A genetic algorithm-based approach for single-machine scheduling with learning effect and release time

Der Chiang Li, Peng Hsiang Hsu, Chih Chieh Chang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The way to gain knowledge and experience of producing a product in a firm can be seen as new solution for reducing the unit cost in scheduling problems, which is known as "learning effects." In the scheduling of batch processing machines, it is sometimes advantageous to form a nonfull batch, while in other situations it is a better strategy to wait for future job arrivals in order to increase the fullness of the batch. However, research with learning effect and release times is relatively unexplored. Motivated by this observation, we consider a single-machine problem with learning effect and release times where the objective is to minimize the total completion times. We develop a branch-and-bound algorithm and a genetic algorithm-based heuristic for this problem. The performances of the proposed algorithms are evaluated and compared via computational experiments, which showed that our approach has superior ability in this scenario.

Original languageEnglish
Article number249493
JournalMathematical Problems in Engineering
Volume2014
DOIs
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Engineering(all)

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