A Robust Possibilistic Programming Approach to Drug Supply Chain Master Planning

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The provision of an efficient master plan which is able to integrate the procurement, production and distribution plans is a critical need in the way of achieving the competitive advantage in today’s marketplace. In this paper, a supply chain master planning problem of a drug supply chain is taken into account. The considered drug supply chain includes multiple suppliers, one manufacturer and multiple distribution centers.
In this paper, a multi-objective possibilistic mixed integer linear programming model (MOPMILP) which minimizes the total logistics cost and maximizes the total value of supplier selection aggregate function is developed. It should be noted that both economic and environmental criteria are considered in the supplier selection objective function to support the green and sustainable purchasing approach. Then to cope with the input parameters tainted with high degree of uncertainty, a new effectual robust possibilistic programming (RPP) model is elaborated. The proposed robust possibilistic programming model is able to appropriately adjust the degree of feasibility and optimality robustness of output decisions against business-as-usual uncertainty. Also the proposed robust optimization model can be appropriately applied in the cases in which reliable and sufficient historical data is not available for imprecise parameters (i.e., most of the real-life problems). To show the usefulness and effectiveness of the proposed robust possibilistic programming model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and felicity of the developed approach.

Keywords

Main Subjects

References

[1] Mahallati,V., (2012),“Management of Drug Supply Chain at 2025".
[2] Torabi, S.A., Hassini,E., (2008), “An interactive possibilistic programming approach for multiple objective supply chain master planning”, Fuzzy Sets Syst. 159: 193-214.
[3] Mula, J., Poler, R., Garcia, J.P., (2006), “MRP with flexible constraints: a fuzzy mathematical programming approach”, Fuzzy Sets and Systems 157: 74-97.
[4] Alem, D. J., Morabito, R., (2012), “Production planning in furniture settings via robust optimization”, Computers & Operations Research 39 (2):139-150.
[5] Sousa, R., shah. N., Papageorgiou, L., (2005), “Global Supply Chain Network Optimisation for Pharmaceuticals”, European Symposium on Computer Aided Process Engineering 20: 1189-1194.
[6] Susarla, N., Karimi, I.A., (2012), “Integrated supply chain planning for multinational pharmaceutical enterprises”, Computers and Chemical Engineering 42: 168– 177.
[7] Kao, C., Hsu, W., (2002), “A Single-Period Inventory Model with Fuzzy Demand”, Computers and Mathematics with Applications 43: 841-848.
[8] Liang, T.F., (2006), “Distribution planning decisions using interactive fuzzy multi-objective linear programming”, Fuzzy Sets and Systems 157: 1303-1316.
[9] Liang, T.F., (2006), “Integrating production–transportation planning decision with fuzzy multiple goals in supply chains”, International Journal of Production Research 46(6): 1477-1494.
[10] Maitya, K., Maitib, M., (2008), “A numerical approach to a multi-objective optimal inventory control problem for deteriorating multi-items under fuzzy inflation and discounting”, Computers and Mathematics with Applications 55: 1794-1807.
[11] Selim, H., Ozkarahan, I., (2008), “A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach”, Internet. J. Adv. Manuf. Technol 36(3): 401-418.
[12] Handfield, R., Warsing, D., (2009), “(Q, R) Inventory policies in a fuzzy uncertain supply chain environment”, European Journal of Operational Research 197(2): 609-619.
[13] Tanthatemee T., Phruksaphanrat B., Member, IAENG., (2012), “Fuzzy Inventory Control System for Uncertain Demand and Supply”, proceeding of the International Multi Conference of Engineers and Computer scientisits, March 14-16, Hong kong.
[14] Gutierrez, G., Kouvelis, P., Kurawala, A., (1996), “A robustness approach to uncapacitated network design problems”, European Journal of Ope1995 rational Research 94: 362-76.                       
[15] Leung, S. C. H., Tsang, S. O. S., Ng, W. L., Wu, Y., (2007), “A robust optimization model for multi-site production planning problem in an uncertain environment”, European Journal of Operational Research 181: 224-238.                     
[16] Pan, F., Nagi, R., (2010), “Robust supply chain design under uncertain demand in agile manufacturing”, Computers & Operations Research, 37: 668-683.                                                 
[17] Alem, D. J., Morabi, R., (2012), “Production planning in furniture settings via robust optimization”, Computers & Operations Research 39(2): 139-150. 
[18] Azar, A., Rabieh, M., Modarres yazdi, M., Fetanat fard haghighi, M., (2010), “A Mathematical Model for Fuzzy-Robust multi objective supply selection: An Approach in management of risk of Iran Khodro Chain”, management operations in Iran 15(1): 51-76.
[19] Pishvaee, M.S., Razmi, J., and Torabi, S., (2012), “Robust possibilistic programming for socially responsible supply chain network design: A new approach”, Fuzzy sets and systems 206: 1-20
[20] Zahiri, B., Tavakkoli-Moghaddam,R., Pishvaee, M.S., (2014), “A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty”, Computers & Industrial Engineering 74: 139-148.
[21] Hatami Varzaneh, M., (2002), Secret health with herbs. Fahmideh Shahid Publishing: 254.
[22]. رشیدی، شمس‌اله. فرجی، هوشنگ. جهانبین، داریوش، میرفردی، اصغر (1390). ارزیابی میزان آگاهی، اعتقاد و عملکرد مردم شهر یاسوج، نسبت به گیاهان دارویی، فصلنامه گیاهان دارویی 1(8).
[23] Van Landeghem., H., Vanmaele, H., (2002), “Robust planning:a new paradigm for demand chain planning”, Journal of Operations Management 20: 769-783.
[24] Rezaie, K., Ostadi, B., Torabi, S.A., (2008), “Activity-based costing in flexible manufacturing systems with a case study in a forging industry”, International Journal of Production Research 46(4): 1047-1069.
[25] Degraeve, Z., Roodhooft, F., Van Doveren, B., (2005), “The Use of Total Cost of Ownership for Strategic Procurement: A Company-Wide” Management Information System, J. Oper. Res. Soc. 56: 51-59.
[26] Kanyalkar, A.P., Adil, G.K., (2007), “Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment”, Internat. J. Prod. Res 45(22):5329-5355.
[27] Agha Mohammad Ali Kermani, M., Malaei, A., Nasiri, M., (2011), “Presenting a mathematical programming model for green supplier selection”, Proceedings of the 41st International Conference on Computers & Industrial Engineering, USA: 648-653.
[28] Kleijnen J.P.C., (2011), “Ethical issues in modeling: Some reflections”, E. J. of Operational Research. 130: 223-230.
[29] Pishvaee, M.S., Torabi S.A., (2010), “A possibilistic programming approach for closed loop supply chain network design under uncertainty”, Fuzzy Sets and Systems 161: 2668-2683.
[30] Jimenez, M., Arenas, M., Bilbao, A., Rodriguez, M.V., (2007), “Linear programming with fuzzy parameters: an interactive method resolution”, European Journal of Operational Research 177: 1599-1609.
[31] Gupte, A., Ahmmed, S., Cheon,M.S., Dey, S., (2013), “Solving Mixed Integer Bilinear Problems Using MILP Formulation”, SIMA Journal on optimization, 23(2), 721-744.
Journal of Industrial Engineering Research in Production Systems
Volume 4, Issue 7 - Serial Number 7
Spring and Summer 2016
September 2016
Pages 49-67

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