ارائه مدل بهینه برنامه ریزی تولید، تعمیرات و نگهداری و زمان بندی نیروی کار در شرایط عدم قطعیت و حل آن با الگوریتم مورچگان
محورهای موضوعی :
مدیریت صنعتی
mohammd sharifzadegan
1
,
Tahmourth sohrabi
2
,
Ahmad Jafarnejad Chaghoshi,
3
1 - Department of Industrial Managemnet, Tehran Central Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
تاریخ دریافت : 1399/07/28
تاریخ پذیرش : 1399/11/10
تاریخ انتشار : 1400/02/13
کلید واژه:
تعمیرات و نگهداری,
مدل ریاضی,
کلمات کلیدی: برنامه ریزی تولید,
برنامه ریزی نیروی انسانی,
الگوریتم مورچگان,
چکیده مقاله :
چکیده: در شرایط رقابتی امروز، بهره وری تولید یک موضوع بسیار مهم و کلیدی است. این در حالی است که تمامی بخش‌های واحد‌تولیدی به یکدیگر وابسته هستند. پاسخگویی سریع به نیاز مشتریان، تنوع پذیری، اطمینان و اعتماد پذیری و هزینه بر بودن تجهیزات و ماشین آلات با توجه به محدودیت‌های گسترده در منابع تولیدی به رقابت پذیری و کسب سهم بازار در شرایط عدم قطعیت، نیاز است برای اتخاذ تصمیم‌های مدیریتی از قالبی یکپارچه که عوامل حیاتی را درخود جای داده است استفاده شود. بر همین اساس، در این تحقیق به یکپارچه‌‌سازی سه حوزه مهم در شرکت‌های تولیدی پرداخته شده است. این حوزه‌ها شامل برنامه ریزی تولید، نگهداری و تعمیرات و برنامه ریزی نیروی انسانی می‌باشد. در این خصوص یک مدل ریاضی با هدف استفاده بهینه از نیروی کار و افزایش حجم تولید ارائه شده است. در این مدل تجربه کارگران، نرخ به کارگیری ماشین و نرخ خرابی ماشین آلات به صورت غیر قطعی و با اعداد فازی بیان شده است. برای حل این مدل از الگوریتم فراابتکاری مورچگان استفاده شده است. نتایج عددی حاصل از پیاده‌‌سازی در یک شرکت صنعتی نشان می‌دهد که الگوریتم مورد استفاده، می‌تواند در یک زمان معقول و منطقی، جواب‌هایی با حداقل خطای ممکن ارائه کند. همچنین تحلیل حساسیت انجام شده نشان می‌دهد که نرخ خرابی ماشین قبل و پس از تعمیرات و نگهداری، تاثیر بسیار زیادی روی مقدار تابع هدف مدل ریاضی دارد.
چکیده انگلیسی:
Abstract: In today's competitive environment, production productivity is a very important and key issue. However, all department of the production unit are interdependent. Rapid response to customer needs, diversity, reliability and cost of equipment and machinery due to the widespread limitations in production resources to be competitive and gain market share in conditions of uncertainty, is needed to make management decisions from Use an integrated format that incorporates important elements. Accordingly, in this study, the integration of three important areas in manufacturing companies has been addressed. These areas include production planning, maintenance, and manpower planning. In this way, a mathematical model with the aim of optimal use of labor and increasing production volume is presented. In this model of workers' experience, machine utilization rate and machine failure rate are expressed uncertainly and with fuzzy numbers. To solveing this model, the ants meta-innovation algorithm has been used. Numerical results obtained from the implementation in an industrial company show that the algorithm used can provide answers with the least possible error in a reasonable time. Sensitivity analysis also shows that the failure rate of the machine before and after repairs and maintenance has a great impact on the value of the objective function of the mathematical model.
منابع و مأخذ:
Aghezzaf, E. H., & Najid, N. M. (2008). Integrated production planning and preventive maintenance in deteriorating production systems. Information Sciences, 178(17), 3382-3392.
Akbari Mohammad, (2017), Scheduling of Temporary Employees with Variable Productivity. Management Research in Iran, 21(3), 47-25.
Alimian, M., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2020). New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems. Journal of Manufacturing Systems, 56, 341-358.
Alimian, M., Saidi-Mehrabad, M., & Jabbarzadeh, A. (2019). A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures. Journal of Manufacturing Systems, 50, 263-277.
Behnezhad, A. R., & Khoshnevis, B. (1988). The effects of manufacturing progress function on machine requirements and aggregate planning problems. International journal of production research, 26(2), 309-326.
Bensmain, Y., Dahane, M., Bennekrouf, M., & Sari, Z. (2019). Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: A hybrid genetic algorithm based approach. Reliability Engineering & System Safety, 185, 546-566.
Berrichi, A., Amodeo, L., Yalaoui, F., Châtelet, E., & Mezghiche, M. (2009). Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. Journal of Intelligent Manufacturing, 20(4), 389.
Ekin, T. (2018). Integrated maintenance and production planning with endogenous uncertain yield. Reliability Engineering & System Safety, 179, 52-61.
Ettaye, G., El Barkany, A., & El Khalfi, A. (2017). Modeling and optimization a production/maintenance integrated planning. In International Journal of Engineering Research in Africa (Vol. 28, pp. 169-181). Trans Tech Publications Ltd.
