摘要
随着信息与制造技术的加速革新和产品生命周期的缩短，企业及供应链管理中的库存与生产运作管理环节面临着越来越多的不确定性，其中最大的来源是需求的不确定性。系统地分析和解决不确定性需求下的库存与生产运作管理问题成为当今研究学者和实践者研究的热门课题。解析式和仿真式方法在求解复杂性、移植性、领域知识和不确定信息的表达能力等方面存在局限。启发式方法可以有效利用专家领域知识并适用于各种复杂和变化的资源约束环境下，特别是人工智能启发式方法还可以良好地表达和处理各种不确定信息。面对不断动态突变的库存与生产运作环境，伴随着频繁的重新规划和关联的资源约束，研究学者和实践者越来越趋于应用人工智能启发式方法。
置信规则推理方法是在证据推理理论和产生式规则专家系统的基础上提出来的解决不确定性问题的人工智能启发式方法。本文对置信规则推理理论进行深入研究，并应用于不确定需求下的库存与生产运作管理问题，主要研究工作如下：
论述了置信规则推理理论的基础知识，总结出置信规则推理方法的应用模式和应用机制，提出在区间不确定性信息输入下的置信规则前向推理算法。将置信规则推理方法的应用模式分为系统逼近器和系统控制器。应用机制提供了置信规则推理方法的关键环节，为置信规则推理方法的改进和应用提供了指导和支撑作用。
针对不确定需求下的库存控制问题，分别考虑缺货补偿和缺货丢失两种情况，提出基于置信规则推理的库存控制方法。证明了正态随机预测误差下的最优基本库存策略作为定量专家知识初始化置信规则库，为定性专家知识缺失或不可信时提供良好的依据。给出一个数值案例和一个4S店实例进行仿真验证，并与现有的适应性、启发式、近似最优、确定等价控制和鲁棒优化等方法进行比较分析。
针对置信规则库的结构识别和参数识别，提出了同步识别和异步识别方法。在异步识别中提出置信K均值聚类算法对置信规则库的结构进行识别。对于不确定需求信息下的企业集约生产规划问题，提出基于置信规则推理的层次化集约生产规划方法，并构建了连续和切换生产模式。给出某机动车和匹兹堡油漆厂两个生产规划案例，将同步识别和异步识别方法进行了比较分析，并将置信规则推理方法与非线性区间数规划、线性决策规则和生产切换启发式方法进行比较，同时提供了针对不同成本结构下置信规则推理方法的敏感性分析。
针对区间信息输入下的置信规则前向推理算法、异步识别中的结构识别和参数识别算法，分别提出了相应的遗传共轭梯度求解方法，并应用于不确定需求情况下的集中式供应链管理问题。对于由单个生产商和单个分销商组成的两级串型供应链系统，提出生产商和分销商的层次化置信规则推理框架，构建了生产商和分销商的置信规则推理订货策略。在某机动车制造商和分销商组成的两级串型供应链实例中分析了遗传共轭梯度求解方法相对于已有求解方法的优越性，并将置信规则推理方法与鲁棒优化方法进行了比较。
With the rapid development of information and manufacturing technologies andgradually shortened product life cycle, inventory and production operations management inenterprise and supply chain management are facing more and more uncertainties, in whichthe biggest source is demand uncertianty. It has become a hot spot nowadays whichattracted much attention from researchers and practitioners to systematically analyze andsolve inventory and production operations management problems under uncertainties. Theanalytical method and simulation method have limitations with respects to computationalcomplexity, portability, and the expression ability of domain knowledge and uncertaininformation. Heuristic methods can effectively use expert domain knowledge and aresuitable for complex, changing and resource constrained environment, especially theartificial intelligence based heuristic methods have favorable capability to express andtransact uncertainty. Facing dynamically changing inventory and production operationalenvironments with frequent replanning and interconnected resource constraints, researcherand practitioner more and more tend to use AI based heuristic methods.
As an AI based heuristic method, belief-rule-based inference (BRBI) method isproposed based on evidential reasoning theory and production rule based expert system fordealing with uncertain problems. This thesis studies deeply the BRBI method, and applies itto inventory and production operations management problems under uncertainty. The mainresearch works are as follows:
This thesis introduces the foundation of BRBI theory, outlines the application modesand application mechanisms of BRBI method, and proposes a BRBI approach with intervaluncertain inputs. The application modes of the BRBI model are classified into systemapproximator and system controller. The application methanisms provide the key elimentsof the BRBI model, which has instruction and support functions to improvement andapplication of the BRBI method.
