Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing

Wenjun Xu; Xin Zhong; Yuanyuan Zhao; Zude Zhou; Lin Zhang; Duc Truong Pham

doi:10.1007/978-3-319-61994-1_9

Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing

Wenjun Xu^*
, Xin Zhong
, Yuanyuan Zhao
, Zude Zhou
, Lin Zhang
, Duc Truong Pham

^*Corresponding author for this work

Mechanical Engineering

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

1 Citation (Scopus)

Abstract

During the execution process of a cloud manufacturing (CMfg) system, manufacturing service may become faulty to cause the violation of whole production processes against the predefined constraints. It is necessary to timely adjust service aggregation process to the runtime failure during manufacturing process. Therefore it is significant to do service reconfiguration to enhance the reliability of service-oriented manufacturing applications. The issues of the runtime service process reconfiguration based on QoS and energy consumption have been studied. In this paper, by contrast, an effective reconfiguration strategy is proposed to identify reconfiguration regions rather than the whole service process. Moreover, a hybrid bees algorithm (HBA) combining discrete bees algorithm (DBA) with discrete particle swarm optimization (DPSO) is developed to explore the replaceable services during service reconfiguration process. The experiment results show that most of manufacturing service aggregation processes can be repaired by replacing only a small number of services, and HBA is more efficient when finding the replaceable manufacturing services set compared with the existing algorithms.

Original language	English
Title of host publication	Challenges and Opportunity with Big Data
Subtitle of host publication	19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers
Editors	Lin Zhang, Lei Ren, Fabrice Kordon
Publisher	Springer Verlag
Pages	87-98
Number of pages	12
ISBN (Print)	9783319619934
DOIs	https://doi.org/10.1007/978-3-319-61994-1_9
Publication status	Published - 1 Jan 2017
Event	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10228 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016
Country/Territory	China
City	Beijing
Period	8/10/16 → 11/10/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cloud manufacturing
Hybrid bees algorithm
Manufacturing service reconfiguration
Reconfiguration optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-61994-1_9Licence: None: All rights reserved

https://link.springer.com/chapter/10.1007%2F978-3-319-61994-1_9#aboutcontentLicence: None: All rights reserved

Cite this

Xu, W., Zhong, X., Zhao, Y., Zhou, Z., Zhang, L., & Pham, D. T. (2017). Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. In L. Zhang, L. Ren, & F. Kordon (Eds.), Challenges and Opportunity with Big Data : 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers (pp. 87-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61994-1_9

Xu, Wenjun ; Zhong, Xin ; Zhao, Yuanyuan et al. / Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. Challenges and Opportunity with Big Data : 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers. editor / Lin Zhang ; Lei Ren ; Fabrice Kordon . Springer Verlag, 2017. pp. 87-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5130ea0dc9424787b7638ffb00c1107a,

title = "Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing",

abstract = "During the execution process of a cloud manufacturing (CMfg) system, manufacturing service may become faulty to cause the violation of whole production processes against the predefined constraints. It is necessary to timely adjust service aggregation process to the runtime failure during manufacturing process. Therefore it is significant to do service reconfiguration to enhance the reliability of service-oriented manufacturing applications. The issues of the runtime service process reconfiguration based on QoS and energy consumption have been studied. In this paper, by contrast, an effective reconfiguration strategy is proposed to identify reconfiguration regions rather than the whole service process. Moreover, a hybrid bees algorithm (HBA) combining discrete bees algorithm (DBA) with discrete particle swarm optimization (DPSO) is developed to explore the replaceable services during service reconfiguration process. The experiment results show that most of manufacturing service aggregation processes can be repaired by replacing only a small number of services, and HBA is more efficient when finding the replaceable manufacturing services set compared with the existing algorithms.",

keywords = "Cloud manufacturing, Hybrid bees algorithm, Manufacturing service reconfiguration, Reconfiguration optimization",

author = "Wenjun Xu and Xin Zhong and Yuanyuan Zhao and Zude Zhou and Lin Zhang and Pham, \{Duc Truong\}",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-61994-1\_9",

