جایگاه نقاط بی تفاوتی در مسائل تصمیم گیری چند معیاره (مطالعه موردی ارزیابی تامین کنندگان شرکت آب و فاضلاب شهری استان زنجان)
الموضوعات :
Reza Radfar
1
,
ARSHAD FARAHMANDIAN
2
,
Mohammad Ali Afshar Kazemi
3
1 - Technology Management, Faculty of Management and Economy, Azad University of Science and Research, Tehran, Iran
2 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Associate Professor.,Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
تاريخ الإرسال : 23 الأحد , ربيع الثاني, 1440
تاريخ التأكيد : 28 الأحد , رمضان, 1440
تاريخ الإصدار : 20 الخميس , محرم, 1441
الکلمات المفتاحية:
الگوریتم‌های فراابتکاری,
ماتریس‌های هم ارز,
نرخ نهایی جانشینی,
منحنی‌های بی تفاوتی,
ملخص المقالة :
تصمیمات مربوط به فرایند ارزیابی و انتخاب تأمین کنندگان باید با بررسی همه گزینه‌های ممکن انجام گیرد، در غیر اینصورت در مرحله پیاده سازی و اجرا، سازمان با مشکلات عدیده‌ای روبرو خواهد شد. هدف مطالعه حاضر تعیین نقاط بی تفاوتی ارزیابی تأمین کنندگان شرکت آب و فاضلاب شهری استان زنجان می‌باشد.مطالعه حاضر از نوع توصیفی می‌باشد. داده‌های این مطالعه مربوط به ارزیابی تأمین کنندگان یکی از پروژه های شرکت آب و فاضلاب شهری استان زنجان می‌باشد. داده‌ها بر اساس نظرات 10 نفر از خبرگان که دارای حداقل مدرک کارشناسی و همچنین حداقل 5 سال سابقه کار در شرکت را داشتند بر اساس "فرم ارزیابی تأمین کنندگان " جمع آوری شد. داده‌ها با استفاده از نرم افزار متلب نسخه 2014 تحلیل شده اند.تعداد 10 مورد ماتریس هم ارز با ماتریس اولیه تصمیم گیری به تفکیک برای هر روش شناسایی و تولید شده است. روش TOPSIS-GA=2 ، TOPSIS- PSO = 3 ، AHP- GA= 2و روش AHP- PSO = 3 . ازبین ماتریس های هم ارز شناسایی شده ماتریس مربوط به خروجی دوم TOPSIS-GA براساس نظر خبرگان شرکت به عنوان گزینه مطلوب انتخاب به عنوان نسخه نهایی جهت تطبیق در اختیار پیمانکار مربوطه قرار گرفته شده است.
المصادر:
Alborzi, M. (2008) .Augmenting System Dynamics with Genetic Algorithm andTopsis Multivariate Ranking Module for Multi-Criteria Optimization. Proceedings ofthe International Conference of the System Dynamics Society.
Ali, R. (2017). Principleof Management. Tehran: Samat Publication.
Asgharpour, M. J. 2017. Multiple Criteria Decision Making, Tehran University
Chen, D., Zhou, Z. & Pham, D. (2008). Research onthe Grey Relational Evaluation Method of Core Competencies of Virtual Enterprise Members. Kybernetes, 37, 1250-1256.
Faghih, N., Montazeri, M. M. (2008), Genetic Algorithms for Assembly Line Balancing Problem. Journal of Industrial Management, 1(1), 107-124.
Hwang, Y., A. & Wang, B., S. (2016). A Matrix Approach to the Associated Consistency With Respect to the Equal Allocation of Non-Separable Costs. Operations Research Letters, 44, 826-830.
Intriligator, M. D. (1971). Mathematical Optimization and Economic Theory.Phi Learning; 1st edition.
Leili, M, M. S., Ekradi, E, Parvin, E., Fazeli, H. (2017). Studying the Relationship between Managers' Decision Making Styles withThe Level Of Creativity And Participative Management In Guidance Schools. InnovCreat Hum Sci Journal, 4, 19.
Mohamadi, A, M. S., Dostmohammadi, A., Khaleghi, A.(2015). Management Assessment and Selection of Logistics Providers atthe Social Security Hospital of Imam Hossein in Zanjan. International Conference on Management. Istanbul, Turkey.
Mulliner, E., Malys, N. & Maliene, V. (2016). Comparative Analysis ofMcdm Methods forthe Assessment Of Sustainable Housing Affordability. Omega, 59, 146-156.
Radfar, R, K. N. (2015). Identify And Ranking The Factors Affecting The Efficiency Of Using By Dematel. Productivity Management Journal, 35, 19.
