Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/1174
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dc.contributor.advisorOlugbara, Oludayo O.-
dc.contributor.advisorAdeyemo, Josiah-
dc.contributor.authorAbayomi, Adekanmbi Oluwole-
dc.date.accessioned2015-01-14T12:12:32Z-
dc.date.available2015-01-14T12:12:32Z-
dc.date.issued2015-01-14-
dc.identifier.other618435-
dc.identifier.urihttp://hdl.handle.net/10321/1174-
dc.descriptionSubmitted in fulfillment of the requirements of the Master of Technology Degree in Information Technology, Durban University of Technology, Durban, South Africa, 2014.en_US
dc.description.abstractThis dissertation reports on the original study that applies the differential evolution algorithm to support farmers with optimal strategic decision making in the crop planning system. The analysis and modelling of crop planning decision making process are attractive for producing formalized knowledge on cropping plans and choices of farmers under uncertainty. The formalization of the decision making process is generally becoming a crucial focal point for developing decision support systems that go beyond the limitation of formerly developed prescriptive approaches. This dissertation makes a distinctive contribution to the development of a formalized methodology to study the decision making process in crop farming systems. The research reported in this dissertation formulates crop-mix planning problems by concurrently maximizing net profit and crop production, while minimizing the total land in hectare used to determine optimal cropping patterns. Different optimal crop-mix problems formulated in this research were solved using a mathematical methodology of generalized differential evolution 3 algorithm to obtain globally optimal solutions. The methodology of this research strikes a balance between mathematical formulations of crop planning problems and effective implementation of crop planning decision models. Simulation experiments were conducted using the non-dominated sorted genetic algorithm II to validate the performance of the generalized differential evolution 3 algorithm for solving optimal crop planning problems. The empirical results of this study generally indicate that generalized differential evolution 3 algorithm is a viable alternative for optimal crop-mix planning decision. Based on the performance of the generalized differential evolution 3 algorithm, the design of a decision support system was realized which promises to assist farmers and decision-makers within the agricultural sector to make optimal decisions pertaining to crop planning.en_US
dc.format.extent138 pen_US
dc.language.isoenen_US
dc.subject.lcshCrop science--South Africaen_US
dc.subject.lcshAgricultural systems--South Africa--Decision makingen_US
dc.subject.lcshCropping systems--South Africaen_US
dc.subject.lcshMathematical optimizationen_US
dc.titleDifferential evolution algorithm for optimal strategic decision making in crop farming systemen_US
dc.typeThesisen_US
dc.description.levelMen_US
item.grantfulltextopen-
item.languageiso639-1other-
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Appears in Collections:Theses and dissertations (Accounting and Informatics)
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