A Hybrid Reinforcement Learning-Based Model for the Vehicle Routing Problem in Transportation Logistics

Abstract

Currently, the number of deliveries handled by transportation logistics is rapidly increasing because of the significant growth of the e-commerce industry, resulting in the need for improved functional vehicle routing measures for logistic companies. The effective management of vehicle routing helps companies reduce operational costs and increases its competitiveness. The vehicle routing problem (VRP) seeks to identify optimal routes for a fleet of vehicles to deliver goods to customers while simultaneously considering changing requirements and uncertainties in the transportation environment. Due to its combinatorial nature and complexity, conventional optimization approaches may not be practical to solve VRP. In this paper, a new optimization model based on reinforcement learning (RL) and a complementary tree-based regression method is proposed. In our proposed model, when the RL agent performs vehicle routing optimization, its state and action are fed into the tree-based regression model to assess whether the current route is feasible according to the given environment, and the response received is used by the RL agent to adjust actions for optimizing the vehicle routing task. The procedure repeats iteratively until the maximum iteration is reached, then the optimal vehicle route is returned and can be utilized to assist in decision making. Multiple logistics agency case studies are conducted to demonstrate the application and practicality of the proposed model. The experimental results indicate that the proposed technique significantly improves profit gains up to 37.63% for logistics agencies compared with the conventional approaches.

A Tailored Meta-Heuristic for the Autonomous Electric Vehicle Routing Problem Considering the Mixed Fleet

“…Solving large sized problems to optimality in reasonable time is usually unrealistic. Therefore, developing efficient heuristic or meta-heuristic algorithms to quickly obtain good enough solutions would be a better alternative [34].…”

Research on multi-objective Green Permutation Flow Shop Scheduling Problem Method based on Deep Reinforcement Learning

“…Lin et al 29 established an end-to-end DRL framework to effectively address the electric VRP considering time windows. Phiboonbanakit et al 30 formulated a novel optimization model that integrates RL with a complementary tree-based regression method to efficiently solve the VRP under changing requirements and uncertainties in the transportation environment. Fellek et al 31 introduced an attention-based end-to-end DRL model that incorporates edge information between nodes to enable comprehensive graph representation learning for solving the VRP.…”

Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment

“…In [19], the proximal policy optimization (PPO) is utilized to address the multiagent formation control with obstacle avoidance. Phiboonbanakit et al develop a hybrid optimization model via RL and a complementary tree-based regression method to solve the vehicle routing problem in transportation logistics [20]. An improved Dyna-Q algorithm is proposed to deal with the mobile robot path planning in an unknown environment [21], in which the action-selection strategy, ε -greedy policy, and heuristic reward function and actions are utilized to enhance the performance.…”