یک مدل ریاضی چندهدفه تولید سلول پویا برای آنالیز پاسخ استواری در کارگران با مهارت متفاوت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مدیریت صنعتی ، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران.

2 دانشجوی دکتری ،گروه مدیریت صنعتی ، واحد قزوین، دانشگاه آزاد اسلامی ، قزوین ، ایران

dmej/dmej.2020.8.1004

چکیده

هدف اصلی این تحقیق، بهینه­سازی مجموعه جواب­های بهینه حاصل از اهداف با نام جبهه بهینه پارتویی به‌منظور ارائه به تصمیم‌گیرنده نهایی است. انتخاب نهایی یک نقطه از جبهه پارتو جهت پیاده­سازی در سیستم موردنظر توسط تصمیم‌گیرنده، معمولاً فقط بر اساس اهداف ارائه‌شده در مدل ریاضی مسئله صورت می­پذیرد. یکی از معیارهای مهم در این تحقیق، میزان استواری پاسخ­های تولیدی در مقابل تغییرات پارامترهای مسئله است. در این مقاله، یک مدل ریاضی جهت طراحی سلول‌های تولیدی با در نظر گرفتن دو هدف ناهمجواری و میزان جابجایی بین سلولی قطعات، ارائه و تجزیه‌وتحلیل می‌گردد. ایجاد همکاری بین کارگران موجود در یک سلول می­تواند تأثیر زیادی در زمان اتمام عملیات داشته باشد؛ بنابراین یکی از نکات مهم در استفاده از سیستم تولید سلولی، کنترل تمامی اجزای سیستم در طی فرآیند تولید است. به این معنا که تعداد کارگران، ماشین‌آلات و قطعات اختصاص داده‌شده به یک سلول باید در یک سطح قابل‌کنترلی باشند. برای تجزیه‌وتحلیل و بررسی استواری راه­حل­های تولیدشده از تکنیک آنالیز استواری به کمک شبیه­سازی مونت‌کارلو استفاده‌شده است. این‌روش موجب افزایش کیفیت راه‌حل­ نهایی انتخابی توسط تصمیم­گیرنده می­شود. در پایان نتایج حاصل از حل و تجزیه‌وتحلیل مسئله، در قالب مطالعه موردی در شرکت امرسان ارائه می­گردد.

کلیدواژه‌ها


عنوان مقاله [English]

A Multi-Objective Mathematical Model of Dynamic Cellular Production for Analysis Robust Reply in the Different Skills of Workers

نویسندگان [English]

  • Reza Ehtesham Rasi 1
  • Akram Ali Kazemi 2
1 Assistant Professor, Department of Industrial Management, Islamic Azad University Qazvin Branch, Qazvin, Iran.
2 Ph.D. Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
چکیده [English]

The multi-objective optimization issue is the main objective of creating optimal set of objectives known as Pareto optimal front in order to provide the ultimate decision-maker.
The last determination of a point of Pareto front in order to actualize the framework by the decision maker, generally just done dependent on the objectives introduced in the mathematical model. In addition to these objectives, it is better to consider other criteria to select the final answer. One of the most important criteria, the manufacturing firm response to changes in the parameters of the problem. In this paper, a mathematical model for the design of production cells by taking two non-contiguous objectives between cellular components, presentation and analysis. Cooperation between workers in a cell can have a large impact at the time of completion. So one of the important points in the cell production system, control all system components during the manufacturing process. This means that the number of workers, machinery and parts devoted to the cells must be at a manageable level. To analyze and evaluate the stability of the solution produced a solid analysis techniques using Monte Carlo simulation has been used. This method increases the quality of the final stages to be chosen by the decision maker.

کلیدواژه‌ها [English]

