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Enerji verimli sıra-bağımlı hazırlık zamanlı karışık modelli robotik çift taraflı montaj hattı dengeleme problemlerinin çözümü için bir hibrit genetik algoritma

Yıl 2024, Cilt: 30 Sayı: 7, 944 - 956, 28.12.2024

Öz

Son on yılda küresel ısınma ve iklim değişikliği gibi çevresel sorunlar, kamuoyunun giderek dikkatini çekmiştir. Parasal teşviklerin yanısıra, çevresel koruma ve sürdürülebilir enerji kaynağı arayışı nedeniyle endüstride enerji kullanımı daha da önem arz etmiştir. Aynı zamanda, enerji verimliliği sorunu, araştırmacılar ve üreticiler için de önemli bir odak noktası olarak öne çıkmaktadır. Verimli montaj hattı dengeleme, üretim etkinliğini artırmada önemli bir rol oynamaktadır. Robotik çift taraflı montaj hattı dengeleme problemi (RÇMHDP), yüksek hacimde büyük ürünler üreten üretim tesislerinde yaygın olarak karşılaşılan bir problemdir. Bu montaj hattında, ürünü üretmek için her montaj hattı istasyonunda birden fazla robot bulunur. İki taraflı montaj hatlarında robotların kullanımı, özellikle yüksek işçilik maliyetleri nedeniyle yaygın bir şekilde tercih edilmektedir. Ancak, bu durumda da enerji maliyetleri sorunu ortaya çıkmaktadır. Bu nedenle bu çalışmada; sıra- bağımlı hazırlık zamanlı karışık modelli robotik çift taraflı montaj hattı dengeleme problemleri için, uyarlanabilir yerel arama mekanizmasını içeren yeni bir hibrit genetik algoritma önerilmiştir. Bu algoritmanın iki ana amacı vardır: çevrim süresini (zamana dayalı yaklaşım) ve toplam enerji tüketimini (enerjiye dayalı yaklaşım) en aza indirmek. Yönetimsel önceliklere bağlı olarak, farklı üretim zaman dilimleri için zamana dayalı veya enerjiye dayalı model seçilebilir.

