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PhD Position in Developing Exact Algorithms for Routing Problems

Self-funded 🎓 Computer Science exact algorithms routing problems branch-and-price logistics optimization pickup-and-delivery multi-echelon distribution

Explore advanced exact algorithms for routing and logistics optimization with Professor Marilène Cherkesly at UQAM. Tackle complex urban and emergency transportation challenges by developing novel mathematical models and scalable solutions.

AI-generated overview

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Why This Research Matters

This research directly addresses critical challenges in optimizing transportation and supply chain systems, improving logistical efficiency and sustainability. Enhanced routing algorithms can lead to smarter city planning and more effective emergency evacuations, benefiting both industry and society by reducing costs and environmental impact.

Operations research

Project Description

Project Overview

This PhD project centers on developing exact algorithms for routing problems, which lie at the crossroads of operations research, computer science, and industrial engineering. Key challenges involve pickup-and-delivery networks and multi-echelon distribution systems with applications to urban logistics and emergency evacuation. The project emphasizes state-of-the-art mathematical modeling and exact solution methods such as branch-and-price decomposition.

What You Will Do

You will propose and model innovative logistics systems and develop cutting-edge exact algorithms to solve large-scale routing instances. This will involve collaboration within research groups such as CIRRELT, GERAD, and CRI2GS, participation in seminars and workshops, and international research exchanges. The focus is on producing both algorithmic innovations and actionable managerial insights.

Expected Outcomes

The research aims to deliver efficient and scalable optimization methods that enhance logistics and transportation operations. Expected outcomes include improved solution techniques for real-world routing problems, contributing to smarter and more sustainable city planning and emergency response systems.

Why This Matters

Routing problem solutions are critical for optimizing supply chains, enabling faster and more reliable distribution networks and emergency evacuations. The project's outcomes promise to positively impact industrial efficiency, environmental sustainability, and public safety, affording significant societal and economic benefits.

Entry Requirements

A Master's degree in operations research, computer science, industrial engineering, or a related field. Strong programming skills (e.g., Python, C++). Interest in mathematical modeling and combinatorial optimization. Additional knowledge in integer programming, network optimization, or decomposition methods is advantageous.

How to Apply

Submit curriculum vitae (including research projects and publications), Master's transcripts, a brief research statement (max 1 page), and references' contact details. Applications are reviewed on a rolling basis. For details and to apply, see: https://www.linkedin.com/posts/marilenecherkesly_phd-position-in-optimization-ugcPost-7447288457334411264-fbmE

Eligibility

UK/Home
EU
International

Supervisor Profile

PM
Professor Marilène Cherkesly
Université du Québec à Montréal

Professor Marilène Cherkesly is a recognized expert in exact algorithms for routing problems, focusing on optimization in operations research and logistics. Her research emphasizes developing mathematical models and solution methods such as branch-and-price decomposition. She is affiliated with top Canadian research groups including CIRRELT, GERAD, and CRI2GS, and is known for her contributions to combinatorial optimization and transportation applications.

Key Publications

2015 102 citations
A population-based metaheuristic for the pickup and delivery problem with time windows and LIFO loading
2016 84 citations
Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks
2015 84 citations
Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and last-in-first-out loading
2017 58 citations
The pickup and delivery problem with time windows and handling operations
2019 42 citations
Community healthcare network in underserved areas: Design, mathematical models, and analysis

Research Contributions

Developed advanced branch-price-and-cut algorithms for various pickup and delivery problems with time window constraints and loading policies such as LIFO and multiple stacks.
Improved optimization techniques for logistics and transportation management, enabling more efficient and feasible routing solutions.
Designed mathematical models and heuristics for healthcare network planning in underserved areas and humanitarian logistics.
Supported better planning and deployment of mobile clinics and healthcare resources in challenging or underserved environments.

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