Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach

Zhaowang Ji; Anthony Chen; Kitti Subprasom

Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach

Zhaowang Ji, Anthony Chen, Kitti Subprasom

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

15 Citations (Scopus)

Abstract

Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and Intelligent Transportation System (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.

Original language	English
Title of host publication	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages	174-180
Number of pages	7
Volume	1
Publication status	Published - 13 Sept 2004
Externally published	Yes
Event	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States Duration: 19 Jun 2004 → 23 Jun 2004

Conference

Conference	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Country/Territory	United States
City	Portland, OR
Period	19/06/04 → 23/06/04

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach",

abstract = "Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and Intelligent Transportation System (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.",

author = "Zhaowang Ji and Anthony Chen and Kitti Subprasom",

year = "2004",

month = sep,

day = "13",

language = "English",

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volume = "1",

pages = "174--180",

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note = "Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 ; Conference date: 19-06-2004 Through 23-06-2004",

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Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach. / Ji, Zhaowang; Chen, Anthony; Subprasom, Kitti.
Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004. Vol. 1 2004. p. 174-180.

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

TY - GEN

T1 - Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach

AU - Ji, Zhaowang

AU - Chen, Anthony

AU - Subprasom, Kitti

PY - 2004/9/13

Y1 - 2004/9/13

N2 - Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and Intelligent Transportation System (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.

AB - Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and Intelligent Transportation System (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.

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M3 - Conference article published in proceeding or book

SN - 0780385152

SN - 9780780385153

VL - 1

SP - 174

EP - 180

BT - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

T2 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

Y2 - 19 June 2004 through 23 June 2004

ER -

Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach

Abstract

Conference

ASJC Scopus subject areas

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