Models for the discrete berth allocation problem: A computational comparison

Models for the discrete berth allocation problem: A computational comparison

► We investigate several mixed integer models for the dynamic berth allocation problem. ► The best of the models can solve a large number of previously unsolved instances. ► The quality of existing heuristics for the problem is evaluated using the new results. In this paper we consider the problem o...

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Bibliographic Details
Published in:Transportation research. Part E, Logistics and transportation review Vol. 47; no. 4; pp. 461 - 473
Main Authors: Buhrkal, Katja, Zuglian, Sara, Ropke, Stefan, Larsen, Jesper, Lusby, Richard
Format: Journal Article
Language:English
Published: Exeter Elsevier India Pvt Ltd 01-07-2011
Elsevier
Elsevier Sequoia S.A
Series:Transportation Research Part E: Logistics and Transportation Review
Subjects:
Allocations
Berth allocation problem
Berth allocation problem Container terminals Mixed integer programming
Comparative analysis
Computation
Container terminals
Containers
Dynamic tests
Dynamics
Mathematical models
Mixed integer programming
Numerical analysis
Operations research
Resource allocation
Studies
Terminals
Transportation
Transportation terminals
Container terminals
Berth allocation problem
Mixed integer programming
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Summary:► We investigate several mixed integer models for the dynamic berth allocation problem. ► The best of the models can solve a large number of previously unsolved instances. ► The quality of existing heuristics for the problem is evaluated using the new results. In this paper we consider the problem of allocating arriving ships to discrete berth locations at container terminals. This problem is recognized as one of the most important processes for any container terminal. We review and describe three main models of the discrete dynamic berth allocation problem, improve the performance of one model, and, through extensive numerical tests, compare all models from a computational perspective. The results indicate that a generalized set-partitioning model outperforms all other existing models.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2010.11.016