Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modi...

In this study, we investigate the best remanufacturing strategy for the original equipment manufacturer (OEM) and independent remanufacturer (IR) in an innov...

We use stochastic dynamic programming to design and solve an extended structural setting for which the illiquidity of the firm's assets under liquidation i...

Providing the right data to a machine learning model is an important step to insure the performance of the model. Non-compliant training data instances may l...

In this paper, we study a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Specifically, we addres...

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the adverti...

We consider the generalized successive overrelaxation (GSOR) method for solving a class of block three-by-three saddle-point problems. Based on the necessary...

We study how the financial literature has evolved in scale, research team composition, and article topicality across 32 finance-focused academic journals fro...

Hybrid renewable energy systems (HRES), which co-locate two or more renewable energy sources, have proven to be promising frameworks for harnessing complemen...

Position estimation in Multi-Robot Systems (MRS) relies on relative angle or distance measurements between the robots, which generally deteriorate as dista...

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations ei...

Crew pairing problems (CPP) are regularly solved by airlines to produce crew schedules. The goal of CPPs is to find a set of pairings (sequence of flights a...

The multi-depot scheduling problem (MDVSP) is one of the most studied problem in public transport service planning. It consists of assigning buses to each ti...

Personnel scheduling consists in determining employee work schedules (sequences of work shifts and days off) to cover the demands of multiple jobs over a pl...

Statistical learning models are proposed for the prediction of the probability of a spike in the electricity DART (day-ahead minus real-time price) spread. A...

We study an integrated multi-product production and distribution problem considering a network of multiple plants and customers, who are geographically dispe...

We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is ...

The conjugate gradient (CG) method is a classic Krylov subspace method for solving symmetric positive definite linear systems. We introduce an analogous sem...

We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt(2018). Focu...

A new factor-based representation of implied volatility surfaces is proposed. The factors adequately capture the moneyness and maturity slopes, the smile att...