An optimization model for the inland repositioning of empty containers

Alessandro Olivo, Massimo Di Francesco and Paola Zuddas
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Alessandro Olivo: 1] Department of Civil Engineering, Environment and Architecture, University of Cagliari, Piazza D’Armi 16, Cagliari 09123, Italy[2] Network Optimisation Research and Educational Centre (CRIFOR), University of Cagliari, via San Giorgio 12, Cagliari 09124, Italy. E-mails: olivo@unica.it; mdifrance@unica.it; zuddas@unica.it
Massimo Di Francesco: 1] Network Optimisation Research and Educational Centre (CRIFOR), University of Cagliari, via San Giorgio 12, Cagliari 09124, Italy. E-mails: olivo@unica.it; mdifrance@unica.it; zuddas@unica.it[2] Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, Cagliari 09124, Italy
Paola Zuddas: 1] Network Optimisation Research and Educational Centre (CRIFOR), University of Cagliari, via San Giorgio 12, Cagliari 09124, Italy. E-mails: olivo@unica.it; mdifrance@unica.it; zuddas@unica.it[2] Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, Cagliari 09124, Italy

Maritime Economics & Logistics, 2013, vol. 15, issue 3, 309-331

Abstract: The inland repositioning of empty containers is a crucial problem for shipping companies providing door-to-door transport services to customers. This activity consists of the allocation of heterogeneous fleets of empty containers between inland depots and ports, so that they can be properly positioned in anticipation of future customer requests. This article describes how shipping companies perform this complex activity and its links with truck routing problems and the repositioning of empty containers on maritime networks. To address the inland repositioning of empty containers, we propose a time-extended optimization model, whose innovative elements are decision variables and constraints on the so-called flexible leased containers, which can be on-hired and off-hired according to a number of clauses, and substitution options between container types. The experimentation shows that the model is an effective instrument to support the current decision-making process on this issue, because realistic size instances can be solved within time limits imposed by planning operations.

Date: 2013
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