Solving manufacturing cell design problems using the black hole algorithm

Ricardo Soto; Broderick Crawford; Nicolás Fernandez; Víctor Reyes; Stefanie Niklander; Ignacio Araya

doi:10.1007/978-3-319-62434-1_32

Solving manufacturing cell design problems using the black hole algorithm

Ricardo Soto, Broderick Crawford, Nicolás Fernandez, Víctor Reyes, Stefanie Niklander, Ignacio Araya

SCHOOL OF COMPUTER ENGINEERING

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

2 Scopus citations

Abstract

In this paper we solve the Manufacturing Cell Design Problem. This problem considers the grouping of different machines into sets or cells with the objective of minimizing the movement of material. To solve this problem we use the Black Hole algorithm, a modern population-based metaheuristic that is inspired by the phenomenon of the same name. At each iteration of the search, the best candidate solution is selected to be the black hole and other candidate solutions, known as stars, are attracted by the black hole. If one of these stars get too close to the black hole it disappears, generating a new random star (solution). Our approach has been tested by using a well-known set of benchmark instances, reaching optimal values in all of them.

Original language	English
Title of host publication	Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings
Editors	Oscar Herrera-Alcantara, Grigori Sidorov
Publisher	Springer Verlag
Pages	391-398
Number of pages	8
ISBN (Print)	9783319624334
DOIs	https://doi.org/10.1007/978-3-319-62434-1_32
State	Published - 2017
Event	15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, Mexico Duration: 23 Oct 2016 → 28 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10061 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Mexican International Conference on Artificial Intelligence, MICAI 2016
Country/Territory	Mexico
City	Cancun
Period	23/10/16 → 28/10/16

Keywords

Black Hole algorithm
Manufacturing cell design problems
Metaheuristics

Access to Document

10.1007/978-3-319-62434-1_32

Cite this

Soto, R., Crawford, B., Fernandez, N., Reyes, V., Niklander, S., & Araya, I. (2017). Solving manufacturing cell design problems using the black hole algorithm. In O. Herrera-Alcantara, & G. Sidorov (Eds.), Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings (pp. 391-398). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10061 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-62434-1_32

Soto, Ricardo ; Crawford, Broderick ; Fernandez, Nicolás et al. / Solving manufacturing cell design problems using the black hole algorithm. Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. editor / Oscar Herrera-Alcantara ; Grigori Sidorov. Springer Verlag, 2017. pp. 391-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Solving manufacturing cell design problems using the black hole algorithm",

abstract = "In this paper we solve the Manufacturing Cell Design Problem. This problem considers the grouping of different machines into sets or cells with the objective of minimizing the movement of material. To solve this problem we use the Black Hole algorithm, a modern population-based metaheuristic that is inspired by the phenomenon of the same name. At each iteration of the search, the best candidate solution is selected to be the black hole and other candidate solutions, known as stars, are attracted by the black hole. If one of these stars get too close to the black hole it disappears, generating a new random star (solution). Our approach has been tested by using a well-known set of benchmark instances, reaching optimal values in all of them.",

keywords = "Black Hole algorithm, Manufacturing cell design problems, Metaheuristics",

author = "Ricardo Soto and Broderick Crawford and Nicol{\'a}s Fernandez and V{\'i}ctor Reyes and Stefanie Niklander and Ignacio Araya",

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Soto, R , Crawford, B, Fernandez, N, Reyes, V, Niklander, S & Araya, I 2017, Solving manufacturing cell design problems using the black hole algorithm. in O Herrera-Alcantara & G Sidorov (eds), Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10061 LNAI, Springer Verlag, pp. 391-398, 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancun, Mexico, 23/10/16. https://doi.org/10.1007/978-3-319-62434-1_32

Solving manufacturing cell design problems using the black hole algorithm. / Soto, Ricardo ; Crawford, Broderick; Fernandez, Nicolás et al.
Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. ed. / Oscar Herrera-Alcantara; Grigori Sidorov. Springer Verlag, 2017. p. 391-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10061 LNAI).

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

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AU - Crawford, Broderick

AU - Fernandez, Nicolás

AU - Reyes, Víctor

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AU - Araya, Ignacio

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

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N2 - In this paper we solve the Manufacturing Cell Design Problem. This problem considers the grouping of different machines into sets or cells with the objective of minimizing the movement of material. To solve this problem we use the Black Hole algorithm, a modern population-based metaheuristic that is inspired by the phenomenon of the same name. At each iteration of the search, the best candidate solution is selected to be the black hole and other candidate solutions, known as stars, are attracted by the black hole. If one of these stars get too close to the black hole it disappears, generating a new random star (solution). Our approach has been tested by using a well-known set of benchmark instances, reaching optimal values in all of them.

AB - In this paper we solve the Manufacturing Cell Design Problem. This problem considers the grouping of different machines into sets or cells with the objective of minimizing the movement of material. To solve this problem we use the Black Hole algorithm, a modern population-based metaheuristic that is inspired by the phenomenon of the same name. At each iteration of the search, the best candidate solution is selected to be the black hole and other candidate solutions, known as stars, are attracted by the black hole. If one of these stars get too close to the black hole it disappears, generating a new random star (solution). Our approach has been tested by using a well-known set of benchmark instances, reaching optimal values in all of them.

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Soto R , Crawford B, Fernandez N, Reyes V, Niklander S, Araya I. Solving manufacturing cell design problems using the black hole algorithm. In Herrera-Alcantara O, Sidorov G, editors, Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. Springer Verlag. 2017. p. 391-398. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-62434-1_32

Solving manufacturing cell design problems using the black hole algorithm

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Keywords

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