An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Peiliang Wu,Wenbai Chen,Hongnian Yu,Qingyu Yang,Bingyi Mao,Zhile Yang

doi:10.1155/2020/3450180

Abstract

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.

Highlights

With the advancement of Industry 4.0, the demographicdividend is gradually replaced by the technology-dividend [1, 2]
The permutation flowshop scheduling problem (PFSSP) has been researched for more than half a century due to its complexity. It can be described as N jobs that are processed on M different machines in the same order. e processing time of the n-th job on the m-th machine is known in advance and fixed. e task is to solve the processing order of each job so that the objective function is optimal [3,4,5,6]
We propose an improved genetic-shuffled frog-leaping algorithm (GSFLA) called as IGSFLA and apply it to solve the PFSSP. is paper has three contributions: (1) a heuristic optimal-insert method with fitness constrain is applied to generate the frog group; (2) the crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and make the evolution faster; (3) the disturbance mechanism is applied both in the initialization step and the local searching step to evolve the frogs with the same optimal fitness

Summary

Introduction

With the advancement of Industry 4.0, the demographicdividend is gradually replaced by the technology-dividend [1, 2]. Kurdi [42] combines the genetic algorithm, simulated annealing, and NEH to construct a memetic algorithm with novel semiconstructive evolution operators to solve the permutation flowshop scheduling problem, and the proposed MASC can be considered as one of the best-so-far methods for PFSSP. SFLA has been applied to multiobjective flexible job shop scheduling problem (MOFJSSP), and one of an approximate optimal solution can be obtained by iterating the global search several times [47]. The memeplex grouping SFLA is proposed and applied to solve the distributed two-stage hybrid flowshop scheduling problem (DTHFSSP) in a multifactory environment to minimize manufacturing time and the amount of delayed work [32]. While as shown in our experiments, the traditional GSFLA is not good enough to solve the PFSSP, and in this paper, we propose an improved GSFLA and apply it to solve the PFSSP better

Improved GSFLA for PFSSP

Experiments and Analysis

Algorithm Parameter Testing

Conclusion