Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Bilal Khurshid,Uzair Asad,Muhammad Omair,Muhammad Saad,Biswajit Sarkar,Shahid Maqsood

doi:10.1109/access.2021.3066446

Abstract

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.

Highlights

Scheduling is the key factor for maintaining a competitive environment in manufacturing systems and production planning [1]
WORKS In this paper, first, a comprehensive review is carried out of various techniques used to minimize the makespan of Permutation Flow Shop Scheduling Problems (PFSSP)
For fine-tuning of the results, the mutation rate is reduced with the increase in the number of iterations

Summary

Introduction

Scheduling is the key factor for maintaining a competitive environment in manufacturing systems and production planning [1]. Small improvements such as saving processing times and improving production efficiency can results in significant profit for the company. PFSSP is the key part of scheduling in manufacturing systems for one piece of mass production. Demonstrated that makespan minimization for PFSSP is an NP-hard problem. This NP-hard distinctive and its extensive application in the engineering field makes it a hot topic in the engineering and research field. The allocation of resources (e.g. Machines) to tasks (e.g. Jobs) over a period of time is termed as scheduling and is used to optimize one or more objectives [3] and provides a pivoting role for enhancing production performance in manufacturing systems, the development of efficient and robust technique is mandatory for manufacturing systems.

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Conclusion