نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته دکترای مهندسی صنایع، دانشکده مهندسی صنایع و سیستم های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران.

2 دانشیار و عضو هیات علمی دانشکده مهندسی صنایع و سیستم های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران

3 استاد و عضو هیات علمی دانشکده مهندسی صنایع و سیستم های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران.

چکیده

هدف این مقاله ارائه یک مدل بهینه‌سازی تسهیم دانش بر مبنای نظریه بازی است که تصمیمات سازمان و دانشکاران را بصورت همزمان بهینه‌سازی کند. بدین منظور از برنامه‌ریزی دوسطحی استفاده شد. در سطح بالا تصمیمات سازمان در خصوص پاداش و در سطح پایین تصمیمات دانشکار در خصوص زمان و تلاش تخصیص یافته به تسهیم دانش مدلسازی شد. طراحی فرمولاسیون ریاضی رفتار دانشکاران بر اساس ادبیات و در چارچوب انگیزش-فرصت-توانایی انجام شد. مدلسازی دوسطحی ارائه شده نسبت به مدل‌های قبلی امکان بررسی پارامترهای بیشتری را فراهم می‌کند و علاوه بر انگیزش به دو عامل دیگر یعنی توانایی و فرصت نیز توجه دارد. همچنین ساختار تابع عایدی بصورت غیرخطی در نظر گرفته شده است و لذا انطباق بیشتری با واقعیت دارد. علاوه بر این، میزان زمان در دسترس به عنوان یک عامل کلیدی در آن آمده است. مدل دوسطحی در نرم افزار GAMS و با سینتکس EMP کدنویسی شد و برای مجموعه‌ای از داده‌ها که بصورت تصادفی تولید شدند، با استفاده از الگوریتم بارون حل شد. نتایج نشان می‌دهد که افزایش میزان کاربردپذیری دانش و ضریب تأثیر مقایسه اجتماعی می‌تواند منجر به بهبود عملکرد سازمان و صرفه‌جویی در هزینه‌های پاداش شود. نادیده گرفتن این دو عامل می‌تواند منجر به اتخاذ تصمیمات غیربهینه شود. این تحقیق با فراهم نمودن بستری برای در نظر گرفتن پارامترهای بیشتر، به بهبود تصمیمات سازمان در این زمینه کمک می‌کند. همچنین، بر اساس این تحقیق الگوریتم بارون برای مسائل در اندازه بزرگ نیاز به بهبود دارد و پیشنهاد می‌شود تحقیقات آتی الگوریتم‌های کاراتری برای این مسئله ارائه نمایند.

کلیدواژه‌ها

عنوان مقاله [English]

Knowledge Sharing Optimization Based on the Game Theory: A Bi-Level Programming Application

نویسندگان [English]

  • Mohsen Tabatabaei 1
  • Abbas Afrazeh 2
  • Abbas Seifi 3

1 Ph.D. in Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

2 Associate Professor, Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran,

3 Professor, Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

چکیده [English]

This paper aims to propose a knowledge sharing optimization model based on the game theory that optimizes both employer and employee(s) decisions simultaneously. This model is a bi-level programming model. The upper-level problem includes employer decision about the reward, and the lower-level problem contains employee(s) decisions about time and effort allocation to knowledge sharing activity. Mathematical formulation of the model designed based on previous literature and in the framework of Motivation-Opportunity-Ability. The proposed bi-level programming model provides a foundation to investigate more different parameters comparing with previous models introduced in the literature. This model considers opportunity and ability factors in addition to the motivation. Also, payoff functions in this model are non-linear and therefore is more consistent with real cases relative to previous linear models. Additionally, this model analysis the effects of available time as a key factor. The bi-level model coded in GAMS using EMP syntax and solved for a set of randomly generated data using BARON algorithm. Results show that the increase of applicability of codified knowledge and impact coefficient of social comparison could improve organizational performance and also save the cost of reward system. Therefore, neglecting these two parameters in designing a reward system could lead to non-optimized decision making. This research provides a basis to consider more parameters simultaneously and help to improve organizational decisions. However, based on the results, BARON algorithm is not efficient to solve big problems, so developing a more efficient algorithm is needed

کلیدواژه‌ها [English]

  • Knowledge Sharing
  • Bi-Level Programming
  • Game Theory
  • Motivation-Opportunity-Ability Framework
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