MPM Job Shop Scheduling Problem: a bi-objective approach
Dimitrios C. Tselios and Ilias K. Savvas (University of Thessaly, Greece); Tahar M Kechadi (University College Dublin, Ireland)
This paper presents a Recurrent Neural Network approach for the multipurpose machines Job Shop Scheduling Problem. This case of JSSP can be utilized for the modelling of project portfolio management besides the well known adoption in factory environment. Therefore, each project oriented organization develops a set of projects and it has to schedule them as a whole. In this work, we extended a bi-objective system model based on the JSSP modelling and formulated it as a combination of two recurrent neural networks. In addition, we designed an example within its neural networks that are focused on the Makespan and the Total Weighted Tardiness objectives. Moreover, we present the findings of our approach using a set of well known benchmark instances and the discussion about them and the singularity that arises.
Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V14
Published: Feb 28, 2013