Implementation of a Binary Kidney-Inspired Algorithm Based on a Parallel Method for Solving the Job Shop Scheduling Problem

Abstract

This paper proposes a binary kidney-inspired algorithm (KA) to tackle the job shop scheduling problem (JSSP), and its solution is essential in manufacturing, especially in real industrial engineering. The job shop scheduling problem is a computer science optimization problem, and it is among the most significant and challenging issues in the field of production scheduling. The proposed algorithm is based on a parallel method with many threads. This algorithm is checked using certain job shop benchmark problems. The findings are compared with those retrieved using two techniques: a genetic algorithm (GA) and a particle swarm optimization (PSO) method. These techniques indicate that applying the binary kidney-inspired in parallel is an efficient algorithm for solving the JSSP, shows remarkable competitiveness, and considerably accelerates speedups, especially in large-scale instances. The achieved results are based on four threads; the speedup is 3.13 for the FT06 instance, while the execution time is 2.24 seconds.