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پیکره بندی شبکة زنجیرة تأمین یکپارچة راهبردی تصادفی | ||
| مدیریت صنعتی | ||
| مقاله 6، دوره 7، شماره 1، فروردین 1394، صفحه 83-105 اصل مقاله (1.03 MB) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2015.50682 | ||
| نویسندگان | ||
حمیدرضا فلاح لاجیمی   1؛ احمد جعفرنژاد2؛ محمدرضا مهرگان2؛ لعیا الفت3
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| 1دانشجوی دکتری مدیریت صنعتی (تحقیق در عملیات) دانشکدة مدیریت، دانشگاه تهران، تهران، ایران. | ||
| 2استاد گروه مدیریت صنعتی دانشکدة مدیریت، دانشگاه تهران، تهران، ایران. | ||
| 3دانشیار گروه مدیریت صنعتی دانشکدة مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران | ||
| چکیده | ||
| امروزه یکپارچه‌سازی فرایندهای زنجیرة تأمین به‌عنوان مزیت رقابتی شرکت‌ها مطرح است و معمولاً بدون درنظرگرفتن این دو عامل کلیدی متحمل هزینه‌های زیادی می‌شوند. بررسی پیکربندی‌های زنجیرة تأمین در سطح راهبردی با داده‌های تصادفی می‌تواند مکمل مناسبی برای رویکرد‌های قبلی باشد. این تحقیق ابزاری را برای بهنیه‌سازی زنجیرة تأمین فراهم می‌آورد تا مدیران در طراحی شبکه‌های تولید و توزیع در شرایط بی‌اطمینانی از آن استفاده کنند. مسئلة مورد نظر در این تحقیق ایجاد زیرساخت‌های زنجیرة تأمین، خرید، انتقال و موجودی مواد اولیه، تولید، انتقال موجودی محصول نهایی برای دستیابی به حداکثر سود و حداقل‌کردن تغییرپذیری سود و تقاضای برآورده‌نشده است. در این تحقیق، مدل بهینه‌سازی استوار عدد صحیح مختلط آرمانی چندهدفه با داده‌های تقاضای تصادفی ارائه می‌شود. به‌منظور درک بیشتر، مثال عددی ارائه می‌شود که با استفاده از نرم‌افزار LINDO حل و سپس اعتبارسنجی مدل بررسی شد که بیانگر بهبود اهداف در مدل است. | ||
| کلیدواژه‌ها | ||
| برنامه ریزی تصادفی؛ بهینه سازی استوار؛ زنجیرة تأمین راهبردی | ||
| عنوان مقاله [English] | ||
| Configuring integrated supply chain network stochastic strategic | ||
| نویسندگان [English] | ||
| Hamidreza Fallah Lajimi1؛ Ahmad Jafarnejad2؛ Mohammadreza Mehrgan2؛ Laaya Olfat3 | ||
| 1PhD Student in Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran | ||
| 2Prof., Faculty of Management, University of Tehran, Tehran, Iran | ||
| 3Associate Prof., Faculty of Management and Accounting, Allame Tabatabaei University, Tehran, Iran | ||
| چکیده [English] | ||
| This research provides an optimization tool for use by supply chain managers in the design and operation of manufacturing- distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing profit variability and unsatisfied demand. This research presented a model to supply chain infrastructure design. In this research, a multi-period, multi objective mixed integer robust optimization formulation of the strategic model is presented to account for the probabilistic demand data. For this purpose, numerical examples are presented and solved by LINDO software. | ||
| کلیدواژه‌ها [English] | ||
| Robust optimization, Stochastic Programming, strategic supply chain | ||
| مراجع | ||
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Agrawal, V. and & S. Seshadri, S. (2000). Risk intermediation in supply chains. IIE Transactions, 32, :819-831.
Alonso-Ayuso, A., et al. (2003). An approach for strategic supply chain planning under uncertainty based on stochastic 0-1 programming. Journal of Global Optimization, 26: 97-124.
Amid, S. H. & Ghodsypour, C. (2006). O’Brien, Fuzzy multi objective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104: 394 – 407.
Applequist, G. E., Pekny, J. F., & Reklaitis, G. V. (2000). Risk and uncertainty in managing chemical manufacturing supply chains. Comput. Chem. Eng, 24: 2211–2222.
Azaron, A., Brown, K. N., Tarim, S. A. & M. Modarres. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116: 129-138.
Bai, D., Carpenter, T. & Mulvey, J. (1997). Making a case for robust optimization models. Management Science, 43(7): 895-907.
Beamon, B. M. (1998). Supply chain design and analysis: models and methods. International Journal of Production Economics, 55: 281-294.
Ben-Tal, A. & Nemirovski, A. (2002). Robust optimization methodology and applications. Mathematical Programming Series B, 92: 453-480.
Chan, F. T. S. & Chan, H. K. (2005). Simulation modeling for comparative evaluation of supply chain management strategies. International Journal of Advanced Manufacturing Technology, 25: 998-1006.
Chang, Y. & Harris, M. (2001). Supply chain modeling using simulation. International Journal of Simulation: Systems, Science & Technology, 2(1), 24–30.
