سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

قانعی, نادر, اسماعیلیان, غلامرضا, خیرخواه, امیرسامان. (1402). بهینه‌سازی چیدمان سلولی پویا و پایدار بر اساس راهبرد مقیاس‌بندی سرعت پردازش و مسیریابی فرایند در مصرف انرژی و ایمنی محیط کار. , 13(4), 313-375. doi: 10.48308/jimp.13.4.313
نادر قانعی; غلامرضا اسماعیلیان; امیرسامان خیرخواه. "بهینه‌سازی چیدمان سلولی پویا و پایدار بر اساس راهبرد مقیاس‌بندی سرعت پردازش و مسیریابی فرایند در مصرف انرژی و ایمنی محیط کار". , 13, 4, 1402, 313-375. doi: 10.48308/jimp.13.4.313
قانعی, نادر, اسماعیلیان, غلامرضا, خیرخواه, امیرسامان. (1402). 'بهینه‌سازی چیدمان سلولی پویا و پایدار بر اساس راهبرد مقیاس‌بندی سرعت پردازش و مسیریابی فرایند در مصرف انرژی و ایمنی محیط کار', , 13(4), pp. 313-375. doi: 10.48308/jimp.13.4.313
قانعی, نادر, اسماعیلیان, غلامرضا, خیرخواه, امیرسامان. بهینه‌سازی چیدمان سلولی پویا و پایدار بر اساس راهبرد مقیاس‌بندی سرعت پردازش و مسیریابی فرایند در مصرف انرژی و ایمنی محیط کار. , 1402; 13(4): 313-375. doi: 10.48308/jimp.13.4.313

بهینه‌سازی چیدمان سلولی پویا و پایدار بر اساس راهبرد مقیاس‌بندی سرعت پردازش و مسیریابی فرایند در مصرف انرژی و ایمنی محیط کار

چشم‌انداز مدیریت صنعتی
دوره 13، شماره 4 - شماره پیاپی 52، 1402، صفحه 313-375    اصل مقاله (1.87 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.48308/jimp.13.4.313
نویسندگان
نادر قانعی1؛ غلامرضا اسماعیلیان* 2؛ امیرسامان خیرخواه3
1دانشجوی دکتری، گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران.
2استادیار، گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران.
3دانشیار، گروه مهندسی صنایع، دانشگاه بو علی سینا، همدان، ایران.
چکیده
افزایش نگرانی‌ها در خصوص مسائل زیست‌محیطی، کمبود منابع و مسائل رفاهی کارکنان، سازمان‌ها را به سمت تجدیدنظر در مورد استراتژی‌های تولید و چیدمان تسهیلات سوق داده است تا چیدمانی ارائه دهند که به تمام ابعاد پایداری (اقتصادی، رفاه ‌اجتماعی و زیست‌محیطی) توجه داشته باشد؛ بنابراین در این پژوهش، یک مدل ریاضی چندهدفه به‌منظور ایجاد توازن بین کاهش هزینه‌های چیدمان، کاهش میزان مصرف انرژی الکتریکی و افزایش ایمنی محیط تولید برای کارکنان توسعه یافته است. ویژگی‌ برجسته مدل پیشنهادی، درنظرگرفتن راهبرد مقیاس‌بندی سرعت پردازش عملیات در ماشین‌ها در کنار مسیریابی فرایند است که با مفاهیمی نظیر قابلیت اطمینان ماشین‌ها، تعادل بار کاری، تخصیص اپراتور ترکیب شده است. به‌منظور اعتبارسنجی و کاربردپذیری مدل پیشنهادی از روش معیار جامع و نمونه‌های برگرفته از مبانی نظری موضوع استفاده شده است. با توجه به پیچیدگی مدل و ناتوانایی نرم‌افزار گمز در ارائه جواب برای مسائل با ابعاد بزرگ در زمان مطلوب، از الگوریتم ژنتیک رتبه‌بندی نامغلوب (NSGA-II) استفاده شده است. درنهایت رویکرد پیشنهادی به‌صورت یک مطالعه موردی واقعی در یک کارگاه تولید اجاق‌گاز و فرهای صنعتی مورداستفاده قرار گرفت. نتایج، دستیابی به صرفه‌جویی 62 درصدی در هزینه‌های تولید را از اعمال روش پیشنهادی نشان می‌دهد.
کلیدواژه‌ها
چیدمان سلولی؛ پایداری؛ مسیریابی جایگزین فرایند؛ چیدمان ایمن؛ مصرف انرژی
عنوان مقاله [English]
Optimization of Dynamic and Sustainable Cellular Layout Based on the strategy of scaling Processing Speed and Process Routing in Energy Consumption and Workplace Safety
نویسندگان [English]
Nader Ghanei1؛ Gholam Reza Esmaeilian2؛ Amir Saman Kheirkhah3
1Ph.D Student, Department of Industrial Engineering, Payam Noor University, Tehran, Iran.
