A knowledge-based evolutionary proactive scheduling approach in the presence of machine breakdown and deterioration effect

This paper considers proactive scheduling in response to stochastic machine breakdown under deteriorating production environments, where the actual processing time of a job gets longer along with machine's usage and age. It is assumed that a job's processing time is controllable by allocating extra resources and the machine breakdown can be described using a given probability distribution. If a machine breaks down, it needs to be repaired and is no longer available during the repair. To absorb the repair duration, the subsequent unfinished jobs are compressed as much as possible to match up the baseline schedule. This work aims to find the optimal baseline sequence and the resource allocation strategy to minimize the operational cost consisting of the total completion time cost and the resource consumption cost of the baseline schedule, and the rescheduling cost consisting of the match-up time cost and additional resource cost. To this end, an efficient multi-objective evolutionary algorithm based on elitist non-dominated sorting is proposed, in which a support vector regression (SVR) surrogate model is built to replace the time-consuming simulations in evaluating the rescheduling cost, which represents the solution robustness of the baseline schedule. In addition, a priori domain knowledge is embedded in population initialization and offspring generation to further enhance the performance of the algorithm. Comparative results and statistical analysis show that the proposed algorithm is effective in finding non-dominated tradeoff solutions between operational cost and robustness in the presence of machine breakdown and deterioration effect.