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Selection of subcontractors using ordinal ranking methods based on Condorcet approach
Sławomir Biruk
Katedra Inżynierii Procesów Budowlanych; Wydział Budownictwa i Architektury; Politechnika Lubelska (Polska)
Piotr Jaśkowski
Katedra Inżynierii Procesów Budowlanych; Wydział Budownictwa i Architektury; Politechnika Lubelska (Polska)
Abstrakt
A choice of a subcontractor may have critical impact on realization of the project, it has influence on the cost, duration, and quality. Selection of the best sucontractor can be defined as multiple criteria decision making problem (MCDM) of choosing a proper offer from set of alternatives evaluated by using set of criteria. Decision maker should determine the criteria as objective and measurable. Significance of decision making problem is presented by large amount of theories and methods developed for solving MCDM problems and number of criteria considered in these problems. A Condorcet method (formulated over two centuries ago) is commonly accepted for democratic (majority of criteria determines the winner) and fair election – a Condorcet winner is the alternative which is preferred in all pair-wise comparisons. According to social choice theory where a Condorcet winner cannot be obtained from a set of alternatives, the best solution is close to being a Condorcet winner. The paper presents four selection methods of the best alternative that is as close as possible to being a Condorcet winner and contains examples of a subcontractor selection using only ordinal scales of evaluation of alternatives.
Słowa kluczowe:
project management, subcontractors selection, social choice theory, the Condorcet winnerBibliografia
[1] Marzouk M.M., El Kherbawy A.A. and Khalifa M. Factors Influencing Sub-contractors Selection in Construction Projects. HBRC Journal 9 (2013) 150-158.
[2] Ng S.T., Skitmore M. and Chung,W.F. Ten Basic Factors to Indentify Suitable Subcontractors for Construction Projects. Proc. CIB TG 23 International Conference, Hong Kong, 2003.
[3] Taruna D., Rajiv Bhatt R. and Bhavsar J.J. Methodology for Ranking of Factors Affecting Selection of Subcontractor for Construction Contractors of Gujarat. International Journal of Engineering Trends and Technology (IJETT) 32(1) (2016) 19-25.
[4] Al-Harbi K.M.A.-S. Application of the AHP in Project Management. International Journal of Project Management 19(1) (2001) 19-27.
[5] Alptekin O. Multi-Criteria Decision Making Approach in Contractor Selection. International Journal of Natural and Engineering Sciences 8(2) (2014) 6-9.
[6] Anagnostopoulos K.P., Vavatsikos A.P. An AHP Model for Construction Contractor Prequalification. Operational Research. An International Journal 6(3) (2006) 333-346.
[7] Fong P.S.-W., Choi S.K.-Y. Final Contractor Selection Using the Analytical Hierarchy Process. Construction Management and Economics 18 (2000) 547-557.
[8] Saaty R.W. The Analytic Hierarchy Process – What It Is and How It Is Used. Mathematical Modelling 9(3-5) (1987) 161-176.
[9] Mohaghar A., Faqhei M.S. Contractor Selection Using Extended TOPSIS Technique with Interval – Valued Triangular Fuzzy Numbers. Global Business and Economics Research Journal 2(5) (2013) 55-65.
[10] Plebankiewicz E. A Fuzzy Sets Based Contractor Prequalification Procedure. Automation in Construction 22 (2012) 433-443.
[11] Arrow K.J., Raynaud H. Social Choice and Multicriterion Decision Making. M.I.T. Press, Cambridge, 1986.
[12] Slater P. Inconsistencies in a Schedule of Paired Comparisons. Biometrika 48 (1961) 303-312.
[13] Hudry O. On the Difficulty of Computing the Winners of a Tournament. Annales du LAMSADE n 6, actes du Workshop on Voting Theory and Preference Modelling, DIMACS (2006) 181-191.
[14] Charon I., Hudry O. A branch-and-Bound Algorithm to Solve the Linear Ordering Problem for Weighted Tournaments. Discrete Applied Mathematics 154 (2006) 2097-2116.
[15] Festa P., Pardalos P.M., Resende M.G.C. Feedback Set Problems, [in:] Handbook of Combinatorial Optimization (Eds. Du D.Z., Pardalos P.M.). Vol. A, Kluwer 2009, 209-258.
[16] Sukegawa N., Mizuno S. Redundancy of the Transitivity Constraints in the Ordering Problem. Department of Industrial Engineering and Management, Technical Report No. 2014-2, Tokyo Institute of Technology JAPAN, 2104.
[17] Young H.P. Extending Condorcet’s Rule. Journal of Economic Theory 16 (1997) 335-353.
[18] Betzler N., Guo J., Niedermeier R. Parameterized Computational Complexity of Dodgson and Young Elections. Information and Computation 208(2) (2010) 165-177.
[19] Caragiannis I., Cove, J.A., Feldma, M., Homan C.M., Kaklamanis C., Karanikolas N., Procaccia A.D., Rosenschein J.S. On the Approximability of Dodgson and Young Elections. Artificial Intelligence (187–188) (2012) 31-51.
[20] Bartholdi III J., Tovey C.A., Trick M.A. Voting Schemes for Which It Can Be Difficult to Tell Who Won the Election. Social Choice and Welfare 6(2) (1989) 157-165.
[21] Tideman T.N. Independence of Clones as a Criterion for Voting Rules. Social Choice and Welfare 4(3) (1987) 185– 206.
[22] Nurmi H. Settings of Consensual Processes: Candidates, Verdicts, Policies, [in:] Consensual Processes. Studies in Fuzziness and Soft Computing. (Eds. Herrera-Viedma E., García-Lapresta J.L., Kacprzyk J., Fedrizzi M., Nurmi H. and Zadrożny S.), Berlin Heidelberg, Springer, 2011, 159-178.
[23] Smith W.D. Descriptions of Single-Winner Voting Systems. temple.edu/wds/ homepage/votedesc.pdf, 2006.
[2] Ng S.T., Skitmore M. and Chung,W.F. Ten Basic Factors to Indentify Suitable Subcontractors for Construction Projects. Proc. CIB TG 23 International Conference, Hong Kong, 2003.
[3] Taruna D., Rajiv Bhatt R. and Bhavsar J.J. Methodology for Ranking of Factors Affecting Selection of Subcontractor for Construction Contractors of Gujarat. International Journal of Engineering Trends and Technology (IJETT) 32(1) (2016) 19-25.
[4] Al-Harbi K.M.A.-S. Application of the AHP in Project Management. International Journal of Project Management 19(1) (2001) 19-27.
[5] Alptekin O. Multi-Criteria Decision Making Approach in Contractor Selection. International Journal of Natural and Engineering Sciences 8(2) (2014) 6-9.
[6] Anagnostopoulos K.P., Vavatsikos A.P. An AHP Model for Construction Contractor Prequalification. Operational Research. An International Journal 6(3) (2006) 333-346.
[7] Fong P.S.-W., Choi S.K.-Y. Final Contractor Selection Using the Analytical Hierarchy Process. Construction Management and Economics 18 (2000) 547-557.
[8] Saaty R.W. The Analytic Hierarchy Process – What It Is and How It Is Used. Mathematical Modelling 9(3-5) (1987) 161-176.
[9] Mohaghar A., Faqhei M.S. Contractor Selection Using Extended TOPSIS Technique with Interval – Valued Triangular Fuzzy Numbers. Global Business and Economics Research Journal 2(5) (2013) 55-65.
[10] Plebankiewicz E. A Fuzzy Sets Based Contractor Prequalification Procedure. Automation in Construction 22 (2012) 433-443.
[11] Arrow K.J., Raynaud H. Social Choice and Multicriterion Decision Making. M.I.T. Press, Cambridge, 1986.
[12] Slater P. Inconsistencies in a Schedule of Paired Comparisons. Biometrika 48 (1961) 303-312.
[13] Hudry O. On the Difficulty of Computing the Winners of a Tournament. Annales du LAMSADE n 6, actes du Workshop on Voting Theory and Preference Modelling, DIMACS (2006) 181-191.
[14] Charon I., Hudry O. A branch-and-Bound Algorithm to Solve the Linear Ordering Problem for Weighted Tournaments. Discrete Applied Mathematics 154 (2006) 2097-2116.
[15] Festa P., Pardalos P.M., Resende M.G.C. Feedback Set Problems, [in:] Handbook of Combinatorial Optimization (Eds. Du D.Z., Pardalos P.M.). Vol. A, Kluwer 2009, 209-258.
[16] Sukegawa N., Mizuno S. Redundancy of the Transitivity Constraints in the Ordering Problem. Department of Industrial Engineering and Management, Technical Report No. 2014-2, Tokyo Institute of Technology JAPAN, 2104.
[17] Young H.P. Extending Condorcet’s Rule. Journal of Economic Theory 16 (1997) 335-353.
[18] Betzler N., Guo J., Niedermeier R. Parameterized Computational Complexity of Dodgson and Young Elections. Information and Computation 208(2) (2010) 165-177.
[19] Caragiannis I., Cove, J.A., Feldma, M., Homan C.M., Kaklamanis C., Karanikolas N., Procaccia A.D., Rosenschein J.S. On the Approximability of Dodgson and Young Elections. Artificial Intelligence (187–188) (2012) 31-51.
[20] Bartholdi III J., Tovey C.A., Trick M.A. Voting Schemes for Which It Can Be Difficult to Tell Who Won the Election. Social Choice and Welfare 6(2) (1989) 157-165.
[21] Tideman T.N. Independence of Clones as a Criterion for Voting Rules. Social Choice and Welfare 4(3) (1987) 185– 206.
[22] Nurmi H. Settings of Consensual Processes: Candidates, Verdicts, Policies, [in:] Consensual Processes. Studies in Fuzziness and Soft Computing. (Eds. Herrera-Viedma E., García-Lapresta J.L., Kacprzyk J., Fedrizzi M., Nurmi H. and Zadrożny S.), Berlin Heidelberg, Springer, 2011, 159-178.
[23] Smith W.D. Descriptions of Single-Winner Voting Systems. temple.edu/wds/ homepage/votedesc.pdf, 2006.
Biruk, S. i Jaśkowski, P. (2016) „Selection of subcontractors using ordinal ranking methods based on Condorcet approach”, Budownictwo i Architektura, 15(4), s. 033–040. doi: 10.24358/Bud-Arch_16_154_04.
Autorzy
Sławomir BirukKatedra Inżynierii Procesów Budowlanych; Wydział Budownictwa i Architektury; Politechnika Lubelska Polska
Autorzy
Piotr JaśkowskiKatedra Inżynierii Procesów Budowlanych; Wydział Budownictwa i Architektury; Politechnika Lubelska Polska
Statystyki
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