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Kaynak atölyesi çizelgeleme probleminin değişken iş istasyonu kısıtlaması altında optimizasyonu: Arena simülasyonu tabanlı genetik algoritma ile endüstriyel bir uygulama

Yıl 2022, Cilt: 28 Sayı: 1, 139 - 147, 28.02.2022

Öz

İş atölyesi planlaması, 'emek yoğun proje tipi üretim' için zor bir konudur. Çünkü bu tür bir üretimde, gerçek işlem süreleri üretim bitene kadar tam olarak bilinmez ve bu işlem süreleri siparişin teknik özelliklerine göre değişir. İşlem sürelerini tahmin etmek için olasılık dağılımlarını kullanmak uygun bir yöntemdir. Bu makale, emek-yoğun proje tipi çalışan kaynak atölyesinin değişken iş istasyonu kısıtlamaları altında planlanması için endüstriyel bir uygulama sunmaktadır. Bu kısıt, ürünlerin boyuna bağlı olarak ortaya çıkan özel bir üretim şeklinin sonucudur. Amaç, bir grup bekleyen iş emrinin tamamlanma süresini en aza indirmektir. Genetik algoritma (GA) bu amaçla, atölyeye girmeyi bekleyen iş emirlerinin atölyeye giriş sırasını oluşturmak ve bunları 6 özdeş kaynak istasyonuna göndermek için kullanılır. Atölyenin dinamik koşulları, Arena simülasyon programı ile simüle edilir. Algoritmanın girdi verileri olarak stokastik işlem süreleri kullanılır. Kaynak iş istasyonu çizelgeleme için değişken iş istasyonu kısıtlaması altında stokastik işleme sürelerinin kullanılması daha önce araştırılmamıştır. Deneysel sonuçlara göre, GA ve Arena simülasyonu birlikte, değişken iş istasyonu kısıtlaması altında bu tür problemlerde bir grup işin toplam tamamlanma zamanını etkili bir şekilde azaltmaktadır. GA destekli Arena çizelgesi, bu sorun için GA kullanmadan önerilen çizelgeden daha iyi performans gösterir. Simülasyon sonuçları, bekleyen siparişlerin toplam üretim süresinin, GA kullanılmadan önerilen çizelgelerle karşılaştırıldığında yaklaşık % 9,25 oranında azaldığını göstermektedir

