In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount. The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model. Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model. A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model. The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran. The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans. The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach.
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Ahmadi, A., Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2018). OR applications in pharmaceutical supply chain management. In C. Kahraman & Y. Topcu (Eds.), Operations research applications in health care management (pp. 461–491). Cham: Springer.
Akbarpour, M., Torabi, S. A., & Ghavamifar, A. (2020). Designing an integrated pharmaceutical relief chain network under demand uncertainty. Transportation Research Part E: Logistics and Transportation Review,136, 101867.
Alkaabneh, F., Diabat, A., & Gao, H. O. (2020). Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. Computers & Operations Research,113, 104751.
Alnaji, L., & Ridha, M. (2013). The role of supply chain applications in Jordanian pharmacies: A case study on Pharmacies in the capital city Amman. Industrial Engineering Letters,3, 65–71.
Baboli, A., Fondrevelle, J., Tavakkoli-Moghaddam, R., & Mehrabi, A. (2011). A replenishment policy based on joint optimization in a downstream pharmaceutical supply chain: centralized vs. decentralized replenishment. The International Journal of Advanced Manufacturing Technology,57(1–4), 367–378.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location–routing–inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production,260, 120842.
Chao, C., Zhihui, T., & Baozhen, Y. (2019). Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network. Annals of Operations Research,273(1–2), 111–134.
Charnes, A., Cooper, W. W., Sun, B., & Huang, Z. (1990). Polyhedral cone-ratio models with an application to large commercial banks. Journal of Econometrics, 46, 73–91.
Chintapalli, P. (2015). Simultaneous pricing and inventory management of deteriorating perishable products. Annals of Operations Research,229(1), 287–301.
Darbari, J. D., Kannan, D., Agarwal, V., & Jha, P. C. (2019). Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem. Annals of Operations Research,273(1–2), 693–738.
Diabat, A., Abdallah, T., & Le, T. (2016). A hybrid tabu search based heuristic for the periodic distribution inventory problem with perishable goods. Annals of Operations Research,242(2), 373–398.
Gatica, G., Papageorgiou, L. G., & Shah, N. (2003). Capacity planning under uncertainty for the pharmaceutical industry. Chemical Engineering Research and Design,81(6), 665–678.
Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production,242, 118317.
Kannan, D., Mina, H., Nosrati-Abarghooee, S., & Khosrojerdi, G. (2020). Sustainable circular supplier selection: A novel hybrid approach. The Science of the Total Environment,722, 137936–137936.
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.
Levis, A. A., & Papageorgiou, L. G. (2004). A hierarchical solution approach for multi-site capacity planning under uncertainty in the pharmaceutical industry. Computers & Chemical Engineering,28(5), 707–725.
Mardan, E., Govindan, K., Mina, H., & Gholami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. Journal of Cleaner Production,235, 1499–1514.
Masoumi, A. H., Yu, M., & Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research Part E: Logistics and Transportation Review,48(4), 762–780.
Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering,82, 115–128.
Nagurney, A., & Li, D. (2015). A supply chain network game theory model with product differentiation, outsourcing of production and distribution, and quality and price competition. Annals of Operations Research,226(1), 479–503.
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.
Nasrollahi, M., & Razmi, J. (2019). A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty. Operational Research, 1–28.
Nematollahi, M., Hosseini-Motlagh, S. M., Ignatius, J., Goh, M., & Nia, M. S. (2018). Coordinating a socially responsible pharmaceutical supply chain under periodic review replenishment policies. Journal of Cleaner Production,172, 2876–2891.
Oh, H. C., & Karimi, I. A. (2004). Regulatory factors and capacity-expansion planning in global chemical supply chains. Industrial and Engineering Chemistry Research,43(13), 3364–3380.
Ozsen, L., Coullard, C. R., & Daskin, M. S. (2008). Capacitated warehouse location model with risk pooling. Naval Research Logistics (NRL),55(4), 295–312.
Pakzad-Moghaddam, S. H., Mina, H., & Mostafazadeh, P. (2019). A novel optimization booster algorithm. Computers & Industrial Engineering,136, 591–613.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering,33(12), 1931–1938.
Papageorgiou, L. G., Rotstein, G. E., & Shah, N. (2001). Strategic supply chain optimization for the pharmaceutical industries. Industrial and Engineering Chemistry Research,40(1), 275–286.
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering,126, 657–672.
Savadkoohi, E., Mousazadeh, M., & Torabi, S. A. (2018). A possibilistic location–inventory model for multi-period perishable pharmaceutical supply chain network design. Chemical Engineering Research and Design,138, 490–505.
Shah, N. (2004). Pharmaceutical supply chains: Key issues and strategies for optimisation. Computers & Chemical Engineering,28(6–7), 929–941.
Shaw, K., Irfan, M., Shankar, R., & Yadav, S. S. (2016). Low carbon chance constrained supply chain network design problem: A Benders decomposition based approach. Computers & Industrial Engineering,98, 483–497.
Singh, S. K., & Goh, M. (2018). Multi-objective mixed integer programming and an application in a pharmaceutical supply chain. International Journal of Production Research,57, 1–24.
Souza, V. D., Bloemhof-Ruwaard, J., & Borsato, M. (2019). Exploring ecosystem network analysis to balance resilience and performance in sustainable supply chain design. International Journal of Advanced Operations Management,11(1–2), 26–45.
Susarla, N., & Karimi, I. A. (2012). Integrated supply chain planning for multinational pharmaceutical enterprises. Computers & Chemical Engineering,42, 168–177.
Taleizadeh, A. A., Haji-Sami, E., & Noori-daryan, M. (2019). A robust optimization model for coordinating pharmaceutical reverse supply chains under return strategies. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03200-7.
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.
Weraikat, D., Zanjani, M. K., & Lehoux, N. (2019). Improving sustainability in a two-level pharmaceutical supply chain through vendor-managed inventory system. Operations Research for Health Care,21, 44–55.
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., Jula, P., & Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences,423, 257–283.
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.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45–55.
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Zandkarimkhani, S., Mina, H., Biuki, M. et al. A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Ann Oper Res (2020). https://doi.org/10.1007/s10479-020-03677-7
- Pharmaceutical supply chain
- Perishable products
- Inventory–location–routing problem
- Goal programming approach