|Budget Amount *help
¥1,900,000 (Direct Cost : ¥1,900,000)
Fiscal Year 1992 : ¥400,000 (Direct Cost : ¥400,000)
Fiscal Year 1991 : ¥300,000 (Direct Cost : ¥300,000)
Fiscal Year 1990 : ¥1,200,000 (Direct Cost : ¥1,200,000)
Forest planning problems are typically complex, wlcked, and multiobjective, invoiving tradeoffs among efficiency, equity, environmental protection, administrative requirements, and risk. Resolution of these types of problems is far from trivial since substantial quantities of valuable resources are involved and large numbers of people affected. Multiple objective programming methods encompass a general class of a mathematical programming techniques for solving problems in which several objectives are considered simultaneously. The use of mathematical programming techniques in multiobjective forest planning has been limited mainly to linear programming, and especially goal programming. it has been applied in land use planning, forest management, and multiple use forestry. While GP has been reported to a suitable planning tool for forest management planning system, some criticisms have been raised concerning its use. One of the central criticisms is that GP is the simplistic, determinist
ic, and static nature of these models. Parametric programming has been proposed to analyze the sensitivity of solutions to some changes in the parameters, but it also falls short in capturing the dynamics, diversity, and complexity of the forest ecosystem (Mendoza and Sprouse 1989).
This report describes six modeling approaches for multlobjective forest planning problems. The first approach describes the use of fuzzy set theory in linear programming and goal programming problems. The second approach is called fuzzy modeling to generate alternatives(FMGA) method which is designed to provide the analyst or decision maker with a set of alternatives that are good with respect to modeled objectives and wldely different from each other. the third technique illustrates the method of evaluation and prioritization of alternatives using fuzzy approach. The fourth approach describes the possibility of applying a regression analysis to human preference. The fifth method is on the fuzzy identification of human preferece structure developed by Takagi and Sugeno(1985). And finally,the use of neural network technology and fuzzy set theory in a decision support system for forest land use planning is illustrated.