An application of EDA and GA for permutation based spare part allocation problem

Nouf Alkaabi; Siddhartha Shakya; Adriana Gabor; Andrzej Sluzek; Beum Seuk Lee; Gilbert Owusu

doi:10.1007/978-3-030-63799-6_31

An application of EDA and GA for permutation based spare part allocation problem

Nouf Alkaabi, Siddhartha Shakya, Adriana Gabor, Andrzej Sluzek, Beum Seuk Lee, Gilbert Owusu

British Telecommunications plc

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

1 Scopus citations

Abstract

Enterprise Resource management is crucial to the success of any service organizations. Having right resource at the right time at the right place can make a big difference to the quality of their service offering. This paper focuses on spare parts management in a telecom industry as part of the enterprise resource management problem. The traditional way of moving the spare parts within the network is done manually by expert planners. However, this is not efficient as they may not have a global view of supply and demand, considering a large number of spares and potential locations that have to be taken into account when making distribution decisions. We investigate two evolutionary algorithms to solve this problem. The objective is twofold: 1) to identify and implement a permutation based Estimation of Distribution Algorithm for this problem, 2) to perform detail experimental analysis and compare the performance EDA to that of GA, with the goal of enhancing existing spare management software.

Original language	British English
Title of host publication	Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings
Editors	Max Bramer, Richard Ellis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	393-399
Number of pages	7
ISBN (Print)	9783030637989
DOIs	https://doi.org/10.1007/978-3-030-63799-6_31
State	Published - 2020
Event	40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020 - Cambridge, United Kingdom Duration: 15 Dec 2020 → 17 Dec 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12498 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020
Country/Territory	United Kingdom
City	Cambridge
Period	15/12/20 → 17/12/20

Keywords

Genetic algorithm
Resource management
RK-EDA

Access to Document

10.1007/978-3-030-63799-6_31

Cite this

Alkaabi, N., Shakya, S., Gabor, A., Sluzek, A., Lee, B. S., & Owusu, G. (2020). An application of EDA and GA for permutation based spare part allocation problem. In M. Bramer, & R. Ellis (Eds.), Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings (pp. 393-399). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12498 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63799-6_31

Alkaabi, Nouf ; Shakya, Siddhartha ; Gabor, Adriana et al. / An application of EDA and GA for permutation based spare part allocation problem. Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings. editor / Max Bramer ; Richard Ellis. Springer Science and Business Media Deutschland GmbH, 2020. pp. 393-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Enterprise Resource management is crucial to the success of any service organizations. Having right resource at the right time at the right place can make a big difference to the quality of their service offering. This paper focuses on spare parts management in a telecom industry as part of the enterprise resource management problem. The traditional way of moving the spare parts within the network is done manually by expert planners. However, this is not efficient as they may not have a global view of supply and demand, considering a large number of spares and potential locations that have to be taken into account when making distribution decisions. We investigate two evolutionary algorithms to solve this problem. The objective is twofold: 1) to identify and implement a permutation based Estimation of Distribution Algorithm for this problem, 2) to perform detail experimental analysis and compare the performance EDA to that of GA, with the goal of enhancing existing spare management software.",

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Alkaabi, N, Shakya, S , Gabor, A, Sluzek, A, Lee, BS & Owusu, G 2020, An application of EDA and GA for permutation based spare part allocation problem. in M Bramer & R Ellis (eds), Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12498 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 393-399, 40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020, Cambridge, United Kingdom, 15/12/20. https://doi.org/10.1007/978-3-030-63799-6_31

An application of EDA and GA for permutation based spare part allocation problem. / Alkaabi, Nouf; Shakya, Siddhartha ; Gabor, Adriana et al.
Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings. ed. / Max Bramer; Richard Ellis. Springer Science and Business Media Deutschland GmbH, 2020. p. 393-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12498 LNAI).

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

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AU - Shakya, Siddhartha

AU - Gabor, Adriana

AU - Sluzek, Andrzej

AU - Lee, Beum Seuk

AU - Owusu, Gilbert

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

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Y1 - 2020

N2 - Enterprise Resource management is crucial to the success of any service organizations. Having right resource at the right time at the right place can make a big difference to the quality of their service offering. This paper focuses on spare parts management in a telecom industry as part of the enterprise resource management problem. The traditional way of moving the spare parts within the network is done manually by expert planners. However, this is not efficient as they may not have a global view of supply and demand, considering a large number of spares and potential locations that have to be taken into account when making distribution decisions. We investigate two evolutionary algorithms to solve this problem. The objective is twofold: 1) to identify and implement a permutation based Estimation of Distribution Algorithm for this problem, 2) to perform detail experimental analysis and compare the performance EDA to that of GA, with the goal of enhancing existing spare management software.

AB - Enterprise Resource management is crucial to the success of any service organizations. Having right resource at the right time at the right place can make a big difference to the quality of their service offering. This paper focuses on spare parts management in a telecom industry as part of the enterprise resource management problem. The traditional way of moving the spare parts within the network is done manually by expert planners. However, this is not efficient as they may not have a global view of supply and demand, considering a large number of spares and potential locations that have to be taken into account when making distribution decisions. We investigate two evolutionary algorithms to solve this problem. The objective is twofold: 1) to identify and implement a permutation based Estimation of Distribution Algorithm for this problem, 2) to perform detail experimental analysis and compare the performance EDA to that of GA, with the goal of enhancing existing spare management software.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Y2 - 15 December 2020 through 17 December 2020

ER -

Alkaabi N, Shakya S , Gabor A, Sluzek A, Lee BS, Owusu G. An application of EDA and GA for permutation based spare part allocation problem. In Bramer M, Ellis R, editors, Artificial Intelligence XXXVII - 40th SGAI International Conference on Artificial Intelligence, AI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 393-399. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63799-6_31

An application of EDA and GA for permutation based spare part allocation problem

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