Multi-objective Forest Planning


Book Description

Most of the scientific methods devised for forest planning support timber production ignoring the existence of forest functions other than wood production. Fortunately, the realisation that the forest planning methods available today do not correspond to the needs of today's forestry has activated forest researchers to develop and adopt new methodologies and approaches, which are specifically aimed at multi-objective situations. This book is about the quantitative approach to multi-objective forest planning. The emphasis is on topics that are rather new and not yet systematically applied in forest planning practice. The topics and methodologies discussed in this book include: measurement of preferences, multiple criteria decision analysis, use of GIS to support multi-objective forest management, heuristic optimization, spatial optimisation, and the measurement of non-wood forest outputs. By reading the book, a planning specialist, student or a researcher will get an insight into some of the current developments in forest planning research.







Multi-criteria decision models for forestry and natural resources management


Book Description

Foresters and natural resource managers must balance conflicting objectives when developing land-management plans. Conflicts may encompass economic, environmental, social, cultural, technical, and aesthetic objectives. Selecting the best combination of management uses from numerous objectives is difficult and challenging. Multi-Criteria Decision Models (MCDM) provide a systematic means for comparing tradeoffs and selecting alternatives that best satisfy the decisionmakergass objectives. In recent years, the use of MCDM in forestry and natural resources management has generated a substantial body of literature. This annotated bibliography includes 124 important references ranging from theoretical studies to real-world applications of MCDM.




Multi-Objective Programming and Goal Programming


Book Description

Most real-life problems involve making decisions to optimally achieve a number of criteria while satisfying some hard or soft constraints. In this book several methods for solving such problems are presented by the leading experts in the area. The book also contains a number of very interesting application papers which demonstrate theoretical modelling, analysing and solution of real-life problems.




Advances in Multiple Objective and Goal Programming


Book Description

Within the field of multiple criteria decision making, this volume covers the latest advances in multiple objective and goal programming as presented at the 2nd International Conference on Multi-Objective Programming and Goal Programming, Torremolinos, Spain, May 16 - 18, 1996. The book is an undispensable source of the latest research results, presented by the leading experts of the field.




Decision Support for Forest Management


Book Description

This updated and expanded second edition adds the most recent advances in participatory planning approaches and methods, giving special emphasis to decision support tools usable under uncertainty. The new edition places emphasis on the selection of criteria and creating alternatives in practical multi-criteria decision making problems.







Operations Research Applied to Forestry Management


Book Description

People want to use forests for their benefits as much as possible but environmental impacts of their actions should be minimized. This leads to difficult land management problems with multiple, conflicting objectives. Forest land management analysts have developed and utilized sophisticated planning methods to address complex issues involving multiple objectives. An intensive literature review of these techniques is presented. The most popular multiobjective technique among forester is Goal Programming. Multiobjective Genetic Algorithms are relatively new optimization techniques which have not yet been used in forestry. Two multiobjective forestry problems are solved using a Multiobjective Genetic Algorithm and the results are compared to Goal Programming solutions. It is shown that the Multiobjective Genetic Algorithm can find solutions with better tradeoffs between conflicting objectives.