2Assistant Professor, Department of Industrial Engineering, Payam Noor University, Tehran, Iran.
3Associate Professor, Department of Industrial Engineering, Bu-Ali Sina University, Hamadan, Iran.
چکیده [English]
Increasing concerns about environmental issues, resources constraint and social issues of employees have moved organizations to review production strategies and facility layout to provide an arrangement that takes into account all dimensions of sustainability (economic, social, and environmental). Therefore, in this research, a multi-objective mathematical model has been developed to achieve a balance between reducing layout costs, reducing electrical energy consumption, and improving the safety of the production environment for the operators. One prominent feature of the proposed model is the consideration of the strategy of scaling the processing speed of operations in machines alongside process routing. This is combined with concepts such as machine reliability, workload balancing, and operator allocation. In order to validate and assess the usability of the proposed model, the LP-metric method and examples derived from the relevant literature have been utilized. Considering the complexity of the model and the limitations of the GAMS software in providing timely solutions for large-scale problems, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been employed. Finally, the proposed approach was used as a real case study in a gas stove and industrial oven production workshop. The results show a 62% savings in production costs from applying the proposed method.
کلیدواژه‌ها [English]
Cellular Layout, Sustainability, Process Alternative Routing, Safe Layout, Energy Eonsumption
مراجع
  1. Aalaei, A. Hamid. D., (2017). A robust optimization model for cellular manufacturing system into supply chain management. International Journal of Production Economics, 183, 667-679.
  2. Aghajani-Delavar, N., Mehdizadeh, E., Tavakkoli Moghaddam, R., & Haleh, H. (2020). A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation. Scientia Iranica, 29(4), 2041-2068. (In Persian)
  3. Aghazadeh, S. M., Hafeznezami, S., Najjar, L., & Huq, Z. (2011). The influence of work‐cells and facility layout on the manufacturing efficiency. Journal of facilities management, 9(3), 213-224.
  4. Akbari, M., Dorri Nokarani, B., & Zandieh, M. (2012). Scheduling Working Shifts for Multi-skilled Workforces with Genetic algorithm Approach. The Journal of Industrial Management Perspective, 2(3), 87-102. (In Persian)
  5. Ayough, A., Zandieh, M., Farsijani, H., & Dorri Nokarani, B. (2014). Job Rotation Scheduling in a New Arranged Lean Cell, a Genetic Algorithm Approach. The Journal of Industrial Management Perspective, 4(3), 33-59. (In Persian)
  6. Bakhshi-Khaniki, H., & Fatemi Ghomi, S. M. T. (2023). Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand. International Journal of Engineering, 36(2), 348-359. (In Persian)
  7. Bagheri, M., & Bashiri, M. (2014). A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment. Applied Mathematical Modelling, 38(4), 1237-1254.
  8. Benjaafar, S. (2002). Modeling and analysis of congestion in the design of facility layouts. Management science, 48(5), 679-704.
  9. Bougain, S., Gerhard, D., Nigischer, C., & Uĝurlu, S. (2015). Towards energy management in production planning software based on energy consumption as a planning resource. Procedia CIRP, 26, 139-144.
  10. Chang, C. C., Wu, T. H., & Wu, C. W. (2013). An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems. Computers & Industrial Engineering, 66(2), 438-450.
  11. de Lira-Flores, J. A., López-Molina, A., Gutiérrez-Antonio, C., & Vázquez-Román, R. (2019). Optimal plant layout considering the safety instrumented system design for hazardous equipment. Process Safety and Environmental Protection, 124, 97-120.
  12. Delgoshaei, A., Ariffin, M. K. A., & Ali, A. (2017). A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS. International Journal of Production Research, 55(4), 997-1039.
  13. Doroudyan, M., & Khoshghalb, A. (2021). Robust design for facility layout problem in cellular manufacturing systems with uncertain demand. Journal of Industrial and Systems Engineering, 13(Special issue: 17th International Industrial Engineering Conference), 1-11. (In Persian)
  14. Drake, D. F., & Spinler, S. (2013). Sustainable operations management: An enduring stream or a passing fancy? Manufacturing & Service Operations Management, 15(4), 689-700.
  15. Fakhrzad, M. B., Barkhordary, F., & Afari Nodoushan, A. J. (2022). A Mathematical Model for Dynamic Cell Formation Problem Based on Scheduling, Worker Allocation, and Financial Resources Constraint. Industrial Management Journal, 13(3), 435-463. (In Persian)
  16. Feng, H., Xi, L., Xia, T., & Pan, E. (2018). Concurrent cell formation and layout design based on hybrid approaches. Applied Soft Computing, 66, 346-359.
