Trailer allocation and truck routing using bipartite graph assignment and deep reinforcement learning

Trailer allocation and truck routing are critical components of truck transportation management. However, in real-world applications, inter-influence between selecting the best trailers and trucks, strict fulfillment of Pickup or Delivery (PD) orders, and the size of the fleet are some of the challenges that need to be dealt with in a large truck company. In addition, trailer allocation and truck routing problems are considered to be NP-hard combinatorial optimization (CO) problems. Therefore, we use deep reinforcement learning (DRL), which has the capability of solving routing problems with a single set of hyperparameters. This is significant progress toward finding strong heuristics for a special case of the trailer allocation to customers and truck routing problem presented in this article. Given a set of trailers, trucks, customers, and orders we propose a novel two-phase framework based on Bipartite Graph Assignment (BGA) and attention-based DRL to minimize the total traveling distance traveled from trucks to trailers and then to customers. The BGA heuristic finds the minimum traveling distance from the trailers to the customers based on the edge information and the encoder-decoder helps DRL to get useful node and graph feature representations and trains the model to find the proper solutions for the trailer allocation and truck routing problem. Our experiments on three different problem sizes showcase the effectiveness of ARTT-DRL. The results indicate that ARTT-DRL produces desirable outcomes and has strong generalization capabilities.