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  • توسعه الگوریتم موازنه به هنگام بار در سیستم های کارگاهی ( مطالعه کیفی)

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آدرس مقاله


کد مقاله : IMJ-2002-1333 (R4) بازدید : 71 صفحه: 16 - 30

نوع مقاله: پژوهشی

توسعه الگوریتم موازنه به هنگام بار در سیستم های کارگاهی ( مطالعه کیفی)

محورهای موضوعی : مدیریت صنعتی

Nima Rahmani 1 (Department of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Alireza Irajpour 2 (Assistant Professor, Department of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Naser Hamidi 3 (Associate Professor, Department of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Akbar Alamtabriz 4 (Professor, Department of industrial management, Shahid Beheshti University, Tehran, Iran)
Reza Ehtesham Rasi 5 (industrial Management,)

تاریخ دریافت : 1398/11/16 تاریخ پذیرش : 1399/04/15 تاریخ انتشار : 1399/06/17

کلید واژه: کوپراس, قابلیت عملیاتی, مطالعه کیفی, سوآرا, متوازن سازی بر خط تولید,

چکیده مقاله :

یکی از مهم ترین مشکلات در سطح تاکتیکی ،تولید انبوه سفارشات مختلف است که هر یک از این سفارشات با توجه به شرایط تولید و اهمیت مشتریان دارای اولویت های مختلف هستند ،خط تولید متشکل از چندین ایستگاه کاری و ماشین است که هر یک در تواتر تولید با هم در ارتباط اند و ورودی یکی خروجی دیگری است در این حالت هر ماشین و فرآیندی که باید محصول را تولید نماید با محدودیت های فرآیندی و تکنولوژی به لحاظ زمان و حجم و وزن هر کار و سلامت فیزیکی ماشین آلات ،راندمان نیروی انسانی ، مواجه است .این مقاله با هدف لحاظ نمودن تمامی عوامل تاثیر گذار بر متوازن سازی خط تولید که در برنامه ریزی تولید کارگاهی اهمیت ویژه دارد، صورت می پذیرد و با بررسی تحلیلی در سوابق حل مسایل متوزان سازی خط تولید و با بهره گیری از خبرگان، مولفه هایی را که در برنامه ریزی و زمان بندی خط تولید موثر اند را شناسایی و با بهره گیری از روش ترکیبی سوآرا و کوپراس ،این مولفه های تاثیر گذار را به الگوریتم متوازن سازی بر خطدر سیستم های تولیدی می افزاید . نتایج این تحقیق نشان می دهد که بروز رسانی صورت گرفته در الگوریتم متوازن سازی بر خط تولید نسبت به روش های پیشین دارای قابلیت عملیاتی و کیفیت در نتایج است.

چکیده انگلیسی:

One of the most important problems at the tactical level is the mass production of different orders, each of which has different priorities according to the production conditions and the importance of customers. The production line consists of several workstations and machines, each in production frequency. They are interconnected and the input is one output. In this case, each machine and process that must produce the product is faced with process and technology limitations in terms of time, volume and weight of each work and physical health of machines, manpower efficiency. This article aims to take into account all the factors affecting the balancing of the production line, which is of particular importance in workshop production planning, and by analyzing the history of solving the problems of the production line and using experts, the component Identify the ones that are effective in planning and scheduling the production line, and using the combined method of Swara and Copras, these effective components add to the balancing algorithm in time in production systems. The results of this study show that the update made in the balancing algorithm during the production line compared to the previous methods has operational capability and quality in results

منابع و مأخذ:


