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dc.contributor.authorKibis, Eyyub Y.
dc.contributor.authorBuyuktahtakin, Esra
dc.identifier.citationKibis, Eyyub Y.; Buyuktahtakin, I. Esra. 2017. Optimizing invasive species management: A mixed-integer linear programming approach. European Journal of Operational Research, vol. 259:no. 1, 16 May 2017:pp 308–321en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractControlling invasive species is a highly complex problem. The intricacy of the problem stems from the nonlinearity that is inherent in biological systems, consequently impeding researchers to obtain timely and cost-efficient treatment strategies over a planning horizon. To cope with the complexity of the invasive species problem, we develop a mixed-integer programming (MIP) model that handles the problem as a full dynamic optimization model and solves it to optimality for the first time. We demonstrate the applicability of the model on a case study of sericea (Lespedeza cuneata) infestation by optimizing a spatially explicit model on a heterogeneous 10-by-10 grid landscape for a seven-year time period. We evaluate the solution quality of five different linearization methods that are used to obtain the MIP model, We also compare the model with its mixed-integer nonlinear programming (MINLP) equivalent and nonlinear programming (NLP) relaxation in terms of solution quality. The computational superiority and realism of the proposed MIP model demonstrate that our model has the potential to constitute the basis for future decision-support tools in invasive species management.en_US
dc.description.sponsorshipNational Science Foundation CAREER Award under Grant # CBET-1554018.en_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofseriesEuropean Journal of Operational Research;v.259:no.1
dc.subject(S) Complexity theoryen_US
dc.subjectSpatially explicit large-scale optimizationen_US
dc.subjectMixed-integer programming (MIP)en_US
dc.subjectLinearization; Big-Men_US
dc.titleOptimizing invasive species management: A mixed-integer linear programming approachen_US
dc.rights.holder© 2016 Elsevier B.V. All rights reserved.en_US

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  • ISME Research Publications
    Research works published by faculty and students of the Department of Industrial, Systems, and Manufacturing Engineering

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