An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm

doi:10.1016/S1672-6529(13)60234-6

摘要/Abstract

关键词: supply chain, performance evaluation, 5DBSC, bionics, LMBP neural network

Abstract:

A high efficient Supply Chain (SC) would bring great benefits to an enterprise such as integrated resources, reduced lo-gistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various na-ture-inspired algorithms are reviewed and their applications for SC Performance Evaluation (PE) are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard (5DBSC) and LMBP (Levenberg–Marquardt Back Propa-gation) neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. The convergence speed is faster than that in the previous work.

Key words: supply chain, performance evaluation, 5DBSC, bionics, LMBP neural network

Xuemei Fan, Shujun Zhang, Longzhao Wang, Yinsheng Yang, Kevin Hapeshi. [J]. J4, 2013, 10(3): 383-395.

Xuemei Fan, Shujun Zhang, Longzhao Wang, Yinsheng Yang, Kevin Hapeshi. An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm[J]. J4, 2013, 10(3): 383-395.

参考文献

[1] Beamon B M. Measuring supply chain performance. Inter-national Journal of Operations and Production Manage-ment, 1999, 19, 275–292.
[2] Chen K. Research and Practice of the Models of Supply Chain Performance in Manufacturing Industry, PhD thesis, Tianjin University, 2009. (in Chinese)
[3] Bolch G, Greine S, Meer H D, Trivedi K S. Queuing Net-works and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, 2nd Edi-tion, John Wiley&Sons, Inc., 2006.
[4] Yi K G. KPI assessment: Study of implication, workflow and counter measures. Technological Economy, 2005, 24, 48–49. (in Chinese)
[5] Estampe D, Lamouri S, Paris T. A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 2013, 142, 247–258.
[6] Stewart G. Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management. Logistics Information Manage-ment, 1997, 10, 62–67.
[7] Kaplan R S, Norton D P. The Balanced Scorecard: Measures that drive performance. Harvard Business Review, 1992, 70, 71–79.
[8] Boxwell R. Benchmarking for Competitive Advantage, McGraw-Hill, New York, USA, 1994.
[9] Vincent J F V, Bogatyreva O A, Bogatyrev N R, Bowyer A, Pahl A-K. Biomimetics: Its practice and theory. Journal of the Royal Society Interface, 2006, 3, 471–482.
[10] Ren L. Progress in the bionic study on anti-adhesion and resistance reduction of terrain machines. Science in China Series E: Technological Sciences, 2009, 52, 273–284.
[11] Richardson P. Fitness for the Future: Applying Biomimetics to Business Strategy, PhD thesis, Bath University, UK, 2010.
[12] Li Y. Application of GA and SVM to the performance evaluation of supply chain. Computer Engineering and Ap-plication, 2010, 46, 246–248. (in Chinese)
[13] Shi C D, Chen J H, Hu J. Study of supply chain perform-ances using rough sets and neural network. Computer En-gineering and Application, 2007, 43, 203–245. (in Chinese)
[14] Xi Y F, Wang C, Nie X X. Study of the evaluation methods of supply chain performances using fuzzy neural network. Information Journal, 2007, 9, 77–79. (in Chinese)
[15] Chen K, Shaw Y. Applying back propagation network to cold chain temperature monitoring. Advanced Engineering Informatics, 2011, 25, 11–22.
[16] Wu X Z, Zhou H, Liang C H. Optimisation of multi-layer supply chains using particle swarm optimisation algorithm. Computer Integrated Manufacturing Systems, 2010, 16, 127–132. (in Chinese)
[17] Zhao X, Dian J. Analysis of the current situations of methods for evaluating supply chain performance. Business Re-search, 2005, 2, 60–62.
[18] Lummus R R, Vokurka R J. Strategic supply chain planning. Production and Inventory Management Journal, 1998, 39, 49–58.
[19] Farris P W, Bendle N T, Pfeifer P E, Reibstein D J. Mar-keting Metrics: The Definitive Guide to Measuring Mar-keting Performance, Pearson Education, New Jersey, USA, 2010.
[20] Saaty T L, Alexander J M. Conflict Resolution: The Analytic Hierarchy Approach, Praeger Pub, New York, USA, 1989.
[21] Kaplan R S, Norton D P. Using the balanced scorecard as a strategic management system. Harvard Business Review, 2007, 74 ,75–85.
[22] Wong W P. A review on benchmarking of supply chain performance measures. Benchmarking, 2008, 15, 25–51.
