Uğur Arikan | Operations Research & Data Science

Operations research and data science leader with 12+ years of experience turning mathematical optimization, network science, and machine learning into production decision-support systems. I lead complex planning projects from problem formulation and algorithm design through deployment and stakeholder adoption across transportation, aviation, and network operations. I bring particular expertise in Rust for high-performance and optimization software, alongside broader experience designing production systems, APIs, visualizations, and data workflows in Python, C#, TypeScript/React, and SQL.

Core areas

Operations research
Optimization and modeling
Decision-support systems

Network planning
Routing and capacity
Applied data science

Software engineering
Rust and parallel computation
Open-source contribution

Professional experience

Principal Operations Research & Data Scientist | DHL Data & AI | Bonn, Germany

2018 - Present

Lead and contribute to optimization projects across air capacity planning, network design, linehaul planning, freight consolidation, regional transport optimization, and last-mile routing, from problem formulation through deployment and operational use.
Develop decision-support tools for air-capacity allocation and network planning, supporting strategic and tactical decisions in complex transportation systems.
Design optimization models for network structure, facility location, routing, freight consolidation, and container loading, combining exact methods, heuristics, and scenario analysis.
Architect and standardize reusable production engines and services for network optimization, routing, and loading, improving consistency, maintainability, and reuse across analytical applications.
Work closely with business, network, and technical stakeholders to translate planning questions into tractable mathematical models and usable decision-support applications.
Deliver cloud-based optimization solutions to support broader deployment, scalability, and sustainable operational use across teams and workflows.

Postdoctoral Researcher | Singapore University of Technology and Design | Singapore

2014 - 2018

Conducted research in transportation systems, utility theory, discrete choice models, and robust optimization; supervised student projects and contributed to multidisciplinary design teams.

Software engineering & open source

Extend this engineering work through open-source contributions in optimization, numerical methods, and data-driven planning, with attention to documentation, testing, benchmarking, and reusable abstractions. The orx-parallel repository and broader orx-* ecosystem provide public examples, with crates published via crates.io.
Led the adoption of Rust for optimization-focused software within DHL Data & AI, establishing a production-oriented approach that enabled live Rust-based solutions for operational decision support.

Education

PhD, Industrial Engineering & Operations Research \| Middle East Technical University Dissertation: Airline Disruption Management	2009 - 2014
MSc, Industrial Engineering & Operations Research \| Middle East Technical University Dissertation: Two-Sided Assembly Line Balancing: Models and Heuristics	2006 - 2009
BSc, Industrial Engineering & Operations Research \| Middle East Technical University, Ankara, Turkey	2002 - 2006

Further training

Modeling and Simulation of Transportation Networks \| Massachusetts Institute of Technology	Jul 2015
Operations Research Peripatetic Postgraduate Program \| JKU Linz	Jul 2013

Research & publications

Research and peer-reviewed publications in airline disruption management, network-based recovery, traffic equilibrium, and applied optimization in logistics and transportation.

Flight Network-Based Approach for Integrated Airline Recovery with Cruise Speed Control
INFORMS Transportation Science

Integrated Aircraft and Passenger Recovery with Cruise Time Controllability
Annals of Operations Research

On the flexibility of using marginal distribution choice models in traffic equilibrium
Transportation Research Part B: Methodological

Distributionally Robust Markovian Traffic Equilibrium
INFORMS Transportation Science

Human-Centric Parcel Delivery with Operations Research and Machine Learning
INFORMS Interfaces

Selected talk

A practitioner perspective on applying Rust design principles to optimization software.

Composing Zero-Cost Abstractions in Route Optimization
Talk on the intersection of Operations Research and Rust

Selected technical capabilities

Optimization

Mixed-integer optimization
Network optimization
Routing and facility location
Heuristics, decomposition, and local search
Scenario analysis and decision support

Engineering

Rust, Python, C#, TypeScript/React, SQL
Parallel computation and concurrency
Production service design
Cloud deployment and integration
Profiling, benchmarking, and reusable library design

Languages

English (fluent) | German (intermediate) | Turkish (native)