Article

An agent-based approach for managing symbiotic simulation of semiconductor assembly and test operation

Authors:

Malcolm Yoke Hean Low,

Peter Lendermann,

Stephen John Turner,

Reman Tat Wee Chim,

Surya Hadisaputra LeoAuthors Info & Claims

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

Pages 85 - 92

https://doi.org/10.1145/1082473.1082809

Published: 25 July 2005 Publication History

Abstract

The rapid changing business environment of high-tech asset intensive enterprises such as semiconductor manufacturing constantly drives production managers to look for better solutions to improve the manufacturing process. Simulation, though identified to be the most appropriate technique to generate and test out possible execution plans, suffers from long cycle-time in the process of model update, analysis and verification. It is thus very difficult to carry out prompt "what-if' analysis to respond to abrupt changes in these systems. Symbiotic simulation systems have been proposed as a way of solving this problem by having the simulation and the physical system interact in a mutually beneficial manner. In this paper, we describe our work in developing a prototype proof-of-concept symbiotic simulation system that employs software agents in the monitoring, optimization and control of a semiconductor assembly and test operation.

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cover image ACM Conferences

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

July 2005

1407 pages

ISBN:1595930930

DOI:10.1145/1082473

Program Chairs:
Michal Pechoucek
Czech Republic
,
Donald Steiner
USA
,
Simon Thompson
UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2005

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AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005

July 25 - 29, 2005

The Netherlands

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Naing HCai WNan HTiantian WLiang Y(2022)Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term MemoryACM Transactions on Modeling and Computer Simulation10.1145/355855532:4(1-27)Online publication date: 5-Nov-2022
https://dl.acm.org/doi/10.1145/3558555
Naing HCai WHu NWu TYu LDiallo STolk AGiabbanelli P(2021)Data-driven Microscopic Traffic Modelling and Simulation using Dynamic LSTMProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459258(1-12)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3437959.3459258
Li HHuh WSampaio MKeng N(2021)Planning Production and Equipment Qualification under High Process FlexibilityProduction and Operations Management10.1111/poms.1343930:10(3369-3390)Online publication date: 1-Oct-2021
https://doi.org/10.1111/poms.13439
Werling JYugma CSoukhal AMohr T(2020)An agent-based simulation model with human resource integration for semiconductor manufacturing facilityProceedings of the Winter Simulation Conference10.5555/3466184.3466387(1801-1812)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466387
Onggo BCorlu CJuan AMonks Tde la Torre R(2020)Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-makingEnterprise Information Systems10.1080/17517575.2020.1777587(1-18)Online publication date: 18-Jun-2020
https://doi.org/10.1080/17517575.2020.1777587
Oakley DOnggo BWorthington D(2019)Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-methodHealth Care Management Science10.1007/s10729-019-09485-1Online publication date: 3-Jun-2019
https://doi.org/10.1007/s10729-019-09485-1
Onggo BMustafee NSmart AJuan AMolloy OJohansson BJain S(2018)Symbiotic simulation systemProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320685(1358-1369)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320685
Huh JPark ILim SPaeng BPark JKim K(2018)Learning to Dispatch Operations with Intentional Delay for Re-Entrant Multiple-Chip Product Assembly LinesSustainability10.3390/su1011412310:11(4123)Online publication date: 9-Nov-2018
https://doi.org/10.3390/su10114123
Yang CShen WWang X(2018)The Internet of Things in Manufacturing: Key Issues and Potential ApplicationsIEEE Systems, Man, and Cybernetics Magazine10.1109/MSMC.2017.27023914:1(6-15)Online publication date: Jan-2018
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Suh WHenclewood DGuin AGuensler RHunter MFujimoto R(2017)Dynamic data driven transportation systemsMultimedia Tools and Applications10.1007/s11042-016-4318-x76:23(25253-25269)Online publication date: 1-Dec-2017
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