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Multicriteria optimization of nondif...

Schaper, James Carl.

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Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize./
作者:	Schaper, James Carl.
面頁冊數:	244 p.
附註:	Source: Dissertation Abstracts International, Volume: 60-07, Section: B, page: 3393.
Contained By:	Dissertation Abstracts International60-07B.
標題:	Engineering, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9936602
ISBN:	0599376643

Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize.
Schaper, James Carl.

Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize. - 244 p.

Source: Dissertation Abstracts International, Volume: 60-07, Section: B, page: 3393.

Thesis (Ph.D.)--Michigan State University, 1998.

A mathematical model of an irrigated maize enterprise, from preseason manure application to the transport of grain to market, is constructed and used to simulate crop production in southern Michigan under linguistically categorized climatic scenarios. The simulation deploys a multi-variable sequential-random-search algorithm that incorporates a simple adaptive evolution strategy to optimize both economic net return and nitrate leaching for a given soil type. The parameters of optimization describe the genetic coefficients of maize, irrigation technology, and agricultural practices. Net return is found to be greatest with cultivars adapted to the specific growing season. Scheduling irrigation in conjunction with a weather forecast reduces the amount of nitrate leached. Simultaneous scheduling of all controllable resources is found to be beneficial for an enterprise that is managed to achieve multiple criteria at a Pareto frontier.

ISBN: 0599376643Subjects--Topical Terms:

1019504
Engineering, Agricultural.

Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize.
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245 1 0 $a Multicriteria optimization of nondifferentiable stochastic biosystems: Technology management of irrigated maize.
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500 $a Source: Dissertation Abstracts International, Volume: 60-07, Section: B, page: 3393.
502 $a Thesis (Ph.D.)--Michigan State University, 1998.
520 $a A mathematical model of an irrigated maize enterprise, from preseason manure application to the transport of grain to market, is constructed and used to simulate crop production in southern Michigan under linguistically categorized climatic scenarios. The simulation deploys a multi-variable sequential-random-search algorithm that incorporates a simple adaptive evolution strategy to optimize both economic net return and nitrate leaching for a given soil type. The parameters of optimization describe the genetic coefficients of maize, irrigation technology, and agricultural practices. Net return is found to be greatest with cultivars adapted to the specific growing season. Scheduling irrigation in conjunction with a weather forecast reduces the amount of nitrate leached. Simultaneous scheduling of all controllable resources is found to be beneficial for an enterprise that is managed to achieve multiple criteria at a Pareto frontier.
520 $a A methodology is developed which permits model-referenced optimal control in highly nonlinear (nondifferentiable) representations of biological production systems that are managed according to conflicting and environmentally-conditioned goals. The methodology also lends itself to parameter estimation on the enterprise model contained within a hierarchical ecological network structure which conformally maps economic performance to the mass-energy flows and transformations in the enterprise. Based on a definition for compromise, the optimization continuously seeks a more desirable multi-goal target while simultaneously searching for the management strategy which encompasses the stochastic influences of weather. The search evolves, subject to explicit and implicit constraints which are defined a priori by boundaries and rules.
520 $a The traditional method to accommodate an implicit constraint requires additional iterations of the model with a new trial solution vector each time the constraint is violated. The implicit constraints in the irrigated maize enterprise model that demanded this procedure were related to cumulative environmental measures and resulted in tedious nested optimizations. A subset of implicit constraints, characterized by the influence of weather on the scheduling of agricultural operations, was efficiently resolved with functional adjustments to the trial solution vector rather than with nested optimizations.
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