Solving the Long-Term Forest Treatment Scheduling Problem

The Long-Term Forest Treatment Scheduling Problem (LTFTSP) is the task of allocating treatments in a forest such that both sustainability and economic outcome is maximized. Solving such problems is demanded in more and more countries and the task is increasingly more complex because one must adhere to local legislation, environmental issues, and public interests. To be able to handle most aspects of the LTFTSP with adjacency constraints (which is the problem we solve), a rich, spatial model which is parameterized, is required. We present a model defined on discrete land units and time points, where the treatments to perform are parameterized. Many of the most commonly used criteria in the form of constraints and objective components in long-term forestry scheduling are included. Such criteria may be defined for the complete forest region in question, or for specific sub-regions.

[1]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[2]  Felipe Caro,et al.  A 2-Opt Tabu Search Procedure for the Multiperiod Forest Harvesting Problem with Adjacency, Greenup, Old Growth, and Even Flow Constraints , 2003 .

[3]  Andres Weintraub,et al.  Aggregation Procedures in Forest Management Planning Using Cluster Analysis , 1997, Forest Science.

[4]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

[5]  Ljusk Ola Eriksson,et al.  Allowing for spatial consideration in long-term forest planning by linking linear programming with simulated annealing , 2002 .

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

[7]  John Wilson Model Solving in Mathematical Programming , 1993 .

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

[9]  John Sessions,et al.  Intensifying a heuristic forest harvest scheduling search procedure with 2-opt decision choices , 1999 .

[10]  Kevin A. Crowe,et al.  Solving the area-restricted harvest-scheduling model using the branch and bound algorithm , 2003 .

[11]  Francisco Barahona,et al.  Harvest Scheduling Subject to Maximum Area Restrictions: Exploring Exact Approaches , 2005, Oper. Res..

[12]  Marc E. McDill,et al.  Harvest scheduling with area-based adjacency constraints , 2002 .

[13]  José G. Borges,et al.  Using dynamic programming and overlapping subproblems to address adjacency in large harvest scheduling problems , 1998 .

[14]  Kevin Crowe,et al.  An indirect search algorithm for harvest-scheduling under adjacency constraints , 2003 .

[15]  Alan T. Murray Spatial restrictions in harvest scheduling , 1999 .

[16]  H. P. Williams,et al.  Model Building in Mathematical Programming , 1979 .

[17]  Kevin A. Crowe,et al.  An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks , 2005 .

[18]  Jianping Zhu,et al.  Landscape-level optimization using tabu search and stand density-related forest management prescriptions , 2007, Eur. J. Oper. Res..

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

[20]  Marc E. McDill,et al.  Using the branch and bound algorithm to solve forest planning problems with adjacency constraints , 2001 .

[21]  Magnus Hindsberger,et al.  Tabu Search - a guided tour , 2000 .

[22]  Geir Hasle,et al.  Interactive planning for sustainable forest management , 2000, Ann. Oper. Res..

[23]  Timo Pukkala,et al.  A comparison of one- and two-compartment neighbourhoods in heuristic search with spatial forest management goals , 2004 .

[24]  Fred Glover,et al.  Tabu Search: A Tutorial , 1990 .