AFSO-JSSP: Artificial Fish Swarm Optimization for Efficient Job Shop Scheduling

Abstract

Job Shop Scheduling Problem (JSSP) has become one of the key issues in a contemporary manufacturingsystem in which the task is to optimally schedule jobs to the machines to reduce the time and resources used inproduction. Good scheduling is critical in enhancing the productivity and competitiveness of manufacturing industries.In this research, Artificial Fish Swarm Optimization (AFSO) algorithm is used to optimize the JSSP in minimizingmakespan, total work load and maximum work load in machines. The AFSO strategy models the swarm behaviour offishes to search and forage the search space in an efficient manner to prevent its early convergence to local optima. Themodel incorporates a disturbed state in the world to improve the direction in search and the speed of convergence. Theeffectiveness of the suggested AFSO method is compared and tested with the traditional and sophisticated optimizationalgorithms like Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) methods. The experimentalfindings prove that the offered technique provides better results in convergence rate and solution quality. The resultsprove that AFSO is a useful and promising method of solving complex problems in production system scheduling.