Kaynakça

  • [1] United Nations Climate Change. “Key aspects of the Paris Agreement” https://unfccc.int/most-requested/key-aspects-of-the-paris-agreement (26.09.2023).
  • [2] Sun B, Wang L, Peng Z. “Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing”. Computers & Industrial Engineering, 146, 1-34, 2020.
  • [3] Scholl A. Balancing and Sequencing of Assembly lines. 1st ed. Heidelberg, Germany, Physica-Verlag, 1995.
  • [4] Boysen N, Fliedner M, Scholl A. “Sequencing mixed-model assembly lines: Survey, classification and model critique”. European Journal of Operational Research, 192(2), 349-373, 2009.
  • [5] Sivasankaran P, Shahabudeen P. “Literature review of assembly line balancing problems”. The International Journal of Advanced Manufacturing Technology, 73(9-12), 1665-1694, 2014.
  • [6] Becker C, Scholl A. “A survey on problems and methods in generalized assembly line balancing”. European Journal of Operational Research, 168(3), 694-715, 2006.
  • [7] Boysen N, Fliedner M, Scholl A. “A classification of assembly line balancing problems”. European Journal of Operational Research, 183(2), 674-693, 2007.
  • [8] Bartholdi JJ. “Balancing two-sided assembly lines: a case study”. International Journal of Production Research, 31(10), 2447-2461, 1993.
  • [9] Li Z, Kucukkoc I, Nilakantan JM. “Comprehensive review and evaluation of heuristics and meta-heuristics for two-sided assembly line balancing problem”. Computers & Operations Research, 84, 146-161, 2017.
  • [10] Soysal-Kurt H, İşleyen SK. “Multi-objective optimization of cycle time and energy consumption in parallel robotic assembly lines using a discrete firefly algorithm”. Engineering Computations, 39(6), 2424-2448, 2022.
  • [11] Zhou B, Wu Q. “Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems”. Journal of Manufacturing Systems, 55, 30-43, 2020.
  • [12] Rubinovitz J, Bukchin J, Lenz E. “RALB-A Heuristic Algorithm for Design and Balancing of Robotic Assembly Lines”. CIRP Annals, 42(1), 497-500, 1993.
  • [13] Aghajani M, Ghodsi R, Javadi B. “Balancing of robotic mixed-model two-sided assembly line with robot setup times”. The International Journal of Advanced Manufacturing Technology, 74(5-8), 1005-1016, 2014.
  • [14] Çil ZA, Mete S, Ağpak K. “Analysis of the type II robotic mixed-model assembly line balancing problem”. Engineering Optimization, 49(6), 990-1009, 2017.
  • [15] Chutima P. “A comprehensive review of robotic assembly line balancing problem”. Journal of Intelligent Manufacturing, 33(1), 1-34, 2022.
  • [16] Nilakantan JM, Huang GQ, Ponnambalam SG. “An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems”. Journal of Cleaner Production, 90, 311-325, 2015.
  • [17] Li Z, Tang Q, Zhang L. “Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm”. Journal of Cleaner Production, 135, 508-522, 2016.
  • [18] Nilakantan JM, Li Z, Tang Q, Nielsen P. “Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems”. Journal of Cleaner Production, 156, 124-136, 2017.
  • [19] Zhang Z, Tang Q, Zhang L. “Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem”. Journal of Cleaner Production, 215, 744-756, 2019.
  • [20] Zhang Z, Tang Q Li Z, Zhang L. “Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems”. International Journal of Production Research, 57(17), 5520-5537, 2019.
  • [21] Baş İ, Tosun Ö, Bayram V. “Line balancing optimization under robot location and worker-station assignment considerations: A case study of a dishwasher factory”. Pamukkale University Journal of Engineering Sciences, 27(4), 495-503, 2021.
  • [22] Aslan Ş. “Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times”. Optimization and Engineering, 24(2), 989-1016, 2023.
  • [23] Li Z, Janardhanan MN, Tang Q, Ponnambalam SG. “Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times”. Swarm and Evolutionary Computation, 50, 1-17, 2019.
  • [24] Scholl A, Boysen N, Fliedner M. “The assembly line balancing and scheduling problem with sequence-dependent setup times: problem extension, model formulation and efficient heuristics”. OR Spectrum, 35(1), 291-320, 2013.
  • [25] Yun Y. “Hybrid genetic algorithm with adaptive local search scheme”. Computers & Industrial Engineering, 51(1), 128-141, 2006.
  • [26] Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. 1st ed. Boston, USA, Addison-Wesley, 1989.
  • [27] Goldberg DE, Lingle R. “Alleles, Loci, and the traveling salesman problem”. First International Conference on Genetic Algorithms and their Applications, Pittsburgh, USA, 24-26 July 1985.
  • [28] Kim YK, Kim Y, Kim YJ. “Two-sided assembly line balancing: A genetic algorithm approach”. Production Planning & Control, 11(1), 44-53, 2000.
  • [29] Lee TO, Kim Y, Kim YK. “Two-sided assembly line balancing to maximize work relatedness and slackness”. Computers & Industrial Engineering, 40(3), 273-292, 2001.
  • [30] Özcan U, Toklu B. “Balancing of mixed-model two-sided assembly lines”. Computers & Industrial Engineering, 57(1), 217-227, 2009.
  • [31] Delice Y, Kızılkaya Aydoğan E, Özcan U, İlkay MS. “A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing”. Journal of Intelligent Manufacturing, 28(1), 23-36, 2017.

