A long-term, spatially constrained harvest scheduling model for Eucalyptus plantations in the southeast of Mexico

Persistent Link:
http://hdl.handle.net/10150/289157
Title:
A long-term, spatially constrained harvest scheduling model for Eucalyptus plantations in the southeast of Mexico
Author:
Cruz-Bello, Gustavo Manuel
Issue Date:
2000
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In the states of Tabasco and Chiapas, Mexico there is a lack of long-term harvest scheduling models that consider the effects of the harvest activities on the surrounding areas. Additionally, these problems are combinatorial in nature, which makes them hard to solve. Consequently, only harvest scheduling for small areas can be solved to optimality using traditional approaches such as integer programming (IP). In this study, a genetic algorithms (GA) model was used to generate multiple viable solutions for long-term spatially constrained problems on large areas with a great number of management units. This model enables consideration of regeneration and reharvest in forest planning. The flexibility of the model allows it to handle a different set of time periods, database sizes, different species and diverse tree growth models. The data set employed corresponds to a eucalyptus plantation with a cutting cycle seven years and a planning horizon of 10 rotation periods. Total plantation area is 300,000 ha, divided in 5,388 harvest units. IP was used as a standard to validate the efficiency and accuracy of the GA method. The GA performance with different combinations of genetic operators was tested. Scheduled volume flow for simulated communities was computed. Additionally, three different volume assignment scenarios (low, medium and high) were compared to estimate the effect of volume assignment on the spatial optimization output. The significant findings of this research are: (1) a long-term spatially constrained robust solution was found through the use of genetic algorithms for a large area with more harvest units than those reported elsewhere, (2) the solution allowed re-harvest in the same planning horizon, (3) most of the genetic algorithms runs performed better than the integer programming, and (4) on average the volume scheduled for every simulated community was comparable for the two methods used in the work. In both cases, the percentages of the potential volume ranged between 7 and 29%.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Biology, Ecology.; Agriculture, Forestry and Wildlife.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Renewable Natural Resources
Degree Grantor:
University of Arizona
Advisor:
Ball, George

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA long-term, spatially constrained harvest scheduling model for Eucalyptus plantations in the southeast of Mexicoen_US
dc.creatorCruz-Bello, Gustavo Manuelen_US
dc.contributor.authorCruz-Bello, Gustavo Manuelen_US
dc.date.issued2000en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn the states of Tabasco and Chiapas, Mexico there is a lack of long-term harvest scheduling models that consider the effects of the harvest activities on the surrounding areas. Additionally, these problems are combinatorial in nature, which makes them hard to solve. Consequently, only harvest scheduling for small areas can be solved to optimality using traditional approaches such as integer programming (IP). In this study, a genetic algorithms (GA) model was used to generate multiple viable solutions for long-term spatially constrained problems on large areas with a great number of management units. This model enables consideration of regeneration and reharvest in forest planning. The flexibility of the model allows it to handle a different set of time periods, database sizes, different species and diverse tree growth models. The data set employed corresponds to a eucalyptus plantation with a cutting cycle seven years and a planning horizon of 10 rotation periods. Total plantation area is 300,000 ha, divided in 5,388 harvest units. IP was used as a standard to validate the efficiency and accuracy of the GA method. The GA performance with different combinations of genetic operators was tested. Scheduled volume flow for simulated communities was computed. Additionally, three different volume assignment scenarios (low, medium and high) were compared to estimate the effect of volume assignment on the spatial optimization output. The significant findings of this research are: (1) a long-term spatially constrained robust solution was found through the use of genetic algorithms for a large area with more harvest units than those reported elsewhere, (2) the solution allowed re-harvest in the same planning horizon, (3) most of the genetic algorithms runs performed better than the integer programming, and (4) on average the volume scheduled for every simulated community was comparable for the two methods used in the work. In both cases, the percentages of the potential volume ranged between 7 and 29%.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectBiology, Ecology.en_US
dc.subjectAgriculture, Forestry and Wildlife.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineRenewable Natural Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBall, Georgeen_US
dc.identifier.proquest9983845en_US
dc.identifier.bibrecord.b40802152en_US
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