基于GA和DE的逆向物流网络建模与优化
|
摘要
近年来随着人们的保护环境和节约资源意识不断的增强,逆向物流越来越引起了政府、制造商和消费者广泛的重视,成为近年来的热点研究领域。逆向物流不仅节约资源、减少排放废弃物,降低企业成本,而且可以提高客户满意度,于是许多企业都纷纷建立自己的逆向物流体系。逆向物流网络是整个逆向物流体系运作的基础和关键。建立经济、高效的逆向物流网络是许多企业的目标,因此如何设计出合理的逆向物流网络在逆向物流领域具有实际的研究价值。
论文首先介绍了选题的背景、意义和创新之处。现在对逆向物流的研究还处于起步阶段,逆向物流网络建模与优化有着广泛的应用背景。另外,本文分别从宏观和微观论述了选题的重要意义,提出了本研究的主要目标和内容。
其次,论文总结了选址理论和差异演化算法(DE)的国内外研究现状,概述了基本选址模型。论文还指出了一些国内选址理论研究的特点和不足之处。对选址问题中经常使用启发式算法做了较为详细的介绍。
第三,论文以排队论为基础研究逆向物流网络建模与优化。逆向物流中的处理回收物品的工厂被看作是服务台,回收物品被看作是顾客,而且回收的物品有不同的处理方法。本文先基于GI/G/1模型,为每个工厂从几个备选生产能力中选择一个建立逆向物流网络。为了求解该模型设计了遗传算法(GA)。
第四,在GI/G/1模型的基础上将模型扩展为基于GI/G/m的逆向物流网络模型。随着求解问题的复杂性及难度的增加,提高GA的运行速度便显得尤为突出,并行遗传算法不仅提高了求解速度,而且由于种群规模的扩大和各子种群的隔离,使种群的多样性得以丰富和保持,减少了未成熟收敛的可能性,提高了求解质量。本文设计了并行遗传算法求解基于GI/G/m的网络优化模型。
第五,在研究多层逆向物流网络建模与优化方面。考虑由居民、初级收集点、处理中心组成的多层逆向物流网络优化问题。在满足居民的需求的前提下,确定建立收集点和处理工厂的位置和数量。每个初级收集点有一个最大容量限制。本文建立了一个非线性整数规划模型,目标是最大化逆向物流收益。求解该模型用了自适应遗传算法。
第六,在上面模型的基础上,把研究内容扩展为包括正向物流和逆向物流的网络结构。该模型要求同时优化正向和逆向物流网络。模型中生产销售商品的工厂和处理回收物品的工厂可以是同一个工厂,销售商店和初级回收点是不同的。销售商店和初级回收点以及工厂有容量限制的但是可以通过扩建来满足消费者的需求。本文设计了差异演化算法求解模型,该算法稳健性强、收敛速度快。
最后,把多层逆向物流网络优化模型扩展为多期,多层,有容量限制,同时包括正向和逆向的网络优化模型。为了求解模型设计了模糊自适应差异演化算法。目前,模糊控制朝着自适应、自学习方向发展,使得模糊控制参数或规则在控制过程中自动地调整、修改和完善,从而使系统的控制性能不断改善,达到最佳的控制效果。通常差异演化算法采用固定的变异算子F和交叉算子CR,本文将模糊控制原理和差异演化算法结合,提出了一种模糊自适应差异演化算法,根据种群前后两代总体的差异和每个个体的差异,自动调整F和CR。
Recently, the concept of protecting environment and saving resource prevail. Government, producer and customers pay a close attention to reverse logistics, and it has become a hot research field. There is a concern, in people's mind, that the reverse logistics is not only the effective way to save resource, decrease waste, but also it can reduce the cost and improve the satisfaction degree of customers. Therefore, reverse logistics were built by many companies. The network of reverse logistics is the key of the whole reverse logistics. Many companies want to set up economic and high effective reverse logistics network, so how to built better reverse logistics network has practical value on research.
Firstly, the thesis introduces the backgrounds, the significance, the innovative achievements and the motivations of choosing this topic. Although, the research on reverse logistics is justat beginning phase, but the design of network reverse logistics has huge applied fields. Also, this thesis, from macroscopic concept and microcosmic concept, presents the goal and main content of the research.
Secondly, the thesis reviews the literatures of location theories and differential evolution algorithm, and summarizes the fundamental models of location theories. On the base of these, it analyzes the characteristics and insufficiencies of theoretic research of locating. Further more, the thesis introduce the heuristic search methods, such as greedy algorithm, genetic algorithm and differential evolution algorithm.
Thirdly, based on the queuing theory, the writer studies the design of reverse logistics. The factories are regarded as service station, and returned goods are regarded as customers. There are many treatments to deal with the returned goods. Above all, this thesis, according to GI/G/1 model, selects production ability from many choices for a factory and write self-adaptive genetic algorithm to solve the model.
Fourthly, this thesis extends the model based on GI/G/1 to GI/G/m model. Along with the complexity increasing, the running speed of genetic algorithm plays an important role. Parallel genetic algorithm (PGA) improves the speed of solving problem and, owing to the huge population and insulated sub-populations, enhances the diversity of population. So PGA can effectively prevent convergence of immature individual. The writer designs PGA to solve complicated optimizing model.
Fifthly, the thesis studies the research on multi-echelon reverse logistics. Customers, collection points and factories form the multi-echelon reverse logistics network, and we select the site and quantity of the collection points and factories, after every customer's satisfactory is met. Every collection point has the maximum capacity of collecting returned products. The model is nonlinear integer programming model and the goal of the model is to achieve the maximum revenue.
Sixthly, the thesis extends the model to integrate and optimize the reverse logistics and positive supply chain, at the same time. The factories can be the same one to producs goods and deal with the returned product. But the departments and collections are different. The departments, collection points and factories can be extended to satisfy customers' need. The differential evolution algorithm was designed to solve the model. To the optimization problem of nonconve, multi-modal and non-linear function, the algorithm is much more robust and quicker in convergence than other evolution algorithms.
Finally, the thesis turns the model into a more complicated one. It is multi-period, multi-echelon, has capacity limitation and integrates the reverse logistics and positive logistics. In order to solve the model, a fuzzy self-adaptive differential evolution algorithm was developed. At present, fuzzy control develops towards adaptive control and self-learning control. The adaptive controllers modify the parameters and rules, so the best performance of the control system can be obtained, hi the classic differential evolution algorithm, the F and CR are fixed, the thesis, combining the fuzzy control and differential algorithm, develops a fuzzy self-adaptive differential evolution algorithm that modifies the mutation factor and crossover rate by using the difference of twe generations.
