Genetic Simulated-Annealing Algorithm for Robust Job Shop Scheduling

摘要

This paper discusses the job shop scheduling problem with uncertain processing times, which are characterized by scenarios. A robust optimization criterion is proposed to balance the average performance and the robustness. In order to solve the uncertain scheduling problem with special criterion, a hybrid heuristic algorithm, which integrates the genetic algorithm and the simulated annealing algorithm, is developed in this paper. The specific genetic operators and the specific simulated annealing operators are appropriately designed to cooperate in this hybrid algorithm. An extensive experiment was conducted to investigate the convergence and the effectiveness of the developed hybrid algorithm as well as the advantages of the proposed robust optimization model. The computational results show that the genetic simulated-annealing hybrid algorithm can effectively address the problem discussed in this paper at more rapidly converging speed and can achieve better solution qualities than the individual genetic or simulated annealing algorithm.