Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass PersonalizationCitation formats

Standard

Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization. / Ogunsakin, Rotimi; Mehandjiev, Nikolay; Marín, César A.

From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings. ed. / Poramate Manoonpong; Jørgen Christian Larsen; Xiaofeng Xiong; John Hallam; Jochen Triesch. Springer Nature, 2018. p. 250-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10994 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Ogunsakin, R, Mehandjiev, N & Marín, CA 2018, Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization. in P Manoonpong, JC Larsen, X Xiong, J Hallam & J Triesch (eds), From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10994 LNAI, Springer Nature, pp. 250-264, 15th International Conference on the Simulation of Adaptive Behavior, SAB 2018, Frankfurt/Main, Germany, 14/08/18. https://doi.org/10.1007/978-3-319-97628-0_21

APA

Ogunsakin, R., Mehandjiev, N., & Marín, C. A. (2018). Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization. In P. Manoonpong, J. C. Larsen, X. Xiong, J. Hallam, & J. Triesch (Eds.), From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings (pp. 250-264). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10994 LNAI). Springer Nature. https://doi.org/10.1007/978-3-319-97628-0_21

Vancouver

Ogunsakin R, Mehandjiev N, Marín CA. Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization. In Manoonpong P, Larsen JC, Xiong X, Hallam J, Triesch J, editors, From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings. Springer Nature. 2018. p. 250-264. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-97628-0_21

Author

Ogunsakin, Rotimi ; Mehandjiev, Nikolay ; Marín, César A. / Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization. From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings. editor / Poramate Manoonpong ; Jørgen Christian Larsen ; Xiaofeng Xiong ; John Hallam ; Jochen Triesch. Springer Nature, 2018. pp. 250-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{25b31fd9e50f460289d55d36166a68c5,
title = "Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization",
abstract = "One of the goals of Flexible Manufacturing System (FMS) is the mass production of personalized goods at cost comparable to the mass produced goods. This paradigm is referred to as mass personalization. To achieve this, the system has to seamlessly translate flexibility that can be achieved through the software that is responsible for the control of such system directly to the physical system, such that multiple distinct products can be produced in a non-batch mode. However, the present rigid design of Flexible Manufacturing Systems, which is characterized by static processing stations and rigid roll conveyor for part and material transportation, hampers this dream. In this paper, we propose a distributed architecture, which is implemented as Self-Organizing Flexible Manufacturing System (SoFMS), characterized by mobile processing stations that are capable of autonomously re-adjusting their location in real time on the shop floor to form an optimal layout depending on the mix of order inflow. This is achieved using the BEEPOST algorithm, an algorithm inspired by young honeybees{\textquoteright} collective behavior of aggregation in a temperature gradient field. An agent-based simulation paradigm is used to evaluate the viability and performance of the proposed system. The result of the simulation shows that processing stations are able to autonomously and optimally adjust their location depending on the mix of order inflow using the BEEPOST algorithm. This capability also results in higher throughput when compare to a similar system with static processing stations. This approach is expected to engender the capability for production of one-lot-size order in FMS, which is a requirement for mass-personalization.",
keywords = "BEEPOST algorithm, Flexible Manufacturing System, Mass personalization",
author = "Rotimi Ogunsakin and Nikolay Mehandjiev and Mar{\'i}n, {C{\'e}sar A.}",
year = "2018",
doi = "10.1007/978-3-319-97628-0_21",
language = "English",
isbn = "9783319976273",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "250--264",
editor = "Poramate Manoonpong and Larsen, {J{\o}rgen Christian} and Xiaofeng Xiong and John Hallam and Jochen Triesch",
booktitle = "From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings",
address = "United States",
note = "15th International Conference on the Simulation of Adaptive Behavior, SAB 2018 ; Conference date: 14-08-2018 Through 17-08-2018",

}

RIS

TY - GEN

T1 - Bee-Inspired Self-Organizing Flexible Manufacturing System for Mass Personalization

AU - Ogunsakin, Rotimi

AU - Mehandjiev, Nikolay

AU - Marín, César A.

PY - 2018

Y1 - 2018

N2 - One of the goals of Flexible Manufacturing System (FMS) is the mass production of personalized goods at cost comparable to the mass produced goods. This paradigm is referred to as mass personalization. To achieve this, the system has to seamlessly translate flexibility that can be achieved through the software that is responsible for the control of such system directly to the physical system, such that multiple distinct products can be produced in a non-batch mode. However, the present rigid design of Flexible Manufacturing Systems, which is characterized by static processing stations and rigid roll conveyor for part and material transportation, hampers this dream. In this paper, we propose a distributed architecture, which is implemented as Self-Organizing Flexible Manufacturing System (SoFMS), characterized by mobile processing stations that are capable of autonomously re-adjusting their location in real time on the shop floor to form an optimal layout depending on the mix of order inflow. This is achieved using the BEEPOST algorithm, an algorithm inspired by young honeybees’ collective behavior of aggregation in a temperature gradient field. An agent-based simulation paradigm is used to evaluate the viability and performance of the proposed system. The result of the simulation shows that processing stations are able to autonomously and optimally adjust their location depending on the mix of order inflow using the BEEPOST algorithm. This capability also results in higher throughput when compare to a similar system with static processing stations. This approach is expected to engender the capability for production of one-lot-size order in FMS, which is a requirement for mass-personalization.

AB - One of the goals of Flexible Manufacturing System (FMS) is the mass production of personalized goods at cost comparable to the mass produced goods. This paradigm is referred to as mass personalization. To achieve this, the system has to seamlessly translate flexibility that can be achieved through the software that is responsible for the control of such system directly to the physical system, such that multiple distinct products can be produced in a non-batch mode. However, the present rigid design of Flexible Manufacturing Systems, which is characterized by static processing stations and rigid roll conveyor for part and material transportation, hampers this dream. In this paper, we propose a distributed architecture, which is implemented as Self-Organizing Flexible Manufacturing System (SoFMS), characterized by mobile processing stations that are capable of autonomously re-adjusting their location in real time on the shop floor to form an optimal layout depending on the mix of order inflow. This is achieved using the BEEPOST algorithm, an algorithm inspired by young honeybees’ collective behavior of aggregation in a temperature gradient field. An agent-based simulation paradigm is used to evaluate the viability and performance of the proposed system. The result of the simulation shows that processing stations are able to autonomously and optimally adjust their location depending on the mix of order inflow using the BEEPOST algorithm. This capability also results in higher throughput when compare to a similar system with static processing stations. This approach is expected to engender the capability for production of one-lot-size order in FMS, which is a requirement for mass-personalization.

KW - BEEPOST algorithm

KW - Flexible Manufacturing System

KW - Mass personalization

U2 - 10.1007/978-3-319-97628-0_21

DO - 10.1007/978-3-319-97628-0_21

M3 - Conference contribution

AN - SCOPUS:85051415255

SN - 9783319976273

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

SP - 250

EP - 264

BT - From Animals to Animats 15 - 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Proceedings

A2 - Manoonpong, Poramate

A2 - Larsen, Jørgen Christian

A2 - Xiong, Xiaofeng

A2 - Hallam, John

A2 - Triesch, Jochen

PB - Springer Nature

T2 - 15th International Conference on the Simulation of Adaptive Behavior, SAB 2018

Y2 - 14 August 2018 through 17 August 2018

ER -