Annika Kangas & Jyrki Kangas 
Decision Support for Forest Management [PDF ebook] 

Preface. Acknowledgements. 1. Introduction. 1.1 Planning and decision support. 1.2 Forest management planning. 1.3 History of forest planning.- Discrete problems. 2. Unidimensional problems. 2.1 Decisions under risk and uncertainty. 2.2 Measuring utility and value. 2.2.1 Estimating a utility function. 2.2.2 Estimating a value function.- 3. Multi-criteria decision problems. 3.1 Theoretical aspects. 3.2 Multi-attribute utility functions. 3.2.1 Function forms. 3.2.2 Basis for estimating the weights. 3.2.3 SMART. 3.3 Even Swaps. 3.4 Analytic hierarchy process. 3.4.1 Decision problem. 3.4.2 Phases of AHP. 3.4.3 Uncertainty in AHP. 3.4.4 ANP. 3.5 A’WOT.- 4. Uncertainty in multi-criteria decision making. 4.1 Nature of uncertainty. 4.2 Fuzzy set theory. 4.2.1 Membership functions and fuzzy numbers. 4.2.2 Fuzzy goals in decision making. 4.2.3 Fuzzy additive weighting. 4.3 Possibility theory in decision making. 4.4 Evidence theory. 4.5 Outranking methods. 4.5.1 Outline. 4.5.2 PROMETHEE method. 4.5.3 ELECTRE method. 4.5.4 Other outranking methods. 4.6 Probabilistic uncertainty in decision analysis. 4.6.1 Stochastic multicriteria acceptability analysis (SMAA). 4.6.2 SMAA-O. 4.6.3 Pairwise probabilities.- Continuous problems. 5. Optimization. 5.1 Linear programming. 5.1.1 Primal problem. 5.1.2 Dual problem. 5.1.3 Forest planning problem with several stands. 5.1.4 JLP software. 5.2 Goal programming. 5.3 Integer programming. 5.4 Uncertainty in optimization. 5.5 Robust portfolio modelling. 5.5.1 Principles of the method. 5.5.2 Use of RPM in forest planning.- 6. Heuristic optimization. 6.1 Principles. 6.2 Objective function forms. 6.3 HERO. 6.4 Simulated annealing and threshold accepting. 6.5 Tabu search. 6.6 Genetic algorithms. 6.7 Improving the heuristic search. 6.7.1 Parameters of heuristic optimisation techniques. 6.7.2 Expanding the neighbourhood. 6.7.3 Combining optimisation techniques.- Cases with several decision makers. 7. Group decision making and participatory planning. 7.1 Decision makers and stakeholders. 7.2 Public participation process. 7.2.1 Types of participation process. 7.2.2 Success of the participation process. 7.2.3 Defining the appropriate process. 7.3 Tools for eliciting the public preferences. 7.3.1 Surveys. 7.3.2 Public hearings. 7.4 Problem structuring methods. 7.4.1 Background. 7.4.2 Strategic options development and analysis (SODA). 7.4.3 Soft systems methodology (SSM). 7.5 Decision support for group decision making.- 8. Voting methods. 8.1 Social choice theory. 8.1.1 Outline. 8.1.2 Evaluation criteria for voting systems. 8.2 Positional voting schemes. 8.2.1 Plurality voting. 8.2.2 Approval voting. 8.2.3 Borda count. 8.3 Pairwise voting. 8.4 Fuzzy voting. 8.5 Probability voting. 8.6 Multicriteria approval. 8.6.1 Original method. 8.6.2 Fuzzy MA. 8.6.3 Multicriteria approval voting.- Application viewpoints. 9. Behavioural aspects. 9.1 Criticism towards decision theory. 9.1.1 Outline. 9.1.2 Satisficing or maximizing?. 9.1.3 Rules or rational behaviour?. 9.2 Image theory. 9.3 Prospect theory.- 10. Practical examples of using MCDS methods. 10.1 Landscape ecological planning. 10.2 Participatory planning. 10.3 Spatial objectives and heuristic optimisation in practical forest planning.- 11. Final remarks.-