Yuyang Ma

Yuyang (Johnny) Ma

Ph.D. Student @ USC | Industrial & Systems Engineering

Yuyang Ma is a Ph.D. student in the Daniel J. Epstein Department of Industrial and Systems Engineering at University of Southern California. He received a master's degree in operations research from Georgia Institute of Technology in 2022. Prior to that, he received a bachelor's degree in industrial engineering from the University of Pittsburgh (Pittsburgh, United States) and a bachelor's degree in industrial engineering from Sichuan University (Chengdu, China). Yuyang's research interests lie in optimization under uncertainty and corresponding applications. He is currently working with Dr. Karmel S. Shehadeh.

News

Nov 2025

Our paper "Integrated Planning of Drone-Based Disaster Relief: Facility Location, Inventory Prepositioning, and Fleet Operations under Uncertainty" can be now accessed on SSRN 🥳.

Aug 2025

I am now continuing my Ph.D. journey at University of Southern California.

Dec 2022

I am honored to obtain Master's degree from Georgia Tech.

Selected Works

Integrated Planning of Drone-Based Disaster Relief: Facility Location, Inventory Prepositioning, and Fleet Operations under Uncertainty

Submitted

Yuyang Ma, Karmel S. Shehadeh, Man-Yiu Tsang

PDF SSRN

Teaching

Lehigh University

Georgia Institute of Technology

Contact

The best way to contact is through e-mail (yuyangm 'at' usc 'dot' edu). Please indicate the purpose of the e-mail in the subject line, and I will try to respond as soon as possible. Besides, you can also reach me through the following ways:

Office Address

3650 McClintock Ave, Room 304
Los Angeles, CA 90089

Visitor Map

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Miscellaneous Moments

When I am not solving optimization problems, I enjoy exploring nature, photography, and spending time with my cats.

Xiaobao 2024

Submitted

Integrated Planning of Drone-Based Disaster Relief: Facility Location, Inventory Prepositioning, and Fleet Operations under Uncertainty

Authors: Yuyang Ma, Karmel S. Shehadeh, Man-Yiu Tsang Status: Under Review

Abstract

We introduce a two-stage robust optimization (RO) framework for the integrated planning of a drone-based disaster relief operations problem (DDROP). Given sets of demand points, candidate locationsforestablishingdrone-supportedrelieffacilities, facilitytypes, dronetypes, andreliefitems types, our first-stage problem solves the following problems simultaneously: (i) a location problem that determines the number of facilities to establish and where to establish them, (ii) an inventory prepositioning problem that decides the quantity of relief items to store at each established facility, (iii) a fleet sizing problem that decides the number of drones to deploy, and (iv) an assignment problem that assigns drones to established facility and demand points. In the second stage, the model determines a relief distribution plan and enables the reassignment of drones to demand nodes. We equip the RO model with an uncertainty set that captures the relationship between facility disruptions, demand for relief items, drone operational status, and the remaining usable fraction of pre-positioned supplies after the disaster. Additionally, we explicitly model the dependency of post- disaster drone functionality on the pre-disaster assignment decisions. To address the challenges of integer recourse, we derive a K-adaptability approximation of the RO model and develop a column-and-constraint generation (C&CG) algorithm to solve it. Moreover, we introduce several strategies to enhance the computational performance of C&CG, including an efficient warm-starting mechanism, symmetry-breaking constraints, and valid inequalities. We present extensive numerical experiments that demonstrate the computational efficiency of our framework and provide valuable insights.

Resources

Download PDF View on SSRN