  1. Asfidani, Mohammad. &Kimasi, Masood. &Roustie, Ahmad.  (2018). Identifying Corporate Customer Behavior Pattern and Its Relationship with Corporate Banking Strategies in Iranian Banking Industry. Shahed University Business Strategies Journal, 2(1)  1-4. (in persian).
    2.
  2. Baudin, M. (2002). Lean assembly: the nuts and bolts of making assembly operations flow. New York, USA: Productivity Press.
    3.
  3. Bouranta, Nancy, and Evangelos, Psomas. (2017). A comparative analysis of competitive priorities and business performance between manufacturing and service firms. International Journal of Productivity and Performance Management, 66(7): 914-931.
    4.
  4. Boysen, N. &Fliedner, M. &Scholl A.A. (2007). Classification of assembly line balancing problems. European Journal of Operational Research, 183: 674– 693.
    5.
  5. Bukchin, Yossi. &Raviv, Tal. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78: 57-68.
    6.
  6. Byrne, M. (2001). Sampling for qualitative research. AORN J. 73(2): 494- 498.
    7.
  7. Caramia, M. &Dell’Olmo, P. (2006). Effective Resource Management in Manufacturing Systems Optimization Algorithms for Production Planning. Springer series in advanced manufacturing.
    8.
  8. Chryssolouris, G. (1991). An Approach for Allocating Manufacturing Resources to Production Tasks. Journal of Manufacturing Systems, 10 (5):368-374.
    9.
  9. Dangayach, G.S., & Deshmukh, S.G. (2001). Manufacturing strategy: literature review and some issue. Int. J. of Operations & Production Management, 21(7): 884-932.
    10.
  10. Dehnavi, Alireza. &Nasiri Aghdam, Iman. &Pradhan, Biswajeet. &Morshed Varzandeh, Mohammad Hossein. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA, 135: 122-148.
    11.
  11. Eisenhardt, Kathleen M. &Melissa, E. (2007). Theory building from cases: Opportunities and challenges. Academy of management journal, 50(1): 25-32.
    12.
  12. Fisel, Johannes. &Exner, Yannick. &Stricker, Nicole. &Lanza, Gisela. (2019). Changeability and flexibility of assembly line balancing as a multi-objective optimization problem. Journal of Manufacturing Systems, 53: 150-158.
    13.
  13. Graham, R. L. (1996). Bounds for certain multiprocessing anomalies. Bell Syst. Tech. J, 45:1563–1581.
    14.
  14. Jordi, Pereira. &Álvarez-Miranda, Eduardo. (2017). An exact approach for the robust assembly line balancing problem. Omega, 78: 85-98.
    15.
  15. Kersuliene, V. &Zavadskas, E. Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11 (2): 243-258.
    16.
  16. Kim, Thai. &Young. (2018). Improving warehouse responsiveness by job priority management. A European distribution centre field study, Computers & Industrial Engineering. https://doi/org/10/1016/j/cie/2018/12/011
    17.
  17. Kroes, J.R. &Ghosh, S. (2010). Outsourcing congruence with competitive priorities: impact on supply chain and firm performance. Journal of Operations Management, 28 (2): 124-143.
