رتبه‏بندی شرکت‏های حمل‏ونقل بین‏المللی ریلی ایران با استفاده از مدل تصمیم‏گیری چندمعیاره پویا و سیستم استنتاج فازی

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران

2 استاد، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران

3 استادیار، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران

چکیده

هدف: حمل بار از طریق ریل با توجه به مزایایی همچون آلودگی کمتر در مقایسه با حمل جاده‌ای، صرف زمان کمتر در قیاس با حمل دریایی و هزینه کمتر نسبت به حمل هوایی، از جمله موضوعاتی است که در حوزه حمل‏ونقل اهمیت دارد. از همین رو شرکت‏های حمل‏ونقلی، بیش از پیش به فعالیت در زمینه حمل بار ریلی و به‌ویژه، حمل بار بین‌المللی ریلی تمایل نشان می‏دهند. از جمله چالش‌های شرکت راه‏آهن جمهوری اسلامی ایران، در حوزه حمل‏ونقل بین‌المللی، رتبه‏بندی این شرکت‏هاست که در تصمیم‌های گوناگونی همچون اعطای تخفیفات و تسهیلات، واگذاری واگن‌های خارجی، اولویت‌دهی به تأمین خواسته‌ها و رسیدگی به شکایات و موضوعاتی از این دست تأثیرگذار است. به همین منظور، در این مقاله مدل تصمیم‏گیری چندمعیاره پویا، برای رتبه‏بندی شرکت‏های حمل‏ونقل بین‌المللی ریلی ایران پیاده‏سازی شده است.
روش: با توجه به ماهیت پویای مسئله، مدل تصمیم‏گیری چندمعیاره پویا، بر اساس داده‏های واقعی حمل بار بین‌المللی، شامل صادرات، واردات و ترانزیت، توسط شرکت‏های حمل‏ونقل بین‌المللی ریلی، در پنج سال اجرا شد و به‌منظور برآورد داده‏های عملکردی شرکت‏ها در آینده، از سیستم استنتاج فازی بهره گرفته شد.
یافته‌ها: با لحاظ‌کردن عدم قطعیت از دو جهت که یکی تأثیرپذیری فعالیت شرکت‏ها در قالب یک سیستم استنتاج فازی و دیگری، پویایی مدل تصمیم‏گیری چندمعیاره پویا بود، اولویت 15 شرکت حمل‏ونقل بین‌المللی ریلی، برای شرکت راه‏آهن جمهوری اسلامی ایران مشخص شد.
نتیجه‌گیری: نتایج کلی بیانگر آن است که مواجهه با موضوع رتبه‏بندی شرکت‏های حمل‏ونقل بین‌المللی ریلی به‌صورت پویا، خروجی مطلوب‌تری را ارائه می‏دهد. همچنین کاربردی‌بودن روش ارائه‌شده برای بهره‌مندی از سیستم استنتاج فازی در مدل تصمیم‏گیری چندمعیاره پویا، از دیگر نتایجی است که از این پژوهش حاصل شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Ranking Iran's International Rail Transportation Companies Using Dynamic Multi-criteria Decision-making and Fuzzy Inference System

نویسندگان [English]

  • Mahdis Nejatnia 1
  • Ahmad Makui 2
  • Armin Jabbarzadeh 3
1 Ph.D. Candidate, Department of Industrial Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Prof., Department of Industrial Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3 Assistant Prof., Department of Industrial Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده [English]

Objective
Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies.
 
Methods
Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data.
 
Results
Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results.
 
Conclusion
This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.
Objective
Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies.
 
Methods
Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data.
 
Results
Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results.
 
Conclusion
This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.

کلیدواژه‌ها [English]

  • Dynamic multi-criteria decision-making
  • Fuzzy inference system
  • International rail transport companies in Iran

