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<p>In the pandemic era, high-level services and effective sustainable inventory strategies are essential objectives for every pharmaceutical company and hospital. Medicine shortages and improper use of pharmaceuticals can lead to not only financial losses but also have a significant impact on patients. Many health systems and hospitals face difficulties to achieving these goals because they have not addressed how the medicines are managed, supplied, used to save lives and improve the health. Many pharmaceutical companies and hospitals make efforts to resolve these issues, but they face difficulties in solving managerial problems related to supplies and the utilization of pharmaceuticals before reaching their decaying stage. To address these managerial challenges, we have developed a novel technique based on the evidential theory approach for an integrated production-inventory model for a pharmaceutical supply chain involving a single vendor (pharmaceutical company) and multiple buyers (hospitals) with uncertain deterioration rates and uncertain lead times. During pandemics, meeting hospital demands often involves excess transportation facilities, contributing to environmental issues. In our study, we have designed sustainable inventory strategies for pandemics, considering transport costs proportional to pandemic intensity (COVID-19) and incorporating carbon emissions with the intention to reduce them through green investment. Our primary objective is to minimize the joint total cost for a pharmaceutical company and multiple hospitals. We achieve this by determining optimal lead time, deterioration rates, cycle time, pharmaceutical prices, and production lot sizes in the pharmaceutical supply chain. To fulfill this objective, we have formulated an integer nonlinear programming model, and for optimization, we applied three hybrid meta-heuristic algorithms: particle swarm optimization, genetic algorithm, and a hybrid using Python Software. The study provides theoretical derivations and numerical examples, along with sensitivity analysis to extract essential managerial insights into the impact of the pandemic (COVID-19).</p>

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Optimizing healthcare supply chains strategically: analyzing the impact of COVID-19 on pharmaceutical inventory costs through the integration of evidence theory and hybrid evolutionary algorithms

  • 期刊论文

  • 发表日期：

    2025年2月9日

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  • Vinita Dwivedi,
  • Mamta Keswani,
  • Uttam Kumar Khedlekar

摘要

In the pandemic era, high-level services and effective sustainable inventory strategies are essential objectives for every pharmaceutical company and hospital. Medicine shortages and improper use of pharmaceuticals can lead to not only financial losses but also have a significant impact on patients. Many health systems and hospitals face difficulties to achieving these goals because they have not addressed how the medicines are managed, supplied, used to save lives and improve the health. Many pharmaceutical companies and hospitals make efforts to resolve these issues, but they face difficulties in solving managerial problems related to supplies and the utilization of pharmaceuticals before reaching their decaying stage. To address these managerial challenges, we have developed a novel technique based on the evidential theory approach for an integrated production-inventory model for a pharmaceutical supply chain involving a single vendor (pharmaceutical company) and multiple buyers (hospitals) with uncertain deterioration rates and uncertain lead times. During pandemics, meeting hospital demands often involves excess transportation facilities, contributing to environmental issues. In our study, we have designed sustainable inventory strategies for pandemics, considering transport costs proportional to pandemic intensity (COVID-19) and incorporating carbon emissions with the intention to reduce them through green investment. Our primary objective is to minimize the joint total cost for a pharmaceutical company and multiple hospitals. We achieve this by determining optimal lead time, deterioration rates, cycle time, pharmaceutical prices, and production lot sizes in the pharmaceutical supply chain. To fulfill this objective, we have formulated an integer nonlinear programming model, and for optimization, we applied three hybrid meta-heuristic algorithms: particle swarm optimization, genetic algorithm, and a hybrid using Python Software. The study provides theoretical derivations and numerical examples, along with sensitivity analysis to extract essential managerial insights into the impact of the pandemic (COVID-19).