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Optimizing invasive species management: A mixed-integer linear programming approach
- Source :
- European Journal of Operational Research. 259:308-321
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Controlling 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.
- Subjects :
- 021110 strategic, defence & security studies
Mathematical optimization
021103 operations research
Information Systems and Management
General Computer Science
Basis (linear algebra)
Computer science
0211 other engineering and technologies
Time horizon
02 engineering and technology
Management Science and Operations Research
Grid
Industrial and Manufacturing Engineering
Nonlinear programming
Nonlinear system
Linearization
Modeling and Simulation
Relaxation (approximation)
Integer programming
Subjects
Details
- ISSN :
- 03772217
- Volume :
- 259
- Database :
- OpenAIRE
- Journal :
- European Journal of Operational Research
- Accession number :
- edsair.doi...........14d976bc6a7a79834afa3a740d6d52da