A Multicriteria Decision Making Model for Outsourcing Heavy Transportation Activities: (Case Study: Iran’s Oil, Gas, and Petrochemical Industries
Subject Areas :
Industrial Management
Kiarash Vazirizadeh
1
(
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
)
Hamidreza Izadbakhsh
2
(
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
)
Hamed Davari-Ardakani
3
(
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
)
Received: 2016-05-01
Accepted : 2017-10-17
Published : 2017-12-24
Keywords:
Abstract :
Heavy transportation plays a leading role in the construction and development of infrastructure of industries. Consequently, transportation of heavy, expensive equipment has drawn the attention of many organizations around the world. This paper proposes a multicriteria decision-making model to assess the performance of firms dealing with the logistics and transportation of heavy equipment. First, a number of evaluation criteria were determined by reviewing research works in this area, interviewing experts and using the Delphi method. Afterward, a panel of experts compared the evaluation criteria, and the Analytic Hierarchy Process (AHP) was used to determine the relative importance of the evaluation criteria. Then, Fuzzy Technique of Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) was utilized to assess the performance of firms active in the area of heavy equipment transportation. Finally, the assessment procedure was conducted by ELimination Et Choice Translating Reality (ELECTRE). A case study of firms active in the heavy transportation within oil, gas and petrochemical industries in Iran was used to implement the proposed multicriteria framework. Results of both Fuzzy TOPSIS and ELECTRE methods showed that the A7 organization is the leading one in the transportation of heavy industrial equipment. In addition, both these methods provided a similar ranking of other organizations active in the transportation of heavy industrial equipment.
References:
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Adler, M., & Ziglio, E. (1996). Gazing into the Oracle: the Delphi method and its application to social policy and public health (1st ed.). London: Jessica Kingsley Publishers.
Azar. A., & Memariani, A. (1995). A novel technique for group decision making. Journal of Management Knowledge, 27-28, 22-32.
Bagherinejad, J., & Amal Nik, M. S. (2012). A model to select the third party logistic company in Iran. Supply Chain Management, 14, 36. 4-19.
Efendigil, T., Onut, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54, 269-287.
Ferizani Farsangi, H. (2012). Selection of the logistic transport company using AHP and Fuzzy TOPSIS. The 3rd National Conference on Industrial and Systems Engineering, Islamic Azad University: South Tehran Branch, Tehran, Iran.
Gürcan, Ö. F., Yazıcı, I., Beyca, Ö. F., Arslan, Ç. Y., & Eldemir, G. (2016). Third Party Logistics (3PL) Provider Selection with AHP Application. Procedia - Social and Behavioral Sciences, 235, 226-234.
Lin, Y. T., Lin, C. L., Yu, H. C., & Tzeng, G. (2010). A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan. Expert Systems with Applications, 37, 4796-4804.
Liu, H. T., & Wang, W. K. (2009). An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert Systems with Applications, 36, 4387-4398.
Louw, W. J. A., Kok, M. C., & Sanderson, C. (2006). Contractor selection: A quantitative, consensus friendly, transparent and objective method. Southern African Forestry Journal, 206, 42-35
Mahdi, I. M., Riley, M. J., Fereig, S. M., & Alex, A. P. (2002). A multi-criteria approach to contractor selection. Engineering. Construction and Architectural Management, 9, 29-37.
Rastegar, S. (2015). Third party supplier selection in reverse logistics using data envelopment analysis (MSc thesis). University of Kurdistan, Kurdistan, Iran.
Rouhbakhsh Meyari Dovom, A., Mashadi Farahani, M. A., & Kazemi, M. (2015). Assessment and ranking the most appropriate criteria to select the logistic service supplier using QFD and Fuzzy AHP. Journal of Operational Research and Its Applications, 12, 61-78.
Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15, 353-375.
Separi, Z., Karbasian, M., Sajadi, S. M., & Shirouyehzad, H. (2012). A supplier selection method in green supply chain using VIKOR method. The 1st International Conference on Industrial and Systems Engineering, Islamic Azad University: Najafabad Branch, Isfahan, Iran.
Shojaie, A. A., Soltani, A. R., & Soltani, M. R. (2016). A fuzzy integrated approach for evaluating third-party logistics. International Journal of Modeling and Optimization, 6, 206-210.
