Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption

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Soysal-Kurt H., İŞLEYEN S. K., GÖKÇEN H.

Flexible Services and Manufacturing Journal, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s10696-024-09533-1
  • Dergi Adı: Flexible Services and Manufacturing Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Balancing and sequencing problem, Energy consumption, Mixed-model, NSGA-II, Parallel, Robotic assembly line
  • Gazi Üniversitesi Adresli: Evet

Özet

As technology advances, the integration of robots in the assembly line has become widespread. While robots offer numerous benefits, such as increased productivity and improved product quality, they also result in higher energy usage. Finding the optimal line balance while considering energy consumption is a challenging task in a robotic assembly line that produces multiple product models in a mixed sequence. This paper addresses the mixed-model parallel robotic assembly line balancing and model sequencing (MPRALB/S) problem. The objectives of this problem are to minimize cycle time and energy consumption. The authors have not found any existing research on this topic in the literature. To solve the MPRALB/S problem, a modified non-dominated sorting genetic algorithm II (MNSGA-II) is developed. Since there is no existing benchmark data for the MPRALB/S problem, new datasets are generated for this study. The MPRALB/S problem is illustrated through a numerical example. The performance of MNSGA-II is evaluated with non-dominated sorting genetic algorithm II (NSGA-II) and restarted simulated annealing through commonly used performance metrics including hypervolume ratio (HVR), ratio of non-dominated solutions (RP) and generational distance (GD). According to the results of the computational study, MNSGA-II outperforms NSGA-II in approximately 81% of the problem instances for HVR, 71% for RP, and 76% for GD. The results show that MNSGA-II is an effective approach for solving the MPRALB/S problem and achieves competing performance compared to other algorithms.