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  • شناسایی و رتبه بندی بنگاه های اقتصادی حمل و نقلی بر اساس شاخص های ارزیابی عملکرد ‌با استفاده از روش مولتی مورا

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آدرس مقاله


کد مقاله : IMJ-1901-1180 (R1) بازدید : 92 صفحه: 56 - 71

نوع مقاله: پژوهشی

شناسایی و رتبه بندی بنگاه های اقتصادی حمل و نقلی بر اساس شاخص های ارزیابی عملکرد ‌با استفاده از روش مولتی مورا

محورهای موضوعی : مدیریت صنعتی

gholam reza einy sarkalleh 1 (exper)
hosien MAHMODY 2 (مدیر کل)
Hossien Afzali 3 (Researcher at the institute for Trade Student and Research (ITSR))
MOSTEFA REZVANY DOST 4 (STUDENT)
hafez ashkan 5 (هیئت علمی)

تاریخ دریافت : 1397/10/19 تاریخ پذیرش : 1397/11/06 تاریخ انتشار : 1397/11/01

کلید واژه: تصمیم گیری چند معیاره, حمل و نقل, بنگاههای اقتصادی, تصمیم گیری گروهی,

چکیده مقاله :

امروزه صنعت حمل و نقل در کشور پیشرفت روز افزون داشته و در عین حال هزینه های عملیاتی زیادی را به خود اختصاص داده است. شناسایی و ارزیابی عملکرد بنگاههای اقتصادی و در نهایت بازخور و تحلیل شاخص های ارزیابی یکی از موضوعات اساسی در صنعت حمل و نقل می باشد. در این تحقیق با استفاده از تکنیک های تصمیم گیری چند معیاره و روش خبرگی (دلفی) به شناسایی شاخص های تاثیر گذار در عملکرد یک بنگاه اقتصادی حمل و نقلی پرداخته و سپس با استفاده از روش تصمیم گیری مولتی مورا که یکی از روش های نوین در تصمیم گیری چند معیاره می باشد و با در نظر گرفتن معیارهای تعیین شده، اولویت بندی و رتبه بندی بین شاخص های عملکردی پیشنهادی صورت می پذیرد. مقاله حاضر علاوه بر شناسایی شاخص های تاثیرگذار در ارزیابی عملکرد بنگاه های اقتصادی حمل و نقلی به مطالعه موردی در خصوص پنج شرکت حمل و نقلی و رتبه بندی آن، به تشریح گام های روش مولتی مورا و نیز پرداخته شده است.

چکیده انگلیسی:

Today, the transportation industry is growing in the country, and at the same time it has increased operational costs. Identifying and assessing the performance of economics enterprises and, finally, feedback and analysis of evaluation indicators is one of the main issues in the transportation industry In this research, multi-criteria decision-making techniques and Delphi methodology have been used to identify the indicators that affect the performance of a transport business, then the MULTIMOORA decision making method has been applied which is one of the novel methods of decision making There are several criteria and according to the established criteria, prioritization and ranking among the proposed performance indicators is performed. The present paper, in addition to identifying the indicators of the effect of Godard on the evaluation of the performance of the transport enterprises, tends to describe the steps of the MULTIMOORA method in brief in its methodology. Keywords: Transportation, Enterprise economics, Multi-criteria Decision Making, Group Decision Making, Group Decision Making, MultiMoora Method

منابع و مأخذ:


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  5. Einy Sarkalleh, Gholam Reza, Afzali, Hossien, Khademy Nejad, Mojtba, Miandoabchi, Elnaz. (2017). Proposing a New Genetic Algorithm Multi-capacity to Solve the Multi-Storage Routing problem with Multi-capacity Vehicles. Journal of Industrial Management, 12(42), 87-98.
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  6. Evaluating Significance of Green Manufacturing Enablers Using MOORA Method for Indian Manufacturing Sector, 303-314.
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  12. Hafezalkotob, A., Hafezalkotob, A. (2015).  Extended MULTIMOORA method based on Shannon entropy weight for materials selection, Journal of Ind. Eng. Int., 12(1), 1–13.
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  13. Karimzadeh, R. (2008). Selection of favorable projects in transport companies using Bernardo's decision-making method. Journal of Transportation, 4(4), 329-338.
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  14. Leonidovna Zaytseva, A., Anatol'yevna Menukhova, T. (2016). On the Issue of the Innovation Policy at the Road Transport Enterprises, 11(4), 2206-2211.
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  15. Rajak, S., Parthiban, P., Dhanalakshmi, R. (2016). Sustainable transportation systems performance evaluation using fuzzy logic, 503-513.
    16.
  16. Skrypnikov, A., Dorokhin, S., Kozlov, V. G., Chernyshova, E. V. (2017).  Mathematical Model of Statistical Identification of Car Transport Informational Provision, 12, (2).
    17.
  17. Sun, J., Yuan, Y., Yang, R., Ji, X., Wu, J. (2018). Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis, 75-86.
    18.
  18. Thokala, P., Devlin, N., Marsh, K., Stuart Peacock, S. (2016). Multiple Criteria Decision Analysis for Health Care Decision Making an Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force, 1–13.
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  19. Wang, J., Wu, J., Wang, J., Zhang, H., Chen, X. (2015). TOPSIS method for multi-attribute group decision-making, under single-valued neutrosophic environment.
    20.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

