Abstract:
© 2020 Informa UK Limited, trading as Taylor & Francis Group. Due to the emergency role of pharmaceutical supply chains, there has been rapid development of optimisation techniques as one of efficient tools to improve pharmaceutical supply chain network design. To improve the pharmaceutical supply chain network, this study proposes a novel location-allocation-inventory model as a multi-objective, multi-echelon, multi-product, multi-period and multi-modal transportation system for pharmaceutical supply chain network design under uncertainty. The proposed model aims to optimise multiple objectives, including minimising the total costs and the delivery time while maximising the transportation system reliability simultaneously. To control the impacts of uncertain parameters including the ordering, delivery, purchase, and transportation costs and the capacity of vehicles, warehouses and of distribution centres, a robust fuzzy optimisation approach is developed. An efficient modification of a state-of-the-art evolutionary algorithm called the red deer algorithm (RDA) in its multi-objective form abbreviated as IMORDA is developed and compared with itself and well-established algorithms in the literature such as non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimisation (MOPSO). The results confirm the applicability and efficiency of the IMORDA for the proposed model and encourage further development of this new metaheuristic.
