ReACT-TTC: Capacity-Aware Top Trading Cycles for Post-Choice Reassignment in Shared CPS

TL;DR

This paper introduces a capacity-aware top trading cycles framework for post-deviation reassignment in shared cyber-physical systems, improving efficiency and user satisfaction despite user non-compliance.

Contribution

It extends the classical TTC mechanism to handle capacity constraints and unassigned resources, formalizes the structural cases, and incorporates a Prospect-Theoretic preference model.

Findings

Increases user satisfaction in EV charging scenarios.

Maintains Pareto efficiency, individual rationality, and strategy-proofness.

Effectively handles non-compliance in shared resource management.

Abstract

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.