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- Publisher Website: 10.1109/LRA.2023.3314349
- Scopus: eid_2-s2.0-85171560838
- WOS: WOS:001071746200007
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Article: Fast Approximations for Dynamic Behavior in Manufacturing Systems With Regular Orders: An Aggregation Method
Title | Fast Approximations for Dynamic Behavior in Manufacturing Systems With Regular Orders: An Aggregation Method |
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Authors | |
Keywords | Aggregation method approximation algorithm customer demand dynamic behavior manufacturing system |
Issue Date | 1-Nov-2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Robotics and Automation Letters, 2023, v. 8, n. 11, p. 7122-7129 How to Cite? |
Abstract | Both manufacturing systems and inventory models have received substantial research attention, but they are mainly investigated independently without considering their interactions. In fact, from the supply chain network perspective, manufacturing systems and warehouses may interact. This article considers the manufacturing systems with regular orders. In addition to machines and work-in-process buffers usually considered in current literature, a finished goods buffer is added to the manufacturing system to store the final products. For a fixed shipping period, a certain amount of products are shipped to the customers. For these systems, the production process and inventory level may fluctuate when goods are shipped. As a result, they are in dynamic modes in part or all the processing cycles, and an unacceptable error occurs when employing the steady-state analysis algorithm. Therefore, this article develops effective algorithms for analyzing the dynamic behavior in manufacturing systems with regular orders. Specifically, the system with one machine and finished goods buffer isolated from the manufacturing system is first modeled and estimated. Then, based on it, we proposed an aggregation method, which can be employed in larger systems. The approach substantially decreases the state space of the problem and leads to the modeling and estimation of the system solvable. Numerical experiments show that the proposed methods maintain high accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/336531 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.119 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, J | - |
dc.contributor.author | Shen, ZJM | - |
dc.date.accessioned | 2024-02-16T03:57:31Z | - |
dc.date.available | 2024-02-16T03:57:31Z | - |
dc.date.issued | 2023-11-01 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, 2023, v. 8, n. 11, p. 7122-7129 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336531 | - |
dc.description.abstract | Both manufacturing systems and inventory models have received substantial research attention, but they are mainly investigated independently without considering their interactions. In fact, from the supply chain network perspective, manufacturing systems and warehouses may interact. This article considers the manufacturing systems with regular orders. In addition to machines and work-in-process buffers usually considered in current literature, a finished goods buffer is added to the manufacturing system to store the final products. For a fixed shipping period, a certain amount of products are shipped to the customers. For these systems, the production process and inventory level may fluctuate when goods are shipped. As a result, they are in dynamic modes in part or all the processing cycles, and an unacceptable error occurs when employing the steady-state analysis algorithm. Therefore, this article develops effective algorithms for analyzing the dynamic behavior in manufacturing systems with regular orders. Specifically, the system with one machine and finished goods buffer isolated from the manufacturing system is first modeled and estimated. Then, based on it, we proposed an aggregation method, which can be employed in larger systems. The approach substantially decreases the state space of the problem and leads to the modeling and estimation of the system solvable. Numerical experiments show that the proposed methods maintain high accuracy. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Robotics and Automation Letters | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Aggregation method | - |
dc.subject | approximation algorithm | - |
dc.subject | customer demand | - |
dc.subject | dynamic behavior | - |
dc.subject | manufacturing system | - |
dc.title | Fast Approximations for Dynamic Behavior in Manufacturing Systems With Regular Orders: An Aggregation Method | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LRA.2023.3314349 | - |
dc.identifier.scopus | eid_2-s2.0-85171560838 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 7122 | - |
dc.identifier.epage | 7129 | - |
dc.identifier.eissn | 2377-3766 | - |
dc.identifier.isi | WOS:001071746200007 | - |
dc.identifier.issnl | 2377-3766 | - |