مهندسی و مدیریت کیفیت

توسعه نمودارهای کنترل جهت پایش آماری شبکه دینامیک خدمات فوریتی

نوع مقاله : مقاله پژوهشی

نویسندگان

حوریه نجفی 1
عباس سقایی 2

1 دانشجوی دکتری، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، گروه مهندسی صنایع، تهران، ایران

2 استاد، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، گروه مهندسی صنایع، تهران، ایران

https://doi.org/10.48313/jqem.2022.160371
چکیده
در دنیای واقعی علاوه بر شبکه‌های اجتماعی، طیف گسترده‌ای از مساله‌ها وجود دارد که با راهکار پایش شبکه‌ها قابل تحلیل و بهبود هستند، شبکه‌هایی مانند شبکه‌های حمل‌و‌نقل، عرضه و تقاضا، تبادلات مالی، شبکه‌های موجود در بهداشت و درمان و غیره، که پایش آن‌ها و تحلیل نتایج حاصل می‌تواند منافع قابل توجهی برای ذینفعان شبکه داشته باشد. این تحقیق برمبنای شناسایی و حل مساله واقعی شکل گرفته است، به این معنا که یک مساله واقعی در کشور شناسایی شده و برای حل آن‌ متدولوژی طراحی و اجرا می‌گردد. موردکاوی مورد نظر، پایش شبکه‌ای از مراکز یک مجموعه است که خدمات فوریتی در شهرها ارائه می‌دهد. ماهیت این شبکه دینامیک، مبتنی بر ویژگی، جهت‌دار و وزن‌دار می‌باشد. نتایج این تحقیق نشان می‌دهد با مدل کردن سیستم‌های پیچیده به عنوان یک شبکه و پایش مداوم آن می‌توان وضعیت‌های غیرعادی را زودهنگام شناسایی و مدیریت نمود و از رخداد بحران در شهرها جلوگیری کرد.

کلیدواژه‌ها

شبکه های مبتنی بر ویژگی
آزمون نسبت درستنمایی (LRT)
شبکه های وزن دار

عنوان مقاله English

Developing Control Charts for Statistical Monitoring of a Dynamic Network of Emergency Service

نویسندگان English

Hoorieh Najafi 1
Abbas Saghaei 2
1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده English

Nowadays, statistical analysis and monitoring of networks and early detection of anomalies with a significant growth rate have received more attention than before in recent years. In the real world, there is a wide range of networks analyzed and improved through network monitoring solutions, such as transportation, supply-demand, financial exchanges, health care, as well as the social ones, the analysis of the results can be beneficial to the stakeholders. The basis of the research is on identifying and solving the real problem. In other words, a real problem is identified in the country and a methodology is developed to solve it. The case study is the monitoring of a network of centers that provide emergency services in cities. The nature of this network is dynamic, feature-based, directed and weighted. The results of this study show that by modeling complex systems as a network and its continuous monitoring, abnormal situations can be identified and managed early and crises in cities can be prevented.

