کاربرد مدل‌سازی مفهومی در شبیه‌سازی عامل‌بنیان تخصیص افراد به پست‌های سازمانی
محورهای موضوعی :
مدیریت صنعتی
Alireza Moumivand
1
(Department of Industrial Management, Science and Research branch, Islamic Azad
University, Tehran, Iran)
adel azar
2
(Department of Management, Faculty of Management and Economics, Tarbiat Modares
University, Tehran, Iran)
Abbass Toloie Ashlaghi
3
(Department of Industrial Management, Science and Research branch, Islamic Azad University, Tehran, Iran)
تاریخ دریافت : 1398/12/11
تاریخ پذیرش : 1399/08/17
تاریخ انتشار : 1399/09/02
کلید واژه:
شبیه‌سازی عامل‌بنیان,
تخصیص پست,
ساختار سازمانی,
مدل‌سازی مفهومی,
ارتقاء شغلی,
چکیده مقاله :
دراین مقاله با بررسی ادبیات موضوع شبیه‌سازی عامل‌بنیان و کاربرد‌های آن ،یک مدل کلی شامل: مدل مفهومی ، مدل عامل‌بنیان ، روابط آنها (بایکدیگر و دنیای بیرون)به منظور بهبود مشکلات سازمانی پیچیده ارائه شده است. مدل مفهومی و جزئیات آن جهت ساختار‌دهی شبیه‌سازی به کمک مدل‌سازان و ذینفعان مدل‌سازی با تعیین اهداف شبیه‌سازی، ورودی‌ها، خروجی‌ها ، فعالیت‌های مدل‌سازی( رفتار عامل‌های اصلی ومحیطی) وحدود (مرزهای) شبیه‌سازی عامل‌بنیان معرفی شده و با بررسی اعتبار مدل مفهومی، مدل عامل‌بنیان در رایانه ساخته و ضمن تصدیق و صحه گذاری مدل رایانه‌ای ، امکان بررسی سناریوهای مختلف و در نهایت پاسخ مناسب به مسئله فراهم شده است. به منظور کاربردی کردن مدل ارائه شده این پژوهش در قالب مطالعه موردی، مشکل نارضایتی کارمندان شرکت او-جی به دلیل عدم ترفیع شغلی، توسط مدل عامل‌بنیان به صورت سیستمیک مورد بررسی قرار گرفته است. به گونه‌ای که پس از مدل‌سازی فرایند ترفیع شغلی در چارچوب ساختار سازمانی شرکت، سه سناریو برای حل مشکل یاد شده پیشنهاد و در مدل عامل‌بنیان، اجرا شده است. سناریوی اول پیشنهاد کاهش مدت زمان جهت دریافت ریالی جبرانی ناشی از دست رفتن ارتقاء برای هر کارمند، سناریوی دوم، پیشنهاد افزایش یک رتبه به رتبه سازمانی تمامی پست‌های سازمان و سناریوی سوم، ترکیب سناریوی اول و دوم بوده که با اجرای این سناریوها محتمل است که میزان نارضایتی کارمندان در 10 سال آتی به ترتیب میزان 57 ، 42 و 78 درصدکاهش یابد. در نهایت مدل‌سازان سناریو آخر را جهت اجرا به مدیران که خود از اعضای تیم مدل‌سازی بودند، پیشنهاد دادند.
چکیده انگلیسی:
The present paper introduces a model by investigating studies that have applied Agent-Based Modelling to improve organization system problems. The model is made of two parts (a conceptual model and an Agent-based model) and their interactions. The conceptual model structures the simulation by determining input, output, modelling activities, and system boundaries. Then, the computer model based on the validated and verified conceptual model was built. For practical application of the model, employees’ dissatisfaction of OG companies over promotion system was modeled. Regarding the agent based simulation of promotion process, modelling team with multiple perspectives involving managers, employees, and modelers suggest three scenarios to address the problem. The first scenario suggests financial compensation for employees; the second scenario recommends one grade increase in all the company job-grades; and the last scenario is a combination of them. The results of this investigation indicate that by implementing the first, second and third scenarios the overall dissatisfaction of the company will decrease 57, 42, and 78% over 10 years respectively. Finally, the modelling team proposes the last scenario for implementing in company.
