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Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop
conference contribution
posted on 2016-07-29, 14:45 authored by Abdulaziz Alotaibi, Niels LohseNiels Lohse, Tuong Manh VuNowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result.
Funding
The reported work has been partially funded by the EPSRC Centre for Innovated Manufacturing in Intelligent Automation (EP/I033467/1).
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016)Citation
ALOTAIBI, A., LOHSE, N. and MANH VU, T., 2016. Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop. Procedia Cirp, 57, pp.728-733.Publisher
© The Authors. Published by ElsevierVersion
- NA (Not Applicable or Unknown)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2016-05-25Publication date
2016Notes
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution NonCommercial-NoDerivatives 4.0 Unported Licence (CC BY-NC-ND). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/4.0/ It was presented at the 49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016), Stuttgart, Germany, May 25-27th.ISSN
2212-8271Publisher version
Language
- en