research-article

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Authors:

George Q. Huang,

Chen XuAuthors Info & Claims

Advanced Engineering Informatics, Volume 29, Issue 4

Pages 799 - 812

https://doi.org/10.1016/j.aei.2015.01.002

Published: 01 October 2015 Publication History

Abstract

An RFID-event driven mechanism is used to integrate planning and scheduling.RFID production shopfloor data was used to obtain APPS parameters.Release strategy is efficient to reduce the total tardiness by 44.46% averagely. Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.

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Cited By

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A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing
1. Applied computing
2. Theory of computation
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    1. Approximation algorithms analysis
    2. Online algorithms
      1. Online learning algorithms
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    1. Machine learning theory
      1. Reinforcement learning

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cover image Advanced Engineering Informatics

Advanced Engineering Informatics Volume 29, Issue 4

October 2015

370 pages

ISSN:1474-0346

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Copyright © Elsevier Ltd.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2015

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Zheng JLin ZXie X(2024)Improved multi-objective brain storm optimization algorithm for RFID network planningWireless Networks10.1007/s11276-023-03543-x30:2(1055-1068)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s11276-023-03543-x
Coito TFirme BMartins MCostigliola ALucas RFigueiredo JVieira SSousa J(2022)Integration of industrial IoT architectures for dynamic schedulingComputers and Industrial Engineering10.1016/j.cie.2022.108387171:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.cie.2022.108387
Yun LMa SLi LLiu Y(2022)CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturingAdvanced Engineering Informatics10.1016/j.aei.2022.10153452:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.aei.2022.101534
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