Kaynakça

  • [1] Jia Z, Lu X, Yang J, Jia D. “Research on job-shop scheduling problem based on genetic algorithm”. International Journal of Production Research, 49(12), 3585-3604, 2011.
  • [2] Azadeh A, Negahban A, Moghaddam M. “A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems”. International Journal of Production Research, 50(2), 551-566, 2012.
  • [3] Huang XW, Zhao XY, Ma XL. “An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility”. International Journal of Simulation Modelling, 13(4), 510-522, 2014.
  • [4] Aydemir E, Koruca HI. “A new production scheduling module using priority-rule based genetic algorithm”. International Journal of Simulation Modelling, 14(3), 450-462, 2015.
  • [5] Ba L, Li Y, Yang MS, Gao XQ, Liu Y. “Modelling and simulation of a multi-resource flexible job-shop scheduling”. International Journal of Simulation Modelling, 15(1), 157-169, 2016.
  • [6] Deng Q, Gong G, Gong X, Zhang L, Liu W, Ren Q. “A bee evolutionary guiding nondominated sorting genetic algorithm II for multiobjective flexible job-shop scheduling”. Computational Intelligence and Neuroscience, 2017. https://doi.org/10.1155/2017/5232518.
  • [7] Ocaktan MAB, Kucukkoc I, Karaoglan AD, Cicibas A, Buyukozkan K. “Scheduling Customized Orders: A Case Study at BEST Transformers Company”. 6th International Conference on Mechanics and Industrial Engineering (ICMIE’17), Rome, Italy, 8-10 June, 2017.
  • [8] Zhang W, Wen JB, Zhu YC, Hu Y. “Multi-objective scheduling simulation of flexible job-shop based on multipopulation genetic algorithm”. International Journal of Simulation Modelling, 16(2), 313-321, 2017.
  • [9] Hu HX, Lei WX, Gao X, Zhang Y. “job-shop scheduling problem based on improved cuckoo search algorithm”. International Journal of Simulation Modelling, 17(2), 337-346, 2018.
  • [10] Jiang T, Zhang C, Zhu H, Deng G. “Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode”. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/8574892
  • [11] Jiang T, Zhang C, Zhu H, Gu J, Deng G. “Energy-efficient scheduling for a job shop using an improved whale optimization algorithm”. Mathematics, 2018. https://doi.org/10.3390/math6110220
  • [12] Seng DW, Li JW, Fang XJ, Zhang XF, Chen J. “low-carbon flexible job-shop scheduling based on improved nondominated sorting genetic algorithm-II”. International Journal of Simulation Modelling, 17(4), 712-723, 2018.
  • [13] Karaoglan AD, Ocaktan MAB, Ocaktan DG, Oral A, Kundakci SS, Tuncer C. “Scheduling customized orders by considering the ergonomic constraints: a case study at Yemtar company”. 7th International Conference on Mechanics and Industrial Engineering (ICMIE’18), Madrid, Spain, 16-18 August, 2018.
  • [14] Zhang HP, Ye JH, Yang XP, Muruve NW, Wang JT. ”Modified binary particle swarm optimization algorithm in lotsplitting scheduling involving multiple techniques”. International Journal of Simulation Modelling, 17(3), 534-542, 2018.
  • [15] Zhong Q, Yang H, Tang T. “Optimization algorithm simulation for dual-resource constrained job-shop scheduling”. International Journal of Simulation Modelling, 17(1), 147-158, 2018.
  • [16] Sel C, Hamzadayi A. "A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(4), 665-674, 2018.
  • [17] Fu HC, Liu P. “A multi-objective optimization model based on non-dominated sorting genetic algorithm”. International Journal of Simulation Modelling, 18(3), 510-520, 2019.
  • [18] Tang H, Chen R, Li Y, Peng Z, Guo S, Du Y. “Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop”. Applied Soft Computing, 78, 176-194, 2019.
  • [19] Liao J, Lin C. “Optimization and simulation of job-shop supply chain scheduling in manufacturing enterprises based on particle swarm optimization”. International Journal of Simulation Modelling, 18(1), 187-196, 2019.
  • [20] Wang Y, Yang O, Wang SN. “A solution to single-machine inverse job-shop scheduling problem”. International Journal of Simulation Modelling, 18(2), 335-343, 2019.
  • [21] Zhang Z, Guan ZL, Zhang J, Xie X. “A novel job-shop scheduling strategy based on particle swarm optimization and neural network”. International Journal of Simulation modeling, 18(4), 699-707, 2019.
  • [22] Zhu J, Shao ZH, Chen C. “An improved whale optimization algorithm for job-shop scheduling based on quantum computing”. International Journal of Simulation Modelling, 18(3), 521-530, 2019.
  • [23] Karaoglan AD, Cetin E. Industrial Engineering in the Big Data Era. Editors: Calisir F, Cevikcan E, Akdag HC. Part I: Industrial Engineering, Artificial Bee Colony Algorithm for Labor Intensive Project Type Job Shop Scheduling Problem: A Case Study, 79-88, Cham, Switzerland, Springer, 2019.
  • [24] Shi DL, Zhang BB, Li Y. “A multi-objective flexible job-shop scheduling model based on fuzzy theory and immune genetic algorithm”. International Journal of Simulation Modelling, 19(1), 123-133, 2020.
  • [25] Gu J, Jiang T, Zhu H, Zhang C. “Low-carbon job shop scheduling problem with discrete genetic-grey wolf optimization algorithm”. Journal of Advanced Manufacturing Systems, 19(1), 1-14, 2020.
  • [26] Vital-Soto A, Azab A, Mohammed FB. “Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility”. Journal of Manufacturing Systems, 54, 74-93, 2020.
  • [27] Rao Y, Meng R, Zha J, Xu X. “Bi-objective mathematical model and improved algorithm for optimisation of welding shop scheduling problem”. International Journal of Production Research, 58(9), 2767-2783, 2020.
  • [28] Holland JH. Adaptation in Natural and Artificial Systems, Cambridge, Massachusetts, USA, MIT Press, 1975.
  • [29] Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA, Addison-Wesley Longman Publishing, 1989.
  • [30] Lim C, Sim E, “Production planning in manufacturing/remanufacturing environment using genetic algorithm”. Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (GECCO’05), Washington, USA, 25-29 June, 2005.
  • [31] Wall M. A Genetic Algorithm for Resource-Constrained Scheduling. PhD Thesis, Massachusetts Institute of Technology, Cambridge-Massachusetts, USA, 1996.
  • [32] Kachitvichyanukul V. "Comparison of three evolutionary algorithms: GA, PSO, and DE". Industrial Engineering & Management Systems, 11(3), 215-223, 2012.
  • [33] Rosetti MD. Simulation Modeling and Arena. 2nd ed. New Jersey, Hoboken, USA, John Wiley & Sons Inc, 2010.

