Équilibrage des chaînes de montage avec robots collaboratifs

Field: Decision Sciences (Social Sciences)

Human-robot collaboration (HRC) has become a transformative strategy in modern manufacturing, promising to enhance efficiency, flexibility, and productivity in assembly line operations. This thesis will explore developing and applying innovative mathematical models to optimize task scheduling and resource allocation in assembly systems that integrate human workers and collaborative robots (cobots). It will solve them using promising algorithms. Also, it will address the challenges of balancing assembly lines while considering setup times, potential cobot downtimes, cobot failures, and budget constraints, ultimately seeking to improve operational efficiency and product quality. Additionally, it will examine practical limitations, including the financial investment required for cobots, the need for adaptable system designs to accommodate production variations, and the complexity of assigning tasks between humans and cobots based on precision, safety, and skill requirements. The study will explore predictive and optimization algorithms to enhance system reliability and minimize disruptions caused by cobot failures, ensuring robust and efficient collaboration in assembly lines. The significance of this thesis will arise from the growing need for cost-effective and resilient human-robot collaboration in modern manufacturing. Budget constraints will often limit the implementation of cobots, making it essential to develop optimization models that will ensure efficient resource allocation without exceeding financial limits. Setup times, a crucial factor in real-world assembly lines, will introduce additional complexities that impact scheduling and overall productivity. Furthermore, predicting cobot failures will minimize unexpected downtimes and enhance system reliability. By integrating multi-skilled human workers into assembly line balancing, this study will acknowledge the diverse capabilities of human operators, utilizing their adaptability to complement cobot functionalities. Addressing these challenges through advanced mathematical modeling, metaheuristic algorithms, and machine learning techniques will ensure the development of robust, cost efficient, and adaptable manufacturing systems capable of handling real-world uncertainties. This dissertation will introduce several ground breaking contributions to assembly line balancing and human-robot collaboration. First, two novel mathematical formulations will be developed to incorporate budget constraints into HRC-based assembly line balancing. These formulations will ensure optimal task allocation between human workers and cobots while adhering to financial limitations, enhancing the practical feasibility of implementing HRC in real-world manufacturing environments. Various problem instances from the SALBP dataset will be employed to validate these models, providing a robust performance evaluation benchmark. The proposed models will be solved using advanced metaheuristic algorithms, specifically simulated annealing (SA) and genetic algorithms (GA), and their results will be systematically compared against the Gurobi optimizer to assess efficiency and solution quality. Additionally, a comprehensive performance analysis will be conducted across diverse operational settings to evaluate the robustness and adaptability of the proposed approaches. Secondly, the thesis will develop a novel mathematical model to cope with assembly line balancing, incorporating setup times for the first time. This extension will allow for a more realistic representation of assembly line operations, where setup times will be crucial in determining overall efficiency. A significant novelty of this research will be the presentation of machine learning-based algorithms to predict cobot failure in an adapted assembly line balancing model that will include setup times. (please read more in the PDF)