Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Integer programming for optimizing habitat network permeability

Integer programming for optimizing habitat network permeability Purpose – Negative effects of habitat isolation that arise from landscape fragmentation can be mitigated, by connecting natural areas through a network of habitat corridors. To increase the permeability of a given network, i.e. to decrease the resistance to animal movements through this network, often many developments can be made. The available financial resources being limited, the most effective developments must be chosen. This optimization problem, suggested in Finke and Sonnenschein, can be treated by heuristics and simulation approaches, but the method is heavy and the obtained solutions are sub‐optimal. The aim of the paper is to show that the problem can be efficiently solved to optimality by mathematical programming. Design/methodology/approach – The moves of the individual in the network are modeled by an absorbing Markov chain and the development problem is formulated as a mixed‐integer quadratic program, then this program is linearized, and the best developments to make are determined by mixed‐integer linear programming. Findings – First, the approach allows the development problem to be solved to optimality contrary to other methods. Second, the definition of the mathematical program is relatively simple, and its implementation is immediate by using standard, commercially available, software. Third, as it is well known with mixed‐integer linear programming formulation it is possible to add new constraints easily if they are linear (or can be linearized). Research limitations/implications – With a view to propose a simple and efficient tool to solve a difficult combinatorial optimization problem arising in the improvement of permeability across habitat networks, the approach has been tested on simulated habitat networks. The research does not include the study of some precise species movements in a real network. Practical implications – The results provide a simple and efficient decision‐aid tool to try to improve the permeability of habitat networks. Originality/value – The joint use of mathematical programming techniques and Markov chain theory is used to try to lessen the negative effects of landscape fragmentation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management of Environmental Quality An International Journal Emerald Publishing

Integer programming for optimizing habitat network permeability

Loading next page...
 
/lp/emerald-publishing/integer-programming-for-optimizing-habitat-network-permeability-JpX6RS0Kwq

References (15)

Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
1477-7835
DOI
10.1108/14777831011067890
Publisher site
See Article on Publisher Site

Abstract

Purpose – Negative effects of habitat isolation that arise from landscape fragmentation can be mitigated, by connecting natural areas through a network of habitat corridors. To increase the permeability of a given network, i.e. to decrease the resistance to animal movements through this network, often many developments can be made. The available financial resources being limited, the most effective developments must be chosen. This optimization problem, suggested in Finke and Sonnenschein, can be treated by heuristics and simulation approaches, but the method is heavy and the obtained solutions are sub‐optimal. The aim of the paper is to show that the problem can be efficiently solved to optimality by mathematical programming. Design/methodology/approach – The moves of the individual in the network are modeled by an absorbing Markov chain and the development problem is formulated as a mixed‐integer quadratic program, then this program is linearized, and the best developments to make are determined by mixed‐integer linear programming. Findings – First, the approach allows the development problem to be solved to optimality contrary to other methods. Second, the definition of the mathematical program is relatively simple, and its implementation is immediate by using standard, commercially available, software. Third, as it is well known with mixed‐integer linear programming formulation it is possible to add new constraints easily if they are linear (or can be linearized). Research limitations/implications – With a view to propose a simple and efficient tool to solve a difficult combinatorial optimization problem arising in the improvement of permeability across habitat networks, the approach has been tested on simulated habitat networks. The research does not include the study of some precise species movements in a real network. Practical implications – The results provide a simple and efficient decision‐aid tool to try to improve the permeability of habitat networks. Originality/value – The joint use of mathematical programming techniques and Markov chain theory is used to try to lessen the negative effects of landscape fragmentation.

Journal

Management of Environmental Quality An International JournalEmerald Publishing

Published: Aug 10, 2010

Keywords: Markov processes; Simulation; Mathematical programming; Linear programming

There are no references for this article.