ارائه مدلی ریاضی جهت انتخاب اعضای تیم تحقیق‌وتوسعه و حل آن با استفاده از الگوریتم‌ شبیه‌سازی تبرید (مورد مطالعه: شرکت کیسون)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

3 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

امروزه ضرورت تشکیل تیم بیش‌ازپیش اهمیت یافته است و سازمان‌ها نیز بر روی یافتن افرادی با مهارت‌های تیمی بالا تمرکز می‌کنند. این امر به‌ویژه در رابطه با واحدهای تحقیق‌وتوسعه (R&D) که عنصر اصلی حفظ نوآوری سازمانی هستند، اهمیت بسیاری دارد. سازمان‌ها به‌منظور توسعه فنّاوری‌ها و محصولات جدید، بر تیم‌های R&D تکیه می‌کنند. ازاین‌رو، هدف از پژوهش حاضر، انتخاب شایسته‌ترین افراد به‌عنوان اعضای تیم تحقیق‌وتوسعه است. تاکنون از روش­های متعددی برای انتخاب اعضا استفاده شده و در این ‌بین، مدل‌سازی ریاضی به‌عنوان رویکردی کارآمد مدنظر محققان قرار گرفته است. این تحقیق ازنظر هدف کاربردی بوده و در دسته‌ تحقیقات توصیفی–تحلیلی قرار دارد. در این مقاله، هدف از مدل‌سازی ریاضی، انتخاب افرادی است که بیش‌ترین امتیاز را کسب نموده‌اند و متغیرهای تصمیم از نوع صفر-یک بوده که به معنای انتخاب یا عدم انتخاب فرد می‌باشد. ویژگی‌های فردی بر مبنای مدل شایستگی شناسایی شده و وزن‌های مربوطه با استفاده از روش سیموس تجدیدنظر شده و مطابق با نظر خبرگان تعیین شده است. در مرحله بعد، وزن ویژگی­های مشترک با استفاده از شاخص نوع مایرز-بریگز حاصل گشته و در انتها نیز از الگوریتم چندهدفه شبیه‌سازی تبرید (MOSA) برای حل مدل استفاده می‌شود. نهایتاً مدل‌سازی صورت گرفته برای انتخاب اعضای یکی از بزرگ‌ترین پروژه­های تحقیق‌وتوسعه شرکت کیسون مورداستفاده قرار گرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

Proposing a mathematical model for selection team member in R&D teams and solving it using Multi-Objective Simulated Annealing (Case study: Keyson Company)

نویسندگان [English]

  • Mehrnoosh Khorram 1
  • mohammadreza taghizadeh yazdi 2
  • Jalil Heidary Dahouei 3
1 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
2 Associate Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
3 Associate Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
چکیده [English]

Today, the necessity of forming teams has become more important than ever and firms begin to concentrate on finding proper people with team skills. This is especially true in research and development (R&D) organizations that are critical for maintaining organizational innovation. Organizations rely on R&D teams to develop new technologies and products. Thus, the aim of this research is to select the best individuals as R&D team members. So far, several methods have been used to select members and mathematical modeling has been considered as an efficient approach. This research is an applied and descriptive – analytical research. For this purpose, the objective function is to choose individuals who earn the most points and decision variable is 0-1 if the candidate is selected or not. Individual characteristics of team members are derived from existing competency models. Individual weights will be obtained by using revised Simos and according to experts and Myers-Briggs Type Indicator will be employed to gain collaborative weights. Finally, Multi Objective Simulated Annealing algorithm will be used to solve the problem. The modeling was used for team member selection of one of the largest Keyson Company R&D projects.

کلیدواژه‌ها [English]

  • R&D Team member selection
  • Revised Simos method
  • Competency model
  • Myers-Briggs Type Indicator (MBTI)
  • Multi-Objective Simulated Annealing (MOSA) algorithm

[1]        Omidi, M. Razavi, H. Mah Peykar, M.R., The selection of project team members based on the criteria of effectiveness of the PROMETHEE method, Journal of Industrial Management Perspective, 1, 2011, 113-134. (Persian).

[2]        Baltos, G. & Mitsopoulou, Z., team formation under normal versus crisis situations: leaders’ Assessments of Task Requirements and selection of Team Members. Naval postgraduate school, Monterey, California, 2007.

[3]        Hsu, S. C., Weng, K. W., Cui, Q., & Rand, W., Understanding the complexity of project team member selection through agent-based modeling. International Journal of Project Management, 2015, 1-12.

[4]        Hartenian, L. S., Team member acquisition of team knowledge, skills, and abilities. Team Performance Management: An International Journal,9(1/2), 2003, 23-30.

[5]        Levi, D., & Slem, C., Team work in research and development organizations: The characteristics of successful teams. International Journal of Industrial Ergonomics, 16(1), 1995, 29-42.

[6]        Huang, C. C., Knowledge sharing and group cohesiveness on performance: An empirical study of technology R&D teams in Taiwan. Technovation, 29, 2009, 786-797.

[7]        Fan, Z. P., Feng, B., Jiang, Z. Z., & Fu, N., A method for member selection of R&D teams using the individual and collaborative information. Expert Systems with Applications, 36, 2009, 8313-8323.

[8]        Zhang, L., & Zhang, X., Multi-objective team formation optimization for new product development. Computers & Industrial Engineering, 64, 2013, 804-811.

[9]        Feng, B., Jiang, Z. Z., Fan, Z. P., & Fu, N., A method for member selection of cross-functional teams using the individual and collaborative performances. European Journal of Operational Research, 203, 2010, 652-661.

