Articles containing the keyword 'goal programming'

An alternative approach to formulating a forestry goal programming problem is presented. First, single objective optima levels are solved. The Analytical Hierarchy Process is applied in the estimation of a priori weights of deviations from the goal target levels. The ratios of the weights can be interpreted as relative importance of the goals, respectively. The sum of the weighted deviations from all single optima levels associated with the management goals is minimized. Instead of absolute deviations, relative ones are used. A case study problem of forest management planning with several objectives, measured in different units, is analysed.

The PDF includes an abstract in Finnish.

• Kangas, E-mail: jk@mm.unknown
• Pukkala, E-mail: tp@mm.unknown

The applicability of five mathematical programming methods, namely standard linear programming, parametric programming, goal programming, mixed integer programming and integer programming is discussed as a planning tool for the choice of wood procurement method.

Theoretically, the goal programming approach seems to be the best routine for mathematical handling of problems related to wood procurement. The parametric approach must include enough large post-optimality analysis routine. If the effect of the variables expressed with different measures is to be studied, interpretation of the economic information given by the approach becomes a problem. The other drawback is that the approach does not allow determination of the hierarchy of the goals objectively as they depend on the subjective preferences of the decision maker.

From the practical point of view, standard linear programming is the best method if the objective function can be formulated in economic terms, for instance. If there are several goals to be attained or satisfied the best method is goal programming.

According to the sub-studies, every method under consideration can be used as a solution routine for the minimization of wood procurement costs. In cost minimization the best methods are goal programming and standard linear programming. The best method for harvesting system evaluation purposes is parametric because it allows varied cost calculations within a certain cost range. The best method for harvesting equipment investment planning is mixed integer programming with binary decision variables.

The more complicated and restricted the problem environment is, the better the mathematical programming approach will be, also in harvesting related problems.

The PDF includes a summary in English.

• Mikkonen, E-mail: em@mm.unknown

This work aimed to tackle a timber harvest scheduling problem by simultaneously integrating into the analysis two forestry products derived from the same species: the timber and the pine nut. For this purpose, three management scenarios were proposed: two in which each of the productions is maximised separately, and a third mixed where, in each management unit, the product to which the silvicultural effort should be devoted is decided. After defining a set of objectives, and optimising the rotation length, a multi-criteria model based on goal programming was considered since no feasible solutions have been obtained when employing linear programming. The results in our case study show how the feasible solutions reached can be more attractive for the manager. Specifically, the area to be devoted to timber and cone/pine-nut production was computed in a scenario where the optimal silviculture (oriented towards timber or pine nuts) in each stand was selected, and it was concluded that the area allocated to pine nuts should be notably greater. This situation is the opposite of the current management.

• Pereira, Technical University of Madrid, ETS Ingenieros de Montes, Ciudad Universitaria s/n, 28040 Madrid, Spain E-mail: spereirasaez@gmail.com
• Prieto, Technical University of Madrid, ETS Ingenieros de Montes, Ciudad Universitaria s/n, 28040 Madrid, Spain E-mail: antonio.prieto@upm.es
• Calama, Dpto. Selvicultura y Gestión Forestal, INIA-CIFOR, Ctra. A Coruña km 7.5, 28040 Madrid, Spain E-mail: rcalama@inia.es
• Diaz-Balteiro, Technical University of Madrid, ETS Ingenieros de Montes, Ciudad Universitaria s/n, 28040 Madrid, Spain E-mail: luis.diaz.balteiro@upm.es

• Bettinger, School of Forestry and Natural Resources, 180 E. Green Street, University of Georgia, Athens, Georgia, USA 30602 E-mail: pbettinger@warnell.uga.edu
• Demirci, General Directorate of Forestry, Ministry of Forest and Water Affairs, Republic of Turkey E-mail: mehmetdemirci@yahoo.com
• Boston, Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, USA E-mail: Kevin.Boston@oregonstate.edu