ارائه مدلی برای تخصیص بهینه منابع انسانی به فرآیندهای عملیاتی با استفاده از مدل مارکویتز: مطالعه موردی در واحد اورولوژی یک مرکز فوق‌تخصصی کلیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مدیریت سیستم و بهره وری، دانشکده مهندسی صنایع و سیستم ها، دانشگاه تربیت مدرس، تهران، ایران

2 دانشجوی دکترای مهندسی صنایع، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران

3 دانشگاه تربیت مدرس، دانشکده مهندسی صنایع و سیستم‌ها،گروه سیستم‌های اقتصادی و اجتماعی

4 استاد، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران

چکیده

آنالیز فرآیند ارتباط تنگاتنگی با بهینه‌سازی فرایند دارد ودرآن بحث منابع نقش اصلی دارد. ازآنجا که مجموعه‌ی بهینه منابع به ویژه درشرایط رخداد حوادث غیر منتظره دارای اثرات متغیر در ریسک، عملکرد فرآیند و بازدهی آن است می‌تواند مشابه مدل انتخاب سبد بهینه سهام تعریف شود. هدف این مقاله، ارائه مدلی ریاضی برای تخصیص منابع انسانی بااستفاده از مدل مارکویتز براساس مهارت‌ها و هزینه‌ها و باهدف مینیمم کردن ریسک و ماکسیمم کردن بازده منابع است. مدل ارائه شده شامل ترکیبی از ارزیابی ریسک وبازده جهت پیدا کردن بهترین سبد ترکیب منابع در شرایط بحرانی است و روش حل محدودیت اپسیلون است. ازجمله نوآوری‌ها، مدل مارکوئیتز برای بهینه‌سازی تخصیص منابع و تخصیص مبتنی بر ریسک منابع است. مدل پیشنهادی در مطالعه موردی واحد اورولوژی یک مرکز فوق‌تخصصی کلیه استفاده شده است. نتایج نشاندهنده تخصیص منابع با بازده وریسک بهینه و بیشترین میزان خروجی در شرایط بحرانی باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Presenting a model for the optimal allocation of human resources to operational processes using the Markowitz model: A case study in urology unit at a kidney center

نویسندگان [English]

  • Bakhtiar Ostadi 1
  • Mahnaz Ebrahimi-Sadrabadi 2
  • Ali Husseinzadeh Kashan 3
  • Mohammad Mehdi Sepehri 4
1 Associate Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
2 PhD Student, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
3 Associate Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
4 Professor of Healthcare Systems Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Process analysis is closely related to process optimization and in the optimization, the discussion of resources plays a major role. Since the optimal set of resources, especially in the event of unexpected events will have variable effects on risk, process performance and efficiency can be similar to the optimal stock portfolio selection model. The purpose of this paper is to provide a mathematical model for allocating human resources using the Markowitz model to the days of the week based on skills and costs, which the objective function minimizes the risk and maximizes the efficiency of resources. The proposed model includes a combination of risk and return assessments to find the best resource portfolio in critical situations. The study also used the Epsilon constraint method. The innovations of this research is the application of Markowitz model to optimize resource allocation and risk-based allocation of resources in the process. The proposed model has been used in a case study of a urology subspecialty kidney center. Numerical results show that using the designed model, resources can be allocated in such a way that it has optimal returns and risk, in order to produce the maximum amount of output in critical situations.
 

کلیدواژه‌ها [English]

