Intelligent Solution System towards Parts Logistics Optimization
This work addresses a real-world vehicle routing problem for automotive logistics, though it appears incremental as it builds on existing intelligent algorithms.
The authors tackled the complex parts logistics optimization problem for SAIC Motor by proposing an integrated intelligent system combining heuristic initialization, Tabu Search with a novel bundle technique, and post-optimization, resulting in a solution superior to manual planning in performance, customizability, and expandability.
Due to the complication of the presented problem, intelligent algorithms show great power to solve the parts logistics optimization problem related to the vehicle routing problem (VRP). However, most of the existing research to VRP are incomprehensive and failed to solve a real-work parts logistics problem. In this work, towards SAIC logistics problem, we propose a systematic solution to this 2-Dimensional Loading Capacitated Multi-Depot Heterogeneous VRP with Time Windows by integrating diverse types of intelligent algorithms, including, a heuristic algorithm to initialize feasible logistics planning schemes by imitating manual planning, the core Tabu Search algorithm for global optimization, accelerated by a novel bundle technique, heuristically algorithms for routing, packing and queuing associated, and a heuristic post-optimization process to promote the optimal solution. Based on these algorithms, the SAIC Motor has successfully established an intelligent management system to give a systematic solution for the parts logistics planning, superior than manual planning in its performance, customizability and expandability.