مدل چندهدفه تعیین اندازه بهینه بافر و تخصیص افزونگی ـ دسترس‌پذیری به‌صورت هم‌زمان در سیستم‌های تولیدی

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران.

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران.

3 استادیار، گروه مهندسی صنایع، دانشکده مهندسی صنایع و مکانیک، دانشگاه آزاد اسلامی، قزوین، ایران.

4 دانشجوی دکتری، مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران

چکیده

هدف: این پژوهش با هدف بررسی هم‌زمان دو دسته از مسائل پرکاربرد در حوزه تولید و عملیات شامل مسئله تخصیص افزونگی و تخصیص بافر انجام شده است. در این پژوهش، به‌دنبال بهینه‌سازی اهدافی از جمله دسترس‌پذیری، هزینه‌های سیستم و ظرفیت بافرها هستیم و بدین منظور متغیرهایی از جمله میزان بهینه ظرفیت بافرهای بین ماشین‌آلات، تعداد ماشین‌آلات با قابلیت اطمینان زیاد و تخصیص آنها و برنامه زمانی مناسب تعمیر و نگهداری بررسی شده است.
روش: با توجه به دسته‌بندی خرابی‌های اضطراری و پیشگیرانه برای ماشین‌آلات، لحاظ کردن هزینه هر نوع خرابی برای ماشین‌آلات و در نظرگیری تابع توزیع غیرنمایی و فزاینده برای انواع خرابی‌ها که به‌موجب آن، به‌دست آوردن و محاسبه توابع ریاضی مربوط به اهداف دسترس‌پذیری و هزینه به‌صورت صریح، بسیار سخت می‌شود، بنابراین رویکرد ترکیبی شبیه‌سازی، طراحی آزمایش‌ها و شبکه عصبی به‌منظور برآورد این دو تابع هدف به‌کارگرفته شد. همچنین به‌منظور تحلیل کارایی الگوریتم مورد استفاده، از الگوریتم MOPSO استفاده شد و بر اساس شاخص‌های سنجش کارایی الگوریتم‌ها با یکدیگر مقایسه شدند.
یافته‌ها: بعد از پیاده‌سازی مثال عددی با رویکرد مورد استفاده، نتایج تحقیق، حاکی از اعتبار متدلوژی پیشنهادی برای مسئله مورد بررسی بود.
نتیجه‌گیری: بر اساس مجموعه جواب‌های به‌دست آمده از الگوریتم‌های استفاده شده، می‌توان ترکیب‌های مختلفی از متغیرهای مورد بررسی (شامل تعداد ماشین‌آلات در هر ایستگاه، ظرفیت بافر و مدت زمان انجام تعمیرها) را مورد استفاده قرار داد تا به سطح مناسبی از اهداف مسئله دست یابیم.

کلیدواژه‌ها


عنوان مقاله [English]

Multi-Objective Model for determining Optimal Buffer Size and Redundancy-Availability Allocation Simultaneously in Manufacturing Systems

نویسندگان [English]

  • Maghsoud Amiri 1
  • Mohammad Taghi Taghavifard 2
  • Parham Azimi 3
  • Mojtaba Aghaei 4
1 Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
2 Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.
3 Assistant Prof., Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
4 Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran
چکیده [English]

Objective: This research was carried out with the aim of simultaneously examining the two categories of the most commonly encountered issues in the field of production and operations including the redundancy allocation and the buffers allocation. The study sought to optimize goals such as accessibility, system costs, and buffer capacity and for this purpose, variables such as the optimum capacity of buffers between machines, the number of high-reliability machines and their allocation, and the appropriate time schedule for maintenance and repair were investigated.
Methods: Considering the categorization of emergency and preventive failures for machinery, taking into account the cost of any failure for machinery, and considering the non-exponential and increasing distribution function for a variety of failures, it is very difficult to obtain and calculate mathematical functions related to the objectives of Availability and Cost explicitly. Therefore, a combination of simulation, experimental design, and neural network approach was used to estimate these two objective functions. In order to solve the proposed model, the NSGA-II algorithm was coded in MATLAB. Also, in order to analyze the efficiency of the suggested Algorithm, the MOPSO Algorithm was used and the Algorithms were compared with each other based on the performance measures of the algorithms.
Results: After applying the numerical example with the approach used, the results of the research indicate the validity of the proposed methodology for the problem under study.
Conclusion: Based on the set of solutions obtained from the algorithms used, different combinations of variables (including the number of machines per station, buffer capacity and duration of repairs) can be used to achieve the appropriate level of objectives.

