不确定环境下供应商选择及契约研究
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摘要
当前供应商选择是供应链管理面临的热点之一,供应商作为整个供应链的源头,是供应链运行的基础,它的好坏直接影响到供应链整体的运行效率.供应链协调是供应链管理面临的又一大挑战,供应链收入共享契约作为供应链契约的一种重要机制,它能有效的协调供应链各成员,优化供应链系统,已经成为越来越多的企业应对日趋激烈的市场竞争的有效手段.由于在供应链管理中存在着大量不确定性,通常表现为随机性和模糊性.因此,研究模糊环境下的供应商选择问题与收入共享契约问题不仅具有重要的理论意义,而且也具有较高的实用价值.本文研究内容具体如下：
本文研究了不确定环境下的供应商选择问题,以供应商供货价格、质量、准时供货为选择标准,将产品价格、产品次品率、产品交货延迟率看作模糊变量,使产品总花费、次品数、交货延迟最小,建立了多目标模糊机会约束模型(CCP),结合模糊模拟和遗传算法的混合智能算法进行求解,并给出相应的计算实例验证所建模型和求解算法的有效性.
本文研究了供应链协调中的收入共享契约问题,利用模糊变量来刻画市场的需求.在需求不确定的情况下,综合考虑缺货损失成本和剩余产品残值收益,建立了供应链收入共享契约的模糊期望收益最优模型,并利用基于模糊模拟和神经网络的粒子群算法对模型进行求解,最后用数值例子验证了所建模型及求解算法的有效性.
Supplier selection is becoming a challenge of present supply chain manage-ment.The supplier is the source of supply chain,which is the basis of stable opera-tion and its performance is directly affects the efficiency of supply chain.Supplier coordinate is another challenge of supply chain management, how to coordinate the various members of the overall supply chain goals and objectives of the con-flict between the decentralized supply chain is the central issue of coordination. The revenue sharing contract as an important mechanism of supply chain coor-dination can effective coordinate the members and optimal the system, which becomes an effective way to deal with the increasingly fierce market competition. Stochastic and fuzzy are two main aspects of the uncertainty in the supply chain management. Therefore, study in the fuzzy environment supplier selection prob-lems not only of great the oretical value, but also has high practical significance. In this thesis, the detailed contents are as follows:
This thesis studies supplier selection in the fuzzy environment, describe sup-plier delivery price, quality, on-time criteria for the evaluation of suppliers, and the fuzziness is associated with the product price,defective and delivery, mini-mize the total product cost, the number of defective goods, the number of de-livery delay, finally establish a multi-objective fuzzy chance-constrained model (CCP), combination of fuzzy simulation and genetic algorithm hybrid intelligent algorithm, and examples are given to verify the calculation of the corresponding algorithm.
The revenue-sharing contract in supply chain contract coordination is studied in this thesis.This paper characterizes the market demand as a fuzzy variables put forward by uncertainty theory. Considering the loss of out-of-stock products cost and residual value of the remaining proceeds, developed the fuzzy expected value model of revenue - sharing contract coordination in supply chain with underage cost. To solve the problem, by simulating the decision process a hybrid algorithm based on particle swarm optimizer (PSO), neural network and fuzzy simulations is developed and numerical example is provided to illustrate the effectiveness of the algorithm.
本文研究了不确定环境下的供应商选择问题,以供应商供货价格、质量、准时供货为选择标准,将产品价格、产品次品率、产品交货延迟率看作模糊变量,使产品总花费、次品数、交货延迟最小,建立了多目标模糊机会约束模型(CCP),结合模糊模拟和遗传算法的混合智能算法进行求解,并给出相应的计算实例验证所建模型和求解算法的有效性.
本文研究了供应链协调中的收入共享契约问题,利用模糊变量来刻画市场的需求.在需求不确定的情况下,综合考虑缺货损失成本和剩余产品残值收益,建立了供应链收入共享契约的模糊期望收益最优模型,并利用基于模糊模拟和神经网络的粒子群算法对模型进行求解,最后用数值例子验证了所建模型及求解算法的有效性.
