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Simultaneous optimization of harvest schedule and data quality

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Kangas, A., Hartikainen, M., & Miettinen, K. (2015). Simultaneous optimization of harvest schedule and data quality. Canadian Journal of Forest Research, 45(8), 1034-1044. https://doi.org/10.1139/cjfr-2014-0443
Published in
Canadian Journal of Forest Research
Authors
Kangas, Annika |
Hartikainen, Markus |
Miettinen, Kaisa
Date
2015
Discipline
TietotekniikkaMathematical Information Technology
Copyright
© 2015 NRC Research Press. This is a final draft version of an article whose final and definitive form has been published by National Research Council Canada. Published in this repository with the kind permission of the publisher.

 
In many recent studies, the value of forest inventory information in harvest scheduling has been examined. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. The imperfect information is obtained with a specific scenario tree formulation. Our decision problem includes three types of decisions: harvest decisions, measurement decisions, and decisions about measurement quality. In addition, the timing of the harvests and measurements must be decided. These decisions are evaluated based on two objectives: discounted aggregate income for the planning periods and the end value of the forest at the end of the planning horizon. Solving the bi-objective optimization problem formed using the ε-constraint method showed that imperfect information was mostly sufficient for the harvest timing decisions during the planning horizon but perfect information was required to meet the end-value constraint. The relative importance of the two objectives affects the measurements indirectly by increasing or decreasing the number of certain decisions (i.e., situations in which the optimal decision is identical in all scenarios). ...
Publisher
NRC Research Press; National Research Council Canada (NRC)
ISSN Search the Publication Forum
0045-5067
Keywords
information economics data quality forest inventory stochastic optpmization constraints bi-objective optimization Pareto optimality multi-objective optimization päätöksenteko
DOI
https://doi.org/10.1139/cjfr-2014-0443
URI

http://urn.fi/URN:NBN:fi:jyu-201508052618

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/24808639

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  • Informaatioteknologian tiedekunta [1967]

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