کلیدواژه‌ها [English]

  • Buffer allocation problem
  • Redundancy Allocation Problem
  • simulation
  • NSGA-II Algorithm

اسماعیلیان، مجید؛ بکران، هاجر (1393). زمان‎بندی تعمیرات پیشگیرانه با استفاده از برنامه‎ریزی عدد صحیح و برنامه‌ریزی محدودیتی. مدیریت صنعتی، 6 (3)، 433-452.

شهرخی، محمود (1397). ارائه رویکردی برای محاسبه قابلیت اطمینان فازی بر پایه آهنگ خرابی فازی. مدیریت صنعتی، 10(2)، 183-200.

قاضی میرسعید، سید محمود؛ نجفی، امیرعباس؛ شهریاری، حمید (1393). ارائه روش حل دقیق برای بهبود پایایی سیستم‌های k  ازn  در مسئله تخصیص مازاد با انتخاب راهبرد مازاد. مدیریت صنعتی، 6(1)، 97- 110.

 

References

Abouei Ardakan, M., Zeinal Hamadani, A. & Alinaghian, M. (2015). Optimizing bi-objective redundancy allocation problem with a mixed redundancy strategy. ISA Transactions, 55, 116–128.

Alrabghi, A., & Tiwari, A. (2016). A novel approach for modeling complex maintenance systems using discrete event simulation. Reliability Engineering and System Safety, 154, 160–170.

Ameen, W., AlKahtani, M., Khan Mohammed, M., Abdulhameed, O., & El-Tamimi, A.M. (2018).Investigation of the effect of buffer storage capacity and repair rate on production line efficiency. Journal of King Saud University – Engineering Sciences, 30, 243–249.

Amiri, M., & Mohtashami, A. (2012). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal Advanced Manufacturing Technology, (62), 371–383.

Attar, A., Raissi, S., & Khalili-Damghani, K. (2017). A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems. Reliability Engineering and System Safety, 157, 177–191.

Chambari, A., Rahmati, S.H., Najafi A.A., & Karimi, A. (2012). A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Computers & Industrial Engineering, 63(1), 109–119.

Chang, Kuo-Hao., & Kuo P.Y. (2018). An efficient simulation optimization method for the generalized redundancy allocation problem. European Journal of Operational Research, 265, 1094–1101.

Chern, M. S. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters, 11(5), 309–315.

Coello Coello C.A., Lamont G.B. & Van Veldhuizen D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Second Edition. Springer Science & Business Media.

Costa, A., Alfieri, A., & Fichera, S. (2015). A parallel tabu search for solving the primal buffer allocation problem in serial production systems. Computers & Operations Research, (64), 97-112.

Durieux, S., & Pierreval, H. (2003). Regression meta-modeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource. International Journal of Production Economics, 89(1), 1–10.

Esfe, M.H., Razi, P., Hajmohammad, M.H., Rostamian, S.H., Sami Sarsam, W., Akbar Abbasian Arani, A., & Dahari, M. (2017). Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN. International Communications in Heat and Mass Transfer, 82, 154-160.

Esmaelian, M., & Bakran, H. (2014). Preventive maintenance scheduling with integer programming and constraint programming. Industrial Management Journal, 6(3), 433-452.

Gershwin, S. B., & Schor, J. E. (2000). Efficient algorithms for buffer space allocation. Annals of Operations Research, (93), 117–144.

Ghazi Mirsaeid, S.M., Najafi, A.A., & Shahriari, H. (2014). An exact method for solving reliability redundancy allocation problem of k-out-of-n systems with a choice of redundancy strategy. Industrial Management Journal, 6(1), 97-110.

Heydari, M., Sullivan, KM. (2018). An Integrated Approach to Redundancy Allocation and Test Planning for Reliability Growth.Computers & Operations Research, 92, 182-193.

Huang, M., Guang, C., Pao, L., & Chou, Y. (2002). Buffer allocation in flow shop-type production system with general arrival and service patterns. Computer and Operation Research, 29(2), 103–121.

Huang, X., Coolen, F. P.A., Tahani, C.M. (2019). A heuristic survival signature based approach for reliability-redundancy allocation. Reliability Engineering and System Safety, 185, 511–517.

Ida, K., Gen, M. and Yokota, T. (1994). System Reliability Optimization with Several Failure Modes by Genetic Algorithm, Proceeding of the 16th International Conference on Computers and Industrial Engineering, Ashikaga of Japan, 82.

