Advertisement

A Framework for Locating Logistic Facilities with Multi-Criteria Decision Analysis

  • Gilberto Montibeller
  • Hugo Yoshizaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

Abstract

Locating logistic facilities, such as plants and distribution centres, in an optimal way, is a crucial decision for manufacturers, particularly those that are operating in large developing countries which are experiencing a process of fast economic change. Traditionally, such decisions have been supported by optimising network models, which search for the configuration with the minimum total cost. In practice, other intangible factors, which add or reduce value to a potential configuration, are also important in the location choice. We suggest in this paper an alternative way to analyse such problems, which combines the value from the topology of a network (such as total cost or resilience) with the value of its discrete nodes (such as specific benefits of a particular location). In this framework, the focus is on optimising the overall logistic value of the network. We conclude the paper by discussing how evolutionary multi-objective methods could be used for such analyses.

Keywords

multi-criteria analysis logistics facility location multi-attribute value theory multi-objective optimisation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aikens, C.H.: Facility location models for distribution planning. European Journal of Operational Research 22, 263–279 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  2. Badri, M.A.: Combining the analytic hierarchy process and goal programming for global facility location-allocation problem. International Journal of Production Economics 62, 237–248 (1999)CrossRefGoogle Scholar
  3. Badri, M.A., Mortagy, A.K., Alsayed, A.: A multi-objective model for locating fire stations. European Journal of Operational Research 110, 243–260 (1998)CrossRefzbMATHGoogle Scholar
  4. Ballou, R.H.: Business Logistics/Supply Chain Management. Prentice-Hall, New Jersey (2004)Google Scholar
  5. Belton, V., Elder, M.D.: Decision support systems: Learning from visual interactive modelling. Decision Support Systems 12, 355–364 (1994)CrossRefGoogle Scholar
  6. Bowersox, D.J., Closs, D.J., Cooper, M.B.: Supply Chain Logistics Management, 2nd edn. McGraw-Hill, New York (2007)Google Scholar
  7. Branke, J., Deb, K.: Integrating user preferences into evolutionary multi-objective optimisation. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 461–478. Springer, Berlin (2005)CrossRefGoogle Scholar
  8. Cheng, S., Chan, C.W., Huang, G.H.: An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Engineering Applications of Artificial Intelligence 16, 543–554 (2003)CrossRefGoogle Scholar
  9. Current, J., Min, H., Schilling, D.: Multiobjective analysis of facility location decisions. European Journal of Operational Research 49, 295–307 (1990)CrossRefzbMATHGoogle Scholar
  10. Daskin, M.S.: Network and discrete location: models, algorithms, and applications. Wiley, New York (1995)CrossRefzbMATHGoogle Scholar
  11. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, New York (2001)zbMATHGoogle Scholar
  12. de Geus, A.P.: Planning as Learning. Harvard Business Review, 70–74 (March-April 1988)Google Scholar
  13. Farahani, R.Z., Asgari, N.: Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research 176, 1839–1858 (2007)CrossRefzbMATHGoogle Scholar
  14. Franco, A., Montibeller, G.: Invited Review - Facilitated modeling in Operational Research. European Journal of Operational Research 205, 489–500 (2010)CrossRefzbMATHGoogle Scholar
  15. Giannikos, I.: A multiobjective programming model for locating treatment sites and routing hazardous wastes. European Journal of Operational Research 104, 333–342 (1998)CrossRefzbMATHGoogle Scholar
  16. Hugo, A., Pistikopoulos, E.N.: Environmentally conscious long-range planning and design of supply chain networks. Journal of Cleaner Production 13, 1471–1491 (2005)CrossRefGoogle Scholar
  17. Jones, D.F., Mirrazavi, S.K., Tamiz, M.: Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research 137, 1–9 (2002)CrossRefzbMATHGoogle Scholar
  18. Keeney, R.L.: Evaluation of Proposed Storage Sites. Operations Research 27, 48–64 (1979)CrossRefGoogle Scholar
  19. Keeney, R.L.: Value-Focused Thinking. Harvard University Press, Cambridge (1992)zbMATHGoogle Scholar
  20. Keeney, R.L.: Common mistakes in making value trade-offs. Operations Research 50, 935–945 (2002)CrossRefzbMATHGoogle Scholar
  21. Klose, A., Drexl, A.: Facility location models for distribution system design. European Journal of Operational Research 162, 4–29 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  22. Koksalan, M., Phelps, S.: An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions. INFORMS J. on Computing 19, 291–301 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  23. Malczewski, J., Ogryczak, W.: The multiple criteria location problem: 2. Preference-based techniques and interactive decision support. Environment and Planning A 28, 69–98 (1996)Google Scholar
  24. Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidisc. Optim. 26, 369–395 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  25. Min, H.: Location analysis of international consolidation terminals using the Analytic Hierarchy Process. Journal Of Business Logistics 15, 25–44 (1994)Google Scholar
  26. Mirchandani, P.B., Reilly, J.M.: Spatial distribution design for fire fighting units. In: Ghosh, A., Rushton, G. (eds.) Spatial Analysis and Location-Allocation Models, pp. 186–223. Van Nostrand Reinhold, New York (1987)Google Scholar
  27. Melo, M.T., Nickel, S., Saldanha-da-Gama, F.: Facility location and supply chain management - a review. European Journal of Operational Research 196, 401–412 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  28. Nickel, S., Puerto, J., Rodriguez-Chia, A.M.: MCDM location problems. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple criteria decision making: state of the art surveys, pp. 761–795. Springer, Berlin (2005)CrossRefGoogle Scholar
  29. Stewart, T.J.: Robustness of Additive Value Function Methods in MCDM. Journal of Multi-Criteria Decision Analysis 5, 301–309 (1996)CrossRefzbMATHGoogle Scholar
  30. Tamiz, M., Jones, D., Romero, C.: Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research 111, 569–581 (1998)CrossRefzbMATHGoogle Scholar
  31. von Winterfeld, D.: On the relevance of behavioral decision research for decision analysis. In: Shanteau, S., Mellers, B.A., Schum, D.A. (eds.) Decision science and technology: reflections on the contributions of Ward Edwards, pp. 133–154. Kluwer, Norwell (1999)CrossRefGoogle Scholar
  32. Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S., Deb, K.: Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead. Management Science 54, 1336–1349 (2008)CrossRefzbMATHGoogle Scholar
  33. Yang, L., Jones, B.F., Yang, S.-H.: A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. European Journal of Operational Research 181, 903–915 (2007)CrossRefzbMATHGoogle Scholar
  34. Yurimoto, S., Masui, T.: Design of a decision support system for overseas plant location in the EC. Int. J. Production Economics 41, 411–418 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gilberto Montibeller
    • 1
  • Hugo Yoshizaki
    • 2
  1. 1.Management Science Group, Dept. of ManagementLondon School of EconomicsEnglandUnited Kingdom
  2. 2.Dept. of Production EngineeringUniversity of Sao PauloBrazil

Personalised recommendations