Bottleneck machine identification method based on constraint transformation for job shop scheduling with genetic algorithm

Practical production scheduling problems usually involve some “bottleneck” machines, the scheduling policies for which could significantly affect the final solution quality. Therefore, it is beneficial to identify the bottleneck machines beforehand, so that we can intensify the optimization for these machines in the subsequent solving stage. To this end, a bottleneck machine identification algorithm is proposed in this paper for the job shop scheduling problem with the objective of minimizing total tardiness. In order to obtain the instance-specific information about bottleneck machine distribution, we first propose a new optimization model which relaxes some conventional constraints of the standard job shop problem. Then, a simulated annealing algorithm is applied to solve this newly defined problem. Based on the optimization result, the bottleneck characteristic value (which is a measure of bottleneck level) is calculated for each machine. To utilize the obtained bottleneck information for job shop scheduling, we design a genetic algorithm which allocates more computational resources to the identified bottleneck machines by using a hybrid encoding scheme. Computational results verify the effectiveness and the robustness of the proposed bottleneck identification procedure. It is shown that intensifying the local search effort for the bottleneck machines will generally result in higher solution quality within reasonably short computational time.