A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling

Most scheduling problems are highly complex combinatorial problems, However, stochastic methods such us 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. A 10-jobs/6-machines example shows the effectiveness of the developed method.