Multiple solution harvest scheduling

Application of the Metropolis algorithm for forest harvest scheduling is extended by automating the relative weighting of objective function components. Previous applications of the Metropolis algorithm require the user to specify these weights, which demands substantial trial and error in practice. This modification allows for general incorporation of objective function components that are either periodic or spatial in nature. A generic set of objective function components is developed to facilitate harvest scheduling for a wide range of problems. The resulting algorithm generates multiple feasible solutions rather than a single optimal solution.

[1]  Atsushi Yoshimoto,et al.  Comparative analysis of algorithms to generate adjacency constraints , 1994 .

[2]  J. G. Jones,et al.  Formulating adjacency constraints in linear optimization models for scheduling projects in tactical planning , 1991 .

[3]  Alan T. Murray.  Ecosystem management or infeasible guidelines? Implications of adjacency restrictions for wildlife habitat and timber production , 1998 .

[4]  Charles ReVelle,et al.  Temporal and spatial harvesting of irregular systems of parcels , 1996 .

[5]  Martin G. Raphael,et al.  Some mathematical programming approaches for optimizing timber age-class distributions to meet multispecies wildlife population objectives , 1993 .

[6]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ibrahim H. Osman,et al.  Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem , 1993, Ann. Oper. Res..

[8]  F. Helles,et al.  Spatial optimization by simulated annealing and linear programming , 1997 .

[9]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[10]  J. Douglas Brodie,et al.  Adjacency constraints in harvest scheduling: an aggregation heuristic , 1990 .

[11]  Paul C. Van Deusen.  Habitat and harvest scheduling using Bayesian statistical concepts , 1996 .

[12]  Charles B. Moss,et al.  Reply: Ecosystem management or infeasible guidelines? Implications of adjacency restrictions for wildlife habitat and timber production , 1997 .

[13]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[14]  Richard L. Church,et al.  Heuristic solution approaches to operational forest planning problems , 1995 .

[15]  F. Glover,et al.  In Modern Heuristic Techniques for Combinatorial Problems , 1993 .

[16]  F. Barahona,et al.  A column generation algorithm for solving general forest planning problems with adjacency constraints , 1994 .

[17]  Brian Kent,et al.  An integer programming approach for spatially and temporally optimizing wildlife populations , 1994 .

[18]  David G. Mayer,et al.  Tabu search not an optimal choice for models of agricultural systems , 1998 .

[19]  John Sessions,et al.  Ensuring the compatibility of aquatic habitat and commodity production goals in eastern oregon with a tabu search procedure , 1998 .

[20]  C. Lockwood,et al.  Harvest scheduling with spatial constraints: a simulated annealing approach , 1993 .

[21]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[22]  Jeremy York,et al.  Use of the Gibbs Sampler in Expert Systems , 1992, Artif. Intell..

[23]  L. Joyce,et al.  A mixed integer linear programming approach for spatially optimizing wildlife and timber in managed forest ecosystems , 1993 .

[24]  Karl R. Walters,et al.  Spatial and temporal allocation of stratum-based harvest schedules , 1993 .

[25]  John Sessions,et al.  Using Tabu search to schedule timber harvests subject to spatial wildlife goals for big game , 1997 .