A Model to Optimize Knowledge Flow in Team Working
Peyman Akhavan, Ali Shahabipour and Reza Hosnavi
Malek Ashtar University of Technology, Tehran, Iran

Abstract: This paper presents a mathematical model with the necessary variables that can serve to identify employees with right knowledge, skills, and abilities to optimize learning in team working. The paper sets out to achieve a bridging of the fields of human resource development and multi-criteria decision making/operations management. The human resource development implications of supplier management are under-explored. After identifying the factors affecting the knowledge flow, supplier involvement was selected as a human resource development practice to optimize the knowledge flow. Based on the Motivation-Opportunity-Ability framework, selecting appropriate members from among the suppliers and buyers was formulated as a multi-objective decision-making model. Using a meta-heuristic algorithm the model was solved. To reach a high convergence and avoid getting stuck in a local optimum point, the evolutionary algorithms were combined with the classical method. To find the Pareto front for non-dominated solutions, the imperialist competition algorithm was utilized. Then, the multi-attitude decision-making model was applied to prioritize these solutions and find the best solution. The results of applying the proposed method for an industrial company showed its effectiveness in selecting appropriate members for supplier involvement. Organizational managers will be able to select optimum members to exchange between suppliers and inner experts to minimize the cost and maximize the expertise for both supplier and buyer. It will increase the success probability of the joint action; facilitate the maintenance of knowledge acquired during the project lifecycle and lead to supplier development. The paper tries to adopt a mathematical and systematic approach to a human resource selection problem which requires a qualitative approach traditionally.

Keywords: Human Resource Management; Knowledge Flow, Supplier Development, Meta-Heuristic, Imperialist Competition Algorithm