About

Dr. Maged M. Dessouky is the Tryon Chair in Industrial and Systems Engineering and Chair of the Daniel J. Epstein Department of Industrial and Systems Engineering at USC. He earned his B.S. and M.S. degrees in Industrial Engineering from Purdue University in 1984 and 1987, respectively, and completed his Ph.D. in Industrial Engineering and Operations Research at the University of California at Berkeley in 1992. His research has been supported by prominent organizations including the National Science Foundation, Society of Manufacturing Engineers, PATH, Caltrans, FTA, Department of Defense, and the Department of Homeland Security's National Center for Risk and Economic Analysis of Terrorism Events (CREATE). Dr. Dessouky's work has been recognized with the 2007 Transportation Science & Logistics Best Paper Prize for his research on optimal slack time in schedule-based transit operations. He is a Fellow of both the Institute of Operations Research and Management Science (INFORMS) and the Institute of Industrial and Systems Engineers (IISE). Throughout his career, he has received numerous teaching awards such as the IISE Operations Research Division Excellence in Teaching Award, the USC Associates Award in Teaching, Alpha Pi Mu/Omega Rho Outstanding Teacher of the Year in Industrial Systems Engineering, the USC Award for Excellence in Teaching, and the TRW School of Engineering Teacher Award. Dr. Dessouky has served as associate editor and editorial board member for several leading journals in transportation and industrial engineering, including Transportation Research Part B: Methodological and Transportation Research Part E: Logistics and Transportation Review. From 2010 to 2018, he was the Director of The Daniel J. Epstein Institute. His research contributions focus on transportation systems, logistics, and operations research, with an emphasis on transit operations, freight routing, ridesharing, and optimization of transportation networks.

Research topics

• Computer Science
• Transport engineering
• Engineering
• Business
• Environmental economics
• Simulation
• Waste management
• Environmental science
• Automotive engineering
• Mathematical optimization
• Risk analysis (engineering)
• Natural resource economics
• Mathematics
• Operations research
• Economics
• Computer network

Selected publications

• Incentivized Personalized Coordinated Freight Routing Considering System Optimization With Driver-in-Loop Utility Learning

  IEEE Transactions on Intelligent Transportation Systems · 2026-01-01

  article

  With the growth of cities and the expansion of urban populations, traffic congestion has become a major challenge in urban areas. Congestion significantly worsens economic and environmental conditions, and is particularly problematic in areas with heavy truck traffic. In this paper, we introduce a coordinated freight routing system aimed at optimizing the overall utility of the network and alleviating congestion through personalized routing instructions and incentives. This system specifically tailors the allocation of incentives and payments to individual drivers, considering both current traffic conditions and their specific routing pReferences. We employ a mixed logit model with a linear utility specification to model drivers’ route choice preferences and decisions. Participation in the system is voluntary, and the system ensures that for most drivers, the combined expected utility, including incentives, surpasses their anticipated utility under User Equilibrium (UE), thereby motivating a substantial number of drivers to follow the assigned routes. The system collects data on the drivers’ routing choices, subsequently updating estimates of utility parameters based on their recent decisions. Ahierarchical Bayes estimator is used for the estimation of individual-specific utility parameters. By integrating driver behavior into the routing process, our system actively adapts and updates parameters in response to real driver actions, offering a dynamic and accurate representation of evolving driver preferences. Numerical experiments on the Sioux Falls network demonstrate the effectiveness of the proposed method.

  PublisherDOI

• An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering En-Route Launching and Recovery Points.

  California Digital Library · 2026-01-01

  otherOpen access1st authorCorresponding

  The logistics industry faces significant challenges in keeping up with evolving demand and supply conditions, especially in urban areas. Traffic congestion during peak hours makes on-time delivery hard. Moreover, time-sensitive products, such as emergency blood and medicine, must be delivered to the customer at the desired time. Drones are a viable solution to urban logistics problems, as they offer several benefits for package delivery. Drones are resilient to traffic delays since they function independently of road infrastructure, unlike conventional vehicles. However, drones have capacity and other constraints; therefore, collaborating with a drone and a truck can make the delivery system more efficient. Although there has been significant research interest in developing truck-drone routing algorithms, a gap remains in developing models that allow for en-route drone launching points and recovery points. The prior research on truck-drone routing assumes that the truck can only reconnect with a drone at a customer location. In this proposed research, we will expand on the prior work to develop optimization models and algorithms to allow with enroute meet points. This added dimension has the potential to reduce truck vehicle miles and subsequently congestion. Our solution framework will employ a dynamic programming-based algorithm for the initial solution and a synchronized drone dispatch algorithm to determine the launching and recovery points along the truck route. The proposed algorithm will be able to provide solutions for real-world large instances.

