A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows

K. C. Tan; T. H. Lee; Y. H. Chew; L. H. Lee

doi:10.1109/ICSMC.2003.1243842

A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows

K. C. Tan, T. H. Lee, Y. H. Chew, L. H. Lee

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

22 Citations (Scopus)

Abstract

Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW problems. The proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace.

Original language	English
Title of host publication	2003 IEEE International Conference on Systems, Man and Cybernetics
Publisher	IEEE
Pages	361-366
Volume	1
ISBN (Print)	0-7803-7952-7
DOIs	https://doi.org/10.1109/ICSMC.2003.1243842
Publication status	Published - 2003
Externally published	Yes
Event	System Security and Assurance - Washington, DC, United States Duration: 5 Oct 2003 → 8 Oct 2003

Conference

Conference	System Security and Assurance
Place	United States
City	Washington, DC
Period	5/10/03 → 8/10/03

Research Keywords

Multiobjective evolutionary optimization
Vehicle routing with time windows

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10.1109/ICSMC.2003.1243842

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@inproceedings{6202a697ec0d473eba52667045a24160,

title = "A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows",

abstract = "Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW problems. The proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace.",

keywords = "Multiobjective evolutionary optimization, Vehicle routing with time windows",

author = "Tan, {K. C.} and Lee, {T. H.} and Chew, {Y. H.} and Lee, {L. H.}",

year = "2003",

doi = "10.1109/ICSMC.2003.1243842",

language = "English",

isbn = "0-7803-7952-7 ",

volume = "1",

pages = "361--366",

booktitle = "2003 IEEE International Conference on Systems, Man and Cybernetics",

publisher = "IEEE",

address = "United States",

note = "System Security and Assurance ; Conference date: 05-10-2003 Through 08-10-2003",

}

A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. / Tan, K. C.; Lee, T. H.; Chew, Y. H. et al.
2003 IEEE International Conference on Systems, Man and Cybernetics. Vol. 1 IEEE, 2003. p. 361-366.

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

TY - GEN

T1 - A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows

AU - Tan, K. C.

AU - Lee, T. H.

AU - Chew, Y. H.

AU - Lee, L. H.

PY - 2003

Y1 - 2003

N2 - Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW problems. The proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace.

AB - Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW problems. The proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace.

KW - Multiobjective evolutionary optimization

KW - Vehicle routing with time windows

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DO - 10.1109/ICSMC.2003.1243842

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VL - 1

SP - 361

EP - 366

BT - 2003 IEEE International Conference on Systems, Man and Cybernetics

PB - IEEE

T2 - System Security and Assurance

Y2 - 5 October 2003 through 8 October 2003

ER -

A multiobjective evolutionary algorithm for solving vehicle routing problem with time windows

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