بهینه‌سازی نگهداری و تعمیرات Proactive و کنترل موجودی با استفاده از فرایند تصمیم‌ مارکوف و شبیه‌سازی در محیط اینترنت اشیاء صنعتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت صنعتی، ‌دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

4 دانشیار، گروه مدیریت، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

اجرای برنامه‌های نگهداری و تعمیرات در زمان مناسب و مدیریت و کنترل همزمان موجودی با در نظرداشتن تغییرات و رشد تکنولوژی و همچنین بهره‌گیری از فناوری‌های نوین، موضوعی است که می‌تواند علاوه بر تأثیر بر کیفیت تولید، به‌عنوان یک مزیت رقابتی محسوب شود. هدف از انجام این پژوهش بهینه‌سازی نرخ نقص مورد انتظار در دو مقوله "نگهداری و تعمیرات" و "برنامه‌ریزی و کنترل موجودی" بر اساس زمان و هزینه است. به گونه‌ای که برای این منظور، سیاست بهینه با توجه به رویدادهای شناسایی‌شده بر اساس زمان و هزینه، با استفاده از فرایند تصمیم مارکوف، پیشنهاد و توصیه شده و مقادیر احتمالات در حالت‌های مختلف سیستم، محاسبه شده است. در ادامه برای تعیین زمان اثربخشی و تأثیرگذاری سیاست‌ها، از مفهوم اینترنت اشیاء صنعتی بهره‌گیری و مسئله در محیط شبیه‌ساز OPNET، مدلسازی و شبیه‌سازی شده است و بر اساس مقادیر زمانی جدید، مقادیر بهینه محاسبه شده است. برای انجام پژوهش، داده‌های تاریخی مربوط به اجرای برنامه‌های نت و ارزیابی ریسک در شبکه انتقال گاز مورد استفاده قرار گرفته است و بر اساس تغییر در میانگین نرخ وقوع رویدادها، زمان انجام شبیه‌سازی و تغییر در مقادیر پارامترهای آماریِ شبکه، تحلیل حساسیت و اعتبارسنجی مدل انجام شده است. نتایج پژوهش، میزان بهبود و نرخ بهینگی در نرخ نقص مورد انتظار را مبتنی بر "زمان اجرای اقدام اصلاحی یا سیاست نت"، "زمان تأثیرگذاری اقدام اصلاحی یا سیاست نت" و "زمان سفارش قطعات یدکی و اقدامات لجستیکی"، بر اساس هزینه نشان می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Optimize Proactive Maintenance and Inventory control by Using the Markov Decision Process and simulation in the frame of Industrial Internet of Things (IIOT)

نویسندگان [English]

  • Mohammadsadegh Behrooz 1
  • mohammad ali afshr kazemi 2
  • Adel Azar 3
  • Ezatolah Asgharizadeh 4
1 PhD student in Industrial Management, School of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran
2 Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
3 Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
4 Associate Professor, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran
چکیده [English]

Implementation of maintenance programs at the right time and simultaneous management and control of inventory, taking into account changes of technology, as well as the use of new technologies, is an issue that can affect the quality of production, as Be considered a competitive advantage. The purpose of this study is to optimize the Expected Loss Rate in the two concept of "maintenance" and "inventory planning and control" based on time and cost. For this purpose, the optimal policy is proposed according to the identified events based on time and cost, using the Markov Decision Process and the values of probabilities in different states of the system. To determine the effectiveness of time and policies, the concept of Industrial IoT has been used and the problem with the OPNET simulator has been modeled and simulated, and based on the new time values, the optimal values have been calculated. For conducting the research, historical data related to the implementation of maintenance and risk assessment in the gas pipeline network have been used. Based on the change in the average occurrence rate of events, the time of simulation and change in the values of network statistical parameters, Sensitivity analysis and model validation are performed. The results of the study indicate the rate of improvement and the optimal rate of the Expected Loss Rate based on time to" implement maintenance policy ", " effect of maintenance policy " and " order spare parts and logistics spare parts", presents based on cost.

کلیدواژه‌ها [English]

