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Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) pp 465–478Cite as

Recent Trends in Supply Chain Management: A Soft Computing Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 202))

Abstract

Increasing globalization, diversity of the product range and increasing customer awareness are making the market highly competitive thereby forcing different supply chains to adapt to different stimuli on a continuous basis. It is also well recognized that overall supply chain focus should be given an overriding priority over the individual goals of the players, if one were to improve overall supply chain surplus. Therefore, supply chain performance has attracted researcher’s attention. A variety of soft computing techniques have been employed to improve effectiveness and efficiency in various aspects of supply chain management. The aim of this paper is to summarize the findings of existing research concerning the application of soft computing techniques to supply chain management.

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References

  1. Christopher, M.: Logistics and Supply Chain Management, 2nd ed., Prentice Hall, Norfolk, 2004.

    Google Scholar 

  2. Harrison, A., Hoek, R.: Logistics Management and Strategy, 2nd ed., Prentice Hall, Essex, 2005.

    Google Scholar 

  3. Silva, C.A., Sousa, J.M.C., Runkler, T., Palm, R.: Soft computing optimization methods applied to logistic processes, International Journal of Approximate Reasoning 40 (3) (2005) 280–301.

    Google Scholar 

  4. Rushton, A., Oxley, J., Croucher, P.: The Handbook of Logistics and Distribution Management, 2nd ed., Kogan Page, London, 2000.

    Google Scholar 

  5. Roy, R., Furuhashi, T., Chawdhry, P.K.: Advances in Soft Computing: Engineering Design and Manufacturing, Springer, London, 1999.

    Google Scholar 

  6. Sivanandam, S.N., Deepa, S.N.:Principles of Soft Computing, 2nd Edition copyright, 2011 Wiley India Pvt. Ltd.

    Google Scholar 

  7. Tettamanzi, A., Tomassini, M.: Soft computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer, Heidelberg, 2001.

    Google Scholar 

  8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Massachusetts, 1989.

    Google Scholar 

  9. Sinha, N.K., Gupta, M.M.: Soft Computing and Intelligent Systems: Theory and Applications, Academic Press, San Diego, 2000.

    Google Scholar 

  10. Musilek, P., Gupta, M.M.: Neural networks and fuzzy systems, in: Sinha, N.K, Gupta M.M. (Eds.), Soft Computing and Intelligent Systems: Theory and Applications, Academic Press, San Diego, 2000.

    Google Scholar 

  11. Kartalopoulos, S.V.: Understanding Neural Networks and Fuzzy Logic, IEEE Press, New York, 1996.

    Google Scholar 

  12. Ross, T.J., Fuzzy Logic with Engineering Applications, 2nd ed., John Wiley and Sons, West Sussex, 2004.

    Google Scholar 

  13. Du, T.C., Wolfe, P.M.: Implementation of fuzzy logic systems and neural networks in industry, Computers in Industry 32 (3) (1997) 261–272.

    Google Scholar 

  14. Selen, W., & Soliman, F. (Operations in today’s demand chain management framework. Journal of Operations Management, (2002), 20. 667–673.

    Google Scholar 

  15. Kuo, R.J., Xue, K.C.: Fuzzy neural networks with application to sales forecasting, Fuzzy Sets and Systems 108 (2) (1999) 123–143.

    Google Scholar 

  16. Bermúdeza, J.D., Segurab, J.V., Verchera, E..: A decision support system methodology for forecasting of time series based on soft computing Computational Statistics & Data Analysis 51 (2006) 177 – 191.

    Google Scholar 

  17. Sprecher, D.A.: On the structure of continuous functions of several variables, Transactions of the American Mathematical Society 115 (1965) 340–355.

    Google Scholar 

  18. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting 14 (1) (1998) 35–62.

    Google Scholar 

  19. Wall, B., Higgins, P., Browne, J., Lyons, G.: A prototype system for short-term supply planning, Computers in Industry 19 (1) (1992) 1–19.

    Google Scholar 

  20. Ansuj, A.P., Camargo, M.E., Radharamanan, R., Petry, D.G.: Sales forecasting using time series and neural networks, Computers & Industrial Engineering 31 (1–2) (1996) 421–424.

