Consolidation of the IFM with the JSSP through neural networks as model for software projects

Pandelis Ipsilandis; Dimitrios Tselios; Vassilis C. Gerogiannis

doi:10.1109/AIMS.2014.16

Consolidation of the IFM with the JSSP through neural networks as model for software projects

Pandelis Ipsilandis
, Dimitrios Tselios
, Vassilis C. Gerogiannis

TEI of Thessaly

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a consolidation of the Incremental Funding Method (IFM) with the Job Shop Scheduling Problem (JSSP) through Neural Networks, in order to develop a model for software projects. More specifically, it formulates the IFM method in terms of JSSP model and then gives a solution method based on Recurrent Neural Networks (RRNs). The IFM is a financial approach to software project management aiming at maximizing the net present value (NPV) and it can be used in cooperation with other software development processes. However, there are few proposed algorithms that focus on the IFM modeled problem's solving with limited results. Our goal is to employ the JSSP, a well established model, in order to model a IFM problem as JSSP problem and to propose a Neural Network solving method that gives promising results.

Original language	English
Title of host publication	Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	33-38
Number of pages	6
ISBN (Electronic)	9781479975990
DOIs	https://doi.org/10.1109/AIMS.2014.16
Publication status	Published - 5 May 2014
Event	2nd IEEE International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014 - Madrid, Spain Duration: 18 Nov 2014 → 20 Nov 2014

Other

Other	2nd IEEE International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014
Country/Territory	Spain
City	Madrid
Period	18/11/14 → 20/11/14

Keywords

Incremental Funding Method
Job Scheduling Problem
Minimum Marketable Feature
Multi-objective
Neural Network
Software Project

Access to Document

10.1109/AIMS.2014.16

Cite this

Ipsilandis, P., Tselios, D., & Gerogiannis, V. C. (2014). Consolidation of the IFM with the JSSP through neural networks as model for software projects. In Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014 (pp. 33-38). Article 7102431 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIMS.2014.16

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title = "Consolidation of the IFM with the JSSP through neural networks as model for software projects",

abstract = "This paper proposes a consolidation of the Incremental Funding Method (IFM) with the Job Shop Scheduling Problem (JSSP) through Neural Networks, in order to develop a model for software projects. More specifically, it formulates the IFM method in terms of JSSP model and then gives a solution method based on Recurrent Neural Networks (RRNs). The IFM is a financial approach to software project management aiming at maximizing the net present value (NPV) and it can be used in cooperation with other software development processes. However, there are few proposed algorithms that focus on the IFM modeled problem's solving with limited results. Our goal is to employ the JSSP, a well established model, in order to model a IFM problem as JSSP problem and to propose a Neural Network solving method that gives promising results.",

keywords = "Incremental Funding Method, Job Scheduling Problem, Minimum Marketable Feature, Multi-objective, Neural Network, Software Project",

author = "Pandelis Ipsilandis and Dimitrios Tselios and Gerogiannis, \{Vassilis C.\}",

year = "2014",

month = may,

day = "5",

doi = "10.1109/AIMS.2014.16",

language = "English",

pages = "33--38",

booktitle = "Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2nd IEEE International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014 ; Conference date: 18-11-2014 Through 20-11-2014",

}

Ipsilandis, P, Tselios, D & Gerogiannis, VC 2014, Consolidation of the IFM with the JSSP through neural networks as model for software projects. in Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014., 7102431, Institute of Electrical and Electronics Engineers Inc., pp. 33-38, 2nd IEEE International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014, Madrid, Spain, 18/11/14. https://doi.org/10.1109/AIMS.2014.16

Consolidation of the IFM with the JSSP through neural networks as model for software projects. / Ipsilandis, Pandelis; Tselios, Dimitrios; Gerogiannis, Vassilis C.
Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 33-38 7102431.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tselios, Dimitrios

AU - Gerogiannis, Vassilis C.

PY - 2014/5/5

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N2 - This paper proposes a consolidation of the Incremental Funding Method (IFM) with the Job Shop Scheduling Problem (JSSP) through Neural Networks, in order to develop a model for software projects. More specifically, it formulates the IFM method in terms of JSSP model and then gives a solution method based on Recurrent Neural Networks (RRNs). The IFM is a financial approach to software project management aiming at maximizing the net present value (NPV) and it can be used in cooperation with other software development processes. However, there are few proposed algorithms that focus on the IFM modeled problem's solving with limited results. Our goal is to employ the JSSP, a well established model, in order to model a IFM problem as JSSP problem and to propose a Neural Network solving method that gives promising results.

AB - This paper proposes a consolidation of the Incremental Funding Method (IFM) with the Job Shop Scheduling Problem (JSSP) through Neural Networks, in order to develop a model for software projects. More specifically, it formulates the IFM method in terms of JSSP model and then gives a solution method based on Recurrent Neural Networks (RRNs). The IFM is a financial approach to software project management aiming at maximizing the net present value (NPV) and it can be used in cooperation with other software development processes. However, there are few proposed algorithms that focus on the IFM modeled problem's solving with limited results. Our goal is to employ the JSSP, a well established model, in order to model a IFM problem as JSSP problem and to propose a Neural Network solving method that gives promising results.

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KW - Job Scheduling Problem

KW - Minimum Marketable Feature

KW - Multi-objective

KW - Neural Network

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BT - Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014

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ER -

Consolidation of the IFM with the JSSP through neural networks as model for software projects

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