This work proposes a bi-objective mathematical optimization model and a two-stage heuristic for a real-world application of the heterogeneous Dynamic Dial-a-Ride Problem with no rejects, i.e., a patient transportation system. The problem consists of calculating route plans to meet a set of transportation requests by using a given heterogeneous vehicle fleet. These transportation requests can be either static or dynamic, and all of them must be attended to. In the first stage of the proposed heuristic, the problem’s static part is solved by applying a General Variable neighborhood Search based algorithm. In the second stage, the dynamic requests are dealt with by implementing a simple insertion heuristic. We create different instances based on the real data provided by a Brazilian city’s public health care system and test the proposed approach on them. The analysis of the results shows that the higher the level of dynamism, i.e., the number of urgent requests on each instance, the smaller the objective function value will be in the static part. The results also demonstrate that a higher level of dynamism increases the chance of a time window violation happening. Besides, we use the weighted sum method of the two conflicting objectives to analyze the trade-off between them and create an approximation for the Pareto frontier.

这项工作提出了一个双目标数学优化模型和一个两阶段启发式方法，用于不带拒绝项的异构动态“无人驾驶”问题（即患者运输系统）的实际应用。问题包括通过使用给定的异构车队来计算路线计划以满足一组运输要求。这些运输请求可以是静态的也可以是动态的，所有这些都必须得到照顾。在所提出的启发式方法的第一阶段，通过应用基于通用变量邻域搜索的算法来解决问题的静态部分。在第二阶段，通过实现简单的插入试探法来处理动态请求。我们根据巴西城市公共卫生保健系统提供的真实数据创建不同的实例，然后对它们进行测试。结果分析表明，动态级别越高，即每个实例上的紧急请求数越多，则静态函数中的目标函数值越小。结果还表明，较高的动态性会增加发生时间窗违规的机会。此外，我们使用两个冲突目标的加权和方法来分析它们之间的折衷，并为两个目标建立一个近似值。结果还表明，较高的动态性会增加发生时间窗违规的机会。此外，我们使用两个冲突目标的加权和方法来分析它们之间的折衷，并为两个目标建立一个近似值。结果还表明，较高的动态性会增加发生时间窗违规的机会。此外，我们使用两个冲突目标的加权和方法来分析它们之间的折衷，并为两个目标建立一个近似值。帕累托边疆。