Integrated process planning and scheduling by an agent-based ant colony optimization

C. W. Leung; T. N. Wong; K. L. Mak; R. Y K Fung

doi:10.1016/j.cie.2009.09.003

Integrated process planning and scheduling by an agent-based ant colony optimization

C. W. Leung, T. N. Wong, K. L. Mak, R. Y K Fung

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

150 Citations (Scopus)

Abstract

This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm. © 2010 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	166-180
Journal	Computers and Industrial Engineering
Volume	59
Issue number	1
DOIs	https://doi.org/10.1016/j.cie.2009.09.003
Publication status	Published - Aug 2010

Research Keywords

Ant colony optimization
Multi-agent system
Process planning and scheduling

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10.1016/j.cie.2009.09.003

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title = "Integrated process planning and scheduling by an agent-based ant colony optimization",

abstract = "This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm. {\textcopyright} 2010 Elsevier Ltd. All rights reserved.",

keywords = "Ant colony optimization, Multi-agent system, Process planning and scheduling",

author = "Leung, {C. W.} and Wong, {T. N.} and Mak, {K. L.} and Fung, {R. Y K}",

year = "2010",

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TY - JOUR

T1 - Integrated process planning and scheduling by an agent-based ant colony optimization

AU - Leung, C. W.

AU - Wong, T. N.

AU - Mak, K. L.

AU - Fung, R. Y K

PY - 2010/8

Y1 - 2010/8

N2 - This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm. © 2010 Elsevier Ltd. All rights reserved.

AB - This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm. © 2010 Elsevier Ltd. All rights reserved.

KW - Ant colony optimization

KW - Multi-agent system

KW - Process planning and scheduling

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77955268719&origin=recordpage

U2 - 10.1016/j.cie.2009.09.003

DO - 10.1016/j.cie.2009.09.003

M3 - RGC 21 - Publication in refereed journal

SN - 0360-8352

VL - 59

SP - 166

EP - 180

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

IS - 1

ER -

Integrated process planning and scheduling by an agent-based ant colony optimization

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