Fuzzy random based mean variance model for agricultural production planning
Observation and measurement data are the basis of an analysis which usually contains uncertainties. The uncertainties in data need to be properly described as they may increase error in the prediction model. The collected data which contains uncertainty should be adequately treated before analysi...
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| Language: | en |
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| Online Access: | http://eprints.uthm.edu.my/3496/1/KP%202020%20%2875%29.pdf http://eprints.uthm.edu.my/3496/ https://doi.org/10.1007/978-3-030-36056-6_2 |
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| author | Othman, Mohammad Haris Haikal Arbaiy, Nureize Che Lah, Muhammad Shukri Pei-, Chun Lin |
| author_facet | Othman, Mohammad Haris Haikal Arbaiy, Nureize Che Lah, Muhammad Shukri Pei-, Chun Lin |
| author_sort | Othman, Mohammad Haris Haikal |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Observation and measurement data are the basis of an analysis which
usually contains uncertainties. The uncertainties in data need to be properly
described as they may increase error in the prediction model. The collected data
which contains uncertainty should be adequately treated before analysis. In the
portfolio selection problem, uncertainty involves are characterized as fuzzy and
random. Hence fuzzy random variables are accounted as input values in the
portfolio selection analysis. It is important to preprocess the data sufficiently due
to the uncertainties issue. However, only a few studies discuss the systematic
procedure for data processing whereby the uncertainties exist. Hence, this study
introduces a structure for fuzzy random data processing which deals with
fuzziness and randomness in data for building a portfolio selection model. The
fuzzy number is utilized to treat the fuzziness and the probability distribution
used to treat randomness. The proposed model is applied for agricultural
planning. Five types of industrial plants are assessed using the proposed method.
The result of this study demonstrates that the proposed method of fuzzy random
based data Pre-processing can treat the uncertainties. The systematic procedure
of fuzzy random data Pre-processing in this study is important to enable data
uncertainties treatment and to reduce error in the early stage of problem model
building. |
| format | Conference or Workshop Item |
| id | my.uthm.eprints-3496 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| record_format | eprints |
| spelling | my.uthm.eprints-34962022-01-23T05:19:19Z http://eprints.uthm.edu.my/3496/ Fuzzy random based mean variance model for agricultural production planning Othman, Mohammad Haris Haikal Arbaiy, Nureize Che Lah, Muhammad Shukri Pei-, Chun Lin T Technology (General) TS155-194 Production management. Operations management Observation and measurement data are the basis of an analysis which usually contains uncertainties. The uncertainties in data need to be properly described as they may increase error in the prediction model. The collected data which contains uncertainty should be adequately treated before analysis. In the portfolio selection problem, uncertainty involves are characterized as fuzzy and random. Hence fuzzy random variables are accounted as input values in the portfolio selection analysis. It is important to preprocess the data sufficiently due to the uncertainties issue. However, only a few studies discuss the systematic procedure for data processing whereby the uncertainties exist. Hence, this study introduces a structure for fuzzy random data processing which deals with fuzziness and randomness in data for building a portfolio selection model. The fuzzy number is utilized to treat the fuzziness and the probability distribution used to treat randomness. The proposed model is applied for agricultural planning. Five types of industrial plants are assessed using the proposed method. The result of this study demonstrates that the proposed method of fuzzy random based data Pre-processing can treat the uncertainties. The systematic procedure of fuzzy random data Pre-processing in this study is important to enable data uncertainties treatment and to reduce error in the early stage of problem model building. Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/3496/1/KP%202020%20%2875%29.pdf Othman, Mohammad Haris Haikal and Arbaiy, Nureize and Che Lah, Muhammad Shukri and Pei-, Chun Lin Fuzzy random based mean variance model for agricultural production planning. In: The 4th International Conference on Soft Computing and Data Mining (SCDM 2020), 22-23 January 2020, Melaka, Malaysia. https://doi.org/10.1007/978-3-030-36056-6_2 |
| spellingShingle | T Technology (General) TS155-194 Production management. Operations management Othman, Mohammad Haris Haikal Arbaiy, Nureize Che Lah, Muhammad Shukri Pei-, Chun Lin Fuzzy random based mean variance model for agricultural production planning |
| title | Fuzzy random based mean variance model for agricultural production planning |
| title_full | Fuzzy random based mean variance model for agricultural production planning |
| title_fullStr | Fuzzy random based mean variance model for agricultural production planning |
| title_full_unstemmed | Fuzzy random based mean variance model for agricultural production planning |
| title_short | Fuzzy random based mean variance model for agricultural production planning |
| title_sort | fuzzy random based mean variance model for agricultural production planning |
| topic | T Technology (General) TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/3496/1/KP%202020%20%2875%29.pdf http://eprints.uthm.edu.my/3496/ https://doi.org/10.1007/978-3-030-36056-6_2 |
| url_provider | http://eprints.uthm.edu.my/ |