Research on Multi-Objective Optimal Scheduling for Power Battery Reverse Supply Chain

In the context of carbon neutralization, electric vehicle and energy storage market is growing rapidly. As a result, battery recycling an important work with consideration advent retirement resource constraints, environmental factors, regional price factors. Based on theoretical research intelligent algorithm mathematical models, integer programming model urban power reverse supply chain scheduling was established goal highest customer satisfaction least total cost logistics distribution, to study influence resources operation status built city center dismantling chain. The includes load, demand point range, service capacity constraints. This collected image data, conducted analysis, further designed improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization suitable solve global problem by introducing improvement strategy convergence rate, particle search, traditional elite individual retention. results verified practicability model, ability problem, speed through comparing obtained from basic algorithm. A reasonable comprehensive solution for location path also obtained. Multi-objective carried out in scheduling, facility construction, construction. integrated software were compared. We found that scheme provided can significantly reduce enterprise. new insights enterprises effectively utilize optimize battery.