As supply chain costs constitute a large portion of hospitals' operating expenses and with $27.7 billion spent by the US hospitals on drugs alone in 2009, improving medication inventory management provides a great opportunity to decrease the cost of healthcare. This study investigates different management approaches for a system consisting of one central storage location, the main pharmacy, and multiple dispensing machines located in each department. Each medication has a specific unit cost, availability from suppliers, criticality level, and expiration date. Event-driven simulation is used to evaluate the performance of several inventory policies based on the total cost and patient safety (service level) under various arrangements of the system defined by the number of drugs and departments, and drugs' criticality, availability, and expiration levels. Our results show that policies that incorporate drug characteristics in ordering decisions can address the tradeoff between patient safety and cost. Indeed, this study shows that such policies can result in higher patient safety and lower overall cost when compared to traditional approaches. Additional insights from this study allow for better understanding of the medication inventory system's dynamics and suggest several directions for future research in this topic. Findings of this study can be applied to help hospital pharmacies with managing their inventory.
This article optimizes the design and configuration of algal biofuel supply chain networks (SCN) under economic and environmental objectives. Minimization of the total supply chain cost and the total life cycle greenhouse gas emission are the economic and environmental objectives, respectively. The SCN has been modeled by a multiobjective mixed integer linear programming approach which incorporates multiple production pathways and time periods, seasonality factors, water evaporation, recycling opportunities, and other major traits of the algal biofuel SCN. The model determines the optimal strategic and tactical level decisions of all SCN echelons. A fuzzy solution-based ε-constraint method has been utilized to obtain Pareto-optimal solutions that illustrate the trade-off between economic and environmental objectives. The performance of the model has been assessed in a case study carried out in seven states of the U.S which intends to develop the algal biofuel SCN from the year 2018 to the year 2024. Essential information with regard to the future of different technological pathways, relative importance of various supply chain factors, and sensitivity analysis has been discussed with respect to the case study results.
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