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INTERSHIP DYNALOG- Cooperative game-based decision-making for AMR fleet operations in a logistics warehouse using discrete-event simulation (FlexSim)

CESI · La Couronne (16) · Stage

Publiée le 13 avril 2026

Title: Cooperative game-based decision-making for AMR fleet operations in a logistics warehouse using discrete-event simulation (FlexSim)

Titre : Prise de décision coopérative par théorie des jeux pour le pilotage d’une flotte d’AMR en entrepôt, via simulation à événements discrets (FlexSim)

Abstract

Catchphrase: Enabling robust and efficient AMR fleet coordination through cooperative decision mechanisms under real-time warehouse uncertainty.

Keywords: AMR, cooperative game theory, multi-robot task allocation, warehouse logistics, discrete-event simulation, FlexSim, negotiation, charging scheduling, congestion

Autonomous Mobile Robots (AMRs) are increasingly deployed for internal transport in warehouses, but their performance depends on continuous decisions under uncertainty (new requests, changing priorities, congestion, charging needs, robot downtime). This internship focuses on the design and evaluation of cooperative decision-making mechanisms among robots, grounded in cooperative game theory, to dynamically allocate tasks and coordinate robot actions..

The work will rely on a discrete-event simulation model implemented in FlexSim to represent warehouse flows, task arrivals, resources, and disruptions. On top of this simulation layer, cooperative coordination strategies will be developed (e.g., coalition formation, cost-sharing rules, bargaining mechanisms, cooperative allocation schemes) to decide: (i) task-to-robot assignment, (ii) mission sequencing, and (iii) charging/availability management while mitigating congestion effects. Strategies will be assessed through KPIs such as service level for high-priority tasks, throughput, energy/distance, utilization rate, fairness among robots, and robustness to disruptions.

Résumé

Slogan : Coordonner efficacement une flotte d’AMR par coopération (théorie des jeux) malgré les incertitudes et la dynamique temps réel d’un entrepôt.

Mots clés : AMR, théorie des jeux coopératifs, allocation de tâches multi-robots, entrepôt logistique, simulation à événements discrets, FlexSim, négociation, recharge, congestion

Les robots mobiles autonomes (RMA) sont de plus en plus utilisés pour le transport interne en entrepôt, mais leurs performances dépendent d’une prise de décision continue face à des événements (arrivées de nouvelles missions, changements de priorités, congestion, recharge, indisponibilités). Ce stage vise à concevoir et évaluer des mécanismes de décision coopératifs entre robots, basés sur la théorie des jeux coopératifs, afin d’améliorer la coordination d’une flotte d’AMR.

Le travail s’appuiera sur un modèle de simulation à événements discrets sous FlexSim, décrivant les flux, les ressources (AMR, stations, chargeurs), et les perturbations. Au-dessus de cette couche simulation, des stratégies coopératives seront développées (coalitions, partage de coût/gain, mécanismes de négociation, schémas d’allocation coopératifs) pour décider : (i) l’affectation des tâches aux AMR, (ii) le séquencement des missions, et (iii) la gestion de la recharge/disponibilité tout en limitant la congestion. Les politiques seront comparées via des indicateurs : niveau de service des tâches prioritaires, débit, distance/énergie, taux d’utilisation, équité entre robots, robustesse aux aléas.

Skills

Scientific and technical skills:

• Multi-robot coordination and AMR fleet management
• Cooperative game theory for decision-making (coalitions, bargaining, cost sharing)
• Discrete-event simulation of warehouse operations (FlexSim)
• Optimization / algorithm design and data analysis in Python
• Intralogistics knowledge (transport, dispatching, charging, congestion effects)

Soft skills:

• Autonomy and scientific curiosity
• Strong coding discipline (tests, reproducibility, documentation)
• Clear communication (KPI-driven comparisons, reporting)
• Teamwork with researchers and industrial stakeholders

Compétences

Compétences scientifiques et techniques :

• Gestion de flotte d’AMR et coordination multi-robots
• Théorie des jeux coopératifs appliquée à la décision (coalitions, négociation, partage de coûts)
• Simulation à événements discrets d’un entrepôt (FlexSim)
• Conception d’algorithmes et analyse de données en Python
• Intralogistique (transport interne, dispatching, recharge, congestion)

Compétences relationnelles :

• Autonomie et curiosité scientifique
• Rigueur de développement (tests, reproductibilité, documentation)
• Communication orientée indicateurs (KPIs) et résultats
• Travail en équipe (chercheurs/ingénieurs, partenaires industriels)

