We empirically test the prediction of Pastor, stambaugh, and Taylor (2020) that green firms outperform brown firms when concerns about climate change increas...

Scholars and practitioners have long recognised the importance of data-driven operations and supply chain management (OSCM), which typically centres on produ...

We consider the problem of designing vehicle routes in a distribution system that are at the same time cost-effective and visually attractive. In this pape...

This paper studies disjunctive cutting planes in Mixed-Integer Conic Programming. Building on conic duality, we formulate a cut-generating conic program for...

Data acquisition and recording in the form of databases are routine operations. The process of collecting data, however, may experience irregularities, res...

Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications...

Let \(G=(V,E)\) be a graph and let \(S\subseteq V\) be a subset of its vertices. If the subgraph of \(G\) induced by \(V\setminus S\) is acyclic, the...

Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

The determinantal point process (DPP) provides a promising and attractive alternative to simple random sampling in cluster analysis or classification, for th...

The crew pairing problem is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of...

With sufficient layers, enough training data, enough time, and often a custom tailored architecture, modern deep learning methods can be extremely successf...

Deploying applications having many processes in a multi-cloud environment is challenging. The cloud manager has goals such as minimizing power consumption, w...

Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the d...

A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph repre...

We introduce the conditional \(p\)-dispersion problem (c-pDP), an incremental variant of the \(p\)-dispersion problem (pDP). In the c-pDP, one is given a...

In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood fun...

Significant progress has been made in the field of computer vision, due to the development of supervised machine learning algorithms, which efficiently extra...

The deepening penetration of renewable power generation is challenging how the minute balancing of supply and demand is carried out by power system operators...

We consider three colouring problems which are variations of the basic vertex-colouring problem, and are motivated by applications from various domains. We g...

Reducing the number of features used in data classification can remove noisy or redundant features, reduce the cost of data collection, and improve the accur...