The Stochastic Pseudo-Star Degree Centrality Problem

We introduce the stochastic pseudo-star degree centrality problem, which focuses on a novel probabilistic group-based centrality metric. The goal is to identify a feasible induced pseudo-star, which is defined as a collection of nodes forming a star network with a certain probability, such that it maximizes the sum of the individual probabilities of unique assignments … Read more

The Star Degree Centrality Problem: A Decomposition Approach

We consider the problem of identifying the induced star with the largest cardinality open neighborhood in a graph. This problem, also known as the star degree centrality (SDC) problem, has been shown to be 𝒩𝒫-complete. In this work, we first propose a new integer programming (IP) formulation, which has a fewer number of constraints and … Read more

Optimizing the Response for Arctic Mass Rescue Events

We study a model that optimizes the response to a mass rescue event in Arctic Alaska. The model contains dynamic logistics decisions for a large-scale maritime evacuation with the objectives of minimizing the impact of the event on the evacuees and the average evacuation time. Our proposed optimization model considers two interacting networks – the … Read more

Optimizing the Recovery of Disrupted Multi-Echelon Assembly Supply Chain Networks

We consider optimization problems related to the scheduling of multi-echelon assembly supply chain (MEASC) networks that have applications in the recovery from large-scale disruptive events. Each manufacturer within this network assembles a component from a series of sub-components received from other manufacturers. We develop scheduling decision rules that are applied locally at each manufacturer and … Read more