Optimizing invasive species management: A mixed-integer linear programming approach

Eyyüb Y. Kıbış, Esra Büyüktahtakın

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish
Pages (from-to)308-321
Number of pages14
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - 16 May 2017


  • (S) Complexity theory
  • Big-M
  • Linearization
  • Mixed-integer programming (MIP)
  • Spatially explicit large-scale optimization


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