Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fu...Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization （SSPSO） algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search （SS） algorithm into the frame of particle swarm optimization （PSO） algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution （DE） scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.展开更多
基金supported by National Natural Science Foundation of China(Nos.61174040 and 61104178)Shanghai Commission of Science and Technology(No.12JC1403400)the Fundamental Research Funds for the Central Universities
文摘Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization （SSPSO） algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search （SS） algorithm into the frame of particle swarm optimization （PSO） algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution （DE） scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.
