Aggregation method with dynamic search direction for multi-objective problems

The job-shop scheduling problem (JSP) is one of the hardest problems (NP-complete problem). In lots of cases, the combination of goals and resources has exponentially increasing search space. The objective of resolution of such a problem is generally, to maximize the production with a lower cost and makespan. In this paper, we explain how to modify the objective function of genetic algorithms to treat the multiobjective problem and to generate a set of diversified "optimal" solutions in order to help decision maker. The solutions proposed by our approach are compared to the lower bound of the cost and makespan in order to prove the qualities and robustness of our proposed approach.