An effective Markov random fields based estimation of distribution algorithm and scheduling of flexible job shop problem

Xinchang Hao, Jing Tian, Hao Wen Lin, Tomohiro Murata

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.

Original languageEnglish
Pages (from-to)796-805
Number of pages10
JournalIEEJ Transactions on Electronics, Information and Systems
Volume134
Issue number6
DOIs
Publication statusPublished - 2014

Fingerprint

Scheduling
Heuristic methods
Simulated annealing
Particle swarm optimization (PSO)
Planning
Job shop scheduling

Keywords

  • Estimation of distribution algorithm
  • Flexible job shop problem
  • Markov random fields
  • Network probability model

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

An effective Markov random fields based estimation of distribution algorithm and scheduling of flexible job shop problem. / Hao, Xinchang; Tian, Jing; Lin, Hao Wen; Murata, Tomohiro.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 134, No. 6, 2014, p. 796-805.

Research output: Contribution to journalArticle

@article{a71c8a62af964f8b8247e556876470f5,
title = "An effective Markov random fields based estimation of distribution algorithm and scheduling of flexible job shop problem",
abstract = "During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.",
keywords = "Estimation of distribution algorithm, Flexible job shop problem, Markov random fields, Network probability model",
author = "Xinchang Hao and Jing Tian and Lin, {Hao Wen} and Tomohiro Murata",
year = "2014",
doi = "10.1541/ieejeiss.134.796",
language = "English",
volume = "134",
pages = "796--805",
journal = "IEEJ Transactions on Electronics, Information and Systems",
issn = "0385-4221",
publisher = "The Institute of Electrical Engineers of Japan",
number = "6",

}

TY - JOUR

T1 - An effective Markov random fields based estimation of distribution algorithm and scheduling of flexible job shop problem

AU - Hao, Xinchang

AU - Tian, Jing

AU - Lin, Hao Wen

AU - Murata, Tomohiro

PY - 2014

Y1 - 2014

N2 - During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.

AB - During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.

KW - Estimation of distribution algorithm

KW - Flexible job shop problem

KW - Markov random fields

KW - Network probability model

UR - http://www.scopus.com/inward/record.url?scp=84901759785&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901759785&partnerID=8YFLogxK

U2 - 10.1541/ieejeiss.134.796

DO - 10.1541/ieejeiss.134.796

M3 - Article

VL - 134

SP - 796

EP - 805

JO - IEEJ Transactions on Electronics, Information and Systems

JF - IEEJ Transactions on Electronics, Information and Systems

SN - 0385-4221

IS - 6

ER -