Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

Shoujing Zhang,Shoufeng Jin,Sebastian Borucki,Haotian Du,Zhixiong Li,Tiantian Hou

doi:10.3390/machines9060108

Abstract

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

Highlights

From the average of the calculation results of the four types of algorithms, the average maximum completion times of genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and improved quantum genetic algorithm (IQGA) are 58.3 min, 56.6 min, 56.3 min, and 55.3 min, respectively, which can prove the superiority of the algorithm in this paper
A process-based quantum code is designed for the DRCFJSP problem, and the niche initialization population, dynamic rotation angle, and quantum mutation strategy are used to improve the quantum genetic algorithm to improve the efficiency of the algorithm and avoid premature convergence of the algorithm
Optimizes 17.6% and 6.7% of the time compared to the algorithms in literature [33,34], respectively; in Kacem03, IQGA optimizes 7.7% and 7.7% in comparison with the algorithms in literature [34,35]; in Kacem04, IQGA optimizes 12.5% of the time compared to the algorithm in [33]; and in Kacem05, IQGA optimizes 14.3% of the time compared to the algorithm in [35]

Summary

Introduction

JSP is involved with a set of machines to process a set of work parts. Each work part is formed by a series of processes with sequential constraints. Each process requires only one machine, which is always available and can process one operation at one time without interruption [3]. Flexible job shop scheduling problem (FJSP) is an extension of the traditional JSP, in which several processes of workpieces are allowed to be processed by several machines at the same time. In various complex man-machine systems, about 60–90% of failures are attributed to operator errors [4]. The research on dual resource constrained flexible job shop scheduling problem (DRCFJSP) has theoretical significance and practical value

Results

Discussion

Conclusion