Phd Position F - M Clustering Approaches For Logistics Route Planning Via Adaptive Districting In a Stochastic Environment H/F - INRIA

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.
PhD Position F/M Clustering Approaches for Logistics Route Planning via Adaptive Districting in a Stochastic Environment
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac +5 ou équivalent

Autre diplôme apprécié : Master degree in Operations research / Industrial Engineering

Fonction : Doctorant

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.

For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).

Contexte et atouts du poste

This PhD thesis is part of the collaboration between INRIA and La Poste, the French national postal service provider.

The distribution operations of urban logistics operators, such as La Poste, have historically relied on a stable territorial structure: delivery routes are defined as fixed sets of delivery points, often grouped into districts, which are aggregated to form daily routes. This organizational structure offers advantages in terms of operational resilience, local knowledge, and social stability. However, it is now showing its limits due to an increasing demand variability.

Indeed, volumes are becoming increasingly volatile depending on the day (busy days vs. low days), the period (seasonal peaks, sales, exceptional events), geographic areas, and customer types. Added to this are structural uncertainties linked to e-commerce, operational contingencies, and growing environmental constraints.

In this context, a fixed route organization leads either to underutilization of resources on low days or to significant operational strain on busy days. A promising approach is to consider adaptive districting-basic territorial units that can be dynamically merged to form routes with the expected workload.

The underlying problem can be stated as: how can districts be grouped, day by day, into coherent, robust, and operationally feasible routes, while accounting for demand uncertainty and operational constraints?

This thesis aims to make a scientific and contribution to this problem by integrating modeling, solution methods, and validation using real-world cases from La Poste.

Mission confiée

The overall objective of this thesis is to propose a comprehensive methodological framework for the adaptive clustering-baseddesign of logistics routesin a stochastic environment. More specifically, the thesis will pursue the following objectives:

- Model the route construction problem as a clustering problem under uncertainty, explicitly incorporatingdemand variability and operational constraints.
- Develop solution methods capable of producing, within a reasonable computation time, robust and efficient district assemblies suitable for operational use.
- Evaluate the proposed approaches using real-world data, analyzing potential gains, trade-offs, and field acceptability conditions.

Principales activités

The work plan for this thesis will be as follows:

Contribution 1 - Modeling the stochastic dynamic clustering of delivery routes
The first contribution will focus on the mathematical and conceptual modeling of the problem.
Districts will be considered as basic units characterized by a geographic location, travel times and distances,
a stochastic distribution of volumes and subject tooperational constraints.

The problem will consist to design routes based onthe districts. The routes may vary over time, and one or more objectives may beconsidered. Several modeling approaches will be studied.

Contribution 2 - Solution Methods and Algorithms
The second contribution will focus on developing solution-tailoredmethods. Since the problem under study is large-scale, combinatorial, and stochastic. The thesis will therefore primarily explore matheuristics.

Particular emphasis will be placed on the algorithms' ability to produce robust solutions with respect to demand variability. The issues of computation time and integration into existing tools will also be central.

Contribution 3 - Field Application and Validation on Real-World CasesThe third contribution will focus on the application and validation of the research on cases proposed by La Poste.Using real-world data, the proposed methods will be tested across several representative regions. The results will be analyzed across several dimensions: operational performance, robustness to traffic variations, stability of the planned routesrelative to reference routes, and operational acceptability, using simple, interpretable indicators.

This phase will identify the conditions under which clustering-based approaches provide real value and the practical limitations. It willbe a bridge between academic research and industrial deployment.

Compétences

Technical skills and level required :

- Good knowledge in combinatorial opimization, integer programming, heuristics
- Coding: C++, Java, Julia

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

€2,300 gross per month