This thesis is structured in two main parts that explore different research directions related to warehousing logistics and human factors. As a first objective, this thesis studies the integration of planning problems in manual picker-to-parts warehouses. Order Picking (OP) is widely considered the most resource-intensive operation in warehousing logistics, and recent research has highlighted the potential gains of jointly optimizing OP planning problems. The work achieved during the preparation of this thesis advances the state-of-the-art on the topic in several dimensions. First, we study the tractability of the Picker Routing Problem (PRP) in conventional warehouses. Although it has been established that the problem is polynomial for a single-block warehouse, the complexity for general multi-block warehouses remained an open question. This gap is addressed as we prove that the problem is NP-hard in the strong sense. Moving forward, we explore the integration of the storage and routing decisions, motivated by modern industrial practices due to the development of e-commerce, where storage decisions are becoming increasingly dynamic. We introduce an extended formulation for the problem, based on a Dantzig-Wolfe reformulation, further strengthened by the introduction of a novel family of non-robust valid inequalities. From this formulation, we propose a generic Branch-Cut-and-Price algorithm able to solve a large class of variants of the problem. Finally, we propose efficient move evaluation and neighborhood exploration routines for integrated OP problems. These results are based on the introduction of a novel PRP heuristic based on lower and upper bounds for the problem. To highlight the potential of these contributions, we develop a Large Neighborhood Search algorithm to jointly optimize routing decisions with either storage or batching decisions.

As a secondary objective, this thesis studies the integration of human factors and ergonomics in optimization problems, with a focus on logistics and manufacturing systems. Human-aware optimization is becoming an important research stream with the advent of Industry 5.0, placing the human operator at the core of the process design. Our main contribution to the topic is an extensive review of the literature. In this work, we present a holistic picture of human-aware optimization of logistics and manufacturing systems. After a semi-systematic search of the literature, a comprehensive analysis of the collected material is carried out to map the related literature by class of problems encountered in logistics and manufacturing. Subsequently, we investigate the mathematical programming techniques used to integrate human factors into optimization models. Finally, the existing \textit{Human-Aware Models} are classified and reviewed by domain. Particular attention is paid to the field validity of each method, its relevance to specific use cases, the required data collection, and its usability within mathematical optimization models.

Jury:

• Kris Braekers, Associate Professor, Hasselt University (Reviewer)
• François Clautiaux, Professeur, Université de Bordeaux (Reviewer)
• Yasemine Arda, Professeure, HEC Liège (Examiner)
• Maxime Ogier, Maître de Conférences, Centrale Lille (Examiner)
• Stefan Røpke, Professor, Technical University of Denmark (Examiner)
• Nabil Absi, Professeur, Mines Saint-Etienne (Thesis director)
• Valeria Borodin, Maître Assistante, IMT Atlantique (Thesis advisor)
• Diego Cattaruzza, Maître de Conférences, Centrale Lille (Thesis advisor).