Associate Professor
Industrial & Systems Engineering
University at Buffalo
E-mail: cmurray3@buffalo.edu
Phone: (716) 645-4716
Office: 309 Bell Hall
My research interests involve the application of operations research (OR) techniques to solve problems encountered by industry and the military. In particular, I’m interested in leveraging the capabilities of (semi-) autonomous vehicles for logistics and surveillance. This includes routing and scheduling of unmanned aerial vehicles (UAVs, also known as drones). I am a Federal Aviation Administration (FAA) certified drone pilot.
Latest News
New Research: Drone Scheduling with Weather Considerations (Posted Nov. 16, 2022) – Ph.D. student Lan Peng has released the first paper from his dissertation, entitled “Parallel Drone Scheduling Traveling Salesman Problem with Weather Impacts”. This research provides a model for scheduling drone and truck operations when inclement weather (e.g., rain, wind, low visibility) restricts flights. A mixed integer linear programming formulation is presented, as well as an efficient heuristic for solving problems of practical size.
SOAR Open House Event (Posted Sept. 17, 2021) – Join us on Thursday, Sept. 30, for demonstrations of drone research at the University at Buffalo’s Structure for Outdoor Autonomy Research (SOAR) flight test center. Students and faculty from the Departments of Computer Science, Electrical Engineering, Mechanical & Aerospace Engineering, and Industrial & Systems Engineering will showcase some of their latest research with live demonstrations.
Click here for a full list of demonstrations, directions to the SOAR facility, and more…
Truck/Drone deliveries with road traffic (Posted Jan. 17, 2021) – New research on “The Time-Dependent Multiple Flying Sidekicks Traveling Salesman Problem”, from soon-to-be-graduating Ph.D. student Ritwik Raj, is now available. Ritwik’s research examines the impacts of road network congestion in a last-mile delivery system consisting of a truck and multiple drones. Existing literature in this area assumes that the truck travel time between two nodes is fixed. However, this assumption may be dangerous considering that unmanned aerial vehicles (UAVs, or drones) have limited endurance, as congestion may delay the truck’s arrival at retrieval locations, potentially causing crashes. This work introduces the time-dependent multiple flying sidekicks traveling salesman problem, in which truck travel times on each road segment may vary throughout the day. A mixed integer linear programing formulation is provided for small-scale problems, while an ant-pair colony system heuristic is proposed for problems of realistic scale. Results indicate significant improvement in terms of feasibility and time savings over the case in which schedules are generated by neglecting congestion. Furthermore, the analysis demonstrates the ability of the proposed model to increase the utilization of UAVs and smaller roads during high congestion to minimize overall delivery time.
VeRoViz version 0.4.3 now available (Posted Jan. 4, 2021) – VeRoViz is a vehicle routing visualization toolkit. The latest version introduces 5 new utility functions, but was mostly focused on some bug fixes (see the change log for details). We have also submitted a paper on VeRoViz for publication considerations; you may view the paper here.
Introducing “tex2solver” (Posted Oct. 12, 2020) –
tex2solver makes it easy to transfer LaTeX-typeset optimization models to programming code for use in a solver. Simply copy-and-paste your LaTeX code (or take a screenshot of your model), then choose your desired solver and programming language. tex2solver will generate the code you need to be able to solve instances of your model. With tex2solver, it’s easy to keep your math model and solver code in sync. tex2solver currently works with PuLP, but support for other popular solvers (including CPLEX and Gurobi) is forthcoming.
Visit tex2solver.com to get started for free.
Fly Slower to Reduce Drone Delivery Times? (Updated Sept. 30, 2020) – New research with Ritwik Raj reveals that flying drones at slower speeds can actually lead to a significant reduction in overall delivery times. Check out the pre-publication accepted paper (free), or download directly from the publisher (paywalled), for an explanation, and to see how truck travel distances, waiting times, and drone energy consumption are affected. All test problems are available for download (and source code for the heuristic is also now available).
New Flying Sidekick Paper Published (Dec. 23, 2019) – Our latest paper on the “multiple flying sidekicks traveling salesman problem” has been published in Transportation Research Part C. You may download the pre-publication version (free) or download directly from the publisher (paywalled). We’re also excited to share all test problems and source code for solving these problems.
VeRoViz – Vehicle Routing Visualization package (Oct. 21, 2019) – We are excited to announce the release of VeRoViz, an open-source software package for visualizing vehicle routing problems. Visit veroviz.org for more information, including installation instructions, detailed documentation, and numerous examples.
Using Images to Compare MILPs (Aug. 16, 2019) – New research with Ph.D. student Zachary Steever explores the use of images to find relationships among mixed integer linear programs (MILPs). Visit mic.optimatorlab.org to see some interesting pictures, then download the working paper to learn more.
New Research Papers Available – Pre-publication versions of two of the Optimator Lab’s papers are now posted:
- “Robotics in Order Picking: Evaluating Warehouse Layouts for Pick, Place, and Transport Vehicle Routing Systems,” forthcoming in International Journal of Production Research
- “Dynamic Courier Routing for a Food Delivery Service,” forthcoming in Computers & Operations Research
Featured Research
Vehicle Routing Visualization (VeRoViz) – This open-source software package, for Python and with web-based components, is designed to help vehicle routing researchers easily create test problems, generate time and distance matrices, and visualize solutions with dynamic 3D movies. Visit veroviz.org for installation instructions, documentation, and examples.
Drone Flight Training and Simulation System – This application helps pilots, both new and experienced, to improve their drone-flying skills. It was originally developed to ensure that students in the Optimator Lab at the University at Buffalo were proficient in operating a drone before flying our hardware outside. However, we have added numerous features that make the system …read more…
Multi-user Multi-vehicle Mission Control (M3C) – The M3C package provides mission planning and execution capabilities for teams of UAVs (drones) and UGVs (rovers). We developed this system to enable command and control of autonomous vehicles. Key features of the system include …read more…
UAVs in Logistics – Motivated by Amazon’s “Prime Air” UAV for small parcel delivery, we have developed algorithms that coordinate traditional delivery trucks with quadcopters. These algorithms minimize the total time required for deliveries, helping customers to receive packages faster and improving the overall effectiveness of the delivery process. Read more…