  • Pareto Front
  • Robust
  • Cell Production
  • Monte Carlo Simulation
Adenzo-Diaz B, Lozano S. (2008).”A model for the design of dedicated manufacturing cells”. Int J Prod Res,46(2),301–319.
Adenso-Diaz, B., Lozano, S., Racero, J., and Guerrero, F.(2001).”Machine cell formation in generalized group technology”. Computers and Industrial engineering, 41(3),227-240
Ang, C. L., & Willey, P. C. T. (1984). “A comparative study of the performance of pure and hybrid group technology manufacturing systems using computer simulation techniques”. International Journal of Production Research, 22(2), 193–233.
Askin, R.G. and Estrada, S. (1999).”Investigation of cellular manufacturing practices”, in Irani, S.A. (Ed.): Handbook of Cellular Manufacturing Systems”. John Wiley, New York ,6(4),.25–34 .
Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). "Theory and applications of robust optimization". SIAM Review, 53(3), 464–501.
Bertsimas, D., Sim, M., (2004). “The Price of Robustness. Operations research”, 52(1), 35–53.
Bertsimas, D., & Sim, M. (2003)."Robust discrete optimization and network flows". Mathematical Programming (Series B), 98(3), 49–71.
Ben-Tal, A., & Nemirovski, A.(1998)."Robust convex optimization." Mathematics of Operations Research, 23(3), 769–805.
Ben-Tal, A., & Nemirovski, A. (2000). "Robust solutions of linear programming problems contaminated with uncertain data". Mathematical Programming, 88(4), 411–424.
Bulent Erenay, Gursel A. Suer, Jing Huang, Sripathi Maddisetty.(2015).”Comparison of layered cellular manufacturing system design approaches”. Computers & Industrial Engineering 85(14) , 346–358.
Chin-Chih Chang, Tai-Hsi Wub, Chien-Wei Wuc.(2013).”An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems”. Computers & Industrial Engineering 66(3) , 438–450.
C.R. Shiyas, V. Madhusudanan Pillai. “A mathematical programming model for manufacturing cell formation to develop multiple configurations”. Journal of Manufacturing Systems 33 (2014) 149– 158.
Deb, K., & Gupta, H. (2006)." Introducing robustness in multi-objective optimization".Evolutionary Computation, 14(4), 463–494.
Douglas Jose´ Alem, Reinaldo Morabito,(2012).”Production planning in furniture settings via robust optimization”, Computers & Operations Research,6(2), 139–150
Figueira, J. R., Greco, S., Mousseau, V., & Słowin´ ski, R. (2008).”Interactive multiobjective optimization using a set of additive value functions”. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS ,52(52), 97–119.
Gokhan Egilmeza, Bulent Erenayb, Gürsel A. Süer.(2014).”Stochastic skill-based manpower allocation in a cellularmanufacturing system”. Journal of Manufacturing Systems,23(2),115-119.
George Mavrotas, Olena Pechak, Eleftherios Siskos, Haris Doukas, John Psarras.)2015).”Robustness analysis in Multi-Objective Mathematical Programming using Monte Carlo simulation”. European Journal of Operational Research 240(11) , 193–201.
George Mavrotas, José Rui Figueira, Eleftherios Siskos.(2015). “Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection”. Omega 52(15)142–155.
Kamal Deepa, Pardeep K.(2015).”Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm”. Journal of Manufacturing Systems, 35(1),155–163.
Khaksar-Haghani, Fahimeh,Kia, Reza, Mahdavi,Iraj,Kazemi,Mohammad.(2013).”A genetic algorithm for solving a multi-floor layout design model of a cellular manufacturing system with alternative process routings and flexible configuration”.International  Journal Advanced Manufacturing Technolgy , 66(2),845–865.
Kumar KR, Chandrasekharan MP.(1990).”Grouping efficacy: a quantitative criterion forgoodness of block diagonal forms of binary matrices in group technology”. Int JProd Res ,28(2),233–43.
Kouvelis, P., & Yu, G.(1997)."Robust discrete optimization and its applications. Nonconvex-optimization and its applications". Dordrecht: Kluwer Academic Press,14.
Kusiak, Chow W. (1987).” Efficient solving of the group technology problem”. Journal  Manufacturing Systems, 6(2), 117–24.
Jensen, J.B., Malhotra, M.K., and Philipoom, P.R.(1996).”Machine dedication and process flexibility in a group technology environment”. Journal of Operations Management, 14(5),19-39.
Jeon G, Leep HR.(2006).”Forming part families by using genetic algorithm and designing machine cells under demand change”. Computers & Operations Research,33(3),263–83.
Lian, J., Liu, C., Li, W., & Yin, Y. (2018). “A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity”. Computers & Industrial Engineering, 118(6), 366-382.
Liesio, J., Mild, P., & Salo, A.(2007)."Preference programming for robust portfolio modeling and project selection". European Journal of Operational Research, 181(5), 1488–1505.
