A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop

Tung Sun Chan, King Lun Tommy Choy, Bibhushan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)


This paper presents a genetic algorithm-based job-shop scheduler for a flexible multi-product, parallel machine sheet metal job shop. Most of the existing research has focused only on permutation job shops in which the manufacturing sequence and routings are strictly in a predefined order. This effectively meant that only the jobs shops with little or no flexibility could be modeled using these models. The real life job shops may have flexibility of routing and sequencing. Our paper proposes one such model where variable sequences and multiple routings are possible. Another limitation of the existing literature was found to be negligence of the setup times. In many job shops like sheet metal shops, setup time may be a very sizable portion of the total make-span of the jobs, hence setup times will be considered in this work. One more flexibility type arises as a direct consequence of the routing flexibility. When there are multiple machines (parallel machines) to perform the same operation, the job could be routed to one or more of these machines to reduce the make-span. This is possible in situations where each job consists of a pre-defined quantity of a specified product. In other words, same job is quantity-wise split into two or more parts whenever it reduces the makespan. This effectively assumes that the setup cost is negligible. This model has been implemented on a real-life industry problem using VB.Net programming language. The results from the scheduler are found to be better than those obtained by simple sequencing rules.
Original languageEnglish
Pages (from-to)8703-8715
Number of pages13
JournalExpert Systems with Applications
Issue number7
Publication statusPublished - 1 Jul 2011


  • Flexible job shop
  • Genetic algorithm
  • Multi-processor job shop
  • Scheduling
  • Simulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • General Engineering


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