ارائه مدل دو هدفه تصادفی کاهش هزینه و زمان در زنجیره تامین دارو
محورهای موضوعی :
مدیریت صنعتی
Farshid Jouyban
1
,
mahdi yousefi
2
,
Ensiyeh Neyshaboori
3
1 - Industrial engineering group, Islamic Azad University, Bonab branch, Bonab, Iran
2 - IAU
3 - IAU
تاریخ دریافت : 1397/01/17
تاریخ پذیرش : 1397/06/01
تاریخ انتشار : 1397/07/01
کلید واژه:
مدل سازی ریاضی,
مکان یابی- تخصیص چند دوره ای,
طراحی شبکه زنجیره تامین دارو,
مدل دو هدفه,
چکیده مقاله :
در این مقاله، یک مدل برنامه ریزی خطی عدد صحیح مختلط (BOMILP) برای یک مساله طراحی شبکه زنجیره تامین دارویی توسعه داده می شود. این مدل کمک می کند تا تصمیمات متفاوتی در باره موضوعات استرتژیک زنجیره از قبیل افتتاح مراکز تولید دارویی و مراکز توزیع اصلی یا محلی همراه با جریان های مواد بهینه در طول یک افق برنامه ریزی میان مدت به عنوان تصمیمات تاکتیکی اتخاذ کنیم. این مدل کمک می کند تا هزینه های کل و مدت زمان های حمل و نقل به ترتیب به عنوان توابع هدف اول و دوم به طور همزمان مینیمم گردند. با توجه به اینکه پارامترهای بحرانی مساله با درجه بالایی از عدم قطعیت ناشی از دانش بشر همراه می باشند، یک رویکرد برنامه ریزی استوارربه کار می بریم تا پارامترهای غیر قطعی مدل را کنترل کنیم. برای اینکه از صحت مدل توسعه داده مطمعن شویم، آن را بر روی یک مطالعه موردی واقعی (کپسول آموکسی سیلین 500 میلی گرم براساس داده ها و اطلاعات جمع آوری شده از سازمان غذا و داروی ایران) تست می کنیم.
چکیده انگلیسی:
In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufacturing centers and main/local distribution centers along with optimal material flows over a mid-term planning horizon as the tactical decisions. It aims to concurrently minimize the total costs and flow times as the first and second objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, a robust possibilistic programming approach is used to handle uncertain parameters. In order to verify and analyze the proposed model, it is tested on a real case study according to Iran’s National Organization of Food & Drug’s data. Finally, a well-known multi-objective decision making (MODM) techniques i.e. the ɛ-constraint method is applied to yield both trade-off surface and final preferred compromise solution for the real case study whose results were also comprehensively analyzed.
منابع و مأخذ:
Guoqin, Z., Dzever, S., & Renwu, T. (2017). Assessing the Impact of the New Medical Reform on China’s Pharmaceutical Supply Chain: The Case of Essential Medicines Distribution in Yuping, Luochan, and Minhang Regions. In E. Paulet & C. Rowley (Eds.), The China Business Model (pp. 119–144).
Haimes, Y. Y. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, 1(3), 296–297.
Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225–244.
Jetly, G., Rossetti, C. L., & Handfield, R. (2012). A multi-agent simulation of the pharmaceutical supply chain. Journal of Simulation, 6(4), 215–226.
Kao, C., & Hsu, W.-K. (2002). A single-period inventory model with fuzzy demand. Computers & Mathematics with Applications, 43(6), 841–848.
Kelle, P., Woosley, J., & Schneider, H. (2012). Pharmaceutical supply chain specifics and inventory solutions for a hospital case. Operations Research for Health Care, 1(2–3), 54–63.
La’inez, J. M., Schaefer, E., & Reklaitis, G. V. (2012). Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry. Computers & Chemical Engineering, 47, 19–28.
Mokrini, A. El, Kafa, N., Dafaoui, E., Mhamedi, A. El, & Berrado, A. (2016). Evaluating outsourcing risks in the pharmaceutical supply chain: Case of a multi-criteria combined fuzzy AHP-PROMETHEE approach. IFAC-PapersOnLine, 49(28), 114–119.
Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers and Chemical Engineering, 82, 115–128.
Narayana, S. A., Pati, R. K., & Vrat, P. (2012). Research on management issues in the pharmaceutical industry: a literature review. International Journal of Pharmaceutical and Healthcare Marketing, 6(4), 351–375.
Nicholson, L., Vakharia, A. J., & Erenguc, S. S. (2004). Outsourcing inventory management decisions in healthcare: Models and application. European Journal of Operational Research, 154(1), 271–290.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Pishvaee, M. S., Torabi, S. A., & Razmi, J. (2012). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624–632.
Sadghiani, N. S., Torabi, S. A., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95–114.
Settanni, E., Harrington, T. S., & Srai, J. S. (2017). Pharmaceutical supply chain models: A synthesis from a systems view of operations research. Operations Research Perspectives, 4, 74–95.
