Solving Flexible Job Shop Scheduling Problem with Transportation Time Based on Neuro-Fuzzy Suggested Metaheuristics

Abstract

The flexible job shop problem (FJSP) represents an extension of the classical job shop problem (JSP). The paper deals with a FJSP in an available set of machines with additional transportation time between machines. This type of problem belongs to the group of NP-hard problems. To solve the FJSP, artificial intelligence was used by applying three improved metaheuristic algorithms: Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colonies (ABC) algorithm and Genetic Algorithm (GA). The new approach in solving planning and scheduling problems with additional transportation time in combination with artificial intelligence and the developed neuro-fuzzy system represents the main research subject in the paper. The aim of the paper is to reduce the objective function with regard to time and increase productivity. Based on the case study optimization, experimental results show that the proposed mathematical model and the metaheuristic algorithms lead to an efficient outcome.