رتبه بندی شرکت های حمل ونقل بین المللی ریلی ایران با استفاده از مدل تصمیم گیری چندمعیاره پویا و سیستم استنتاج فازی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
هدف: حمل بار از طریق ریل با توجه به مزایایی همچون آلودگی کمتر در مقایسه با حمل جاده ای، صرف زمان کمتر در قیاس با حمل دریایی و هزینه کمتر نسبت به حمل هوایی، از جمله موضوعاتی است که در حوزه حمل ونقل اهمیت دارد. از همین رو شرکت های حمل ونقلی، بیش از پیش به فعالیت در زمینه حمل بار ریلی و به ویژه، حمل بار بین المللی ریلی تمایل نشان می دهند. از جمله چالش های شرکت راه آهن جمهوری اسلامی ایران، در حوزه حمل ونقل بین المللی، رتبه بندی این شرکت هاست که در تصمیم های گوناگونی همچون اعطای تخفیفات و تسهیلات، واگذاری واگن های خارجی، اولویت دهی به تأمین خواسته ها و رسیدگی به شکایات و موضوعاتی از این دست تأثیرگذار است. به همین منظور، در این مقاله مدل تصمیم گیری چندمعیاره پویا، برای رتبه بندی شرکت های حمل ونقل بین المللی ریلی ایران پیاده سازی شده است. روش: با توجه به ماهیت پویای مسئله، مدل تصمیم گیری چندمعیاره پویا، بر اساس داده های واقعی حمل بار بین المللی، شامل صادرات، واردات و ترانزیت، توسط شرکت های حمل ونقل بین المللی ریلی، در پنج سال اجرا شد و به منظور برآورد داده های عملکردی شرکت ها در آینده، از سیستم استنتاج فازی بهره گرفته شد. یافته ها: با لحاظ کردن عدم قطعیت از دو جهت که یکی تأثیرپذیری فعالیت شرکت ها در قالب یک سیستم استنتاج فازی و دیگری، پویایی مدل تصمیم گیری چندمعیاره پویا بود، اولویت 15 شرکت حمل ونقل بین المللی ریلی، برای شرکت راه آهن جمهوری اسلامی ایران مشخص شد. نتیجه گیری: نتایج کلی بیانگر آن است که مواجهه با موضوع رتبه بندی شرکت های حمل ونقل بین المللی ریلی به صورت پویا، خروجی مطلوب تری را ارائه می دهد. همچنین کاربردی بودن روش ارائه شده برای بهره مندی از سیستم استنتاج فازی در مدل تصمیم گیری چندمعیاره پویا، از دیگر نتایجی است که از این پژوهش حاصل شد.Ranking Iran's International Rail Transportation Companies Using Dynamic Multi-criteria Decision-making and Fuzzy Inference System
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.
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