[23] Akkermans H, Orschot K V. Developing a balanced score-card with system dynamics. Proceedings of 20th Interna-tional System Dynamics Conference, Palermo, Italy, 2002.
[24] Zheng P. Study of Methods for Evaluating the Performance of Dynamic Supply Chains, PhD thesis, Hunan University, China, 2008. (in Chinese)
[25] Dorigo M. Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italy, 1992. (In Italian)
[26] Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B. Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 2007, 11, 651–665.
[27] Seeley T D. The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies, Harvard University Press, Cam-bridge, UK, 1996.
[28] Cha S H, Tappert C C. A genetic algorithm for constructing compact binary decision trees. Journal of Pattern Recogni-tion Research, 2009, 4, 1–13.
[29] Yang X S. Firefly algorithms for multimodal optimization. Lecture Notes in Computer Sciences, 2009, 5792, 169–178.
[30] Fukushima K. Neocognitron: A self-organizing neural net-work model for a mechanism of pattern recognition un-affected by shift in position. Biological Cybernetics, 1980, 36, 193–202.
[31] Hinton G E, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18, 1527–1554.
[32] Mcculloch W, Pitts W. A logical calculus of the ideas im-manent in nervous activity. Bulletin of Mathematical Bio-physics, 1943, 5, 115–133.
[33] Fogel L J. Evolutionary programming in perspective: The top-down view. In Zurada J M, Marks II R J, Robinson C J, Eds., Computational Intelligence: Imitating Life, IEEE Press, Piscataway, USA, 1994, 135–146.
[34] Shi Y H, Eberhart R C. Empirical study of particle swarm optimization. Proceedings of the Congress on Evolutionary Computation, Washington DC, USA, 1999.
[35] Zandieh M, Ghomi F, Husseini M. An immune algorithm approach to hybrid flow shops scheduling with se-quence-dependent setup times. Applied Mathematics and Computation, 2006, 189, 111–127.
[36] Zhang F M, Cao Q K, Fang Q Y. A comparative inquiry into supply chain performance appraisal based on support vector machine and neural network. 2008 International Conference on Management Science & Engineering, Long Beach, USA, 2008, 370–377.
[37] Yu J M. GA and Its Application to Design of Supply Network, Master’s thesis, South China University of Technology, China, 2011. (in Chinese)
[38] Gabbert P, Brown D E, Huntley C L, Markowicz B P, Sappington D E. A system for learning routes and sched-ules with genetic algorithms. The Fourth International Conference on Genetic Algorithms (ICGA'91), 1991, 430–436.
[39] Liu C. Modelling and Theoretical Design of the Optimisa-tion of Supply Networks, PhD thesis, Changsha Central South University, China, 2006. (in Chinese)
[40] Pawlak Z. Rough sets decision algorithms and Bays' theory. European Journal of Operational Research, 2002, 136, 181–189.
[41] Liu Y Z, Xuan H Y. ACA and its application to vehicle routine problems. Information and Control, 2004, 33, 249–252. (in Chinese)
[42] Li J J. Study of Theory of Ecological Chain of Enterprises, PhD thesis, Jilin University, China, 2011. (in Chinese)
[43] Sabelis M W, Odiekmann O, Jansen V A A. Metapopulation persistence despite local extinction: Predator-prey patch models of the Lotka-Volterra type. Biological Journal of the Linnean Society, 1991, 42, 267–283.
[44] Li X F, Liu G Z. The modification and application of artifi-cial neural network BP algorithm. Journal of Sichuan Uni-versity (Engineering Edition), 2000, 32, 105–109. (in Chi-nese)
[45] Huang J Y, H J Z. BP network and its application in SCM performance evaluation. Shanghai Management Science, 2006, 6, 75–57. (in Chinese)
[46] Coulibalya P, Anctilb F, Bobee B. Daily reservoir in?ow forecasting using arti?cial neural networks with stopped training approach. Journal of Hydrology, 2000, 230, 244–257.
[47] Rumelhart D E, Hinton G E, Williams R J. Learning repre-sentations by back-propagating errors. Nature, 1986, 323, 533–536.
[48] Masters T. Advanced Algorithms for Neural Networks: A C++ Sourcebook, 1st, Wiley, New York, USA, 1995.
[49] Hornik K M, Stinch M, White H. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2, 359–366.