Approaches to integrated strategic/tactical forest planning

Traditionally forest planning is divided into a hierarchy of planning phases. Strategic planning is conducted to make decisions about sustainable harvest levels while taking into account legislation and policy issues. Within the frame of the strategic plan, the purpose of tactical planning is to schedule harvest operations to specific areas in the immediate few years and on a finer time scale than in the strategic plan. The operative phase focuses on scheduling harvest crews on a monthly or weekly basis, truck scheduling and choosing bucking instructions. Decisions at each level are to a varying degree supported by computerized tools. A problem that may arise when planning is divided into levels and that is noted in the literature focusing on decision support tools is that solutions at one level may be inconsistent with the results of another level. When moving from the strategic plan to the tactical plan, three sources of inconsistencies are often present; spatial discrepancies, temporal discrepancies and discrepancies due to different levels of constraint. The models used in the papers presented in this thesis approaches two of these discrepancies. To address the spatial discrepancies, the same spatial resolution has been used at both levels, i.e., stands. Temporal discrepancies are addressed by modelling the tactical and strategic issues simultaneously. Integrated approaches can yield large models. One way of circumventing this is to aggregate time and/or space. The first paper addresses the consequences of temporal aggregation in the strategic part of a mixed integer programming integrated strategic/tactical model. For reference, linear programming based strategic models are also used. The results of the first paper provide information on what temporal resolutions could be used and indicate that outputs from strategic and integrated plans are not particularly affected by the number of equal length strategic periods when more than five periods, i.e. about 20 year period length, are used. The approach used in the first paper could produce models that are very large, and the second paper provides a two-stage procedure that can reduce the number of variables and preserve the allocation of stands to the first 10 years provided by a linear programming based strategic plan, while concentrating tactical harvest activities using a penalty concept in a mixed integer programming formulation. Results show that it is possible to use the approach to concentrate harvest activities at the tactical level in a full scale forest management scenario. In the case study, the effects of concentration on strategic outputs were small, and the number of harvest tracts declined towards a minimum level. Furthermore, the discrepancies between the two planning levels were small

Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Models and heuristics for forest management with environmental restrictions

Tese de doutoramento, Estatística e Investigação Operacional (Otimização), Universidade de Lisboa, Faculdade de Ciências, 2018The main focus of this thesis was to develop mathematical models and methods in integer programming for solving harvest scheduling problems with environmental restrictions. Constraints on maximum clearcut area, minimum total habitat area, minimum total core area and inter-habitat connectivity were addressed for this purpose. The research was structured in a collection of three papers, each one describing the study of a different forest harvest scheduling problem with respect to the environmental constraints. Problems of papers 1 and 2 aim at maximizing the net present value. A bi objective problem is considered in paper 3. The objectives are the maximization of the net present value and the maximization of the inter-habitat connectivity. The tree search methods branch-and-bound and multiobjective Monte Carlo tree search were designed specifically to solve the problems. The methods could be used as heuristics, as a time limit of 2 hours was imposed. All harvest scheduling problems were based on the socalled cluster formulation. The proposed models and methods were tested with sixteen real and hypothetical instances ranging from small to large. The results obtained for branch-and-bound and Monte Carlo tree search show that these methods were able to find solutions for all instances. The results suggest that it is possible to address the environmental restrictions with small reductions of the net present value. With respect to the forestry fragmentation caused by harvestings, the results suggest that, although clearcut size constraints tend to disperse clearcuts across the forest, compromising the development of large habitats, close to each other, the proposed models, with the other environmental constraints, attempt to mitigate this effect

Location Analytics for Transitioning to Fire Resilient Landscapes

Wildfire risk is significant for forest and vegetative landscapes, particularly in regions where climate change is resulting in prolonged droughts and extended fire seasons that are a fire risk to people and property. An important component of mitigation is restoration programs that transition landscapes to be more fire resilient. A collaborative partnership between the US Forest Service and university researchers is reported that takes advantage of location intelligence. This paper reviews this general planning problem and details location analytic based approaches for informing mitigation efforts. Application of results highlight the ability to optimize goals and objectives while maintaining project area needs and treatment thresholds

Dificultades y posibilidades del algoritmo de optimización de enjambre de partículas para la planificación contemporánea espacial del bosque

