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Group Decision and Negotiation

, Volume 22, Issue 6, pp 1021–1050 | Cite as

TOPSIS Based Approach to Scoring Negotiating Offers in Negotiation Support Systems

  • Tomasz WachowiczEmail author
  • Paweł Błaszczyk
Open Access
Article

Abstract

In this paper we analyze the possibility of applying the technique for order preferences by similarity to ideal solution (TOPSIS) to building the scoring system for negotiating offers. TOPSIS is a multiple criteria decision making method that is based on measuring distances between alternatives under consideration and two bipolar reference alternatives, a positive and negative ideal. Thus the criteria used for the evaluation of alternatives should be described using strong scales. However, in the negotiation, the issues are very often described qualitatively, which results in ordinal or even nominal variables that must be taken into consideration in offers’ evaluation process. What is more, TOPSIS may be applied to solving the discrete decision problems while the negotiation space may be defined by the means of continuous variables too. In this paper we try to modify the TOPSIS algorithm to make it applicable to negotiation support and, moreover, discuss the following methodological issues: using TOPSIS for a negotiation problem with continuous negotiation space; selecting the distance measure for adequate representation of negotiator’s preferences and measuring distances for qualitative issues. Finally, we propose a simple additional mechanism that allows for building the TOPSIS-based scoring system for negotiating offers and does not involve negotiators in time consuming and tiresome preference elicitation process. This mechanism requires from negotiators to construct examples of offers that represent some categories of quality and then by using a goal programming approach it infers all the parameters required by the TOPSIS algorithm. We also show a simple prototype software tool that applies the TOPSIS modified algorithm and may be used in electronic negotiation support.

Keywords

Negotiation support Electronic negotiation Preference analysis Scoring system for negotiating offers Continuous negotiation space TOPSIS Distance measures Goal programming 

Notes

Acknowledgments

This research was partially supported by the grant of the Polish Ministry of Science and Higher Education (N N111 234936).

