Scheduling problems under uncertain demand

摘要

The Job-shop Scheduling Problem (JSP) is already an NP-complete problem, when resources and demands are supposed to be well-known. In practical industrial issues, demand is rather unknown and precisely is subject to uncertainty. In this paper, we show how to generate a set of diversified "optimal" solutions in order to help decision maker in uncertain environment. The list of demands is split into firm (certain) demands and predicted demands (uncertain). We aim to maximize the produced quantity while minimizing the makespan, in priority, and the production costs. Genetic algorithms are used to find the scheduling solution of the firm jobs. The predicted jobs will be inserted in the real solutions. The approach proposed allows us to select the solution of the firm demands which is the best solution after insertion of the predicted demands.