Pharmaceutical Fuzzy Optimization

Abstract

In response to the urgent demands of pharmaceutical-supply-chains (PSC), there has been a rapid emergence of optimization processes as effective tools for enhancing the working of these chain-networks. This study introduces an innovative approach called the location-allocation-inventory model, which addresses the complexities of a multi-modal transportation-system within the PSCN, considering uncertainties. The primary goal of this model is to optimize various objectives, including minimizing overall-costs and delivery-time while maximizing the reliability of the transportation-system. To mitigate the effects of uncertain-parameters such as ordering of products, delivery of the products, purchase, and transportation-costs, as well as the vehicle capacity, warehouses, and distribution-centres, a robust-fuzzy-optimization approach is employed. The study also presents a modified version of the state-of-the-art evolutionary-algorithm known as the RDA(Red-Deer-Algorithm), referred to as the IMORDA(Improved-Multi-Objective-Red-Deer-Algorithm), and compares its performance with other well-established algorithms such as the Non-dominated-Sorting-Genetic-Algorithm and the MOPSO(Multi-Objective-Particle-Swarm-Optimization). The findings of the study validate the effectiveness and suitability of the IMORDA for the proposed-model, thereby encouraging further advancements in this promising meta-heuristic approach.