Supplier selection is becoming a challenge of present supply chain manage-ment.The supplier is the source of supply chain,which is the basis of stable opera-tion and its performance is directly affects the efficiency of supply chain.Supplier coordinate is another challenge of supply chain management, how to coordinate the various members of the overall supply chain goals and objectives of the con-flict between the decentralized supply chain is the central issue of coordination. The revenue sharing contract as an important mechanism of supply chain coor-dination can effective coordinate the members and optimal the system, which becomes an effective way to deal with the increasingly fierce market competition. Stochastic and fuzzy are two main aspects of the uncertainty in the supply chain management. Therefore, study in the fuzzy environment supplier selection prob-lems not only of great the oretical value, but also has high practical significance. In this thesis, the detailed contents are as follows:
This thesis studies supplier selection in the fuzzy environment, describe sup-plier delivery price, quality, on-time criteria for the evaluation of suppliers, and the fuzziness is associated with the product price,defective and delivery, mini-mize the total product cost, the number of defective goods, the number of de-livery delay, finally establish a multi-objective fuzzy chance-constrained model (CCP), combination of fuzzy simulation and genetic algorithm hybrid intelligent algorithm, and examples are given to verify the calculation of the corresponding algorithm.
The revenue-sharing contract in supply chain contract coordination is studied in this thesis.This paper characterizes the market demand as a fuzzy variables put forward by uncertainty theory. Considering the loss of out-of-stock products cost and residual value of the remaining proceeds, developed the fuzzy expected value model of revenue - sharing contract coordination in supply chain with underage cost. To solve the problem, by simulating the decision process a hybrid algorithm based on particle swarm optimizer (PSO), neural network and fuzzy simulations is developed and numerical example is provided to illustrate the effectiveness of the algorithm.
引文
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[3]W. Benton, Quantity discount decision of multiple items, multiple suppliers lim-nation, International Journal of Production Research 29 (1992) 1953-1961.
[4]F. Bernstein, A. Federgruen, Decentralized supply chains with competing retailers under demand uncertainty, Management Science 51 (2005) 18-29.
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[12]S. Chauhan, J. Proth, Analysis of a supply chain partnership with revenue sharing, Internet Journal of Production Econonics 97 (2005) 44-51.
[13]T. Chen, Optimizing supply chain collaboration based on joint replenishment and channel coordination, Transportation Research Part E 41(2005) 261-285.
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[45]B. Pasternack, Optimal pricing and return policies for perishable commodities, Marketing Scienc 4 (1985) 166-176.
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[55]A. Tsay, Managing retail channel overstock:markdown money and return policies, Journal of Retailing 77 (2001) 457-492.
[56]J. Veen, V. Venugopal, Using revenue sharing to create win-win in the video rental supply chain, Journal of Operational Research Society 56 (2005) 757-762
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[59]J. Wang, Y. Shu, A possibilistic decision model for new product supply chain design, European Journal of Operational Research 177 (2007) 1044-1061.
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[62]Z. Weng, Coordinating order quantities betweenthe manufacturer and the buyer-A generalized newsvendor model, European Journal of Operational Research 156 (2004) 148-161
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[2]P. Bender, R. Brown, H. Isaac, Improving purchasing productivity at IMB with a normative decision support system, Interfaces 15 (1985) 106-115.
[3]W. Benton, Quantity discount decision of multiple items, multiple suppliers lim-nation, International Journal of Production Research 29 (1992) 1953-1961.
[4]F. Bernstein, A. Federgruen, Decentralized supply chains with competing retailers under demand uncertainty, Management Science 51 (2005) 18-29.
[5]F. Buffa, W. Jackson, A goal programming model for purchase planning, Journal of Purchasing and Materials Management 19 (1983) 27-34.
[6]G. Cachon, M. Lariviere, Capacity choice and allocaiton:strategic behavior and supply chain performance, Management Science 47 (1997) 629-646.
[7]G. Cachon, M. Lariviere, Contracting to assure supply:How to share demand forecasts in a supply chain, Management Science 47 (2001) 537-556.
[8]G. Cachon, Supply chain coordination with contracts:in Handbooks in Oper-ations Research and Management Science:Supply Chain Management,Elsevier Publishing Company 2003.
[9]G. Cachon, M. Lariviere, Supply chain coordination with revenue-sharing con-tracts:strengths and limitations, Management Science 51 (2005) 30-44.
