Supporting Circular Economy Through Using Digital Transformation in Sustainable Pharmaceutical Reverse Logistics: Multi-objective Bi-level Modeling

Pharmaceutical waste management has become one of the major challenges in reverse logistics (RL) over the past decade. As drugs are classified as hazardous materials, their uncontrolled disposal poses significant risks to both the environment and public health. One potential solution is the recycling of surplus (unused) drugs by the citizens. This research aims to implement a RL system for surplus drugs and prevent the release of hazardous pharmaceutical waste into the environment. By leveraging Digital Transformation, the study proposes solutions to encourage citizens to sell their surplus drugs and participate more actively in implementing RL of drugs. In this regard, this research focuses on the proper collection and management of surplus drugs, particularly strategic drugs, by outsourcing this task to a Third-Party Reverse Logistics Provider (3PRLP). In this system, the government plays a crucial role in overseeing RL management. The primary objective is to recover raw materials and safely eliminate hazardous waste through a circular economy approach. To achieve this, 3PRLP companies utilize a three-channel platform powered by information technologies, including geographic information system (GIS), Cloud Computing (CC), and blockchain. Citizens can use these technologies to register and request the sale of surplus drugs. The study introduces a bi-level model with a mixed-integer nonlinear programming structure to optimize a multi-objective sustainable model at the upper level and minimize the costs associated with drug collection at the lower level. In addition, the research determines optimal drug pricing based on the Generalized Axiom of Revealed Preference, and presents a new demand function based on the Cobb-Douglas production function, incorporating relevant risks. The Lagrangian relaxation method is employed to address load-balancing issues and calculate CC costs. Applying this model allows for the optimization of energy consumption in cloud centers. In addition, the model helps develop the concept of the circular economy and achieve sustainability in the RL of drugs through purchasing citizens' surplus drugs and recycling. Furthermore, the proposed approach can lead to substantial currency savings for the country and ensure a more efficient supply of essential drugs, particularly for patients with special needs. In the numerical analysis, the large-size instances of the multi-objective model are solved using a linearization method. Examining the Pareto front using the epsilon-constraint method shows the high correlation between the base price of purchasing drugs from citizens by 3PRLPs and the price offered by the government to 3PRLPs.