منابع و مأخذ:
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Ameyaw, C. and H. W. Alfen (2018). Two Strands Model of the Soft System Methodology Analysis of Private Sector Investment in Power Generation Sector in Ghana. Systemic Practice and Action Research 31(4),395-419.
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Davoodi, S. M. F. c., Shahab. (2019). Dynamic Analysis of Ordering System in the Supply Chain with the Dynamics of Systems Approach. journal of Industrial management, 14(48), 51-60.
Fioretti, G. (2013). Agent-based simulation models in organization science. Organizational Research Methods, 16(2), 227-242.
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Heyne, D., & Mönch, L. (2011). An agent-based planning approach within the framework of distributed hierarchical enterprise management. Journal of Management Control, 22(2), 205-236.
Holm, L. B., Dahl, F. A., & Barra, M. (2013). Towards a multimethodology in health care–synergies between Soft Systems Methodology and Discrete Event Simulation. Health Systems, 2(1), 11-23.
Jackson, M. C., & Keys, P. (1984). Towards a system of systems methodologies. Journal of the Operational Research Society, 35(6), 473-486.
Jiang, G., Hu, B., & Wang, Y. (2011). Agent-based simulation approach to understanding the interaction between employee behavior and dynamic tasks. Simulation, 87(5), 407-422.
Jonker, J., & Pennink, B. (2010). The essence of research methodology: A concise guide for master and PhD students in management science: Springer Science & Business Media.
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Robinson, S. (1999). Simulation verification, validation and confidence: a tutorial. Transactions of the Society for Computer Simulation, 16(2), 63-69.
Robinson, S. (2001). Soft with a hard centre: discrete-event simulation in facilitation. Journal of the Operational Research Society, 52(8), 905-915.
Robinson, S. (2004a). Simulation: the practice of model development and use: Wiley Chichester.
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Robinson, S. (2008b). Conceptual modelling for simulation Part II: a framework for conceptual modelling. Journal of the Operational Research Society, 59(3), 291-304.
Rosenhead, J., & Mingers, J. (2001). Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict: Wiley Chichester.
Sang, T. X. (2013). Multi-criteria decision making and task allocation in multi-agent based rescue simulation. Japan Graduate School of Science and Engineering, Saga University, Japan.
46.Sargent, R. G. (2010). Verification and validation of simulation models. Paper presented at the Proceedings of the 2010 Winter Simulation Conference.
47.Secchi, D. (2015). A case for agent-based models in organizational behavior and team research. Team Performance Management.
Sierhuis, M., Jonker, C., Van Riemsdijk, B., & Hindriks, K. (2009). Towards organization aware agent-based simulation. practice, 16, 17.
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Sobkowicz, P. (2016). Agent based model of effects of task allocation strategies in flat organizations. Physica A: Statistical Mechanics and its Applications, 458, 17-30.
Tako, A. A., N. Tsioptsias and S. Robinson (2020). "Can we learn from simplified simulation models? An experimental study on user learning." Journal of Simulation: 1-15.
Vahidi, A. and A. Aliahmadi (2019). "Describing the necessity of multi-methodological approach for viable system model: Case study of viable system model and system dynamics multi-methodology." Systemic Practice and Action Research 32(1): 13-37.
van der Zee, D.-J. (2019). "Model simplification in manufacturing simulation–Review and framework." Computers & Industrial Engineering 127: 1056-1067.
Wall, F. (2016). Agent-based modeling in managerial science: an illustrative survey and study. Review of Managerial Science, 10(1), 135-193.
Waters, D., & Waters, C. D. J. (2008). Quantitative methods for business: Pearson Education.
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo: MIT Press.
Williams, T. (2009). Management Science in Practice: JW.
Wu, J., Hu, B., Zhang, Y., Spence, C., Hall, S. B., & Carley, K. M. (2009). An agent-based simulation study for exploring organizational adaptation. Simulation, 85(6), 397-413.
Yan, C., Yanlif, Y., Lina, Y., & Ning, L. (2006). Agent-based dynamic organization structure for production scheduling in a dynamic manufacturing environment, Paper presented at International Technology and Innovation Conference 2006.
