Optimization of kitting material distribution of mixed-model assembly line under robot-operator picking environment
To improve the intelligent level of distribution in automobile manufacturing enterprises and solve the problems of low efficiency and high cost of manual picking,a robot-operator picking mode based on kitting policy was introduced.The total costs,including robot usage,labor and WIP inventory costs were minimized by optimizing the number of labors,ro-bots and tour period.To solve this optimization problem,an improved quantum-inspired ant colony optimization algorithm was constructed.The superposition of quantum bits was used to increase the population diversity and avoid the algorithm falling into local optimization.An improved quantum rotation gate update mechanism and a non-optimal individual optimiza-tion strategy based on differential evolution operation were designed to improve the convergence speed and quality of the al-gorithm.Finally,numerical experiments were presented to demonstrate the correctness of the model and the effectiveness of the algorithm,and the influence of picking batch size on cost was analyzed.
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