Design of Reverse Logistics Network for Remanufacturing Waste Machine Tools Based on Multi-Objective Gray Wolf Optimization Algorithm

The high uncertainty of the recovery time, quantity and quality of waste machine tools has led to dynamic changes in the recycling logistics network and is difficult to plan. Considering factors such as recycling efficiency, cost, and carbon emissions, an optimized model for the recycling network of waste machine tool recycling with the goal of minimizing total operating costs and total carbon tax penalties was proposed. The optimization of the combination of recycling efficiency, cost and carbon emissions of waste machine tools has been achieved. For model solving, an optimization model solving algorithm based on the multi-object gray wolf algorithm was proposed. Problems that are difficult to apply due to too slow convergence speed and too many solving parameters were solved. Finally, the recycling process of waste machine tools of a machine tool remanufacturing enterprise was taken as an example, and the proposed model and algorithm were used to optimize the logistics network of waste machine tools recycling. The results show that the optimal scheme of the optimization model of the recycling network of waste machine tools can be obtained from the proposed model. The gray wolf algorithm is superior to the multi-objective non-dominated sorting genetic algorithm in both the convergence speed and the total cost of recovered logistics. Therefore, the validity and feasibility of the model and algorithm in this paper have been verified.