language = "English",

isbn = "9783319619934",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "87--98",

editor = "Zhang, \{Lin \} and Ren, \{Lei \} and \{Kordon \}, \{Fabrice \}",

booktitle = "Challenges and Opportunity with Big Data",

note = "19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

}

Xu, W, Zhong, X, Zhao, Y, Zhou, Z, Zhang, L & Pham, DT 2017, Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. in L Zhang, L Ren & F Kordon (eds), Challenges and Opportunity with Big Data : 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10228 LNCS, Springer Verlag, pp. 87-98, 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016, Beijing, China, 8/10/16. https://doi.org/10.1007/978-3-319-61994-1_9

Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. / Xu, Wenjun; Zhong, Xin; Zhao, Yuanyuan et al.
Challenges and Opportunity with Big Data : 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers. ed. / Lin Zhang; Lei Ren; Fabrice Kordon . Springer Verlag, 2017. p. 87-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS).

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

TY - GEN

T1 - Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing

AU - Xu, Wenjun

AU - Zhong, Xin

AU - Zhao, Yuanyuan

AU - Zhou, Zude

AU - Zhang, Lin

AU - Pham, Duc Truong

PY - 2017/1/1

Y1 - 2017/1/1

N2 - During the execution process of a cloud manufacturing (CMfg) system, manufacturing service may become faulty to cause the violation of whole production processes against the predefined constraints. It is necessary to timely adjust service aggregation process to the runtime failure during manufacturing process. Therefore it is significant to do service reconfiguration to enhance the reliability of service-oriented manufacturing applications. The issues of the runtime service process reconfiguration based on QoS and energy consumption have been studied. In this paper, by contrast, an effective reconfiguration strategy is proposed to identify reconfiguration regions rather than the whole service process. Moreover, a hybrid bees algorithm (HBA) combining discrete bees algorithm (DBA) with discrete particle swarm optimization (DPSO) is developed to explore the replaceable services during service reconfiguration process. The experiment results show that most of manufacturing service aggregation processes can be repaired by replacing only a small number of services, and HBA is more efficient when finding the replaceable manufacturing services set compared with the existing algorithms.

AB - During the execution process of a cloud manufacturing (CMfg) system, manufacturing service may become faulty to cause the violation of whole production processes against the predefined constraints. It is necessary to timely adjust service aggregation process to the runtime failure during manufacturing process. Therefore it is significant to do service reconfiguration to enhance the reliability of service-oriented manufacturing applications. The issues of the runtime service process reconfiguration based on QoS and energy consumption have been studied. In this paper, by contrast, an effective reconfiguration strategy is proposed to identify reconfiguration regions rather than the whole service process. Moreover, a hybrid bees algorithm (HBA) combining discrete bees algorithm (DBA) with discrete particle swarm optimization (DPSO) is developed to explore the replaceable services during service reconfiguration process. The experiment results show that most of manufacturing service aggregation processes can be repaired by replacing only a small number of services, and HBA is more efficient when finding the replaceable manufacturing services set compared with the existing algorithms.

KW - Cloud manufacturing

KW - Hybrid bees algorithm

KW - Manufacturing service reconfiguration

KW - Reconfiguration optimization

UR - https://www.scopus.com/pages/publications/85028541237

U2 - 10.1007/978-3-319-61994-1_9

DO - 10.1007/978-3-319-61994-1_9

M3 - Conference contribution

AN - SCOPUS:85028541237

SN - 9783319619934

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

SP - 87

EP - 98

BT - Challenges and Opportunity with Big Data

A2 - Zhang, Lin

A2 - Ren, Lei

A2 - Kordon , Fabrice

PB - Springer Verlag

T2 - 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Xu W, Zhong X, Zhao Y, Zhou Z, Zhang L, Pham DT. Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing. In Zhang L, Ren L, Kordon F, editors, Challenges and Opportunity with Big Data : 19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016, Revised Selected Papers. Springer Verlag. 2017. p. 87-98. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61994-1_9

Manufacturing service reconfiguration optimization using hybrid bees algorithm in cloud manufacturing

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this