Abstract
Potential production policies and distribution strategies for pharmaceutical products have attracted significant attention to sustainable development. End-of-life products have a severe impact on the ecological system. Thus, this paper has developed sustainable objectives in the pharmaceutical supply chain optimization framework with different constraints under uncertainty. The trade-off between socio-economic and environmental objectives is identified by ensuring the optimal allocation of different products among various echelons. Furthermore, three different solution approaches have suggested solving the proposed model. The Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking method is used to rank the different solution sets. Apart from TOPSIS, multiple criteria such as baseline design, co-efficient of variance, and degree of desirability have been depicted to select the best compromise solution set. Finally, an industrial case study of a pharmaceutical company is presented to validate and demonstrate the modeling and optimization approach. The outcomes facilitate wholesome decision-making policies with the achievement of sustainable objectives and provide opportunities to further align business practices with social needs and expectations, which then promote long-term market value—recognizing the need for inter-dependence and cooperation results in a reduction in total economic costs and improvements in customers’ services, and less impact on the environment. Furthermore, by increasing customer coverage distance, the firm earns customer loyalty, which results in repurchasing behaviors. Similarly, customer satisfaction is a vital social factor concerning the TBL objectives and significantly impacts sustainable PSC planning. The in-depth managerial insights are also addressed based on the presented work. After reviewing the literature, this is the first attempt to analyze the socio-economic performance of the pharmaceutical supply chain considering uncertain parameters.
•Sustainable pharmaceutical supply chain planning problem under uncertainty.•Multiobjective robust-optimization technique.•Opportunity to select the best optimal solution based on different criteria.•Proposed model implemented and analyzed on a pharmaceutical case study data.