You are here

A framework to generate a smart manufacturing system configurations using agents and optimization

Download pdf | Full Screen View

Date Issued:
2016
Abstract/Description:
Manufacturing is a crucial element in the global economy. During the last decade, the national manufacturing sector loses nearly 30% of its workforce and investments. Consequently, the quality of the domestic goods, global share, and manufacturing capabilities has been declined. Therefore, innovative ways to optimize the usage of the Smart Manufacturing Systems (SMS) are required to form a new manufacturing era. This research is presenting a framework to optimize the design of SMS. This includes the determination of the suitable machines that can perform the job efficiently, the quantity of those machines, and the potential messaging system required for sharing information.Multiple reviews are used to form the framework. Expert machine selection matrix identifies the required machines and machine parameter matrix defines the specifications of those machines. While business process modeling and notation (BPMN) captures the process plan in object-oriented fashion. In addition, to agent unified modeling language (AUML) that guides the application of message sequence diagram and statecharts. Finally, the configuration is obtained from a hybrid simulation model. Agent based-modeling is used to capture the behavior of the machines where discrete event simulation mimics the process flow. A case study of a manufacturing system is used to verify the study. As a result, the framework shows positive outcomes in supporting upper management in the planning phase of establishing a SMS or evaluating an existing one.
Title: A framework to generate a smart manufacturing system configurations using agents and optimization.
19 views
5 downloads
Name(s): Nagadi, Khalid, Author
Rabelo, Luis, Committee Chair
Lee, Gene, Committee Member
Elshennawy, Ahmad, Committee Member
Ahmad, Ali, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2016
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Manufacturing is a crucial element in the global economy. During the last decade, the national manufacturing sector loses nearly 30% of its workforce and investments. Consequently, the quality of the domestic goods, global share, and manufacturing capabilities has been declined. Therefore, innovative ways to optimize the usage of the Smart Manufacturing Systems (SMS) are required to form a new manufacturing era. This research is presenting a framework to optimize the design of SMS. This includes the determination of the suitable machines that can perform the job efficiently, the quantity of those machines, and the potential messaging system required for sharing information.Multiple reviews are used to form the framework. Expert machine selection matrix identifies the required machines and machine parameter matrix defines the specifications of those machines. While business process modeling and notation (BPMN) captures the process plan in object-oriented fashion. In addition, to agent unified modeling language (AUML) that guides the application of message sequence diagram and statecharts. Finally, the configuration is obtained from a hybrid simulation model. Agent based-modeling is used to capture the behavior of the machines where discrete event simulation mimics the process flow. A case study of a manufacturing system is used to verify the study. As a result, the framework shows positive outcomes in supporting upper management in the planning phase of establishing a SMS or evaluating an existing one.
Identifier: CFE0006540 (IID), ucf:51311 (fedora)
Note(s): 2016-05-01
Ph.D.
Engineering and Computer Science, Industrial Engineering and Management Systems
Doctoral
This record was generated from author submitted information.
Subject(s): Smart Manufacturing Systems -- Internet of Things -- Agent Based Modeling
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0006540
Restrictions on Access: public 2016-11-15
Host Institution: UCF

In Collections