_||_

  1. Arzu Akyuz, G., Erman Erkan, T., Mahallesi, K. (2010). Supply chain performance measurement: a literature review, 5137–5155.
    2.
  2. Bergquist,  K., Fink, C., Raffo, J. (2017). Identifying and ranking the world’s largest clusters of inventive activity, No. 34.
    3.
  3. Biswas, P., Pramanik, S., Giri, B. (2015). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment.
    4.
  4. Carvalho, L., Meier, S., W. Wang, S. (2016). Poverty and Economic Decision-Making: Evidence from Changes in Financial Resources at Payday, 260-84.
    5.
  5. Einy Sarkalleh, Gholam Reza, Afzali, Hossien, Khademy Nejad, Mojtba, Miandoabchi, Elnaz. (2017). Proposing a New Genetic Algorithm Multi-capacity to Solve the Multi-Storage Routing problem with Multi-capacity Vehicles. Journal of Industrial Management, 12(42), 87-98.
    6.
  6. Evaluating Significance of Green Manufacturing Enablers Using MOORA Method for Indian Manufacturing Sector, 303-314.
    7.
  7. FHWA/FTA. (1993). U.S. Department of Transportation. Metropolitan Planning Process: Major Metropolitan Transportation Investments, Federal Register, Part II,
    8.
  8. Guerra, E., de Lara, A., Malizia, Díaz, P. (2009). MULTICRITERIA DECISION MAKING: Advances in MCDM Models, Algorithms, Theory, and Applications, 51(4).
    9.
  9. Gunasekaran, A.,  Kobu, B. (2007).  Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications.
    10.
  10. Gunasekaran, A., Patel, C., Tirtiroglu, E. (2016). Performance measures and metrics in a supply chain environment, 71 - 87.
    11.
  11. Hafezalkotob, A. Hafezalkotob, A. (2015). Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications, Mater. Dec., 87, 949–959.
    12.
  12. Hafezalkotob, A., Hafezalkotob, A. (2015).  Extended MULTIMOORA method based on Shannon entropy weight for materials selection, Journal of Ind. Eng. Int., 12(1), 1–13.
    13.
  13. Karimzadeh, R. (2008). Selection of favorable projects in transport companies using Bernardo's decision-making method. Journal of Transportation, 4(4), 329-338.
    14.
  14. Leonidovna Zaytseva, A., Anatol'yevna Menukhova, T. (2016). On the Issue of the Innovation Policy at the Road Transport Enterprises, 11(4), 2206-2211.
    15.
  15. Rajak, S., Parthiban, P., Dhanalakshmi, R. (2016). Sustainable transportation systems performance evaluation using fuzzy logic, 503-513.
    16.
  16. Skrypnikov, A., Dorokhin, S., Kozlov, V. G., Chernyshova, E. V. (2017).  Mathematical Model of Statistical Identification of Car Transport Informational Provision, 12, (2).
    17.
  17. Sun, J., Yuan, Y., Yang, R., Ji, X., Wu, J. (2018). Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis, 75-86.
    18.
  18. Thokala, P., Devlin, N., Marsh, K., Stuart Peacock, S. (2016). Multiple Criteria Decision Analysis for Health Care Decision Making an Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force, 1–13.
    19.
  19. Wang, J., Wu, J., Wang, J., Zhang, H., Chen, X. (2015). TOPSIS method for multi-attribute group decision-making, under single-valued neutrosophic environment.
    20.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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