Montgomery D C. Design and Analysis of Experiments [M]. New York: Wiley, 2008
[2]
Wang S Y, Wang L, Liu M, Xu Y. An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem [J]. International Journal of Production Economics, 2013, 145(1): 387-396
[3]
Yue D, You F. Sustainable scheduling of batch processes under economic and environmental criteria with MINLP models and algorithms [J]. Computers & Chemical Engineering, 2013, 54: 44-59
[4]
Liang Tao(梁涛), Li Qiqiang(李歧强). Self-organizing approach to multistage batch scheduling with batching optimization [J]. Control and Decision(控制与决策), 2011, 26(12): 1818-1823
[5]
Pan Q K, Wang L, Gao L, Li W D. An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers [J]. Information Sciences, 2011, 181(3): 668-685
[6]
Akkerman R, Van Donk D P, Gaalman G. Influence of capacity-and time-constrained intermediate storage in two-stage food production systems [J]. International Journal of Production Research, 2007, 45(13): 2955-2973
[7]
Belaid R, T'kindt V, Esswein C. Scheduling batches in flowshop with limited buffers in the shampoo industry [J]. European Journal of Operational Research, 2012, 223(2): 560-572
[8]
Zhou Xiaohui(周晓慧), Chen Chun(陈纯), Wu Peng(吴鹏), Zheng Junling(郑骏玲). Optimized scheduling of production process based on continuous-time in printing and dyeing industry [J]. CIESC Journal(化工学报), 2010, 61(8): 1877-1881
[9]
Li Z, Ierapetritou M. Process scheduling under uncertainty: review and challenges [J]. Computers & Chemical Engineering, 2008, 32(4): 715-727
[10]
Gu Xingsheng(顾幸生). A survey of production scheduling under uncertainty [J]. Journal of East China University of Science and Technology(华东理工大学学报), 2000, 26(5): 441-446
[11]
Kennedy J, Eberhart R C. Particle swarm optimization//Proceeding of IEEE International Conference on Neural Networks[C]. Perth, Australian, 1995: 1942-1948
[12]
Larra？aga P, Lozano J A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation[M]. Boston: Kluwer Academic Publishers, 2002
[13]
Sakawa M, Kubota R. Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms [J]. European Journal of Operational Research, 2000, 120(2): 393-407
[14]
Lee E S, Li R J. Comparison of fuzzy numbers based on the probability measure of fuzzy events [J]. Computers & Mathematics with Applications, 1988, 15(10): 887-896
[15]
Niu Q, Jiao B, Gu X. Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time [J]. Applied Mathematics and Computation, 2008, 205(1): 148-158
[16]
Jarboui B, Eddaly M, Siarry P. An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems [J]. Computers & Operations Research, 2009, 36(9): 2638-2646
[17]
Pan Q K, Ruiz R. An estimation of distribution algorithm for lot-streaming flow shop problems with setup times [J]. Omega, 2012, 40(2): 166-180
[18]
Nawaz M, Enscore E, Ham I. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem [J]. The International Journal of Management Sciences, 1983, 11(1): 91-95
[19]
Omar A G. A bi-criteria optimization: minimizing the integral value and spread of the fuzzy makespan of job shop scheduling problems [J]. Applied Soft Computing, 2003, 2(3): 197-210
[20]
Reeves C R. A genetic algorithm for flowshop sequencing [J]. Computers & Operations Research, 1995, 22(1): 5-13
[21]
Wang L, Zhang L, Zheng D Z. An effective hybrid genetic algorithm for flow shop scheduling with limited buffers [J]. Computers & Operations Research, 2006, 33(10): 2960-2971
[22]
Eberhart R C, Shi Y. Particle swarm optimization: developments, applications and resources//Proceedings of the IEEE Congress on Evolutionary Computation [C]. Seoul, Korea, 2001: 81-86