    18.
  18. Levi, D. S. &Kaminsky, P. &Levi, E. S. (2003). Designing and managing the supply chain: Concepts, strategies, and case studies, McGraw-Hill.
    19.
  19. Miltenburg, J. (2008). Setting manufacturing strategy for a factory-within-a- factory, International Journal of Production Economics, 113: 307-323.
    20.
  20. Motaghi, Hayedeh. (2015). Production and Operations Management. Avaya Shervin Publishing. (in persian).
    21.
  21. Muchiri, P. (2011). Development of maintenance function performance measurement framework and indicators. International Journal of Production Economics, 131:295-302.
    22.
  22. Paksoy, T. &Özceylan, E. &Gökçen, H. (2012). Supply chain optimization with assembly line balancing. International Journal of Production Research, 50:3115-3136.
    23.
  23. Peng, Kunkun. &Wen, Long. & Li Ran. & Gao, Liang. &Li, Xinyu. (2018). An Effective Hybrid Algorithm for Permutation Flow Shop Scheduling Problem with Setup Time.Procedia CIRP, 72: 1288-1292.
    24.
  24. Rafie, Majid. &Mohammadi Talab, Attieh. (2016). Presenting a Mathematical Model with a Stable Optimization Approach for Designing a Dynamic Cellular Production System Considering Multifunction Machines. A Journal of Industrial Engineering Research in Production Systems, 7(9):  281-295. (in persian).
    25.
  25. Ramezanian, Reza. &Ezzatpanah, Abdullah. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87:  74–80.
    26.
  26. Scholl, A. &Klein, R. &Domschke, W. (1998). Pattern based vocabulary building for effectively sequencing mixed model assembly lines. Journal of Heuristics, 4: 359–381.
    27.
  27. Shirazi, Hassan. &Hassanavi, Reza. &Kavian, Mohammad Hossein. (2018). Presenting a Model of Industrial Production Control System. Journal of Control Command, 2(3) : 79-91. (in Persian).
    28.
  28. Sikora, Celso. &Gustavo, Stall. &Lopes Thiago, Cantos. &Schibelbain Magatã, Daniel Leandro. (2017). Integer based formulation for the simple assembly line balancing problem with multiple identical tasks. Computers & Industrial Engineering, 104: 134-144.
    29.
  29. Valipour, A. N. &Yahaya, N. (2017). Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study. Journal of Civil Engineering and Management, 23 (4): 524-532.
    30.
  30. Wei, Jiayin. &Xu, Daoyun. &Qin, Yongbin. (2017). On-Line Load Balancing With Task Buffer. Computing and Informatics, 36: 1207-1234.
    31.
  31. Yücenur, G. &Nilay, Şeyma Çaylak. (2020). An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy, 145: 2587-2597.
    32.
  32. Zavadskas, E.K. &Kaklauskas, Vilutiene. T. (2009). Multi criteria evaluation of apartment blocks maintenance contractors: Lithuanian case study. International Journal of Strategic Property Management, 13 (4): 319-338.
    33.
_||_

