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

Anurag Satpathy, Arindam Khanda, Chittaranjan Swain, Sajal K. Das

TL;DR

ReACT-TTC provides a capacity-aware, post-deviation reassignment mechanism for shared-resource CPS by extending the Top-Trading-Cycle framework to handle many-to-one allocations and idle capacity. It preserves core economic guarantees—Pareto efficiency, individual rationality, and strategy-proofness—while integrating Prospect-Theoretic satisfaction to model realistic user preferences. The method operates on top of any initial allocation and only activates when deviations occur, enabling real-time, decentralized reallocation with low overhead. Validated on EV charging data, the approach yields substantial improvements in user satisfaction and assignment quality, demonstrating its practical utility as a lightweight, domain-agnostic solution for resilient human-in-the-loop CPS.

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.

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

TL;DR

ReACT-TTC provides a capacity-aware, post-deviation reassignment mechanism for shared-resource CPS by extending the Top-Trading-Cycle framework to handle many-to-one allocations and idle capacity. It preserves core economic guarantees—Pareto efficiency, individual rationality, and strategy-proofness—while integrating Prospect-Theoretic satisfaction to model realistic user preferences. The method operates on top of any initial allocation and only activates when deviations occur, enabling real-time, decentralized reallocation with low overhead. Validated on EV charging data, the approach yields substantial improvements in user satisfaction and assignment quality, demonstrating its practical utility as a lightweight, domain-agnostic solution for resilient human-in-the-loop CPS.

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.
Paper Structure (21 sections, 5 theorems, 4 equations, 9 figures, 2 algorithms)

This paper contains 21 sections, 5 theorems, 4 equations, 9 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Non-compliant Behavior in CPS
  4. Dynamic Resource Allocation in CPS
  5. Exchange and Matching Mechanisms in CPS
  6. Research Gaps
  7. System Model and Problem Formulation
  8. System Model
  9. Mapping Ranks to Satisfaction
  10. Prospect-Theoretic Satisfaction Model
  11. Problem Formulation
  12. Proposed Solution
  13. Resource Allocation as Assignment Problem
  14. TTC: Limitations in Constrained CPS
  15. Solution Approach
  16. ...and 6 more sections

Key Result

Theorem 1

TTC terminates after a finite number of rounds and returns a feasible assignment.

Figures (9)

  • Figure 1: An electric vehicle (EV) to charging point (CP) assignment scenario with compliance and non-compliance.
  • Figure 2: TTC example instances with full and partial endowments.
  • Figure 3: Case A, example 1: Preferences, augmented graph, and the final satisfactions ($\alpha = 0.5$).
  • Figure 4: Case A, example 2: Preferences, augmented graph, and the final satisfactions ($\alpha = 0.5$).
  • Figure 5: Case B: Endowments, augmented graph, and two cycle-resolution sequences.
  • ...and 4 more figures

Theorems & Definitions (11)

  • Definition 1: Preferences
  • Definition 2: Assignment
  • Definition 3: Pareto Optimality
  • Definition 4: Individual Rationality
  • Definition 5: Core Stability
  • Definition 6: Strategy-proofness
  • Theorem 1: Termination
  • Theorem 2: Individual rationality
  • Theorem 3: Pareto Optimality
  • Theorem 4: Core Stability
  • ...and 1 more