For inventory control problem under nonstationary uncertain demand, abelief-rule-based inventory control (BRB-IC) method is proposed considering bothbackorder case and lost sales case. An optimal base stock policy under normal forecast erroris proved as a quantitative expert knowledge to initialize the belief-rule-base. A numericalexample and an auto4S store case study are provided to examine the BRB-IC method bycomparison with existing adaptive method, heuristic method, myopic method,certainty-equivalent-control, and robust optimization.
For structure and parameter identifications of the belief-rule-base, a simultaneous identification approach and an asynchronous identification approach are developed andcompared. In the asynchronous approach, a belief K-means clustering algorithm is putforward for structure identification. For aggregate production planning (APP) underuncertain demand, a hierarchical BRBI method for APP is proposed including bothcontinuous and switching modes. The simultaneous identification approach andasynchronous identification approach are applied to identify the structure and parameter ofBRB. An automotive case study and the Pittsburgh paint factory example are provided. TheBRBI method is compared with nonlinear interval number programming, linear decisionrule and production switching heuristic. The sensitivity of BRBI method is analyzed underdifferent cost structures.
For belief-rule-inference model under interval inputs, structure identification andparameter identification models in the asynchronous approach, corresponding genetic-conjugate gradient algorithms are proposed and applied into centralized supply chainmanagement problem under uncertain demand. The producer and distributor’s hierarchicalBRBI framework is constructed and their inference models and order policies are provided.In an automotive case study, the superiority of the genetic-conjugate gradient algorithmsover existing algorithms are provided, and the BRBI method is compared with the robustoptimization method.
引文
[1] E. A. Silver, D. F. Pyke, R. Peterson. Inventory Management and ProductionPlanning and Scheduling.(3rd Ed.). New Jersey: John Wiley and Sons,1998
[2] H. Stadtler, C. Kilger. Supply Chain Management and Advanced Planning: Concepts,Models, Software, and Case Studies.(4th Ed.). Berlin: Springer-Verlag,2008
[3] S. C. Graves. Uncertainty and production planning. Planning Production andInventories in the Extended Enterprise. International Series in Operations Researchand Management Science. New York: Springer-Verlag,2011,151:83~101
[4] X. H. Zhang, M. Prajapati, E. Peden. A stochastic production planning model underuncertain seasonal demand and market growth. International Journal of ProductionResearch,2011,49(7):1957~1975
[5] J. Mula, R. Poler, J. P. Garcia-Sabater, F. C. Lario. Models for production planningunder uncertainty: a review, International Journal of Production Economics,2006,103(1):271~285
[6] J. B. Yang, J. Liu, J. Wang, H. S. Sii, H. W. Wang. Belief rule-base inferencemethodology using the evidential reasoning approach-RIMER. IEEE Transactionson System, Man and Cybernetics-Part A: Systems and Humans,2006,36(2):266~285
[7] J. B. Yang, J. Liu, D. L. Xu, J. Wang, H. W. Wang. Optimization models for trainingbelief rule based systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans,2007,37(4):569~585
[8] J. B. Yang, M. G. Singh. An evidential reasoning approach for multiple attributedecision making with uncertainty. IEEE Transactions on Systems, Man, andCybernetics,1994,24(1):1~18
[9] J. B. Yang, P. Sen. A general multi-level evaluation process for hybrid MADM withuncertainty. IEEE Transactions on Systems, Man, and Cybernetics,1994,24(10):1458~1473
[10] J. B. Yang. Rule and utility based evidential reasoning approach for multipleattribute decision analysis under uncertainty. European Journal of OperationalResearch,2001,131(1):31~61
[11] J. B. Yang, D. L. Xu. On the evidential reasoning algorithm for multiattributedecision analysis under uncertainty. IEEE Transactions on Systems, Man, andCybernetics Part A: Systems and Humans,2002,32(3):289~304
