A Framework for Flexible Job Shop Scheduling Problem Using Simulation-Based Cuckoo Search Optimization

Cuckoo search optimization (CSO) is an evolutionary, nature-inspired, swarm intelligence meta-heuristic algorithm, which is compatible to optimize non-polynomial combinatorial problems. It works based on the living style of cuckoos. They have obligatory brood parasitism conduct. Basically, the CSO adopted the process of egg emplacing and breeding of cuckoos. CSO algorithm begins with initial population as if genetic algorithm, particle swarm optimization and other evolutionary algorithms. Basically, the CSO works based on the cuckoos endeavor to survive amid nests and societies. Due to this struggle, several cuckoos or eggs or chicks demise and the lived cuckoos colonize toward healthier environment. So, the process of “egg laying and breeding” starts again. The survival exertion of cuckoos unites them to form a society which has similar fitness quality of each cuckoo. Hence, this algorithm has been well known to solve complicated optimization problems. Therefore, in the present work a novel approach, based on CSO algorithm, has been framed to handle the flexible job shop scheduling problem. This problem belongs to the typical class of production scheduling having various manufacturing flexibilities as if the real-world manufacturing environment. Simulation is preferred as to compare the mathematical formulation, because it imitates the performance near to a realistic system. Quality function of CSO algorithm has been planned to determine through ProModel^© simulation software. The proposed algorithm handles multiple objectives such as makespan, mean flow time and mean tardiness.