MPM Job Scheduling Problem: a bi-objective approach
|Title:||MPM Job Scheduling Problem: a bi-objective approach||Authors:||Tselios, Dimitrios
Savvas, Ilias K.
|Permanent link:||http://hdl.handle.net/10197/6551||Date:||Feb-2013||Abstract:||This paper presents a Recurrent Neural Network approach for the multi purpose 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 formulate dit as a combination of two recurrent neural networks. In addition, we designed an example within its neural networks that are focused on the Make span 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||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||United Kingdom Simulation Society||Journal:||International Journal of Simulation Systems, Science & Technology||Volume:||14||Issue:||1||Start page:||49||End page:||58||Keywords:||Machine learning; Statistics; Recurrent neural network; Multipurpose machines; Job scheduling problem; Bi-objective; Singularity||Other versions:||http://ijssst.info/Vol-14/No-1/paper7.pdf||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
Insight Research Collection
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