Search results for "monitavoiteoptimointi"
showing 10 items of 81 documents
Interactivized : Visual Interaction for Better Decisions with Interactive Multiobjective Optimization
2022
In today’s data-driven world, decision makers are facing many conflicting objectives. Since there is usually no solution that optimizes all objectives simultaneously, the aim is to identify a solution with acceptable trade-offs. Interactive multiobjective optimization methods are iterative processes in which a human decision maker repeatedly provides one’s preferences to request computing new solutions and compares them. With these methods, the decision maker can learn about the problem and its limitations. However, advanced optimization software usually offer simple visualization tools that can be significantly improved. On the other hand, current approaches for multiobjective optimization…
Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty
2022
Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel intera…
Optimization and analysis of processes with moving materials subjected to fatigue fracture and instability
2013
We study systems of traveling continuum modeling the web as a thin elastic plate of brittle material, traveling between a system of supports at a constant velocity, and subjected to bending, in-plane tension and small initial cracks. We study crack growth under cyclic in-plane tension and transverse buckling of the web analytically. We seek optimal in-plane tension that maximizes a performance vector function consisting of the number of cycles before fracture, the critical velocity and process effectiveness. The present way of applying optimization in the studies of fracture and stability is new and affords an analytical tool for process analysis. peerReviewed
Interactive Multiobjective Robust Optimization with NIMBUS
2018
In this paper, we introduce the MuRO-NIMBUS method for solving multiobjective optimization problems with uncertain parameters. The concept of set-based minmax robust Pareto optimality is utilized to tackle the uncertainty in the problems. We separate the solution process into two stages: the pre-decision making stage and the decision making stage. We consider the decision maker’s preferences in the nominal case, i.e., with the most typical or undisturbed values of the uncertain parameters. At the same time, the decision maker is informed about the objective function values in the worst case to support her/him to make an informed decision. To help the decision maker to understand the behavio…
IRA-EMO : Interactive Method Using Reservation and Aspiration Levels for Evolutionary Multiobjective Optimization
2019
We propose a new interactive evolutionary multiobjective optimization method, IRA-EMO. At each iteration, the decision maker (DM) expresses her/his preferences as an interesting interval for objective function values. The DM also specifies the number of representative Pareto optimal solutions in these intervals referred to as regions of interest one wants to study. Finally, a real-life engineering three-objective optimization problem is used to demonstrate how IRA-EMO works in practice for finding the most preferred solution. peerReviewed
A Simple Indicator Based Evolutionary Algorithm for Set-Based Minmax Robustness
2018
For multiobjective optimization problems with uncertain parameters in the objective functions, different variants of minmax robustness concepts have been defined in the literature. The idea of minmax robustness is to optimize in the worst case such that the solutions have the best objective function values even when the worst case happens. However, the computation of the minmax robust Pareto optimal solutions remains challenging. This paper proposes a simple indicator based evolutionary algorithm for robustness (SIBEA-R) to address this challenge by computing a set of non-dominated set-based minmax robust solutions. In SIBEA-R, we consider the set of objective function values in the worst c…
Interactive Inverse Modeling Based Multiobjective Evolutionary Algorithm
2018
An interactive version of the inverse modeling based multiobjective evolutionary algorithm is presented. Instead of generating a representation of the whole Pareto optimal front, the algorithm aims at producing solutions in the regions where the decision maker is interested in. This is facilitated through an interactive solution process where the decision maker iteratively evaluates a set of solutions shown to her/him and the preference information obtained is used to adapt the search process of the algorithm. peerReviewed
NAUTILUS Navigator : free search interactive multiobjective optimization without trading-off
2019
We propose a novel combination of an interactive multiobjective navigation method and a trade-off free way of asking and presenting preference information. The NAUTILUS Navigator is a method that enables the decision maker (DM) to navigate in real time from an inferior solution to the most preferred solution by gaining in all objectives simultaneously as (s)he approaches the Pareto optimal front. This means that, while the DM reaches her/his most preferred solution, (s)he avoids anchoring around the starting solution and, at the same time, sees how the ranges of the reachable objective function values shrink without trading-off. The progress of the motion towards the Pareto optimal front is…
Multi-scenario multi-objective robust optimization under deep uncertainty: A posteriori approach
2021
This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well as an alternative way for scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate and test the novel approach, we use the classic shallow lake problem. We compare the results obtained with the novel approach to those obtained with previously used approaches. We show that the novel approach guarantees the feasibility and robust efficiency of the produced solutions under all selected scenarios, while decreasing computation cost, addresses the scenario-dependency issues, and enables the decision-makers to explore the trade-off …
The price of multiobjective robustness : Analyzing solution sets to uncertain multiobjective problems
2021
Defining and finding robust efficient solutions to uncertain multiobjective optimization problems has been an issue of growing interest recently. Different concepts have been published defining what a “robust efficient” solution is. Each of these concepts leads to a different set of solutions, but it is difficult to visualize and understand the differences between these sets. In this paper we develop an approach for comparing such sets of robust efficient solutions, namely we analyze their outcomes under the nominal scenario and in the worst case using the upper set-less order from set-valued optimization. Analyzing the set of nominal efficient solutions, the set of minmax robust efficient …