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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 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 languageEnglish
Title of host publication2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
PublisherIEEE
Pages331-335
Number of pages5
ISBN (Print)9781424426294
DOIs
Publication statusPublished - 1 Jan 2008
EventIEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management - Singapore, Singapore
Duration: 8 Dec 200811 Dec 2008

Conference

ConferenceIEEM 2008. IEEE International Conference on Industrial Engineering and Engineering Management
CountrySingapore
CitySingapore
Period8/12/0811/12/08

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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