Designing and solving a knowledge flow network model among an organization's employees using integer programming method - دانشکده فنی و مهندسی
Designing and solving a knowledge flow network model among an organization's employees using integer programming method
نوع: Type: thesis
مقطع: Segment: masters
عنوان: Title: Designing and solving a knowledge flow network model among an organization's employees using integer programming method
ارائه دهنده: Provider: Armin Makarchi
اساتید راهنما: Supervisors: Dr.Hamidreza Dezfoulian
اساتید مشاور: Advisory Professors: Dr.Parvaneh Samouei
اساتید ممتحن یا داور: Examining professors or referees: Dr.Vahid Khodakarami and Dr.Amirsaman Kheirkhah
زمان و تاریخ ارائه: Time and date of presentation: 13 اسفند 1399 ساعت 14:00
مکان ارائه: Place of presentation:
چکیده: Abstract: Knowledge transfer can be done at both intra-organizational and inter-organizational levels. Learning knowledge from outside the organization requires a significant budget and time, while by mastering and relying on the existing knowledge in the organization that is with its employees, it is possible to create a network of knowledge flow between employees to improve their knowledge. In this way, the knowledge level of the organization can be upgraded with the least cost and time. Designing a model of knowledge flow network between employees of the organization according to the level of professional and personal trust, training and learning ability, level of knowledge of employees, level of organizational commitment, type of knowledge and importance of each knowledge as well as uncertainty of knowledge transfer is an issue with objective functions. Maximizing the level of knowledge and minimizing the time of knowledge transfer were investigated and modeled in the form of a mixed integer programming mathematical model. This model was solved using GAMS software, Lagrange release algorithm and innovative algorithm. The results obtained from solving the model in all the presented sizes show the high efficiency of the Lagrange release algorithm in finding the upper bound for the main problem in all 3 sizes: small, medium and large. Also, the innovative algorithm presented in this research in medium and large sizes responds much faster than the other two solution methods. The results show that the organizational commitment parameter has a greater impact on training capacity and also the training capacity parameter has a greater impact on learning time on knowledge transfer time
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