نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه مهندسی صنایع، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران

2 دانشجوی دکتری مدیریت صنعتی، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران

چکیده

ازآنجایی‌که مکان‌یابی انبارهای توزیع به‌راحتی قابل انجام نیستند، استفاده از سیستم پشتیبان تصمیم‌گیری کمک مناسبی به بالا بردن اثربخشی تصمیمات در این زمینه خواهد نمود. بر این اساس در این پژوهش، هدف به‌کارگیری مدلی است علاوه بر معرفی مناسب‌ترین مکان برای احداث انبار مرکزی، میزان مطلوبیت مکان موردنظر را با استفاده از برنامه‌ریزی خطی چند هدفه پیشنهادی محاسبه می‌کند تا مشخص کند مکان‌های بالقوه و پیشنهادی تا چه اندازه می‌توانند مکانی مناسب برای احداث انبار مرکزی باشند. روش‌های بهینه برای تصمیم‌گیری همواره موردتوجه بوده و از آن در بسیاری از مسائل مهم صنعتی استفاده می‌شود. در پژوهش حاضر، از مطلوبیت تجمعی ستاره که یکی از مهم‌ترین روش‌ها برای محاسبه و استخراج توابع مطلوبیت می‌باشد، استفاده شده است. نتایج حاکی از این است که مکان انبار  حائز رتبه اول دارای مطلوبیت 84 درصدی به‌عنوان مطلوب‌ترین مکان انبار مرکزی شرکت فولاد دامغان می‌باشد. همچنین نکته حائز اهمیت نتایج به‌دست‌آمده این است که هیچ‌یک از گزینه‌های پیشنهادی ، نمی‌توانند در آینده به‌عنوان مطلوب‌ترین مکان برای دیگر بخش‌ها باشند.
 

کلیدواژه‌ها

عنوان مقاله [English]

Decision Support System Based on Multi-Objective Programming for Central Warehouse Location

نویسندگان [English]

  • mohammad Ehsanifar 1
  • Fatemeh Dekamini 2

1 Associate Professor, Department of Industrial Engineering Arak Branch, Islamic Azad University, Arak, Iran

2 Ph.D. Student in Industrial Management, Faculty of Management, Arak Branch, Islamic Azad University, Arak, Iran

چکیده [English]

Introduction
Since locating distribution warehouses is not easy to do, using a decision support system will help increase the effectiveness of decisions in this area. Accordingly, in this study, the aim is to use a model in addition to introducing the most suitable location for the construction of the central warehouse, to calculate the desirability of the desired location using multi-objective linear planning to determine potential and proposed locations. To what extent can they be a good place to build a central warehouse?

Literature Review
Proper location of distribution warehouses are the main key to productivity, which can be used to achieve various goals of the organization, such as providing optimal services to customers and at the same time reducing distribution costs. Warehouse location is related to the placement and orientation of a piece of land according to the location of consumers and suppliers of the warehouse, and it consists of determining the location of the warehouse in such a way that its goals are met. Determining possible locations for warehouses varies from organization to organization and from one situation to another. Decision making in this case requires careful planning and proper forecasting and some analysis. However, the scientific method of planning directs the existing experiences into an optimal plan.

Methodology
Previous researches that have been conducted in the field of warehouse location have used different decision making methods to design a decision support system and introduced the appropriate location for building a warehouse. Now, the question that is raised is whether the same decision-making methods of the past can be the criteria for choosing the location of the central warehouse, or whether a new method should be used to design the decision support system based on the importance of the studied problem? Based on this, in this research, the purpose of using a model is to introduce the most suitable place for the construction of a central warehouse, it calculates the desirability of the desired location using the proposed multi-objective linear programming, to determine the potential and proposed locations. To what extent can they be a suitable place to build a central warehouse? Optimal methods for decision making have always been considered and used in many important industrial issues. In the present study, the cumulative utility of the star, which is one of the most important methods for calculating and extracting utility functions, has been used.

Results
According to the planning of the managers for the development of the factory for new products, the need to have a central warehouse to store the goods and to choose the right place requires attention to this matter. Based on this, the managers of Foulad Company are trying to take an important step by choosing the right location for the central warehouse, in order to reduce costs and the level of good service to the nearby workshops, as well as to increase profits. In this research, due to the importance of choosing a warehouse location and the complexity of making such a decision, we have presented a decision support system based on a multi-objective linear programming model, which is able to provide appropriate assistance by considering the minimum information from the researchers. Increase the effectiveness of decisions and help managers and researchers in the process of making optimal decisions.

Discussion
According to the findings, it can be argued that by using the obtained results, it will create the possibility for companies to make decisions to optimize the support system in their company and with this, in addition to reducing Costs to increase their competitiveness.

Conclusion
On the one hand, according to the results of multi-objective linear planning, he has calculated and presented the overall desirability of the reference options, which include potential and proposed options for building a central warehouse location. Also, on the other hand, it deals with the sensitivity analysis of options to help managers and researchers in the process of making a favorable and optimal decision. The results indicate that the warehouse location has the first rank with 84% desirability as the most desirable central warehouse location of Damghan Steel Company. Also, the important point of the obtained results is that none of the proposed options can be the most desirable place for other sectors in the future.

کلیدواژه‌ها [English]

  • Central Warehouse Location
  • star additive utility method
  • Multi-Objective Programming
  • Decision Support System
 
Ashrafzadeh, M., Mokhatab Rafiei, F., Mollaverdi Isfahani, N., & Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of Warehouse Location: A Case Study. Interdisciplinary Journal of Contemporary Research in Business, 3(9), 655-671.
Beuthe, M., & Scannella, G. (2001). Comparative analysis of UTA multicriteria methods. European Journal of Operational Research, 130(2), 246-262.
Caunhye, A. M., Zhang, Y., Li, M. and Nie, X. (2016). “A location-routing model for prepositioning and distributing emergency supplies”, Transportation Research Part E: Logistics and Transportation Review, Vol. 90, PP. 161–176.
Chu, X, Yan Zhong, Q. (2015), Post-earthquake allocation approach of medical rescue teams, Nat Hazards, Volume 79, Issue 3, 1809–1824.
Demirel, T., Demirel, N. Ç., & Kahraman, C. (2010). Multi-criteria warehouse location selection using Choquet integral. Expert Systems with Applications, 37(5), 3943-3952.
Ehsanifar, M., & Rezaei, Z. (2018). Combination of Classic Linear Assignment Method and MOLP for Evaluation of Alternatives Ranking. Sharif Journal of Industrial Engineering & Management, 34-1(1.2), 129-136.
Ehsanifar, M., Toloie Eshlaghi, A., Keramati, M. A., & Nazemi, J. (2013). The Development of UTASTAR Method in Fuzzy Environment for Supplier Selection. European Journal of Scientific Research, 108(3), 317-326.
Janssen, L., Diabat, A., Sauer, J., Herrmann, F. (2018). A stochastic micro-periodic age-based inventory replenishment policy for perishable goods. Transportation Research Part E: Logistics and Transportation Review. 118, 445-465.
Janssen, L., Sauer, J., Claus, T.U. (2018). Nehls Development and simulation analysis of a new perishable inventory model with a closing days constraint under non-stationary stochastic demand. Computers & Industrial Engineering. 118, 9-22.
Karmaker, C. L., & Saha, M. (2015). Optimization of warehouse location through fuzzy multi-criteria decision making methods. Decision Science Letters, 4(3), 315-334.
Korpela, J., Lehmusvaara, A., & Nisonen, J. (2007). Warehouse operator selection by combining AHP and DEA methodologies. International Journal of Production Economics, 108(1), 135-142.
Mohamadi, A, Yaghoubi, S, (2017), A bi-objective stochastic model for emergency medical services network design with backup services for disasters under disruptions: An earthquake case study, International Journal of Disaster Risk Reduction, Volume 23, 204-217.
Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779.
Ozgen, D., & Gulsun, B. (2014). Combining possibilistic linear programming and fuzzy AHP for solving the multi-objective capacitated multi-facility location problem. Information Sciences, 268, 185-201.
Qiu, Y., Qiao, J., Pardalos, P.M. (2019). Optimal production, replenishment, delivery, routing and inventory management policies for products with perishable inventory. Omega. 82, 193-204.
Siskos, Y., Grigoroudis, E., & Matsatsinis, N. F. (2005). UTA Methods. In: Multiple Criteria Decision Analysis: State of the Art Surveys. In International Series in Operations Research & Management Science (Vol. 78). New York, USA: Springer.
Turban, E., Sharda, R., & Delen, D. (2011). Decision Support and Business Intelligence Systems: Pearson Education. New Jersey, USA: Inc., Upper Saddle River.
Vlachopoulou, M., Silleos, G., & Manthou, V. (2001). Geographic information systems in warehouse site selection decisions. International Journal of Production Economics, 71(1), 205-212.
Yang, L., Jones, B. F., & Yang, S.-H. (2007). A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. European Journal of Operational Research, 181(2), 903-915.
Zanjirani Farahani, R., & Asgari, N. (2007). Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research, 176(3), 1839-1858.