MPM Job Scheduling Problem: a bi-objective approach

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Title: MPM Job Scheduling Problem: a bi-objective approach
Authors: Tselios, Dimitrios
Savvas, Ilias K.
Kechadi, Tahar
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 learningStatisticsRecurrent neural networkMultipurpose machinesJob scheduling problemBi-objectiveSingularity
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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