A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics

The pharmaceutical supply chain has features that distinguish it from other supply chains. Medicine is considered a strategic commodity, and the smallest disruption in its supply chain may cause severe crises. This is why the distribution of pharmaceutical products needs to combine the minimization of costs with strong compliance with service standards while taking into account risks due to uncertainty. In this study, we present a new multi-objective multi-echelon multi-product multi-period pharmaceutical supply chain network (PSCN) along with the production–distribution–purchasing–ordering–inventory holding-allocation-routing problem under uncertainty. We formulate the problem as a Mixed-Integer Non-Linear Programming model and develop a novel robust fuzzy programming method to cope with uncertainty parameters. To find optimal solutions, several multi-objective metaheuristic algorithms, namely, MOSEO, MOSAM MOKA, and MOFFA considering different criteria and multi-objective assessment metrics are suggested. Since there are no benchmarks existing in the literature, 10 numerical instances in large and small sizes are generated and also the trapezoidal fuzzy numbers of the uncertain parameters were randomly generated based on a uniform distribution. The required parameters were set and also the simulated data were examined in an exact method and by metaheuristic algorithms. The results confirm the efficiency of the MOFFA algorithm to detect a near-optimal solution within a logical CPU time. The solution methods are complemented with several sensitivity analyses on the input parameters of the proposed model. •Integrating production, distribution and purchasing in pharmaceutical supply chain.•Considering uncertainty for ordering, purchasing, and delivery costs.•Developing a novel robust fuzzy approach to manage uncertainty.•Several multi-objective meta-heuristic algorithms are applied and compared.•Providing several sensitivity analyses on the main parameters of PSCN.