A multi-objective evolutionary algorithm for berth allocation in a container port

C. Y. Cheong; C. J. Lin; K. C. Tan; D. K. Liu

doi:10.1109/CEC.2007.4424569

A multi-objective evolutionary algorithm for berth allocation in a container port

C. Y. Cheong^*, C. J. Lin, K. C. Tan, D. K. Liu

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

12 Citations (Scopus)

Abstract

This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of make span, number of crossings, and waiting time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation
Publisher	IEEE
Pages	927-934
ISBN (Electronic)	978-1-4244-1340-9
ISBN (Print)	978-1-4244-1339-3
DOIs	https://doi.org/10.1109/CEC.2007.4424569
Publication status	Published - Sept 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Place	Singapore
Period	25/09/07 → 28/09/07

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title = "A multi-objective evolutionary algorithm for berth allocation in a container port",

abstract = "This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of make span, number of crossings, and waiting time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation. ",

author = "Cheong, {C. Y.} and Lin, {C. J.} and Tan, {K. C.} and Liu, {D. K.}",

year = "2007",

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doi = "10.1109/CEC.2007.4424569",

language = "English",

isbn = "978-1-4244-1339-3",

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booktitle = "2007 IEEE Congress on Evolutionary Computation",

publisher = "IEEE",

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note = "2007 IEEE Congress on Evolutionary Computation, CEC 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

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AU - Lin, C. J.

AU - Tan, K. C.

AU - Liu, D. K.

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N2 - This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of make span, number of crossings, and waiting time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation.

AB - This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of make span, number of crossings, and waiting time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation.

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DO - 10.1109/CEC.2007.4424569

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4244-1339-3

SP - 927

EP - 934

BT - 2007 IEEE Congress on Evolutionary Computation

PB - IEEE

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

A multi-objective evolutionary algorithm for berth allocation in a container port

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