Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system

Wayan F. Mahmudy; Romeo M. Marian; Lee H.S. Luong

doi:10.1109/CyberneticsCom.2013.6865795

Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system

Wayan F. Mahmudy
, Romeo M. Marian
, Lee H.S. Luong

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

24 Citations (Scopus)

Abstract

This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. Hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.

Original language	English
Title of host publication	Proceeding - IEEE CYBERNETICSCOM 2013
Subtitle of host publication	IEEE International Conference on Computational Intelligence and Cybernetics
Publisher	IEEE Computer Society
Pages	126-130
Number of pages	5
ISBN (Print)	9781467360531
DOIs	https://doi.org/10.1109/CyberneticsCom.2013.6865795
Publication status	Published - 2013
Event	2nd IEEE International Conference on Computational Intelligence and Cybernetics, IEEE CYBERNETICSCOM 2013 - Yogyakarta, Indonesia Duration: 3 Dec 2013 → 4 Dec 2013

Publication series

Name	Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics

Conference

Conference	2nd IEEE International Conference on Computational Intelligence and Cybernetics, IEEE CYBERNETICSCOM 2013
Country/Territory	Indonesia
City	Yogyakarta
Period	3/12/13 → 4/12/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

flexible manufacturing system
hybrid genetic algorithms
machine loading problem
multi-period production planning
part type selection problem

Access to Document

10.1109/CyberneticsCom.2013.6865795

Cite this

Mahmudy, W. F., Marian, R. M., & Luong, L. H. S. (2013). Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system. In Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics (pp. 126-130). Article 6865795 (Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics). IEEE Computer Society. https://doi.org/10.1109/CyberneticsCom.2013.6865795

Mahmudy, Wayan F. ; Marian, Romeo M. ; Luong, Lee H.S. / Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system. Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics. IEEE Computer Society, 2013. pp. 126-130 (Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics).

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title = "Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system",

abstract = "This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. Hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.",

keywords = "flexible manufacturing system, hybrid genetic algorithms, machine loading problem, multi-period production planning, part type selection problem",

author = "Mahmudy, \{Wayan F.\} and Marian, \{Romeo M.\} and Luong, \{Lee H.S.\}",

year = "2013",

doi = "10.1109/CyberneticsCom.2013.6865795",

language = "English",

isbn = "9781467360531",

series = "Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics",

publisher = "IEEE Computer Society",

pages = "126--130",

booktitle = "Proceeding - IEEE CYBERNETICSCOM 2013",

note = "2nd IEEE International Conference on Computational Intelligence and Cybernetics, IEEE CYBERNETICSCOM 2013 ; Conference date: 03-12-2013 Through 04-12-2013",

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Mahmudy, WF, Marian, RM & Luong, LHS 2013, Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system. in Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics., 6865795, Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics, IEEE Computer Society, pp. 126-130, 2nd IEEE International Conference on Computational Intelligence and Cybernetics, IEEE CYBERNETICSCOM 2013, Yogyakarta, Indonesia, 3/12/13. https://doi.org/10.1109/CyberneticsCom.2013.6865795

Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system. / Mahmudy, Wayan F.; Marian, Romeo M.; Luong, Lee H.S.
Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics. IEEE Computer Society, 2013. p. 126-130 6865795 (Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system

AU - Mahmudy, Wayan F.

AU - Marian, Romeo M.

AU - Luong, Lee H.S.

PY - 2013

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N2 - This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. Hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.

AB - This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. Hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.

KW - flexible manufacturing system

KW - hybrid genetic algorithms

KW - machine loading problem

KW - multi-period production planning

KW - part type selection problem

UR - https://www.scopus.com/pages/publications/84906895807

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M3 - Conference contribution

AN - SCOPUS:84906895807

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T3 - Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics

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BT - Proceeding - IEEE CYBERNETICSCOM 2013

PB - IEEE Computer Society

T2 - 2nd IEEE International Conference on Computational Intelligence and Cybernetics, IEEE CYBERNETICSCOM 2013

Y2 - 3 December 2013 through 4 December 2013

ER -

Mahmudy WF, Marian RM, Luong LHS. Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system. In Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics. IEEE Computer Society. 2013. p. 126-130. 6865795. (Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics). doi: 10.1109/CyberneticsCom.2013.6865795

Hybrid genetic algorithms for multi-period part type selection and machine loading problems in flexible manufacturing system

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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