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)

药品库存管理至关重要，因为药品不足及其滥用会影响人类的生活并造成经济损失。相反，恶化的物品和交货时间在药品库存问题中具有特殊的重要性，尤其是在两者都出现不确定性的情况下。考虑不同的程序来处理不确定性，包括模糊系统和概率技术。本研究根据证据理论方法开发了一种新技术，旨在解决单供应商（制药公司）多买方（医院）库存系统中出现随机劣化率和交货时间的库存模型。似乎证据方法在处理间隔、不完善、不准确和缺失（无知）数据方面是有效的。医院的交货期需求是对数正态的，医院的短缺是彻底的延期交货。粒子群优化（PSO）和遗传算法（GA）解决了这个问题。最后，使用数值示例和主要参数的影响来说明结果。

强调

• 基于证据理论方法开发新的药品供应链模型

• 设计具有随机劣化率和交货时间的单供应商（制药公司）多买方（医院）系统

• 考虑医疗保健系统中的间隔、不完整、不精确和缺失（无知）数据，以在库存问题中做出决策

• 使用遗传算法（GA）和粒子群优化算法（PSO）