A new framework to solve flexible jobshop scheduling problems in the context of Industry 5.0
Résumé
Human intervention is reduced in Industry 4.0 through automation of the manufacturing process. Today, Industry 5.0 puts human beings at the center of the industry, using their creativity and expertise in collaboration with powerful, intelligent and precise machines with two goals: to bring back a human touch in the industry and to have more personalized products. The aim of this work is to provide a framework that uses a decision maker preference elicitation algorithm in the form of an RMP model in the first step, and then incorporates it in resolving a multi-objective flexible job shop scheduling problem in the second step. Three objectives to be minimized are considered: the makespan, the total machining time, and the quadratic difference of the workload between all machines with the average load. A hybrid Tabu search method is proposed and tested on publicly available instances. The link between our approach and Industry 5.0 will then be established.