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Vehicle routing and scheduling for regular mobile healthcare services

机译：常规移动医疗保健服务的车辆路由和调度

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We propose our solution to a particular practical problem in the domain of vehicle routing and scheduling. The generic task is finding the best allocation of the minimum number of mobile resources that can provide periodical services in remote locations. These mobile resources are based at a single central location. Specifications have been defined initially for a real-life application that is the starting point of an ongoing project. Particularly, the goal is to mitigate health problems in rural areas around a city in Romania. Medically equipped vans are programmed to start daily routes from county capital, provide a given number of examinations in townships within the county and return to the capital city in the same day. From the health care perspective, each van is equipped with an ultrasound scanner, and they are scheduled to investigate pregnant woman each trimester aiming to diagnose potential problems. The project is motivated by reports currently ranking Romania as the country with the highest infant mortality rate in the European Union. We developed our solution in two phases: modeling of the most relevant parameters and data available for our goal and then design and implement an algorithm that provides an optimized solution. The most important metric of an output scheduling is the number of vans that are necessary to provide a given amount of examination time per township, followed by total travel time or fuel consumption, number of different routes, and others. Our solution implements two probabilistic algorithms out of which we chose the one that performs the best.
机译：我们提出了对车辆路由和调度领域的特定实际问题的解决方案。通用任务正在找到最佳移动资源的最佳分配，可以在远程位置提供周期性服务。这些移动资源基于单个中心位置。最初已经定义了规范，用于是正在进行的项目的起点的实际应用程序。特别是，目标是减轻罗马尼亚城市周围的农村地区的健康问题。手工上的货车被编程为从县资本的日常航线开始，在县内的乡镇提供一定数量的考试，并在同一天返回首都。从医疗保健角度来看，每个面包车都配备了超声波扫描仪，他们计划调查每个妊娠期孕妇的孕妇，旨在诊断潜在的问题。该项目受到目前将罗马尼亚排名为欧盟婴儿死亡率最高的国家的报告。我们以两个阶段开发了解决方案：建模最相关的参数和数据可用于我们的目标，然后设计和实现提供优化解决方案的算法。输出调度最重要的指标是提供每乡镇给定金属考试时间所需的面包机的数量，然后是总旅行时间或燃料消耗，不同的路线数量等。我们的解决方案实现了两个概率算法，我们选择了表现最佳的概率算法。

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