A Decomposition Method for Finding Optimal Container Stowage Plans

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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A Decomposition Method for Finding Optimal Container Stowage Plans. / Roberti, Roberto; Pacino, Dario.

In: Transportation Science, Vol. 52, No. 6, 2018, p. 1297-1588.

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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@article{b4451e0636a74c04b806a9267eca21d3,
title = "A Decomposition Method for Finding Optimal Container Stowage Plans",
abstract = "In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.",
author = "Roberto Roberti and Dario Pacino",
year = "2018",
doi = "10.1287/trsc.2017.0795",
language = "English",
volume = "52",
pages = "1297--1588",
journal = "Transportation Science",
issn = "0041-1655",
publisher = "Institute for Operations Research and the Management Sciences (I N F O R M S)",
number = "6",

}

RIS

TY - JOUR

T1 - A Decomposition Method for Finding Optimal Container Stowage Plans

AU - Roberti, Roberto

AU - Pacino, Dario

PY - 2018

Y1 - 2018

N2 - In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.

AB - In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.

U2 - 10.1287/trsc.2017.0795

DO - 10.1287/trsc.2017.0795

M3 - Journal article

VL - 52

SP - 1297

EP - 1588

JO - Transportation Science

JF - Transportation Science

SN - 0041-1655

IS - 6

ER -