Abstract To address the increasingly relevant challenge of routing autonomous agents within contested environments, this research formulates and examines the Multiple Shortest Path Problem with Path Deconfliction (MSPP-PD) to balance agent routing efficiency with group vulnerability. Within the general model formulation, multiple agents are routed between respective source and terminus nodes while minimizing both the total distance travelled and a measure of path conflict, where path conflict occurs for any instance of more than one agent traversing an arc and/or node. Within this modeling structure, this research presents and inspects a set of alternative, conceptually-motivated penalty metrics to inhibit path conflict between agents. Illustrative testing demonstrates the distinguishability of different MSPP-PD variants as they relate to optimal agent routing solutions, as well as the non-dominated solutions attainable via different relative priorities over the objective functions. Subsequent empirical testing over a set of synthetic instances demonstrates the effect of different penalty function metrics on both optimal solutions and the computational effort required to identify them. Concluding the work are recommendations about the utility of the MSPP-PD model variants, both individually and collectively.

中文翻译：

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具有路径冲突的多重最短路径问题

摘要 为了解决在竞争环境中路由自主代理日益相关的挑战，本研究制定并检验了具有路径解冲突的多最短路径问题 (MSPP-PD)，以平衡代理路由效率与组脆弱性。在通用模型公式中，多个代理在各自的源节点和终点节点之间路由，同时最小化总行进距离和路径冲突的度量，其中路径冲突发生在多个代理穿越弧线和/或节点的任何实例中。在这个建模结构中，本研究提出并检查了一组替代的、概念上激励的惩罚指标，以抑制代理之间的路径冲突。说明性测试证明了不同 MSPP-PD 变体的可区分性，因为它们与最佳代理路由解决方案有关，以及通过目标函数的不同相对优先级可获得的非支配解决方案。随后对一组合成实例的实证测试证明了不同惩罚函数度量对最优解和识别它们所需的计算工作的影响。总结工作是关于 MSPP-PD 模型变体的实用性的建议，无论是单独还是集体。随后对一组合成实例的实证测试证明了不同惩罚函数度量对最优解和识别它们所需的计算工作的影响。总结工作是关于 MSPP-PD 模型变体的实用性的建议，无论是单独还是集体。随后对一组合成实例的实证测试证明了不同惩罚函数度量对最优解和识别它们所需的计算工作的影响。总结工作是关于 MSPP-PD 模型变体的实用性的建议，无论是单独还是集体。