[10]S. Chaudhry, F. Fomt, J. Zydiak, A mulficritefia approach to Mlocming order quantity among vendors,Production and Inventory Management Joumal 3 (1991) 82-86.
[11]S. Chaudhry, F. Fomt, Vendor selectian with price breaks, European Joumal of Operational Research 70 (1993) 52-66.
[12]S. Chauhan, J. Proth, Analysis of a supply chain partnership with revenue sharing, Internet Journal of Production Econonics 97 (2005) 44-51.
[13]T. Chen, Optimizing supply chain collaboration based on joint replenishment and channel coordination, Transportation Research Part E 41(2005) 261-285.
[14]D. Dubois, H. Prade, Possibility Theory:An Approch to Computerized Processing of Uncertainty, New York:Plenum,1988.
[15]Z. Degraeve, E. Labm, Rodohofot, An evaluation of vendor selection models from a total cost of ownership perspective, European Journal of Operational Research 125 (2000) 34-58.
[16]P. Desai, K. Srinivasan, Demand signaling under unobservable effort in franchising: Linear and nonlinear price contracts, Management Science 41 (1995) 1608-1623.
[17]V. Deshpande, L. Schwarz, Optimal Capacity Allocation in Decentralized Supply Chains, Working Paper, Purdue University 2002.
[18]G. Dickson, An analysis of vendor selection systems and decisions, Journal of Purchasing 2 (1996) 5-17.
[19]R. Eberhart, J.Kennedy A new optimizer using particle swarm theory, Proc.6th lnt Symposium on Micro Machine and Human Science, Nagoya,1995:39-43
[20]H. Emmons, S. Gilbert, The role of returns policies in pricing and inventory deci-sions for catalogue goods, Management Science 44 (1998) 276-283.
[21]A. Gaballa, Minimum cost allocation of tenders. Operational Research Quarterly, 25 (1974) 389-398.
[22]J. Gao, B. Liu, Fuzzy multilevel programming with a hybrid intelligent algorithem, Computers and Mathematics Applications 40 (2005) 1539-1548.
[23]I. Giannoccaro, P. Pontrandolfo, Supply chain cordination by revenue sharing contracts, International Journal of Production Economics 89 (2004) 131-139.
[24]J. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor:The Uni-versity of Michigan Press,1975.
[25]A. Kaufmann, M. Gupta, Introduction to Fuzzy Arithmetic:Theory and Appli-cations, New York:Van Nostrand Reinhold,1985.
[26]M. Kumar, P. Vrat, R. Shankar, A fuzzy programming approach for vendor selec-tion problem in a supply chain, International Journal of Production and Economics 101 (2006) 273-285.
[27]M. Lariviere, Supply chain contracting and coordination with stochastic demand, quantitative models for supply chain management, Kluwer Academic Publishers Boston,1999,233-268
[28]M. Lariviere, E. Porteus, Selling to the newsvendor:An analysis of price-only contracts, Msom 5 (2001) 293-305.
[29]H. Lee, S. Whang, Decentralized multi-echelon supply chains:Incentives and information, Management Science 45 (1999) 633-640.
[30]D. Li, C. Brien, Integrated decision modeling of supply chain efficiency, Interna-tional Journal of Production economics 59 (1999) 147-157.
[31]B. Liu, Toward fuzzy optimization with mathematical ambiguity, Fuzzy Optimiza-tion and Decision Making 1 (2002) 43-63.
[32]B. Liu, Theory and Practice of Uncertain Programming, Heidelberg:Physica-Verlag,2002.
[33]B. Liu, Uncertainty Theory:An Introduction to Its Axiomatic Foundations, Berlin:Springer-Verlag,2004.
[34]B. Liu, A survey of credibility theory, Fuzzy Optimization and Decision Making 5 (2006) 387-408.
[35]B. Liu, K. Iwamura, Chance constrainted programming with fuzzy parameters, Fuzzy Sets and Systems 94 (1998) 227-237.
[36]B. Liu, K. Iwamura, A note on chance constrainted programming with fuzzy co-efficients, Fuzzy Sets and Systems 100 (1998) 229-233.
