Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell
This paper presents position-based optimization methods to schedule the production of automatic cells of a wheel manufacturing factory. Real-time schedule is challenging when a cell is interrupted by various order changes. Given a sequence of orders to be scheduled, it is sorted based on an earliest...
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doaj-e4d84922174449b791aa8a47b191485d2021-03-29T20:01:50ZengIEEEIEEE Access2169-35362017-01-0153369337510.1109/ACCESS.2016.26163667589021Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing CellChih-Hua Hsu0https://orcid.org/0000-0002-8175-436XHaw-Ching Yang1Department of Information Management, Chang Jung Christian University, Tainan City, TaiwanGraduate Institute of Electrical Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, TaiwanThis paper presents position-based optimization methods to schedule the production of automatic cells of a wheel manufacturing factory. Real-time schedule is challenging when a cell is interrupted by various order changes. Given a sequence of orders to be scheduled, it is sorted based on an earliest due day policy, a mixed integer linear programming model is formulated, and then rolling-horizon optimization methods are used to timely find the near-optimal schedule by minimizing earliness and tardiness penalties with setup times of a manufacturing cell. In addition, an original schedule can be partial rescheduled with the preset order sequence by using the linear programming model. Experimental results show that the proposed method enables a wheel manufacturing cell to reschedule its three to five daily orders within the cycle time of a rim when there exist order changes, e.g., rush orders and customized orders. Hence, these proposed methods are promising to promptly derive the near-optimal schedule for satisfying the objective of mass customization for industry 4.0.https://ieeexplore.ieee.org/document/7589021/Earliness and tardiness costmixed integer linear programmingreal-time schedulingrolling horizon optimizationsetup timessingle machine scheduling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chih-Hua Hsu Haw-Ching Yang |
spellingShingle |
Chih-Hua Hsu Haw-Ching Yang Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell IEEE Access Earliness and tardiness cost mixed integer linear programming real-time scheduling rolling horizon optimization setup times single machine scheduling |
author_facet |
Chih-Hua Hsu Haw-Ching Yang |
author_sort |
Chih-Hua Hsu |
title |
Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell |
title_short |
Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell |
title_full |
Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell |
title_fullStr |
Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell |
title_full_unstemmed |
Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell |
title_sort |
real-time near-optimal scheduling with rolling horizon for automatic manufacturing cell |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2017-01-01 |
description |
This paper presents position-based optimization methods to schedule the production of automatic cells of a wheel manufacturing factory. Real-time schedule is challenging when a cell is interrupted by various order changes. Given a sequence of orders to be scheduled, it is sorted based on an earliest due day policy, a mixed integer linear programming model is formulated, and then rolling-horizon optimization methods are used to timely find the near-optimal schedule by minimizing earliness and tardiness penalties with setup times of a manufacturing cell. In addition, an original schedule can be partial rescheduled with the preset order sequence by using the linear programming model. Experimental results show that the proposed method enables a wheel manufacturing cell to reschedule its three to five daily orders within the cycle time of a rim when there exist order changes, e.g., rush orders and customized orders. Hence, these proposed methods are promising to promptly derive the near-optimal schedule for satisfying the objective of mass customization for industry 4.0. |
topic |
Earliness and tardiness cost mixed integer linear programming real-time scheduling rolling horizon optimization setup times single machine scheduling |
url |
https://ieeexplore.ieee.org/document/7589021/ |
work_keys_str_mv |
AT chihhuahsu realtimenearoptimalschedulingwithrollinghorizonforautomaticmanufacturingcell AT hawchingyang realtimenearoptimalschedulingwithrollinghorizonforautomaticmanufacturingcell |
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1724195463170621440 |