[12] J. Liu, J. B. Yang, J. Wang, H. S. Sii, Y. M. Wang. Fuzzy rule-based evidentialreasoning approach for safety analysis. International Journal of General Systems,2004,33(2-3):183~204
[13] J. Liu, J. B. Yang, J. Wang, H. S. Sii. Engineering system safety analysis andsynthesis using fuzzy rule-based evidential reasoning approach. Quality andReliability Engineering International,2005,21:387~411
[14] J. Liu, J. B. Yang, D. Ruan, L. Martinez, J. Wang. Self-tuning of fuzzy belief rulebases for engineering system safety analysis. Annals of Operations Research,2008,163(1):143~168
[15] J. Liu, D. Ruan, H. Wang, L. Martinez. Improving nuclear safeguards evaluationthrough enhanced belief rule-based inference methodology. International Journal ofNuclear Knowledge Management,2009,3(3):312~339
[16] J. Liu, L. Martinez, D. Ruan, R. Rodriguez, A. Calzada. Optimization algorithm forlearning consistent belief rule base from examples. Journal of Global Optimization,2010, DOI:10.1007/s10898-010-9605-x
[17]常瑞,王红卫,杨剑波.基于梯度法和二分法的置信规则库参数训练方法.系统工程(增刊),2007:287~291
[18] Tang D W, Yang J B, Chin K S, Wong Z S Y, Liu X B. A methodology to generate abelief rule base for customer perception risk analysis in new product development.Expert Systems with Applications,2011,38(5):5373~5383
[19] Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, D. H. Zhou. Online updatingbelief-rule-based system for pipeline leak detection under expert intervention.Expert Systems with Applications,2009,36(4):7700~7709
[20] Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, M. Y. Chen, D. H. Zhou. A sequentiallearning algorithm for online constructing belief rule based systems. Expert Systemswith Applications,2010,37(2):1790~1799
[21] Y. W. Chen, J. B. Yang, D. L. Xu, Z. J. Zhou, D. W. Tang. Inference analysis andadaptive training for belief rule based systems. Expert Systems with Applications,2011,38(10):12845~12860
[22] D. L. Xu, J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson, J. Ren. Inference andlearning methodology of belief-rule-based expert system for pipeline leak detection.Expert Systems with Applications,2007,32(1):103~113
[23] Z. J. Zhou, C. H. Hu, D. L. Xu, J. B. Yang, D. H. Zhou. Bayesian reasoningapproach based recursive algorithm for online updating belief rule based expertsystem of pipeline leak detection. Expert Systems with Applications,2011,38(4):3937~3943
[24] Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, D. H. Zhou. Online updatingbelief-rule-based systems using the RIMER approach. IEEE Transactions onSystems, Man, and Cybernetics-Part A: Systems and Humans,2011,41(6):1225~1243
[25] Z. J. Zhou, C. H. Hu, D. L. Xu, J. B. Yang, D. H. Zhou. New model for systembehaviour prediction based on belief-rule-based systems. Information Sciences,2010,180(24):4834~4864
[26] H. S. Sii, J. Wang, A. G. Eleye-Datubo, J. B. Yang, J. Liu. Safety assessment ofFPSO turret-mooring system using approximate reasoning and evidential reasoning.Marine Technology and SNAME News,2005,42(2):88~102
[27] G. L. Kong, D. L. Xu, X. B. Liu, J. B. Yang. Applying a belief rule-base inferencemethodology to a guideline-based clinical decision support system. Expert Systems,2009,26(5):391~408
[28] G. L. Kong, D. L. Xu, J. B. Yang, R. Body, K. Mackway-Jones, S. Carley. A beliefrule-based decision support system for clinical risk assessment of cardiac chest pain.European Journal of Operational Research,2011, doi:10.1016/j.ejor.2011.10.044
[29] Y. M. Wang, J. B. Yang, D. L. Xu, K. S. Chin. Consumer preference prediction byusing a hybrid evidential reasoning and belief rule-based methodology. ExpertSystems with Applications,2009,36(4):8421~8430
[30] X. S. Si, C. H. Hu, J. B. Yang, Q. Zhang. On the dynamic evidential reasoningalgorithm for fault prediction. Expert Systems with Applications,2011,38(5):5061~5080
[31]司小胜,胡昌华,周志杰.基于证据推理的故障预报模型.中国科学:信息科学,2010,40(7):954~967
[32] C. H. Hu, X. S. Si, J. B. Yang. Dynamic evidential reasoning algorithm for systemsreliability prediction. International Journal of Systems Science,2010,41(7):783~796
[33] C. H. Hu, X. S. Si and J. B. Yang. System reliability prediction model based onevidential reasoning algorithm with nonlinear optimization. Expert Systems withApplications,2010,37(3):2550~2562
