Transportation Modeling · Operations Research

Connor
Godoy

Transportation Modeling Analyst · C&S Wholesale Grocers
Incoming M.S. Operations Research · Georgia Tech

I develop optimization tools and dashboards that simplify operational decision-making and the evaluation of transportation trade-offs across outbound distribution networks.

Schedule a conversation → [email protected]
Connor Godoy
Current role
C&S Wholesale Grocers
>10%
reduction in outbound truck miles identified
$30K+
annual savings from IE process improvement at UPS
3.77
GPA, graduated with honors from UC Riverside
2026
incoming M.S. Operations Research · Georgia Tech
About

Turning complex logistics into clear decisions

I specialize in the intersection of mathematical optimization and transportation networks — formulating real operational problems as rigorous mathematical models and translating the results into decisions stakeholders can act on.

Currently focused on outbound distribution optimization at C&S Wholesale Grocers, where I encode delivery operations as vehicle routing problems and evaluate what-if scenarios to drive network-level strategy.

01Vehicle routing & CVRPTW formulation
02Delivery schedule optimization
03Facility sourcing optimization
04What-if scenario modeling
05Dashboard deployment & stakeholder communication

"Connor formulated a bin assignment problem as a linear optimization model, carefully designing objective function coefficients to reflect operational priorities… This level of initiative and applied thinking is uncommon at the undergraduate level."

— Dr. Mohsen El Hafsi, Professor of Operations & Supply Chain Management, UC Riverside

Experience
JUN 2025 — PRESENT
C&S Wholesale Grocers
Current position · Remote

Transportation Modeling Analyst

Formulate and solve large-scale vehicle routing problems to optimize outbound grocery delivery operations. Analyses have identified opportunities to reduce outbound truck miles by more than 10% while minimizing network disruption.

  • Formulate outbound grocery delivery operations as a CVRPTW, encoding capacity, time window, and routing constraints for use in a proprietary Julia-based optimization solver
  • Evaluate what-if scenarios — new customers, constraint changes, delivery schedule adjustments — to support network-level decision making
  • Present quantitative results to stakeholders, translating model outputs into actionable operational recommendations
JUN — SEP 2024
United Parcel Service

Industrial Engineering Intern

  • Led process improvement project reducing route stop time, generating $29,872.92 in annual savings
  • Independently developed a mathematical optimization model to address a real bin assignment operational problem, formulating it as a linear program with objective coefficients reflecting employee productivity and travel time
  • Wrote SQL queries to extract and validate operational data for ROI analysis
  • Built Power BI dashboards tracking KPIs for package sorting performance
JUN — AUG 2023
General Atomics

Supply Chain Intern

  • Designed an Excel-based tool to standardize subcontractor data tracking and improve reporting efficiency across supply chain operations
Education
● Incoming · Fall 2026

Georgia Institute of Technology

M.S. Operations Research · H. Milton Stewart School of ISyE

  • Nationally ranked #1 industrial engineering program
  • Focus on stochastic processes, integer programming, and simulation
  • Admitted with a strong recommendation from UC Riverside faculty
Graduated June 2025

University of California, Riverside

B.S. Business Administration · Operations & Supply Chain Management
Minor in Mathematics

GPA 3.77 · Graduated With Honors
  • Coursework: Optimization, Linear Programming, Machine Learning, Numerical Analysis, Simulation, Applied Linear Algebra
  • Dean's Honors List — multiple quarters
  • Chancellor's Honors List — Spring 2024 & Spring 2025 academic years
Projects
Mathematical Optimization

Load-to-Bin Assignment Optimizer

Formulated a real-world UPS operational problem as a linear program, optimizing employee-to-workstation assignments by encoding productivity and travel time constraints into objective function coefficients. Developed in collaboration with Dr. Mohsen El Hafsi at UC Riverside.

Linear Programming Python Operations Research
Stochastic Simulation

Disneyland Single Rider Queue Simulation

Built a discrete-event simulation of Disneyland's single rider queue system to analyze wait time distributions and queue dynamics under variable demand. Modeled probabilistic arrival patterns and service rates to surface operational insights.

Simulation Python Queueing Theory
Capacity Planning

Capacity Optimization Under Demand Uncertainty

Developed a capacity level optimization framework that balances investment costs against uncertain demand scenarios. Models the trade-off between over- and under-capacity across probabilistic demand outcomes to identify optimal capacity decisions.

Stochastic Optimization Excel Decision Analysis
Machine Learning

Inventory Classification with K-Means Clustering

Applied K-means clustering to segment inventory items by demand volume, variability, and lead time characteristics, enabling differentiated replenishment policies across SKU classes. Bridges unsupervised ML with supply chain decision-making.

K-Means Clustering Python Inventory Management
Technical Skills
Languages & Tools
Julia Python R SQL Microsoft Excel Power BI
Methods
Vehicle Routing (CVRPTW) Linear & Integer Programming Stochastic Optimization Discrete-Event Simulation Machine Learning Algorithms What-If Scenario Analysis
Domains
Transportation Networks Supply Chain Optimization Outbound Distribution Inventory Management Capacity Planning Stakeholder Communication

Available for freelance engagements

Let's work on your
transportation problem

Whether you need a routing model built, a what-if analysis run, or a decision dashboard deployed — I'd be glad to discuss how I can help. Reach out to schedule a conversation.

Schedule a meeting → [email protected]