Modeling and solving a multimodal transportation problem with flexible-time and scheduled services
Publication: Research - peer-review › Journal article – Annual report year: 2011
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Modeling and solving a multimodal transportation problem with flexible-time and scheduled services. / Moccia, Luigi; Cordeau, Jean-François; Laport, Gilbert; Røpke, Stefan; Valentini, Maria Pia.
In: Networks, Vol. 57, No. 1, 2011, p. 53-68.Publication: Research - peer-review › Journal article – Annual report year: 2011
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TY - JOUR
T1 - Modeling and solving a multimodal transportation problem with flexible-time and scheduled services
A1 - Moccia,Luigi
A1 - Cordeau,Jean-François
A1 - Laport,Gilbert
A1 - Røpke,Stefan
A1 - Valentini,Maria Pia
AU - Moccia,Luigi
AU - Cordeau,Jean-François
AU - Laport,Gilbert
AU - Røpke,Stefan
AU - Valentini,Maria Pia
PY - 2011
Y1 - 2011
N2 - This article studies a transportation problem in a multimodal network with shipment consolidation options. A freight forwarder can use a mix of flexible-time and scheduled transportation services. Time windows are a prominent aspect of the problem. For instance, they are used to model pickup and delivery time slots. The various features of the problem can be described as elements of a digraph and their integration leads to a holistic graph representation. This allows an origin-destination integer multi-commodity flow formulation with nonconvex piecewise linear costs, time windows, and side constraints. Column generation algorithms are designed to compute lower bounds. These column generation algorithms are also embedded within heuristics aimed at finding feasible integer solutions. Computational results with real-life data are presented and show the efficacy of the proposed approach.
AB - This article studies a transportation problem in a multimodal network with shipment consolidation options. A freight forwarder can use a mix of flexible-time and scheduled transportation services. Time windows are a prominent aspect of the problem. For instance, they are used to model pickup and delivery time slots. The various features of the problem can be described as elements of a digraph and their integration leads to a holistic graph representation. This allows an origin-destination integer multi-commodity flow formulation with nonconvex piecewise linear costs, time windows, and side constraints. Column generation algorithms are designed to compute lower bounds. These column generation algorithms are also embedded within heuristics aimed at finding feasible integer solutions. Computational results with real-life data are presented and show the efficacy of the proposed approach.
U2 - 10.1002/net.20383
DO - 10.1002/net.20383
JO - Networks
JF - Networks
IS - 1
VL - 57
SP - 53
EP - 68
ER -