Search results for "monitavoiteoptimointi"
showing 10 items of 81 documents
Sectoral policies as drivers of forest management and ecosystems services: A case study in Bavaria, Germany
2023
European countries have national sectoral polices to regulate and promote the provision of a wide range of forest ecosystems services (FES). However, potential incoherencies among these policies can negatively affect the efficient provision of FES. In this work, we evaluated the coherence among three national policies from Germany and their ability to effectively provide FES in the future: the Forest Strategy 2020 (FS), the National Strategy on Biological Diversity (BDS), and the German National Policy Strategy on Bioeconomy (BES). Using forest inventory data from the Federal State of Bavaria, we simulated a range of forest management options under three climate trajectories for 100 years i…
Trade-offs among intensive forestry, ecosystem services and biodiversity in boreal forests
2018
Finnish forests are used extensively for timber production but are also providers of other ecosystem services and harbor unique biodiversity. The ecosystem services approach has so far been used marginally in the context of Finnish forestry; however, due to the multiple values associated with Finnish forests and the impacts forestry operations have on forest ecosystems, it is clearly applicable in this context. In this thesis, I studied the occurrence and severity of trade-offs among ecosystem services and biodiversity conservation in Finnish forests. I used forest inventory data, forest growth simulations, and multi-objective optimization to reveal how the severity of the trade-offs varies…
A visualization technique for accessing solution pool in interactive methods of multiobjective optimization
2015
<pre>Interactive methods of <span>multiobjective</span> optimization repetitively derive <span>Pareto</span> optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the <span>Pareto</span> optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the sol…
Interactive multiobjective optimization for finding the most preferred exercise therapy modality in knee osteoarthritis
2022
Abstract Background There are no explicit guidelines or tools available to support clinicians in selecting exercise therapy modalities according to the characteristics of individual patients despite the apparent need. Objective This study develops a methodology based on a novel multiobjective optimization model and examines its feasibility as a decision support tool to support healthcare professionals in comparing different modalities and identifying the most preferred one based on a patient’s needs. Methods Thirty-one exercise therapy modalities were considered from 21 randomized controlled trials. A novel interactive multiobjective optimization model was designed to characterize the effic…
Interactive methods for multiobjective robust optimization
2018
Practical optimization problems usually have multiple objectives, and they also involve uncertainty from different sources. Various robustness concepts have been proposed to handle multiple objectives and the involved uncertainty simultaneously. However, the practical applicability of the proposed concepts in decision making has not been widely studied in the literature. Developing solution methods to support a decision maker to find a most preferred robust solution is an even more rarely studied topic. Thus, we focus on two goals in this thesis including 1) analyzing the practical applicability of different robustness concepts in decision making and 2) developing interactive methods for sup…
Multi-objective Optimization for Computation Offloading in Fog Computing
2018
Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay,…
Interactively Learning the Preferences of a Decision Maker in Multi-objective Optimization Utilizing Belief-rules
2020
Many real life problems can be modelled as multiobjective optimization problems. Such problems often consist of multiple conflicting objectives to be optimized simultaneously. Multiple optimal solutions exist to these problems, and a single solution cannot be said to be the best without preferences given by a domain expert. Preferences can be used to find satisfying solutions: optimal solutions, which best match the expert’s preferences. To model the preferences of the expert, and aid him/her in finding satisfying solutions, a novel method is proposed. The method utilizes machine learning combined with belief-rule based systems to adaptively train a belief rule based system to learn a domai…
Automatic surrogate modelling technique selection based on features of optimization problems
2019
A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the liter…
Exact extension of the DIRECT algorithm to multiple objectives
2019
The direct algorithm has been recognized as an efficient global optimization method which has few requirements of regularity and has proven to be globally convergent in general cases. direct has been an inspiration or has been used as a component for many multiobjective optimization algorithms. We propose an exact and as genuine as possible extension of the direct method for multiple objectives, providing a proof of global convergence (i.e., a guarantee that in an infinite time the algorithm becomes everywhere dense). We test the efficiency of the algorithm on a nonlinear and nonconvex vector function. peerReviewed
Interpreting wind damage risk-how multifunctional forest management impacts standing timber at risk of wind felling
2022
AbstractLandscape multifunctionality, a widely accepted challenge for boreal forests, aims to simultaneously provide timber, non-timber ecosystem services, and shelter for biodiversity. However, multifunctionality requires the use of novel forest management regimes optimally combined over the landscape, and an increased share of sets asides. It remains unclear how this combination will shape stand vulnerability to wind disturbances and exposed timber volume. We combined forest growth simulations and multi-objective optimization to create alternative landscape level forest management scenarios. Management choices were restricted to 1) rotation forestry, 2) continuous cover forestry, and 3) a…