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...

In a Real Business Cycle model, monetary shock does not affect real variables, and economic agents are assumed to understand the model’s structure. This article shows how it is possible to build a macroeconomic agent-based simulation from standard textbook Real Business Cycle model and how to...

An adaptive filter (AF) is a linear filter whose transfer function is defined by a set of parameters that are continuously adjusted according to an iterative algorithm like the least mean squares (LMS). However, sometimes AFs cannot be properly designed because they trade convergence speed for...

In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To...