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

Yıl 2022, Cilt: 28 Sayı: 1, 139 - 147, 28.02.2022

Öz

Job-shop scheduling is a difficult issue for 'labor-intensive project type manufacturing'. Because in this type of production, the actual processing times are not exactly known until the production is finished and these processing times vary depending on the order’s technical specifications. It is an appropriate method to use probability distributions to forecast the processing times. This paper provides an industrial application for the scheduling of a labor-intensive project type working welding job-shop under variable workstation constraints. This constraint is consequence of a special production type that is depending on the length of the products. The aim is minimizing the makespan of a group of waiting orders. Genetic algorithm (GA) is used for this purpose to establish the entry sequence of the job-shop's waiting orders and dispatching them to the 6 identical welding stations. The dynamic conditions of the job-shop are simulated by the Arena simulation program. Stochastic processing times are used as the input data of the algorithm. Using stochastic processing times under variable workstation constraint for welding job-shop scheduling is not investigated previously. According to the experimental results, GA and Arena simulation together effectively reduces the makespan in this type of problem under variable workstation constraint. The GA aided Arena schedule outperforms the schedules proposed without using GA for this problem. Simulation results indicate that the total manufacturing time of pending orders is nearly 9.25% reduced when compared with the schedules proposed without using GA.

Kaynakça

  • [1] Jia Z, Lu X, Yang J, Jia D. “Research on job-shop scheduling problem based on genetic algorithm”. International Journal of Production Research, 49(12), 3585-3604, 2011.
  • [2] Azadeh A, Negahban A, Moghaddam M. “A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems”. International Journal of Production Research, 50(2), 551-566, 2012.
  • [3] Huang XW, Zhao XY, Ma XL. “An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility”. International Journal of Simulation Modelling, 13(4), 510-522, 2014.
  • [4] Aydemir E, Koruca HI. “A new production scheduling module using priority-rule based genetic algorithm”. International Journal of Simulation Modelling, 14(3), 450-462, 2015.
  • [5] Ba L, Li Y, Yang MS, Gao XQ, Liu Y. “Modelling and simulation of a multi-resource flexible job-shop scheduling”. International Journal of Simulation Modelling, 15(1), 157-169, 2016.
  • [6] Deng Q, Gong G, Gong X, Zhang L, Liu W, Ren Q. “A bee evolutionary guiding nondominated sorting genetic algorithm II for multiobjective flexible job-shop scheduling”. Computational Intelligence and Neuroscience, 2017. https://doi.org/10.1155/2017/5232518.
  • [7] Ocaktan MAB, Kucukkoc I, Karaoglan AD, Cicibas A, Buyukozkan K. “Scheduling Customized Orders: A Case Study at BEST Transformers Company”. 6th International Conference on Mechanics and Industrial Engineering (ICMIE’17), Rome, Italy, 8-10 June, 2017.
  • [8] Zhang W, Wen JB, Zhu YC, Hu Y. “Multi-objective scheduling simulation of flexible job-shop based on multipopulation genetic algorithm”. International Journal of Simulation Modelling, 16(2), 313-321, 2017.
  • [9] Hu HX, Lei WX, Gao X, Zhang Y. “job-shop scheduling problem based on improved cuckoo search algorithm”. International Journal of Simulation Modelling, 17(2), 337-346, 2018.
  • [10] Jiang T, Zhang C, Zhu H, Deng G. “Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode”. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/8574892
  • [11] Jiang T, Zhang C, Zhu H, Gu J, Deng G. “Energy-efficient scheduling for a job shop using an improved whale optimization algorithm”. Mathematics, 2018. https://doi.org/10.3390/math6110220
  • [12] Seng DW, Li JW, Fang XJ, Zhang XF, Chen J. “low-carbon flexible job-shop scheduling based on improved nondominated sorting genetic algorithm-II”. International Journal of Simulation Modelling, 17(4), 712-723, 2018.
  • [13] Karaoglan AD, Ocaktan MAB, Ocaktan DG, Oral A, Kundakci SS, Tuncer C. “Scheduling customized orders by considering the ergonomic constraints: a case study at Yemtar company”. 7th International Conference on Mechanics and Industrial Engineering (ICMIE’18), Madrid, Spain, 16-18 August, 2018.
  • [14] Zhang HP, Ye JH, Yang XP, Muruve NW, Wang JT. ”Modified binary particle swarm optimization algorithm in lotsplitting scheduling involving multiple techniques”. International Journal of Simulation Modelling, 17(3), 534-542, 2018.
  • [15] Zhong Q, Yang H, Tang T. “Optimization algorithm simulation for dual-resource constrained job-shop scheduling”. International Journal of Simulation Modelling, 17(1), 147-158, 2018.
  • [16] Sel C, Hamzadayi A. "A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(4), 665-674, 2018.
  • [17] Fu HC, Liu P. “A multi-objective optimization model based on non-dominated sorting genetic algorithm”. International Journal of Simulation Modelling, 18(3), 510-520, 2019.
  • [18] Tang H, Chen R, Li Y, Peng Z, Guo S, Du Y. “Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop”. Applied Soft Computing, 78, 176-194, 2019.
  • [19] Liao J, Lin C. “Optimization and simulation of job-shop supply chain scheduling in manufacturing enterprises based on particle swarm optimization”. International Journal of Simulation Modelling, 18(1), 187-196, 2019.
  • [20] Wang Y, Yang O, Wang SN. “A solution to single-machine inverse job-shop scheduling problem”. International Journal of Simulation Modelling, 18(2), 335-343, 2019.
  • [21] Zhang Z, Guan ZL, Zhang J, Xie X. “A novel job-shop scheduling strategy based on particle swarm optimization and neural network”. International Journal of Simulation modeling, 18(4), 699-707, 2019.
  • [22] Zhu J, Shao ZH, Chen C. “An improved whale optimization algorithm for job-shop scheduling based on quantum computing”. International Journal of Simulation Modelling, 18(3), 521-530, 2019.
  • [23] Karaoglan AD, Cetin E. Industrial Engineering in the Big Data Era. Editors: Calisir F, Cevikcan E, Akdag HC. Part I: Industrial Engineering, Artificial Bee Colony Algorithm for Labor Intensive Project Type Job Shop Scheduling Problem: A Case Study, 79-88, Cham, Switzerland, Springer, 2019.
  • [24] Shi DL, Zhang BB, Li Y. “A multi-objective flexible job-shop scheduling model based on fuzzy theory and immune genetic algorithm”. International Journal of Simulation Modelling, 19(1), 123-133, 2020.
  • [25] Gu J, Jiang T, Zhu H, Zhang C. “Low-carbon job shop scheduling problem with discrete genetic-grey wolf optimization algorithm”. Journal of Advanced Manufacturing Systems, 19(1), 1-14, 2020.
  • [26] Vital-Soto A, Azab A, Mohammed FB. “Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility”. Journal of Manufacturing Systems, 54, 74-93, 2020.
  • [27] Rao Y, Meng R, Zha J, Xu X. “Bi-objective mathematical model and improved algorithm for optimisation of welding shop scheduling problem”. International Journal of Production Research, 58(9), 2767-2783, 2020.
  • [28] Holland JH. Adaptation in Natural and Artificial Systems, Cambridge, Massachusetts, USA, MIT Press, 1975.
  • [29] Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA, Addison-Wesley Longman Publishing, 1989.
  • [30] Lim C, Sim E, “Production planning in manufacturing/remanufacturing environment using genetic algorithm”. Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (GECCO’05), Washington, USA, 25-29 June, 2005.
  • [31] Wall M. A Genetic Algorithm for Resource-Constrained Scheduling. PhD Thesis, Massachusetts Institute of Technology, Cambridge-Massachusetts, USA, 1996.
  • [32] Kachitvichyanukul V. "Comparison of three evolutionary algorithms: GA, PSO, and DE". Industrial Engineering & Management Systems, 11(3), 215-223, 2012.
  • [33] Rosetti MD. Simulation Modeling and Arena. 2nd ed. New Jersey, Hoboken, USA, John Wiley & Sons Inc, 2010.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makine Müh. / Endüstri Müh.
Yazarlar

