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Manuscript ID : 669129 Visit : 209 Page: 37 - 47

Article Type: Original Research

Indifferent Points in The Multicriteria Decision Making Problems (A Case Study of Suppliers’ Evaluation in Zanjan Province Gas Company)

Subject Areas : policy making

Arshad Farahmandian 1 , reza radfar 2 , mohammad ali afshar kazemi 3

1 - ph.D. Student in industrial Management,Faculty of management, Science and Research
2 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - 1. Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2018-02-11 Accepted : 2018-08-16 Published : 2018-08-01

Keywords: * Indifference Points, * Marginal Rate of Substitution, * Metaheuristic Algorithms, * Parallel Matrixes,

Abstract :

Evaluating and selecting the right contractors can increase the chances of success of a project and the organization. Considering the intense competition faced by organizations today, proper cost management to enhance profitability and customer satisfaction has attracted a lot of attention. The evaluation of contractors is usually a process thatis based on various criteria.By the end of it, theappropriate options are selected. Given the diversity in the criteria and among thedecision-making subjects, no singleway has been offered to suggest substitution between criteria.The desirability indifference on the curve ofconsumption of various goods (selection ofdecision-making options) are the same. This paper seeks to identify parallel matrices with the initial decision-making matrix of contractors that have the same results and desirability for decision-makers (indifference points). At first, the initial rating using the AHP and TOPSIS methods andthe particle swarm optimization (PSO) and genetic algorithm (GA)techniques, along withMATLAB software,was used to identify theparallel matrices. According to the obtained results, sixparallel matrixes with the initial decision-making matrix that had been prepared by experts fromthe company were produced.Out of them, the matrix related to The point of indifference is the fifth output5 AHP-PSO, based on the company experts' opinions was selected as the final version.

References:

