IEEE Access (Jan 2022)

Research on the Scheduling of Mobile Robots in Mixed-Model Assembly Lines Considering Workstation Satisfaction and Energy Consumption

  • Xiaoyun Ma,
  • Xiaoye Zhou

DOI
https://doi.org/10.1109/ACCESS.2022.3197791
Journal volume & issue
Vol. 10
pp. 84738 – 84753

Abstract

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With the intensification of the global energy crisis, the production costs of manufacturing companies have increased significantly. To reduce the production energy consumption and costs in mixed-model assembly lines while improving efficiency and workstation satisfaction, novel line-integrated supermarkets and mobile robots are introduced. Considering the split delivery caused by workstation satisfaction and the mobile robot’s energy limitation, a multiobjective mathematical model of mobile robot scheduling in a mixed-model assembly line with a fuzzy time window is presented with the goal of maximizing workstation satisfaction while minimizing energy consumption. On this basis, according to the problem’s characteristics, a nondominated sorting genetic algorithm II with variable neighborhood search (VNSGA-II) is developed that constructs the initial solution using a heuristic method, improves crossover operation, and performs neighborhood search using three operators: exchange, insertion, and 2-opt to improve the solution’s quality. Finally, two numerical experiments are used to validate the model and algorithm. The results demonstrate that: 1) The scheduling model for mobile robots in a mixed-model assembly line that allows for spilt delivery and uses a normal fuzzy membership function to characterize workstation satisfaction is more in line with production practice. 2) The VNSGA-II algorithm can quickly establish a reasonable scheduling scheme for mobile robots in a mixed-model assembly line, and provide managers with a basis for making scientific decisions. Compared to MOPSO and NSGA-II, workstation satisfaction has improved by 0.91% and 1.12%, respectively, and mobile robots’ energy consumption has decreased by 12.53% and 13.66%, respectively.

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