A. (Alessandro) Bombelli

A. (Alessandro) Bombelli


Main research topics

My research focuses on:

  • Air cargo operations
  • Vehicle routing problems
To address these problems, I use different methodologies, including:
  •  Mixed integer linear programming
  •  Meta-heuristics
  • Complex network theory
  • Unsupervised learning (mainly clustering techniques)


I am originally from Italy, where I studied aerospace engineering (BSc) and space engineering (MSc) at Politecnico di Milano. Then, I moved to the University of California, Irvine, where I obtained my PhD in the Air Traffic Management research field in August 2017. During my PhD, I was twice a visiting researcher at NASA Ames, where I performed part of my research. From late 2017 to late 2019, I was a postdoctoral researcher at the Transport & Planning department (TU Delft), working on optimization models for the landside air cargo supply chain.

I am currently a lecturer at the Air Transport and Operations group, at the Faculty of Aerospace Engineering. My main duties are - helping assistant professors with MSc track courses, - supervising MSc students working on air cargo supply chain and urban air mobility topics, - conducting individual research on the same topics.

Publication highlights

Title: Integrators' global networks: A topology analysis with insights into the effect of the COVID-19 pandemic   
Description: We propose, to the best of our knowledge, the first analysis of the global networks of integrators FedEx, UPS, and DHL using network science. While noticing that all three networks rely on a “hub-and-spoke” structure, the network configuration of DHL leans towards a multi-“hub-and-spoke” structure that reflects the different business strategy of the integrator. 
Title: Analysis of the air cargo transport network using a complex network theory perspective   
Description: We present a complex network analysis of the air transport network using the air cargo, instead of the passenger, perspective. To the best of our knowledge, this is the first work where a global cargo network comprising passenger airlines, full-cargo airlines, and integrators’ capacity was studied.
Title: Improved Clustering for Route-Based Eulerian Air Traffic Modeling   
Description:   In the paper, a new approach is developed for identifying and approximating well-traveled routes in a historical dataset of flight trajectories. The approximate routes are intended for use in a route-based Eulerian model of air traffic flow for strategic planning but are useful for other route-based strategic planning. The approach involves coarse clustering, outlier detection, fine clustering, and aggregate route construction.
Title: Strategic Air Traffic Planning with Fréchet Distance Aggregation and Rerouting
Description:  An aggregate route model for strategic air traffic flow management is presented: it is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Two examples demonstrate the model formulation and results of strategic planning. First, ground delays are introduced to manage high demand in the Los Angeles center; second, ground holding and predeparture rerouting are used to manage a convective weather scenario in the same center.

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