Classification of Location Models and Location Softwares

  • Sajedeh Tafazzoli
  • Marzieh Mozafari
Part of the Contributions to Management Science book series (MANAGEMENT SC.)


According to the importance and advantages of classification, the first section of this chapter is dedicated to some presented classifications of location models, which help in having more disciplined understanding of location models. In the second section, some location softwares will be introduced briefly.

Nowadays, with the increasing development of science in all branches, need for a systematic arrangement or proposing a classification scheme for easy access to scientific researches seems necessary. Location science is a branch of optimization science, which formally introduce by Alfred Weber in 1909. It has been growing so rapidly for years that now without a systematic classification of models, continuing the procedure of researches would be so difficult. Therefore, several efforts in classifying location models have been made that, some of them will be mentioned in this section.


Nickel Nash 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bender T, Hennes H, Kalcsics J, Melo MT, Nickel S (2002) Location software and interface with GIS and supply chain management. In: Drezner Z, Hammacher H (eds) Facility location: applications and theory. Berlin, SpringerGoogle Scholar
  2. Brandeau ML (1992) Characterization of the stochastic median queue trajectory in a plane with generalized distances. Oper Res 40(2):331–341CrossRefGoogle Scholar
  3. Brandeau ML, Chiu SS (1989) An overview of representative problems in location research. Manage Scie 35:645–674CrossRefGoogle Scholar
  4. Burkard RE, Cela E, Dollani H (2000) 2-Medians in trees with pos/neg weights. Discrete Appl Math 105:51–71CrossRefGoogle Scholar
  5. Carrizosa EJ, Conde E, Munoz M, Puerto J (1995) The generalized weber problem with expected distances. RAIRO 29:35–57Google Scholar
  6. Colebrook M, Sicilia J (2007) A polynomial algorithm for the multicriteria cent-dian location problem. Eur J Oper Res 179:1008–1024CrossRefGoogle Scholar
  7. Daskin MS (1995) Network and discrete location: models, algorithms, and applications. Wiley Interscience, NYCrossRefGoogle Scholar
  8. Daskin MS (2002) SITATION-facility location software. Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL.
  9. Eiselt HA, Laporte G (1993) The existence of equilibria in the 3-facility Hotelling model in a tree. Transport Sci 27(1):39–43CrossRefGoogle Scholar
  10. Eiselt HA, Laporte G, Thisse JF (1993) Competitive location models: a framework and bibliography. Transport Sci 27:44–54CrossRefGoogle Scholar
  11. Eliosoff J, Unger R (1998). MEC – minimum enclosing circle applet.
  12. Erkut E, Tansel BC (1992) On parametric medians of trees. Transport Sci 26(2):149–156CrossRefGoogle Scholar
  13. Fliege J (2001) OLAF – a general modeling system to evaluate and optimize the location of an air polluting facility. OR Spektrum 23:117–136CrossRefGoogle Scholar
  14. Francis RL, White JA (1974) Facility layout and location: an analytical approach. Prentice-Hall, Englewood CliffsGoogle Scholar
  15. Gomes H, Ribeiro AB, Lobo V (2007) Location model for CCA-treated wood waste remediation units using GIS and clustering methods. Environ Model Softw 22:1788–1795CrossRefGoogle Scholar
  16. Graham RE, Lawler EL, Lenstra JK, Rinnoy Kan AHG (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discrete Math 4:287–326CrossRefGoogle Scholar
  17. Hamacher HW, Nickel S (1998) Classification of location models. Location Sci. 6:229–242CrossRefGoogle Scholar
  18. Hamacher HW, Klamroth K, Nickel S, Schoebel A (1996) Library of location algorithms. University of Kaiserslautern.
  19. Handler GY, Mirchandani PB (1979) Location on networks theory and algorithms. MIT Press, CambridgeGoogle Scholar
  20. Jia H, Ordonez F, Dessouky M (2007) A modeling framework for facility location of medical services for large-scale emergencies. IIE Trans 39:41–55CrossRefGoogle Scholar
  21. Kendall D (1951) Some problems in the theory of queues. J R Stat Soc 13:151–153Google Scholar
  22. Klamroth K (2004) Algebraic properties of location problems with one circular barrier. Eur J Oper Res 154:20–35CrossRefGoogle Scholar
  23. Klose A, Drexl A (2004) Facility location models for distribution system design. Eur J Oper Res 162:4–29CrossRefGoogle Scholar
  24. Mihelic J (2004) Jure Mihelic k-center algorithms. Department of Computer and Information Science, University of LjubljanaGoogle Scholar
  25. Mirchandani P, Kohli R, Tamir A (1996) Capacitated location problems on a line. Transport Sci 30(1):75–80CrossRefGoogle Scholar
  26. Nagy G, Salhi S (2007) Location-routing: issues, models and methods. Eur J Oper Res 177:649–672CrossRefGoogle Scholar
  27. Nickel S, Hamacher HW (1992) RLP: a program package for solving restricted 1-facility location problems in a user friendly environment. Eur J Oper Res 62:116–117CrossRefGoogle Scholar
  28. ReVelle CS, Eiselt HA, Daskin MS (2008) A bibliography for some fundamental problem categories in discrete location science. Eur J Oper Res 184:817–848CrossRefGoogle Scholar
  29. Sirigos S, Photis YN (2005) S-distance software. Department of Planning and Regional Development (DPRD), University of Thessaly, GreeceGoogle Scholar
  30. Tansel BC, Francis RL, Lowe TJ (1983) Location on networks: a survey. Part I: the p-center and p-median problems. Manage Sci 29:482–497Google Scholar
  31. Tiefelsdorf M, Boots B (1997) GAMBINI multiplicative weighted voronoi diagrams.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Industrial EngineeringAmirkabir University of TechnologyTehranIran

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