Sousa, R. T., Liu, S., Papageorgiou, L. G., & Shah, N. (2011). Global supply chain planning for pharmaceuticals. Chemical Engineering Research and Design, 89(11), 2396–2409.
Sousa, R. T., Shah, N., & Papageorgiou, L. G. (2005). Global supply chain network optimisation for pharmaceuticals. Computer Aided Chemical Engineering, 20, 1189–1194.
Susarla, N., & Karimi, I. A. (2012). Integrated supply chain planning for multinational pharmaceutical enterprises. Computers & Chemical Engineering, 42, 168–177.
Uthayakumar, R., & Priyan, S. (2013). Pharmaceutical supply chain and inventory management strategies: Optimization for a pharmaceutical company and a hospital. Operations Research for Health Care, 2(3), 52–64.
Weraikat, D., Zanjani, M. K., & Lehoux, N. (2016). Two-echelon pharmaceutical reverse supply chain coordination with customers incentives. International Journal of Production Economics, 176, 41–52.
WHO | Handbook of supply management at first-level health care facilities. (2013). WHO
Yu, X., Li, C., Shi, Y., & Yu, M. (2010). Pharmaceutical supply chain in China: current issues and implications for health system reform. Health Policy, 97(1), 8–15.
Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109–142.
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Guoqin, Z., Dzever, S., & Renwu, T. (2017). Assessing the Impact of the New Medical Reform on China’s Pharmaceutical Supply Chain: The Case of Essential Medicines Distribution in Yuping, Luochan, and Minhang Regions. In E. Paulet & C. Rowley (Eds.), The China Business Model (pp. 119–144).
Haimes, Y. Y. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, 1(3), 296–297.
Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225–244.
Jetly, G., Rossetti, C. L., & Handfield, R. (2012). A multi-agent simulation of the pharmaceutical supply chain. Journal of Simulation, 6(4), 215–226.
Kao, C., & Hsu, W.-K. (2002). A single-period inventory model with fuzzy demand. Computers & Mathematics with Applications, 43(6), 841–848.
Kelle, P., Woosley, J., & Schneider, H. (2012). Pharmaceutical supply chain specifics and inventory solutions for a hospital case. Operations Research for Health Care, 1(2–3), 54–63.
La’inez, J. M., Schaefer, E., & Reklaitis, G. V. (2012). Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry. Computers & Chemical Engineering, 47, 19–28.
Mokrini, A. El, Kafa, N., Dafaoui, E., Mhamedi, A. El, & Berrado, A. (2016). Evaluating outsourcing risks in the pharmaceutical supply chain: Case of a multi-criteria combined fuzzy AHP-PROMETHEE approach. IFAC-PapersOnLine, 49(28), 114–119.
Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers and Chemical Engineering, 82, 115–128.
Narayana, S. A., Pati, R. K., & Vrat, P. (2012). Research on management issues in the pharmaceutical industry: a literature review. International Journal of Pharmaceutical and Healthcare Marketing, 6(4), 351–375.
Nicholson, L., Vakharia, A. J., & Erenguc, S. S. (2004). Outsourcing inventory management decisions in healthcare: Models and application. European Journal of Operational Research, 154(1), 271–290.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Pishvaee, M. S., Torabi, S. A., & Razmi, J. (2012). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624–632.
Sadghiani, N. S., Torabi, S. A., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95–114.
Settanni, E., Harrington, T. S., & Srai, J. S. (2017). Pharmaceutical supply chain models: A synthesis from a systems view of operations research. Operations Research Perspectives, 4, 74–95.
Sousa, R. T., Liu, S., Papageorgiou, L. G., & Shah, N. (2011). Global supply chain planning for pharmaceuticals. Chemical Engineering Research and Design, 89(11), 2396–2409.
Sousa, R. T., Shah, N., & Papageorgiou, L. G. (2005). Global supply chain network optimisation for pharmaceuticals. Computer Aided Chemical Engineering, 20, 1189–1194.
Susarla, N., & Karimi, I. A. (2012). Integrated supply chain planning for multinational pharmaceutical enterprises. Computers & Chemical Engineering, 42, 168–177.
Uthayakumar, R., & Priyan, S. (2013). Pharmaceutical supply chain and inventory management strategies: Optimization for a pharmaceutical company and a hospital. Operations Research for Health Care, 2(3), 52–64.
Weraikat, D., Zanjani, M. K., & Lehoux, N. (2016). Two-echelon pharmaceutical reverse supply chain coordination with customers incentives. International Journal of Production Economics, 176, 41–52.
WHO | Handbook of supply management at first-level health care facilities. (2013). WHO
Yu, X., Li, C., Shi, Y., & Yu, M. (2010). Pharmaceutical supply chain in China: current issues and implications for health system reform. Health Policy, 97(1), 8–15.
Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109–142.
