The Weighted Tardiness as Objective Function of a RNN Model for the Job Scheduling Problem

This paper proposes a Neural Network approach for the project portfolio management problem. The modern organizations such as the IT firms schedule and perform a set of projects that share common rare resources. Therefore, each IT organization develops a set of IT projects and it has to execute them simultaneously. In this work we reviewed the literature and extended a multi-objective system model based on the job shop scheduling problem modelling and expressed it as recurrent neural network. Moreover, we produced an example within its neural network that is focused on the Weighted Tardiness objective function. In addition, we use an initial solution by amending a greedy algorithm that has been proposed in a previous work for the Makespan objective function.