توسعۀ مدل مکان‌یابی فراملیتی با استفاده از ترکیب روش‌های تصمیم‌گیری چندمعیاره و مدل‌های پوششی مکان‌یابی در شرایط عدم اطمینان

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی صنایع، دانشکدة صنایع، دانشگاه علم‌وصنعت، تهران، ایران

2 استادیار مهندسی صنایع، دانشکدة صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

3 کارشناس ارشد مهندسی صنایع، دانشکدة صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

چکیده

تصمیم‌های مختلف شرکت‌هایی که در محیط پویای بین‌الملل درحال فعالیت‌اند (مانند تصمیم‌های مربوط به مکان‌یابی این شرکت‌ها) در معرض عدم  فراوانی قرار دارد. در این پژوهش، به مسئلة استقرار تسهیلات کارخانه‌ای در سطح فراملیتی در شرایط نبود اطمینان پرداخته می‌شود و مدلی یکپارچه برای برنامه‌ریزی راهبردی و عملیاتی مکان‌یابی یک شرکت با این شرایط ارائه می‌شود. مدل ارائه‌شده در سطح راهبردی با توجه به شاخص‌های اقتصاد بین‌الملل، ارزش کشورهای بالقوه را برای استقرار تسهیلات تعیین می‌کند. سه هدف اصلی مورد نظر عبارت‌اند از: پیداکردن کمترین تعداد واحدهای تسهیلاتی، استقرار آنها در بهترین نقاط بالقوه (کشورها) و کمینه‌کردن هزینه‌های حمل‌ونقل در تخصیص واحدهای تسهیلاتی به کشورهای متقاضی با واردکردن پارامترهای غیرقطعی در توابع هدف و محدودیت‌های مدل. برای تشریح بیشتر کاربرد این رویکرد، یک مسئلة مکان‌یابی برای یک شرکت بین‌المللی فعال در صنعت داروسازی اروپا تشریح شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Development of International facility location model applying the combination of MCDM and location covering techniques under uncertainty

نویسندگان [English]

  • Fahimeh Rouhi 1
  • Seyed Babak Ebrahimi 2
  • Hamid Ketabian 3
1 MSc., Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
2 Assistant Professor, Faculty of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
3 MSc., Faculty of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
چکیده [English]

Different decisions, including location decisions of companies which are operating in international dynamic environment are subject to significant uncertainty.So in this research an international facility location problem under uncertainty is considered and an integrated model for strategic and operational planning is presented. In strategic level countries are assessed according to international economic indices available in Global Competitiveness Report (GCR (and in operational level regarding the obtained values and factors like establishment costs, transportation costs, capacity and demand a multi objective optimization model is developed.The problem includes three objective functions: finding the least number of facilities, locating them in the best possible locations and minimizing transportation costs. A demonstration of the application of the methodology is presented by investigating the location problem of an international pharmaceutical company in Europe.

کلیدواژه‌ها [English]

  • covering techniques
  • international facility location
  • MCDM
  • uncertainty
Arntzen, B. C., Brown, G. G., Harrison, T. P. & Trafton, L. L. (1995). Global supply chain management at digital equipment corporation, Interfaces, 25(1): 69- 93.

Badri, M. A. (1999). Combining the analytic hierarchy process and goal programming for global facility location-allocation problem, International Journal of Production Economics, 62(3): 237- 248.

Canbolat, Y. B., Chelst, K. & Garg, N. (2007). Combining decision tree and MAUT for selecting a country for a global manufacturing facility, Omega, 35(3): 312- 325.

Canel, C. & Das, S. R. (2002). Modeling global facility location decisions: Integrating marketing and manufacturing decisions, Industrial Management and Data Systems, 102(2):110- 118.

Canel, C. & Khumawala, B. M. (1997). Multi period international facility location: An algorithm and application, International Journal of Production Research, 35(7): 1891- 1910.

 

Cheng-Chang, C. & Ju-Long, C. (2012). A foreign expansion model with multi-site locations in service industries, International Journal of Production Economics, 136(1): 102– 109.

