arXiv subjects

Jorge A. Sefair

Publications and source records attributed to Jorge A. Sefair.

3 recordsLinked to original sources

arXiv2026

The Bidirectionality-Preserving Feedback Arc Set Problem with an Application to Faculty Hiring Network Analysis

This work introduces a novel combinatorial optimization problem to uncover strict and non-strict hierarchical structures underlying digraphs with bidirected arcs. We show that the problem includes the seminal feedback arc set problem as a special case, and that its solution extends a tournament solution known as Slater's rule. We introduce and compare two binary programming formulations and also introduce a polynomial-time search algorithm for obtaining an ordered hierarchy from their solution. The methodology is applied to the faculty hiring network problem using a dataset previously collected by Del Castillo et al. (2020) to characterize the prestige/rank of Industrial/Systems/Operations Research (IEOR) departments using faculty hiring transactions.

cs.DM↗

arXiv2020

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Tested on artificially generated swarm motion data, the network achieves high levels of prediction accuracy and imitation authenticity. We compare our model to previous approaches for modelling interaction systems and show how modifying components of other models gradually approaches the performance of ours. Finally, we also discuss an extension of SwarmNet that can deal with nondeterministic, noisy, and uncertain environments, as often found in robotics applications.

cs.NE↗

arXiv2020

Robust Assortment Optimization under a Ranking-based Choice Model with Product Unavailability Effect

The assortment planning problem is a central piece in the revenue management strategy of any company in the retail industry. In this paper, we study a robust assortment optimization problem for substitutable products under a sequential ranking-based choice model. Our modeling approach incorporates the cumulative effect of finding multiple unavailable products on the customers' purchase decisions. To model the highly uncertain order in which a customer explores the products to buy, we present a bi-level optimization approach to maximize the expected revenue under the worst-case order of products in the preference lists of customers. We provide a polynomial-time algorithm that optimally solves a special case of the unconstrained assortment planning problem under our choice model. For the general constrained version of the problem, we devise a solution procedure that includes a single-level reformulation and a cutting-plane approach to iteratively tighten the solution space. We also provide a greedy algorithm that can quickly solve large instances with small optimality gaps.

math.OC↗