[10]     Kousari, Sh., Designing a decision support system for managers of sports teams in the group matches, to select team members: A Case Study in Volleyball, M.A. thesis, Payam Noor University, Tehran, 2011. (Persian).

[11]     Mohaghar, A. & Mostafavi, A., Designing a Model for Selecting Project Team Based on Fuzzy Approach. Management Research in Iran, 11(3), 2007, 207-232. (Persian).

[12]     Tavakkoli Moghaddam, M., Najafi, E. & Yazdani, M., Project Manager Selection by using a Fuzzy Hybrid Delphi-VIKOR approach. Management Research in Iran, 16(4), 2013, 19-44 (Persian).

[13]     Afshari, A. R., Yusuff, R. M., & Derayatifar, A. R., Linguistic extension of fuzzy integral for group personnel selection problem. Arabian Journal for Science and Engineering, 38(10), 2013, 2901-2910.

[14]     Roudi, A. & Khalili Jafar Abad, A., Explaining the staff selection model in private companies active in the field of information technology, Journal of Information Technology Management, 7(3), 2015, 614-595. (Persian).

[15]     Saremi, M., Mousavi, S. F., & Sanayei, A., TQM consultant selection in SMEs with TOPSIS under fuzzy environment. Expert Systems with Applications, 36(2), 2009, 2742-2749.

[16]     Safari, H., Cruz-Machado, V., Zadeh Sarraf, A., & Maleki, M., MUltidimensional personnel selection through combination of TOPSIS and Hungary assignment algorithm. Management and Production Engineering Review, 5(1), 2014, 42-50.

[17]     Zzkarian, A., & Kusiak, A., Forming teams: an analytical approach.IIE transactions, 31(1), 1999, 85-97.

[18]     Boon, B. H., & Sierksma, G., Team formation: Matching quality supply and quality demand. European Journal of Operational Research, 148(2), 2003, 277-292.

[19]     Tseng, T. L. B., Huang, C. C., Chu, H. W., & Gung, R. R., Novel approach to multi-functional project team formation. International Journal of Project Management, 22, 2004, 147-159.

[20]     Wi, H., Oh, S., Mun, J., & Jung, M., A team formation model based on knowledge and collaboration. Expert Systems with Applications, 36(5), 2009, 9121-9134.

[21]     Abdelsalam, H. M., Multi-objective Team Forming Optimization for Integrated Product Development Projects. Foundations of Computational Intelligence, of the Series Studies in Computational Intelligence, 3, 2009, 461–478.

[22]     Strnad, D., & Guid, N., A fuzzy-genetic decision support system for project team formation. Applied soft computing, 10(4), 2010, 1178-1187.

[23]     D’Souza, G. C., & Colarelli, S. M., Team member selection decisions for virtual versus face-to-face teams. Computers in Human Behavior, 26(4), 2010, 630-635.

[24]     Kabak, M., Burmaoğlu, S., & Kazançoğlu, Y., A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications, 39(3), 2012, 3516-3525.

[25]     Ahmed, F., Deb, K., & Jindal, A., Multi-objective optimization and decision making approaches to cricket team selection. Applied Soft Computing, 13(1), 2013, 402-414.

[26]     Tavana, M., Azizi, F., Azizi, F., & Behzadian, M., A fuzzy inference system with application to player selection and team formation in multi-player sports. Sport Management Review, 16(1), 2013, 97-110.

[27]     Lemmer, H. H., Team selection after a short cricket series. European Journal of Sport Science, 13(2), 2013, 200-206.

[28]     Amin, G. R., & Sharma, S. K., Cricket team selection using data envelopment analysis. European journal of sport science, 14(sup1), 2014, 369-376.

[29]     Nikukar, GH., Alidadi Talkhestani, Y., Mahdavi Mazdeh, S.J., Providing a Non-dominated Sorting Genetic Algorithm-version 2 (NSGA-II) for Integrated model of research and development team members. Journal of Industrial Management, 6(2), 2014, 385-410 (Persian).

[30]     Wang, D., Extension of TOPSIS method for R&D personnel selection problem with interval grey number. In Management and Service Science, 2009. MASS'09. International Conference on (pp. 1-4), 2009, IEEE.

[31]     Kratzer, J., Leenders, R. T. A., & Van Engelen, J. M., Managing creative team performance in virtual environments: an empirical study in 44 R&D teams. Technovation, 26(1), 2006, 42-49.

[32]     Hu, L., Li, H., & Yu, R., A competency model of R&D personnel in High-tech manufacturing enterprises. International Conference on Management and Service Science, 2011, 1-5.

[33]     Siskos, E., & Tsotsolas, N., Elicitation of criteria importance weights through the Simos method: A robustness concern.  European Journal of Operational Research, 246, 2015, 543-553.

[34]     Figueira, J., & Roy, B., Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure. European Journal of Operational Research, 139(2), 2002, 317-326.

[35]     Merikh Bayat, F., Optimization algorithms inspired by nature, Nas, Tehran, 2012 (Persian).

[36]     Valiolah, F., Jalali, S.A. & Shir Khanlou, Z., The inspection of Myers-Briggs Type Indicator (MBTI) psychometric properties. Journal Management System, 6(21), 2013, 80-99. (Persian).

[37]     Myers-Briggs Type Indicator (MBTI) questionnaire, Psychology Laboratory, Faculty of Psychology & Education, University of Tehran (Persian).

Tiger, P. & Barron-Tiger, B., Do What You Are: Discover the Perfect Career for You Through the Secrets of Personality Type, Gharacheh Daghi, Mehdi & Rahim Monfared, Hossein. Naghsho Negar, Tehran, 2004 (Persian).