A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Yıl 2024, Cilt: 30 Sayı: 7, 944 - 956, 28.12.2024

Öz

Serious environmental challenges such as global warming and climate change have captured a growing amount of public awareness in the last decade. Besides monetary incentives, the drive for environmental preservation and the pursuit of a sustainable energy source have contributed to an increased recognition of energy usage within the industrial sector. Meanwhile, the challenge of energy efficiency stands out as a major focal point for researchers and manufacturers alike. Efficient assembly line balancing plays a vital role in enhancing production effectiveness. The robotic two-sided assembly line balancing problem (RTALBP) commonly arises in manufacturing facilities that produce large-sized products in high volumes. In this scenario, multiple robots are placed at each assembly line station to manufacture the product. The utilization of robots is extensive within two-sided assembly lines, primarily driven by elevated labour expenses. However, this adoption has resulted in the challenge of increasing energy consumption. Therefore, in this study, a new hybrid genetic algorithm is introduced, incorporating an adaptive local search mechanism. for the mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. This algorithm has two main objectives: minimizing cycle time (time-based approach) and overall energy consumption (energy-based approach). Depending on managerial priorities, either the time-based or energy-based model can be chosen for different production timeframes.

Kaynakça

  • [1] United Nations Climate Change. “Key aspects of the Paris Agreement” https://unfccc.int/most-requested/key-aspects-of-the-paris-agreement (26.09.2023).
  • [2] Sun B, Wang L, Peng Z. “Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing”. Computers & Industrial Engineering, 146, 1-34, 2020.
  • [3] Scholl A. Balancing and Sequencing of Assembly lines. 1st ed. Heidelberg, Germany, Physica-Verlag, 1995.
  • [4] Boysen N, Fliedner M, Scholl A. “Sequencing mixed-model assembly lines: Survey, classification and model critique”. European Journal of Operational Research, 192(2), 349-373, 2009.
  • [5] Sivasankaran P, Shahabudeen P. “Literature review of assembly line balancing problems”. The International Journal of Advanced Manufacturing Technology, 73(9-12), 1665-1694, 2014.
  • [6] Becker C, Scholl A. “A survey on problems and methods in generalized assembly line balancing”. European Journal of Operational Research, 168(3), 694-715, 2006.
  • [7] Boysen N, Fliedner M, Scholl A. “A classification of assembly line balancing problems”. European Journal of Operational Research, 183(2), 674-693, 2007.
  • [8] Bartholdi JJ. “Balancing two-sided assembly lines: a case study”. International Journal of Production Research, 31(10), 2447-2461, 1993.
  • [9] Li Z, Kucukkoc I, Nilakantan JM. “Comprehensive review and evaluation of heuristics and meta-heuristics for two-sided assembly line balancing problem”. Computers & Operations Research, 84, 146-161, 2017.
  • [10] Soysal-Kurt H, İşleyen SK. “Multi-objective optimization of cycle time and energy consumption in parallel robotic assembly lines using a discrete firefly algorithm”. Engineering Computations, 39(6), 2424-2448, 2022.
  • [11] Zhou B, Wu Q. “Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems”. Journal of Manufacturing Systems, 55, 30-43, 2020.
  • [12] Rubinovitz J, Bukchin J, Lenz E. “RALB-A Heuristic Algorithm for Design and Balancing of Robotic Assembly Lines”. CIRP Annals, 42(1), 497-500, 1993.
  • [13] Aghajani M, Ghodsi R, Javadi B. “Balancing of robotic mixed-model two-sided assembly line with robot setup times”. The International Journal of Advanced Manufacturing Technology, 74(5-8), 1005-1016, 2014.
  • [14] Çil ZA, Mete S, Ağpak K. “Analysis of the type II robotic mixed-model assembly line balancing problem”. Engineering Optimization, 49(6), 990-1009, 2017.
  • [15] Chutima P. “A comprehensive review of robotic assembly line balancing problem”. Journal of Intelligent Manufacturing, 33(1), 1-34, 2022.
  • [16] Nilakantan JM, Huang GQ, Ponnambalam SG. “An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems”. Journal of Cleaner Production, 90, 311-325, 2015.
  • [17] Li Z, Tang Q, Zhang L. “Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm”. Journal of Cleaner Production, 135, 508-522, 2016.
  • [18] Nilakantan JM, Li Z, Tang Q, Nielsen P. “Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems”. Journal of Cleaner Production, 156, 124-136, 2017.
  • [19] Zhang Z, Tang Q, Zhang L. “Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem”. Journal of Cleaner Production, 215, 744-756, 2019.
  • [20] Zhang Z, Tang Q Li Z, Zhang L. “Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems”. International Journal of Production Research, 57(17), 5520-5537, 2019.
  • [21] Baş İ, Tosun Ö, Bayram V. “Line balancing optimization under robot location and worker-station assignment considerations: A case study of a dishwasher factory”. Pamukkale University Journal of Engineering Sciences, 27(4), 495-503, 2021.
  • [22] Aslan Ş. “Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times”. Optimization and Engineering, 24(2), 989-1016, 2023.
  • [23] Li Z, Janardhanan MN, Tang Q, Ponnambalam SG. “Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times”. Swarm and Evolutionary Computation, 50, 1-17, 2019.
  • [24] Scholl A, Boysen N, Fliedner M. “The assembly line balancing and scheduling problem with sequence-dependent setup times: problem extension, model formulation and efficient heuristics”. OR Spectrum, 35(1), 291-320, 2013.
  • [25] Yun Y. “Hybrid genetic algorithm with adaptive local search scheme”. Computers & Industrial Engineering, 51(1), 128-141, 2006.
  • [26] Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. 1st ed. Boston, USA, Addison-Wesley, 1989.
  • [27] Goldberg DE, Lingle R. “Alleles, Loci, and the traveling salesman problem”. First International Conference on Genetic Algorithms and their Applications, Pittsburgh, USA, 24-26 July 1985.
  • [28] Kim YK, Kim Y, Kim YJ. “Two-sided assembly line balancing: A genetic algorithm approach”. Production Planning & Control, 11(1), 44-53, 2000.
  • [29] Lee TO, Kim Y, Kim YK. “Two-sided assembly line balancing to maximize work relatedness and slackness”. Computers & Industrial Engineering, 40(3), 273-292, 2001.
  • [30] Özcan U, Toklu B. “Balancing of mixed-model two-sided assembly lines”. Computers & Industrial Engineering, 57(1), 217-227, 2009.
  • [31] Delice Y, Kızılkaya Aydoğan E, Özcan U, İlkay MS. “A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing”. Journal of Intelligent Manufacturing, 28(1), 23-36, 2017.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Algoritmalar ve Hesaplama Kuramı, Veri Yapıları ve Algoritmalar
Bölüm Makale
Yazarlar

