The sharing economy is among the top trending topics in operations research. In this paper, we introduce a novel sharing application encountered in the construction industry. The problem under research, which has been developed in close cooperation with an industrial partner, is concerned with the on-site accommodation of trades in large-scale construction projects. In such projects, a construction

Renewable liquid fuels produced from biomass, hydrogen, and carbon dioxide play an important role in reaching climate neutrality in the transportation sector. For large-scale deployment, production facilities and corresponding logistics have to be established. However, the implementation of such a large-scale renewable fuel production network requires acceptance by citizens. To gain insights into the

In most Swiss municipalities, a curbside system consisting of heavy trucks stopping at almost each household is used for non-recoverable waste collection. Due to the many stops of the trucks, this strategy causes high fuel consumption, emissions and noise. These effects can be alleviated by reducing the number of stops performed by the collection vehicles. One possibility consists of selecting a subset

De novo programming (DNP) is an efficient technique for optimal system design. This paper explores the ability to link the DNP technique with Simon’s satisficing theory to deal with a system design that is satisfactory rather than optimal. To achieve this aim, the ideal vector is replaced by an aspiration-level vector, and the solutions are determined by minimising the Lp-distance metric between the

Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the stati...

We propose a robust optimization (RO) framework that immunizes some of the central linear algebra problems in the presence of data uncertainty. Namely, we formulate linear systems, matrix inversion, eigenvalues-eigenvectors and matrix factorization under uncertainty, as robust optimization problems using appropriate descriptions of uncertainty. The resulting optimization problems are computationally

In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and quay crane utilization. However, many existing studies rely on strong assumptions that often overlook the uncertainties and dynamics innate to real-life

We investigate a condition-based group maintenance problem for multi-component systems, where the degradation process of a specific component is affected only by its neighbouring ones, leading to a special type of stochastic dependence among components. We formulate the maintenance problem into a factored Markov decision process taking advantage of this dependence property, and develop a factored value

Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates of intractable posterior expectations as the number of iterations tends to infinity. However, in large data applications...

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to find the low-dimensional structure of data. Dimension reduction for large, high-dimensional data is not merely a way to reduce the data; the new representations and descriptors obtained by ML reveal the geometric shape of high-dimensional point clouds and allow one to visualize, denoise, and interpret them.

Maps provide a data framework for the statistical analysis of georeferenced data observations. Since the middle of the twentieth century, the field of spatial statistics has evolved to address key inferential questions relating to spatially defined data, yet many central statistical properties do not translate to spatially indexed and spatially correlated data, and the development of statistical inference

This review provides an overview of concepts relating to the communication of statistical and empirical evidence in times of crisis, with a special focus on COVID-19. In it, we consider topics relating to both the communication of numbers, such as the role of format, context, comparisons, and visualization, and the communication of evidence more broadly, such as evidence quality, the influence of changes

Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic modeling, and discuss how to use it to analyze the Multi-Attribute

In this note, the authors propose correcting one erroneous formula from [Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). A unified solution framework for multi-attribute vehicle routing problems. European Journal of Operational Research, 234(3), 658-673] in charge of lunch breaks. In the original paper, the authors propose to compute several attribute values from the solution of a vehicle

A finite order multivariate Hermite polynomial expansion, as an approximation of a joint density function, can handle complex correlation structures. However, it does not construct copulas, because the density function can take negative values. In this study, we propose a formulation of multivariate Hermite polynomial expansion suitable for the application of correction that recovers non-negativity

Given the considerable number of actors in the humanitarian space, coordination is essential for successful disaster response. Furthermore, the sheer size of challenges and limited resources increasingly highlight the need for improved cooperation and collaboration in humanitarian supply chains. A significant number of studies in the literature explore the 3Cs (coordination, cooperation and collaboration)

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods

Advances in information technology have increased the availability of time-stamped relational data, such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions

Published in Journal of the American Statistical Association (Just accepted, 2023)

Agents may form coalitions. Each coalition shares its endowment among its agents by applying a sharing rule. The sharing rule induces a coalition formation problem by assuming that agents rank coalitions according to the allocation they obtain in the corresponding sharing problem. We characterize the sharing rules that induce a class of stable coalition formation problems as those that satisfy a natural

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within

Published in Journal of the American Statistical Association (Just accepted, 2023)

In this paper, we present a survey on vehicle routing problems with multiple commodities. In most routing problems, only one commodity is explicitly considered. This may be due to the fact that, indeed, a single commodity is involved, or multiple commodities are transported, but they are aggregated and modeled as a single commodity, as no specific requirement imposes their explicit consideration. However

This paper studies purchasing a commodity or a perishable item under stochastically evolving and correlated prices for a distribution system environment. We consider the central purchasing of the commodity under the demand process correlated with the random price and decide on the timing and quantity of allocation to demand locations. As an implementation of the physical pooling concept, we investigate

We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models

In this work, the authors address the Optimal Transport (OT) problem on graphs using a proximal stabilized Interior Point Method (IPM). In particular, strongly leveraging on the induced primal–dual regularization, the authors propose to solve large scale OT problems on sparse graphs using a bespoke IPM algorithm able to suitably exploit primal–dual regularization in order to enforce scalability. Indeed

The design of experiments involves a compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and describes an experimental design that allows expe...

When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocation policy, polic...

Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the ...

Abstract–We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of netw...

The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities

This paper proposes efficient exact and heuristic approaches for minimizing the makespan on a single machine scheduling problem with sequence-dependent setup times and release dates. The exact procedure consists of a branch-and-price (B&P) algorithm implemented over an arc-time-indexed formulation with a pseudo-polynomial number of variables and constraints. Our B&P algorithm includes several modern

Retailers are embracing cryptocurrency payments to gain a competitive edge. However, the fierce volatility of traditional cryptocurrencies like Bitcoin deters risk-averse consumers from using them regularly. This issue is particularly pronounced in retail markets with high product return rates, as consumers may bear the volatility risk by directly holding cryptocurrencies after claiming a refund. In

This paper characterises dynamic linkages arising from shocks with heterogeneous degrees of persistence. Using frequency domain techniques, we introduce measures that identify smoothly varying links of a transitory and persistent nature. Our approach allows us to test for statistical differences in such dynamic links. We document substantial differences in transitory and persistent linkages among US

Abstract– We propose a new measure of dependence between a categorical random variable and a random vector with potentially high dimensions, named semi-distance correlation. It is an interesting ex...

Motivated by the simultaneous association analysis with the presence of latent confounders, this paper studies the large-scale hypothesis testing problem for the high-dimensional confounded linear ...

Third-party sellers on online platforms primarily rely on banks to meet their financing requirements and are often constrained by the lack of sufficient working capital. Online platforms such as Amazon and Alibaba have ushered new dynamics in the e-commerce financing landscape by offering working capital loans to these sellers, thereby directly competing with banks and influencing market competition

In several areas of economics and engineering, it is often necessary to fit discrete data points or approximate non-linear functions with continuous functions. Piecewise linear (PWL) functions are a convenient way to achieve this. PWL functions can be modeled in mathematical problems using only linear and integer variables. Moreover, there is a computational benefit in using PWL functions that have

A common approach that governments use to combat the potential environmental harm caused by industry is to set an environmental standard for firms to either comply with by a specified deadline or face a penalty. Two penalty policies that governments often rely on to pressure firms to comply with a new standard are a per-period penalty policy and a per-unit penalty policy. We examine how a government

Online portfolio selection is a practical problem in financial engineering and quantitative trading. Many empirical studies show that stock performance in the market is likely to follow mean reversion, and strategies based on mean reversion show better return performance than the market average. However, the existing mean reversion strategies are not universal and short selling is not allowed, which

The COVID-19 pandemic was responsible for the cancellation of both the men’s and women’s 2020 National Collegiate Athletic Association (NCAA) Division I basketball tournaments. Starting from the po...

We consider single machine scheduling problems in the context of adversarial bilevel optimization where two agents, the leader and the follower, take decisions on the same jobset and the leader acts first with the aim of inducing the worst possible solution for the follower. Thus, the follower schedules the jobs in order to optimize a given criterion. The considered criteria are the total completion

The traditional classification algorithms focus on the maximization of classification accuracy which might lead to poor performance in practice by forcing classifiers to overfit to the majority class. In order to overcome this issue, various approaches focus on the optimization of alternative loss functions such as the Area Under the Curve (AUC). AUC is a Receiver Operating Characteristics (ROC) metric

Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the ordered weighted averaging (OWA) criterion can be applied to aggregate scenarios or objectives into a single function. Formulating this preference, however, can be challenging, as we need to make explicit what is usually

Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining

Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data. This fundamental problem has been known and studied for many years in many disciplines. In the past thirty years, however, the amount of empirical as well as methodological research

New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that

We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper i...

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries

This paper studies the Zero Emission passenger Vessel Service Network Design Problem (ZEVSNDP) in order to investigate how technical and economic challenges related to diffusion of battery electric vessels can be alleviated by appropriate planning of services. The ZEVSNDP considers decisions that are strategic (i.e., vessel fleet and charging locations), tactical (i.e., routes, whether to omit servicing

B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bay...

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where...

The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Res...

In the big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mai...

This paper shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of...

Despite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter e...

Low-frequency earthquakes (LFEs) are small magnitude earthquakes with frequencies of 1-10?Hertz which often occur in overlapping sequence forming persistent seismic tremors. They provide insights i...

A special-topics undergraduate course about the history of statistics which was taught in Spring 2023 at the University of South Carolina is described. We review other similar courses (past and cur...

Most analytics models are built on complex internal learning processes and calculations, which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based decisions must be transparent and intuitive to foster greater human acceptability and confidence in analytics. Explainable analytics models are transparent models in which the primary factors and weights that lead to a prediction