Hybridization of Artificial Bee Colony algorithm with Particle Swarm Optimization algorithm for flexible Job Shop Scheduling

Abstract

Job shop scheduling represents a Non deterministic polynomial (NP)-Hard combinatory in the domain of the scheduling problems. The Job Shop Scheduling Problem (JSSP) has emerged as one of the most appealing scheduling models now in vogue which is concerned with the toughest combinatorial optimization issues. The job shop scheduling menace can be successfully tackled by means of a narrative technique, whereby overlapping in functions and client requirement may be compensated with the manifold requirement for each and every job, where demand invariably exerts an incredible thrust on the volume of each and every completed task demanded by the client. The proposed methodology hybridization of the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) optimization techniques minimizes the makespan time of the shops. Here twenty different types of bench mark problems are considered for the JSSP process. In the ABC technique the scout bee operation based on the PSO technique updates the process velocity and position of particles. The optimal solutions are obtained in the HPA compared to the ABC and PSO. From the results the optimal makespan time fitness function accuracy of the proposed method is 94.23% compared to other optimization processes.