  • Resource allocation
  • Markowitz model
  • Operational processes
  • Risk and return
  • Alkaabneh, Faisal, Ali Diabat, and Huaizhu Oliver Gao. Unified Framework for Efficient, Effective, and Fair Resource Allocation by Food Banks: Approximate Dynamic Programming Approach. Omega, 102300.‏
  • Abdolmanafi, Seyed Ebrahim, and Sina Karamad. A new approach for resource allocation for black spot treatment (case study: The road network of Iran). Journal of Safety Research, 2019. 69. 95-100.‏
  • Abdalla, I. M. Fatality risk assessment and modeling of drivers responsibility for causing traffic accidents in Dubai. Journal of safety research. 2002. 33(4), 483-496.‏
  • Brindley, C. (Ed.). Supply chain risk. 2017. Taylor & Francis.  
  • Bozejko, Wojciech, et al., Optimization of production process for resource utilization. Archives of Civil and Mechanical Engineering ,2019. 19 . 1251-1258.‏
  • Deng, Yu-Jing, et al., Optimal defense resource allocation for attacks in wireless sensor networks based on risk assessment model. Chaos, Solitons & Fractals, 2020. 137. ‏
  • Firouzi, J. F., & Aghajannejad, A. A Model to Improve the Allocation of Hospital Resources Using Queuing Theory. 2018.‏
  • Khakzad, N., Khan, F., & Amyotte, P. Dynamic risk analysis using bow-tie approach. Reliability Engineering & System Safety, 2012. 104, 36-44.‏
  • Kalantarnia, M., Khan, F., & Hawboldt, K. Dynamic risk assessment using failure assessment and Bayesian theory. Journal of Loss Prevention in the Process Industries, 2009 . 22(5), 600-606.‏
  • Ketabi, S., Ghandehari, M., & Bolandi, D. Efficiency Analysis and Hospital Resource Allocation Using Centralized Data Envelopment Analysis. Journal of Production and Operations Management, 2020.3: 1-16.‏
  • Li, W., Cao, Q., He, M., & Sun, Y. Industrial non-routine operation process risk assessment using job safety analysis (JSA) and a revised Petri net. Process Safety and Environmental Protection, 2018. 533-538.‏
  • Lozano, S., Gabriel Villa, and B. Adenso-Dıaz. Centralised target setting for regional recycling operations using DEA. Omega, 2004. 32.2. 101-110.‏
  • Mutlu, N. G., & Altuntas, S. Risk analysis for occupational safety and health in the textile industry: Integration of FMEA, FTA, and BIFPET methods. International Journal of Industrial Ergonomics, 2019. 72, 222-240.‏
  • Mashayekhi, Z., & Omrani, H. An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 2016. 38, 1-9.‏
  • Marhavilas, P. K., & Koulouriotis, D. E. A combined usage of stochastic and quantitative risk assessment methods in the worksites: Application on an electric power provider. Reliability Engineering & System Safety, 2012. 97(1), 36-46.‏
  • Mokhtarian Daloie. R, Ostadi. B. Developing a discrete-event simulation model for improving the quality of services: A case study in urology unit at a kidney center. Journal of Engineering and Quality Management, 2019. 9(3), 244-260, Persian.
  • Markowitz, H. Portfolio Selection. The Journal of Finance, 1952. Vol 7: 77-91.
  • Msengwa, Amina S.; Rashidi, Jumanne; Mniachi, R. A. Waiting time and Resource Allocation for Out-patient Department: A case of Mwananyamala Hospital in Dar es Salaam, Tanzania. Tanzania Journal for Population studies and Development, 2020.1.‏
  • Najarian, M., & Lim, G. J. Optimizing infrastructure resilience under budgetary constraint. Reliability Engineering & System Safety, 2020. 198, 106801.‏
  • Ostadi B, Seifi M.M., Husseinzadeh Kashan A. A multi-objective model for resource allocation in disaster situations to enhance the organizational resilience and maximize the value of business continuity with considering events interactions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2021. February. doi:10.1177/1748006X21991027
  • Ostadi, B, Ghorbani, & Mokhtarian Deloui. Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry). Journal of Industrial Engineering, 2018. 52 (4), 509-521, Persian.
  • Ostadi, B., Sedeh, O. M., & Kashan, A. H. Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model. Energy, 2020. 191, 116516.‏
  • Ostadi, B & Abbasi Harofteh, S. A novel risk assessment approach using Monte Carlo simulation based on co-occurrence of risk factors: A case study of a petrochemical plant construction. Scientia Iranica: Transactions on Industrial Engineering (E), 2020. doi: 10.24200/sci.2020.55513.4258.
  • Rezaie, K., Amalnik, M. S., Gereie, A., Ostadi, B., & Shakhseniaee, M. Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties. Applied Mathematics and Computation, 2007. 190(2), 1492-1501.‏
  • Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. Integrated business continuity and disaster recovery planning: Towards organizational resilience. European Journal of Operational Research, 2015. 242(1), 261-273.
  • Suppapitnarm, N., & Pongpirul, K. Model for allocation of medical specialists in a hospital network. Journal of healthcare leadership, 2018. 10: 45.‏
  • Torabi, S. A., Giahi, R., & Sahebjamnia, N. An enhanced risk assessment framework for business continuity management systems. Safety science, 2016. 89, 201-218.‏
  • Tran, T. D., Nguyen, U. V., Nong, V. M. and Tran, B. X. Patient waiting time in the outpatient clinic at a central surgical hospital of Vietnam: Implications for resource allocation. F1000Research, 2017. 6:454, 1-12
  • Vercher, E. & Bermudez, J. Portfolio optimization using a credibility meanabsolute semi-deviation model. Expert Systems with Applications, 2015. 42(20), 79–90.
  • Wu, D., Li, J., Xia, T., Bao, C., Zhao, Y., & Dai, Q. A multiobjective optimization method considering process risk correlation for project risk response planning. Information Sciences, 2018. 467, 282-295.‏
  • Withanachchi, N, Uchida, Y., Nanayakkara, S., Samaranayake, D. and Okitsu, A. Resource allocation in public hospitals: Is it effective?. Health Policy, 2007, 80 (2), 308-313.‏
  • Wangyang, Y., Menghan, J., Xianwen, F., Yao, L. and Jianchun, X. ‏Modeling and analysis of medical resource allocation based on Timed Colored Petri net. Future Generation Computer Systems, 111, 2020, 368-374