We describe here an example of applying particle swarm optimization (PSO) — a population-based heuristic technique — to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.Se describe aquí un ejemplo de la aplicación de la optimización de enjambre de partículas (PSO) — una técnica heurística basada en la población — para maximizar el valor presente neto de un moderno plan de gestión del bosque del sur de los Estados Unidos, que incluye limitaciones espaciales y restricciones del flujo de madera. Cuando se inicia con condiciones iniciales factibles definidas aleatoriamente, y en sintonía con algunas modificaciones adecuadas, el algoritmo PSO converge gradualmente sobre su solución final y suministra los valores de la función objetivo. Sin embargo, sólo el 86% del valor global óptimo podría lograrse usando la heurística PSO modificada. Los resultados de este estudio sugieren que bajo condiciones de arranque aleatorio de la población inicial, la heurística PSO puede tener una aplicación más bien limitada a los problemas de planificación forestal con objetivos económicos, restricciones de flujo de madera y consideraciones espaciales. Las dificultadas incluyen la necesidad de modificar la estructura de PSO para abordar tanto las limitaciones espaciales como para reparar las partículas, y la necesidad de modificar algunos de los supuestos básicos de PSO para abordar mejor los problemas contemporáneos de la planificación forestal. Nuestros resultados, y por lo tanto nuestra aportación, son contrarios a trabajos anteriores que ilustran el impresionante potencial de PSO cuando se aplica a problemas de planificación forestal a nivel de rodal o cuando se aplica a una población de calidad inicial alta

Multi-objective models for the forest harvest scheduling problem in a continuous-time framework

In this study we present several multi-objective models for forest harvest scheduling in forest with single-species, even-aged stands using a continuous formulation. We seek to maximize economic profitability and even-flow of timber harvest volume, both for the first rotation and for the regulated forest. For that, we design new metrics that allow working with continuous decision variables, namely, the harvest time of each stand. Unlike traditional combinatorial formulations, this avoids dividing the planning horizon into periods and simulating alternative management prescriptions before the optimization process. We propose to combine a scalarization technique (weighting method) with a gradient-type algorithm (L-BFGS-B) to obtain the Pareto frontier of the problem, which graphically shows the relationships (trade-offs) between objectives, and helps the decision makers to choose a suitable weighting for each objective. We compare this approach with the widely used in forestry multi-objective evolutionary algorithm NSGA-II. We analyze the model in a Eucalyptus globulus Labill. forest of Galicia (NW Spain). The continuous formulation proves robust in forests with different structures and provides better results than the traditional combinatorial approach. For problem solving, our proposal shows a clear advantage over the evolutionary algorithm in terms of computational time (efficiency), being of the order of 65 times faster for both continuous and discrete formulationsS

Mathematical programming with uncertainty and multiple objectives for sustainable development and wildfire management

Mathematical Programming is a well-placed field of Operational Research to tackle problems as diverse as those that arise in Logistics and Disaster Management. The fundamental objective of Mathematical Programming is the selection of an optimal alternative that meets a series of restrictions. The criterion by which the alternatives are evaluated is traditionally only one (for example, minimizing cost), however it is also common for several objectives to want to be considered simultaneously, thus giving rise to the Multi-criteria Decision. If the conditions to be met by an alternative or the evaluation of said alternative depend on random (or unknown) factors, we are in an optimization context under uncertainty. In the first chapters of this thesis the fields of multicriteria decision and optimization with uncertainty are studied, in two applications in the context of sustainable development and disaster management. Optimization with uncertainty is introduced through an application to rural electrification. In rural areas, access to electricity through solar systems installed in consumers' homes is common. These systems have to be repaired when they fail, so the decision of how to size a maintenance network is affected by great uncertainty. A mathematical programming model is developed by treating uncertainty in an unexplained way, the objective of which is to obtain a maintenance network at minimum cost. This model is later used as a tool to obtain simple rules that can predict the cost of maintenance using little information. The model is validated using information from a real program implemented in Morocco. When studying Multicriteria Optimization it is considered a problem in forest fire management. To mitigate the effects of forest fires, it is common to modify forests, with what is known as fuel treatment. Through this practice, consisting of the controlled felling or burning of trees in selected areas, it is achieved that when fires inevitably occur, they are easier to control. Unfortunately, modifying the flora can affect the existing fauna, so it is sensible to look for solutions that improve the situation in the face of a fire but without great detriment to the existing species. In other words, there are several criteria to take into account when optimizing. A mathematical programming model is developed, which suggests which zones to burn and when, taking into account these competing criteria. This model is applied to a series of simulated realistic cases. The following is a theoretical study of the field of Multiobjective Stochastic Programming (MSP), in which problems that simultaneously have various criteria and uncertainty are considered. In this chapter, a new solution concept is developed for MSP problems with risk aversion, its properties are studied and a linear programming model capable of obtaining said solution is formulated. A computational study of the model is also carried out, applying it to a variation of the well-known backpack problem. Finally, the problem of controlled burning is studied again, this time considering the existing uncertainty as it is not possible to know with certainty how many controlled burns can be carried out in a year, due to the limited window of time in which these can be carried out. The problem is solved using the multi-criteria and stochastic methodology with risk aversion developed in the previous chapter. Finally, the resulting model is applied to a real case located in southern Spain

Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications