A genetic algorithm approach to job shop scheduling problems with a batch allocation issue

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ConferenceThe 17th International Workshop on Artificial Intelligence: 01 August 2001, Seattle, Washington
Pages126131; # of pages: 6
AbstractThis paper presents a robust genetic algorithm approach to solve job shop scheduling problems with more practical considerations. Considerations have been given not only to job processing time but also to setup time, which leads to a batch allocation for jobs. Incorporating the batch allocation into job shop scheduling promises great potential to reduce work in progress, an essential factor for production planning. The system uses genetic algorithms to evolve an appropriate permutation for dispatching rules and batch sizes with an encode-decode strategy. It then uses a non-delay heuristic algorithm to construct a feasible solution according to the permutation. Testing results show that makespan has been dramatically reduced when the batch allocation is accounted.
Publication date
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number18924
NPARC number20377741
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Record identifier01ccb97e-b175-4c56-ba51-6e4fe5542a47
Record created2012-07-24
Record modified2016-05-09
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