مراجع

منابع
ابوالقاسمی، مریم؛ قوسی، روزبه؛ یاریان، روناک و محمودی، کوروش (1396). رتبه‏بندی سالن‌های تولیدی گروه خودروسازی سایپا بر اساس معیارهای آلودگی صدا و با رویکرد داده‌کاوی. ششمین همایش ملی مدیریت آلودگی هوا و صدا، تهران.
انواری رستمی، علی اصغر؛ حسینیان، شهامت و رضایی اصل، مرتضی (1391). رتبه‏بندی مالی شرکت‏های بورس اوراق بهادار تهران با استفاده از روش‏های تصمیم‏گیری چندشاخصه و مدل‏های ترکیبی. تحقیقات مالی، 14(1)، 31-54.
پناهنده خوجین، غلامرضا؛ طلوعی اشلقی، عباس و افشار کاظمی، محمدعلی (1400). ارائه مدل تحلیل پوششی داده‏ها بر پایه برنامه‏ریزی آرمانی و محدودیت وزنی برای ارزیابی کارایی و رتبه‏بندی واحدهای تصمیم‏گیرنده در بانک قوامین. مدیریت صنعتی، 13(1)، 155-169.
خانمحمدی، معصومه و حیدری، حسین (1397). بررسی کارایی و رتبه‏بندی شرکت‏های سیمانی فعال در بورس با استفاده از روش بهبودیافته تلفیق تحلیل پوششی داده‏ها و فرایند تحلیل سلسله مراتبی. فصلنامه تصمیم‏گیری و تحقیق در عملیات، 3(2)، 25-48.
خواجوی، شکراله؛ سیدعلیخانی، امیر و غیوری مقدم، علی (1401). بهره‏گیری  از سیستم استنتاج فازی در رویکرد پویایی‌شناسی سیستم به‌منظور الگوسازیِ کسب‌وکارِ شرکت‏های پخش در ایران. مدیریت صنعتی، 14(2)، 250-266.
رادسر، مصطفی؛ کاظمی، عالیه؛ مهرگان، محمدرضا و رضوی حاجی‌آقا، سیدحسین (1400). طراحی یک الگوریتم بر پایه تحلیل پوششی داده‏های شبکه‌ای با شاخص‏های خوب و بد به‏منظور ارزیابی صنعت برق ایران. مدیریت صنعتی، 13(1)، 1-26.
سراییان ورنوسفادرانی، سهیلا و شاطالبی حسین آبادی، بدری (1395). دسته‌بندی و رتبه‏بندی شعب بیمه پارسیان استان اصفهان با استفاده از رویکرد تلفیقی تحلیل پوششی داده‏ها و داده‌کاوی. کنفرانس علمی مدیریت، حسابداری، اقتصاد و بیمه، زنجان.
صادقی، محمد صادق؛ جباری، حسین و شفیعی، مرتضی (1391). بررسی کاربرد فناوری داده‌کاوی در رتبه‏بندی شرکت‏های برتر بورس اوراق بهادار تهران. کنفرانس ملی حسابداری، مدیریت مالی و سرمایه‌گذاری، گرگان.
گریوانی، فاطمه؛ درخشانی، مجید؛ نوری، مصطفی و احمدی‌شادمهری، محمدطاهر (1396). رتبه‏بندی شرکت‏های بیمه استان خراسان شمالی به روش TOPSIS. اقتصاد پولی، مالی، 13(1)، 69-87.
نعمتی، زهرا؛ مهرگان، محمدرضا و حسین‌زاده، مهناز (1400). توسعه تئوری چشم‌انداز با نقاط مرجع چندگانه در تصمیم‏گیری. مدیریت صنعتی، 13(4)، 580-605.
 