    Google Scholar 

  21. Luxhoj, J.T., Riis, J.O., Stensballe, B.: A hybrid econometric—neural network modeling approach for sales forecasting, International Journal of Production Economics 43 (2–3) (1996) 175–192.

    Google Scholar 

  22. Kimbrough, S.O., Wu, D.J.,Zhong, F.: Computers play the beer game: can artificial agents manage supply chains? Decision Support Systems 33 (3) (2002) 323–333.

    Google Scholar 

  23. Strozzi, F., Bosch, J., Zaldivar, J.M.: Beer game order policy optimization under changing customer demand, Decision Support Systems 42 (4) (2007) 2153– 2163.

    Google Scholar 

  24. Liang, W., Huang, C.: Agent-based demand forecast in multi-echelon supply chain, Decision Support Systems 42 (1) (2006) 390–407.

    Google Scholar 

  25. http://knowscm.blogspot.in/2008/02/bullwhip-effect-in-supply-chain.html/25/07/(2012.

  26. Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting, European Journal of Operational Research 184 (3) (2008) 1140–1154.

    Google Scholar 

  27. Douglas M. Lambert and Terrance L. Pohlen, Supply Chain Metrics, The International Journal of Logistics Management, Vol. 12, No. 1, (2001), pp. 1-19.

    Google Scholar 

  28. Braunscheidel, M.J., Suresh, N.C., 2009. The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management 27, 119–140.

    Google Scholar 

  29. Roghanian, E., Sadjadi, S.J., Aryanezhad, M.B.: A probabilistic bi-level linear multiobjective programming problem to supply chain planning, Applied Mathematics and Computation 188 (1) (2007) 786–800.

    Google Scholar 

  30. Chiu, M., Lin, G.: Collaborative supply chain planning using the artificial neural network approach, Journal of Manufacturing Technology Management 15 (8) (2004) 787–796.

    Google Scholar 

  31. Moon, C., Kim, J., Hur, S.: Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain, Computers & Industrial Engineering 43 (1–2) (2002) 331–349.

    Google Scholar 

  32. Huin, S.F., Luong, L.H.S., Abhary, K.: Knowledge-based tool for planning of enterprise resources in ASEAN SMEs, Robotics and Computer-Integrated Manufacturing 19 (5) (2003) 409–414.

    Google Scholar 

  33. Huang, G.Q., Zhang, X.Y., Liang, L.: Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains, Journal of Operations Management 23 (3–4) (2005) 267–290.

    Google Scholar 

  34. Nasab, M.K., Konstantaras, I.: A random search heuristic for a multi-objective production planning Computers & Industrial Engineering 62 (2012) 479–490.

    Google Scholar 

  35. Xie, J., Dong, J.: Heuristic genetic algorithms for general capacitated lot-sizing problems, Computers & Mathematics with Applications 44 (1–2) (2002) 263– 276.

    Google Scholar 

  36. Ossipov, P.: Heuristic optimization of sequence of customer orders, Applied Mathematics and Computation 162 (3) (2005) 1303–1313.

    Google Scholar 

  37. Kampf, M., Kochel, P.: Simulation-based sequencing and lot size optimisation for a production-and-inventory system with multiple items, International Journal of Production Economics 104 (1) (2006) 191–200.

    Google Scholar 

  38. Bjork, K., Carlsson, C.: The effect of flexible lead times on a paper producer, International Journal of Production Economics 107 (1) (2007) 139–150.

    Google Scholar 

  39. Verwijmeren, M., Vlist, P., Donselaar, K.: Networked inventory management I formation systems: Materializing supply chain management, International Journal of Physical Distribution and Logistics Management 26 (6) (1996) 16–31.

    Google Scholar 

  40. Chang, P., Yao, M., Huang, S., Chen, C.: A genetic algorithm for solving a fuzzy economic lot-size scheduling problem, International Journal of Production Economics 102 (2) (2006) 265–288.

    Google Scholar 

  41. Wang, X., Tang, W., Zhao, R.: Fuzzy economic order quantity inventory models without backordering, Tsinghua Science & Technology 12 (1) (2007) 91–96.

    Google Scholar 

  42. Maiti, M.K.: Fuzzy inventory model with two warehouses under possibility measure on fuzzy goal, European Journal of Operational Research 188 (3) (2008) 746–774.

