one of many camps for internally displaced persons after the January 2010 earthquake in Haiti, and we describe other current uses of technology in camp management. Motivated by these examples and the interest among humanitarian organizations in expanding the use of such tools to facilitate coordination, we introduce a cooperative game theory model and explore insights about the conditions under which multi‐agency coordination is feasible and desirable. We also outline an agenda for future research in the area of technology‐enabled collaboration in the humanitarian sector.
Many real world systems operate in a decentralized manner, where individual operators interact with varying degree of cooperation and self motives. In this paper, we study transportation networks that operate as an alliance among different carriers. In particular, we study alliance formation among carriers in liner shipping. We address tactical problems such as the design of large scale networks (which result from integrating the service networks of different carriers in an alliance) and operational problems such as the allocation of limited capacity on a transportation network among the carriers in the alliance. We utilize concepts from mathematical programming and game theory and design a mechanism to guide the carriers in an alliance to pursue an optimal collaborative strategy. The mechanism provides side payments to the carriers, as an added incentive, to motivate them to act in the best interest of the alliance while maximizing their own profits. Our computational results suggest that the mechanism can be used to help carriers form sustainable alliances.
In the highly fragmented truckload transportation industry a substantial fraction of truck movements involves empty trucks, i.e., involves moves that reposition trucks. However, reducing the amount of truck repositioning is difficult because the need for a carrier to reposition its trucks depends on the interactions between the shippers the carrier is serving. Through collaboration, shippers may be able to identify and submit sequences of continuous loaded movements to carriers, reducing the carriers' need for repositioning, and thus lowering the carriers' costs. A portion of the carriers' cost savings may be returned to the shippers in the form of lower prices. We discuss optimization technology that can be used to assist in the identification of repeatable, dedicated truckload continuous move tours with little truck repositioning. Timing considerations are critical to practical viability and are a key focus of our efforts. We demonstrate the effectiveness of the algorithms developed on various randomly generated instances as well as on instances derived from data obtained from a strategic sourcing consortium for a $14 billion dollar sized US industry.
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