Chopra, S., Reinhardt, G. & Mohan, U. (2007). The importance of decoupling recurrent and disruptions risks in a supply chain. Naval Research Logistics, 54(5): 544-555.
Deleris, L. A. & Erhun, F. (2005). Risk management in supply networks using Monte-Carlo simulation. Proceeding of the 2005 Winter Simulation Conference, 1643-1649.
Deshpande, P., Shukla, D., Tiwari, M. (2011). Fuzzy goal programming for inventory management: a bacterial foraging approach. European Journal of Operational Research, 212: 325–336.
E. Aghezzaf. (2005). Capacity planning and warehouse location in supply chains with uncertain demands, J. Oper. Res. Soc. 56: 453–462.
Eppen, G. D., Martin, R. K. & Schrage, L. (1989). A Scenario approach to capacity planning. Operations Research, 37(4): 527-527.
Greenberg, H. J. & Morrison, T. (2008). Robust optimization. In A. R. Ravindran (Ed.), Operations Research and Management Science Handbook, CRC Press. Boca Raton, FL.
Guillén, G., et al. (2005). Multiobjective supply chain design under uncertainty, Chemical Engineering Science, 60: 1535-1553.
Gunasekaran, A., Patel, C. & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3): 333– 347.
Gupta, A. & Maranas, C. D. (2003). Managing demand uncertainty in supply chain planning. Computers and Chemical Engineering, 27: 1219-1227.
Hax, A. C. (1974). A comment on the distribution system simulator. Management Science, 21(2): 233-236.
Huang, G. Q., Lau, J. S. K. & Mak, K. L. (2003). The impacts of sharing production information on supply chain dynamics: a review of the literature. International Journal of Production Research, 41(7): 1483–1517.
Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey. International Journal of Production Economics, 87: 171-184.
Karimi Dastjerd. D., Akbari Jokar, M. & Feizabadi, J. (2009). Developing a Configuration for Classifying Supply Chains Based on RBV in Automotive Industry. Journal of industrial management, (1)2: 121-138. (In Persian)
Leung, S. C. H. & Wu, Y. (2004). A robust optimization model for stochastic aggregate production planning. Production Planning and Control, 15(5): 502-514.
Leung, S. C. H., Wu, Y., & Lai, K. K. (2006). A stochastic programming approach for multi-site aggregate production planning. Journal of the Operational Research Society, 57: 123-132.
Melachrinoudis, E. (1999). Bicriteria location of a semi-obnoxious facility. Computers & Industrial Engineering, 37: 581-593.
Mele, F. D., Guillén, G. Espuña, A. & Puigjaner, L. (2007). Anagent-based approach for supply chain retrofitting under uncertainty. Computer and Chemical Engineering, 31: 722-735.
Min, H. & Melachrinoudis, E. (1999). The relocation of a hybrid manufacturing/ distribution facility from supply chain perspectives: a case study. Omega, 27: 75-85.
Mulvey, J. M., Vanderbei, R. J. & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2): 264-281.
Peidro, D., Mula, J., Poler, R. l., Lario, F. C. (2009). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3/4): 400–420.
Ruiz-Femenia, R., Guille´n-Gosa´lbez, G., Jime´nez, L. & Caballero, J. A. (2013). Multi objective optimization of environ mentally conscious chemical supply chains under demand uncertainty. Chemical Engineering Science, 95: 1 –11
Sadeghi Moghadam, M. R., Soroush, M., Momeni, Nalchigar, (2009). integrated planning, and schedule production and distribution supply chain with employing genetic algorithm. Journal of industrial management. 2(1): 88-71. (In Persian)
Santoso, T., Ahmed, S., Goetschalckx, M. & A. Shapiro (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167: 96-115.
Sarmad, Z., Bazaegan, A. & Hejazi, E. (2004 ), Research Methodologies in Behavioral Sciences, Agah Publications. Tehran. (In Persian)
Shapiro. (2000). Modeling supply chain, Duxbury Press.
Shapiro, A . (2006). Worst-case distribution a nalysis of stochastic programs. Math. Program, Ser. B 107: 91–96.
Sodhi, M. (2003). How to do strategic supply chain planning. Sloan Management Review, 45(1): 69–75.
Surana, A., Kumara, S., Greaves, M. & Raghavan, N. (2005). Supply chain networks: a complex adaptive system perspective. International Journal of Production Research, 43(20): 4235–4265.
Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5): 639-657. Tsiakis, P., Shah, N., & Pantelides, C. C. (2001). Design of multi-echelon supply chain networks under demand uncertainty. Industrial & Engineering Chemical Research, 40: 3585-3604.
Wu, Y. (2006). Robust optimization applied to uncertain production loading problems with import quota limits under the global supply chain management environment. International Journal of Production Research, 44(5): 849-882.
Xu, J., Huang, X. & Yan, N. (2007). A multi-objective robust operation model for electronic market enabled supply chain with uncertain demands. Journal of Systems Science and Systems Engineering, 16(1): 74-87.
Xie, Y., Petrovic, D. & Burnham, K. (2006). A heuristic procedure f or the two-level control of serial supply chains under fuzzy customer demand. International Journal of Production Economics, 102: 37 – 50.
Yu, C. S. & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64: 385-397.
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