The identification of the business cycle turn points is a challenging task. In this context, the evolution of new data typologies as internet searches data strongly impacted the construction of approaches practical to forecast macroeconomic variables. This work emphasizes the significance of...

Customer service agents serve a critical role towards post-purchase customer satisfaction, retention, and loyalty. However, properly evaluating their performance involves several challenges: commonly used customer survey measures suffer from scarce, biased, and delayed responses. This study...

The sudden emergence of epidemics, such as COVID-19, entails economic and social challenges requiring immediate attention from policy makers. An essential building block in implementing mitigation policies (e.g., lockdowns, testing, and vaccination) is the identification of potential hotspots,...

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the...

Economic forecasting during structural breaks is challenging due to the possible systematic failure of existent models. Robust forecast devices are able to provide unbiased forecasts just after structural change but at the cost of higher variance in normal times. Reinforcement learning (RL)...

This is an argumentative review article that intends to establish consciousness shifts as a region base concept for metamorphic learning (ML)s. Our theoretical framework is based on ingenuity, self-empathy, and kinship. Kinship includes every aspect that strengthens the consanguine nature of...

In this paper, we investigate optimization of an aircraft maintenance routing problem (AMRP), which is currently a hot research topic in the air transport industry. The optimization of AMRP can help airline companies improve their management capacity and operational efficiency. However, there...

We present Causal ABM, a methodology to derive causal structures describing complex underlying behavioral phenomena. Agent-based models (ABMs) have powerful advantages for causal modeling that have not been explored sufficiently. Unlike traditional causal estimation approaches which often result...

We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is backordered, and any excess inventory is carried to the next period. In contrast...

This work presents a set of neural network (NN) models specifically designed for accurate and efficient fluid dynamics forecasting. In this work, we show how neural networks training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic...