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Logistics Network Models

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Network Models and Optimization

Part of the book series: Decision Engineering ((DECENGIN))

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Abstract

With the development of economic globalization and extension of worldwide electronic marketing, global enterprise services supported by universal supply chain and world-wide logistics become imperative for the business world. How to manage logistics system efficiently thas hus become a key issue for almost all of the enterprises to reduce their various costs in today’s keenly competitive environment of business, especially for many multinational companies. Today’s pervasive internet and full-fledged computer aided decision supporting systems (DSS) certainly provide an exciting opportunity to improve the efficiency of the logistics systems. A great mass of research has been done in the last few decades. However, weltering in giving perfect mathematical representations and enamored with developing various type of over-intricate techniques in solution methods, most researchers have neglected some practical features of logistics. In this chapter, the logistics network models are introduced, consolidating different aspects in practical logistics system. A complete logistics system covers the entire process of shipping raw materials and input requirements from suppliers to plants, the conversion of the inputs into products at certain plants, the transportation of the products to various warehouse of facilities, and the eventual delivery of these products to the final customers. To manage the logistics system efficiently, the dynamic and static states of material flows – transportation and storage – are key points that we need to take into consideration.

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References

  1. Sabri, E. H. & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic & operational planning in supply chain design, Omega, 28, 581–598.

    Article  Google Scholar 

  2. Thomas, D. J. & Griffin, P. M. (1996). Coordinated supply chain management, European Journal of Operational Research, 94, 1–115.

    Article  MATH  Google Scholar 

  3. Vidal, C. J. & Goetschalckx, M. (1997). Strategic production-distribution models: A critical review with emphasis on global supply chain models, European Journal of Operational Research, 98, 1–18.

    Article  MATH  Google Scholar 

  4. Beamon, B. M. (1998). Supply chain design and analysis: models and methods, International Journal of Production Economics, 55, 281–294.

    Article  Google Scholar 

  5. Erenguc, S. S., Simpson, N. C. & Vakharia, A. J. (1999). Integrated production/distribution planning in supply chains: an invited review, European Journal of Operational Research, 115, 219–236.

    Article  Google Scholar 

  6. Pontrandolfo, P. & Okogbaa, O. G. (1999). Global manufacturing: a review and a framework for planning in a global corporation, International Journal of Production Economics, 37(1), 1–19.

    MATH  Google Scholar 

  7. Jayaraman, V. & Pirkul, H. (2001). Planning and coordination of production and distribution facilities for multiple commodities, European Journal of Operational Research, 133, 394– 408.

    Article  MATH  Google Scholar 

  8. Jayaraman, V. & Ross, A. (2003). A simulated annealing methodology to distribution network design & management, European Journal of Operational Research, 144, 629–645.

    Article  MathSciNet  MATH  Google Scholar 

  9. Yan, H., Yu Z. & Cheng, T. C. E. (2003). A strategic model for supply chain design with logical constraints: formulation and solution, Computers & Operations Research, 30(14), 2135– 2155.

    Article  MATH  Google Scholar 

  10. Syam, S. S. (2002). A model and methodologies for the location problem with logistical components, Computers and Operations Research, 29, 1173–1193.

    Article  MathSciNet  MATH  Google Scholar 

  11. Syarif, A., Yun, Y. & Gen, M. (2002). Study on multi-stage logistics chain network: a spanning tree-based genetic algorithm approach, Computers & Industrial Engineering, 43, 299– 314.

    Article  Google Scholar 

  12. Amiri, A. (2004). Designing a distribution network in a supply chain system: formulation and efficient solution procedure, European Journal of Operational Research, in press.

    Google Scholar 

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

    Article  Google Scholar 

  14. Truong, T. H. & Azadivar, F. (2005). Optimal design methodologies for configuration of supply chains, International Journal of Production Researches, 43(11), 2217–2236.

    Article  Google Scholar 

  15. Gen, M., Altiparamk, F. & Lin, L. (2006). A genetic algorithm for two-stage transportation problem using priority-based encoding, OR Spectrum, 28(3), 337–354.

    Article  MATH  Google Scholar 

  16. Chan, F. T. S. & Chung, S. H. (2004). A multi-criterion genetic algorithm for order distribution in a demand driven supply chain, International Journal of Computer Integrated Manufacturing, 17(4), 339–351.

    Article  Google Scholar 

  17. Chen, C. & Lee, W. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands & prices, Computers and Chemical Engineering, 28, 1131–1144.

    Article  Google Scholar 

  18. Erol, I. & Ferrell Jr. W. G. (2004). A methodology to support decision making across the supply chain of an industrial distributor, International Journal of Production Economics, 89, 119–129.

    Article  Google Scholar 

  19. Guillen, G., Mele, F. D., Bagajewicz, M. J., Espuna, A. & Puigjaner, L. (2005). Multiobjective supply chain design under uncertainty, Chemical Engineering Science, 60, 1535–1553.

    Article  Google Scholar 

  20. Chan, F. T. S., Chung, S. H. & Wadhwa, S. (2004). A hybrid genetic algorithm for production and distribution, Omega, 33, 345–355.

    Article  Google Scholar 

  21. Altiparmak, F., Gen, M., Lin, L. & Paksoy, T. (2006). A genetic algorithm approach for multiobjective optimization of supply chain networks, Computers & Industrial Engineering, 51(1), 197–216.

    Article  Google Scholar 

  22. Altiparmak, F., Gen, M., Lin, L. & Karaoglan, I. (2007). A steady-state genetic algorithm for multi-product supply chain network design, Computers and Industrial Engineering, & [online].Available :

    Google Scholar 

  23. Gen, M. & Cheng, R. (2000). Genetic algorithms & engineering optimization, Wiley: New York.

    Google Scholar 

  24. Dimopoulos, C. & Zalzala, A. M. S. (2000). Recent developments in evolutionary computation for manufacturing optimization: problems, solutions and comparisons, IEEE Transactions on Evolutionary Computation, 4(2), 93–113.

    Article  Google Scholar 

  25. Aytug, H., Khouja, M. & Vergara, F. E. (2003). Use of genetic algorithms to solve production and operations management: a review, International Journal of Production Researches, 41(17), 3955–4009.

    Article  Google Scholar 

  26. Guo, J. (2001). Third-party Logistics - Key to rail freight development in China, Japan Railway & Transport Review, 29, 32–37. [Online]. available : http://www.jrtr.net/jrtr29/f32 jia.html.

    Google Scholar 

  27. Michalewicz, Z., Vignaux, G. A. & Hobbs, M. (1991). A non-standard genetic algorithm for the nonlinear transportation problem, ORSA Journal on Computing, 3(4), 307–316.

    MATH  Google Scholar 

  28. Gen, M. & Cheng, R. W. (1997). Genetic Algorithms and Engineering Design, New York: John Wiley & Sons.

    Google Scholar 

  29. Gen, M., Li, Y. Z. & Ida, K. (1999). Solving multiobjective transportation problems by spanning tree-based genetic algorithm, IEICE Transaction on Fundamentals, E82-A(12), 2802– 2810.

    Google Scholar 

  30. Simchi-Levi, D., Kaminsky, P. & Simchi-Levi, E. (2003). Designing and Managing the Supply Chain: Concepts Strategies & Case Studies, Second Edition, McGraw-Hill, Irwin, Boston, MA.

    Google Scholar 

  31. Council of Supply Chain Management Professionals, [Online]. Available : http://www.cscmp.org/Website/AboutCSCMP/Definitions/Definitions.asp

    Google Scholar 

  32. Cooper, L. (1963). Location-allocation problems, Operations Research, 11, 331–343.

    MathSciNet  MATH  Google Scholar 

  33. Gong, D., Yamazaki, G. & Gen, M. (1996). Evolutionary program for optimal design of material distribution system, Proceeding of IEEE International Conference on Evolutionary Computation, 131–134.

    Google Scholar 

  34. Hitchcock, F. L. (1941). The distribution of a product from several sources to numerous localities, Journal of Mathematical Physics, 20, 24–230.

    MathSciNet  Google Scholar 

  35. Tilanus, B. (1997). Introduction to information system in logistics and transportation. In B. Tilanus, Information systems in logistics and transportation, Amsterdam: Elsevier, 7–16.

    Google Scholar 

  36. Geoffrion, A. M. & Graves, G. W. (1974). Multicommodity distribution system design by benders decomposition, management science, 20, 822–844.

    Article  MathSciNet  MATH  Google Scholar 

  37. Pirkul, H. & Jayaraman, V. (1998). A multi-commodity, multi-plant capacitated facility location problem: formulation and efficient heuristic solution, Computer Operations Research, 25(10), 869–878.

    Article  MathSciNet  MATH  Google Scholar 

  38. Heragu, S. (1997). Facilities Design, PSW Publishing Company.

    Google Scholar 

  39. Hindi, K. S., Basta, T. & Pienkosz, K. (1998). Efficient solution of a multi-commodity, twostage distribution problem with constraints on assignment of customers to distribution centers, International Transactions on Operational Research, 5(6), 519–527.

    Article  Google Scholar 

  40. Tragantalerngsak, S., Holt, J. & Ronnqvist, M. (2000). An exact method for two-echelon, single-source, capacitated facility location problem, European Journal of Operational Research, 123, 473–489.

    Article  MathSciNet  MATH  Google Scholar 

  41. Amiri, A. (2005). Designing a distribution network in a supply chain system: formulation and efficient solution procedure, in press, European Journal of Operational Research.

    Google Scholar 

  42. Vignaux, G. A. & Michalewicz, Z. (1991). A genetic algorithm for the linear transportation problem, IEEE Transactions on Systems, Man, and Cybernetics, 21(2), 445–452.

    Article  MathSciNet  MATH  Google Scholar 

  43. Li, Y. Z., Gen, M. & Ida, K. (1998). Improved genetic algorithm for solving multi-objective solid transportation problem with fuzzy number, Japanese Journal of Fuzzy Theory and Systems, 4(3), 220–229.

    Google Scholar 

  44. Syarif, A. & Gen, M. (2003). Double spanning tree-based genetic algorithm for two stage transportation problem, International Journal of Knowledge-Based Intelligent Engineering System, 7(4).

    Google Scholar 

  45. Lee, J. E., Gen, M. & Rhee, K. G. (2008). A multi-stage reverse logistics network problem by using hybrid priority-based genetic algorithm, IEEJ Transactions on Electronics, Information & Systems, in Press.

    Google Scholar 

  46. Garey, M. R. & Johnson, D. S. (1979). Computers and intractability: a guide to the theory of NP-completeness, Freeman.

    Google Scholar 

  47. [Online]. available : http://www.fbk.eur.nl/OZ/REVLOG/PROJECTS/TEMMINOLOGY/defreverselogistics.html.

    Google Scholar 

  48. Stock, J. K. (1992). Reverse logistics : White paper, Council of Logistics Management, Oak Brook, IL.

    Google Scholar 

  49. Kroon, L. & Vrijens, G. (1995). Returnable containers : An example of reverse logistics, International Journal of Physical Distribution and Logistics Management, 25(2), 56–68.

    Article  Google Scholar 

  50. Pati, R. K., Vrat, P. & Kumar, P. A. (2008). Goal programming model for paper recycling system, International Journal of Management Science, Omega, 36(3), 405–417.

    Google Scholar 

  51. Kim, K. B., Song, I. S. & Jeong, B. J. (2006). Supply planning model for remanufacturing system in reverse logistics environment, Computer & Industrial Engineering, 51(2), 279–287.

    Article  Google Scholar 

  52. Lee, J. E., Rhee, K. G. & Gen, M. (2007). Designing a reverse logistics network by prioritybased genetic algorithm, Proceeding of International Conference on Intelligent Manufacturing Logistics System, 158–163.

    Google Scholar 

  53. Goetschalckx, M., Vidal, C. J. & Dogan, K. (2002). Modeling & design of global logistics systems: A review of integrated strategic & tactical models & design algorithms, European Journal of Operational Research, 143, 1–18.

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  55. Nakatsu, R. T. (2005). Designing business logistics networks using model-based reasoning & heuristic-based searching, Expert Systems with Applications, 29(4), 735–745.

    Article  Google Scholar 

  56. Harland, C. (1997). Supply chain operational performance roles, Integrated Manufacturing Systems, 8, 70–78.

    Article  Google Scholar 

  57. Lee, H. L., Padmanabhan, V. & Whang, S. (1997). The bullwhip effect in supply chains, Sloan Management Review, 38(3), 93–102.

    Google Scholar 

  58. Logistics & Technology. [Online].Available:http://www.trafficworld.com/news/log/112904a.asp.

    Google Scholar 

  59. Johnson, J. L. & Umesh, U. N. (2002). The interplay of task allocation patterns and governance mechanisms in industrial distribution channels, Industrial Marketing Management, 31(8), 665–678.

    Article  Google Scholar 

  60. Tsay, A. A. (2002). Risk sensitivity in distribution channel partnerships: implications for manufacturer return policies, Journal of Retailing, 78(2), 147–160.

    Article  Google Scholar 

  61. Yesikökc¸en, G. N. & Wesolowsky, G. O. (1998). A branch-and-bound algorithm for the supply connected location-allocation problem on network, Location Science, 6, 395–415.

    Article  Google Scholar 

  62. Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Program, 3rd ed., New York: Spring-Verlag.

    Google Scholar 

  63. Yun, Y. S. & Moon, C. U. (2003). Comparison of adaptive genetic algorithms for engineering optimization problems, International Journal of Industrial Engineering, 10(4), 584–590.

    Article  Google Scholar 

  64. Lee, C. Y., Yun, Y. S. & Gen, M. (2002). Reliability optimization design for complex systems by hybrid GA with fuzzy logic control and local search. IEICE Transaction on Fundamentals of Electronics Communications & Computer Sciences, E85-A(4), 880–891.

    Google Scholar 

  65. Lee, C. Y., Gen, M. & Kuo, W. (2001). Reliability optimization design using a hybridized genetic algorithm with a neural-network technique. IEICE Transacion on Fundamentals of Electronics Communications & Computer Sciences, E84-A(2), 627–635.

    Google Scholar 

  66. Eiben, A. E., Hinterding, R. & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms, IEEE Transactions on Energy Conversion, 3(2), 124 –141.

    Google Scholar 

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(2008). Logistics Network Models. In: Network Models and Optimization. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-181-7_3

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  • DOI: https://doi.org/10.1007/978-1-84800-181-7_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-180-0

  • Online ISBN: 978-1-84800-181-7

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