The Cumulative Capacitated Vehicle Routing Problem with Time-dependent on Humanitarian Logistics for Disaster Management

Abstract

This study addresses the challenges of optimizing humanitarian logistics during disaster management by developing a Cumulative Capacitated Vehicle Routing Problem with Time-Dependent factors (CCVRP-TD) model. The primary objective is to enhance delivery efficiency by incorporating time-dependent variables such as fluctuating traffic and service durations into route planning. The research contributes a novel Mixed Integer Nonlinear Programming (MINLP) framework that dynamically adapts to real-world conditions like road closures and shifting priorities. Using the MINLP approach, the model was validated through numerical experiments involving four delivery vehicles serving six customers across five routes. Results demonstrated a significant improvement in routing efficiency, with a total cumulative travel distance of 110 km and adherence to specified delivery windows, such as 9:30 AM and 10:30 AM for Customer 1. Additionally, vehicle capacity constraints were effectively managed, with individual route lengths ranging from 20 to 35 km. These findings showcase the model’s ability to balance cost minimization, service reliability, and logistical adaptability. The novelty lies in the integration of time-dependent costs and service benefits into a multi-depot framework, enabling flexible yet precise route optimization under constrained conditions. This research provides a robust tool for enhancing disaster logistics and offers practical implications for improving the responsiveness and effectiveness of humanitarian aid delivery.