A DE-based approach to no-wait flow-shop scheduling

  • Authors:

  • B. Qian;L. Wang;R. Hu;D. X. Huang;X. Wang

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China and Department of Automation, Kunming University ...;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Kunming University of Science and Technology, Kunming 650051, PR China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2009

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Abstract

This paper proposes an effective hybrid differential evolution (HDE) for the no-wait flow-shop scheduling problem (FSSP) with the makespan criterion, which is a typical NP-hard combinational optimization problem. Firstly, a largest-order-value (LOV) rule is presented to transform individuals in DE from real vectors to job permutations so that the DE can be applied for solving FSSPs. Secondly, the DE-based parallel evolution mechanism and framework is applied to perform effective exploration, and a simple but efficient local search developed according to the landscape of FSSP is applied to emphasize problem-dependent local exploitation. Thirdly, a speed-up evaluation method and a fast Insert-based neighborhood examining method are developed based on the properties of the no-wait FSSPs. Due to the hybridization of DE-based evolutionary search and problem-dependent local search as well as the utilization of the speed-up evaluation and fast neighborhood examining, the no-wait FSSPs can be solved efficiently and effectively. Simulations and comparisons based on well-known benchmarks demonstrate the efficiency, effectiveness, and robustness of the proposed HDE.