The Application of the SubChain Salp Swarm Algorithm in the Less-Than-Truckload Freight Matching Problem

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides in the fact that these loads cannot be shipped in a timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays in updating logistic information and higher distribution costs. In order to solve LTL challenges, we developed a novel SubChain Salp Swarm Algorithm (SSSA) by improving the traditional Salp Swarm Algorithm with the utilization of a SubChain operation. Our method aims to find the optimal strategy for maintaining a balance between lower operating costs and customer satisfaction. Our SSSA method combines multiple disconnected SubChain points to separate individual chains to find local optima and obtain better convergence results in the final decision. We have compared our method with mainstream metaheuristic algorithms using logistics datasets from a road freight company in Hangzhou, and the results demonstrate that our method converges faster than other methods and has a lower variance. Our method solves the limitation of local optima observed in other optimization methods and improves customer service in relation to the transportation issue.