Search results for "multiobjective optimization"
showing 10 items of 71 documents
Genetic Algorithms: A Decision Tool in Industrial Disassembly
2008
In the recycling process of the Waste Electrical and Electronic Equipment (WEEE) the disassembly process has a central role. Disassembly is not the reverse of the assembly process, real difficulties occur in the tasks assignment process of the disassembly operations. Since this is a multi objective optimization problem, we prove that genetic algorithms provide a useful multi-criteria decision tool in the industrial disassembly process.
On Generalizing Lipschitz Global Methods forMultiobjective Optimization
2015
Lipschitz global methods for single-objective optimization can represent the optimal solutions with desired accuracy. In this paper, we highlight some directions on how the Lipschitz global methods can be extended as faithfully as possible to multiobjective optimization problems. In particular, we present a multiobjective version of the Pijavskiǐ-Schubert algorithm.
NIMBUS — Interactive Method for Nondifferentiable Multiobjective Optimization Problems
1996
An interactive method, NIMBUS, for nondifferentiable multiobjective optimization problems is introduced. We assume that every objective function is to be minimized The idea of NIMBUS is that the decision maker can easily indicate what kind of improvements are desired and what kind of impairments are tolerable at the point considered.
Managing a boreal forest landscape for providing timber, storing and sequestering carbon
2015
Human well-being highly depends on ecosystem services and this dependence is expected to increase in the future with increasing population and economic growth. Studies that investigate trade-offs between ecosystem services are urgently needed for informing policy-makers. We examine the trade-offs between a provisioning (revenues from timber selling) and regulating (carbon storage and sequestration) ecosystem services among seven alternative forest management regimes in a large boreal forest production landscape. First, we estimate the potential of the landscape to produce harvest revenues and store/sequester carbon across a 50-year time period. Then, we identify conflicts between harvest re…
A Computationally Inexpensive Approach in Multiobjective Heat Exchanger Network Synthesis
2010
We consider a heat exchanger network synthesis problem formulated as a multiobjective optimization problem. The Pareto front of this problem is approximated with a new approximation approach and the preferred point on the approximation is found with the interactive multiobjective optimization method NIMBUS. Using the approximation makes the solution process computationally inexpensive. Finally, the preferred outcome on the Pareto front approximation is projected on the actual Pareto front. peerReviewed
A double-shell design approach for multiobjective optimal design of microgrids
2010
This work develops a new double shell approach to optimal design for multi-objective optimally managed systems. The cost of each design solution can be defined by the evaluation of operational issues and capital costs. In most systems, the correct definition of operational issues can be deduced by means of the solution of a multi-objective optimization problem. The evaluation of each design solution must thus be deduced using the outcome of a multi-objective optimization run, namely a Pareto hyper-surface in the n-dimensional space of operational objectives. In the literature, the design problem is usually solved by considering a single objective formulation of the operational issue. In thi…
An integrated multiobjective design tool for process design
2006
An integrated multiobjective design tool has been developed for chemical process design. This tool combines the rigorous process calculations of the BALAS process simulator and the interactive multiobjective optimization method NIMBUS. With this design tool, the designer can consider several conflicting performance criteria simultaneously. The interactive nature of this tool allows the designer to learn about the behavior of the problem. To illustrate the possibilities of this design tool, two case studies are considered. One of them is related to paper making while the other one is related to power plants.
A solution process for simulation-based multiobjective design optimization with an application in the paper industry
2014
In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, w…
Interactive Nonlinear Multiobjective Optimization Methods
2016
An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide. peerReviewed
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
2018
We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…