Green cargo routing using genetic algorithms

Winson S H Siu; Chi Kong Chan; Chun Bun Henry Chan

Green cargo routing using genetic algorithms

Winson S H Siu, Chi Kong Chan, Chun Bun Henry Chan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

We studied a genetic algorithm-based approach for a multi-objective cargo routing application. Apart from the traditional goal of cost minimization with a time constraint, we also explored the problem of green logistics, where carbon dioxide emission levels are to be treated as both an additional constraint as well as a secondary objective of the problem. We also implemented an adapted Martins' algorithm that is able to produce Pareto optimal solutions, despite its longer running time compared to the GA-based approach, and compared the results with our approach. The results of the simulation suggested that our algorithm was able to achieve Pareto optimality in over 90% of the problem instances, with good system running time compared with Martins' algorithm.

Original language	English
Title of host publication	International MultiConference of Engineers and Computer Scientists, IMECS 2012
Publisher	Newswood Limited
Pages	170-175
Number of pages	6
Volume	2195
ISBN (Print)	9789881925114
Publication status	Published - 1 Jan 2012
Event	2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, Hong Kong Duration: 14 Mar 2012 → 16 Mar 2012

Conference

Conference	2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012
Country/Territory	Hong Kong
City	Kowloon
Period	14/03/12 → 16/03/12

Keywords

Genetic algorithm
Green logistics
Intermodal cargo routing
Multi-objective optimization

ASJC Scopus subject areas

Computer Science (miscellaneous)

Cite this

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title = "Green cargo routing using genetic algorithms",

abstract = "We studied a genetic algorithm-based approach for a multi-objective cargo routing application. Apart from the traditional goal of cost minimization with a time constraint, we also explored the problem of green logistics, where carbon dioxide emission levels are to be treated as both an additional constraint as well as a secondary objective of the problem. We also implemented an adapted Martins' algorithm that is able to produce Pareto optimal solutions, despite its longer running time compared to the GA-based approach, and compared the results with our approach. The results of the simulation suggested that our algorithm was able to achieve Pareto optimality in over 90% of the problem instances, with good system running time compared with Martins' algorithm.",

keywords = "Genetic algorithm, Green logistics, Intermodal cargo routing, Multi-objective optimization",

author = "Siu, {Winson S H} and Chan, {Chi Kong} and Chan, {Chun Bun Henry}",

year = "2012",

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Green cargo routing using genetic algorithms. / Siu, Winson S H; Chan, Chi Kong; Chan, Chun Bun Henry.
International MultiConference of Engineers and Computer Scientists, IMECS 2012. Vol. 2195 Newswood Limited, 2012. p. 170-175.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Green cargo routing using genetic algorithms

AU - Siu, Winson S H

AU - Chan, Chi Kong

AU - Chan, Chun Bun Henry

PY - 2012/1/1

Y1 - 2012/1/1

N2 - We studied a genetic algorithm-based approach for a multi-objective cargo routing application. Apart from the traditional goal of cost minimization with a time constraint, we also explored the problem of green logistics, where carbon dioxide emission levels are to be treated as both an additional constraint as well as a secondary objective of the problem. We also implemented an adapted Martins' algorithm that is able to produce Pareto optimal solutions, despite its longer running time compared to the GA-based approach, and compared the results with our approach. The results of the simulation suggested that our algorithm was able to achieve Pareto optimality in over 90% of the problem instances, with good system running time compared with Martins' algorithm.

AB - We studied a genetic algorithm-based approach for a multi-objective cargo routing application. Apart from the traditional goal of cost minimization with a time constraint, we also explored the problem of green logistics, where carbon dioxide emission levels are to be treated as both an additional constraint as well as a secondary objective of the problem. We also implemented an adapted Martins' algorithm that is able to produce Pareto optimal solutions, despite its longer running time compared to the GA-based approach, and compared the results with our approach. The results of the simulation suggested that our algorithm was able to achieve Pareto optimality in over 90% of the problem instances, with good system running time compared with Martins' algorithm.

KW - Genetic algorithm

KW - Green logistics

KW - Intermodal cargo routing

KW - Multi-objective optimization

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M3 - Conference article published in proceeding or book

SN - 9789881925114

VL - 2195

SP - 170

EP - 175

BT - International MultiConference of Engineers and Computer Scientists, IMECS 2012

PB - Newswood Limited

T2 - 2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012

Y2 - 14 March 2012 through 16 March 2012

ER -

Green cargo routing using genetic algorithms

Abstract

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Keywords

ASJC Scopus subject areas

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