Oberscheider Marco, Hirsch Patrick
Institute of Production and Logistics, University of Natural Resources and Life Sciences, Feistmantelstraße 4, Vienna, 1180, Vienna, Austria.
BMC Health Serv Res. 2016 Sep 13;16(1):487. doi: 10.1186/s12913-016-1727-5.
Efficient transport of non-emergency patients is crucial for ambulance service providers to cope with increased demand resulting from aging Western societies. This paper deals with the optimization of the patient transport operations of the Red Cross of Lower Austria, which is the main provider in this state. Different quality levels of the provided service - expressed by time windows, feasible maximum ride times and exclusive transports - are tested and analyzed on real-life instances to show daily impacts on the provider's resources. Comparisons of the developed solution approach to the recorded manual schedule prove its advantages. In contrast to previous work in this field, non-static service times that depend on the combination of patients, their transport mode, the vehicle type as well as the pickup or delivery locations are used. These service times are based on statistical analyses that have been performed on an anonymized dataset with more than 600,000 requests.
To solve the given problem, a matheuristic solution approach was developed that deals with the exact optimization of combinations of requests as a first stage. Subsequently, the identified combinations are used as an input into a Tabu Search strategy, where the vehicle routing is optimized. Three representative days of the year 2012 were chosen for the four regions of Lower Austria to test five different service levels and the quality of the solution method.
For the standard scenario, the operation time of the manual schedule is reduced in the range from 14.1 % to 19.8 % for all tested instances. Even in the best service scenario, the matheuristic computes better results than the manual schedule. The service level has a high impact on the operation time of providers. The relative savings that are achieved by the algorithm are significantly lowered by introducing higher quality standards. The main reason is that less feasible combinations of patients can be generated. This leads to diminished opportunities for patients to be transported at the same time. The results indicate that the implementation of the developed matheuristic in daily planning decisions could decrease operation times significantly.
Managers have to define minimum standards for the punctuality, exclusive transports and excess ride times. This is crucial in order to find a suitable compromise between the service level and an optimized resource management.
对于救护车服务提供商而言，高效运送非紧急患者对于应对西方社会老龄化导致的需求增加至关重要。本文探讨了奥地利下奥地利州红十字会患者运输业务的优化问题，该红十字会是该州的主要服务提供商。通过时间窗、可行的最长乘车时间和专属运输来体现的不同服务质量水平，在实际案例中进行了测试和分析，以展示对服务提供商资源的日常影响。将所开发的解决方案方法与记录的人工调度进行比较，证明了其优势。与该领域以前的工作不同，使用了取决于患者组合、运输方式、车辆类型以及接送地点的非静态服务时间。这些服务时间基于对一个包含超过60万条请求的匿名数据集进行的统计分析。
为解决给定问题，开发了一种混合启发式解决方案方法，该方法首先对请求组合进行精确优化。随后，将识别出的组合用作禁忌搜索策略的输入，在该策略中对车辆路径进行优化。为测试五种不同服务水平和解决方案方法的质量，选择了2012年的三个代表性日期用于奥地利下奥地利州的四个地区。
对于标准场景，在所有测试案例中，人工调度的运营时间减少了14.1%至19.8%。即使在最佳服务场景下，混合启发式算法计算出的结果也优于人工调度。服务水平对服务提供商的运营时间有很大影响。引入更高质量标准会显著降低算法实现的相对节省。主要原因是可生成的可行患者组合减少。这导致患者同时运输的机会减少。结果表明，在日常规划决策中实施所开发的混合启发式算法可显著减少运营时间。
管理人员必须为准时性、专属运输和超长乘车时间定义最低标准。这对于在服务水平和优化资源管理之间找到合适的折衷方案至关重要。