Chou, T. Y., Hsu, C. L. & Chen, M. C. (2008). A fuzzy multi-criteria decision model for international tourist hotels location selection, International Journal of Hospitality Management, 27(2): 293- 301.

Das, K. & Sengupta, S. (2009). A hierarchical process industry production–distribution planning model, International Journal of Production Economics, 117(2): 402- 419.

De Rosa, V., Hartmann, E., Gebhard, M. & Wollenweber, J. (2014). Robust capacitated facility location model for acquisitions under uncertainty, Computers & Industrial Engineering, 72(1): 206– 216.

Dogan, I. (2012). Analysis of facility location model using Bayesian Networks, Expert Systems with Applications, 39(1): 1092– 1104.

Dou, Y. & Sarkis, J. (2010). A joint location and outsourcing sustainability analysis for a strategic offshoring decision, International journal of production research, 48(2): 567- 592.

Global Competitiveness Report. (2010). Retrieved from: https://members.weforum.org/pdf/GCR09/GCR20092010fullreport.pdf

Global Competitiveness Report. (2012). Retrieved from: http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2012-13.pdf

Goh, M., Lim, J. Y. S. & Meng, F. (2007). A stochastic model for risk management in global supply chain networks, European Journal of Operational Research, 182(1): 164– 173.

Hajidimitriou, Y. A. & Georgiou, A. C. (2002). A goal programming model for partner selection decisions in international joint ventures, European Journal of Operational Research, 138(3): 649- 662.

Hamad, R. & Fares Gualda, N. D. (2008). Model for facilities or vendors location in a global scale considering several echelons in the chain, Networks and Spatial Economics, 8(2): 297- 307.

Hodder, J. E. & Dincer, M. C. (1986). A multifactor model for international plant location and financing under uncertainty, Computers and Operations Research, 13(5): 601- 609.

 

Hodder, J. E. & Jucker, J. V. (1985). International plant location under price and exchange rate uncertainty, Engineering Costs and Production Economics, 9(1): 225- 229.

Hoffman, J. J. & Schniederjans, M. J. (1994). A two-stage model for structuring global facility site selection decisions: The case of the brewing industry, International Journal of Operations and Production Management, 14(4): 79- 96.

 

MacCarthy, B. L. & Atthirawong, W. (2003). Factors affecting location decisions in international operations–A Delphi study, International Journal of Operations and Production Management, 23(7): 794- 818.

Mohamed, Z. M. & Youssef, M. A. (2004). A production, distribution and investment model for a multinational company, Journal of Manufacturing Technology Management, 15(6): 495- 510.

Mohamed, Z. M. (1999). An integrated production-distribution model for a multi-national company operating under varying exchange rates, International Journal of Production Economics, 58(1): 81- 92.

Narasimhan, C., Gupta, M., Foster, G. & Niraj, R. (2006). Customer level profitability implications of satisfaction programs: A retailer satisfaction field study, Available at SSRN 903985.

Porter, M. E. (1998). Competitive advantage of nations, Free press, New York.

Safari, H. & Talebi, J. (2011). Bahman group motors facility location by Fuzzy Topsis and ZLOP, Tehran University Journal of Industrial Management, 3(6): 59- 80. (In Persian)

Sarkis, J. & Sundarraj, R. P. (2002). Hub location at digital equipment corporation: A comprehensive analysis of qualitative and quantitative factors, European Journal of Operational Research, 137(2): 336- 347.

 

Sayadi, M. K., Heydari, M. & Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers, Applied Mathematical Modelling, 33(5): 2257- 2262.

 

Syam, S. S. (2002). A model and methodologies for the location problem with logistical components, Computers and Operations Research, 29(9): 1173- 1193.

Verter, V. (2002). An integrated model for facility location and technology acquisition, Computers and Operations Research, 29(6): 583- 592.

Vidal, C. J. & Goetschalckx, M. (2001). A global supply chain model with transfer pricing and transportation cost allocation, European Journal of Operational Research, 129(1): 134- 158.