Aslan Deniz Karaoglan Bu kişi benim

Yayımlanma Tarihi 28 Şubat 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 28 Sayı: 1

Kaynak Göster

APA Karaoglan, A. D. (2022). Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 139-147.
AMA Karaoglan AD. Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Şubat 2022;28(1):139-147.
Chicago Karaoglan, Aslan Deniz. “Optimization of Welding Job-Shop Scheduling Problem under Variable Workstation Constraint: An Industrial Application With Arena Simulation Based Genetic Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28, sy. 1 (Şubat 2022): 139-47.
EndNote Karaoglan AD (01 Şubat 2022) Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28 1 139–147.
IEEE A. D. Karaoglan, “Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 1, ss. 139–147, 2022.
ISNAD Karaoglan, Aslan Deniz. “Optimization of Welding Job-Shop Scheduling Problem under Variable Workstation Constraint: An Industrial Application With Arena Simulation Based Genetic Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28/1 (Şubat 2022), 139-147.
JAMA Karaoglan AD. Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28:139–147.
MLA Karaoglan, Aslan Deniz. “Optimization of Welding Job-Shop Scheduling Problem under Variable Workstation Constraint: An Industrial Application With Arena Simulation Based Genetic Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 1, 2022, ss. 139-47.
Vancouver Karaoglan AD. Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28(1):139-47.





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