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Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks

Ting Bai, Xinfeng Ru, Shaoyuan Li, Andreas A. Malikopoulos

Abstract

In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation of each truck to minimize the total operational cost of the fleet. The problem is inherently combinatorial and nonlinear due to the coupling between discrete sequencing decisions and continuous charging control, rendering exact optimization intractable for real-time implementation. To address this challenge, we propose a rollout-based dynamic programming framework built upon an inner-outer two-layer structure, which decouples ordering decisions from the schedule optimization, thus enabling efficient policy evaluation and approximation. The proposed method achieves near-optimal solutions with polynomial-time complexity and adapts to dynamic arrivals and time-varying electricity prices. Simulation studies show that the rollout-based approach significantly outperforms conventional heuristics with high computational efficiency, demonstrating its effectiveness and practical applicability for real-time charging management in large-scale transportation networks.

Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks

Abstract

In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation of each truck to minimize the total operational cost of the fleet. The problem is inherently combinatorial and nonlinear due to the coupling between discrete sequencing decisions and continuous charging control, rendering exact optimization intractable for real-time implementation. To address this challenge, we propose a rollout-based dynamic programming framework built upon an inner-outer two-layer structure, which decouples ordering decisions from the schedule optimization, thus enabling efficient policy evaluation and approximation. The proposed method achieves near-optimal solutions with polynomial-time complexity and adapts to dynamic arrivals and time-varying electricity prices. Simulation studies show that the rollout-based approach significantly outperforms conventional heuristics with high computational efficiency, demonstrating its effectiveness and practical applicability for real-time charging management in large-scale transportation networks.

Paper Structure

This paper contains 18 sections, 29 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Modeling of the Charging Process
  4. Ordering Constraints
  5. Charging Scheduling Optimization Problem
  6. Rollout-Based Charging Scheduling Approach
  7. Two-Layer Optimization Structure
  8. Rollout-Based DP Approach
  9. Rollout Policy and Algorithm Design
  10. Optimality and Computational Complexity
  11. Discussion on Optimality
  12. Complexity Analysis
  13. Simulation Studies
  14. Setup
  15. Comparison Results
  16. ...and 3 more sections

Figures (4)

  • Figure E1: Comparison of the total cost ($N\!=\!8$).
  • Figure E2: Comparison of the charging schedules under (a) optimal solution, and (b) RO (EDF) solution, for $N\!=\!8$.
  • Figure E3: Comparison of the total cost ($N\!=\!100$).
  • Figure E4: Comparison of the total charging power ($N\!=\!100$).

Theorems & Definitions (3)

  • Remark 1
  • Remark 2
  • Remark 3