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基于置信规则推理的库存控制方法 被引量:3

Belief-rule-based inference method for inventory control
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摘要 提出一种在非平稳需求以及区间预测需求情况下的基于置信规则推理的库存控制方法.该方法不依赖于需求的分布模型,区间预测需求利用能够处理多种定性和定量不确定性信息的ER(证据推理)框架进行表达,领域专家知识可以用来构建和初始化置信规则库,历史需求信息可以用来训练置信规则库,以得到更加可信的推理.给出了一个汽车4S店库存-销售实例,证实了该方法的可行性及其相对于传统方法的优越性. A belief-rule-based (BRB) inference method, which was independent of customer deman distribution, was proposed for inventory control under nonstatonary demand and interval foreeastin demand. Interval forecasting demand was represented in the ER (evidential reasoning) framewor which can deal with various kinds of qualitative and quantitative uncertain information. Domain exper knowledge was used to construct and initialize a belief rule base. Historical demand information can b used to train the belief rule base to get more reliable inference. Compared with traditional methods the feasibility of the BRB inventory control method and its features were illustrated through an aut, 4S inventory-sales example.
出处 《华中科技大学学报(自然科学版)》 EI CAS CSCD 北大核心 2011年第7期76-79,共4页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助项目(60674085 70572033 70971046 60736026) 国家科技部国际科技交流项目(20072607) 英国工程与物理科学研究委员会资助项目(EP/F024606/1)
关键词 专家系统 库存控制 置信规则库 证据推理 不确定性 expert system inventory control belief-rule-based evidential reasoning uncertainty
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参考文献10

  • 1Gen M, Tsujimura Y, Zheng D Z. An application of fuzzy set theory to inventory control models [J]. Computers and Industrial Engineering, 1997, 33 (3-4): 553-556.
  • 2Axsater S. Inventory Control[M]. 2 Edition, Bei-jing: Tsinghua University Press, 2007.
  • 3Bertsimas D, Thiele A. A robust optimization approach to inventory theory[J]. Operations Research, 2006, 54(1): 150-168.
  • 4Lee H M, Yao J S. Economic production quantity for fuzzy demand quantity and fuzzy production quantity [J]. European Journal of Operational Research, 1998, 109: 203-211.
  • 5傅玉颖,潘晓弘.不确定情况下基于模糊集理论的库存管理研究[J].系统工程理论与实践,2005,25(9):54-58. 被引量:22
  • 6杨杰,华中生.一种基于动态批量的非平稳需求库存管理方法[J].计算机集成制造系统,2007,13(2):387-391. 被引量:12
  • 7I.eungRW K, Lau H CW, KwongCK. Ona responsive replenishment system: a fuzzy logic approach[J]. Expert Systems, 2003, 20(1): 20-32.
  • 8Yang J B, Xu D L. On the evidential reasoning algorithm for muhiattribute decision analysis under uncertainty[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2002, 32(3) : 289-304.
  • 9Yang J B, Liu J, Wang J, et al. A generic rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transactions on System, Man and Cybernetics, Part A: Systems and Humans, 2006, 36(2): 266-285.
  • 10Yang J B, Liu J, Xu D L, et al. Optimization models for training belief rule based systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2007, 37(4): 569-585.

二级参考文献18

  • 1Giannooccaro I, Pontrandolfo P, Scozzi B. A fuzzy echelon approach for in supply chain [ J ]. European Journal of Operation Research,2003,149 : 185 - 196.
  • 2Kacpryzk J, Staniewski P. Long-term inventory policy-making through fuzzy decision-making models[J]. Fuzzy Sets and Systems,1982,8:117- 132.
  • 3Park K S. Fuzzy set theoretic interpretation of economic order quantity[ J ]. IEEE Transactions on Systems, Man, and Cybernetics,1987, (SMC-17) : 1082 - 1084.
  • 4Petrovic D, Petrovic R, Vujosevic M. Fuzzy models for the newsboy problem[ J]. Int. J. Production Economics, 1996,45:435 -441.
  • 5Ishii H, Konno T. A stochastic inventory problem with fuzzy shortages cost[J]. European Journal of Operation Research, 1998,106:90 - 94.
  • 6Chiang Kao, Wen-Kai Hsu. A single-period inventory model with fuzzy demand[J]. Computers and Mathematics with Application,2002,43:841 - 848.
  • 7Lushu Li, Kabadi S N, Nair K P K. Fuzzy models for single-period inventory problem[J]. Fuzzy Sets and Systems,2002,132:273 - 289.
  • 8Petrovic D, Roy R, Petrovic R. Supply chain modeling using fuzzy sets[ J]. Int. J. Production Economics, 1999,59:443- 453.
  • 9Hsieh C H. Optimization of fuzzy production inventory models [J]. Information Sciences,2002,146:29- 40.
  • 10Chen S H, Hsieh C H. Graded mean integration representation of generalized fuzzy number[J] . Journal of Chinese Fuzzy System, 1999,5(2):1-7.

共引文献32

同被引文献24

  • 1Yang J B, Liu J, Xu D L, et al. Optimization models for training belief-rule-based systems[J]. Systems, Man and Cyber- netics, Part A : Systems and Humans, 2007,37 (4) : 569 - 585.
  • 2Xu D L, Liu J, Yang J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak de- tection [ J ]. Expert Systems with Applications,2007,32 ( 1 ) : 103 - 113.
  • 3Zhou Z J, Hu C H, Xu D L, et al. A model for real-time failure prognosis based on hidden Markov model and belief rule base[J]. European Journal of Operational Research,2010,207( 1 ) :269 -283.
  • 4Zhou Z G, Liu F, Jiao L C, et al. A bi-level belief rule based decision support system for diagnosis of lymph node metasta- sis in gastric cancer[J]. Knowledge-Based Systems,2013,54:128 -136.
  • 5Wang Y M, Yang J B, Xu D L, et al. Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology [J]. Expert Systems with Applications,2009,36 (4) : 8421 - 8430.
  • 6Fan Z P, Li Y H, Wang X, et al. Hybrid similarity measure for case retrieval in CBR and its application to emergency re- sponse towards gas explosion[J]. Expert Systems with Applications,2014,41 (5) :2526 -2534.
  • 7BAR-SHAL0M Y , LI X R , KIRUBARAJAN T. Estimationwith applications to tracking and navigation: theory, algorithmsand software [M]. New York: John Wiley & Sons,2001.
  • 8MACAVEIU A , CAMPEANU A. Automotive radar targettracking by Kalman filtering[C]//Proceedings of the 201311th International Conference on Telecommunication inModern Satellite, Cable and Broadcasting Services. Nis,Serbia: IEEE,2013, 2: 553-556.
  • 9KUHN H W. The Hungarian method for the assignment problem[J]. Naval research logistics,2005,52(1) : 7-21.
  • 10SIMON D. Kalman filtering with state constraints: a survey oflinear and nonlinear algorithms [J] . IET control theory andapplication,2010,4 (8 ): 1303-1318.

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