论文首先介绍了选题的背景、意义和创新之处。现在对逆向物流的研究还处于起步阶段,逆向物流网络建模与优化有着广泛的应用背景。另外,本文分别从宏观和微观论述了选题的重要意义,提出了本研究的主要目标和内容。
其次,论文总结了选址理论和差异演化算法(DE)的国内外研究现状,概述了基本选址模型。论文还指出了一些国内选址理论研究的特点和不足之处。对选址问题中经常使用启发式算法做了较为详细的介绍。
第三,论文以排队论为基础研究逆向物流网络建模与优化。逆向物流中的处理回收物品的工厂被看作是服务台,回收物品被看作是顾客,而且回收的物品有不同的处理方法。本文先基于GI/G/1模型,为每个工厂从几个备选生产能力中选择一个建立逆向物流网络。为了求解该模型设计了遗传算法(GA)。
第四,在GI/G/1模型的基础上将模型扩展为基于GI/G/m的逆向物流网络模型。随着求解问题的复杂性及难度的增加,提高GA的运行速度便显得尤为突出,并行遗传算法不仅提高了求解速度,而且由于种群规模的扩大和各子种群的隔离,使种群的多样性得以丰富和保持,减少了未成熟收敛的可能性,提高了求解质量。本文设计了并行遗传算法求解基于GI/G/m的网络优化模型。
第五,在研究多层逆向物流网络建模与优化方面。考虑由居民、初级收集点、处理中心组成的多层逆向物流网络优化问题。在满足居民的需求的前提下,确定建立收集点和处理工厂的位置和数量。每个初级收集点有一个最大容量限制。本文建立了一个非线性整数规划模型,目标是最大化逆向物流收益。求解该模型用了自适应遗传算法。
第六,在上面模型的基础上,把研究内容扩展为包括正向物流和逆向物流的网络结构。该模型要求同时优化正向和逆向物流网络。模型中生产销售商品的工厂和处理回收物品的工厂可以是同一个工厂,销售商店和初级回收点是不同的。销售商店和初级回收点以及工厂有容量限制的但是可以通过扩建来满足消费者的需求。本文设计了差异演化算法求解模型,该算法稳健性强、收敛速度快。
最后,把多层逆向物流网络优化模型扩展为多期,多层,有容量限制,同时包括正向和逆向的网络优化模型。为了求解模型设计了模糊自适应差异演化算法。目前,模糊控制朝着自适应、自学习方向发展,使得模糊控制参数或规则在控制过程中自动地调整、修改和完善,从而使系统的控制性能不断改善,达到最佳的控制效果。通常差异演化算法采用固定的变异算子F和交叉算子CR,本文将模糊控制原理和差异演化算法结合,提出了一种模糊自适应差异演化算法,根据种群前后两代总体的差异和每个个体的差异,自动调整F和CR。
Recently, the concept of protecting environment and saving resource prevail. Government, producer and customers pay a close attention to reverse logistics, and it has become a hot research field. There is a concern, in people's mind, that the reverse logistics is not only the effective way to save resource, decrease waste, but also it can reduce the cost and improve the satisfaction degree of customers. Therefore, reverse logistics were built by many companies. The network of reverse logistics is the key of the whole reverse logistics. Many companies want to set up economic and high effective reverse logistics network, so how to built better reverse logistics network has practical value on research.
Firstly, the thesis introduces the backgrounds, the significance, the innovative achievements and the motivations of choosing this topic. Although, the research on reverse logistics is justat beginning phase, but the design of network reverse logistics has huge applied fields. Also, this thesis, from macroscopic concept and microcosmic concept, presents the goal and main content of the research.
Secondly, the thesis reviews the literatures of location theories and differential evolution algorithm, and summarizes the fundamental models of location theories. On the base of these, it analyzes the characteristics and insufficiencies of theoretic research of locating. Further more, the thesis introduce the heuristic search methods, such as greedy algorithm, genetic algorithm and differential evolution algorithm.
Thirdly, based on the queuing theory, the writer studies the design of reverse logistics. The factories are regarded as service station, and returned goods are regarded as customers. There are many treatments to deal with the returned goods. Above all, this thesis, according to GI/G/1 model, selects production ability from many choices for a factory and write self-adaptive genetic algorithm to solve the model.
Fourthly, this thesis extends the model based on GI/G/1 to GI/G/m model. Along with the complexity increasing, the running speed of genetic algorithm plays an important role. Parallel genetic algorithm (PGA) improves the speed of solving problem and, owing to the huge population and insulated sub-populations, enhances the diversity of population. So PGA can effectively prevent convergence of immature individual. The writer designs PGA to solve complicated optimizing model.
Fifthly, the thesis studies the research on multi-echelon reverse logistics. Customers, collection points and factories form the multi-echelon reverse logistics network, and we select the site and quantity of the collection points and factories, after every customer's satisfactory is met. Every collection point has the maximum capacity of collecting returned products. The model is nonlinear integer programming model and the goal of the model is to achieve the maximum revenue.
Sixthly, the thesis extends the model to integrate and optimize the reverse logistics and positive supply chain, at the same time. The factories can be the same one to producs goods and deal with the returned product. But the departments and collections are different. The departments, collection points and factories can be extended to satisfy customers' need. The differential evolution algorithm was designed to solve the model. To the optimization problem of nonconve, multi-modal and non-linear function, the algorithm is much more robust and quicker in convergence than other evolution algorithms.
Finally, the thesis turns the model into a more complicated one. It is multi-period, multi-echelon, has capacity limitation and integrates the reverse logistics and positive logistics. In order to solve the model, a fuzzy self-adaptive differential evolution algorithm was developed. At present, fuzzy control develops towards adaptive control and self-learning control. The adaptive controllers modify the parameters and rules, so the best performance of the control system can be obtained, hi the classic differential evolution algorithm, the F and CR are fixed, the thesis, combining the fuzzy control and differential algorithm, develops a fuzzy self-adaptive differential evolution algorithm that modifies the mutation factor and crossover rate by using the difference of twe generations.
引文
[1] AbbassH.A, R.Sarker.The pareto differential evolution algorithm [J]. International Journal on Artificial Intelligence Tools, 2002, 11 (4) :531 -552.
