Automation of Employee Workload Management using Random Sample Partition Algorithm

Abstract

In any organization which consists of working employees, under their respective teams, there exist a state where the manager of the team takes the charge of distributing the tasks among the various employees who work under him. In this case there arises a possibility of unequal distribution of the tasks among the employees in terms of workload and complexity of the tasks where the one with increased workload gets even more new tasks assigned to him while the one with less workload continues with the same number of tasks. In this case we have developed a system which assists equal distribution of the tasks among the peers in the team by analyzing the task related data and concurrently representing the results of the analysis in a comprehensible manner to the person responsible for assigning the tasks to the employees. This system takes into account the various attributes like number of tasks and the level of complexity concerned with those tasks and thereby performs analysis on the data by employing the random sample partition algorithm. The random sample partition algorithm acts in order to minimize the load on the analysis and thereby improve the performance by splitting the whole data into simpler blocks of data and then performing analysis on it. The results of the analysis provides the user an overview as to how he can distribute the new tasks which arrive into the system so as to serve the purpose of bringing out equality in distribution of the tasks among the peers in the team.