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Subtask Scheduling for Distributed Robots in Cloud Manufacturing
Wuhan Univ Sci & Technol, Minist Educ, Sch Informat Sci & Engn, Engn Res Ctr Met Automat & Detecting Technol, Wuhan 430081, Peoples R China..
Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada..
St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada..
Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China..
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2017 (English)In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234, Vol. 11, no 2, 941-950 p.Article in journal (Refereed) Published
Abstract [en]

Due to the limitation of capacity in an enterprise, cooperation among these enterprises is necessary to handle a complex production task. Cloud manufacturing (CMF) provides a cooperation platform for efficient utilization of distributed manufacturing resources in regional enterprise cluster. However, effective scheduling of tasks or subtasks to these resources is a challenging problem. Based on the analysis on the procedure of task processing, this paper proposes a CMF scheduling model for efficiently exploiting distributed resources, so industrial robots in different enterprises can cooperatively handle a batch of tasks. Specifically, this paper considers the performance of four robot deployment methods, including random deployment, robot-balanced deployment, function-balanced deployment, and location-aware deployment. Furthermore, three subtask-scheduling strategies are derived for three optimization objectives, including load-balance of robots, minimizing overall cost, and minimizing overall processing time. Moreover, these strategies are implemented by genetic algorithm. Simulation results demonstrate that each strategy can achieve the relevant optimization objective. In addition, the results also show that the physical distance between two enterprises can influence the overall cost, and location-aware deployment leads to smaller transportation cost. Location-aware deployment and function-balanced deployment lead to smaller overall processing time for the low-workload state and high-workload state of the system, respectively.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 11, no 2, 941-950 p.
Keyword [en]
Cloud manufacturing (CMF), deployment, genetic algorithm (GA), manufacturing robot, task scheduling
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-330742DOI: 10.1109/JSYST.2015.2438054ISI: 000404985800055OAI: oai:DiVA.org:uu-330742DiVA: diva2:1147911
Funder
VINNOVA
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2017-10-09Bibliographically approved

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