Operations Research Challenges in Forestry: 33 Open Problems
Purpose of the Article
Forestry and operations research (OR) have developed a symbiotic relationship over the years. Scholars working in this sphere have sought to investigate the interdependent relationship between them with forestry creating challenges for OR, and OR developing models and method-based solutions for forestry. With the help of the current research, these educators seek to cover areas that pose challenges and raise open questions. They include operational, strategic, and tactical planning, and using OR to address environmental concerns, and fire management. The authors of the article, therefore, consider methodological approaches to establish areas that are crucial for forestry, including hierarchical, multiple objectives, and uncertainty planning.
The study proposes that trees serve a wide range of purposes for humans, including revenue from exportation, production of paper and wrappings, aesthetic beauty, reduction of soil erosion, and being homes for wild animals. During the early1960‘s, the USA forestry community developed the first linear programming model that could support long-range harvesting and regeneration decision-making. The diversity of decision problems and planning tasks has since then increased significantly.
Harvesting operations involve challenges connected with direct felling, log bucking, equipment location, harvesting and transportation decisions, environmental impacts on harvesting practices, synchronization of sorting, harvesting, and transportation, and the use of pricing as a coordination mechanism for forest operations. To support planners, the use of Decision Support Systems (DSS) and road databases has increased. The column generation is also used to reduce savings lost in the return flow. For practical collaboration in transportation, ASICAM exploits a simulation-based heuristic to provide a one-day schedule.
In tactical planning, the scope of the model generally integrates harvesting with transportation to solve the issue of stakeholders with individual preferences. R?nnqvist et al. (2015) characterized the Unit Restriction Model and the Area Restriction Model as usable in limiting spatial and environmental conditions during harvesting. In strategic forest management, LP models were developed to allocate lands to mutually exclusive uses of timber production and wildlife habitat. The maximal species-covering problem (MSCP) was one of those formulated by the LP’s to select locations for protection that would maximize the number of species covered.
The authors further initiated research on OR applications in forest fire management. One of the earliest fire management applications used gaming simulation techniques to make fire managers provide subjective assessments of fuel break effectiveness (Silva, Weintraub, Romero, & De la Maza, 2010). The educators also developed one of the first single large fire suppression optimization models and more recently, the USDA Forest Service in 2014. The system now uses the comprehensive Wildland Fire Decision Support Systems (WFDSS) that is designed to support the management of large fires.
Most models that deal with timber management consider the future deterministic methods and take parameters as expected values. Deterministic methods, however, are not developed for uncertainty. Stochastic programming and robust optimization are methods used to handle uncertainty. For larger mixed-integer forest planning problems, another LP model was proposed for the aggregate strategic level. The programming introduced a two-level model linking strategic and tactical levels, including mixed-integer variables and disaggregation-aggregation procedures. With the forests’ contribution to problems of the OR community, rigorous multi-criteria approaches are being considered in LP models to influence the decision maker’s preferences.
Strategic management is necessary to cope with and manage uncertainty in decision-making. The processes of overseeing, designing, and controlling of the production are automated easily with the use of Linear Programming models, an insight into which is well provided by the article. Better and efficient models that can simplify complexities and maximize data solve many discrete problems within management. This article provides a guide to identification, prioritization, and exploitation of opportunities. It provides improved coordination and control of activities while giving an objective view on management problems. Despite the fact that it does not go into details about forest management complexities and the problems within, it discusses to some degree the discipline and formality of the management of a business. Strategic management of forests will help scholars define, analyze, and understand problems tied to forestry.