An exact method to solve a multi-objective dynamic job shop scheduling problem: A case study on Impression Teinture Sahel company

Solving a problem in an optimal way represents a main challenge for any decision maker. The realization of this research is registered in this context. In this paper, a literature review and motivation of this work are presented. An improvement and a reformulation of the presented literature model is made and tow corrects and equivalent models are obtained. An exact method is proposed to solve these models. This method is implemented in python language. An experimental result and some discussion points are presented. A dynamic job shop scheduling problem instances are generated to test the performance of our proposed exact method. The experimental result showed that our resolution method gives equivalent solutions (with the same values of the objective functions and the same values of the decision variables) for models 1 and 2 in each of the instances. We noticed that the two models have the same complexity (in terms of the number of constraints and decision variables) and there is a little variation in the solve time of the same instance of model 1 and 2. We showed that it is possible that this exact method does not have a well-defined path to find the optimal solution. Also, we noticed that for each instance, the solve time variation is small and does not exceed 0.05 seconds in most instances. A case study is presented on Impression Teinture Sahel (ITS) company.