Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems

Ikou Kaku; Yiyong Xiao

doi:10.1109/BICTA.2010.5645313

Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems

Ikou Kaku^*
, Yiyong Xiao

^*Corresponding author for this work

School of Reliability and Systems Engineering

Akita Prefectural University

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

Abstract

Multilevel lot siZing (MLLS) problem is a combinational optimization problem which has been proved NP-hard without restrictive assumption on the product structure. Several evolutionary algorithms were developed to solve the MLLS problem in literature, such as genetic algorithm, simulated annealing, swarm particle optimization, soft optimization based on segmentation, ant colony optimization, variable neighbourhood search and so on. In this paper we investigate implemental techniques and their effectives for those evolutionary algorithms used to solve the MLLS problem. Obtained results can be used to specify the characteristics of the solution of the MLLS problems and to help developing more efficient evolutionary algorithms.

Original language	English
Title of host publication	Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing
Subtitle of host publication	Theories and Applications, BIC-TA 2010
Pages	303-309
Number of pages	7
DOIs	https://doi.org/10.1109/BICTA.2010.5645313
State	Published - 2010
Event	2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010 - Changsha, China Duration: 23 Sep 2010 → 26 Sep 2010

Publication series

Name	Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

Conference

Conference	2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010
Country/Territory	China
City	Changsha
Period	23/09/10 → 26/09/10

Access to Document

10.1109/BICTA.2010.5645313

Cite this

Kaku, I., & Xiao, Y. (2010). Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems. In Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010 (pp. 303-309). Article 5645313 (Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010). https://doi.org/10.1109/BICTA.2010.5645313

Kaku, Ikou ; Xiao, Yiyong. / Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems. Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010. 2010. pp. 303-309 (Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010).

@inproceedings{96fb59fa89744f8cba016ab85f3098cb,

title = "Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems",

abstract = "Multilevel lot siZing (MLLS) problem is a combinational optimization problem which has been proved NP-hard without restrictive assumption on the product structure. Several evolutionary algorithms were developed to solve the MLLS problem in literature, such as genetic algorithm, simulated annealing, swarm particle optimization, soft optimization based on segmentation, ant colony optimization, variable neighbourhood search and so on. In this paper we investigate implemental techniques and their effectives for those evolutionary algorithms used to solve the MLLS problem. Obtained results can be used to specify the characteristics of the solution of the MLLS problems and to help developing more efficient evolutionary algorithms.",

author = "Ikou Kaku and Yiyong Xiao",

year = "2010",

doi = "10.1109/BICTA.2010.5645313",

language = "英语",

isbn = "9781424464388",

series = "Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010",

pages = "303--309",

booktitle = "Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing",

note = "2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010 ; Conference date: 23-09-2010 Through 26-09-2010",

}

Kaku, I & Xiao, Y 2010, Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems. in Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010., 5645313, Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, pp. 303-309, 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, Changsha, China, 23/09/10. https://doi.org/10.1109/BICTA.2010.5645313

Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems. / Kaku, Ikou; Xiao, Yiyong.
Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010. 2010. p. 303-309 5645313 (Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010).

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

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PY - 2010

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N2 - Multilevel lot siZing (MLLS) problem is a combinational optimization problem which has been proved NP-hard without restrictive assumption on the product structure. Several evolutionary algorithms were developed to solve the MLLS problem in literature, such as genetic algorithm, simulated annealing, swarm particle optimization, soft optimization based on segmentation, ant colony optimization, variable neighbourhood search and so on. In this paper we investigate implemental techniques and their effectives for those evolutionary algorithms used to solve the MLLS problem. Obtained results can be used to specify the characteristics of the solution of the MLLS problems and to help developing more efficient evolutionary algorithms.

AB - Multilevel lot siZing (MLLS) problem is a combinational optimization problem which has been proved NP-hard without restrictive assumption on the product structure. Several evolutionary algorithms were developed to solve the MLLS problem in literature, such as genetic algorithm, simulated annealing, swarm particle optimization, soft optimization based on segmentation, ant colony optimization, variable neighbourhood search and so on. In this paper we investigate implemental techniques and their effectives for those evolutionary algorithms used to solve the MLLS problem. Obtained results can be used to specify the characteristics of the solution of the MLLS problems and to help developing more efficient evolutionary algorithms.

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Kaku I, Xiao Y. Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems. In Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010. 2010. p. 303-309. 5645313. (Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010). doi: 10.1109/BICTA.2010.5645313

Implemental techniques and their effectives for evolutionary algorithms used to solve multilevel lot sizing problems

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