Point-of-Dispensing Location and Capacity Optimization via a Decision Support System
Point-of-Dispensing Location and Capacity Optimization via a Decision Support System
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DOI：
10.1111/poms.12323
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发表时间：
2015-08-01
影响因子：
5
通讯作者：
Araz, Ozgur M.
中科院分区：
文献类型：
--
作者：
Ramirez-Nafarrate, Adrian;Lyon, Joshua D.;Araz, Ozgur M.
在紧急情况下，大规模预防的分发对公共卫生至关重要，涉及必须在短时间内做出的复杂决定。本研究提出了一个模型和解决方案的方法，优化点的配药（POD）的位置和容量的决策。这种方法是决策支持系统的一部分，旨在帮助官员准备和应对突发公共卫生事件。该模型从候选集合中选择POD，并建议如何为每个POD配备人员，以使平均旅行和等待时间最小化。遗传算法（GA）基于行程和排队近似（QA）快速解决问题，并且能够在无法满足分配目标时放松软约束。我们表明，所提出的方法返回的解决方案与其他系统相比，它是能够评估替代方案的行动时，资源不足以满足性能目标。
Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point-of-dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets.