In this work, the potentials of data-driven optimization for the well-known berth allocation
problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel …

Feature selection is a recurrent research topic in modern regression analysis, which strives
to build interpretable models, using sparsity as a proxy, without sacrificing predictive power …

Most of the contemporary research published in field of visible-short wave near-infrared (Vis-
SWNIR) fruit spectroscopy is 'derivative'in nature as they primarily showcase the application …

We consider the best subset selection problem in linear regression—that is, finding a
parsimonious subset of the regression variables that provides the best fit to the data …

This study aims to determine suitable locations for artificial recharge of groundwater (ARG)
using the GIS-based analytic hierarchy process (AHP) and order weight average (OWA). To …

Canonical correlation analysis (CCA) is a family of multivariate statistical methods for
extracting mutual information contained in multiple datasets. To improve the interpretability …

This paper discusses the prediction of hierarchical time series, where each upper-level time
series is calculated by summing appropriate lower-level time series. Forecasts for such …

Inferential (or soft) sensors are used in industry to infer the values of imprecisely and rarely
measured (or completely unmeasured) variables from variables measured online (eg …

We study the feature-based newsvendor problem, in which a decision-maker has access to
historical data consisting of demand observations and exogenous features. In this setting …

This study aims to propose a hybrid method for suitability assessment with different risk
levels to construct solar power plants (CSPPs) in southern Iran. The fuzzy-analytic hierarchy …