基于混合整数规划的救护车调度决策模型
A mixed-integer programming model for identifying intuitive ambulance dispatching policies
摘要：
马尔可夫决策过程模型和算法可用于识别用于向空间分布的客户分派救护车的最优策略，其中最优策略指示救护车在每个状态中分派给每个客户类型。由于最优解依赖于马尔可夫状态变量，因此在实际实施策略时，它们可能并不总是对应于一组简单的规则。符合每种类型的客户的优先级列表的受限策略在实践中可能是期望使用的，因为这样的策略是透明的、可解释的并且易于实现。优先级列表策略是救护车的有序列表，指示根据救护车可用性将救护车调度到客户类型的首选顺序。本文提出了一个约束马尔可夫决策过程模型，用于确定最优的优先级列表策略，制定为一个混合整数规划模型，不扩展马尔可夫状态空间，并可以使用标准算法求解。一系列计算示例说明了直观策略的好处。算例的最优混合整数规划解的目标函数值接近于无约束模型的目标函数值，优于经典规划的目标函数值。
英文摘要：
Markov decision process models and algorithms can be used to identify optimal policies for dispatching ambulances to spatially distributed customers, where the optimal policies indicate the ambulance to dispatch to each customer type in each state. Since the optimal solutions are dependent on Markov state variables, they may not always correspond to a simple set of rules when implementing the policies in practice. Restricted policies that conform to a priority list for each type of customer may be desirable for use in practice, since such policies are transparent, explainable, and easy to implement. A priority list policy is an ordered list of ambulances that indicates the preferred order to dispatch the ambulances to a customer type subject to ambulance availability. This paper proposes a constrained Markov decision process model for identifying optimal priority list policies that is formulated as a mixed integer programming model, does not extend the Markov state space, and can be solved using standard algorithms. A series of computational examples illustrate the benefit of intuitive policies. The optimal mixed integer programming solutions to the computational examples have objective function values that are close to those of the unrestricted model and are superior to those of heuristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