Salati F, M. A. (2014). Offer The Value Function (Utility) To Prioritize Research Projects In R & D Centers Using The UtaMethod (Case of Water Resources Company In Iran). Industrial Management Studies, 31, 19-33.
Serrai, W., Abdelli, A., Mokdad, L. & Hammal, Y. (2017). Towards An Efficient And A More Accurate Web Service Selection Using Mcdm Methods. Journal of Computational Science, 22, 253-267.
Shahbazi, L. (2016). At The Same Time Optimize The Planning Problem-Labor-Service Equipment Using Particle Swarm Algorithm. Ms. Thesis,Islamic Azad University, Zanjan Branch.
Taboli, M.(2012). New Method For Solving Multi-Criteria Decision. Industrial Management Studies, 9, 20.
Yang, X.S., (2011). Metaheuristic Optimization. Scholarpedia, 6, 11472.
Yu, X., Zhang, S., Liao, X. & Qi, X. (2018). Electre Methods in Prioritized Mcdm Environment. Information Sciences, 424, 301-316.
Nath, P., Nachiappan, S., & Ramanathan, R. (2019). A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix. International Journal of Operational Research (IJOR), Vol. 34, No. 4,
Schiffels, S., Fliedner, T., & Kolisch, R. (2018). Human behavior in project portfolio selection: Insights from an experimental study. Decision Sciences, 49(6), 1061-1087.
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Alborzi, M. (2008) .Augmenting System Dynamics with Genetic Algorithm andTopsis Multivariate Ranking Module for Multi-Criteria Optimization. Proceedings ofthe International Conference of the System Dynamics Society.
Ali, R. (2017). Principleof Management. Tehran: Samat Publication.
Asgharpour, M. J. 2017. Multiple Criteria Decision Making, Tehran University
Chen, D., Zhou, Z. & Pham, D. (2008). Research onthe Grey Relational Evaluation Method of Core Competencies of Virtual Enterprise Members. Kybernetes, 37, 1250-1256.
Faghih, N., Montazeri, M. M. (2008), Genetic Algorithms for Assembly Line Balancing Problem. Journal of Industrial Management, 1(1), 107-124.
Hwang, Y., A. & Wang, B., S. (2016). A Matrix Approach to the Associated Consistency With Respect to the Equal Allocation of Non-Separable Costs. Operations Research Letters, 44, 826-830.
Intriligator, M. D. (1971). Mathematical Optimization and Economic Theory.Phi Learning; 1st edition.
Leili, M, M. S., Ekradi, E, Parvin, E., Fazeli, H. (2017). Studying the Relationship between Managers' Decision Making Styles withThe Level Of Creativity And Participative Management In Guidance Schools. InnovCreat Hum Sci Journal, 4, 19.
Mohamadi, A, M. S., Dostmohammadi, A., Khaleghi, A.(2015). Management Assessment and Selection of Logistics Providers atthe Social Security Hospital of Imam Hossein in Zanjan. International Conference on Management. Istanbul, Turkey.
Mulliner, E., Malys, N. & Maliene, V. (2016). Comparative Analysis ofMcdm Methods forthe Assessment Of Sustainable Housing Affordability. Omega, 59, 146-156.
Radfar, R, K. N. (2015). Identify And Ranking The Factors Affecting The Efficiency Of Using By Dematel. Productivity Management Journal, 35, 19.
Salati F, M. A. (2014). Offer The Value Function (Utility) To Prioritize Research Projects In R & D Centers Using The UtaMethod (Case of Water Resources Company In Iran). Industrial Management Studies, 31, 19-33.
Serrai, W., Abdelli, A., Mokdad, L. & Hammal, Y. (2017). Towards An Efficient And A More Accurate Web Service Selection Using Mcdm Methods. Journal of Computational Science, 22, 253-267.
Shahbazi, L. (2016). At The Same Time Optimize The Planning Problem-Labor-Service Equipment Using Particle Swarm Algorithm. Ms. Thesis,Islamic Azad University, Zanjan Branch.
Taboli, M.(2012). New Method For Solving Multi-Criteria Decision. Industrial Management Studies, 9, 20.
Yang, X.S., (2011). Metaheuristic Optimization. Scholarpedia, 6, 11472.
Yu, X., Zhang, S., Liao, X. & Qi, X. (2018). Electre Methods in Prioritized Mcdm Environment. Information Sciences, 424, 301-316.
Nath, P., Nachiappan, S., & Ramanathan, R. (2019). A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix. International Journal of Operational Research (IJOR), Vol. 34, No. 4,
Schiffels, S., Fliedner, T., & Kolisch, R. (2018). Human behavior in project portfolio selection: Insights from an experimental study. Decision Sciences, 49(6), 1061-1087.
