A multi parallel mixed-model disassembly line and its balancing optimization for fuel vehicles and pure electric vehicles

TL;DR

This paper develops a multi-parallel mixed-model disassembly line for fuel and electric vehicles, optimizing resource use, energy, and worker fatigue through a novel genetic algorithm, addressing industry challenges with a flexible, efficient solution.

Contribution

It introduces a new disassembly line design and an improved multi-objective genetic algorithm for optimizing hybrid vehicle disassembly under uncertainty.

Findings

The proposed method improves resource utilization and reduces energy consumption.

It alleviates worker fatigue and enhances disassembly efficiency.

The INSGA-III algorithm outperforms existing algorithms in solution quality and stability.

Abstract

With the continuous growth of the number of end-of-life vehicles and the rapid increase in the ownership of pure electric vehicles, the automobile disassembly industry is facing the challenge of transitioning from the traditional fuel vehicles to the mixed disassembly of fuel vehicles and pure electric vehicles. In order to cope with the uncertainty of recycling quantity and the demand of mixed-model disassembly of multiple vehicle types, this paper designs a multi-parallel mixed-model disassembly line (MPMDL), and constructs a corresponding mixed-integer planning model for the equilibrium optimization problem of this disassembly line with the optimization objectives of the minimum number of workstations, the minimum fatigue level of workers and the minimum energy consumption. Combining the differences in disassembly processes between fuel vehicles and pure electric vehicles, an improved non-dominated sorting multi-objective genetic algorithm (INSGA-III) based on the distribution of feasible solutions and dynamic search resource allocation is designed to solve this multi-objective dynamic balance optimization problem, and the two-stage dynamic adjustment strategy is adopted to realize the adaptive adjustment of the disassembly line under the uncertainty of the recycling quantity, and, recently, arithmetic validation is carried out. The results show that the proposed method can effectively improve the resource utilization efficiency, reduce energy consumption, alleviate the workers' load, and provide multiple high-quality disassembly solutions under the multi-objective trade-off. Compared with mainstream multi-objective optimization algorithms, the INSGA-III algorithm shows significant advantages in terms of solution quality, convergence and stability. This study provides a green, efficient and flexible solution for hybrid disassembly of fuel and pure electric vehicles.