We present a logic programming framework that orchestrates multiple variants of an optimization problem and reasons about their results to support high-stakes medical decision-making. The logic programming layer coordinates the construction and evaluation of multiple optimization formulations, translating solutions into logical facts that support further symbolic reasoning and ensure efficient resource allocation -- specifically targeting the "right patient, right platform, right escort, right time, right destination" principle. This capability is integrated into GuardianTwin, a decision support system for Forward Medical Evacuation (MEDEVAC), where rapid and explainable resource allocation is critical. Through a series of experiments, our framework demonstrates an average reduction in casualties by 35.75% compared to standard baselines. Additionally, we explore how users engage with the system via an intuitive interface that delivers explainable insights, ultimately enhancing decision-making in critical situations. This work demonstrates how logic programming can serve as a foundation for modular, interpretable, and operationally effective optimization in mission-critical domains.

翻译：本文提出一种逻辑编程框架，该框架能够协调优化问题的多种变体并对其结果进行推理，以支持高风险医疗决策。逻辑编程层通过协调多种优化模型的构建与评估，将求解结果转化为支持进一步符号推理的逻辑事实，从而确保高效的资源分配——特别针对"正确患者、正确平台、正确护送、正确时间、正确目的地"原则。该能力已集成至GuardianTwin系统（一种用于前方医疗后送（MEDEVAC）的决策支持系统），在此类对快速且可解释的资源分配要求极高的场景中发挥作用。通过系列实验验证，本框架相较于标准基线方法平均降低35.75%的伤亡率。此外，我们通过直观界面探索用户如何与系统交互，该界面能提供可解释的决策洞察，最终提升危急情境下的决策质量。本研究表明逻辑编程可作为关键任务领域中模块化、可解释且具备操作有效性的优化方法的基础。