An integrated ontology and multi-objective optimization model for intercropping decision support systems

Intercropping in rubber plantations is a resource allocation problem to achieve Multiobjective Optimisation (MOO), including higher earnings, low investment, and using less water for cultivation. Moreover, the MOO model must be suitable for area conditions. Since each farmer has different conditions...

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Main Author:	Phoksawat, Kornkanok
Format:	Thesis
Language:	eng eng eng
Published:	2022
Subjects:	Q Science (General)
Online Access:	https://etd.uum.edu.my/10554/1/permission%20to%20use-EMBARGO%204%20JUNE%202023-4%20JUNE%202026.pdf https://etd.uum.edu.my/10554/2/s900776_01.pdf https://etd.uum.edu.my/10554/3/s900776_02.pdf
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id	my-uum-etd.10554
record_format	uketd_dc
spelling	my-uum-etd.105542023-06-28T04:39:44Z An integrated ontology and multi-objective optimization model for intercropping decision support systems 2022 Phoksawat, Kornkanok Mahmuddin, Massudi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School Of Art & Sciences Q Science (General) Intercropping in rubber plantations is a resource allocation problem to achieve Multiobjective Optimisation (MOO), including higher earnings, low investment, and using less water for cultivation. Moreover, the MOO model must be suitable for area conditions. Since each farmer has different conditions, areas, and limitations, therefore the decision process must involve Multiple Criteria Decision-Making (MCDM) approach. This study proposed a new Decision Support System (DSS) model with integrated ontology and Goal Programming model (GP) for solving the MOO intercropping plantation problem by selecting crops that are suitable for each farmer and recommended by experts. The methodology used an ontology with the triangulation method and MOO modelling development. The DSS is designed to work in (i) a plant introduction section and (ii) a multi-purpose resource allocation section. The findings revealed that: (i) the Intercropping in Rubber Plantation Ontology and Recommendation Rules Section provided recommendations with accuracy up to 91.00% and precision equal to 95.79 % based on three rubber planting experts; (ii) the list of plants from the ontology base approved as a decision variable in the GP if the system recommended more than one plant is suitable to allocate resources and constraints of each farmer to achieve the above three objectives; and (iii) to adjust the scale of the values target error from different objectives units of measurement, a minimum percentage of deviation summation is used. The model has improved decision-making by solving the problem of plant selection. This complex MCDM problem requires consideration of both quantitative and qualitative criteria and solving the problem of area allocation that meets the MOO requirements. The intercropping ontology has covered the modelling weakness in a selection problem through a hierarchical structure alone. In addition, the model can be applied to solve many other resource allocation problems with MCDM and MOO. 2022 Thesis https://etd.uum.edu.my/10554/ https://etd.uum.edu.my/10554/1/permission%20to%20use-EMBARGO%204%20JUNE%202023-4%20JUNE%202026.pdf text eng staffonly https://etd.uum.edu.my/10554/2/s900776_01.pdf text eng staffonly https://etd.uum.edu.my/10554/3/s900776_02.pdf text eng staffonly phd doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Mahmuddin, Massudi
topic	Q Science (General)
spellingShingle	Q Science (General) Phoksawat, Kornkanok An integrated ontology and multi-objective optimization model for intercropping decision support systems
description	Intercropping in rubber plantations is a resource allocation problem to achieve Multiobjective Optimisation (MOO), including higher earnings, low investment, and using less water for cultivation. Moreover, the MOO model must be suitable for area conditions. Since each farmer has different conditions, areas, and limitations, therefore the decision process must involve Multiple Criteria Decision-Making (MCDM) approach. This study proposed a new Decision Support System (DSS) model with integrated ontology and Goal Programming model (GP) for solving the MOO intercropping plantation problem by selecting crops that are suitable for each farmer and recommended by experts. The methodology used an ontology with the triangulation method and MOO modelling development. The DSS is designed to work in (i) a plant introduction section and (ii) a multi-purpose resource allocation section. The findings revealed that: (i) the Intercropping in Rubber Plantation Ontology and Recommendation Rules Section provided recommendations with accuracy up to 91.00% and precision equal to 95.79 % based on three rubber planting experts; (ii) the list of plants from the ontology base approved as a decision variable in the GP if the system recommended more than one plant is suitable to allocate resources and constraints of each farmer to achieve the above three objectives; and (iii) to adjust the scale of the values target error from different objectives units of measurement, a minimum percentage of deviation summation is used. The model has improved decision-making by solving the problem of plant selection. This complex MCDM problem requires consideration of both quantitative and qualitative criteria and solving the problem of area allocation that meets the MOO requirements. The intercropping ontology has covered the modelling weakness in a selection problem through a hierarchical structure alone. In addition, the model can be applied to solve many other resource allocation problems with MCDM and MOO.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Phoksawat, Kornkanok
author_facet	Phoksawat, Kornkanok
author_sort	Phoksawat, Kornkanok
title	An integrated ontology and multi-objective optimization model for intercropping decision support systems
title_short	An integrated ontology and multi-objective optimization model for intercropping decision support systems
title_full	An integrated ontology and multi-objective optimization model for intercropping decision support systems
title_fullStr	An integrated ontology and multi-objective optimization model for intercropping decision support systems
title_full_unstemmed	An integrated ontology and multi-objective optimization model for intercropping decision support systems
title_sort	integrated ontology and multi-objective optimization model for intercropping decision support systems
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10554/1/permission%20to%20use-EMBARGO%204%20JUNE%202023-4%20JUNE%202026.pdf https://etd.uum.edu.my/10554/2/s900776_01.pdf https://etd.uum.edu.my/10554/3/s900776_02.pdf
_version_	1776103841541914624

An integrated ontology and multi-objective optimization model for intercropping decision support systems

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