  17. Forghani, K., Fatemi Ghomi, S. M. T., & Kia, R. (2022). Group layout design of manufacturing cells incorporating assembly and energy aspects. Engineering Optimization, 54(5), 770-785.
  18. Ghiasvand Ghiasi, F., Yazdani, M., Vahdani, B., & Kazemi, A. (2022). Meta-Heuristic Algorithms for Multi-Objective Home Health Care Routing and Scheduling Problem Considering Time Windows and Workload Balance of Nurses. The Journal of Industrial Management Perspective, 12(1), 225-260. (In Persian)
  19. Golmohammadi, A. M., Morady Gohareh, M., & Karbasian, M. (2022). Proposing an efficient mathematical model for the continuous layout design in a cellular manufacturing system The real-case of BATA company. Production and Operations Management, 13(1), 25-50. (In Persian)
  20. Guo, L., Wang, Z., Guo, P., Wang, J., & Zhao, D. (2023). Mathematical programming model of process plant safety layout using the equipment vulnerability index. Korean Journal of Chemical Engineering, 40(4), 727-739.
  21. Gupta, M., & Sharma, K. (1996). Environmental operations management: an opportunity for improvement. Production and Inventory Management Journal, 37, 40-46.
  22. Hosseinabad, E. R., & Zaman, M. A. U. (2020). A brief review on cellular manufacturing and group technology. Research Journal of Management Reviews, 5(1), 1-20.
  23. Iqbal, A. Al-Ghamdi, K. A. (2018). Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration. Energy, 163, 863-872.
  24. Jafarzadeh, J., Amoozad Khalili, H., & Shoja, N. (2022). Multi-objective mathematical model for dynamic cellular manufacturing system design under uncertainty; a sustainable approach. Journal of Quality Engineering and Production Optimization, 7(1), 98-120. (In Persian)
  25. Kalantari, M., Pishvaee, M. S., & Yaghoubi, S. (2015). A Multi Objective Model Integrating Financial and Material Flow in Supply Chain Master Planning. The Journal of Industrial Management Perspective, 5(3), 139-167. (In Persian)
  26. Kang, S., Kim, M., & Chae, J. (2018). A closed loop based facility layout design using a cuckoo search algorithm. Expert Systems with Applications, 93, 322-335.
  27. Kargar, B., Pishvaee, M. S., & Barzinpour, F. (2017). A Multi-Objective Mathematical Model for Organ Allocation to Patients in Iran Organ Transplantation Network. The Journal of Industrial Management Perspective, 7(3), 151-177. (In Persian)
  28. Khalafallah, A., & El-Rayes, K. (2004). Safety and cost considerations in site layout planning. Housing and Building Research Centre Journal, 1(1), 141-150.
  29. Kia, R., Baboli, A., Javadian, N., Tavakkoli-Moghaddam, R., Kazemi, M., & Khorrami, J. (2012). Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Computers & Operations Research, 39(11), 2642-2658.
  30. Kia, R., Shirazi, H., Javadian, N., & Tavakkoli-Moghaddam, R. (2015). Designing group layout of unequal-area facilities in a dynamic cellular manufacturing system with variability in number and shape of cells. International Journal of Production Research, 53(11), 3390-3418.
  31. Kidam, K., & Hurme, M. (2012). Design as a contributor to chemical process accidents. Journal of Loss Prevention in the Process Industries, 25(4), 655-666.
  32. Kim, W. S., & Lim, D. E. (2019). On an automated material handling system design problem in cellular manufacturing systems. European journal of industrial engineering, 13(3), 400-419.
  33. Ku, M. Y., Hu, M. H., & Wang, M. J. (2011). Simulated annealing based parallel genetic algorithm for facility layout problem. International Journal of Production Research, 49(6), 1801-1812.
  34. Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of cleaner production, 204, 643-659.
  35. Langer, T., Schlegel, A., Stoldt, J., & Putz, M. (2014). A model-based approach to energy-saving manufacturing control strategies. Procedia CIRP, 15, 123-128.
  36. Lotfi, R., Sheikhi, Z., Amra, M., AliBakhshi, M., & Weber, G.-W. (2021). Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize. International Journal of Logistics Research and Applications, 1-41.
  37. Mansour, H. A., Afefy, I. H., & Taha, S. M. (2021). Heuristic-based approach to solve layout design and workers’ assignment problem in the cellular manufacturing system. International Journal of Management Science and Engineering Management, 1-17.
  38. Mohammadi, M., & Forghani, K. (2016). Designing cellular manufacturing systems considering S-shaped layout. Computers & Industrial Engineering, 98, 221-236.