[37]B. Liu, Uncertain Programming, Wiley, New York,1999
[38]B. Liu, Y. Liu, Excepted value of fuzzy variable and fuzzy expected value models, IEEE Transactions on Fuzzy Systems 10 (2002) 445-450.
[39]Y. Liu, B. Liu, Expected value operator of random fuzzy variable and random fuzzy expected value models, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11 (2003) 195-215.
[40]M. Minsky, S. Papert, Perceptrons, Cambridge:MIT Press,1969.
[41]S. Nahmias, Fuzzy variables, Fuzzy Sets and Systems 1 (1978) 97-110.
[42]R. Narasimhan, K. Stoynoff, Optimising aggregate procurement allocation deci-sions, Journal of Purchasing and Materials Management 22 (1986) 23-30.
[43]V. Padmanabhan, Manufactureg returns policy and retail competition, Marketing Science 6 (1997) 81-94
[44]A. Pan, Allocation of order quantities among suppliers, Journal of Purchasing and Materials Management 7 (1989) 36-39.
[45]B. Pasternack, Optimal pricing and return policies for perishable commodities, Marketing Scienc 4 (1985) 166-176.
[46]H. Pirkul, and O. Aras, Capacitated multiple item ordering problem with quanti-tydiscount, HE Transactions 17 (1985) 206-211
[47]R. Rao, Compensating heterogeneous salesforce:some explicit solutions, Marketing Science 4 (1990) 319-341.
[48]D. Reyniers, C. Tapiero, The Delivery and Contorl of Quality in Supplier-Producer Contracts,Management Science 41 (1985) 1581-1589.
[49]Y. Shi, R. Eberhart, A modified particle swarm optimizer. Proc. IEEE Int. Conf. on Evolutionary Computation, Anchorage,1998:69-73
[50]I. Shimmaru, M. nuiguchi, M. Sakawa, A fuzzy satisfying method for electric power plant coal purchase using genetic algorithms, European Joumal of Operations Research 126 (2000) 218-230.
[51]J. Spengler, Vertical Integration and Anti-trust Policy, Journal of Politieal Econ-omy 58 (1950) 166-178..
[52]K. Stephen, Third-degree price discrimination with independent demand, The Journal of Industrial Economics 46 (1998) 511-524.
[53]T. Talor, Supply chain coordination under channel rebates with sales effort effects, Management Science 48 (2002) 992-1007.
[54]A. Tsay, Quantity-flexibility contract and supplier-customer incentives, Manage-ment science 45 (1999) 1339-1358.
[55]A. Tsay, Managing retail channel overstock:markdown money and return policies, Journal of Retailing 77 (2001) 457-492.
[56]J. Veen, V. Venugopal, Using revenue sharing to create win-win in the video rental supply chain, Journal of Operational Research Society 56 (2005) 757-762
[57]F. Vergera, M. Khouja, M. Michalewics, An evolutionary algorithm for optimizing material flow in supply chains, Computers and Industrial Engineering 43 (2002) 407-421.
[58]J. Wang, Y. Shu, Fuzzy decision modeling for supply chain managment, Fuzzy Sets and Systems 150 (2005) 107-127.
[59]J. Wang, Y. Shu, A possibilistic decision model for new product supply chain design, European Journal of Operational Research 177 (2007) 1044-1061.
[60]Y. Wang, L. Jiang, Z. Shen, Channel performance under consignment contract with revenue sharing, Management Science 50 (2004) 34-47.
[61]C. Weber, A multi objective approach to vendor selection, European Journal of Operational Research 68 (1993) 173-184.
[62]Z. Weng, Coordinating order quantities betweenthe manufacturer and the buyer-A generalized newsvendor model, European Journal of Operational Research 156 (2004) 148-161
[63]R. Wilson, The structure of incentive for decentralization under Uncertainty,La Decision 3 (1969) 171-176
[64]L. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338-353.
[65]L. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1 (1978) 3-28.
[66]H. Zimmermann, Fuzzy programming and linear programming with several objec-tive functions, Fuzzy Sets and Systems 1 (1978) 45-56
[67]焦李成,神经网络系统理论,西安电子科技大学出版社,1990.
[68]刘晓,李海越,王成恩,供应商洗择模型与方法综述,中国管理科学,12(2004)139-148.
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