Research Work

Scientific context

Warehouse intralogistics is rapidly automating with AMR fleets to increase flexibility compared to fixed conveyors and classical AGVs. However, warehouses face high variability (inbound arrivals, urgent orders, shifting priorities) and operational disturbances (charging downtime, maintenance, local congestion). These dynamics require decision methods that are reactive, robust, and scalable. From a scientific standpoint, the problem relates to multi-robot task allocation, dynamic pickup-and-delivery, online scheduling, and distributed coordination mechanisms. This internship investigates cooperative game-theoretic decision models to coordinate AMRs efficiently under uncertainty, evaluated through discrete-event simulation.

Subject

The warehouse contains a set of pickup/drop-off points (input/output stations, storage areas, production/kitting points) generating transport tasks with possible priorities and due times. AMRs execute transport missions while facing constraints such as limited charging capacity, energy autonomy, variable travel times, and congestion.

The objective is to design a cooperative decision mechanism that enables robots to jointly decide:

• Task allocation (which AMR takes which mission),
• Mission sequencing (ordering tasks per robot),
• Charging management (when/where to charge, which robot gets priority), while reacting to real-time events (new tasks, priority updates, robot unavailability, congestion peaks).

Cooperative game theory will be used to formalize collaboration (e.g., coalition values/costs, payoff or cost sharing, bargaining outcomes). The decision layer will interface with the simulation model to test policies under multiple dynamic scenarios.

Prior works in the laboratory

Within the Engineering and Digital Tools team, CESI LINEACT develops approaches for modeling, simulation, optimization and data analysis of cyber-physical systems, as well as decision-support tools for industrial operations. This internship will leverage these competencies to build a FlexSim discrete-event simulation model of warehouse AMR operations and to study cooperative decision strategies suitable for real-time constraints and industrial deployment.

Work program

Planned steps (indicative 6-month schedule):

• State of the art on cooperative multi-robot task allocation, negotiation/coalition mechanisms, AMR fleet management; definition of KPIs and dynamic events.
• Development of a FlexSim discrete-event simulation model (layout, task generators, AMR behaviors, charging, disruptions, congestion indicators).
• Formalization of the decision problem: state variables, constraints, utility/cost functions, and baseline heuristics (priority rules, nearest-task, etc.).
• Design and implementation of cooperative game-based strategies (e.g., coalition formation + cost sharing, bargaining/negotiation, cooperative allocation).
• Integration and testing via simulation on benchmark and/or randomly generated scenarios (priority changes, downtime, congestion, limited charging).
• Results analysis, sensitivity study, and writing of a final report with reproducible code and datasets.

Expected scientific/technical production

Deliverables and impacts:

• A formal problem definition and a suite of dynamic scenarios (task arrivals, priority updates, robot downtime, charging constraints, congestion).
• An implemented set of coordination strategies (baselines + proposed cooperative game-based method) with documented parameters and reproducible experiments.
• A FlexSim discrete-event simulation model of warehouse AMR operations and KPI dashboards (service level, throughput, energy/distance, fairness, robustness).
• A final internship report; potential submission to a conference/journal depending on results and partner constraints.

Context

Lab presentation

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific teams and several application areas.

• Team 1 "Learning and Innovating" mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity and innovation processes.
• Team 2 "Engineering and Digital Tools" mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human-system interactions in particular through digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

• Industry 5.0,
• Construction 4.0 and Sustainable City,
• Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Links to the research axes of the research team involved

This internship aligns with the 'Engineering and Digital Tools' team through (i) modeling and simulation of a cyber-physical warehouse system, (ii) optimization and decision-support for real-time operations, and (iii) digital twin driven evaluation of resilient scheduling policies.

Organisation

Funding: DynaLog Project

Location: CESI LINEACT, Angoulême (France)

Starting date: March 2026

Duration: 6 months.

Supervisor(s):

Abdelkader Mekhalef Benhafssa, Associate Professor, CESI LINEACT, Angoulême (Fance)

M’hammed Sahnoun, Resarch Director, CESI LINEACT, Rouen (Fance)

Belgacem Bettayeb, Associate Professor, CESI LINEACT, Lille (Fance)

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Compétences

• théorie des jeux
• analyse de données
• Python (programmation informatique)
• prendre des décisions
• logistique
• Négociation commerciale
• Travail en équipe

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