[34]胡昌华,司小胜.基于信度规则库的惯性平台健康状态参数在线估计.航空学报,2010,31(7):1454~1465
[35] D. Bertsimas, A. Thiele. Robust and data-driven optimization: moderndecision-making under uncertainty. Tutorials in Operations Research,2006,4:95~122
[36] F. R. Jacobs, R. Chase. Operations and Supply Chain Management. New York:McGraw-Hill,2010
[37] G. Kitagawa. Introduction to Time Series Modeling. CRC Press,2009
[38] M. Z. Baba, Y. Dallery. Dynamic versus static control policies in single stageproduction-inventory systems. International Journal of Production Research,2009,47(2):415~433
[39] Axs ter S. Inventory Control.(2nd Ed.). Beijing: Tsinghua University Press,2007
[40] J. T.Treharne, C. R. Sox. Adaptive inventory control for nonstationary demand andpartial information. Management Science,2002,48(2):607~624
[41] M. Z. Baba, A. A. Syntetos, Y. Dallery, K. Nikolopoulos. Dynamic re-order pointinventory control with lead-time uncertainty: analysis and empirical investigation.International Journal of Production Research,2011,47(9):2461~2483
[42] S. A. Tarim, B. G. Kingsman. Modelling and computing (Rn, S n)policies forinventory systems with non-stationary stochastic demand. European Journal ofOperational Research,2006,174(1):581~599
[43] Z. Hua, J. Yang, F. Huang, X. Xu. A static-dynamic strategy for spare part inventorysystems with nonstationary stochastic demand. Journal of the Operational ResearchSociety,2009,60(9):1254~1263
[44]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.计算机集成制造系统,2007,13(2):387~391
[45] J. S. Song, P. Zipkin. Inventory control in a fluctuating demand environment.Operations Research,1993,41(2):351~370
[46] S. C. Graves. A single-item inventory model for a nonstationary demand process.Manufacturing and Service Operations Management,1999,1(1):50~61
[47] X. W. Lu, J. S. Song, A. Regan. Inventory planning with forecast updates:approximate solutions and cost error bounds. Operations Research,2006,54(6):1079~1097
[48] O. Dey, D. Chakraborty. Fuzzy period review system with fuzzy random variabledemand. European Journal of Operational Research,2009,198(1):113~120
[49] S. Ellon, J. Elmaleh. Adaptive limits in inventory control. Management Science,1970,16(8): B533~B548
[50] C. C. Holt, F. Modigliani, H. A. Simon. A linear decision rule for production andemployment scheduling. Management Science,1955,2(1):1~30
[51] C. C. Holt, F. Modigliani, J. F. Muth. Derivation of a linear decision rule forproduction and employment. Management Science,1956,2(2):159~177
[52] S. Nam, R. Logendran. Aggregate production planning-A survey of models andmethodologies. European Journal of Operational Research,1992,61(3):255~272
[53] S. D. Thompson, D. T. Watanabe, W. J. Davis. A comparative study of aggregateproduction planning strategies under conditions of uncertainty and cyclic productdemands. International Journal of Production Research,1993,31(8):1957~1979
[54] Y. K. Chen, H. C. Liao. An investigation on selection of simplified aggregateproduction planning strategies using MADM approaches. International Journal ofProduction Research,2003,41(14):3359~3374
[55] K. S. Metaxiotis, D. Askounis, J. Psarras. Expert systems in production planning andscheduling: a state-of-the-art survey. Journal of Intelligent Manufacturing,2002,13(4):253~260
[56] J. Mula, R. Poler, J. P. Garcia-Sabater, F. C. Lario. Models for production planningunder uncertainty: a review, International Journal of Production Economics,2006,103(1):271~285
[57] J. F. Tang, D. W. Wang, R. Y. K. Fung. Fuzzy formulation for multi-productaggregate production planning. Production Planning and Control,2000,11(7):670~676
[58] R. Y. K. Fung, J. F. Tang, D. W. Wang. Multiproduct aggregate production planningwith fuzzy demands and fuzzy capacities. IEEE Transactions on Systems, Man, andCybernetics-Part A: Systems and Humans,2003,33(3):302~313
[59] B. Phurksaphanrat. Preemptive possibilistic linear programming: application toaggregate production planning. World Academy of Science, Engineering andTechnology,2011,80:473~480