  • "Proactive Maintenance"
  • " Inventory Control"
  • "Markov Decision Process"
  • "IIoT"
  • "Computer network simulation"
[1]    Rastegar, I., Rezaian, J., Mahdavi, I., & Fatahi, P. (2021). “Modeling the planning and scheduling of production and repair maintenance in a combined workshop flow environment”. Modern Researches in Decision Making. 7, 2, 1-27.
[2]    SaeediSough, Y., Ahmadi, A., & Ramezani, S. (2014). “Combined optimization of spare parts inventory and maintenance activities”. Supply Chain Management, 49, 36-53.
[3]    Niu, G., Yang, B., & Pecht, M. (2010). “Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance”. Reliability Engineering and System Safety, 95, 786–796.
[4]    Bumblauskas, D., Douglas, G., Igou, A., & Anzengruber, J. (2017). “Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics”. Expert Systems with Applications, 90, 303-317.  
[5]    Bousdekis, A., Papageorgiou, N., Magoutas, B., & Apostolou, D. (2018). “Enabling condition-based maintenance decisions with proactive eventdriven computing”. Computers in Industry, 100, 173–183.
[6]    Bousdekis, A., & Mentzas, G. (2019). “A Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things”. Springer Proceedings in Business and Economics, 21-45.
[7]    Li, Y., Tang, Q., Chang, Q., & Brundage, P. (2017). “An event-based analysis of condition-based maintenance decision-making in multistage production systems”. International Journal of Production Research. DOI: 10.1080/00207543.2017.1292063.
[8]    Karabağ, O., Eruguz, A., & Basten, R. (2020). “Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system”. Reliability Engineering and System Safety, 200, 106-955.
[9]    Zou, G., Banisoleiman, K., Gonzalez, A., & Faber, M. (2019). “Probabilistic investigations into the value of information: A comparison of condition-based and time-based maintenance strategies”. Ocean Engineering, 188, 106-181.
[10]    Kim, J., Ahn, Y., & Yeo, H. (2016). “A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy”. Structure and Infrastructure Engineering, 1744-8980.
[11]    Rabbani, A., Zare, H., & Behnia, f. (2012). “Provide a suitable model for the implementation of maintenance system in factories of continuous production lines with the approach of decision-making models and ideal fuzzy planning”. Journal of Industrial Management Studies, 31, 85-100.
[12]    Ravanbakhsh, S. (2017). “Improving the efficiency of strategic equipment by maintenance, failure analysis and simulation methods”. Journal of Maritime Transport Industry, 13-26.
[13]    Nosratpanah, S., & Asadi, A. (2017). “Maintenance and repair policies based on the situation”. Andisheh Amad Quarterly, 61, 141-163.
[14]    Karimabadi, A., Hajiabadi, M., & Kamyab, E. (2015). “An overview of repairs and breakdowns of transmission and substation equipment”. Journal of New Electrical Research, 2.
[15]    Zargar, M. (2018). “Assessment of Barriers to Establishing the Internetof Things in Libraries in Iranbased on a Combined Approach”. Iranian Journal of Information Processing and Technology, 34, 3, 1371-1398. 
[16]    Yazdani, H., Sohrabi, B., & Jalilian Attar, M. (2020). “Identifying effective qualitative indicators on the evaluation of Internet of Things business models based on big data analysis in the smart city”. Modern Researches in Decision Making, 6, 2, 125-154.
[17]    Khan, W., Rehman, M., Zangoti, H., Afzal, M., Armi, N., & Salah, K. (2020). “Industrial internet of things: Recent advances, enabling technologies and open challenges”. Computers and Electrical Engineering, 81, 106-522. 
[18]    Shojaei, Q. (2010). “Conscious aggregation of information by the potential of dynamic routing in wireless sensor networks”. Master Thesis of Islamic Azad University, Rafsanjan, Iran.
[19]    Kumar, A., & Nath, V. (2019). “Study and Design of Smart Embedded System for Smart City Using Internet of Things”. Springer Nature, Singapore Pte.
[20]    Haji Shah Karam, M., & Mohammadi, S. (2015). “Proposed architecture based on the Internet of Things and recommending systems for smartening the city of Tehran”. Journal of Information Processing and Management. 1, 275-295.
[21]    Faqih, N., Baqerpour, M., & Hasanli, S. (2011). “Maintenance and repair planning”. Samt Publishing, Tehran, Iran.
[22]    Azar, A., & Jahanian, S. (2012). “Management science methodology”. University Publishing, 1, Tehran, Iran.
[23]     Modarres, M., & Asefvaziri, A. (2008). “Mathematical programming”. Javan Publishing, 6, Tehran, Iran.
[24]    Naqib Hashemi, S., & Asghari Tochai, A., Binesh Marvasti, M. (2020). “Intelligent passive decision making for sensors in structural monitoring”. Iranian Journal of Electrical and Computer Engineering, 19, 170-182.
[25]    Shekhari Ashkazari, M., & Al-Badawi, A. (2017). “Calculating the lifetime value of customers by considering the dynamics of their behavior using the Markov chain”. Management Research in Iran, 22, 4, 1-22.
[26]    Shakerin, R., Toloui Ashlaghi, A., & Radfar, R. (2019).” Analysis of the service process of the life insurance policy issuance system and future provisioning with the approach of discrete event simulation and scenario”. Management Research in Iran, 24, 4, 19-47.
[27]    Kabiri, M., & Ramezani, M. (2016). “Practical guide to computer network simulation with OPNET”. Fnnavarie novin Publishing, 1, Babol, Iran.
[28]    Farzin, M. (2010). “Simulation using OPNET software”. Sharif Noavaran Publishing, 1, Tehran, Iran.
[29]    Chen, M., Miao, Y., & Humar, I. (2018). “OPNET IoT Simulation”. Springer.
[30]    Alinaghian, M., Izadbakhsh, H., & ZarinBal, M. (2015). “Introduction to the simulation of discrete-event systems”. Mojak publication, 4, Tehran, Iran.