Abstract
An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.
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