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

References

  1. Bellucci E, Zeleznikow J (2006) Developing negotiation decision support systems that support mediators: a case study of the Family_Winner system. J Artif Intell Law 13(2): 233–271CrossRefGoogle Scholar
  2. Chamodrakas I, Alexopoulou N, Martakos D (2009) Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS. Expert Syst Appl 36(4): 7409–7415CrossRefGoogle Scholar
  3. Chen CT (2000) Extension of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Set Syst 114: 1–9CrossRefGoogle Scholar
  4. Cox TF, Cox MAA (2001) Multidimentional scaling, 2nd edn. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  5. Gordon AD (1999) Classification, 2nd edn. Chapman & Hall/CRC, Boca RatonCrossRefGoogle Scholar
  6. Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27: 857–874CrossRefGoogle Scholar
  7. Hellwig Z (1968) Procedure of evaluating high level manpower data and typology of countries by means of the taxonomic method. Prz Stat (Stat Rev) 15(4): 308–327Google Scholar
  8. Hordijk L (1991) Use of the RAINS model in acid rain negotiation in Europe. Environ Sci Technol 25(4): 596–603CrossRefGoogle Scholar
  9. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New YorkCrossRefGoogle Scholar
  10. Jahanshahloo GR, Lotfi FH, Izadikhah M (2006) Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl Math Comput 181: 1544–1551CrossRefGoogle Scholar
  11. Jones DF, Mardle SJ (2004) A distance-metric methodology for the derivation of weights from a pairwise comparison matrix. J Oper Res Soc 55: 869–875CrossRefGoogle Scholar
  12. Kalai E, Smorodinsky M (1975) Other solutions to Nash’s bargaining problem. Econometrica 43(3): 513–518CrossRefGoogle Scholar
  13. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction in cluster analysis. Wiley, New YorkCrossRefGoogle Scholar
  14. Keeney RL, Raiffa H (1976) Decisions with multiple objectives. Wiley, New YorkGoogle Scholar
  15. Kersten GE, Lai H (2007) Negotiation support and E-negotiation systems. Group Decis Negot 16(6): 553–586CrossRefGoogle Scholar
  16. Kersten GE, Noronha SJ (1999) WWW-based negotiation support: design, implementation and use. Decis Support Syst 25(2): 135–154CrossRefGoogle Scholar
  17. Krohling RA, Campanharo VC (2011) Fuzzy TOPSIS for group decision making: a case study for accidents with oil spill in the sea. Expert Syst Appl 38(4): 4190–4197CrossRefGoogle Scholar
  18. Larichev O, Moshkovich H (1997) Verbal decision analysis for unstructured problems. Kluwer, BostonCrossRefGoogle Scholar
  19. Milani AS, Shanian A, El-Lahham C (2008) A decision-based approach for measuring human behavioral resistance to organizational change in strategic planning. Math Comput Model 48(11–12): 1765–1774CrossRefGoogle Scholar
  20. Milani AS, Shanian A, Madoliat R (2005) The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Struct Multidiscip Optim 29(4): 312–318CrossRefGoogle Scholar
  21. Mousseau V (1995) Eliciting information concerning the relative importance of criteria. In: Pardalos P, Siskos Y, Zopounidis C (eds) Advances in multicriteria analysis, nonconvex optimization and its applications. Kluwer, Dordrecht, pp 17–43Google Scholar
  22. Mousseau V, Slowinski R (1998) Inferring an ELECTRE TRI model from assignment examples. J Glob Optim 12(2): 157–174CrossRefGoogle Scholar
  23. Mustajoki J, Hamalainen RP (2000) Web-HIPRE: global decision support by value tree and AHP analysis. INFOR 38(3): 208–220Google Scholar
  24. Nash J (1950) The bargaining problem. Econometrica 18: 155–162CrossRefGoogle Scholar
  25. Paradis N, Gettinger J, Lai H, Surboeck M, Wachowicz T (2010) E-negotiations via Inspire 2.0: the system, users, management and projects. In: de Vreede GJ (ed) Group decision and negotiations 2010. Proceedings. The Center for Collaboration Science, University of Nebraska at Omaha, pp 155–159Google Scholar
  26. Pawełek B (2008) Analyzing the sensitivity of Jeffreys-Matusit and Canberra measures for small changes of variables’ values (text in Polish). Krak Univ Econ Res Pap 797: 143–159Google Scholar
  27. Raiffa H (1953) Arbitration schemes for generalized two-person games. In: Kuhn HW, Tucker AW (eds) Contributions to the theory of games, vol II. Princeton University Press, Princeton, pp 361–388Google Scholar
  28. Raiffa H (1982) The art and science of negotiation. Harvard University Press, CambridgeGoogle Scholar
  29. Raiffa H, Richardson J, Metcalfe D (2002) Negotiation analysis. The science and art of collaborative decision making. The Balknap Press of Harvard University Press, Cambridge, MAGoogle Scholar
  30. Schoop M, Jertila A, List T (2003) Negoisst: a negotiation support system for electronic business-to business negotiations in ecommerce. Data Knowl Eng 47: 371–401CrossRefGoogle Scholar
  31. Sebenius JK (1992) Negotiation analysis: a characterization and review. Manag Sci 38(1): 18–38CrossRefGoogle Scholar
  32. Shih HS, Shyur HJ, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Model 45: 801–813CrossRefGoogle Scholar
  33. Simons T, Tripp TM (2003) The negotiation checklist. In: Lewiski RJ, Saunders DM, Minton JW, Barry B (eds) Negotiation. Reading, excersises and cases. 4th edn. McGraw-Hill/Irwin, New YorkGoogle Scholar
  34. Thiessen EM, Shakun MF (2009) First nation negotiations in Canada: action research using SmartSettle. In: Kilgour DM, Wang Q (eds) Proceedings of GDN 2009: An International Conference on Group Decision and Negotiation, Wilfried Laurier University, TorontoGoogle Scholar
  35. Thiessen EM, Soberg A (2003) Smartsettle described with the montreal taxonomy. Group Decis Negot 12(2): 165–170CrossRefGoogle Scholar
  36. Von Neumann J, Morgenstern O (1944) Theory of games and economic behavior. Princeton University Press, PrincetonGoogle Scholar
  37. Wachowicz T, Kersten GE (2009) Decisions and manners of electronic negotiation system users. In: Kłosiński KA, Biela A (eds) Proceedings of an international scientific conference “A Man And His Decisions”. John Paul II Catholic University of Lublin Press, Lublin, pp 63–74Google Scholar
  38. Wachowicz O (2010) Decision support in software supported negotiations. J Bus Econ Manag 11(4): 576–597CrossRefGoogle Scholar
  39. Wachowicz T (2011) Application of TOPSIS methodology to the scoring of negotiation issues measured on the ordinal scale. In: Trzaskalik T, Wachowicz T (eds) Multiple criteria decision making’10/11. Publisher of The University of Economics in Katowice, Katowice, pp 238–258Google Scholar
  40. Zartman WI (1989) Prenegotiation: phases and functions. Int J 44(2): 237–253CrossRefGoogle Scholar
  41. Zavadskas EK, Vilutiene T, Turskis Z, Tamosaitiene J (2010) Contractor selection for construction works by applying Saw-G and TOPSIS grey techniques. J Bus Econ Manage 11(1): 34–55CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Operation ResearchUniversity of Economics in KatowiceKatowicePoland
  2. 2.Institute of MathematicsUniversity of SilesiaKatowicePoland

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