A hybrid discrete water wave optimization algorithm for the no-idle flowshop scheduling problem with total tardiness criterion. (15th May 2020)

• Record Type: Journal Article
• Authors: Zhao, Fuqing; Zhang, Lixin; Zhang, Yi; Ma, Weimin; Zhang, Chuck; Song, Houbin
• Is Part Of: Expert systems with applications. Volume 146(2020)
• Publication Date: 2020-05-15
• Subjects: Water wave optimization -- No-idle flowshop scheduling problem -- Total tardiness -- Variable neighborhood search; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113166
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A hybrid discrete water wave optimization algorithm is proposed to solve NIFSP. The new initialize method is applied to generate desirable solutions. Three different strategies are introduced to improve the performance of HWWO. The control parameters are calibrated with extensive experiments. The comparisons of HWWO with state-of-the-arts algorithms are presented. Abstract: The no-idle flowshop has attracted enormous attention owing to its widespread application in the manufacturing industry domain. In this paper, a hybrid discrete water wave optimization algorithm, named HWWO, is presented to solve the NIFSP with total tardiness. In order to improve the quality of a population, an initialize method based on a new priority rule combined with the modified NEH method is proposed to generate a population. In the propagation phase, a self-adaption selection neighborhood search structure is introduced to amplify the search range of waves and balance the exploration and exploitation ability of the HWWO. Afterwards, a variable neighborhood search is adopted to strengthen the local search and maintain the diversity of the population in the breaking phase. In the refraction operation, a perturbation sequence is generated and combined with the local optimal solution found by the breaking operation, in order to generate a new solution, and prevent the algorithm from becoming trapped in the local optimum. Furthermore, the control parameters and time complexity are analyzed. The experimental results and comparisons with the other state-of-the-art algorithms evaluated on Taillard's and Ruiz's benchmark sets reveal that the effectiveness and efficiency of the HWWO outperformed the compared algorithms for solving the NIFSP.