کلیدواژه‌ها English

Attribute-based networks
Likelihood Ratio Test (LRT)
Weighted networks
  • McCulloh, I., and Carley, K. M., 2008a. Social network change detection. Technical Report CMU-ISR-08-116, Institute for Software Research, School of Computer Science, Carnegie Mellon University.
  • McCulloh, I., and Carley, K. M., 2008b. Detecting change in human social behavior simulation. Technical Report CMU-ISR-08-135, Institute for Software Research, School of Computer Science, Carnegie Mellon University.
  • McCulloh, I., Carley, K. and Horn, D. B., 2008. Change detection in social networks. Technical Report 1235, United States Army Research Institute for the Behavioral and Social Sciences.
  • McCulloh, I. and Carley, K. M., 2011. Detecting change in longitudinal social networks. Technical report, United States: Military Academy West Point NY Network Science Center (NSC).
  • Tambayong, L. 2014. Change detection in dynamic political networks: the case of Sudan. In Theories and Simulations of Complex Social Systems, pp.43-59.
  • Everton, S. F., Cunningham, D., 2013. Detecting significant changes in dark networks. Behav Sci Terrorism Polit Aggress. Vol.5, No.2, pp.94‐114.
  • Azarnoush, B., Paynabar, K., Jennifer, B., and Runger, G, 2016. Monitoring temporal homogeneity in attributed network streams. Journal of Quality Technology,48, pp.28-43.
  • Zou, N., Li, J., 2017. Modeling and change detection of dynamic network data by a network state space model. IISE Trans. Vol.49, No.1, pp.45‐57.
  • Shi, J., 2006. Stream of variation modeling and analysis for multistage manufacturing processes. CRC Press.
  • Sparks, R., Wilson, J. D., 2016. Monitoring communication outbreaks among an unknown team of actors in dynamic networks. Journal of Quality Technology, Vol.51, No.4, pp. 353-374.
  • Woodall, W. H., Zhao, M. J., Paynabar, K., Sparks, R. and Wilson, J. D., 2017. An overview and perspective on social network monitoring. IISE Transactions, pp.354-365.
  • Zhang, J., Zou, C., Wang, Z., 2010. A control chart based on likelihood ratio test for monitoring process mean and variability. Quality and Reliability Engineering International. Vol.26, pp.63-73.
  • Siegmund, D. and Venkatraman, E.S., 1995. Using the generalized likelihood ratio statistics for sequential detection of a change-point. Annual Statistics.23, pp.255-271.
  • Apley, D.W. and Shi, G.G., 1999. The GLRT for statistical process control of autocorrelated processes. IIE Transaction. Vol.31, pp.1123-1134.
  • Han, D. and Tsung, F., 2005. Comparison of the CUSCORE, GLRT and CUSUM control charts for detecting a dynamic mean change. Annals of the Institute of Statistical Mathematics.57, pp.531-552.
  • Zhang, J., Li, Z. and Wang, Z., 2009. Control chart based on likelihood ratio for monitoring linear profiles. Computer Statistics and Data Analysis.53, pp.1440-1448.
  • Shang, Y., Tsung, F., and Zou, C., 2011. Profile monitoring with binary data and random predictors. Journal of Quality Technology, Vol.43 No.3, pp.196–208.
  • Noorossana, R., Saghaei, A., Izadbakhsh, H., and Aghababaei, O., 2013. Monitoring multinomial log it profiles via log-linear models. International Journal of Industrial Engineering & Production Research, Vol.24 No.2, pp.137–142.
  • Soleymanian, M. E., Khedmati, M., and Mahlooji, H., 2013. Phase II monitoring of binary response profile. Scientia Iranica E, Vol.20 No.6, pp.2238–2246.
  • Qi, D., Wang, Z., Zi, X. and Li, Z., 2016. Phase II monitoring of generalized linear profiles using weighted likelihood ratio charts. Computers & Industrial Engineering, 94, pp.178–187.
  • Sullivan, J. H. and Woodall, W. H., 1996. A Control Chart for Preliminary Analysis of Individual Observations. Journal of Quality Technology, Vol.28 No.3, pp.265-278.
  • Zou, C. and Tsung, F., 2010. Likelihood Ratio-Based Distribution-Free EWMA Control Charts. Journal of Quality Technology, Vol.42 No.2, pp.174.
  • Paynabar, K., Jionghua, J. and Yeh, A. B., 2012. Phase I Risk-Adjusted Control Charts for Monitoring Surgical Performance by Considering Categorical Covariates. Journal of Quality Technology, Vol.44 No.1, pp.39-53.
  • Mazrae Farahani, E., Baradaran Kazemzadeh, R., Albadvi, A. and Teimourpour, B., 2018. GLMM-Based Modeling and Monitoring Dynamic Social Network. International Journal of Industrial Engineering & Production Research, 29, No. 3, pp.247- 259.
  • Fotuhi, H., Amiri, A. and M. Maleki, R., 2018. Phase I monitoring of social networks based on Poisson regression profiles. Quality and Reliability Engineering International,34, pp. 572-588.
  • Mazrae Farahani, E., Baradaran Kazemzadeh, R., Albadvi, A. and Teimourpour, B., 2018. GLMM-Based Modeling and Monitoring Dynamic Social Network. International Journal of Industrial Engineering & Production Research, 29, No. 3, pp.247- 259.
  • Mogouie H., Raissi, G. A., Bahrami, E. and Amiri, A., 2019. Statistical monitoring of binary response attributed social networks considering random effects. Communications in Statistics-Simulation and Computation org/10.1080/03610918.2019.1661471.
  • Najafi, H. and Saghaei, A., 2020. Statistical monitoring for change detection of interactions between nodes in networks: With a case study in financial interactions network. Communications in Statistics- Theory and Methods, DOI:10.1080/03610926.2020.1725830.
  • Shaghaghi, M. and Saghaei, A., 2020. PCA likelihood ratio test approach for attributed social networks monitoring. Communications in Statistics - Theory and Methods, DOI: 10.1080/03610926.2018.1491599.
  • Maboudou-Tchao, E. M. and Hawkins, D. M., 2011. Self-Starting Multivariate Control Charts for Location and Scale. Journal of Quality Technology, Vol.43, No.2, pp.113–126.
  • Capizzi, G. and Masarotto, G., 2010. Self-Starting CUSCORE Control Charts for Individual Multivariate Observations. Journal of Quality Technology,42, No.2, pp. 136–151.
  • Gahrooei Raeisi, M. and Paynabar, K., 2018. Change detection in a dynamic stream of attributed networks. Journal of Quality Technology, Vol.50, pp.418-430.

Woodall, W. H., Zhao, M. J., Paynabar, K., Sparks, R. and Wilson, J. D., 2017. An overview and perspective on social network monitoring. IISE Transactions, pp.354-365

مهندسی و مدیریت کیفیت
دوره 11، شماره 4
زمستان 1400
صفحه 377-392

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