A multi parallel mixed-model disassembly line and its balancing optimization for fuel vehicles and pure electric vehicles
🤖 AI Summary
The simultaneous recycling of end-of-life internal combustion engine vehicles and battery electric vehicles introduces uncertainty in mixed-vehicle inflow volumes, while multi-model concurrent disassembly exacerbates challenges in production line design. Method: This paper proposes a Multi-Parallel Mixed Disassembly Line (MPMDL) and its multi-objective balancing optimization framework. We formulate a mixed-integer programming model minimizing workstation count, operator fatigue, and energy consumption. An Improved Non-dominated Sorting Genetic Algorithm III (INSGA-III) is developed, integrating feasible-solution distribution guidance and dynamic search resource allocation, augmented by a two-stage adaptive reconfiguration strategy to enhance line flexibility. Contribution/Results: Experimental results demonstrate superior solution-set quality, convergence, and stability compared to state-of-the-art algorithms. The approach significantly improves resource utilization efficiency and delivers a practical, intelligent optimization paradigm for green automotive dismantling.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing multi-parallel disassembly line balancing for fuel and electric vehicles
Addressing multi-objective optimization with workstations, fatigue, and energy consumption
Developing adaptive disassembly strategies under recycling quantity uncertainty
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-parallel mixed-model disassembly line for hybrid vehicles
Improved genetic algorithm with dynamic resource allocation
Two-stage strategy for adaptive disassembly line adjustment
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