Low-Carbon Job Shop Scheduling Problem with Discrete Genetic-Grey Wolf Optimization Algorithm

摘要

The workshop scheduling has historically emphasized the production metrics without involving any environmental considerations. Low-carbon scheduling has attracted the attention of many researchers after the promotion of green manufacturing. In this paper, we investigate the low-carbon scheduling problem in a job shop environment. A mathematical model is first established with the objective to minimize the sum of energy-consumption cost and completion-time cost. A discrete genetic-grey wolf optimization algorithm (DGGWO) is developed to solve the problem in this study. According to the characteristics of the problem, a job-based encoding method is first employed. Then a heuristic approach and the random generation rule are combined to fulfill the population initialization. Based on the original GWO, a discrete individual updating method the crossover operation of the genetic algorithm is adopted to make the algorithm directly work in a discrete domain. Meanwhile, a mutation operator is adopted to enhance the population diversity and avoid the algorithm from getting trapped into the local optima. In addition, a variable neighborhood search is embedded to further improve the search ability. Finally, extensive simulations are conducted based on 43 benchmark instances. The experimental data demonstrate that the proposed algorithm can yield better results than the other published algorithms.