title: A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.
abstract: Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.
authors.affiliation: Ecole Nat. d'Ingenieurs de Monastir.
authors.forename: S
authors.initials: S
authors.lastname: Hajri
authors.forename: N
authors.initials: N
authors.lastname: Liouane
authors.forename: S
authors.initials: S
authors.lastname: Hammadi
authors.forename: P
authors.initials: P
authors.lastname: Borne