A Genetic algorithm-based approach to job shop scheduling problem with assembly stage

F.T.S. Chan; T.C. Wong; L.Y. Chan

doi:10.1109/IEEM.2008.4737885

A Genetic algorithm-based approach to job shop scheduling problem with assembly stage

F.T.S. Chan, T.C. Wong, L.Y. Chan

Design, Manufacturing And Engineering Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

Abstract

Assembly job shop scheduling problem (AJSSP) is an extension of classical job shop scheduling problem (JSSP). AJSSP first starts with a JSSP and appends an assembly stage after job completion. In this paper, we extend Lot Streaming (LS) to AJSSP. Hence, the problem is divided into SP1: the determination of LS conditions for all lots and SP2: the scheduling of AJSSP after LS conditions have been determined. To solve the problem, we propose an innovative Genetic Algorithm (GA) approach. To investigate the impacts of LS on AJSSP, several system conditions are examined. To justify the GA, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that equal size LS is the best strategy and GA outperforms PSO for all test problems. Some negative impacts of LS are the increase of work-in-process inventory and total setup cost if the objective is the minimization of total lateness cost.

Original language	English
Title of host publication	2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
Publisher	IEEE
Pages	331-335
Number of pages	5
ISBN (Print)	9781424426294
DOIs	https://doi.org/10.1109/IEEM.2008.4737885
Publication status	Published - 1 Jan 2008
Event	IEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management - Singapore, Singapore Duration: 8 Dec 2008 → 11 Dec 2008

Conference

Conference	IEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management
Country/Territory	Singapore
City	Singapore
Period	8/12/08 → 11/12/08

Keywords

job shop scheduling
particle swarm optimisation
genetic algorithms
assembly systems
bills of materials
costs
genetic alogrithms
lot streaming
assembly job shop

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10.1109/IEEM.2008.4737885

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title = "A Genetic algorithm-based approach to job shop scheduling problem with assembly stage",

abstract = "Assembly job shop scheduling problem (AJSSP) is an extension of classical job shop scheduling problem (JSSP). AJSSP first starts with a JSSP and appends an assembly stage after job completion. In this paper, we extend Lot Streaming (LS) to AJSSP. Hence, the problem is divided into SP1: the determination of LS conditions for all lots and SP2: the scheduling of AJSSP after LS conditions have been determined. To solve the problem, we propose an innovative Genetic Algorithm (GA) approach. To investigate the impacts of LS on AJSSP, several system conditions are examined. To justify the GA, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that equal size LS is the best strategy and GA outperforms PSO for all test problems. Some negative impacts of LS are the increase of work-in-process inventory and total setup cost if the objective is the minimization of total lateness cost.",

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author = "F.T.S. Chan and T.C. Wong and L.Y. Chan",

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Chan, FTS, Wong, TC & Chan, LY 2008, A Genetic algorithm-based approach to job shop scheduling problem with assembly stage. in 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008. IEEE, pp. 331-335, IEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, Singapore, 8/12/08. https://doi.org/10.1109/IEEM.2008.4737885

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AU - Wong, T.C.

AU - Chan, L.Y.

PY - 2008/1/1

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N2 - Assembly job shop scheduling problem (AJSSP) is an extension of classical job shop scheduling problem (JSSP). AJSSP first starts with a JSSP and appends an assembly stage after job completion. In this paper, we extend Lot Streaming (LS) to AJSSP. Hence, the problem is divided into SP1: the determination of LS conditions for all lots and SP2: the scheduling of AJSSP after LS conditions have been determined. To solve the problem, we propose an innovative Genetic Algorithm (GA) approach. To investigate the impacts of LS on AJSSP, several system conditions are examined. To justify the GA, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that equal size LS is the best strategy and GA outperforms PSO for all test problems. Some negative impacts of LS are the increase of work-in-process inventory and total setup cost if the objective is the minimization of total lateness cost.

AB - Assembly job shop scheduling problem (AJSSP) is an extension of classical job shop scheduling problem (JSSP). AJSSP first starts with a JSSP and appends an assembly stage after job completion. In this paper, we extend Lot Streaming (LS) to AJSSP. Hence, the problem is divided into SP1: the determination of LS conditions for all lots and SP2: the scheduling of AJSSP after LS conditions have been determined. To solve the problem, we propose an innovative Genetic Algorithm (GA) approach. To investigate the impacts of LS on AJSSP, several system conditions are examined. To justify the GA, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that equal size LS is the best strategy and GA outperforms PSO for all test problems. Some negative impacts of LS are the increase of work-in-process inventory and total setup cost if the objective is the minimization of total lateness cost.

KW - job shop scheduling

KW - particle swarm optimisation

KW - genetic algorithms

KW - assembly systems

KW - bills of materials

KW - costs

KW - genetic alogrithms

KW - lot streaming

KW - assembly job shop

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BT - 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008

PB - IEEE

T2 - IEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management

Y2 - 8 December 2008 through 11 December 2008

ER -

A Genetic algorithm-based approach to job shop scheduling problem with assembly stage

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