Liesio, J., Mild, P., & Salo, A. (2008).”Robust portfolio modeling with incomplete cost information and project interdependencies”. European Journal of Operational Research, 190(11), 679–695.
Lahdelma, R., Hokkanen, J., & Salminen, P. (1998).”SMAA – Stochastic multiobjective acceptability analysis”. European Journal of Operational Research, 106(1998), 137–143.
M. Sakhaii, R. Tavakkoli-Moghaddam, M. Bagheri, B. Vatani.(2015).”A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines”. Applied Mathematical Modelling,9(3),201-230.
Mahdavi, Iraj. Javadi ,Babak. Fallah-Alipour, Kaveh. Jannes Slomp .(2007).”Designing a new mathematical model for cellular manufacturing system based on cell utilization”. Applied Mathematics and Computation, 190(20), 662–670.
Maral Zafar Allahyaria, Ahmed Azabb.(2015).”A Novel Bi-level Continuous Formulation for the Cellular Manufacturing System Facility Layout Problem”. Procedia CIRP 33,87 – 92.
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). "Robust optimization of largescale systems". Operations Research, 43(2), 264–281.
Nouri H, Tang SH, Hang Tuah BT, Anuar MK.)2010).” BASE: a bacteria foraging algo-rithm for cell formation with sequence data”. Journal  Manufacturing Systems, 29(3),102–110.
Irani SA.(1999). “Handbook of cellular manufacturing systems”. New York: John Wiley& Sons.
Pitombeira Neto AR, Goncalves Filho EV.(2010).”Asimulation-based evolutionary multi objective approach to manufacturing cell formation”. Comput Ind Eng,59(1):64–74.
Raflei H, Ghodsi R.(2008).”A bi-objective mathematical model toward dynamic cell for-mation considering labor utilization”. Appl Math Modell ,37(4):2308–16.
Roland, J., De Smet, Y., & Figueira, J. R. (2012).”On the calculation of stability radius for multi-objective combinatorial optimization problems by inverse optimization”. 4(10), 379–389.
Roy, B. (2010).”Robustness in operational research and decision aiding: A multifaceted issue”. European Journal of Operational Research, 200(5), 629–638.
RG Askin, CR Standridge.) 1993).” Handbook of Modeling and analysis of manufacturing systems”.Chapter 6, John Wiley, New York ,215-218.
Satoglu, S. I., & Suresh, N. C. (2009). "A goal-programming approach for design of hybrid cellular manufacturing systems in dual resource constrained environments". Computers & Industrial Engineering, 56(2), 560–575.
Singh, N.(1993).”Design of cellular manufacturing systems: an invited review”. European Journal of Operational Research, 69, 284-291.
Srinivasan G, Narendran TT, Mahadevan B.(1990).”An assignment model for the part-families problem in group technology”. International  J Prod Res,28(l),145–52.
Sharda B, Banerjee A. (2013).”Robust manufacturing system design using multi objec-tive genetic algorithms, petri nets Bayesian uncertainty representation”. J Manuf Syst,32(5),315–24.
Soyster, A. L. (1973). "Convex programming with set-inclusive constraints and applications to inexact linear programming". Operations Research, 21(5), 1154–1157.
Suer, G. A., & Ortega, M. (1996)." Flexibility considerations in designing manufacturing cells: A case study. In Group technology and cellular manufacturing - methodologies and applications”.Amsterdam, The Netherlands: Gordon and Branch Science,9(3),97-127.
Suer, G. A., Huang, J., & Sripathi, M. (2010). "Design of dedicated, shared and remainder cells in a probabilistic demand environment". International Journal of Production Research, 48(19–20), 5613–5646.
Suresh, N.C., and Slomp, J. (2001).”A multi-objective procedure for labour assignment and grouping in capacitated cell formation problems”. International Journal of Production Research, 39(3), 4103-4131.
Vose, D.(1996).”Quantitative risk analysis: A guide to Monte Carlo simulation modelling”. Wiley.
Wemmerlov U, Hyer NL. (1986).”Procedures for the part family/ machine group identification problem in cellular manufacture”. Journal  Operation Management, 6(3),125–147.
Wang, J., & Zionts, S. (2006).”The aspiration level interactive method (AIM) reconsidered: Robustness of solutions”. European Journal of Operational Research, 175(6), 948–958.
Wemmerlov, U., and Johnson, D.J.(1997).”Cellular manufacturing at 46 user plants: Implementation experiences and Performance Improvements”. International Journal of Production Research, 35, 29-49.
Yasuda K, Hu L, Yin Y.(2005).”A grouping genetic algorithm for the multi-objective cell formation problem”. Int J Prod Res ,43(4):829–53.
Zhen, L., & Chang, D.-F.(2012).”A bi-objective model for robust berth allocation scheduling”.Computers and Industrial Engineering, 63(3), 263–272.