Glawar, R., Karner, M., Nemeth, T., Matyas, K., & Sihn, W. (2018). An approach for the integration of anticipative maintenance strategies within a production planning and control model. Procedia CIRP, 67, 46-51.
Goli, A., Tirkolaee, E. B., Malmir, B., Bian, G. B., & Sangaiah, A. K. (2019). A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing, 101(6), 499-529.
Hamrol, A. (2018). A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9.
Liao, G. L. (2013). Joint production and maintenance strategy for economic production quantity model with imperfect production processes. Journal of Intelligent Manufacturing, 24(6), 1229-1240.
Moussawi-Haidar, L., Daou, H., & Khalil, K. (2021). Joint reserve stock and just-in-time inventory under regular preventive maintenance and random disruptions. International Journal of Production Research, 1-22.
Nourelfath, M., Nahas, N., & Ben-Daya, M. (2016). Integrated preventive maintenance and production decisions for imperfect processes. Reliability engineering & system safety, 148, 21-31.
Samimi, E., & Sydow, J. (2020). Human resource management in project-based organizations: revisiting the permanency assumption. The International Journal of Human Resource Management, 1-35.
Schreiber, M., Klöber-Koch, J., Richter, C., & Reinhart, G. (2018). Integrated Production and Maintenance Planning for Cyber-physical Production Systems. Procedia CIRP, 72, 934-939.
Wang, L., Lu, Z., & Ren, Y. (2020). Integrated production planning and condition-based maintenance considering uncertain demand and random failures. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(1-2), 310-323.
Wang, T., Zhao, Y., & Zhu, X. (2021, January). Advanced Production Plan System of Military Manufacturing Enterprises Based on Linear Programming Model. In Journal of Physics: Conference Series (Vol. 1732, No. 1, p. 012017). IOP Publishing.
Yalaoui, A., Chaabi, K., & Yalaoui, F. (2014). Integrated production planning and preventive maintenance in deteriorating production systems. Information Sciences, 278, 841-861.
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Aghezzaf, E. H., & Najid, N. M. (2008). Integrated production planning and preventive maintenance in deteriorating production systems. Information Sciences, 178(17), 3382-3392.
Akbari Mohammad, (2017), Scheduling of Temporary Employees with Variable Productivity. Management Research in Iran, 21(3), 47-25.
Alimian, M., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2020). New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems. Journal of Manufacturing Systems, 56, 341-358.
Alimian, M., Saidi-Mehrabad, M., & Jabbarzadeh, A. (2019). A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures. Journal of Manufacturing Systems, 50, 263-277.
Behnezhad, A. R., & Khoshnevis, B. (1988). The effects of manufacturing progress function on machine requirements and aggregate planning problems. International journal of production research, 26(2), 309-326.
Bensmain, Y., Dahane, M., Bennekrouf, M., & Sari, Z. (2019). Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: A hybrid genetic algorithm based approach. Reliability Engineering & System Safety, 185, 546-566.
Berrichi, A., Amodeo, L., Yalaoui, F., Châtelet, E., & Mezghiche, M. (2009). Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. Journal of Intelligent Manufacturing, 20(4), 389.
Ekin, T. (2018). Integrated maintenance and production planning with endogenous uncertain yield. Reliability Engineering & System Safety, 179, 52-61.
Ettaye, G., El Barkany, A., & El Khalfi, A. (2017). Modeling and optimization a production/maintenance integrated planning. In International Journal of Engineering Research in Africa (Vol. 28, pp. 169-181). Trans Tech Publications Ltd.
Glawar, R., Karner, M., Nemeth, T., Matyas, K., & Sihn, W. (2018). An approach for the integration of anticipative maintenance strategies within a production planning and control model. Procedia CIRP, 67, 46-51.
Goli, A., Tirkolaee, E. B., Malmir, B., Bian, G. B., & Sangaiah, A. K. (2019). A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing, 101(6), 499-529.
Hamrol, A. (2018). A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9.
Liao, G. L. (2013). Joint production and maintenance strategy for economic production quantity model with imperfect production processes. Journal of Intelligent Manufacturing, 24(6), 1229-1240.
Moussawi-Haidar, L., Daou, H., & Khalil, K. (2021). Joint reserve stock and just-in-time inventory under regular preventive maintenance and random disruptions. International Journal of Production Research, 1-22.
Nourelfath, M., Nahas, N., & Ben-Daya, M. (2016). Integrated preventive maintenance and production decisions for imperfect processes. Reliability engineering & system safety, 148, 21-31.
Samimi, E., & Sydow, J. (2020). Human resource management in project-based organizations: revisiting the permanency assumption. The International Journal of Human Resource Management, 1-35.
Schreiber, M., Klöber-Koch, J., Richter, C., & Reinhart, G. (2018). Integrated Production and Maintenance Planning for Cyber-physical Production Systems. Procedia CIRP, 72, 934-939.
Wang, L., Lu, Z., & Ren, Y. (2020). Integrated production planning and condition-based maintenance considering uncertain demand and random failures. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(1-2), 310-323.
Wang, T., Zhao, Y., & Zhu, X. (2021, January). Advanced Production Plan System of Military Manufacturing Enterprises Based on Linear Programming Model. In Journal of Physics: Conference Series (Vol. 1732, No. 1, p. 012017). IOP Publishing.
Yalaoui, A., Chaabi, K., & Yalaoui, F. (2014). Integrated production planning and preventive maintenance in deteriorating production systems. Information Sciences, 278, 841-861.
