Repair-Integrated Transport Optimization for Returnable Assets

Abstract

Returnable Transport Items (RTIs) play a critical role in sustainable logistics by enabling asset reuse across supply chains. However, damaged RTIs must be efficiently transported to repair centers and reintegrated into the network to maintain service quality and cost-efficiency. This thesis addresses the challenge of optimizing such repair-integrated transport flows within RTI pooling systems, using Container Centralen as a real-world case study. An Integer Linear Programming (ILP) model is developed to jointly determine the optimal allocation of broken RTIs to repair centers and the subsequent redistribution of repaired items to depots, under constraints such as repair capacity, transport time, and demand fulfillment. The model is implemented in Python using Gurobi and embedded in a user-friendly dashboard for decision support. Through simulation experiments based on historical data and realistic scenarios, the integrated approach demonstrates significant improvements over heuristic strategies, including reduced transport distances, better repair center utilization, and improved service reliability. The findings highlight the practical and theoretical value of integrated logistics modeling for RTI networks and propose several avenues for future research, including the extension to multiple RTI types, incorporation of uncertainty, and scalability enhancements. This work contributes to both operations research literature and practical decision-making in circular supply chain management.