[2] Amiri A. The design of service systems with queueing time cost, workload capacities and backup service [J]. European Journal of Operational Research 1998,104:201-217.
[3] Ammons J C, Real. M J, Newton D. Reverse production system design and operation for carpet recycling. Working Paper, Georgia institute of Technology,Atlanta, Georgia, 1997.
[4] Ana Paula Iannoni and Reinaldo Morabito. A discrete simulation analysis of a logistics supply system [J].Transportation Research Part E: Logistics and Transportation Review, 2006,42 (3) : 191-210.
[5] B.V.Babu, M.Mathew, L.Jehan.Differential evolution for multi-objective optimization [A].IEEE Congress on Evolutionary Computation[C], Canberra, 2003,2696-2703.
[6] Barros A, Dekker R, Scholten V.A two-level network for Recycling sand:A case study[J].European Journal of operational Research, 1998, 110 (2) : 199-214.
[7] Berger T, Debaillie B. Location of disassembly centers for reuse to extend an existing distribution network[D] .Unpublished Masters thesis , University of Leuven, Beigium (in Dutch), 1997.
[8] Berman O., Drezner Z., Wesolowsky GO.Locating service facilities whose reliability is distance [J].Computers and Operations Research, 2003, 30 (11) :1683-1695.
[9] Berman O.,Fouska N.,Larson R.C.Optimal location of discretionary service facilities[J] .Transportation Science, 1992,26 (3) :201 -211.
[10] Berman O., Krass D.The generalized maximal covering location problem [J].Computers & Operations Research, 2002, 29:563-581.
[11] Bollapragada R., Camm J., Rao U.S.et al. A two-phase greedy algorithm to locate and allocation hubs for fixed-wireless broadband access [J]. Operation Research Letters,2005,33 (2) : 134-142.
[12] Carbone R.Public facilities under stochastic demand [M]. INFOR, 1974,12 (3) :261-270.
[13] Caruso C.,Colorni A.,Aloi L.Dominant,An algorithm for the p-center problem[J] .European Journal of Operation Research,2003,149:53-64.
[14] Chiang C.I.,Hwang M.J.,Liu Y.H.An alternative formulation for certain fuzzy set-covering problems[J].Mathematical and Computer Modelling,2005,42:363-365.
[15] Church R.L., Meadows M.E.Location modeling utilizing maximum service distance criteria [J].Geographical Analysis, 1979,11:358-373.
[16] Church R.L.COBRA: A new formulation of the class p-median location problem [J]. Annals of Operations Research,2003,122 (1) :103-120.
[17] Cornuejols G, M.Fisher and GL.Nemhauser.On the uncapacitated location problem [ J]. Annals of Discrete Mathmatics, 1977, 1:163-177.
[18] Daskin M.S., Stern E.H.A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 1981,15 (2) :137-152
[19] De Brito M P, Dekker R. Reverse Logistics-a framework [J].Econometric InstituteReport EI2002-38, Erasmus University Rotterdam, the Netherlands,2002.
[20] Der-Hrong Lee, Meng Dong. A heuristic approach to logistics network design for end-of-lease computer products recovery[J] .Transportation research, 2007 ,106:1-20.
[21] Drezner Z.,Wesolowsky GO.A new method for the multifacility minimax location problem[J].Journal of the Operational Research Society,1978,24:1507-1514.
[22] Drezner Z.On the conditional p-median problem [J]. Computer and Operation Research, 1995, 22:525-530.
[23] F. Herrera, M. Lozano, Adaptation of genetic algorithm parameters based on fuzzy logic controllers [J].Genetic Alg. and Soft Computing, 1996, 95-129.
[24] F.Cheong,R.Lai.Designing a hierarchical fuzzy logic controller using differential evolution[C].Proceeding of IEEE on Fuzzy Systems Conference.Korea,1999,1: 277-282.
[25] Fleischmann M. et al. A characterization of logistics networks for product recovery [J]. Omega, 2000, 28 (1) :653-666.
[26] Fleischmann M. Reverse logistics network structures and design[R].ERIM Report Series Research in Management, ERS-2001-52-LIS, Erasmus University Rotterdam, The Netherlands, 2001.
[27] Fleischmann Moritz, Jaequeline .Bloemhof-Ruwaard, Rommert Dekker, Erwin van der Laan, Jo A.E.E.van Nunen, Luk N.Van Wassenhove. Quantitative models for reverse logistics: A review[J].European Journal of operational Research, 1997, 103 (1) :1-17.
[28] GGunawardane.Dynamic versions of set covering type public facility location problems [J]. European Journal of Operational Research, 1982,10:190-195.
[29] Gamperle R,Dmuller S,Koumoutsakos P.A Parameter Study for Differential Evolution [A].International Conference on Advances in Intelligent Systems,Fuzzy Systems, Evolutionary Computation[C], 2002,293-298.
[30] Garey M.R, Johnson D.S. Computers and intractability: a guide to the theory of NP-completeness [M].New York: Freeman, 1979.
[31] Grossman T., Wool A.Computational experience with approximation algorithms for the set covering problem [J].European Journal of Operational Research, 1997,101:81-92.
[32] Guiltinan JP, Nwokoye NG Developing distribution channels and systems in the emerging recycling industries[J].International Journal of Physical Distribution,1975, 6(1) :28-38.
[33] Hakimi S.L.Optimum locations of switching centers and the absolute centers and medians of a graph [J]. Operations Research, 1964, 12:450-459.
[34] Hakimi S.L.P-median theorems for competitive location[J].Annals of Operations Research, 1986,5 (1) :79-88.
[35] Harold Krikkle, Costas P.Pappos, Giannis T.Tsoulfas, Jaequeline Bloemhof.Design principles for closed loop supply chains: optimizing economic, logistic and environmental performance[R].ERIM Report Series Research in Management, ERS-2001-52-LIS, The Netherlands, 2001:1980-1987.
[36] Hassin R., Levin A., Morad D. Lexicographic local search and the p-center problem [J]. European Journal of Operational Research,2003,151 (1) :265-279.
[37] Hodgson J.A flow-capturing location allocation model[J].Geographical Analysis,1990,22 (3) :270-279.
[38] Hokey M., Hyun J., Chang S. A Genetic Algorithm Approach to Developing the Multi-echelon Reverse Logistics Network for Product Returns [J].Omega, 2006, 34:56-69.
