Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process

Malik Haddad; David Sanders; Nils Bausch; Giles Tewkesbury; Alexander Gegov; Mohamed Hassan Sayed

doi:10.1007/978-3-030-01054-6_91

Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process

Malik Haddad, David Sanders, Nils Bausch, Giles Tewkesbury, Alexander Gegov, Mohamed Hassan Sayed

School of Computing

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

287 Downloads (Pure)

Abstract

This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.

Original language	English
Title of host publication	Intelligent Systems and Applications
Subtitle of host publication	Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer
Pages	1303-1316
ISBN (Electronic)	978-3-030-01054-6
ISBN (Print)	978-3-030-01053-9
DOIs	https://doi.org/10.1007/978-3-030-01054-6_91
Publication status	Published - Jan 2019
Event	IntelliSys 2018 - London, United Kingdom Duration: 6 Sept 2018 → 7 Sept 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	868
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	IntelliSys 2018
Country/Territory	United Kingdom
City	London
Period	6/09/18 → 7/09/18

Keywords

Discrete
Intelligent
Selection
Risk
Robustness
Uncertainty
Problem characteristics

Access to Document

10.1007/978-3-030-01054-6_91

Final_Paper_Malik_IntelliSys_2018_Learning_to_make
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-01054-6_91.
Accepted author manuscript (Post-print), 1.67 MBLicence: Unspecified

http://saiconference.com/Conferences/IntelliSys2018

Management and Business Decision Making
Haddad, M. (CoI) & Sanders, D. (PI)
26/09/16 → 29/04/22
Project: Research

IEEE Sponsored SAI Intelligent Systems Conference 2018
Malik Haddad (Presented paper)
6 Sept 2018 → 7 Sept 2018
Activity: Participating in or organising an event types › Participation in conference

Cite this

Haddad, M., Sanders, D., Bausch, N., Tewkesbury, G., Gegov, A., & Hassan Sayed, M. (2019). Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 (pp. 1303-1316). (Advances in Intelligent Systems and Computing; Vol. 868). Springer. https://doi.org/10.1007/978-3-030-01054-6_91

Haddad, Malik ; Sanders, David ; Bausch, Nils et al. / Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer, 2019. pp. 1303-1316 (Advances in Intelligent Systems and Computing).

@inproceedings{843060ac498a4e819623a586fd18c0d2,

title = "Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process",

abstract = "This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.",

keywords = "Discrete, Intelligent, Selection, Risk, Robustness, Uncertainty, Problem characteristics",

author = "Malik Haddad and David Sanders and Nils Bausch and Giles Tewkesbury and Alexander Gegov and {Hassan Sayed}, Mohamed",

note = "Expected pp. 662-668; IntelliSys 2018 ; Conference date: 06-09-2018 Through 07-09-2018",

year = "2019",

month = jan,

doi = "10.1007/978-3-030-01054-6_91",

language = "English",

isbn = "978-3-030-01053-9",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "1303--1316",

editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",

booktitle = "Intelligent Systems and Applications",

}

Haddad, M, Sanders, D, Bausch, N, Tewkesbury, G , Gegov, A & Hassan Sayed, M 2019, Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. Advances in Intelligent Systems and Computing, vol. 868, Springer, pp. 1303-1316, IntelliSys 2018, London, United Kingdom, 6/09/18. https://doi.org/10.1007/978-3-030-01054-6_91

Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. / Haddad, Malik; Sanders, David; Bausch, Nils et al.
Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2019. p. 1303-1316 (Advances in Intelligent Systems and Computing; Vol. 868).

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

TY - GEN

T1 - Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process

AU - Haddad, Malik

AU - Sanders, David

AU - Bausch, Nils

AU - Tewkesbury, Giles

AU - Gegov, Alexander

AU - Hassan Sayed, Mohamed

N1 - Expected pp. 662-668

PY - 2019/1

Y1 - 2019/1

N2 - This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.

AB - This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.

KW - Discrete

KW - Intelligent

KW - Selection

KW - Risk

KW - Robustness

KW - Uncertainty

KW - Problem characteristics

UR - https://www.springer.com/gb/book/9783030010539

UR - https://www.springer.com/gb/book/9783030010560

U2 - 10.1007/978-3-030-01054-6_91

DO - 10.1007/978-3-030-01054-6_91

M3 - Conference contribution

SN - 978-3-030-01053-9

T3 - Advances in Intelligent Systems and Computing

SP - 1303

EP - 1316

BT - Intelligent Systems and Applications

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer

T2 - IntelliSys 2018

Y2 - 6 September 2018 through 7 September 2018

ER -

Haddad M, Sanders D, Bausch N, Tewkesbury G , Gegov A, Hassan Sayed M. Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. Springer. 2019. p. 1303-1316. (Advances in Intelligent Systems and Computing). Epub 2018 Nov 9. doi: 10.1007/978-3-030-01054-6_91

Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Projects

Management and Business Decision Making

Activities

IEEE Sponsored SAI Intelligent Systems Conference 2018

Cite this