ارائه مدل بهینه سازی برون‌سپاری مدیریت دانش با استفاده از الگوریتم ژنتیک

Purpose: The purpose of this study is to develop an optimization model for knowledge management outsourcing using a genetic algorithm. This research aims to provide a framework for evaluating and selecting knowledge management service providers, enhancing decision-making processes for organizational managers.
Methodology: This research is developmental-applied in terms of its objective and descriptive-survey in terms of data collection method. It analyzes the criteria for selecting knowledge management outsourcing providers using a genetic algorithm. In the identification of criteria, the statistical population included eight experts and professors from academia and industry in the field of knowledge management and six contracting companies. Snowball sampling was done and the main data collection tool was a questionnaire.The primary data collection tool was a questionnaire with 33 questions, specifically designed to evaluate the selection criteria for outsourcing providers. The Delphi technique was employed for validation, involving four rounds of expert feedback to reach a consensus. Data from the questionnaires were processed using MATLAB, where the genetic algorithm was applied. The algorithm used a random initial population, with operations for parent selection, crossover, and mutation, iterating until the optimal solution was found. The output helped identify the most effective outsourcing providers, aided managerial decision-making, and enhanced organizational performance.
Findings: In this study, using the roulette wheel method and based on available data, the selection process among six contractors was carried out. The results show that among the six Knowledge Management outsourcing providers, the Future Development Consulting Company, with the highest score, was chosen as the best option. Other companies ranked in the following order: Dana Knowledge Management Consulting Group, Inotex Iran, Nadak Engineering Consulting, Persica, and Rasa Consulting.
Regarding the evaluation of various factors:

Managerial capabilities: Future Development Consulting Company received the highest score of 90.
Adequacy of skilled staff: Both Future Development Consulting Company and Dana Knowledge Management Consulting Group scored 90.
Information technology facilities and capabilities: Future Development Consulting Company and Dana Knowledge Management Consulting Group scored 80 each.
Number of completed Knowledge Management projects: Future Development Consulting Company received the highest score of 90.
Technology readiness: Future Development Consulting Company scored 90, the highest.
Project completion time: Future Development Consulting Company scored 90, the highest.

These results align with previous studies, including those by Lin et al. (2016) and Olajomok (2018), who used genetic algorithms to solve similar issues and confirmed the positive impact of outsourcing on company performance. The results also align with research by Büyüközan and Ersoy (2012), Majidi (2016), and Radfar (2010) regarding factors influencing outsourcing. Furthermore, the findings are consistent with studies by Minooie et al. (2010), Oakville et al. (2004), Lee (2014), Leo et al. (2014), and Fanni et al. (2005) on selecting effective outsourcing factors. The research also matches findings by Gabi et al. (2004) and Chen and Wang (2009) regarding components such as price, cost, quality, delivery time, and technological capabilities.
The genetic algorithm, used as an intelligent tool in this study, allowed for the analysis and optimization of multiple criteria, including managerial capabilities, skilled staff, IT facilities, number of completed projects, technology readiness, and project completion time. This framework, combining fuzzy methods and genetic algorithms, offers a systematic approach for assessing and selecting Knowledge Management outsourcing providers, helping managers make better decisions based on precise, scientific criteria. The findings indicate that the use of genetic algorithms not only provides more accurate data analysis but also reduces the complexity of decision-making and enhances its precision. This framework is applicable not only in Knowledge Management but also in other outsourcing areas, allowing managers to benefit from it in diverse fields.
Research limitations/implications: The limitations of this study include the small number of informed experts consulted, which may limit the generalizability of the results to other contractors. Additionally, due to the inherent characteristics of metaheuristic algorithms, there is no guarantee that genetic algorithms will always lead to the optimal solution. These algorithms typically perform well but generally settle for a satisfactory solution based on the specific conditions of each problem. Furthermore, the selection criteria were chosen in a general manner and are not specific to any particular industry.
Practical implications: To enhance the selection process of Knowledge Management providers, it is recommended to use multi-criteria decision-making techniques, which can offer a more comprehensive analysis of the influencing factors. Additionally, organizations should personalize the selection criteria based on their specific characteristics to better align with their needs and improve performance. This tailored approach could lead to more effective and efficient decision-making. Future research should also explore additional dimensions of Knowledge Management outsourcing, as this study was limited by the length of the questionnaire. Expanding the scope to include more factors may provide a deeper understanding and have a significant impact on the provider selection outcomes. These recommendations aim to optimize the decision-making process and provide valuable insights for both researchers and managers in the field.
Originality/value: This study utilizes genetic algorithms and a fuzzy approach to analyze and evaluate Knowledge Management outsourcing providers, an area that has received limited attention in the existing literature.