1. Planning and Decision Support.- 1.1. What Is Planning?- 1.2. Phases of Decision Making.- 1.3. Classification of Planning Problems.- References.- 2. Forest Management Planning.- 2.1. Forest Management as a Planning Problem.- 2.2. Stand-Level Forest Planning.- 2.3 Development of Sustainable Forest Management Planning Approaches.- 2.3.1 Definitions of Sustainability.- 2.3.2 Fully Regulated Forestry.- 2.3.3 Development Of Optimisation Approaches.- 2.3.4 Development Of Multi-Criteria Approaches.- 2.3.5 Need For Participatory Planning.- References.- 3. Single-Criteria Problems.- 3.1. Decisions Under Risk and Uncertainty.- 3.2. Measuring Utility and Value.- 3.2.1 Estimating a Utility Function.- 3.2.2 Risk Attitude.- 3.2.3 Estimating a Value Function.- References.- 4. Multi-Criteria Decision Problems.- 4.1. Decision Model.- 4.1.1. Objectives and Decision Criteria.- 4.1.2 Selecting the Set of Criteria.- 4.1.3 Designing Alternatives.- 4.1.4 Indifference, Preference and Tradeoff.- 4.1.5 Dominance.- 4.2. Multi-Attribute Utility Functions.- 4.2.1 Function Forms.- 4.2.2 Basis For Estimating The Weights.- 4.2.3 SMART.- 4.2.4 TOPSIS.- 4.2.5 Cautionary Note of Weighting Methods.- 4.3. Analytic Hierarchy Process.- 4.3.1 Decision Problem.- 4.3.2 Phases of AHP.- 4.3.3 Uncertainty in AHP.- 4.4. ANP.- 4.5. Even Swaps.- 4.6. A'WOT.- References.- 5. Uncertainty in Multi-Criteria Decision Making.- 5.1. Nature of Uncertainty.- 5.2. Fuzzy Set Theory.- 5.2.1 Membership Functions and Fuzzy Numbers.- 5.2.2. Fuzzy Goals in Decision Making.- 5.2.3. Fuzzy Additive Weighting.- 5.3. Outranking Methods.- 5.3.1. Outline.- 5.3.2 PROMETHEE Method.- 5.3.3 ELECTRE Method.- 5.3.4. Other Outranking Methods.- 5.4. Probabilistic Uncertainty in Decision Analysis.- 5.4.1 Stochastic Multicriteria Acceptability Analysis (SMAA).- 5.4.2 SMAA-O.- 5.4.3 Pairwise Probabilities.- References.- 6. Linear Programming and its Extensions in Forest Planning.- 6.1. Linear Programming.- 6.1.1 Primal Problem.- 6.1.2 Dual Problem.- 6.2. Forest Planning with LP.- 6.2.1 Formulating a Problem in an Estate Level.- 6.2.2 Even Flow Constraints.- 6.2.3 Production Possibility Frontier.- 6.3. Goal Programming.- 6.2.1 Soft Constraints.- 6.2.2 Balancing Different Goals.- 6.4. General Forest Planning Formulation.- 6.5. Integer Programming.- 6.6. Hierarchical Forest Planning.- 6.7 Spatial Goals and Constraints in Linear Programming.- 6.7.1 Adjacency Constraints and Green-Up Constraints.- 6.7.2 Spatial Goals Suitable for Linear Programming.- References.- 7. Heuristic Optimisation.- 7.1 Principles Of Heuristic Optimisation.- 7.1.1 Definitions.- 7.1.2 Objective Function Forms.- 7.2 HERO.- 7.3. Metaheuristic Methods Using Local Improvements.- 7.3.1 Simulated Annealing And Threshold Accepting.- 7.3.2 Tabu Search.- 7.3.3 Defining the Parameters.- 7.3.4 Defining the Neighborhood.- 7.4. Population Based Methods.- 7.4.1 Genetic Algorithm.- 7.4.2 Other Population-Based Methods.- 7.5. Local/Global Methods.- 7.5.1 Cellular Automaton.- 7.5.2 Reduced Cost Approach.- 7.6. Combining Optimisation Techniques.- References.- 8. Uncertainty in Optimisation.- 8.1. Stochastic Programming.- 8.1.1 Effect of Uncertainty on Optimisation.- 8.1.2 Basics of Stochastic Programming.- 8.1.3 Modeling Forest Planning Problems with Stochastic Programming.- 8.1.4 Value of Information and Stochastic Solution.- 8.1.5 Two-Stage Stochastic Programming.- 8.2. Robust Programming.- 8.3. Chance-Constrained Programming.- 8.4. Robust Portfolio Modeling.- 8.4.1 Principles of the Method.- 8.4.2 Use of RPM in Forest Planning.- References.- 9. Participatory Planning and Group Decision Making.- 9.1. Decision Makers and Stakeholders.- 9.2. Designing the Appropriate Process.- 9.3 Facilitation and Different Facilitator's Roles.- 9.4. Success of the Participation Process.- References.- 10. Voting Methods.- 10.1. Social Choice Theory.- 10.1.1 Outline.- 10.1.2. Evaluation Criteria for Voting Systems.- 10.2. Positional.