  1. Asfidani, Mohammad. &Kimasi, Masood. &Roustie, Ahmad.  (2018). Identifying Corporate Customer Behavior Pattern and Its Relationship with Corporate Banking Strategies in Iranian Banking Industry. Shahed University Business Strategies Journal, 2(1)  1-4. (in persian).
    2.
  2. Baudin, M. (2002). Lean assembly: the nuts and bolts of making assembly operations flow. New York, USA: Productivity Press.
    3.
  3. Bouranta, Nancy, and Evangelos, Psomas. (2017). A comparative analysis of competitive priorities and business performance between manufacturing and service firms. International Journal of Productivity and Performance Management, 66(7): 914-931.
    4.
  4. Boysen, N. &Fliedner, M. &Scholl A.A. (2007). Classification of assembly line balancing problems. European Journal of Operational Research, 183: 674– 693.
    5.
  5. Bukchin, Yossi. &Raviv, Tal. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78: 57-68.
    6.
  6. Byrne, M. (2001). Sampling for qualitative research. AORN J. 73(2): 494- 498.
    7.
  7. Caramia, M. &Dell’Olmo, P. (2006). Effective Resource Management in Manufacturing Systems Optimization Algorithms for Production Planning. Springer series in advanced manufacturing.
    8.
  8. Chryssolouris, G. (1991). An Approach for Allocating Manufacturing Resources to Production Tasks. Journal of Manufacturing Systems, 10 (5):368-374.
    9.
  9. Dangayach, G.S., & Deshmukh, S.G. (2001). Manufacturing strategy: literature review and some issue. Int. J. of Operations & Production Management, 21(7): 884-932.
    10.
  10. Dehnavi, Alireza. &Nasiri Aghdam, Iman. &Pradhan, Biswajeet. &Morshed Varzandeh, Mohammad Hossein. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA, 135: 122-148.
    11.
  11. Eisenhardt, Kathleen M. &Melissa, E. (2007). Theory building from cases: Opportunities and challenges. Academy of management journal, 50(1): 25-32.
    12.
  12. Fisel, Johannes. &Exner, Yannick. &Stricker, Nicole. &Lanza, Gisela. (2019). Changeability and flexibility of assembly line balancing as a multi-objective optimization problem. Journal of Manufacturing Systems, 53: 150-158.
    13.
  13. Graham, R. L. (1996). Bounds for certain multiprocessing anomalies. Bell Syst. Tech. J, 45:1563–1581.
    14.
  14. Jordi, Pereira. &Álvarez-Miranda, Eduardo. (2017). An exact approach for the robust assembly line balancing problem. Omega, 78: 85-98.
    15.
  15. Kersuliene, V. &Zavadskas, E. Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11 (2): 243-258.
    16.
  16. Kim, Thai. &Young. (2018). Improving warehouse responsiveness by job priority management. A European distribution centre field study, Computers & Industrial Engineering. https://doi/org/10/1016/j/cie/2018/12/011
    17.
  17. Kroes, J.R. &Ghosh, S. (2010). Outsourcing congruence with competitive priorities: impact on supply chain and firm performance. Journal of Operations Management, 28 (2): 124-143.
    18.
  18. Levi, D. S. &Kaminsky, P. &Levi, E. S. (2003). Designing and managing the supply chain: Concepts, strategies, and case studies, McGraw-Hill.
    19.
  19. Miltenburg, J. (2008). Setting manufacturing strategy for a factory-within-a- factory, International Journal of Production Economics, 113: 307-323.
    20.
  20. Motaghi, Hayedeh. (2015). Production and Operations Management. Avaya Shervin Publishing. (in persian).
    21.
  21. Muchiri, P. (2011). Development of maintenance function performance measurement framework and indicators. International Journal of Production Economics, 131:295-302.
    22.
  22. Paksoy, T. &Özceylan, E. &Gökçen, H. (2012). Supply chain optimization with assembly line balancing. International Journal of Production Research, 50:3115-3136.
    23.
  23. Peng, Kunkun. &Wen, Long. & Li Ran. & Gao, Liang. &Li, Xinyu. (2018). An Effective Hybrid Algorithm for Permutation Flow Shop Scheduling Problem with Setup Time.Procedia CIRP, 72: 1288-1292.
    24.
  24. Rafie, Majid. &Mohammadi Talab, Attieh. (2016). Presenting a Mathematical Model with a Stable Optimization Approach for Designing a Dynamic Cellular Production System Considering Multifunction Machines. A Journal of Industrial Engineering Research in Production Systems, 7(9):  281-295. (in persian).
    25.
  25. Ramezanian, Reza. &Ezzatpanah, Abdullah. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87:  74–80.
    26.
  26. Scholl, A. &Klein, R. &Domschke, W. (1998). Pattern based vocabulary building for effectively sequencing mixed model assembly lines. Journal of Heuristics, 4: 359–381.
    27.
  27. Shirazi, Hassan. &Hassanavi, Reza. &Kavian, Mohammad Hossein. (2018). Presenting a Model of Industrial Production Control System. Journal of Control Command, 2(3) : 79-91. (in Persian).
    28.
  28. Sikora, Celso. &Gustavo, Stall. &Lopes Thiago, Cantos. &Schibelbain Magatã, Daniel Leandro. (2017). Integer based formulation for the simple assembly line balancing problem with multiple identical tasks. Computers & Industrial Engineering, 104: 134-144.
    29.
  29. Valipour, A. N. &Yahaya, N. (2017). Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study. Journal of Civil Engineering and Management, 23 (4): 524-532.
    30.
  30. Wei, Jiayin. &Xu, Daoyun. &Qin, Yongbin. (2017). On-Line Load Balancing With Task Buffer. Computing and Informatics, 36: 1207-1234.
    31.
  31. Yücenur, G. &Nilay, Şeyma Çaylak. (2020). An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy, 145: 2587-2597.
    32.
  32. Zavadskas, E.K. &Kaklauskas, Vilutiene. T. (2009). Multi criteria evaluation of apartment blocks maintenance contractors: Lithuanian case study. International Journal of Strategic Property Management, 13 (4): 319-338.
    33.

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