A Model for Optimizing Transportation Costs in the Pharmaceutical Supply Chain Using Blockchain Technology

Abstract

This study presents a multi-objective mathematical model for optimizing transportation in the supply chain of perishable pharmaceutical products. By leveraging blockchain technology, the model seeks to reduce logistics costs, increase customer satisfaction, and enhance the transparency and security of information throughout supply chain processes. The proposed model is designed by considering closed-loop characteristics, a heterogeneous transportation fleet, uncertain demand, and environmental constraints. It comprises six objective functions that respectively minimize transportation costs, fleet costs, blockchain contract costs, deviations from the customer satisfaction time window, delivery delays, and operational time. The model is solved using the Particle Swarm Optimization (PSO) metaheuristic algorithm, with optimal parameter settings determined through the Taguchi method, while GAMS software with the CPLEX solver is also employed. The results obtained from solving test problems of different sizes indicate that the proposed model substantially reduces costs while improving delivery time, increasing customer trust, and enhancing the operational performance of the pharmaceutical supply chain. Furthermore, the use of blockchain-based smart contracts plays an effective role in ensuring data security, preventing pharmaceutical counterfeiting, and facilitating product traceability. Accordingly, integrating mathematical optimization with blockchain technology can serve as an efficient approach to managing sensitive and complex supply chains such as those in the pharmaceutical industry. The proposed model is not only adaptable to real-world conditions and operational constraints but can also facilitate digital transformation and improve sustainability in healthcare-oriented logistics systems. Future research is recommended to investigate the human and behavioral dimensions of the supply chain, including trust among supply chain actors and the acceptance of emerging technologies.