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prioriactions - Multi-Action Conservation Planning

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015) <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. See the prioriactions website for more information, documentations and examples.

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conservationconservation-planoptimizationprioritizationthreatscpp

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multiscape - Multi-Objective Spatial Planning

Provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi-objective methods include weighted-sum aggregation, epsilon-constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the 'gurobi' R package, which is distributed with the Gurobi Optimizer installation <https://docs.gurobi.com/projects/optimizer/en/13.0/reference/r/setup.html>, and the 'rcbc' R package, available from GitHub at <https://github.com/dirkschumacher/rcbc>. For background on multi-objective optimisation methods, see Halffmann et al. (2022) <doi:10.1002/mcda.1780>; for the augmented epsilon-constraint method, see Mavrotas (2009) <doi:10.1016/j.amc.2009.03.037>.

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cpp

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