论文标题
一个用于识别直觉救护车派遣政策的混合企业编程模型
A mixed-integer programming model for identifying intuitive ambulance dispatching policies
论文作者
论文摘要
马尔可夫决策过程模型和算法可用于确定用于向空间分布式客户派遣救护车的最佳政策，其中最佳策略表明救护车将每个州的每个客户类型派遣。由于最佳解决方案取决于马尔可夫状态变量，因此在实践中实施策略时，它们可能并不总是与一组简单的规则相对应。符合每种客户的优先级列表的限制政策可能需要在实践中使用，因为这些策略是透明，可解释且易于实施的。优先列表策略是救护车的有序清单，该清单指示首选的命令将救护车派往救护车可用性的客户类型。本文提出了一个受约束的马尔可夫决策过程模型，用于识别作为混合整数编程模型配制的最佳优先级策略，不会扩展马尔可夫状态空间，并且可以使用标准算法来解决。一系列计算示例说明了直观政策的好处。计算示例的最佳混合整数编程解决方案具有接近无限模型的目标函数值，并且优于启发式方法。
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.
