Solving the Liner Shipping Fleet Repositioning Problem with Cargo Flows

Abstract

W e solve a central problem in the liner shipping industry called the liner shipping fleet repositioning problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between routes in a liner shipping network. Liner carriers wish to reposition vessels as cheaply as possible without disrupting cargo flows. The LSFRP is characterized by chains of interacting activities with a multicommodity flow over paths defined by the activities chosen. Despite its industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model and a simulated annealing algorithm for the LSFRP with cargo flows that makes use of a carefully constructed graph; we evaluate these approaches using real-world data from our industrial collaborator. Additionally, we compare the performance of our approach against an actual repositioning scenario, one of many undertaken by our industrial collaborator in 2011. Our simulated annealing algorithm is able to increase the profit from $18.1 to $31.8 million using only a few minutes of CPU time. This shows that our algorithm could be used in a decision support system to solve the LSFRP.

Dynamic Vehicle Scheduling for Working Service Network with Dual Demands

“…Sherali et al [22] proposed a model that integrates certain aspects of the schedule design fleet assignment and aircraft-routing process and designed Benders's decomposition-based method. The liner shipping fleet repositioning problem (LSFRP) was formulated as a novel mathematical model and a simulated annealing algorithm is proposed for the LSFRP by Tierney et al [23]. Hashemi and Sattarvand [24] studied the different management systems of the open pit mining equipment including nondispatching, dispatching, and blending solutions for the Sungun copper mine.…”

Decision support and data visualization for liner shipping fleet repositioning

“…1 An example of a service network from a case study with an industrial collaborator, from [33] Inf Technol Manag literature for this industry is surprisingly low [15,25]. In the area of bulk shipping, Fisher and Rosenwein [18] describe a system that finds optimal fleet schedules for bulk cargo pickup and delivery with a dual algorithm.…”

“…The problem itself and the first mathematical approaches for solving the LSFRP were first introduced in [34]. Furthermore, Tierney et al [33] develops a simulated annealing algorithm with cargo flows that scales to large, real-world problems. The approach finds higher profits compared to a reference scenario from industry.…”

“…When using an SoS, the repositioning vessel uses the slot of the on-service vessel to sail temporarily on the SoS. As described by [33], this activity saves significant amounts of bunker fuel as there is only one vessel where there would have otherwise been two, as the on-service vessel can be laid up at a port or it can be leased out for other operations.…”

“…Repositioning coordinators are therefore still using spreadsheets (or other manual mechanisms) to create their plans. The process of creating a repositioning plan can therefore take up to a couple of days [32], which is significantly more than the several minutes required by modern heuristics [33]. Although systems exist for other problems in the area of maritime transportation [25], they lack the specific requirements of the repositioning problem and thus only offer limited guidance in creating a DSS for the LSFRP.…”