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Alavi, S. N., Isa; Azari, Ali. (2019). Prioritizing Strategic Indicators and Objectives Based on Balanced Scorecard at Iranian Oil Pipelines and Telecommunication Company and Providing Model Dynamics Model. Journal of Industrial management, 14(49), 34-52.
Ameyaw, C. and H. W. Alfen (2018). Two Strands Model of the Soft System Methodology Analysis of Private Sector Investment in Power Generation Sector in Ghana. Systemic Practice and Action Research 31(4),395-419.
Axelrod, R. M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University Press.
Azar, A. K., Farzane; Jalali, Reza. (2013). Soft Operation Research. Tehran: Industrial Management Organization.
Badham, J. (2010). A compendium of modelling techniques.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280-7287.
Brandimarte, P. (2012). Quantitative methods: An introduction for business management: John Wiley & Sons.
Bryman, A., & Bell, E. (2015). Business research methods: Oxford University Press, USA.
Chang, M.-H., & Harrington Jr, J. E. (2006). Agent-based models of organizations. Handbook of computational economics, 2, 1273-1337.
Coyle, R. G. (1996). System dynamics modelling: a practical approach (Vol. 1): CRC Press.
Eldabi, T. (2000). Simulation modelling: problem understanding in healthcare management. Brunel University.
Davoodi, S. M. F. c., Shahab. (2019). Dynamic Analysis of Ordering System in the Supply Chain with the Dynamics of Systems Approach. journal of Industrial management, 14(48), 51-60.
Fioretti, G. (2013). Agent-based simulation models in organization science. Organizational Research Methods, 16(2), 227-242.
Gharajedaghi, J. (2011). Systems thinking: Managing chaos and complexity: A platform for designing business architecture: Elsevier.
Gómez-Cruz, N. A., Loaiza Saa, I., & Ortega Hurtado, F. F. (2017). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313-328.
Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51-59.
Heyne, D., & Mönch, L. (2011). An agent-based planning approach within the framework of distributed hierarchical enterprise management. Journal of Management Control, 22(2), 205-236.
Holm, L. B., Dahl, F. A., & Barra, M. (2013). Towards a multimethodology in health care–synergies between Soft Systems Methodology and Discrete Event Simulation. Health Systems, 2(1), 11-23.
Jackson, M. C., & Keys, P. (1984). Towards a system of systems methodologies. Journal of the Operational Research Society, 35(6), 473-486.
Jiang, G., Hu, B., & Wang, Y. (2011). Agent-based simulation approach to understanding the interaction between employee behavior and dynamic tasks. Simulation, 87(5), 407-422.
Jonker, J., & Pennink, B. (2010). The essence of research methodology: A concise guide for master and PhD students in management science: Springer Science & Business Media.
Kong, Y., Zhang, M., Ye, D., Zhu, J., & Choi, J. (2018). An intelligent agent‐based method for task allocation in competitive cloud environments. Concurrency and Computation: Practice and Experience, 30(3), e4178.
Kotiadis, K., & Mingers, J. (2006). Combining PSMs with hard OR methods: the philosophical and practical challenges. Journal of the Operational Research Society, 57 (7),856-867.
Kotiadis, K., Tako, A. A., & Vasilakis, C. (2014). A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare. Journal of the Operational Research Society, 65(2), 197-213.
Kunc, M., P. Harper and K. Katsikopoulos (2018). "A review of implementation of behavioural aspects in the application of OR in healthcare." Journal of the Operational Research Society: 1-18.
Law, A. (2009). December. How to build valid and credible simulation models. Paper presented at the Simulation Conference (WSC), Proceedings of the.
Li, B., Sun, D., Zhu, R., & Li, Z. (2015). Agent based modeling on organizational dynamics of terrorist network. Discrete Dynamics in Nature and Society, 2015.
Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. Journal of simulation, 10(2), 144-156.
Miller, K. D. (2015). Agent-based modeling and organization studies: A critical realist perspective. Organization Studies, 36(2), 175-196.
Mohammadpor, A. (2010). Meta-Method: Jameeshenasan.
31.Myers, M. D. (1997). Qualitative research in information systems. Management Information Systems Quarterly, 21(2), 241-242.
OG Company Documents. (2019).
