Building robust airline scheduling models involves constructing schedules and routes with reduced levels of flight delays as well as fewer passenger and crew disruptions. In this paper, we study different classes of models to achieve robust airline scheduling solutions, with a focus on the...

Optimization under uncertainty has been a well-studied field, with significant interest generated in this field in the past four decades. This paper is both practical and expository - its purpose is to: discuss the process of generating robust solutions, highlight issues that arise in practice,...

We present an efficient data-driven computational bounding and solution approach for static allocation of ambulance fleet and its dynamic redeployment, where the goal is to position (or re-position) ambulances to bases to maximize the systems service level. We leverage the framework presented,...

Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized...

We present a novel modeling and algorithmic approach, a Maximum Entropy Principle (MEP) heuristic for Routing and Scheduling, for a large class of problems including the Traveling Salesman Problem (TSP), multiple Traveling Salesmen Problem (mTSP), the Vehicle Routing Problem (VRP) and the...

Optimization under uncertainty has been a well-studied field, with significant interest generated in this field in the past four decades. This paper is both practical and expository - its purpose is to: discuss the process of generating robust solutions, highlight issues that arise in practice,...