[39] Hyun Jeung Ko, Gerald W.Evans. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs [J]. Computers&Operations Research, 2007, 34:346-366.
[40] Jayaraman V, Guide JR V D R, Srivastava R.A closed-loop logistics model for remanufacture[J] Journal of the Operational Research Society , 1999 , 50(5) :497-508.
[41] Jiuh-Biing Sheu. A coordinated reverse logistics system for regional management of multi-source hazardous wastes[J].Computers Operations Research, 2007,34:1442-1462.
[42] Joaquin Bautista, Jordi Pereira. Modeling the problem of locating collection areas for urban waste management. An application to the metropolitan area of Barcelona[J].The International Journal of Management Science , 2006(34) :617-629.
[43] Jouni Lampinen. A constraint handling approach for the differential evolution algorithm [A] .Proceedings of the congress on evolutionary computation[C],Honolulu, USA, 2002,1468-1473.
[44] K.E.Parsopoulos,D.K.Tasoulis,N.GPavlidis etc.Vector evaluated differential evolution for multiobjective optimization[A].IEEE Congress on Evolutionary Computation[C],Portland,2004, 204-211.
[45] K.Nateri,Madanvan.Multiobjective optimization using a pareto differential evolution approach[A].Proceeding of the congress on evolutionary computation.Honolulu[C], USA,2002,1145-1150.
[46] Kara, F.Rugrungruang, H.Kaebernick. Simulation modeling of reverse logistics networks[J].International journal of Production economics, 2007, 106:61-69.
[47] Kariv O., Hakimi S.An algorithmic approach to network location problems.II:The p-Medians[J].SIAM Journal on Applied Mathematics, 1979,37:539-560.
[48] Karp R.M.Reducibility among combinatorial problems,in:R.E.Miller and J.W.Thatcher (eds.) , Complexity of Computer Computations [M].New York: Plenium Press, 1972,85-104.
[49] Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing [J].Science, 1983, 220:671-680.
[50] Ko Hyun-Jeung, Min Hokey. The Dynamic design of a reverse logistics network for repair facilities[J].UPSi and LoDI Working Papers, 2003, 11.
[51] Krikke H R, van Harten A, Sehuur P C. Business case: reverse logistics network re-design for copiers[J] .OR Spectrum, 1999, 21 (3) :381-409.
[52] Kroon L and Vrijens G Returnable containers: an example of reverselogistics [J].International Journal of Physical Distribution& Logistics Management, 1995, 25(2) :56-68.
[53] Lambrecht MR, Ivens PL, and Vandaele NJ.ACLIPS: a capacity and lead time integrated procedure for scheduling [J]. Management Science 1998, 44(10) :1548-1561.
[54] Larson. A hypercube queuing model for facility location and redistricting in urban emergency services [J]. Computers and Operations Research, 1974, (1) : 67-95.
[55] Lee C C.Fuzzy Logic in control system: fuzzy logic controller [J]. Part I , II .IEEE Transactions on Systems, Man and Cybernetics, 1990, 20 (2) :404-435.
[56] Levin Y.,Israel A.B.A heuristic method for large-scale multi-facility location problems[J].Computers&Operations Research,2004,31:257-272.
[57] Li-Hsing Shin. Reverse logistics system planning for recycling electrical appliances and computers in Taiwan[J].Resources, Conservation and recyeling,2001,55-72.
[58] Liu, J., Lampinen. J, A fuzzy adaptive differential evolution algorithm. soft computing-a fusion of foundations, methodologies and applications [J] 2005, (6) :448-462.
[59] Lorena L.A.N., Senne E.L.F. A column generation approach to capacitated p-median problem [J].Computers and Operations Research, 2004, 31(6) :863-876.
[60] Louwers D, Kip BJ, peters E, Souren F, Flapper SDP.A facility location allocation model for re-using carpet materials [J]. Computers and Industrial Engineering,1999, 36 (4) :1-15.
[61] M.A. Lee, H. Takagi, Dynamic control of genetic algorithms using fuzzy logic techniques[C].5th Int. Conf. Genetic Algorithms. 1993, 76-83.
[62] Mamdani E H. Application of fuzzy algorithms for control of simple dynamic plant [J]. Proceedings of the Institution of Electrical Engineers, 1974, (121):1585-1588.
[63] Maria Isabel Gomes Salema, Ana Paula Barbosa-Povoa, Augusto Q.Novais. An optimization model for the design of a capacitated multi-Product reverse logistics network with uncertainty[J].2007, 179:1063-1077.
[64] Marianov V, ReVelle C. The queueing maximal availability location problem: a model for the siting of emergency vehicles [J].European Journal of Operational Research 1996,93:110-120.
[65] Marianov V, Serra D. Probabilistic, maximal covering location-allocation models for congested systems [J]. Journal of Regional Science 1998, 38:401-424.
[66] Megiddo N., Supowit K.On the complexity of some common geometric location problems [J].SIAM Journal on Computing, 1984,13:182-196.
[67] Metropolis N, Rosenbluth A, Rosenbluth M et al. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physics, 1953, 21:1087-1092.
[68] Ming Dong, F. Frank Chen, Performance modeling and analysis of integrated logistic chains: An analytic framework [J]. European Journal of Operational Research, 2005,162:83-98.
[69] Nagurney Anna, Fuminori Toyasaki. Reverse supply chain management and electronic waste recycling: a multi-tier network equilibrium framework for e-cycling[J].Transportation Research, 2005 (41) :1-18.
[70] R.K.Ursem, P.Vadstrup.Parameter identification of induction motors using differential evolution [A].Proceedings of the IEEE Congress on Evolutionary Computation[C], 2003, 2:790-796.
[71] R.Storn, K.Price.Differential evolution-a simple and efficient adaptive scheme for Global optimization over continuous spaces[R]. Technical report International Computer Science Institute, Berkley, 1995.
[72] Real M J, Ammons J C, Newton D. Carpet recycling: determining the reverse production system design[J].The Journal of Polymer-Plastics Technology and Engineering, 1999, 38 (3) :547-567.
[73] Rogers D S, Tibben-Lembke R S. Going backwards: reverse logistics trends and practices [M]. Nevada Reno: Reverse Logistics Executive Council, 1999.
[74] Roth R. Computer solutions to minimum cover problems. [J].Operation Research,1969, 17:455-465.
