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The purpose of this study was to evaluate the student’s talent management of a corporate university in Iran by descriptive-analytic method. The statistical population of the study included all 2200 students of the university. Based on the estimated number at Morgan table, 202 respondents completed the survey instrument. The data collecting tool of the questionnaire was ascertained and its reliability was obtained 78 percent by Cronbach's alpha coefficient. Content validity of the tool was also veri-fied by the experts. For data analysis, the binomial test and Structural Equation Modeling (SEM) were used. The results show that none of the components of talent management (deployment and employment, career progression path, practical learning, performance management, knowledge sharing, self-development, training, appreciation and encouragement) in the studied university was in desire conditions. Other findings of the study also show that among organizational factors, compo-nents of “organizational culture”, “supervisor satisfaction”, “organizational dynamics”, “working environment conditions”, “colleagues”, “prestige and brand of the university” and “growth oppor-tunity” were influential on the students’ talent development. Also, the results of the data analysis show that among the components of job factors, the component of “person-job fitness” affects the development of students' talents.

In recent years, many studies on the multi-agent scheduling problems in which agents compete for using the shared resources, have been performed. However, relatively few studies have been undertaken in the field of the multi-agent scheduling in a flowshop environment. To bridge the gap, this paper aims at addressing the two-agent scheduling problem in a two-machine flowshop. Because of the importance of delay penalties and efficient resource utilization in many manufacturing environments, the objective is to find an optimal schedule which has the minimum total tardiness for the first agent’s jobs, under the makespan limitation for the second agent. Since this problem is strongly NP-hard, several theorems and properties of the problem are proposed to apply in exact and meta-heuristic methods. Also, for some instances of the problem for which exact methods cannot achieve optimal solutions in a reasonable amount of time, a tabu search algorithm is developed to achieve near-optimal solutions. Computational results of the tabu search algorithm show that the average absolute error value is lower than 0.18 percent for instances with 20 to 60 jobs in size.