Internship DYNALOG : Dynamic task scheduling for AMR-based transport operations in a logistics warehouse CESI

Laboratory 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 to companies is a determining factor in 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, has 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 also focuses on decision-support tools and on the study of human-system interactions, particularly 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 the one in Rouen dedicated to Factory 5.0 and the one in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Scientific context

Warehouse intralogistics is undergoing rapid automation with AMR fleets to increase flexibility compared to fixed conveyors and AGVs. In parallel, warehouses face high variability (inbound arrivals, urgent orders, shifting priorities) and operational disturbances (charging/maintenance downtime, congestion). These dynamics call for real-time decision methods that combine scheduling and resource allocation with robust performance guarantees. From a scientific standpoint, the problem relates to dynamic pickup-and-delivery, multi-robot task allocation, storage assignment, and online optimization under uncertainty.

Subject

The warehouse contains (i) IN stations where pallets with products arrive and must be stored on shelves, and (ii) OUT stations where pallets must be prepared by collecting products from stock according to a predefined priority order. AMRs perform two task families:

• IN: transport from IN station to a decided storage location (choice of shelf position, considering capacity and future accessibility);
• OUT: transport from a storage location to an OUT station, respecting a dynamic priority list.

The objective is to design a dynamic scheduler that assigns tasks to AMRs, sequences missions for each robot, and selects storage locations for IN tasks, while reacting to events such as new IN/OUT requests, priority changes, and robot unavailability (charging, maintenance). The work will investigate modeling choices (state representation, constraints, objectives), and algorithmic strategies (rolling horizon, re-optimization triggers, hybrid heuristics/optimization).

Prior works in the laboratory

Within the Engineering and Digital Tools team, CESI LINEACT develops methods for modeling, simulation and optimization of cyber-physical systems, including decision-support tools and digital twins. The internship will leverage these competencies to build a simulation model of warehouse flows and to study scheduling/optimization algorithms suited to real-time constraints and industrial deployment.

Work program

Planned steps (indicative 6-month schedule):

• State of the art on AMR task allocation, dynamic pickup-and-delivery, and storage assignment; definition of KPIs and events.

• Formalization of the problem (tasks, priorities, constraints, objective function) and baseline dispatching heuristics.

• Design of a rolling-horizon scheduler (optimization + fast heuristics), including re-optimization triggers.

• Implementation and testing by simulation of discrete events of benchmark and/or randomly generated scenarios (priority changes, downtime, congestion).

• Results analysis, sensitivity study, and writing of a final report with reproducible code and datasets.

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