References
Abolghasemi, M., Ghosi, R., Yaryan, R. & Mahmoudi, K. (2018). Ranking production halls of Saipa Automotive Group based on noise pollution criteria using data mining approach. The Sixth National Conference on Air and Noise Pollution Management, Tehran.
(in Persian)
Anandarao, S., Durai, S. R. S. & Goyari, P. (2019). Efficiency decomposition in two-stage data envelopment analysis: an application to life insurance companies in India. Journal of Quantitative Economics, 17, 271-285.
Anvary Rostamy, A. A., Hoseinian, S. & Rezaei Asl, M. (2012). Financial Ranking of Firms Listed in Tehran Stock Exchange Corporations Using MADM and Mixed Methods. Financial Research Journal, 14(1), 31-54. (in Persian)
Benitez, P., Rocha, E., Varum, H. & Rodrigues, F. (2020). A dynamic multi-criteria decision-making model for the maintenance planning of reinforced concrete structures. Journal of Building Engineering, 27, 100971.
Campanella, G. & Ribeiro, R. A. (2011). A framework for dynamic multiple-criteria decision making. Decision Support Systems, 52(1), 52-60.
Chen, F. H. & Tzeng, G. H. (2015). Probing organization performance using a new hybrid dynamic MCDM method based on the balanced scorecard approach. Journal of Testing and Evaluation, 43(4), 924-937.
Duc, D. A., Van, L. H., Yu, V. F., Chou, S. Y., Hien, N. V., Chi, N. T., ... & Dat, L. Q. (2021). A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation. Plos one, 16(1), e0245187.
Ercan, M. & Onder, E. (2016). Ranking insurance companies in Turkey based on their financial performance indicators using VIKOR method. International Journal of Academic Research in Accounting, Finance and Management Sciences, 6(2), 104-113.
Fan, J. P., Zhang, H. & Wu, M. Q. (2022). Dynamic Multi-Attribute Decision-Making Based on Interval-Valued Picture Fuzzy Geometric Heronian Mean Operators. IEEE Access, 10, 12070-12083.
Geng, R., Bose, I. & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247.
Gerivani, F., Ferakhshani, M., Noori, M. & Ahmadishadmehri, M. (2017). Ranking of the insurance companies of North Khorasan Province TOPSIS method. Monetary & Financial Economics, 24(14), 69-87. (in Persian)
HashemkhaniZolfani, S., Maknoon, R. & Zavadskas, E. K. (2016). An introduction to prospective multiple attribute decision making (PMADM). Technological and Economic Development of Economy, 22(2), 309-326.
HashemkhaniZolfani, S., Maknoon, R. &Zavadskas, E. K. (2016). Multiple attribute decision making (MADM) based scenarios. International Journal of Strategic Property Management, 20(1), 101-111.
İç, Y. T. (2014). A TOPSIS based design of experiment approach to assess company ranking. Applied Mathematics and Computation, 227, 630-647.
Izadikhah, M. & Farzipoor Saen, R. (2020). Ranking sustainable suppliers by context-dependent data envelopment analysis. Annals of Operations Research, 293(2), 607-637.
Jakovljevic, V., Zizovic, M., Pamucar, D., Stević, Ž. & Albijanic, M. (2021). Evaluation of human resources in transportation companies using multi-criteria model for ranking alternatives by defining relations between ideal and anti-ideal alternative (RADERIA). Mathematics, 9(9), 976.
Janackovic, G. L., Savic, S. M. & Stankovic, M. S. (2013). Selection and ranking of occupational safety indicators based on fuzzy AHP: a case study in road construction companies: case study. South African Journal of Industrial Engineering, 24(3), 175-189.
Jassbi, J. J., Ribeiro, R. A. & Varela, L. R. (2014). Dynamic MCDM with future knowledge for supplier selection. Journal of Decision Systems, 23(3), 232-248.
Jassbi, J. J., Ribeiro, R. A. &Dargam, F. (2014, June). Dynamic MCDM for multi group decision making. In Joint International Conference on Group Decision and Negotiation (pp. 90-99). Springer, Cham.
Kahraman, C. &Çebı, S. (2009). A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications, 36(3), 4848-4861.
Karabasevic, D., Paunkovic, J. & Stanujkic, D. (2016). Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods. Serbian Journal of Management, 11(1), 43-53.
Khajavi, S., Sayed Alikhani, A. & Ghayouri Moghadam, A. (2022). Utilization of Fuzzy Inference System in System Dynamics to Design a Business Model for Distribution Companies in Iran. Industrial Management Journal, 14(2), 250-266. (in Persian)
Khanmohamadi, M. & Heydari, H. (2018). Evaluation of efficiency and ranking cement companies active in the market with improved method of integrating DEA and AHP. Journal of decisions and operations research, 3(2), 138-150. (in Persian)
Kornbluth, J. S. H. (1992). Dynamic multi‐criteria decision making. Journal of Multi‐Criteria Decision Analysis, 1(2), 81-92.
Li, G., Kou, G. & Peng, Y. (2015). Dynamic fuzzy multiple criteria decision making for performance evaluation. Technological and Economic Development of Economy, 21(5), 705-719.
Lin, Y. H., Lee, P. C. & Ting, H. I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644.
