This case study deals with a redesign effort to face the overcrowding issue in an Emergency Department (ED). A multidiscinary group of healthcare professionals and engineers worked together to improve the actual processes. We integrate the simulation modeling in a humancentered design method. We use the simulation technique as a learning and experimentation tool into … Read more
In 1988, Barzilai and Borwein published a pioneering paper which opened the way to inexpensively accelerate first-order methods. More in detail, in the framework of unconstrained optimization, Barzilai and Borwein developed two strategies to select the steplength in gradient descent methods with the aim of encoding some second-order information of the problem without computing and/or … Read more
In this paper we study the worst-case complexity of an inexact Augmented Lagrangian method for nonconvex constrained problems. Assuming that the penalty parameters are bounded, we prove a complexity bound of $\mathcal{O}(|\log(\epsilon)|)$ outer iterations for the referred algorithm to generate an $\epsilon$-approximate KKT point, for $\epsilon\in (0,1)$. When the penalty parameters are unbounded, we prove … Read more
Portfolio optimization is an ongoing hot topic of mathematical optimization and management science. Due to the current financial market environment with low interest rates and volatile stock markets, it is getting more and more important to extend portfolio optimization models by other types of investments than classical assets. In this paper, we present a mixed-integer … Read more
We consider a distributionally robust Partially Observable Markov Decision Process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint … Read more
We consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification … Read more
This paper studies the economic desirability of UAV parcel delivery and its e ect on e-retailer distribution network while taking into account technological limitations, government regulations, and customer behavior. We consider an e-retailer o ering multiple same day delivery services including a fast UAV service and develop a distribution network design formulation under service based competition where … Read more
Integer programming problems that arise in practice often involve decision variables with one or two sided bounds. In this paper, we consider a generalization of Chvatal-Gomory inequalities obtained by strengthening Chvatal-Gomory inequalities using the bounds on the variables. We prove that the closure of a rational polyhedron obtained after applying the generalized Chvatal-Gomory inequalities is … Read more
Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that are fully adapted to the uncertainty. Often, e.g. due to contractual constraints, such flexible and adaptive policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of … Read more
We study tactical models for the design of same-day delivery (SDD) systems. Same-day fulfillment in e-commerce has seen substantial growth in recent years, and the underlying management of such services is complex. While the literature includes operational models to study SDD, they tend to be detailed, complex, and computationally difficult to solve, and thus may … Read more