Cyber-physical systems (CPS) increasingly manage shared physical resources in the presence of human decision-making, where system-assigned actions must be executed by users or agents in the physical world. A fundamental challenge in such settings is user non-compliance: individuals may deviate from assigned resources due to personal preferences or local information, degrading system efficiency and requiring light-weight reassignment schemes. This paper proposes a post-deviation reassignment framework for shared-resource CPS that operates on top of any initial allocation algorithm and is invoked only when users diverge from prescribed assignments. We advance the Top-Trading-Cycle (TTC) mechanism to enable voluntary, preference-driven exchanges after deviation events, and extend it to handle many-to-one resource capacities and unassigned resource conditions that are not supported by the classical TTC. We formalize these structural cases, introduce capacity-aware cycle-detection rules, and prove termination along with the preservation of Pareto efficiency, individual rationality, and strategy-proofness. A Prospect-Theoretic (PT) preference model is further incorporated to capture realistic user satisfaction behavior. We demonstrate the applicability of this framework on an electric-vehicle (EV) charging case study using real-world data, where it increases user satisfaction and effective assignment quality under non-compliant behavior.

翻译：信息物理系统（CPS）日益需要在人类决策参与下管理共享物理资源，其中系统分配的动作必须由物理世界中的用户或代理执行。此类场景中的一个根本性挑战在于用户不遵从性：个体可能因个人偏好或局部信息而偏离所分配的资源，从而降低系统效率，并需要轻量级的重分配方案。本文提出一种面向共享资源CPS的偏离后重分配框架，该框架可运行于任何初始分配算法之上，且仅在用户偏离预设分配时被触发。我们改进了顶环交易（TTC）机制，使其能够在偏离事件后支持基于偏好的自愿交换，并将其扩展至处理经典TTC不支持的“多对一”资源容量及未分配资源状态。我们形式化了这些结构性场景，引入了容量感知的环检测规则，并证明了算法的可终止性，同时保持帕累托效率、个体理性与防策略性。进一步引入前景理论（PT）偏好模型以刻画真实的用户满意度行为。通过基于真实数据的电动汽车（EV）充电案例研究，我们验证了该框架的适用性，结果表明其在用户不遵从行为下能有效提升用户满意度与分配质量。