[75] S.Aydin, H.Temeltas.Time optimal trajectory planning for mobile robots by differential evolution algorithm and neural networks [A].2003 IEEE International Symposium on Industrial Electronics[C], IEEE Press, 2003, 1:252-257.
[76] S.Moalla,A.M.Alimi,N.Derbel.Design of beta neural systems using differential evolution[A].Proceeding of IEEE on Systems,Man and Cybernetics Conference[C],Tunisia,2002,3:6-9.
[77] S.Paterlini, T.Krink.High performance clustering with differential evolution [A].Proceeding of the Sixth Congress on Evolutionary Computation[C], IEEE Press,Piscataway, 2004, 2:2004-2011.
[78] Spengler T, PuE ehert H, Penkuhn T, Rentz O. Environmental integrated production and recycling management [J].European Journal of Operational Research, 1997,3:8-26.
[79] Stock J R. Reverse logistics [M].Oak Brook IL: Council of Logistics Management,1992.
[80] Sviridenko M.I.Worse-case analysis of the greedy algorithm for a generalization of maximum p-facility location problem [J]. Operation Research Letters,2000,26 (4):193-197.
[81] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cybernetics, 1985,6(2):116-132.
[82]Thierry M,Salomon M,van Nunen J,van Wassenhove I.Strategic issues in product recovery management[J].California Management Review.1995,37(2):114-135.
[83]Toregas.S.ReVelle R.C.,Bergman L.The location of emergency service facilities [J].Operation Research,1971,19:1363-1373.
[84]Vandaele N,Vannieuwenhuyse I,Cupers S.Optimal grouping for a nuclear magnetic resonance scanner by means of an open queuing model[J].European Journal of Operational Research 2003,151(1):181-192.
[85]Vesterstrom J,Thomsen R.A comparative study of differential evolution,particle swarm optimization and evolutionary algorithms on numerical benchmark problems[A].Congress on Evolutionary Computation[C],2004,1980-1987.
[86]Wesolowsky G.O.Dynamic facility location[J].Management Science,1973,19(11):1241-1248.
[87]Whitt W.Approximations for the GI/G/m queue[J].Production and Operatiors Management 1993,2(2):114-161.
[88]Yung-Chien Lin,Feng-Sheng Wang and Kao-Shing Hwang.A hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems[A].IEEE Proceeding of Evolutionary Computation[C],Piscataway,1999,2159-2166.
[89]Zadeh LA.Fuzzy Sets.[J]Information Control,1965,8:338-353.
[90]Zadeh L A.Outline of a new approach to the analysis of complex systems.IEEE International Conference on Systems[J],Man and Cybernetics,1973,3(1):638-656.
[91]蔡希贤,夏士智编译.物流合理化的数量方法[M].武汉:华中理工大学出版社,1985.
[92]常玉林,王炜.城市紧急服务系统优化选址模型[J].系统工程理论与实践,2000(2):104-108.
[93]达庆利,黄祖庆,张钦.逆向物流系统结构研究的现状及展望[J].中国管理科学.2004,12(1):131-138.
[94]代颖,马祖军,刘飞.再制造闭环物流网络优化设计模型[J].中国机械工程, 2006,6(8):809-814.
[95]戴箐.逆向物流网络设计与库存控制模型评述[J].商业经济与管理,2004,11:22-24.
[96]蒋良奎.平面选址问题的一种混合算法[J].上海海运学院学报,1999,20(4):98-102.
[97]黎青松,袁庆达,杜文.一个结合库存策略的物流选址模型[J].西南交通大学学报,2000,35(3):315-318.
[98]刘海燕,李宗平,叶怀珍.物流配送中心选址模型[J].西南交通大学学报,2000,35(3):311-313.
[99]刘明广.差异演化算法及其改进.系统工程[J].2005,23(2):108-111.
[100]刘曙光.模糊控制技术[M].北京:中国纺织出版社,2001.
[101]马良,蒋馥.约束多目标选址问题及其算法[J].运筹与管理,1998,7(2):7-12.
[102]马祖军,代颖,张殿业.逆向物流网络结构与设计[J].物流技术.2004,4:12-14.
[103]马祖军,代颖.产品回收逆向物流网络优化设计模型[J].管理工程学报,2005,19(4):114-117.
[104]孙林岩,王蓓.逆向物流的研究现状和发展趋势[J].中国机械工程,2005,5(16):928-934.
[105]孙晓晨,陈秋双,李响等.再制造系统废旧产品回收的最优控制策略[J].计算机集成制造系统,2007,13(9):1820-1825.
[106]孙元欣,黄培清.竞争型连锁经营网点选址模型与遗传算法解[J].科学学与科学技术管理.2001(10):60-63.
[107]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学,2002.
[108]魏洁,李军.EPR下的逆向物流回收模式选择研究[J].中国管理科学,2005,13(6):18-22.
[109]吴亮红等.自适应二次变异差分进化算法[J],控制与决策,2006,21(8):898-902.
[110]夏绪辉,刘飞.逆向供应链物流的内涵及研究发展趋势[J].机械工程学报,2005,41(4):103-109.
[111]徐菡,张敏洪.大规模逆向物流网络非线性优化模型的研究[J].中国科学院研究生院学报,2007,24(6):749-755.
[112]薛永吉,李旭宏,程日盛.基于仿真技术的物流中心站台数确定方法研究[J].物流技术,2005,6:37-39.
[113]姚卫新.电子商务条件下闭环供应链物流网络的设计[J].管理科学,2005,18(6):43-49.
[114]袁庆达,杜文,黎青松.区域公共物流中心规模和选址问题[J].交通运输工程学,2001,1(4):97-100.
[115]岳辉.第三方逆向物流决策与网络构建[D].西南交通大学硕士学位论文,2004
[116]张钦红,赵泉午,熊中楷.不对称信息下的易逝品退货物流协调运作研究[J].中国管理科学.2006,14(2):107-111.
[117]张显东等.市场竞争条件下的供应商选址模型研究[J].运筹与管理,1998,7(2):1-6.
[118]周垂日,梁棵,许传永等.逆向物流研究的新进展:文献综述[J].科研管理,2007,28(3):123-132.
[119]周垂日,梁裸,许传永.废旧电器的逆向物流系统规划模型研究[J].预测,2005,24(6):74-78.
[120]周根贵,曹振宇.遗传算法在逆向物流网络选址问题中的应用研究[J].中国管理科学,2005,13(1):42-47.
[121]诸静.模糊控制原理与应用[M].北京:机械工业出版社,1999.
