关键词: 柔性加工车间, 鲁棒对等模型, 遗传算法, 邻域搜索

Abstract: To improve the feasibility of scheduling and ensure the stability of production processes,a robust optimization approach was proposed to address flexible job shop scheduling problem under stochastic processing times.After introducing two uncertain parameters to describe the degree of disturbance volatility and allowable constraints violence respectively,the generalized robust optimization framework for common linear programming problems was formulated,in which the stochastic variables were expressed as probability distributions.Consequently,the intractable problem of scheduling stochastic flexible job shops was reformulated into its deterministic robust counterpart model.Based on this model,a robust optimization algorithm was developed to make double decisions concurrently on operation sequences and machine assignments by formulating a fitness function with stochastic processing times and combining the global optimization of genetic evolution and the local improvement of neighborhood search.Experimental results showed that the proposed approach could obtain the optimal solution within 95% confidence level in a short computational time and at reasonable productivity loss.

Key words: flexible job shop, robust counterpart model, genetic algorithms, neighborhood search