Integrated Optimization Approaches for Unified, Responsible, and Sustainable Logistics

This PhD project focuses on optimizing logistics as a whole, from palletizing products in the warehouse to their final delivery. The proposed approach does not treat each stage separately, but rather considers them in an integrated and centralized manner in order to improve overall efficiency, particularly for optimization objectives that are intrinsically linked to organizational cohesion.

In many logistics systems, the organizational disconnect between internal and external logistics leads not only to reduced efficiency and avoidable environmental impacts, but also to frustration. Indeed, delivery drivers often have to wait their turn at the warehouse for loading or at customer sites for unloading, as customers are not always informed sufficiently in advance of delivery times. These waiting periods, in addition to causing frustration for drivers, result in additional energy consumption to maintain a comfortable cabin temperature.

It is therefore necessary to jointly plan warehouse 1) picking operations, 2) pallet building and stacking, taking product fragility into account, 3) truck loading, and 4) delivery routes. Few studies have addressed such an integrated organization and the benefits it can bring to overall system efficiency.

Another key aspect of this work lies in incorporating the human factor: reducing drivers’ waiting times during loading, integrating mandatory rest periods for both warehouse operators and drivers, and coordinating these constraints with other logistical decisions.

From an environmental perspective, the research also aims to reduce greenhouse gas emissions by designing better-adapted delivery routes, promoting the use of electric trucks, and aligning charging times with mandatory driver breaks and loading operations.

The LIST3N Laboratory for Computing and Digital Society develops methods to address the entire data processing chain, from sensors to final use, including analysis and optimization. Its scientific project aims to create an integrated processing chain, with a focus on traceability, explainability, and robustness to ensure user trust. The laboratory will propose new mathematical strategies to optimize this chain and will validate its approaches in real-world environments. It combines information and communication sciences with social sciences and humanities for multidisciplinary research.The main objectives are: developing tools for the entire data processing chain, proposing specific and multidisciplinary approaches, and addressing societal challenges such as business digitization, healthcare, energy, and agronomy.

Applicants should be in the final year of a Master’s program (Master 2) or third year of an engineering program, or have recently graduated from such programs.

The required theoretical are operations research and/or machine learning.

Proficiency in at least one programming language (C++, Java, Python, Julia, C#, Rust) is necessary.

Understanding the object-oriented programming paradigm is also an important criterion