4.7 Article

A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

Journal

EXPERT SYSTEMS WITH APPLICATIONS
Volume 91, Issue -, Pages 347-363

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.09.028

Keywords

Water Wave Optimization (WWO); Iterated greedy algorithm; No-wait flow shop scheduling problem; Makespan

Funding

  1. National Natural Science Foundation of China [61663023]
  2. Science and Technology Commission Foundation of Gansu Province [17YF1WA160]
  3. Postdoctoral Science Foundation of China
  4. Science Foundation for Distinguished Youth Scholars of Lanzhou University of Technology
  5. Lanzhou Science Bureau [2012M521802, 2013T60889, J201405, 2013-4-64]

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In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP. (C) 2017 Elsevier Ltd. All rights reserved.

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