A Multiobjective Memetic Approach to Job-Shop Scheduling under Uncertainty

International audienceIn recent years, much work has been expended on addressing job-shop scheduling problems (JSP) with uncertain information. There are two primary approaches to uncertainty handling, i.e. using probability theory and possibility theory. In this work, we use the possibilistic approach to deal with JSP where uncertain processing times are modeled by triangular fuzzy numbers (TFNs). Algorithmically, this paper examines the incorporation of a local search into a multiobjective genetic approach. The incorporation results in a simple multiobjective memetic algorithm that is based on the NSGA-II and the N2 neighborhood structure for individual improvement in the Lamarckian learning procedure. An extensive experiment was conducted to confirm the superiority of the algorithm compared to both the single-objective memetic and multiobjective genetic methods