References                                    

  1. ali hedari boyoki, t., &khademi zareh, h.(2016). development DEA method to clustering credit customers Banks. Modeling in Engineering.
    2.
  2. alvani, s.(1995). Decision-making and public policy.tehran: samt.
    3.
  3. amiri, m., rahimi, m., &tabli, h.(2013). New method for solving multi-criteria decision. Journal of Industrial Management Studies,45-65.
    4.
  4. asgharpour, m. j. (2003). multiple criteria decision making.tehran: tehran university.
    5.
  5. azar, a., mahdavi rad, a. r., &mosakhani, m. (2015). Design model combines data mining and multi-criteria decision (Case Study Database subsidies Statistical Center of Iran). Journal of Operational Research and Its Applications ( Applied Mathematics ) - Lahijan Azad University, -95-111.
    6.
  6. baradaran hoseni, m. (2011). Application of genetic algorithms in the Computer-aided process planning (CAPP) various industrial environments.tehran: islamic azad university.
    7.
  7. behnamiyan, j., &fatemei ghomi, s. t. (2011). Providing hybrid algorithm based on PSO and hyper-heuristic method for scheduling factories distributed virtual alliance. Research of Engineering in Manufacturing Systems, 1-11.
    8.
  8. blum, c., &roil, a.(2003). Metaheuristics in combinatorial optimization. Overview and conceptual comparison, 268-311.
    9.
  9. blum, c., &roil, a. (2008). Hybrid Metaheuristics.berlin: springer-verlag.145-160
    10.
  10. borenstein, y., &poli, r.(2006). structure and metaheuristics. 8th annual conference on genetic and evolutionary computation.washington.
    11.
  11. burke, K. E., &Kendall, G. (2005). Introductory Tutorials in Optimization and Decision Support Techniques.new york: springer.68-84
    12.
  12. Denpontin, M., Mascapla, H., &Spronk, J.(1998). A user oriented listing of MCDM. Revue Beige de Researche Operationelle 23, 3-11.
    13.
  13. ebrahimi, b., rahmani, m., &khakzarbfrobi, m. (2016). New models of data envelopment analysis to determine the most efficient units of decision-making with regard to inaccurate data. Research of Engineering in Manufacturing Systems,139-148.
    14.
  14. Ferland, A. J., lavoie, A., &Hertz, A.(1996). An Object-Oriented Methodology for Solving Assignment-Type Problems with Neighborhood Search Technique. operation research44(2), 347-359)
    15.
  15. ghasemeh, r., jamali, g. r., &karimi asl, e.(2016). Large scale analysis of supply chain approach in the cement industry through a combination of multiple criteria decision making techniques. Industrial Management Tehran University,813-836.
    16.
  16. Gonzalez, F. T.(2007). Handbook of Approximation Algorithms and Metaheuristics.Boca Raton: Chapman and Hall.
    17.
  17. Hendrix, T. M., &G-Toth, B.(2010). Goodness of optimization algorithms Introduction to Nonlinear and Global Optimization.new York: Springer.
    18.
  18. hoseni, z., &kazemi, m.(2016). Compare the results of direct extraction utility function and a linear-approximate estimates in solving multi-criteria decision-making models. Journal of Operational Research and Its Applications ( Applied Mathematics ) - Lahijan Azad University,15-28.
    19.
  19. Intriligator, M.(1998). Econometric Models,Techniques andApplications.NewJersey: Hall,Inc.
    20.
  20. james, A., &estoner, E.(2001). management.tehran: calturealre search publication.87-96
    21.
  21. khatami firoozabadi, a.(2010). Providing decision support system in regard to selection and evaluation of suppliers using UTA. Management development.
    22.
  22. Liu, J.(1999). The impact of neighbourhood size on the process of simulated annealing: Computational experiments on the flowshop scheduling problem. computers&Industrial Engineering37(1-2),285-288)).
    23.
  23. LV, P., Yuan, L., &Zhang, J.(2009). Cloud theory-based simulated annealing algorithm and application. Engineering Applications of Artificial Intelligence,22,742-749.
    24.
  24. mahmoodi tehrani, a., &kianpour, m.(2015). optimized geometric partial differential equations with random data. Journal of Operational Research and Its Applications ( Applied Mathematics ),37-59.
    25.
  25. Metaheuristics.(2011)www.metaheuristic.com/metaheuristic_optimization.php.
    26.
  26. mohamad moradi, a., &akhtar kavan, m. (2010). Multi-criteria decision analysis methodologies, models.armanshar.
    27.
  27. mohamadi ranjbarani, d., salimi fard, k., &yosefi, s.(2015). Check the performance of the most common optimization techniques, multi-criteria decision approach. Journal of Operational Research and Its Applications ( Applied Mathematics ) - Lahijan Azad University,65-83.
    28.
  28. momeni, m.(2011). new topics in operations research.tehran: bakhtar.45-61
    29.
  29. pasha, y., mostafaiei, h. r., khlaj, m., &khalaj, f.(2013). Calculate the Uncertainty Interval Based on Entropy and dempster shafer theory of evidence. International Journal of Industrial Engineering&Production Management,216-223.
    30.
  30. radfar, r.(2006). presented a dynamic model for organizational planning solutions based Network-based internal issues.tehran: Science and Research Branch islamic azad university.
    31.
  31. radfar, r., &kiyani, n.(2015). Identify and ranking the factors affecting the efficiency of using by DEMATEL. productivity journal,111-130.
    32.
  32. rezaiyan, a.(1993). perncipal management.tehran: samt.95-117
    33.
  33. salati, f., &makoei, a.(2014). Providing utility function prioritize research projects using research and development centers by UTA. Industrial Management Studies,19-31.
    34.
  34. shafiei nikabadi, m., farajpour, h., &eftekhari, h.(2016). applied combination hybrid approachFA,AHP,TOPSISfor the selection and ranking strategies convenient maintenance and repairs. Industrial Management Studies.
    35.
  35. shahbazi, L.(2016). At the same time optimize the planning problem-labor-service equipment by use particle swarm algorithm.zanjan: islamic azad university zanjan branch.
    36.
  36. Talibi, G. E.(2009). METAHEURISTICS: FROM DESIGN TO IMPLEMENTATION.147-165
    37.
  37. tavakoli moghadam, r., &eslami, s.(2007). Presents a new mathematical model for staffing schedule and solve it using genetic algorithms. Journal of Industrial Engineering,21-31-.
    38.
  38. tavakoli, a., &nafar, m.(1991). Mathematical Economics.Esfahan: Esfahan university.245-278
    39.
  39. Wang, X.(2010). Solving Six-Hump Camel Back Function Optimization Problem by Using Thermodynamics Evolutionary Algorithm.
    40.
  40. Weise, T.(2007). Global Optimization Algorithm: Theory and Application .124-135
    41.
  41. Xin, Y.(1991). Simulated Annealing with Extended Neighborhood. International Journal of Computer Mathematics,40(3-4), 169-189)).
    42.
  42. yaghini, m., &akhvan kazemzadeh, m. r.(2012). metaheuristic optimization algorithms.tehran: jahad amirkabir university.
    43.

 

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    2.

 

 

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