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

Abstract

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.

Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

“…e first item represents the cost of the logistics company to distribute a logistics task, the second item represents the cost of the logistics company to transport the logistics task to the cooperative distribution company, and the third item represents the distribution cost required by the cooperative distribution company. e objective function indicates that the points with F > 0 are selected, and the distribution points requiring transshipment are calculated; equation (2) indicates that the transfer point cannot transfer within the company; equation (3) indicates that only one company can be responsible for a demand task; equation (4) indicates that the distribution weight of the distribution company is not greater than the bearing weight of the company; equation ( 5) is an integer constraint.…”

“…e requirements for logistics are higher in the whole vehicle production plant. Because the automobile is a highly integrated product, nearly 10000 parts of the whole vehicle are basically distributed to the final assembly line of the factory through logistics except the body of the vehicle [3]. With the increasingly fierce competition in the automotive industry and the increasing pressure on quality, cost, and efficiency, the logistics of the final assembly workshop has developed rapidly from rough logistics to lean logistics.…”

Design and Implementation of Energy-Saving Logistics Management System for Route Optimization

“…Repeat this process until the standard measure function starts to converge. The K-calculation steps are as follows: (1) Choose K samples as the initial cluster centers Z 1 ð1Þ, Z 2 ð1Þ,…, Z k ð1Þ, K < N. The number in parentheses indicates the number of iterative operations to find cluster centers [14,15]. (2) Assign the remaining samples X to one of the K cluster centers according to the principle of minimum distance, namely,…”

Transport Optimization of the Reverse Logistics System During Territories Sustainable Development

“…In [18], the authors also took into account the carbon footprint when creating a model for optimizing the reverse logistics network for waste recycling. A solution method based on a multipurpose gray wolf optimization algorithm was proposed, which solves the problems of low convergence rate and many parameters that were difficult to apply.…”