[2] Amiri A. The design of service systems with queueing time cost, workload capacities and backup service [J]. European Journal of Operational Research 1998,104:201-217.
[3] Ammons J C, Real. M J, Newton D. Reverse production system design and operation for carpet recycling. Working Paper, Georgia institute of Technology,Atlanta, Georgia, 1997.
[4] Ana Paula Iannoni and Reinaldo Morabito. A discrete simulation analysis of a logistics supply system [J].Transportation Research Part E: Logistics and Transportation Review, 2006,42 (3) : 191-210.
[5] B.V.Babu, M.Mathew, L.Jehan.Differential evolution for multi-objective optimization [A].IEEE Congress on Evolutionary Computation[C], Canberra, 2003,2696-2703.
[6] Barros A, Dekker R, Scholten V.A two-level network for Recycling sand:A case study[J].European Journal of operational Research, 1998, 110 (2) : 199-214.
[7] Berger T, Debaillie B. Location of disassembly centers for reuse to extend an existing distribution network[D] .Unpublished Masters thesis , University of Leuven, Beigium (in Dutch), 1997.
[8] Berman O., Drezner Z., Wesolowsky GO.Locating service facilities whose reliability is distance [J].Computers and Operations Research, 2003, 30 (11) :1683-1695.
[9] Berman O.,Fouska N.,Larson R.C.Optimal location of discretionary service facilities[J] .Transportation Science, 1992,26 (3) :201 -211.
[10] Berman O., Krass D.The generalized maximal covering location problem [J].Computers & Operations Research, 2002, 29:563-581.
[11] Bollapragada R., Camm J., Rao U.S.et al. A two-phase greedy algorithm to locate and allocation hubs for fixed-wireless broadband access [J]. Operation Research Letters,2005,33 (2) : 134-142.
[12] Carbone R.Public facilities under stochastic demand [M]. INFOR, 1974,12 (3) :261-270.
[13] Caruso C.,Colorni A.,Aloi L.Dominant,An algorithm for the p-center problem[J] .European Journal of Operation Research,2003,149:53-64.
[14] Chiang C.I.,Hwang M.J.,Liu Y.H.An alternative formulation for certain fuzzy set-covering problems[J].Mathematical and Computer Modelling,2005,42:363-365.
[15] Church R.L., Meadows M.E.Location modeling utilizing maximum service distance criteria [J].Geographical Analysis, 1979,11:358-373.
[16] Church R.L.COBRA: A new formulation of the class p-median location problem [J]. Annals of Operations Research,2003,122 (1) :103-120.
[17] Cornuejols G, M.Fisher and GL.Nemhauser.On the uncapacitated location problem [ J]. Annals of Discrete Mathmatics, 1977, 1:163-177.
[18] Daskin M.S., Stern E.H.A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 1981,15 (2) :137-152
[19] De Brito M P, Dekker R. Reverse Logistics-a framework [J].Econometric InstituteReport EI2002-38, Erasmus University Rotterdam, the Netherlands,2002.
[20] Der-Hrong Lee, Meng Dong. A heuristic approach to logistics network design for end-of-lease computer products recovery[J] .Transportation research, 2007 ,106:1-20.
[21] Drezner Z.,Wesolowsky GO.A new method for the multifacility minimax location problem[J].Journal of the Operational Research Society,1978,24:1507-1514.
[22] Drezner Z.On the conditional p-median problem [J]. Computer and Operation Research, 1995, 22:525-530.
[23] F. Herrera, M. Lozano, Adaptation of genetic algorithm parameters based on fuzzy logic controllers [J].Genetic Alg. and Soft Computing, 1996, 95-129.
[24] F.Cheong,R.Lai.Designing a hierarchical fuzzy logic controller using differential evolution[C].Proceeding of IEEE on Fuzzy Systems Conference.Korea,1999,1: 277-282.
[25] Fleischmann M. et al. A characterization of logistics networks for product recovery [J]. Omega, 2000, 28 (1) :653-666.
[26] Fleischmann M. Reverse logistics network structures and design[R].ERIM Report Series Research in Management, ERS-2001-52-LIS, Erasmus University Rotterdam, The Netherlands, 2001.
[27] Fleischmann Moritz, Jaequeline .Bloemhof-Ruwaard, Rommert Dekker, Erwin van der Laan, Jo A.E.E.van Nunen, Luk N.Van Wassenhove. Quantitative models for reverse logistics: A review[J].European Journal of operational Research, 1997, 103 (1) :1-17.
[28] GGunawardane.Dynamic versions of set covering type public facility location problems [J]. European Journal of Operational Research, 1982,10:190-195.
[29] Gamperle R,Dmuller S,Koumoutsakos P.A Parameter Study for Differential Evolution [A].International Conference on Advances in Intelligent Systems,Fuzzy Systems, Evolutionary Computation[C], 2002,293-298.
[30] Garey M.R, Johnson D.S. Computers and intractability: a guide to the theory of NP-completeness [M].New York: Freeman, 1979.
[31] Grossman T., Wool A.Computational experience with approximation algorithms for the set covering problem [J].European Journal of Operational Research, 1997,101:81-92.
[32] Guiltinan JP, Nwokoye NG Developing distribution channels and systems in the emerging recycling industries[J].International Journal of Physical Distribution,1975, 6(1) :28-38.
[33] Hakimi S.L.Optimum locations of switching centers and the absolute centers and medians of a graph [J]. Operations Research, 1964, 12:450-459.
[34] Hakimi S.L.P-median theorems for competitive location[J].Annals of Operations Research, 1986,5 (1) :79-88.
[35] Harold Krikkle, Costas P.Pappos, Giannis T.Tsoulfas, Jaequeline Bloemhof.Design principles for closed loop supply chains: optimizing economic, logistic and environmental performance[R].ERIM Report Series Research in Management, ERS-2001-52-LIS, The Netherlands, 2001:1980-1987.
[36] Hassin R., Levin A., Morad D. Lexicographic local search and the p-center problem [J]. European Journal of Operational Research,2003,151 (1) :265-279.
[37] Hodgson J.A flow-capturing location allocation model[J].Geographical Analysis,1990,22 (3) :270-279.
[38] Hokey M., Hyun J., Chang S. A Genetic Algorithm Approach to Developing the Multi-echelon Reverse Logistics Network for Product Returns [J].Omega, 2006, 34:56-69.
