Search results for "objective"
showing 10 items of 505 documents
Diversity and Social Interaction at Work
2019
Today’s workplaces are characterized by interactions among people with diverse backgrounds. These interactions may occur face-to-face or in technology-mediated settings; as interpersonal exchanges between co-workers, supervisors, and subordinates or between business partners and clients; in small groups and teams or as one-off encounters with customers. The persons engaged in these interactions jointly strive for the shared goals of their organization though they may come from different countries, speak different first languages, or identify with different ethnic, gender, or age groups. This chapter explores the role of diversity in different workplace contexts and situations. First, we unp…
A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations
2019
Abstract The topic of low-energy buildings received a widespread and growing interest in last years, thanks to energy saving policies of developed countries. The design of a low-energy building is addressed with energy saving measures and renewable energy generation, but the correct assessment of phenomena occurring in a building usually requires to perform dynamic simulations and to analyze multiple scenarios to attain the optimal solution. The optimality of a technical solution may be subject to contrasting constraints and objectives. For this reason, designers may employ mathematical optimization techniques, a non-familiar topic to most of building designers. In this paper, a review on o…
An Adaptive Metamodel-Based Optimization Approach for Vehicle Suspension System Design
2014
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/965157 The performance index of a suspension system is a function of the maximum and minimum values over the parameter interval. Thus metamodel-based techniques can be used for designing suspension system hardpoints locations. In this study, an adaptive metamodel-based optimization approach is used to find the proper locations of the hardpoints, with the objectives considering the kinematic performance of the suspension. The adaptive optimization method helps to find the optimum locations of the hardpoints efficiently as it may be unachie…
An evolutionary method for complex-process optimization
2010
10 páginas, 7 figuras, 7 tablas
Black box scatter search for general classes of binary optimization problems
2010
The purpose of this paper is to apply the scatter search methodology to general classes of binary problems. We focus on optimization problems for which the solutions are represented as binary vectors and that may or may not include constraints. Binary problems arise in a variety of settings, including engineering design and statistical mechanics (e.g., the spin glass problem). A distinction is made between two sets of general constraint types that are handled directly by the solver and other constraints that are addressed via penalty functions. In both cases, however, the heuristic treats the objective function evaluation as a black box. We perform computational experiments with four well-k…
SSPMO: A Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization
2007
We describe the development and testing of a metaheuristic procedure, based on the scatter-search methodology, for the problem of approximating the efficient frontier of nonlinear multiobjective optimization problems with continuous variables. Recent applications of scatter search have shown its merit as a global optimization technique for single-objective problems. However, the application of scatter search to multiobjective optimization problems has not been fully explored in the literature. We test the proposed procedure on a suite of problems that have been used extensively in multiobjective optimization. Additional tests are performed on instances that are an extension of those consid…
On the Extension of the DIRECT Algorithm to Multiple Objectives
2020
AbstractDeterministic global optimization algorithms like Piyavskii–Shubert, direct, ego and many more, have a recognized standing, for problems with many local optima. Although many single objective optimization algorithms have been extended to multiple objectives, completely deterministic algorithms for nonlinear problems with guarantees of convergence to global Pareto optimality are still missing. For instance, deterministic algorithms usually make use of some form of scalarization, which may lead to incomplete representations of the Pareto optimal set. Thus, all global Pareto optima may not be obtained, especially in nonconvex cases. On the other hand, algorithms attempting to produce r…
Multiobjective optimization and decision making in engineering sciences
2021
AbstractReal-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how…
Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations
2022
AbstractWe introduce novel concepts to solve multiobjective optimization problems involving (computationally) expensive function evaluations and propose a new interactive method called O-NAUTILUS. It combines ideas of trade-off free search and navigation (where a decision maker sees changes in objective function values in real time) and extends the NAUTILUS Navigator method to surrogate-assisted optimization. Importantly, it utilizes uncertainty quantification from surrogate models like Kriging or properties like Lipschitz continuity to approximate a so-called optimistic Pareto optimal set. This enables the decision maker to search in unexplored parts of the Pareto optimal set and requires …
Data-driven decision support to reduce "driving-under the influence of alcohol" offenses
2018
Extracting valuable knowledge from data to support decision making is a widely practiced trend. Data-driven decision support (DDDS) provides insight for decision makers by exploring and extracting underlying patterns within a dataset. This thesis covers the process of DDDS in reducing driving under the influence of alcohol (DUI) offenses by introducing proposed prison sentences. In this thesis, DDDS is applied to a DUI dataset by analyzing patterns in the dataset and by introducing proposed prison sentences for offenders to reduce the number of DUI cases. Background theories in data mining, machine learning, optimization and decision science that are related to the thesis project are also c…