  39. Morinaga, E., Shintome, Y., Wakamatsu, H., & Arai, E. (2016). Facility layout planning with continuous representation considering temporal efficiency. Transactions of the Institute of Systems, Control and Information Engineers, 29(9), 408-413.
  40. Motahari, R., Alavifar, Z., Zareh Andaryan, A., Chipulu, M., & Saberi, M. (2023). A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems. Computers & Operations Research, 151, 106090.
  41. Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016a). A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment. Journal of Manufacturing Systems, 38, 46-62.
  42. Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016b). A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria. Applied Mathematical Modelling, 40(4), 2674-2691.
  43. O'driscoll, E., & O'donnell, G. E. (2013). Industrial power and energy metering–a state-of-the-art review. Journal of cleaner production, 41, 53-64.
  44. Paydar, M. M., Saidi-Mehrabad, M., & Teimoury, E. (2014). A robust optimisation model for generalised cell formation problem considering machine layout and supplier selection. International Journal of Computer Integrated Manufacturing, 27(8), 772-786.
  45. Pourhassan, M. R., & Raissi, S. (2017). An integrated simulation-based optimization technique for multi-objective dynamic facility layout problem. Journal of Industrial Information Integration, 8, 49-58.
  46. Quiroz-Perez, E., de Lira-Flores, J. A., Gutierrez-Antonio, C., & Vazquez-Roman, R. (2021). A new multiple-risk map approach to solve process plant layout considering safety and economic aspects. Journal of Loss Prevention in the Process Industries, 72, 104524.
  47. Rafiee, M., & Mohamaditalab, A. (2020). Investigation into skill leveled operators in a multi-period cellular manufacturing system with the existence of multi-functional machines. Scientia Iranica, 27(6), 3219-3232. (In Persian)
  48. Rafiei, H., & Ghodsi, R. (2013). A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Applied Mathematical Modelling, 37(4), 2308-2316.
  49. Rahimi, V., Akrat, J., Farughi, H. (2020). A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout. Computers & Industrial Engineering, 143, 106439.
  50. Raoofpanah, H., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2019). Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition. Engineering Optimization, 51(7), 1229-1250.
  51. Roghanian, E., & Cheraghalipour, A. (2019). Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions. Journal of cleaner production, 239, 118081.
  52. Sakhaii, M., Tavakkoli-Moghaddam, R. ;Bagheri, M. ;Vatani, B. (2016). A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, 40(1), 169-191.
  53. Salimpour, S., Pourvaziri, H., & Azab, A. (2021). Semi-robust layout design for cellular manufacturing in a dynamic environment. Computers & Operations Research, 133, 105367.
  54. Sarkis, J. (1998). Evaluating environmentally conscious business practices. European Journal of Operational Research, 107(1), 159-174.
  55. Scalia, G., Micale, R., & Enea, M. (2019). Facility layout problem: Bibliometric and benchmarking analysis. International Journal of Industrial Engineering Computations, 10(4), 453-472.
  56. Shafigh, F., Defersha, F. M., & Moussa, S. E. (2017). A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration. The International Journal of Advanced Manufacturing Technology, 88(1-4), 1119-1140.
  57. Singh, S. P., & Sharma, R. R. K. (2006). A review of different approaches to the facility layout problems. The International Journal of Advanced Manufacturing Technology, 30(5), 425-433.
  58. Singh, S., & Singh, V. (2011). Three-level AHP-based heuristic approach for a multi-objective facility layout problem. International Journal of Production Research, 49(4), 1105-1125.
  59. Tavakkoli-Moghaddam, R., Javadian, N. ,Javadi, B. ,Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation, 184(2), 721-728.
  60. Tayal, A., Gunasekaran, A., Singh, S. P., Dubey, R., & Papadopoulos, T. (2017). Formulating and solving sustainable stochastic dynamic facility layout problem: A key to sustainable operations. Annals of Operations Research, 253(1), 621-655.
  61. Tayal, A., & Singh, S. P. (2017). Designing Flexible Stochastic Dynamic Layout: An Integrated Firefly and Chaotic Simulated Annealing-Based Approach. Global Journal of Flexible Systems Management, 18(2), 89-98.
  62. Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons.
  63. Vikhorev, K., Greenough, R., & Brown, N. (2013). An advanced energy management framework to promote energy awareness. Journal of cleaner production, 43, 103-112.
  64. Wang, J., & Liu, C. (2018). A priority rule based heuristic for virtual cellular manufacturing system with energy consumption. 15th International Conference on Networking, Sensing and Control (ICNSC), 1-5.
  65. Yang, L., Deuse, J., & Jiang, P. (2013). Multiple-attribute decision-making approach for an energy-efficient facility layout design. The International Journal of Advanced Manufacturing Technology, 66(5-8), 795-807.
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