[60] G. R. Bitran, H. H. Yanassee. Deterministic approximation to stochastic productionproblems. Operations Research,1984,32(5):999~1018
[61] B. R. Feiring. Production planning in stochastic demand environments.Mathematical and Computer Modelling,1991,15(10):91~95
[62] B. Kazaz, M. Data, H. Moskowitz. Global production planning under exchange-rateuncertainty. Management Science,2005,51(7):1101~1119
[63]唐加福,汪定伟,许宝栋.多品种集约生产计划问题的模糊方法.管理科学学报,2003,1:44~50
[64] M. K. Zanjani, M. Nourelfath, D. Ait-Kadi. A multi-stage stochastic programmingapproach for production planning with uncertainty in the quality of raw materialsand demand. International Journal of Production Research,2010,48(16):4701~4723
[65] X. H. Zhang, M. Prajapati, E. Peden. A stochastic production planning model underuncertain seasonal demand and market growth. International Journal of ProductionResearch,2011,49(7):1957~1975
[66] S. C. H Leung, S. O. S. Tsang, W. L. Ng, Y. Wu. A robust optimization model formulti-site production planning problem in an uncertain environment. EuropeanJournal of Operational Research,2007,181(1):224~238
[67] E. Aghezzaf, C. Sitompul, N. M. Najid. Models for robust tactical planning inmulti-stage production systems with uncertain demands. Computers and OperationsResearch,2010,37(5):880~889
[68] A. P. Kanyalkar, G. K. Adil. A robust optimisation model for aggregate and detailedplanning of a multi-site procurement-production-distribution system. InternationalJournal of Production Research,2010,48(3):635~656
[69] Y. Y. Lee. Fuzzy set theory approach to aggregate production planning and inventorycontrol.(PhD Thesis). Department of I.E., Kansas State University.1990
[70] D. W. Wang, S. C. Fang. A genetics-based approach for aggregate productionplanning in a fuzzy environment. IEEE Transactions on Systems, Man, andCybernetics-Part A: Systems and Humans,1997,27(5):636~645
[71] S. P. Chen, W. L. Huang. A membership function approach for aggregate productionplanning problems in fuzzy environments. International Journal of ProductionResearch,2010,48(23):7003~7023
[72] R. C. Wang, T. F. Liang. Applying possibilistic linear programming to aggregateproduction planning. International Journal of Production Economics,2005,98(3):328~341
[73] A. Jamalnia, M. A. Soukhakian. A hybrid fuzzy goal programming approach withdifferent goal priorities to aggregate production planning. Computers and IndustrialEngineering,2009,56(4):1474~1486
[74] S. A. Torabi, E. Hassini. Multi-site production planning integrating procurement anddistribution plans in multi-echelon supply chains: an interactive fuzzy goalprogramming approach. International Journal of Production Research,2009,47(19):5475~5499
[75] R. C. Wang, H. H. Fang. Aggregate production planning with multiple objectives ina fuzzy environment. European Journal of Operational Research,2001,133(3):521~536
[76] S. A. Torabi, M. Ebadian, R. Tanha. Fuzzy hierarchical production planning (with acase study). Fuzzy Sets and Systems,2010,161(11):1511~1529
[77] H. Gfrerer, G. Z pfel. Hierarchical model for production planning in the case ofuncertain demand. European Journal of Operational Research,1995,86(1):142~161
[78] S. A. Abass, M. A. Gomaa, G. A. Elsharawy, M. S. Elsaid. Generalized productionplanning problem under interval uncertainty. Egyptian Informatics Journal,2010,11(1):27~31
[79] C. Jiang, X. Han, G. R. Liu, G. P. Liu. A nonlinear interval number programmingmethod for uncertain optimization problems. European Journal of OperationalResearch,2008,188(1):1~13
[80] C. H. Jones. Parametric production planning. Management Science,1967,13(11):843~866
[81] W. H. Taubert. A search decision rule for the aggregate scheduling problem.Management Science,1968,14(6): B343~B359
[82] S. D. Thompson, W. J. Davis. An integrated approach for modeling uncertainty inaggregate production planning. IEEE Transactions on Systems, Man, andCybernetics,1990,20(5):1000~1012
[83] E. H. Bowman. Consistency and optimality in managerial decision making.Management Science,1963,9(2):310~321
[84] J. M. Mellichamp, R. M. Love. Production switching heuristics for the aggregateplanning problem. Management Science,1978,24(12):1242~1251
