Genetic algorithms applied to workshop problems

We evaluate in this paper the qualities of stochastic algorithms, mainly genetic and simulated annealing-type algorithms, against heuristic methods, in the scheduling of workshops. We are particularly interested in flow-shops (minimizing makespan) and one machine schedules (minimizing total tardiness, or minimizing total flow time). Many numerical results for various samples are given, and our conclusions are supported by statistical tests. When the initial population is randomly generated, genetic algorithms are shown to be statistically less efficient than annealing-type algorithms, and better than heuristic methods. But, as soon as at least one good item (e.g., heuristically found) belongs to the initial population, genetic algorithms become as good, or better than annealing-type algorithms. The resolution methods we propose are evaluated and can be used for when scheduling more complicated real workshops.