Asynchronous optimization of part logistics routing problem

The solution to the capacitated vehicle routing problem (CVRP) is vital for optimizing logistics. However, the transformation of real-world logistics problems into the CVRP involves diverse constraints, interactions between various routes, and a balance between optimization performance and computation load. In this study, we propose a systematic model originating from the part logistic routing problem (PLRP), which is a two-dimensional loading capacitated pickup-and-delivery problem that considers time windows, multiple uses of vehicles, queuing, transit, and heterogeneous vehicles. The newly introduced queuing and transit complicate the problem, and to the best of our knowledge, it cannot be solved using existing methods or the standard commercial optimizer. Hence, this problem has caused the existing research to develop, generalize, and extend into the two-dimensional CVRP (2L-CVRP). To solve this problem, we provide a framework that decouples the combination of 2L-CVRP and global optimization engineering and derives an efficient and realistic solver that integrates diverse types of intelligent algorithms. These algorithms include: (1) a heuristic algorithm for initializing feasible solutions by imitating manual planning, (2) asynchronous simulated annealing (SA) and Tabu search (TS) algorithms to accelerate the optimization of global routes based on novel bundling mechanics, (3) dynamic programming for routing, (4) heuristic algorithms for packing, (5) simulators to review associated time-related constraints, and (6) truck-saving processes to promote the optimal solution and reduce the number of trucks. Moreover, the performances of the SA and TS solver algorithms are compared in terms of various size scales of data to obtain an empirical recommendation for selection. The proposed model successfully established an intelligent management system that can provide systematic solutions for logistics planning, resulting in higher performance and lower costs compared to that of manual planning.