Jiansheng G., Zutong, W., Mingfa, Z., Ying, W. (2014). Uncertain multi-objective redundancy allocation problem of repairable systems based on artificial bee colony algorithm. Chinese Journal of Aeronautics, 27(6), 1477–1487.

Kayedpour, F., Amiri, A., Rafizadeh, M., & ShahryariNia, A. (2016). Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection. Reliability Engineering & System Safety, 160, 132-151.

Koenigsberg, E. (1959). Production Lines and Internal Storage -A ReviewManagement Science, 5(4), 410-433.

Kuo, W., & Prasad, R. (2000). An annotated overview of system reliability optimization. IEEE Transactions on Reliability, 49(2), 176–187.

Lavoie, P., Kenne, J., & Gharbi, A. (2009). Optimization of production control policies in failure- prone homogenous transfer lines. IIE Transactions, 41(3), 209–222.

Liu, X., Lei, M., Zeng, Q., & Li, A. (2019). Integrated Optimization of Mixed-Model Assembly Line Balancing and Buffer Allocation Based on Operation Time Complexity. Procedia CIRP, 81, 1040-1045.

Manitz, M. (2008). Queuing model based analysis of assembly lines with finite buffers and general services times. Computers & Operations Research, 35(8), 2520–2536.

Mariano, C.H., & Pece, C.A.Z. (2015). Simulation Optimization Approach to Solve a Complex Multi-objective Redundancy Allocation Problem. In: Mujica Mota M., De La Mota I., Guimarans Serrano D. (eds) Applied Simulation and Optimization. Springer, Cham.

Mohtashami, A. (2014). A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters. The International Journal of Advanced Manufacturing Technology, 74, 1577–1593.

Montgomery, D.C. (2012). Design and Analysis of Experiments, 8th Edition, Wiley.

Okasha, N.M., & Dan, M. (2009). Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA. Structural Safety, 31, 460–74.

Pourkarim Guilani, P., Azimi, P., Niaki, S.T.A., Akhavan Niaki, S.A. (2016). Redundancy allocation problem of a system with increasing failure rates of components based on Weibull distribution: A simulation-based optimization approach. Reliability Engineering and System Safety, 152, 187–196.

Pasandideh, S.H.R., Akhavan Niaki, S.T., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Science, 292, 57–74.

Rigdon, S.E., Basu, A.P. (2000). Statistical Methods for the Reliability of Repairable Systems. Wiley, New York.

Shahrokhi, M. (2018). Developing an Approach to Calculate Fuzzy Reliability Based on Fuzzy Failure Rate. Industrial Management Journal, 10(2), 183-200.

Stenström, C., Parida, A., & Kumar, U. (2016). Measuring and monitoring operational availability of rail infrastructure. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230(5), 1457–1468. 

Tavakkoli-Moghaddam, R., Safari, J., Sassani, F. (2008). Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliability Engineering & System Safety, 93 (4), 550-556.

Tsadiras, A.K., Papadopoulos, C.T., & O’Kelly, M.E.J. (2013). An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Computers & Industrial Engineering, (66), 1150–1162.

Weiss, S., Arne Schwarz, J., & Stolletz R.(2018). The Buffer Allocation Problem in production lines: Formulations, solution methods, and instances. IISE Transactions. DOI: 10.1080/24725854.2018.1442031.

Yahyatabar, A., & Najafi, A.A. (2017). A quadratic reproduction based Invasive Weed Optimization algorithm to minimize periodic preventive maintenance cost for series-parallel systems. Computers & Industrial Engineering, 110, 436-461.

Yelkenci Kose, S., & Kilincci, O. (2015). Hybrid approach for buffer allocation in open serial production lines, Computers & Operations Research, 60(c), 67-78

Yokota, T., Gen, M. and Ida, K. (1995). System Reliability of Optimization Problems with Several Failure Modes by Genetic Algorithm. Japanese Journal of Fuzzy Theory and systems, 7, 117-135.

Yue, H., Xing, K., Hu H., Wu W., Su H.(2018). Resource failure and buffer space allocation control for automated manufacturing systems. Information Sciences, 450, 392–408.

Zheng, Z., Zhou, W., Zheng, Y., & Wu, Y. (2016). Optimal maintenance policy for a system with preventive repair and two types of failures, Computers & Industrial Engineering, 98, 102-112.

Zoulfaghari, H., Zeinal Hamadani, A., & Abouei Ardakan, M. (2014). Bi-objective redundancy allocation problem for a system with mixed repairable and non-repairable components. ISA Transactions, 53, 17–24.