Liu, H., Jiang, L. & Martínez, L. (2018). A dynamic multi-criteria decision making model with bipolar linguistic term sets. Expert Systems with Applications, 95, 104-112.
Melo, R. M. D., Medeiros, D. D. D. & Almeida, A. T. D. (2013). A multicriteria model for ranking of improvement approaches in construction companies based on the PROMETHÉE II method. Production, 25, 69-78.
Morente-Molinera, J. A., Wu, X., Morfeq, A., Al-Hmouz, R. & Herrera-Viedma, E. (2020). A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures. Information Fusion, 53, 240-250.
Mousavizade, F. & Shakibazad, M. (2019). Identifying and ranking CSFs for KM implementation in urban water and sewage companies using ISM-DEMATEL technique. Journal of knowledge management, 23(1), 200-218.
Navas de Maya, B., Arslan, O., Akyuz, E., Kurt, R. E. & Turan, O. (2022). Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports. Ships and Offshore Structures, 17(3), 687-694.
Nemati, Z., Mehregan, M. R. & Hosseinzadeh, M. (2021). Developing Prospect Theory with Multiple Reference Points in Decision Making. Industrial Management Journal, 13(4), 580-605. (in Persian)
Norouzi, N. (2022). A fuzzy multi-criteria decision-making framework for locating a nuclear power plant in Iran. Majlesi Journal of Energy Maqnagement, 11(1), 27-35
Pais, T. C. & Ribeiro, R. A. (2009). Contributions to Dynamic Multicriteria Decision Making Models. In IFSA/EUSFLAT Conf. (pp. 719-724).
Palomares, I., Kalutarage, H., Huang, Y., McCausland, P. M. R. & McWilliams, G. (2017, June). A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) (pp. 1-6). IEEE.
Panahandeh Khojin, G., Toloei Ashlagh, A. & Afsharkazemi, M. A. (2021). Presenting a data envelopment analysis model based on Goal programming and weight Restriction in order to evaluate the efficiency and ranking of decision-making units in Ghavamin Bank. Industrial Management Journal, 13(1), 155-169. (in Persian)
Radsar, M., Kazemi, A., Mehrgan, M. & Razavi Hajiagha, S. H. (2021). Designing an algorithm based on network data envelopment analysis with desirable and undesirable indicators for the evaluation of the Iranian power industry. Industrial Management Journal, 13(1), 1-26. (in Persian)
Saaty, T. L. (2007). Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variables. Mathematical and Computer Modelling, 46(7-8), 860-891.
Sadeghi, M. S., Jabari, H. & Shafiei, M. (2013). Ranking of Firms Listed in Tehran Stock Exchange Corporations Using Data Mining. The First Conference on Accounting, Financial Management and Investment, Gorgan. (in Persian)
Saraian Vernosfaderani, S. & Shatalebi Hosseinabadi, B. (2017). Classification and ranking of branches of Parsian Insurance in Isfahan province using data envelopment analysis and data mining. Scientific Conference on Management, Accounting, Economics and Insurance, Zanjan. (in Persian)
Sehhat, S., Taheri, M. & Sadeh, D. H. (2015). Ranking of insurance companies in Iran using AHP and TOPSIS techniques. American Journal of Research Communication, 3(1), 51-60.
Stević, Ž. & Brković, N. (2020). A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company. Logistics, 4(1), 4.
Tao, R., Liu, Z., Cai, R. & Cheong, K. H. (2021). A dynamic group MCDM model with intuitionistic fuzzy set: Perspective of alternative queuing method. Information Sciences, 555, 85-103.
Thong, N. T., Smarandache, F., Hoa, N. D., Son, L. H., Lan, L. T. H., Giap, C. N. & Long, H. V. (2020). A novel dynamic multi-criteria decision making method based on generalized dynamic interval-valued neutrosophic set. Symmetry, 12(4), 618.
Vo, H. V., Chae, B. & Olson, D. L. (2002). Dynamic MCDM: The case of urban infrastructure decision making. International Journal of Information Technology & Decision Making, 1(02), 269-292.
Watrobski, J., Jankowski, J. & Ziemba, P. (2016). Multistage performance modelling in digital marketing management. Economics & Sociology, 9(2), 101.
Xu, Z. &Yager, R. R. (2008). Dynamic intuitionistic fuzzy multi-attribute decision making. International journal of approximate reasoning, 48(1), 246-262.
Yao, X., Liu, E., Sun, X., Le, W. & Li, J. (2022). Integrating external representations and internal patterns into dynamic multiple-criteria decision making. Annals of Operations Research, 322, 1-24.
Yu, P. L. & Chen, Y. C. (2012). Dynamic multiple criteria decision making in changeable spaces: from habitual domains to innovation dynamics. Annals of Operations Research, 197(1), 201-220.
Yu, P., Yang, Y., Ma, H. & Mba, D. (2022). Evaluation of High-Quality Development of Manufacturing Industry Using a Novel Grey Dynamic Double Incentive Decision-Making Model. Mathematical Problems in Engineering, 2022, 1-10.
Zulueta, Y., Martinez-Moreno, J., Pérez, R. B. & Martinez, L. (2014). A discrete time variable index for supporting dynamic multi-criteria decision making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 22(01), 1-22.
مدیریت صنعتی
دوره 15، شماره 3
1402
صفحه 386-410

فایل ها

هم رسانی

ارجاع به این مقاله

آمار