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Jafari-Eskandari, meisam, Mokhtari, Mehrdad, Abbasi fard, Mohammad. (1399). Design the bi-objective pharmaceutical supply chain network under uncertainty and considering the production, delivery, and drug perishable times. , 6(26), 131-144.
meisam Jafari-Eskandari; Mehrdad Mokhtari; Mohammad Abbasi fard. "Design the bi-objective pharmaceutical supply chain network under uncertainty and considering the production, delivery, and drug perishable times". , 6, 26, 1399, 131-144.
Jafari-Eskandari, meisam, Mokhtari, Mehrdad, Abbasi fard, Mohammad. (1399). 'Design the bi-objective pharmaceutical supply chain network under uncertainty and considering the production, delivery, and drug perishable times', , 6(26), pp. 131-144.
Jafari-Eskandari, meisam, Mokhtari, Mehrdad, Abbasi fard, Mohammad. Design the bi-objective pharmaceutical supply chain network under uncertainty and considering the production, delivery, and drug perishable times. , 1399; 6(26): 131-144.

Design the bi-objective pharmaceutical supply chain network under uncertainty and considering the production, delivery, and drug perishable times

Journal of New Researches in Mathematics
مقاله 12، دوره 6، شماره 26، آذر و دی 2020، صفحه 131-144  XML اصل مقاله (3.17 M)
نوع مقاله: research paper
نویسندگان
meisam Jafari-Eskandari email 1؛ Mehrdad Mokhtari2؛ Mohammad Abbasi fard2
1Industrial Engineering, Payamnoor University, Tehran, Iran
2Department of Industrial Engineering, Payamnoor University, Tehran, Iran
چکیده
In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determines the number and location of potential facilities such as drug manufacture centers and drug distribution centers by considering the minimizing the total costs and minimizing the maximum unsatisfied demand for distribution of drugs to demand zones. In this paper, production time, delivery, and drug perishable time is also considered in modeling. To solve the model, an example is designed and the multi-decision TH method is used. The results obtained the model shows this method is effective in finding the Pareto front at the right time. The results obtained the model shows this method is effective in finding the Pareto front at the right time.The results obtained the model shows this method is effective in finding the Pareto front at the right time.
کلیدواژه‌ها
design the supply chain network؛ drug perishable times؛ production and delivery time؛ robust optimization
مراجع
[1] B. Zahiri, M.S. Pishvaee. Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research. 55(7): 2013-2033 (2017).

[2] S.A.Torabi, E.Hassini. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst. 159: 193-214 (2008).

[3] M.S.Pishvaee, M.Rabbani, S.A.Torabi. A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2): 637-649 (2011).

[4] R.T.Sousa, S.Liu, L.G.Papageorgiou, N. Shah. Global supply chain planning for pharmaceuticals. Chemical Engineering Research and Design.  89(11): 2396-2409 (2011).

[5] N. Susarla, I.A. Karimi. Integrated supply chain planning for multinational pharmaceutical enterprises. Computers & Chemical Engineering.  42: 168-177 (2012).

[6] Z.H. Zhang, H. Jiang. A robust counterpart approach to the bi-objective emergency medical service design problem. Applied Mathematical Modelling. 38(3): 1033-1040 (2014).

[7] B. Zahiri, R. Tavakkoli-Moghaddam, M. Mohammadi, P. Jula. Multi-objective design of an organ transplant network under uncertainty. Transportation Research Part E: Logistics and Transportation Review. 72: 101-124 (2014).

[8] A. Jabbarzadeh, B. Fahimnia, S. Seuring. 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 (2014).

[9] M. Mousazadeh, S.A. Torabi, M.S.  Pishvaee. Green and reverse logistics management under fuzziness. In Supply Chain Management under Fuzziness.  313: 607-637 (2014).

[10] M. Mousazadeh, S.A. Torabi, B. Zahiri. A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering.  82: 115-128 (2015).

[11] S. Martins, P. Amorim, G. Figueira, B. Almada-Lobo. An Optimization-Simulation Approach to the Network Redesign Problem of Pharmaceutical Wholesalers. Computers & Industrial Engineering. 106: 315-328 (2017).

[12] B. Zahiri, P. Jula, R. Tavakkoli-Moghaddam. Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences. 423: 257-283 (2018).

[13] S.S. Kara, S. Onut. A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling. Expert Systems with Applications. 37(9): 6129-6137 (2010).

[14] M. Ramezani, M. Bashiri, R. Tavakkoli-Moghaddam. A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling. 37(1): 328-344 (2013).

[15] S.H. Amin, G. Zhang. A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling. 37(6): 4165-4176 (2013).

[16] H. Soleimani, M. Seyyed-Esfahani, G. Kannan. Incorporating risk measures in closed-loop supply chain network design. International Journal of Production Research. 52(6): 1843-1867 (2014).

[17] K. Subulan, A.S. Taşan, A. Baykasoğlu. Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming. Applied Mathematical Modelling. 39(9): 2661-2702 (2015).

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