PhD thesis position in Combinatorial optimization - Optimization of Human-Centered Production and Logistics Systems

*PhD Thesis in Combinatorial Optimization*

*Title:*Optimization of Human-Centered Production and Logistics Systems

*Laboratory:*Ecole des Mines de Saint-Etienne – Department of 
Manufacturing Sciences and Logistics, Aix-Marseille-Provence Campus, 
Gardanne, Bouches-du-Rhône, France.

*Start date:*October 2026. Applications will be reviewed on a rolling basis.

*Funding:*French National Research Agency.

*Supervision:*

  * *Nabil ABSI*- Professor - Ecole des Mines de Saint-Etienne, [email protected]
  * *Oussama BEN-AMMAR*- Associate Professor - IMT Mines Alès,
    [email protected]

*Keywords:*Operations research, optimization, machine learning, 
production, logistics, multimodal data, sensors, human factors, 
human-machine interaction.

*Context:*

Production and logistics systems rely heavily on the activity, skills, 
and condition of human operators. However, industrial decisions are 
still often made based on partial and static information, while new 
sources of data are becoming available: physiological or biomechanical 
sensors, activity history, skills, age, environmental context, system 
state, interactions with equipment, and so on.

These developments pave the way for new decision-support methods that 
can integrate human factors more accurately into optimization. They also 
raise several scientific challenges: how can heterogeneous and sometimes 
noisy data be transformed into information that is useful for 
decision-making? How can this information be leveraged for tactical or 
operational decisions? How can efficient, robust, and acceptable 
decision-making tools be designed in contexts involving human-machine 
interaction?

*Objective of the PhD:*

The objective of this PhD project is to develop new optimization models 
and methods that exploit multimodal data on operators and their 
environment in order to improve production and logistics decisions. The 
work will focus on:

  * Modeling decision-relevant information from sensor, contextual, and
    organizational data;
  * Developing optimization and decision-support methods at different
    planning horizons, from tactical to operational levels;
  * Designing hybrid approaches combining optimization, learning, and
    simulation;
  * Studying human-machine interaction in assisted decision-making systems.

Depending on the selected direction, the research may be based on one or 
more applications.

*Proposed methods:*

Combinatorial, stochastic, or robust optimization; learning for 
decision-making; simulation; and computational experimentation.

*Candidate profile:*

The candidate should have a strong background in operations research, 
industrial engineering, applied mathematics, decision-support computer 
science, data science, or a related field. A strong interest in 
modeling, optimization, data analysis, and industrial systems is 
expected. Good programming skills and a good command of English are 
required.

*Application Process:*

Please send your application by email as a single file, including a 
detailed CV, academic transcripts, and recommendation letters, where 
applicable, to Nabil ABSI, [email protected], and Oussama BEN-AMMAR, 
[email protected].

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