About me

Hi! I'm Ravit Pichayavet. I'm a researcher working on optimization for supply chain and logistics. I recently finished my Ph.D. at Georgia Tech's ISyE , advised by Professor Alan Erera, and defended in July 2026 with the degree conferred in December 2026.

My work is about high-velocity logistics: the systems behind fast delivery, where speed, cost, and limited capacity all pull against each other. That spans the last mile, routing vehicles through dense cities under delivery-time guarantees, and the middle mile, planning freight flows across national networks and buying truckload capacity ahead of uncertain demand.

My recent CV (last updated on Mar 1, 2026).

Thesis: Data-Driven Optimization of High-Velocity Logistics Systems: Addressing Challenges from Last-mile Delivery to Middle-mile Freight Management
(Successfully Defended: July 2026)

with major in Supply Chain Engineering and minor in Computation and Machine Learning

Expected Graduation: December 2026

The acknowledgments are my favorite part. → [Read them here]

Industry Experience

Applied Scientist Intern

Middle Mile Science, LTL Team

AMAzon, Bellevue, WA |

May 2025 - Aug 2025

Designed and implemented a routing optimization module (e.g., Mixed-Integer Programming, Column generation, Routing heuristics) to improve the efficiency of LTL freight movement in Amazon middle-mile network

Teaching Experience

Graduate Student Instructor

ISyE 3103: Supply Chain Modeling: Logistics

| Spring 2026

Serving as the Instructor for an undergraduate course ISyE3103 Supply Chain Modeling: Logistics, delivering lectures on inventory management, freight transportation, and supply chain network design

Research

An icon of motor bike and its delivery route representing last-mile delivery

Rate-based Vehicle Routing Problem for Delivery in Densely Populated Urban Areas

A branch-and-price method for last-mile routing when orders arrive continuously and every delivery carries a lead-time guarantee.

Improvement Heuristics for Extremely Large-Scale LTL Flow and Load Planning

Local search heuristics that improve flow and load plans for parcel consolidation networks at U.S. national scale.

An icon showing graph and magnifying glass representing data analysis and prediction.

Advance Reservation of 3P-Freight Capacity
under Demand and Capacity Uncertainty

A two-stage stochastic model for reserving truckload capacity in advance when both shipment volume and carrier availability are uncertain, with an exact solution method and a predict-then-optimize deployment.