[39] Hyun Jeung Ko, Gerald W.Evans. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs [J]. Computers&Operations Research, 2007, 34:346-366.
[40] Jayaraman V, Guide JR V D R, Srivastava R.A closed-loop logistics model for remanufacture[J] Journal of the Operational Research Society , 1999 , 50(5) :497-508.
[41] Jiuh-Biing Sheu. A coordinated reverse logistics system for regional management of multi-source hazardous wastes[J].Computers Operations Research, 2007,34:1442-1462.
[42] Joaquin Bautista, Jordi Pereira. Modeling the problem of locating collection areas for urban waste management. An application to the metropolitan area of Barcelona[J].The International Journal of Management Science , 2006(34) :617-629.
[43] Jouni Lampinen. A constraint handling approach for the differential evolution algorithm [A] .Proceedings of the congress on evolutionary computation[C],Honolulu, USA, 2002,1468-1473.
[44] K.E.Parsopoulos,D.K.Tasoulis,N.GPavlidis etc.Vector evaluated differential evolution for multiobjective optimization[A].IEEE Congress on Evolutionary Computation[C],Portland,2004, 204-211.
[45] K.Nateri,Madanvan.Multiobjective optimization using a pareto differential evolution approach[A].Proceeding of the congress on evolutionary computation.Honolulu[C], USA,2002,1145-1150.
[46] Kara, F.Rugrungruang, H.Kaebernick. Simulation modeling of reverse logistics networks[J].International journal of Production economics, 2007, 106:61-69.
[47] Kariv O., Hakimi S.An algorithmic approach to network location problems.II:The p-Medians[J].SIAM Journal on Applied Mathematics, 1979,37:539-560.
[48] Karp R.M.Reducibility among combinatorial problems,in:R.E.Miller and J.W.Thatcher (eds.) , Complexity of Computer Computations [M].New York: Plenium Press, 1972,85-104.
[49] Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing [J].Science, 1983, 220:671-680.
[50] Ko Hyun-Jeung, Min Hokey. The Dynamic design of a reverse logistics network for repair facilities[J].UPSi and LoDI Working Papers, 2003, 11.
[51] Krikke H R, van Harten A, Sehuur P C. Business case: reverse logistics network re-design for copiers[J] .OR Spectrum, 1999, 21 (3) :381-409.
[52] Kroon L and Vrijens G Returnable containers: an example of reverselogistics [J].International Journal of Physical Distribution& Logistics Management, 1995, 25(2) :56-68.
[53] Lambrecht MR, Ivens PL, and Vandaele NJ.ACLIPS: a capacity and lead time integrated procedure for scheduling [J]. Management Science 1998, 44(10) :1548-1561.
[54] Larson. A hypercube queuing model for facility location and redistricting in urban emergency services [J]. Computers and Operations Research, 1974, (1) : 67-95.
[55] Lee C C.Fuzzy Logic in control system: fuzzy logic controller [J]. Part I , II .IEEE Transactions on Systems, Man and Cybernetics, 1990, 20 (2) :404-435.
[56] Levin Y.,Israel A.B.A heuristic method for large-scale multi-facility location problems[J].Computers&Operations Research,2004,31:257-272.
[57] Li-Hsing Shin. Reverse logistics system planning for recycling electrical appliances and computers in Taiwan[J].Resources, Conservation and recyeling,2001,55-72.
[58] Liu, J., Lampinen. J, A fuzzy adaptive differential evolution algorithm. soft computing-a fusion of foundations, methodologies and applications [J] 2005, (6) :448-462.
[59] Lorena L.A.N., Senne E.L.F. A column generation approach to capacitated p-median problem [J].Computers and Operations Research, 2004, 31(6) :863-876.
[60] Louwers D, Kip BJ, peters E, Souren F, Flapper SDP.A facility location allocation model for re-using carpet materials [J]. Computers and Industrial Engineering,1999, 36 (4) :1-15.
[61] M.A. Lee, H. Takagi, Dynamic control of genetic algorithms using fuzzy logic techniques[C].5th Int. Conf. Genetic Algorithms. 1993, 76-83.
[62] Mamdani E H. Application of fuzzy algorithms for control of simple dynamic plant [J]. Proceedings of the Institution of Electrical Engineers, 1974, (121):1585-1588.
[63] Maria Isabel Gomes Salema, Ana Paula Barbosa-Povoa, Augusto Q.Novais. An optimization model for the design of a capacitated multi-Product reverse logistics network with uncertainty[J].2007, 179:1063-1077.
[64] Marianov V, ReVelle C. The queueing maximal availability location problem: a model for the siting of emergency vehicles [J].European Journal of Operational Research 1996,93:110-120.
[65] Marianov V, Serra D. Probabilistic, maximal covering location-allocation models for congested systems [J]. Journal of Regional Science 1998, 38:401-424.
[66] Megiddo N., Supowit K.On the complexity of some common geometric location problems [J].SIAM Journal on Computing, 1984,13:182-196.
[67] Metropolis N, Rosenbluth A, Rosenbluth M et al. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physics, 1953, 21:1087-1092.
[68] Ming Dong, F. Frank Chen, Performance modeling and analysis of integrated logistic chains: An analytic framework [J]. European Journal of Operational Research, 2005,162:83-98.
[69] Nagurney Anna, Fuminori Toyasaki. Reverse supply chain management and electronic waste recycling: a multi-tier network equilibrium framework for e-cycling[J].Transportation Research, 2005 (41) :1-18.
[70] R.K.Ursem, P.Vadstrup.Parameter identification of induction motors using differential evolution [A].Proceedings of the IEEE Congress on Evolutionary Computation[C], 2003, 2:790-796.
[71] R.Storn, K.Price.Differential evolution-a simple and efficient adaptive scheme for Global optimization over continuous spaces[R]. Technical report International Computer Science Institute, Berkley, 1995.
[72] Real M J, Ammons J C, Newton D. Carpet recycling: determining the reverse production system design[J].The Journal of Polymer-Plastics Technology and Engineering, 1999, 38 (3) :547-567.
[73] Rogers D S, Tibben-Lembke R S. Going backwards: reverse logistics trends and practices [M]. Nevada Reno: Reverse Logistics Executive Council, 1999.
[74] Roth R. Computer solutions to minimum cover problems. [J].Operation Research,1969, 17:455-465.
[75] S.Aydin, H.Temeltas.Time optimal trajectory planning for mobile robots by differential evolution algorithm and neural networks [A].2003 IEEE International Symposium on Industrial Electronics[C], IEEE Press, 2003, 1:252-257.
