Multi-objective optimization of cycle time and energy consumption in parallel robotic assembly lines using a discrete firefly algorithm

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Soysal-Kurt H., İŞLEYEN S. K.

ENGINEERING COMPUTATIONS, cilt.39, ss.2424-2448, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1108/ec-12-2020-0747
  • Dergi Adı: ENGINEERING COMPUTATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2424-2448
  • Anahtar Kelimeler: Parallel, Robotic assembly line balancing, Multi-objective optimization, Energy consumption, Discrete firefly algorithm, BALANCING PROBLEMS, GENETIC ALGORITHM, MODEL, CARBON
  • Gazi Üniversitesi Adresli: Evet

Özet

Purpose Assembly lines are one of the places where energy consumption is intensive in manufacturing enterprises. The use of robots in assembly lines not only increases productivity but also increases energy consumption and carbon emissions. The purpose of this paper is to minimize the cycle time and total energy consumption simultaneously in parallel robotic assembly lines (PRAL). Design/methodology/approach Due to the NP-hardness of the problem, A Pareto hybrid discrete firefly algorithm based on probability attraction (PHDFA-PA) is developed. The algorithm parameters are optimized using the Taguchi method. To evaluate the results of the algorithm, a multi-objective programming model and a restarted simulated annealing (RSA) algorithm are used. Findings According to the comparative study, the PHDFA-PA has a competitive performance with the RSA. Thus, it is possible to achieve a sustainable PRAL through the proposed method by addressing the cycle time and total energy consumption simultaneously. Originality/value To the best knowledge of the authors, this is the first study addressing energy consumption in PRAL. The proposed method for PRAL is efficient in solving the multi-objective balancing problem.