Owliya, M., Saadat, M., Anane, R., & Goharian, M. (2012). A new agents-based model for dynamic job allocation in manufacturing shopfloors. IEEE Systems Journal, 6(2), 353-361.
Pereira, T. F., Montevechi, J. A. B., Miranda, R. d. C., & Friend, J. D. (2015). Integrating soft systems methodology to aid simulation conceptual modeling. International Transactions in Operational Research, 22(2), 265-285.
Pidd, M. (2004a). Complementarity in systems modelling. Systems modelling: Theory and practice, 1, 20.
Pidd, M. (2004b). Systems modelling: theory and practice: John Wiley & Sons, Inc.
Rand, W. (2013). The future applications of agent-based modeling in marketing The Routledge Companion to the Future of Marketing (pp. 379-392): Routledge Abingdon.
Robinson, S. (1999). Simulation verification, validation and confidence: a tutorial. Transactions of the Society for Computer Simulation, 16(2), 63-69.
Robinson, S. (2001). Soft with a hard centre: discrete-event simulation in facilitation. Journal of the Operational Research Society, 52(8), 905-915.
Robinson, S. (2004a). Simulation: the practice of model development and use: Wiley Chichester.
Robinson, S. (2004b). Simulation: The Practice of Model Development and Use. Inglaterra: John Willey and Sons. Inc. Cap, 1(2), 5.
Robinson, S. (2008a). Conceptual modelling for simulation Part I: definition and requirements. Journal of the Operational Research Society, 59(3), 278-290.
Robinson, S. (2008b). Conceptual modelling for simulation Part II: a framework for conceptual modelling. Journal of the Operational Research Society, 59(3), 291-304.
Rosenhead, J., & Mingers, J. (2001). Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict: Wiley Chichester.
Sang, T. X. (2013). Multi-criteria decision making and task allocation in multi-agent based rescue simulation. Japan Graduate School of Science and Engineering, Saga University, Japan.
46.Sargent, R. G. (2010). Verification and validation of simulation models. Paper presented at the Proceedings of the 2010 Winter Simulation Conference.
47.Secchi, D. (2015). A case for agent-based models in organizational behavior and team research. Team Performance Management.
Sierhuis, M., Jonker, C., Van Riemsdijk, B., & Hindriks, K. (2009). Towards organization aware agent-based simulation. practice, 16, 17.
Siggelkow, N., & Levinthal, D. A. (2003). Temporarily divide to conquer: Centralized, decentralized, and reintegrated organizational approaches to exploration and adaptation. Organization Science, 14(6), 650-669.
Silva, G. F. P. d., A. L. Pegetti, M. T. Piacesi, M. C. N. Belderrain and N. C. R. Bergiante (2020). "Dynamic modeling of an early warning system for natural disasters." Systems Research and Behavioral Science 37(2): 292-314.
Sobkowicz, P. (2016). Agent based model of effects of task allocation strategies in flat organizations. Physica A: Statistical Mechanics and its Applications, 458, 17-30.
Tako, A. A., N. Tsioptsias and S. Robinson (2020). "Can we learn from simplified simulation models? An experimental study on user learning." Journal of Simulation: 1-15.
Vahidi, A. and A. Aliahmadi (2019). "Describing the necessity of multi-methodological approach for viable system model: Case study of viable system model and system dynamics multi-methodology." Systemic Practice and Action Research 32(1): 13-37.
van der Zee, D.-J. (2019). "Model simplification in manufacturing simulation–Review and framework." Computers & Industrial Engineering 127: 1056-1067.
Wall, F. (2016). Agent-based modeling in managerial science: an illustrative survey and study. Review of Managerial Science, 10(1), 135-193.
Waters, D., & Waters, C. D. J. (2008). Quantitative methods for business: Pearson Education.
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo: MIT Press.
Williams, T. (2009). Management Science in Practice: JW.
Wu, J., Hu, B., Zhang, Y., Spence, C., Hall, S. B., & Carley, K. M. (2009). An agent-based simulation study for exploring organizational adaptation. Simulation, 85(6), 397-413.
Yan, C., Yanlif, Y., Lina, Y., & Ning, L. (2006). Agent-based dynamic organization structure for production scheduling in a dynamic manufacturing environment, Paper presented at International Technology and Innovation Conference 2006.
