ABSTRACT: This study presents the Dynamic Multi-Objective Uncapacitated Facility Location Problem (DMUFLP) model, a novel and forward-thinking approach designed to enhance facility location decisions ...

The growing demand for innovative research in the food industry is driving the adoption of robots in large-scale experimentation, a shift that offers increased precision, repeatability, and efficiency ...

Department of Engineering, University of Cambridge, Cambridge CB2 1CB2 1PZ, U.K.

Abstract: Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free ...

Abstract: Duringthe design of electrical machines, multiple performance objectives need to be considered. Although stochastic optimization algorithms are extensively employed for this purpose, a ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

Next to the primary optimization objectives, scientific optimization problems often contain a series of subordinate objectives, which can be expressed as preferences over either the outputs of an ...

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...

ABSTRACT: Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for ...