[85] H. Hwang, C. N. Cha. An improved version of the production switching heuristic forthe aggregate production planning problem. International Journal of ProductionResearch,1995,33(9):2567~2577
[86] D. B. Rinks. The performance of fuzzy algorithm models for aggregate planningunder differing cost structures. In: M. M. Gupta, E. Sanchez, eds. Fuzzy Informationand Decision Process. Amsterdam: North-Holland,1982:267~278
[87] I. B. Turksen. Approximate reasoning for production planning. Fuzzy Sets andSystems,1988,26(1):23~37
[88] T. L. Ward, P. A. S. Ralston, J. A. Davis. Fuzzy logic control of aggregate productionplanning. Computers and Industrial Engineering,1992,23(1-4):137~140
[89] P. Duchessi, R. M. O’Keefe. A knowledge-based approach to production planning.The Journal of the Operational Research Society,1990,41(5):377~390
[90] S. P. Sethi, H. M. Yan, H. Q. Zhang. Inventory and Supply Chain Management withForecast Updates. International Series in Operations Research and ManagementScience, New York: Springer-Verlag,2005
[91] S. C. Graves, H. C. Meal, S. Dasu, Y. Qui. Two-stage production planning in adynamic environment. In: Axs ter, S., Schneeweiss, C. Silver, E., eds. Multi-StageProduction Planning and Control, Lecture Notes in Economics and MathematicalSystems, Berlin: Springer-Verlag,1986,266:9~43
[92] S. C. Graves, D. B. Kletter, W. B. Hetzel. A dynamic model for requirementsplanning with application to supply chain optimization. Operations Research,1998,46(3): S35~S49
[93] D. C. Heath, P. L. Jackson. Modeling the evolution of demand forecasts withapplication to safety stock analysis in production distribution-systems. IIETransactions,1994,26(3):17~30
[94] L. B. Toktay, L. M. Wein. Analysis of a forecasting-production-inventory systemwith stationary demand. Management Science,2001,47(9):1268~1281
[95] S. Zhou, P. Jackson, R.O. Roundy, R. Q. Zhang. The evolution of family level salesforecasts into product level forecasts: modeling and estimation. IIE Transactions,2007,39(9):831~843
[96] P. Kleindorfer, H. Kunreuther. Stochastic horizons for the aggregate planningproblem. Management Science,1978,24(5):485~497
[97] O. S. S. Filho. An aggregate production planning model with demand underuncertainty. Production Planning and Control,1999,10(8):745~756
[98] H. Widiarta, S. Viswanathan, R. Piplani. Forecasting aggregate demand: Ananalytical evaluation of top-down versus bottom-up forecasting in a productionplanning framework. International Journal of Production Economics,2009,118(1):87-94
[99] J. F. Magee, D. M. Boodman. Production Planning and Inventory Control.(2nd Ed).New York: McGraw-Hill,1967
[100] R. K. Oliver, M. D. Webber. Supply-chain management: Logistics catches up withstrategy (reprint from Outlook1982). In: M. Christopher. Ed. Logistics-theStrategic Issues, London,1992:63~75
[101] A. Thangam, R. Uthayakumar. Modeling of a continuous review supply chain in anuncertain environment. International Journal of Information and ManagementSciences,2009,20:137~159
[102] H. Stadtler. Supply chain management and advanced planning-basics, overview andchallenges. European Journal of Operational Research,2005,163(3):575~588
[103]鲁耀斌,杨光明,杨敏才.供应链管理中不确定性的来源、表现及应对策略.科技进步与对策,2004,21(4):87~89
[104] M. Mahnam, M. R. Yadollahpour, V. Famil-Dardashti, S. R. Hejazi. Supply chainmodeling in uncertain environment with bi-objective approach. Computers andIndustrial Engineering,2009,56(4):1535~1544
[105] A. J. Clark, H. Scarf, Optimal Policies for a Multi-Echelon Inventory Problem.Management Science,1960,6(4):475~490
[106] F. R. Chen, Y. S. Zheng. Evaluating echelon stock (R, nQ) policies in serialproduction/inventory systems with stochastic demand. Management Science,1994,40(10):1262~1275
[107] A. Gupta, C. D. Maranash. Managing demand uncertainty in supply chain planning,Computers and Chemical Engineering,2003,27(8-9):1219~1227
[108] D. Petrovic, R. Roy, R.Petrovic. Supply chain modeling using fuzzy sets.International Journal of Production Economics,1999,59(1-3):443~453
[109] I. Giannoccaro, P. Pontrandolfo, B. Scozzi. A fuzzy echelon approach for inventorymanagement in supply chains. European Journal of Operational Research,2003,149(1):185~196
[110] J. Wang, Y. F. Shu. Fuzzy decision modeling for supply chain management. FuzzySets and Systems,2005,150(1):107~127
[111] G. Y. Xiong, H. Koivisto. Research on Fuzzy Inventory Control under Supply ChainManagement Environment. Lecture Notes in Computer Science,2003,2658:907~916
[112] E. U. Olugua, K. Y. Wong. An expert fuzzy rule-based system for closed-loop supplychain performance assessment in the automotive industry. Expert Systems withApplications,2012,39(1):375~384
[113] D. Bertsimas, A. Thiele. A robust optimization approach to supply chainmanagement. Lecture Notes in Computer Science,2004,3064:86~100
[114]张涛,孙林岩.供应链不确定性管理：技术与策略.北京:清华大学出版社,2005
[115] Y. Chang, H. Makatsoris. Supply chain modeling using simulation, InternationalJournal of Simulation,2001,2(1):24~30
[116] J. P. C. Kleijnen. Supply chain simulation: a survey. Tilburg University, WorkingPaper,2003,103:1~20
[117] G. Berkstresser, T. J. Little, S. C. Fang, R. E. King, H. L. W. Nuttle, J. R. Wilson.Integrated supply chain analysis and decision support,1997, I98-S1:1~10
[118] J. D. Blackburn. Time-based Competition: The Next Battleground in AmericanManufacturing, Homewood, IL: Irwin,1991
[119] H. J. Zimmermann. An application-oriented view of modeling uncertainty. EuropeanJournal of Operational Research,2000,122(2):190~198
[120]梁昌勇,张恩桥,戚筱雯,蔡美菊.不完全信息下基于证据推理的竞争对手分析.系统工程,2010,28(2):44~48
[121]朱卫东,周光中,杨善林.基于二维语言评价信息的群体决策方法.系统工程,2010,27(2):113~118
[122]张洪涛,朱卫东.不完全信息下一种信度判断矩阵及其集结方法.系统工程,2010,28(7):107~110
[123]王红卫,祁超,魏永长,李彬,朱松.基于数据的决策方法综述.自动化学报,2009,35(6):820~833
[124] Y. M. Wang, J. B. Yang, D. L. Xu. Environmental Impact Assessment Using theEvidential Reasoning Approach. European Journal of Operational Research,2006,174(3):1885~1913
[125] Y. M. Wang, J. B. Yang, D. L. Xu. The evidential reasoning approach for multipleattribute decision analysis using interval belief degrees. European Journal ofOperational Research,2006,175(1):35~66
[126] D. L. Xu, J. B. Yang, Y. M. Wang. The ER approach for multi-attribute decisionanalysis under interval uncertainties. European Journal of Operational Research,2006,174(3):1914~1943
[127] M. Guo, J. B. Yang, K. S. Chin, H. W. Wang. Evidential reasoning based preferenceprogramming for multiple attribute decision analysis under uncertainty. EuropeanJournal of Operational Research,2007,182(3):1294~1312
[128] M. Guo, J. B. Yang, K. S. Chin, H. W Wang, X. B. Liu. Evidential reasoningapproach for multi-attribute decision analysis under both fuzzy and intervaluncertainty. IEEE Transactions on Fuzzy Systems,2009,17(3):683~697
[129] R. L. Keeney, H. Raiffa. Decisions with Multiple Objectives.(2nd Ed.). CambridgeUniversity Press,1993
[130] J. B. Yang, Y. M. Wang, D. L. Xu, K. S. Chin. The evidential reasoning approach forMADA under both probabilistic and fuzzy uncertainties. European Journal ofOperational Research,2006,171(1):309~343
[131] J. B. Yang, D. L. Xu. Nonlinear information aggregation via evidential reasoning inmultiattribute decision analysis under uncertainty. IEEE Transactions on Systems,Man, and Cybernetics-Part A: Systems and Humans,2002,32(3):376~393
[132]敖志刚.人工智能与专家系统导论.合肥:中国科学技术大学出版社,2002
[133]李彬,王红卫,杨剑波,祁超,郭敏.置信规则库结构识别的置信K均值聚类算法.系统工程,2011,29(5):85~91
[134] G. Metan, A. C. Thiele. The Value of information in inventory management.INFORMS Annual Meeting, Washington,2008
[135] S. Karlin. Dynamic inventory policy with varying stochastic demands. ManagementScience,1960,6(3):231~258
[136] P. H. Zipkin. Critical number policies for inventory models with periodic data.Management Science,1989,35(1):71~80
[137]汪俊萍,周永务,杨剑波.需求依赖广告费用和销售价格的newsboy型产品库存模型.控制与决策,2010,25(1):89~98
[138] A. Scheller-Wolf, S. Tayur. Reducing international risk through quantity contracts.GSIA Working Paper, Pittsbergh: Carnegie Mellon University,1997
[139] H. Scarf. A min-max solution of an inventory problem. Studies in the MathematicalTheory of Inventory and Production, Stanford University Press,1958
[140] S. Bollapragada, T. E. Morton. A simple heuristic for computing nonstationary (s,S)policies. Operations Research,1999,47(4):576~584
[141] T. Iida. The infinite horizon non-statiionary stochastic inventory problem: nearmyopic policies and weak ergodicity. European Journal of Operational Research,1999,116(2):405~422
[142] G. D. Johnson, H. E. Thompson. Optimality of myopic inventory policies for certaindependent demand processes. Management Science,1975,21(11):1303~1307
[143] H. L. Lee, V. Padmanabhan, S. Whang. Information distortion in a supply chain: thebullwhip effect. Management Science,1997,43(4):546~558
[144] H. L. Lee, K. C. So, C. S. Tang. The value of information sharing in a two-levelsupply chain. Management Science,2000,46(5):626~643
[145] S. Raghunathan. Information sharing in a supply chain: a note on its value whendemand is nonstationary. Management Science,2001,47(4):605~610
[146] A. A. Kurawarwala, H. Matsuo. Forecasting and inventory management of shortlife-cycle products. Operations Research,1996,44(1):131~150
[147] A. Federgruen, A. Heching. Combined pricing and inventory control underuncertainty. Operations Research,1999,47(3):454~475
[148] G. Gallego, G. van Ryzin. Optimal dynamic pricing of inventories with stochasticdemand over finite horizons. Management Science,1994,40(8):999~1020
[149]潘景铭,唐小我.需求不确定条件下柔性供应链生产决策模型及优化.控制与决策,2004,19(4):411~415
[150]赵晓波,黄四民.库存管理.北京:清华大学出版社,2008
[151] D. Petrovic, R. Petrovic, M. Vujosevic. Fuzzy models for the newsboy problem.International Journal of Production Economics,1996,45(1-3):435~441
[152] D. Bertsimas, A. Thiele. A robust optimization approach to inventory theory.Operations Research,2006,54(1):150~168
[153] T. W. Hung, S. C. Fang, H. L. W. Nuttle, R. E. King. A fuzzy-control-based quickresponse reorder scheme for retailing of seasonal apparel. Proceedings of theInternational Conference of Information Sciences,1997,2:300~303
[154] R. W. K Leung, H. C. W. Lau, C. K. Kwong. On a responsive replenishment system:a fuzzy logic approach. Expert Systems,2003,20(1):20~32
[155] L. Kamal, J. L. Sculfort. Fuzzy modeling of inventory control system in uncertainenvironment. International Symposium on Logistics and Industrial Informatics,2007:53~57
[156] M. Gen, Y. Tsujimura, D. Z. Zheng. An application of fuzzy set theory to inventorycontrol models. Computers and Industrial Engineering,1997,33(3-4):553~556
[157] P. Kaminsky, J. M. Swaminathan. Utilizing forecast band refinement for capacitatedproduction planning. Manufacturing and Service Operations Management,2001,3(1):68~81
[158] P. H. Zipkin. Foundations of Inventory Management. New York: McGraw-Hill,2000
[159] T. V. Gopal. Ethics in deploying data to make wise decisions. International Reviewof Information Ethics,2007,7:1~7
[160] C. Ho. Evaluating the impact of operating environments on MRP systemnervousness. International Journal of Production Research,1989,27(7):1115~1135
[161] K. Kilic, O. Uncu, I. B. Turksen. Comparison of different strategies of utilizingfuzzy clustering in structure identification. Information Sciences,2007,177(23):5153~5162
[162] J. B. MacQueen. Some methods for classification and analysis of multivariateobservations. In: Proceedings of5-th Berkeley Symposium on MathematicalStatistics and Probability. Berkeley: University of California Press,1967:281~297
[163] J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algoritms. NewYork: Plenum Press,1981
[164] T. Sillekens, A. Koberstein, L. Suhl. Aggregate production planning in theautomotive industry with special consideration of workforce flexibility. InternationalJournal of Production Research,2010,49(7):5055~5078
[165] Y. M. Wang, J. B. Yang, D. L. Xu. Interval weight generation approaches based onconsistency test and interval comparison matrices. Applied Mathematics andComputation,2005,167(1):252~273
[166] M. A. Omar. The Automotive Body Manufacturing Systems and Processes. JohnWiley and Sons,2011
[167]王小平,曹立明.遗传算法:理论、应用与软件实现.西安:西安交通大学出版社,2002
[168]胡毓达.非线性规划.北京:高等教育出版社,1990
[169] A. Thiele. A robust optimization approach to supply chain and revenue management.(PhD thesis). Massachusetts Institute of Technology,2004
[170] S. Chopra, P. Meindl. Supply Chain Management: Strategy, Planning and Operation.(2nd Ed.). New Jersey: Prentice Hall,2004