Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms

D. E. Koulouriotis, A. S. Xanthopoulos, V. D. Tourassis

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios.

Original languageEnglish
Pages (from-to)2887-2912
Number of pages26
JournalInternational Journal of Production Research
Volume48
Issue number10
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes

Fingerprint

Genetic algorithms
Production control
Discrete event simulation
Manufacturing systems
Assembly systems
Pull
Genetic algorithm
Serials
Simulation optimization
Manufacturing
Kanban
Experiments
Scenarios

Keywords

  • Artificial intelligence
  • Assembly lines
  • Automated manufacturing systems
  • Computer vision
  • Data mining
  • Decision support systems
  • Evolutionary computation
  • Knowledge engineering
  • Robotics
  • Time series analysis

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. / Koulouriotis, D. E.; Xanthopoulos, A. S.; Tourassis, V. D.

In: International Journal of Production Research, Vol. 48, No. 10, 01.2010, p. 2887-2912.

Research output: Contribution to journalArticle

@article{b138ab2b1d754b3cb524e739a464a646,
title = "Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms",
abstract = "Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios.",
keywords = "Artificial intelligence, Assembly lines, Automated manufacturing systems, Computer vision, Data mining, Decision support systems, Evolutionary computation, Knowledge engineering, Robotics, Time series analysis",
author = "Koulouriotis, {D. E.} and Xanthopoulos, {A. S.} and Tourassis, {V. D.}",
year = "2010",
month = "1",
doi = "10.1080/00207540802603759",
language = "English",
volume = "48",
pages = "2887--2912",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis",
number = "10",

}

TY - JOUR

T1 - Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms

AU - Koulouriotis, D. E.

AU - Xanthopoulos, A. S.

AU - Tourassis, V. D.

PY - 2010/1

Y1 - 2010/1

N2 - Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios.

AB - Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios.

KW - Artificial intelligence

KW - Assembly lines

KW - Automated manufacturing systems

KW - Computer vision

KW - Data mining

KW - Decision support systems

KW - Evolutionary computation

KW - Knowledge engineering

KW - Robotics

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=77951135443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951135443&partnerID=8YFLogxK

U2 - 10.1080/00207540802603759

DO - 10.1080/00207540802603759

M3 - Article

VL - 48

SP - 2887

EP - 2912

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 10

ER -