    Google Scholar 

  43. Hwang, H., Choi, B., Lee, M.: A model for shelf space allocation and inventory control considering location and inventory level effects on demand, International Journal of Production Economics 97 (2) (2005) 185–195.

    Google Scholar 

  44. Daniel, J.S.R., Rajendran, C.: Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives, European Journal of Operational Research 175 (1) (2006) 566–592.

    Google Scholar 

  45. Kritchanchai, D., MacCarthy, B.L.: Responsiveness of the order fulfillment process, International Journal of Operations & Production Management, Vol. 19 Iss: 8 pp. 812 - 833(1999).

    Google Scholar 

  46. Thanh P., Bostel N., Pe′ton, O.: A DC programming heuristic applied to the logistics network design problem Int. J. Production Economics 135 (2012) 94–105).

    Google Scholar 

  47. Slater, A.: Specification for a dynamic vehicle routing and scheduling system, International Journal of Transport Management 1 (1) (2002) 29–40.

    Google Scholar 

  48. Pankratz, G.: Dynamic vehicle routing by means of a genetic algorithm, International Journal of Physical Distribution & Logistics Management 35 (5) (2005) 362–383.

    Google Scholar 

  49. Torabi, S.A., Ghomi, S.M.T.F., Karimi, B.: A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains, European Journal of Operational Research 173 (1) (2006) 173–189.

    Google Scholar 

  50. Fu, L., Sun, D., Rilett, L.R.: Heuristic shortest path algorithms for transportation applications: state of the art, Computers & Operations Research 33 (11) (2006) 3324–3343.

    Google Scholar 

  51. Vukadinovic, K., Teodorovic, D.A., Pavkovic, G.: An application of neurofuzzy modeling: the vehicle assignment problem, European Journal of Operational Research 114 (3) (1999) 474–488.

    Google Scholar 

  52. Potvin, J., Shen, Y., Dufour, G., Rousseau, J.: Learning techniques for an expert vehicle dispatching system, Expert Systems with Applications 8 (1) (1995) 101–109.

    Google Scholar 

  53. Jia Shu a, ZhengyiLi a, HoucaiShen b, TingWuc,n, WeijunZhong a A logistics network design model with vendor managed inventory Int. J. Production Economics 135 (2012) 754–761.

    Google Scholar 

  54. Teodorovic, D.: Fuzzy logic systems for transportation engineering: the state of the art, Transportation Research Part A: Policy and Practice 33 (5) (1999) 337– 364.

    Google Scholar 

  55. Sheu, J.: A hybrid fuzzy-based approach for identifying global logistics strategies, Transportation Research Part E: Logistics and Transportation Review 40 (1) (2004) 39–61.

    Google Scholar 

  56. Altiparmak, F., Gen, M., Lin, L., Paksoy, T.: A genetic algorithm approach for multiobjective optimization of supply chain networks, Computers & Industrial Engineering 51 (1) (2006) 196–215.

    Google Scholar 

  57. Ma, H., Davidrajuh, R.: An iterative approach for distribution chain design in agile virtual environment, Industrial Management & Data Systems 105 (6) (2005) 815–834.

    Google Scholar 

  58. Gen, M., Syarif, A.: Hybrid genetic algorithm for multi-time period production/distribution planning, Computers & Industrial Engineering 48 (4) (2005) 799– 809.

    Google Scholar 

  59. Fischer, T., Gehring, H.: Planning vehicle transhipment in a seaport automobile terminal using a multi-agent system, European Journal of Operational Research 166 (3) (2005) 726–740.

    Google Scholar 

  60. Cooper, Robert G., Scott J. Edgett, and Elko J. kleinschmidt, Protfolio management for New products, Reading, MA; Perseus Books,1998.

    Google Scholar 

  61. Tong, L., Liang, Y.: Forecasting field failure data for repairable systems using neural networks and SARIMA model, International Journal of Quality & Reliability Management 22 (4) (2005) 410–420.

    Google Scholar 

  62. Suarez, M.G.: Shelf space assigned to store and national brands: a neural networks analysis, International Journal of Retail & Distribution Management 33 (11) (2005) 858–878.

