Last Update 10/05/2024

Main Research Topics

• Modular design;
• Integrated product process design;
• Product variety management;
• Reconfigurable manufacturing systems;
• Mass Customization.


Is it still ok to mass produce thousands of standard products and put them out there in the market pushing customers to buy them? Do we still need to mass produce huge quantities of products whereas many will not be sold and be transformed into waste?
We are each unique and hence we want unique products and services. These can be obtained when the customer (us) contributes to the design of the product and not only buys what is already produced. Mass customization allows to produce just what the customer wants as he wants it. Hence reducing unneccessary waste and stopping overproduction.
In order to produce high variety products that are each different and customized by the customer and at very small quantities, we need new flexible manufacturing systems, such as reconfigurable manufacturing systems who can be rearranged in order to produce different products.
We also need modularity, that allows us to have different modules that can arranged in many ways to form different products. Modularity saves money and time while allowing to offer high variety and customized products.


Considering the economy's evolution, industrial processes must adapt and meet production needs to address new customer expectations, namely products allowing them to express their individuality. This evolution combines with other challenges such as globalization, technological advancements, the emergence of new materials, market segment diversity, etc., resulting in requirements and specifications of a very different nature. This compels companies to offer a greater variety/diversity of products to better meet market needs and migrate towards flexible production systems. Environmental impact and sustainable performance issues further push companies to produce only what is necessary and demanded by the customer. All these factors drive companies to offer customized or personalized products to the customer. However, offering a greater variety of products or customizable/personalized products increases the complexity of managing industrial processes. This diversity impacts the entire product life cycle and is linked to logistics and pre-and post-sales services. Furthermore, migrating to flexible production systems requires an evolution in production and supply chain management methods and tools, particularly within the framework of Industry 4.0.


My research aims to develop new methods for optimizing production systems or the supply chain to offer highly diverse or customizable products while maximizing their economic and/or environmental performance. The main contributions are as follows:

• Proposal of an approach and a discrete event simulation tool to determine the optimal configuration of the production chain and supply chain to increase performance while offering a wide variety of products.
• Proposal of various mathematical formulations for production management for reconfigurable manufacturing systems with different approximate resolution approaches and reinforcement learning algorithms. Some formulations focus solely on economic performance, while others integrate environmental performance or customer satisfaction.
• Proposal of mathematical formulations for the integrated combinatorial optimization problem of customizable product configuration and production system configuration optimization. This has led to the development of an online demonstrator. The different formulations differ in the considered manufacturing system's reconfigurability level.
• Proposal of a comprehensive distributed and dynamic approach for optimizing the production planning of a cellular manufacturing system producing customizable products. This approach includes real-time supply chain data. Two approximate approaches have been proposed and combined with NSGAII and SMPSO metaheuristics to solve the optimization problem.
• Proposal of a methodology based on the Analytical Hierarchy Problem (AHP) approach for the design and optimization of reverse supply chains.

PhD thesis