[76] S.Moalla,A.M.Alimi,N.Derbel.Design of beta neural systems using differential evolution[A].Proceeding of IEEE on Systems,Man and Cybernetics Conference[C],Tunisia,2002,3:6-9.
[77] S.Paterlini, T.Krink.High performance clustering with differential evolution [A].Proceeding of the Sixth Congress on Evolutionary Computation[C], IEEE Press,Piscataway, 2004, 2:2004-2011.
[78] Spengler T, PuE ehert H, Penkuhn T, Rentz O. Environmental integrated production and recycling management [J].European Journal of Operational Research, 1997,3:8-26.
[79] Stock J R. Reverse logistics [M].Oak Brook IL: Council of Logistics Management,1992.
[80] Sviridenko M.I.Worse-case analysis of the greedy algorithm for a generalization of maximum p-facility location problem [J]. Operation Research Letters,2000,26 (4):193-197.
[81] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cybernetics, 1985,6(2):116-132.
[82]Thierry M,Salomon M,van Nunen J,van Wassenhove I.Strategic issues in product recovery management[J].California Management Review.1995,37(2):114-135.
[83]Toregas.S.ReVelle R.C.,Bergman L.The location of emergency service facilities [J].Operation Research,1971,19:1363-1373.
[84]Vandaele N,Vannieuwenhuyse I,Cupers S.Optimal grouping for a nuclear magnetic resonance scanner by means of an open queuing model[J].European Journal of Operational Research 2003,151(1):181-192.
[85]Vesterstrom J,Thomsen R.A comparative study of differential evolution,particle swarm optimization and evolutionary algorithms on numerical benchmark problems[A].Congress on Evolutionary Computation[C],2004,1980-1987.
[86]Wesolowsky G.O.Dynamic facility location[J].Management Science,1973,19(11):1241-1248.
[87]Whitt W.Approximations for the GI/G/m queue[J].Production and Operatiors Management 1993,2(2):114-161.
[88]Yung-Chien Lin,Feng-Sheng Wang and Kao-Shing Hwang.A hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems[A].IEEE Proceeding of Evolutionary Computation[C],Piscataway,1999,2159-2166.
[89]Zadeh LA.Fuzzy Sets.[J]Information Control,1965,8:338-353.
[90]Zadeh L A.Outline of a new approach to the analysis of complex systems.IEEE International Conference on Systems[J],Man and Cybernetics,1973,3(1):638-656.
[91]蔡希贤,夏士智编译.物流合理化的数量方法[M].武汉:华中理工大学出版社,1985.
[92]常玉林,王炜.城市紧急服务系统优化选址模型[J].系统工程理论与实践,2000(2):104-108.
[93]达庆利,黄祖庆,张钦.逆向物流系统结构研究的现状及展望[J].中国管理科学.2004,12(1):131-138.
[94]代颖,马祖军,刘飞.再制造闭环物流网络优化设计模型[J].中国机械工程, 2006,6(8):809-814.
[95]戴箐.逆向物流网络设计与库存控制模型评述[J].商业经济与管理,2004,11:22-24.
[96]蒋良奎.平面选址问题的一种混合算法[J].上海海运学院学报,1999,20(4):98-102.
[97]黎青松,袁庆达,杜文.一个结合库存策略的物流选址模型[J].西南交通大学学报,2000,35(3):315-318.
[98]刘海燕,李宗平,叶怀珍.物流配送中心选址模型[J].西南交通大学学报,2000,35(3):311-313.
[99]刘明广.差异演化算法及其改进.系统工程[J].2005,23(2):108-111.
[100]刘曙光.模糊控制技术[M].北京:中国纺织出版社,2001.
[101]马良,蒋馥.约束多目标选址问题及其算法[J].运筹与管理,1998,7(2):7-12.
[102]马祖军,代颖,张殿业.逆向物流网络结构与设计[J].物流技术.2004,4:12-14.
[103]马祖军,代颖.产品回收逆向物流网络优化设计模型[J].管理工程学报,2005,19(4):114-117.
[104]孙林岩,王蓓.逆向物流的研究现状和发展趋势[J].中国机械工程,2005,5(16):928-934.
[105]孙晓晨,陈秋双,李响等.再制造系统废旧产品回收的最优控制策略[J].计算机集成制造系统,2007,13(9):1820-1825.
[106]孙元欣,黄培清.竞争型连锁经营网点选址模型与遗传算法解[J].科学学与科学技术管理.2001(10):60-63.
[107]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学,2002.
[108]魏洁,李军.EPR下的逆向物流回收模式选择研究[J].中国管理科学,2005,13(6):18-22.
[109]吴亮红等.自适应二次变异差分进化算法[J],控制与决策,2006,21(8):898-902.
[110]夏绪辉,刘飞.逆向供应链物流的内涵及研究发展趋势[J].机械工程学报,2005,41(4):103-109.
[111]徐菡,张敏洪.大规模逆向物流网络非线性优化模型的研究[J].中国科学院研究生院学报,2007,24(6):749-755.
[112]薛永吉,李旭宏,程日盛.基于仿真技术的物流中心站台数确定方法研究[J].物流技术,2005,6:37-39.
[113]姚卫新.电子商务条件下闭环供应链物流网络的设计[J].管理科学,2005,18(6):43-49.
[114]袁庆达,杜文,黎青松.区域公共物流中心规模和选址问题[J].交通运输工程学,2001,1(4):97-100.
[115]岳辉.第三方逆向物流决策与网络构建[D].西南交通大学硕士学位论文,2004
[116]张钦红,赵泉午,熊中楷.不对称信息下的易逝品退货物流协调运作研究[J].中国管理科学.2006,14(2):107-111.
[117]张显东等.市场竞争条件下的供应商选址模型研究[J].运筹与管理,1998,7(2):1-6.
[118]周垂日,梁棵,许传永等.逆向物流研究的新进展:文献综述[J].科研管理,2007,28(3):123-132.
[119]周垂日,梁裸,许传永.废旧电器的逆向物流系统规划模型研究[J].预测,2005,24(6):74-78.
[120]周根贵,曹振宇.遗传算法在逆向物流网络选址问题中的应用研究[J].中国管理科学,2005,13(1):42-47.
[121]诸静.模糊控制原理与应用[M].北京:机械工业出版社,1999.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |