This thesis investigates the Pick-up and Delivery Problem with Cooperative Robots (PDPCR) and provides mathematical models, theoretical results, and solution methodologies. PDPCR is a novel optimization problem arising from the growing use of reconfigurable and cooperative robotic systems in modern warehouse environments. The work is motivated by the increasing complexity of logistics operations and the need for efficient coordination among multiple robots performing interdependent tasks.

The first part of the thesis establishes the industrial and scientific context of the study. It reviews the state of the art in warehouse automation and related optimization problems, notably the Vehicle Routing Problem with Synchronization constraints and the Resource-Constrained Project Scheduling Problem with Transfer Time, positioning the PDPCR within this broader research landscape and identifying the gaps this work seeks to fill.

We then develop two Mixed Integer Programming (MIP) formulations for the PDPCR and describe a systematic procedure for generating benchmark instances. Computational experiments assess the ability of these formulations to solve small-sized problems to optimality, thereby providing reference solutions for evaluating approximate methods.

To address the scalability limitations of exact approaches, we propose a series of greedy heuristics designed to efficiently handle large-scale instances. These algorithms are evaluated in terms of solution quality and computational effort, demonstrating their effectiveness compared to exact methods.

Building upon these foundations, the thesis introduces a metaheuristic framework based on tabu search, featuring a novel Redistribute neighborhood structure. Extensive numerical experiments show that the proposed tabu search outperforms classical approaches, particularly on large or complex instances. The versatility of the Redistribute neighborhood also suggests potential applicability to other combinatorial optimization problems.

The thesis concludes by summarizing the main findings, discussing their practical implications, and outlining several directions for future research in cooperative robotics and large-scale scheduling optimization.

Jury members:
- Mme. Sonia VANIER, Professeure, École Polytechnique (Rapportrice)
- M. Samuel VERCRAENE, Maître de conférences HDR, INSA de Lyon (Rapporteur)
- Mme. Sophie DEMASSEY, Maitresse de Conférences HDR, École de Mines de Paris (Examinatrice)
- M. Thibaud MONTEIRO, Professeur, INSA de Lyon (Examinateur)
- M. Viet Hung NGUYEN, Professeur, LIMOS, Université Clermont Auvergne (Directeur de thèse)
- M. Jean-Philippe GAYON, Professeur, LIMOS, Université Clermont Auvergne (Co-directeur de thèse)
- M. Anh Son TA, Principal Lecturer, FAMI, Hanoi University of Science and Technology (Co-encadrant de thèse)
- M. Alain QUILLIOT, Professeur émérite, LIMOS, Université Clermont Auvergne (Invité).