化工学报 ›› 2015, Vol. 66 ›› Issue (1): 257-365.doi: 10.11949/j.issn.0438-1157.20141414

• 过程系统工程 • 上一篇    下一篇

不确定条件下中间存储时间有限多产品间歇生产过程调度

耿佳灿, 顾幸生   

  1. 华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237
  • 收稿日期:2014-09-19 修回日期:2014-09-29 出版日期:2015-01-05 发布日期:2015-01-05
  • 通讯作者: 顾幸生 E-mail:xsgu@ecust.edu.cn
  • 基金资助:

    国家自然科学基金项目(61174040, 61104178);上海市科委基础研究重点项目(12JC1403400);中央高校基本科研业务费专项资金。

Time-constrained intermediate storage multiproduct batch process scheduling with uncertainty

GENG Jiacan, GU Xingsheng   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2014-09-19 Revised:2014-09-29 Online:2015-01-05 Published:2015-01-05
  • Supported by:

    supported by the National Natural Science Foundation of China (61174040, 61104178), the Shanghai Commission of Science and Technology (12JC1403400) and the Fundamental Research Funds for the Central Universities.

摘要:

针对产品处理时间不确定条件下中间存储时间有限多产品间歇生产过程调度问题, 采用三角模糊数描述处理时间的不确定性, 通过一种模糊排序的方法建立了以最小化模糊最大完工时间的值以及不确定度作为调度目标的数学模型, 提出一种基于改进粒子群和分布估计的混合算法(IPSO-EDA)。IPSO-EDA算法在粒子群更新公式中引入基于所有粒子自身最优位置的优质个体分布信息, 提高了算法的全局搜索能力, 同时采用NEH初始化获得理想的初始解, 采用NEH局部搜索提高算法的局部搜索能力。通过正交实验设计对算法的参数进行调节, 仿真结果表明了所提出算法的有效性和优越性。

关键词: 间歇过程, 生产调度, 中间存储, 不确定, 粒子群优化, 分布估计算法

Abstract:

Time-constrained intermediate storage multiproduct batch process scheduling with uncertain processing time is concerned in this paper. The triangular fuzzy number is applied to describe the imprecise processing time of products. An approach for ranking fuzzy numbers is used to estimate the value and uncertainty of the makespan which are employed to establish the mathematical model. An improved particle swarm optimization with estimation of distribution algorithm (IPSO-EDA) is proposed. The IPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the particle swarm optimization (PSO), and therefore each particle has comprehensive search ability. Meanwhile, the NEH-based initialization and local search are introduced to construct good initial solutions and enhance the local exploitation, respectively. In addition, the influence of parameter settings of the IPSO-EDA is investigated based on the method of factorial design. The simulation results indicate the superiority of IPSO-EDA in terms of effectiveness and efficiency.

Key words: batch process, scheduling, intermediate storage, uncertain, particle swarm optimization, estimation of distribution algorithm

中图分类号: 

  • TQ021.8
[1] 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
[2] 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
[3] 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
[4] 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
[5] 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
[6] 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
[7] Li Z, Ierapetritou M. Process scheduling under uncertainty: review and challenges [J]. Computers & Chemical Engineering, 2008, 32(4): 715-727
[8] 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
[9] Kennedy J, Eberhart R C. Particle swarm optimization//Proceeding of IEEE International Conference on Neural Networks[C]. Perth, Australian, 1995: 1942-1948
[10] Larrañaga P, Lozano J A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation[M]. Boston: Kluwer Academic Publishers, 2002
[11] 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
[12] 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
[13] 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
[14] 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
[15] 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
[16] 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
[17] 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
[18] Reeves C R. A genetic algorithm for flowshop sequencing [J]. Computers & Operations Research, 1995, 22(1): 5-13
[19] 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
[20] 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
[21] Montgomery D C. Design and Analysis of Experiments [M]. New York: Wiley, 2008
[22] 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
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