Singh, D., & Tiong, R. L. K. (2006). Contractor selection criteria: Investigation of opinions of Singapore construction practitioners. Journal of Construction Engineering and Management, 132, 998-1008.
Soh, S. (2010). A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process. African Journal of Business Management, 4, 339-349.
Topcu, Y. I. (2004). A decision model proposal for construction contractor selection in Turkey. Building and Environment, 39, 469-481.
Vahdani, B., Behzadi, S., & Mousavi, S. M. (2015). An artificial intelligence model based on LS-SVM for third-party logistics provider selection. International Journal of Industrial Mathematics, 7, 301-311
Yan, W., He J. L., &·He, J. L. (2016). The Evaluation and Selection of Third-Party Logistics Service Vendor. International Journal of Hybrid Information Technology, 9, 273-284.
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Adalı, E. A., & Işık, A. T. (2016). Integration of DEMATEL, ANP and DEA methods for third party logistics providers’ selection. Management Science Letters, 6, 325-340.
Adler, M., & Ziglio, E. (1996). Gazing into the Oracle: the Delphi method and its application to social policy and public health (1st ed.). London: Jessica Kingsley Publishers.
Azar. A., & Memariani, A. (1995). A novel technique for group decision making. Journal of Management Knowledge, 27-28, 22-32.
Bagherinejad, J., & Amal Nik, M. S. (2012). A model to select the third party logistic company in Iran. Supply Chain Management, 14, 36. 4-19.
Efendigil, T., Onut, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54, 269-287.
Ferizani Farsangi, H. (2012). Selection of the logistic transport company using AHP and Fuzzy TOPSIS. The 3rd National Conference on Industrial and Systems Engineering, Islamic Azad University: South Tehran Branch, Tehran, Iran.
Gürcan, Ö. F., Yazıcı, I., Beyca, Ö. F., Arslan, Ç. Y., & Eldemir, G. (2016). Third Party Logistics (3PL) Provider Selection with AHP Application. Procedia - Social and Behavioral Sciences, 235, 226-234.
Lin, Y. T., Lin, C. L., Yu, H. C., & Tzeng, G. (2010). A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan. Expert Systems with Applications, 37, 4796-4804.
Liu, H. T., & Wang, W. K. (2009). An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert Systems with Applications, 36, 4387-4398.
Louw, W. J. A., Kok, M. C., & Sanderson, C. (2006). Contractor selection: A quantitative, consensus friendly, transparent and objective method. Southern African Forestry Journal, 206, 42-35
Mahdi, I. M., Riley, M. J., Fereig, S. M., & Alex, A. P. (2002). A multi-criteria approach to contractor selection. Engineering. Construction and Architectural Management, 9, 29-37.
Rastegar, S. (2015). Third party supplier selection in reverse logistics using data envelopment analysis (MSc thesis). University of Kurdistan, Kurdistan, Iran.
Rouhbakhsh Meyari Dovom, A., Mashadi Farahani, M. A., & Kazemi, M. (2015). Assessment and ranking the most appropriate criteria to select the logistic service supplier using QFD and Fuzzy AHP. Journal of Operational Research and Its Applications, 12, 61-78.
Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15, 353-375.
Separi, Z., Karbasian, M., Sajadi, S. M., & Shirouyehzad, H. (2012). A supplier selection method in green supply chain using VIKOR method. The 1st International Conference on Industrial and Systems Engineering, Islamic Azad University: Najafabad Branch, Isfahan, Iran.
Shojaie, A. A., Soltani, A. R., & Soltani, M. R. (2016). A fuzzy integrated approach for evaluating third-party logistics. International Journal of Modeling and Optimization, 6, 206-210.
Singh, D., & Tiong, R. L. K. (2006). Contractor selection criteria: Investigation of opinions of Singapore construction practitioners. Journal of Construction Engineering and Management, 132, 998-1008.
Soh, S. (2010). A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process. African Journal of Business Management, 4, 339-349.
Topcu, Y. I. (2004). A decision model proposal for construction contractor selection in Turkey. Building and Environment, 39, 469-481.
Vahdani, B., Behzadi, S., & Mousavi, S. M. (2015). An artificial intelligence model based on LS-SVM for third-party logistics provider selection. International Journal of Industrial Mathematics, 7, 301-311
Yan, W., He J. L., &·He, J. L. (2016). The Evaluation and Selection of Third-Party Logistics Service Vendor. International Journal of Hybrid Information Technology, 9, 273-284.
