A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

Fuqing Zhao,Xiaotong Hu,Zekai Li,Ling Wang

doi:10.1007/s40747-021-00354-5

Abstract

The distributed manufacturing has become a prevail production mode under the economic globalization. In this article, a memetic discrete differential evolution (MDDE) algorithm is proposed to address the distributed permutation flow shop scheduling problem (DPFSP) with the minimization of the makespan. An enhanced NEH (Nawaz–Enscore–Ham) method is presented to produce potential candidate solutions and Taillard’s acceleration method is adopted to ameliorate the operational efficiency of the MDDE. A new discrete mutation strategy is introduced to promote the search efficiency of the MDDE. Four neighborhood structures, which are based on job sequence and factory assignment adjustment mechanisms, are introduced to prevent the candidates from falling the local optimum during the search process. A neighborhood search mechanism is selected adaptively through a knowledge-based strategy which focuses on the adaptive evaluation for the neighborhood selection. The optimal combinations of parameters in the MDDE algorithm are testified by the design of experiment. The computational results and comparisons demonstrated the effectiveness of the MDDE algorithm for solving the DPFSP.

Highlights

In the context of globalization, intelligent manufacturing has become a common production mode
After running all parameter configurations, the results show that the parameters P1 and N P have no significantly effect on memetic discrete differential evolution (MDDE)
There is no significant difference between MDDE and the comparison algorithms in individual cases, the rank of MDDE is the smallest among all the test cases

Summary

Introduction

In the context of globalization, intelligent manufacturing has become a common production mode. With the increasing cooperation between factories, distributed manufacturing systems have been widely applied [1,2]. The permutation flow shop scheduling problem (PFSP) is one of the classic combinatorial optimization problems, which has been proved as an NP-hard problem [7,8]. The distributed permutation flow shop scheduling problem. According to the literature [32], the DPFSP is described as follows. A set of jobs J is processed in F factories, where J = j1, j2, . Each factory includes an identical flow shop with M machines, M = m1, m2, . The operation of jobs j on machine i is denoted as Oi j. The setting time of the machine and the transmission time between operations are not considered.

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Discussion

Conclusion