Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach
Big data is promoting the development of supply chain design and management. The problem of trustworthy scheduling by using big data is challenging, and it significantly influences the performance of agricultural products supply chain (APSC) management. Currently, there are various approaches to opt...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8451877/ |
| _version_ | 1819320543370805248 |
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| author | Qian Tao Chunqin Gu Zhenyu Wang Joseph Rocchio Weiwen Hu Xinzhi Yu |
| author_facet | Qian Tao Chunqin Gu Zhenyu Wang Joseph Rocchio Weiwen Hu Xinzhi Yu |
| author_sort | Qian Tao |
| collection | DOAJ |
| description | Big data is promoting the development of supply chain design and management. The problem of trustworthy scheduling by using big data is challenging, and it significantly influences the performance of agricultural products supply chain (APSC) management. Currently, there are various approaches to optimize scheduling of APSC, but most of them can only tackle the problem with primary objectives (time and cost) or are limited to small-scale supply chains. The efficient approaches have not been provided for scheduling of APSC in big data environment. This paper aims at proposing a novel trustworthy scheduling optimization approach for APSC by using big data. First, a new management architecture is provided for revealing underexploited values from big data to support the scheduling of APSC. Second, a novel scheduling model is presented to guarantees the trustworthiness of an agricultural product supply chain. At last, an evolutionary algorithm is developed to optimize the scheduling of large-scale supply chains with complex structure. Experiments are performed in 12 various scale test instances of APSC with at most 1 000 000 customer reviews and a 45 000-D search space. The results compiled demonstrate the effectiveness of the proposed approach. |
| first_indexed | 2024-12-24T11:21:15Z |
| format | Article |
| id | doaj.art-e148526f8fb54019a15ffbb15b525272 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T11:21:15Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e148526f8fb54019a15ffbb15b5252722022-12-21T16:58:14ZengIEEEIEEE Access2169-35362018-01-016499905000210.1109/ACCESS.2018.28678728451877Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization ApproachQian Tao0https://orcid.org/0000-0001-7313-2109Chunqin Gu1Zhenyu Wang2Joseph Rocchio3Weiwen Hu4Xinzhi Yu5School of Software Engineering, South China University of Technology, Canton, ChinaSchool of Software Engineering, South China University of Technology, Canton, ChinaSchool of Software Engineering, South China University of Technology, Canton, ChinaDepartment of Chemical Engineering, University of Rhode Island, Kingston, RI, USASchool of Software Engineering, South China University of Technology, Canton, ChinaSchool of Software Engineering, South China University of Technology, Canton, ChinaBig data is promoting the development of supply chain design and management. The problem of trustworthy scheduling by using big data is challenging, and it significantly influences the performance of agricultural products supply chain (APSC) management. Currently, there are various approaches to optimize scheduling of APSC, but most of them can only tackle the problem with primary objectives (time and cost) or are limited to small-scale supply chains. The efficient approaches have not been provided for scheduling of APSC in big data environment. This paper aims at proposing a novel trustworthy scheduling optimization approach for APSC by using big data. First, a new management architecture is provided for revealing underexploited values from big data to support the scheduling of APSC. Second, a novel scheduling model is presented to guarantees the trustworthiness of an agricultural product supply chain. At last, an evolutionary algorithm is developed to optimize the scheduling of large-scale supply chains with complex structure. Experiments are performed in 12 various scale test instances of APSC with at most 1 000 000 customer reviews and a 45 000-D search space. The results compiled demonstrate the effectiveness of the proposed approach.https://ieeexplore.ieee.org/document/8451877/Big datascheduling of APSCoptimizationevolutionary algorithmtrustworthiness |
| spellingShingle | Qian Tao Chunqin Gu Zhenyu Wang Joseph Rocchio Weiwen Hu Xinzhi Yu Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach IEEE Access Big data scheduling of APSC optimization evolutionary algorithm trustworthiness |
| title | Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach |
| title_full | Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach |
| title_fullStr | Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach |
| title_full_unstemmed | Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach |
| title_short | Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach |
| title_sort | big data driven agricultural products supply chain management a trustworthy scheduling optimization approach |
| topic | Big data scheduling of APSC optimization evolutionary algorithm trustworthiness |
| url | https://ieeexplore.ieee.org/document/8451877/ |
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