关键词: 置换流水车间调度, 累积前景理论, 最优觅食算法, 前景价值, 多目标优化

Abstract: For high-dimensional multi-objective Permutation Flow-shop Scheduling Problems (PFSP),an Optimal Foraging Algorithm (OFA) based on Cumulative Prospect Theory (CPT-OFA) was presented to minimize the makespan,maximum delay time,inventory cost and delay cost.In this algorithm,the grey relational analysis,the entropy theory and cumulative prospect theory were combined.The comprehensive-prospect-value model of the Pareto solution was established by setting reference points,defining the value function and the attribute weight.The weights of different objectives were evaluated with entropy theory.The prospect value was adopted to evaluate the quality of the Pareto solution and used as the fitness value to guide the evolution of the algorithm.Moreover,the opposite search mechanism was applied in OFA to avoid the entrapment into local optima and to enhance the search ability.The simulation experiments were carried out with PFSP benchmark instances and a practical PFSP to check the validity of the proposed algorithm.The experimental results demonstrated that CPT-OFA was superior to three relatively novel multi-objective optimization algorithms,and could obtain high-quality Pareto solution for the multi-objective PFSPs.

Key words: permutation flow-shop scheduling, cumulative prospect theory, optimal foraging algorithm, prospect value, multi-objective optimization