A Decison-Making Approach for Job Shop Scheduling with Job Depending Degradation and Predictive Maintenance

Maintenance is a strategic function in a company. It aims to ensure the proper functioning of an equipment. It no longer has the sole objective to repair a system but also to anticipate and prevent its failures. Recently, new failure prediction techniques have emerged. They provide information on the real-time condition of equipment and its remaining useful life (RUL). Thus, new methods of decision-making based on the analysis of these recent developments can be considered. Indeed, this paper presents a heuristic method for solving the makespan and the cost of a resumable job shop problem while predicting and preventing failures. It is based on the elimination of duplication that may exist between the different operations at a given point in time, while selecting a single operation and shifting the others at the end of the execution thereof. The best selection of the operation to assign on the machine depends on the effectiveness of the decision rule used for scheduling. The proposed method has been successfully tested on sonic experimental benchmarks and it allows a satisfactory resolution of relatively important problem in a reasonable time. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.