No abstract

Over the last two decades, forest land management practices have changed in response to ecological issues and the need to improve efficiency to remain competitive in emerging global markets. Decision processes have become more open and complex as information and communication technologies change. The OR/MS community is meeting these challenges by developing new modeling strategies, algorithms, and solution procedures that address spatial requirements, multiresource planning, hierarchical systems, multiple objectives, and uncertainty as they pertain to both industrial timberlands and public forests.

No abstract

Adjacency constraints can be represented by Moore or Neumann neighbourhood adjacency, depending upon how candidate neighbours are assigned at corners adjacent to the target cell. Considering Moore and Neumann neighbourhood adjacency, we investigate the effect of strip cutting under a shelterwood management scheme with adjacency requirements among strips. We compare the effect of creating a strip window within a management unit with the same spatially constrained problem without a strip window. The management scheme comparison is considered as a spatially constrained harvest scheduling problem, which is solved with CPLEX software using an exact solution method. Our experimental analysis shows that the inclusion of additional spatial consideration by strip window creation in the management scheme results in a reduction of the total harvest volume by almost 13% under Moore neighbourhood adjacency, while it has a small effect under Neumann neighbourhood adjacency. Keywords: integer programming; Moore and Neumann neighbourhood adjacency; Shelterwood management strip cutting Consideration of adjacency constraints has been a key issue in harvest scheduling over the last several decades because of environmental, ecological, and aesthetic requirements. ese constraints are often expressed by Moore neighbourhood adjacen-cy in ecological fi elds, where all neighbours sharing adjacent lines and corners with the target cell are considered adjacent. In forest management, on the other hand, Neumann neighbourhood adjacency is often used in harvest scheduling, which only designates those sharing adjacent lines as neighbours. Spatially constrained harvest scheduling problems have been intensively analyzed to resolve harvest scheduling with these adjacency requirements. At an early stage of spatially explicit management problems, harvest constraints are necessary to prevent excessively large harvest openings.

Linear programming is a widely used tool for timber harvest scheduling in North America. However, some potential problems related to infeasible harvest schedules, overly optimistic objective function values, and the need to strictly satisfy all constraints included in deterministic model formulations have been raised. This paper describes a fuzzy approach for explicitly recognizing the imprecise nature of the harvest flow constraints usually included in harvest scheduling models. The objective function and selected constraints are viewed as soft, and satisfactory solutions are derived and discussed for several scenarios. An illustrative sample problem is presented to demonstrate the methodology, and a comparison with solutions derived from a traditional linear programming model is presented.