The new evidence reasoning based pharmaceutical inventory models with stochastic deterioration rates and lead times using PSO and GA

Abstract

Pharmaceutical inventory management is critical because insufficient pharmaceutical items and their misusage affect the lives of humans and cause financial losses. Conversely, deteriorating items and lead-time have special importance in pharmaceutical inventory issues, especially as uncertainty appears in both. Different procedures are considered to deal with uncertainty, including fuzzy systems as well as probabilistic techniques. The present study develops a novel technique according to the evidential theory approach, aimed at solving an inventory model with stochastic deterioration rates and lead-times appearing in a single-vendor (pharmaceutical company) multi-buyer (hospitals) inventory system. It seems that the evidence approach is effective in dealing with interval, imperfect, inaccurate, and missing (ignorance) data. The lead time demand of the hospitals is log-normal, and the shortage of hospitals is thoroughly back-ordered. Particle swarm optimization (PSO) and genetic algorithm (GA) solve this problem. Ultimately, the results are illustrated using the numerical example and the impacts of the main parameters. Highlights Develop the new pharmaceutical supply chain models based on the evidential theory approach Design single-vendor (pharmaceutical company) multi-buyer (hospitals) system with stochastic deterioration rates and lead times Considering interval, incomplete, imprecise and missing (ignorance) data in healthcare systems for make decision in the inventory problem Using genetic algorithm (GA) and particle swarm optimization algorithm (PSO)