  PublisherDOI

• Understanding the traffic pattern impacts of COVID-19 lockdown orders

  Computers & Industrial Engineering · 2025-10-22

  article
  PublisherDOI

• The ridesharing routing problem with flexible pickup and drop-off points

  Transportation Research Part B Methodological · 2025-05-24 · 2 citations

  article
  PublisherDOI

• Metro network operations

  Elsevier eBooks · 2025-02-13

  book-chapter1st authorCorresponding
  PublisherDOI

• Grand challenges in industrial and systems engineering

  International Journal of Production Research · 2025-01-17 · 31 citations

  articleOpen access

  Contemporary society faces a growing set of complex issues representing significant socioeconomic, health and well-being, environmental, and sustainability challenges. The discipline of industrial and systems engineering (ISE) can play an important role in addressing these issues. This paper identifies and discusses eight grand challenges for ISE. These grand challenges are (1) Artificial Intelligence (AI) For Business and Personal Use: Decision-Making and System Design and Operations, (2) Cybersecurity and Resilience, (3) Sustainability: Environment, Energy and Infrastructure, (4) Health Issues, (5) Social Issues, (6) Logistics and Supply Chain, (7) System Integration and Operations: Humans, Automation, and AI, and (8) Industrial and Systems Engineering Education. The discussed grand challenges were derived by accomplished ISE professionals who are the authors of this paper. The implications of the ISE grand challenges for education, training, research, and implementation of ISE principles and methodologies for the benefit of global society are discussed.

  PublisherOA PDFDOI

• Generalized Traffic Equilibrium with Ride-hailing and Customer Waiting

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Optimizing Vaccine Site Locations While Considering Travel Inconvenience and Public Health Outcomes

  arXiv (Cornell University) · 2024-03-26

  preprintOpen access

  During the COVID-19 pandemic, there were over three million infections in Los Angeles County (LAC). To facilitate distribution when vaccines first became available, LAC set up six mega-sites for dispensing a large number of vaccines to the public. To understand if another choice of mega-site location would have improved accessibility and health outcomes, and to provide insight into future vaccine allocation problems, we propose a multi-objective mixed integer linear programming model that balances travel convenience, infection reduction, and equitable distribution. We provide a tractable objective formulation that effectively proxies real-world public health goals of reducing infections while considering travel inconvenience and equitable distribution of resources. Compared with the solution empirically used in LAC in 2020, we recommend more dispersed mega-site locations that result in a 28% reduction in travel inconvenience and avert an additional 1,000 infections.

  PublisherOA PDFDOI

• Load Balancing in Offline and Online Truck Routing Under User Heterogeneity

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• A general coupled morning–evening traffic equilibrium model with rideshare, ride-hailing, and public transit services

  Transportmetrica A Transport Science · 2024-06-04 · 3 citations

  article

  We develop a general equilibrium model to capture the complex interactions between different modes, such as solo driving, public transit, as well as rideshare and ride-hailing services such as Uber and Lyft, under a joint morning and evening commute framework. Formulated as a variational inequality (VI) and equivalently as a mixed complementarity problem (MiCP), the model allows (a) travelers to switch between different transportation modes and (b) passengers from different Origin-Destination (OD) pairs to share a ride together. The computational results on the Sioux-Falls network show that our model captures the possible mode switches and the coupling effects between morning and evening commutes. Furthermore, our numerical examples demonstrate that modelling morning and evening commutes separately tends to overestimate the travelers' disutility and the average Vehicle Miles Traveled (VMT) in the network.

  PublisherDOI

Recent grants

• Supply Chain Consolidation and Cooperation in the Agriculture Industry

  NSF · $320k · 2013–2017

Frequent coauthors

• Fernando Ordóñez

  44 shared

• Pétros Ioannou

  21 shared

• Randolph W. Hall

  University of Southern California

  20 shared

• Z. Shen

  15 shared

• Sushil Kumar Verma

  12 shared

• Luca Quadrifoglio

  11 shared

• Edward John Kazlauskas

  University of Southern California

  11 shared

• Shichun Hu

  11 shared

Education

• Ph.D., Industrial Engineering

  University of Southern California

  1990

• M.S., Industrial Engineering

  University of Southern California

  1986

• B.S., Mechanical Engineering

  University of Alexandria

  1983

Awards & honors

• Transportation Science & Logistics Best Paper Prize (2007)
• IIE Operations Research Division Excellence in Teaching Awar…
• USC Associates Award in Teaching (Top University Award for T…
• Alpha Pi Mu/Omega Rho Outstanding Teacher of the Year in Ind…
• USC Award for Excellence in Teaching