Şehmus Aslan

Yayımlanma Tarihi 28 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 30 Sayı: 7

Kaynak Göster

APA Aslan, Ş. (2024). A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 30(7), 944-956.
AMA Aslan Ş. A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Aralık 2024;30(7):944-956.
Chicago Aslan, Şehmus. “A Hybrid Genetic Algorithm for Solving Energy-Efficient Mixed-Model Robotic Two-Sided Assembly Line Balancing Problems With Sequence-Dependent Setup Times”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30, sy. 7 (Aralık 2024): 944-56.
EndNote Aslan Ş (01 Aralık 2024) A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30 7 944–956.
IEEE Ş. Aslan, “A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 30, sy. 7, ss. 944–956, 2024.
ISNAD Aslan, Şehmus. “A Hybrid Genetic Algorithm for Solving Energy-Efficient Mixed-Model Robotic Two-Sided Assembly Line Balancing Problems With Sequence-Dependent Setup Times”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30/7 (Aralık 2024), 944-956.
JAMA Aslan Ş. A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30:944–956.
MLA Aslan, Şehmus. “A Hybrid Genetic Algorithm for Solving Energy-Efficient Mixed-Model Robotic Two-Sided Assembly Line Balancing Problems With Sequence-Dependent Setup Times”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 30, sy. 7, 2024, ss. 944-56.
Vancouver Aslan Ş. A hybrid genetic algorithm for solving energy-efficient mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30(7):944-56.





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