    Google Scholar 

  63. Tsai, C.: An intelligent adaptive systemfor the optimal variable selections of R&Dand quality supply chains, Expert Systems with Applications 31 (4) (2006) 808–825.

    Google Scholar 

  64. Wang, J., Shu, Y.: A possibilistic decision model for new product supply chain design, European Journal of Operational Research 177 (2) (2007) 1044–1061.

    Google Scholar 

  65. Dale S. Rogers, Douglas M. Lambert, Keely L. Croxton and Sebastián J. García-Dastugue, The Returns Management Process, The International Journal of Logistics Management, Vol. 13, No. 2 (2002), pp. 1-18.

    Google Scholar 

  66. Min, H., Ko, C.S., Ko, H.J.: The spatial and temporal consolidation of returned products in a closed-loop supply chain network, Computers & Industrial Engineering 51 (2) (2006) 309–320.

    Google Scholar 

  67. Lieckens, K., Vandaele, N.: Reverse logistics network design with stochastic lead times, Computers & Operations Research 34 (2) (2007) 395–416.

    Google Scholar 

  68. Min, H., Ko, H.: The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers, International Journal of Production Economics 113 (1) (2008) 176–192.

    Google Scholar 

  69. Herrmann, J. W., & Hodgson, B (2001). SRM: Leveraging the supply base for competitive advantage. Proceedings of the SMTA International Conference, Chicago, Illinois, 1 October, 2001.

    Google Scholar 

  70. Gunasekaran, N., Rathesh, S., Arunachalam, S., Koh, S.C.L.: Optimizing supply chain management using fuzzy approach, Journal of Manufacturing Technology Management 17 (6) (2006) 737–749.

    Google Scholar 

  71. Lau, H.C.W., Pang, W.K., Wong, C.W.Y.: Methodology for monitoring supply chain performance: a fuzzy logic approach, Logistics Information Management 15 (4) (2002) 271–280.

    Google Scholar 

  72. Shore, B., Venkatachalam, A.R.: Evaluating the information sharing capabilities of supply chain partners: a fuzzy logic model, International Journal of Physical Distribution & Logistics Management 33 (9) (2003) 804–824.

    Google Scholar 

  73. Deshpande, U., Gupta, A., Basu, A.: Task assignment with imprecise information for real-time operation in a supply chain, Applied Soft Computing 5 (1) (2004) 101– 117.

    Google Scholar 

  74. Moghadam, M.R.S., Afsar, A., Sohrabi, B.: Inventory lot-sizing with supplier selection using hybrid intelligent algorithm, Applied Soft Computing 8 (4) (2008) 1523–1529.

    Google Scholar 

  75. Wang, J., Zhao, R., Tang, W.: Fuzzy programming models for vendor selection problem in a supply chain, Tsinghua Science & Technology 13 (1) (2008) 106– 111.

    Google Scholar 

  76. Isiklar, G., Alptekin, E., Buyukozkan, G.: Application of a hybrid intelligent decision support model in logistics outsourcing, Computers & Operations Research 34 (12) (2007) 3701–3714.

    Google Scholar 

  77. Choy, K.L., Lee, W.B., Lo, V.: An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing, Expert Systems with Applications 22 (3) (2002) 213–224.

    Google Scholar 

  78. Langer, M., Loidl, S., & Nerb, M. (1999). Customer service management: towards a management information base for an IP connectivity service. The Fourth IEEE Symposium on Computers and Communications, Red Sea, Egypt, pp. 149–155.

    Google Scholar 

  79. Bottani, E., Rizzi, A.: Strategic management of logistics service: a fuzzy QFD approach, International Journal of Production Economics 103 (2) (2006) 585–599.

    Google Scholar 

  80. Robert, S.: Computer Aided Marketing & Selling (1991) Butterworth Heinemann ISBN 978-0-7506-1707-9.

    Google Scholar 

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  1. Indian Institute of Technology, Roorkee, India

    Sunil Kumar Jauha & Millie Pant

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  1. Sunil Kumar Jauha
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  2. Millie Pant
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Correspondence to Sunil Kumar Jauha .

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Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

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Jauha, S.K., Pant, M. (2013). Recent Trends in Supply Chain Management: A Soft Computing Approach. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_40

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  • DOI: https://doi.org/10.1007/978-81-322-1041-2_40

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