Nasir Jamal Abdul, Kuo Yong-Hong, Cheng Reynold
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong.
HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong.
J Transp Health. 2022 Sep;26:101411. doi: 10.1016/j.jth.2022.101411. Epub 2022 Jul 5.
Non-emergency patient transportation (NEPT) services are particularly important nowadays due to the aging population and contagious disease outbreaks (e.g., Covid-19 and SARS). In this work, we study a NEPT problem with a case study of patient transportation services in Hong Kong. The purpose of this work is to study the discomfort and inconvenience measures (e.g., waiting time and extra ride time) associated with the transportation of non-emergency patients while optimizing the operational costs and utilization of NEPT ambulances.
A mixed-integer linear programming (MILP) formulation is developed to model the NEPT problem. This MILP model contributes to the existing literature by not only including the patient inconvenience measures in the objective function but also illustrating a better trade-off among different performance measures through its specially customized formulation and real-life characteristics. CPLEX is used to find the optimal solutions for the test instances. To overcome the computational complexity of the problem, a clustering-based iterative heuristic framework is designed to solve problems of practical sizes. The proposed framework distinctively exploits the problem-specific structure of the considered NEPT problem in a novel way to enhance and improve the clustering mechanism by repeatedly updating cluster centers.
The computational experiments on 19 realistic problem instances show the effective execution of the solution method and demonstrate the applicability of our approach. Our heuristic framework observes an optimality gap of less than 5% for all those instances where CPLEX delivered the result. The weighted objective function of the proposed model supports the analysis of different performance measures by setting different preferences for these measures. An extensive sensitivity analysis performed to observe the behavior of the MILP model shows that when operating costs are given a weightage of 0.05 in the objective function, the penalty value for user inconvenience measures is the lowest; when the weightage value for operating costs varies between 0.8 and 1.0, the penalty value for the same measures is the highest.
This research can assist decision-makers in improving service quality by balancing operational costs and patient discomfort during transportation.
由于人口老龄化和传染病爆发（如新冠疫情和非典），非紧急患者运输（NEPT）服务在当今尤为重要。在这项工作中，我们通过香港患者运输服务的案例研究来探讨非紧急患者运输问题。这项工作的目的是研究与非紧急患者运输相关的不适和不便措施（如等待时间和额外乘车时间），同时优化非紧急患者运输救护车的运营成本和利用率。
开发了一种混合整数线性规划（MILP）公式来对非紧急患者运输问题进行建模。该MILP模型对现有文献的贡献在于，不仅在目标函数中纳入了患者不便措施，还通过其特殊定制的公式和实际特征，说明了不同性能指标之间更好的权衡。使用CPLEX来寻找测试实例的最优解。为了克服问题的计算复杂性，设计了一个基于聚类的迭代启发式框架来解决实际规模的问题。所提出的框架以一种新颖的方式独特地利用了所考虑的非紧急患者运输问题的特定结构，通过反复更新聚类中心来增强和改进聚类机制。
对19个实际问题实例进行的计算实验表明了解决方法的有效执行，并证明了我们方法的适用性。对于所有CPLEX给出结果的实例，我们的启发式框架观察到最优性差距小于5%。所提出模型的加权目标函数通过为这些指标设置不同的偏好，支持对不同性能指标的分析。进行的广泛敏感性分析以观察MILP模型的行为表明，当在目标函数中运营成本的权重为0.05时，用户不便措施的惩罚值最低；当运营成本的权重值在0.8到1.0之间变化时，相同措施的惩罚值最高。
本研究可协助决策者在平衡运营成本和运输过程中患者不适的情况下提高服务质量。
