The resource optimization problem for complex assembly lines

Abstract

This thesis delves into the intricacies of resource optimization, with a specific focus on the Assembly Line Balancing Problem (ALBP) in the context of complex assembly lines. In a world where advanced technologies have permeated various sectors, including industry, medicine, and the military, the efficient management of assembly lines becomes paramount. The fundamental objective of this research is to enhance production processes by addressing the complexities of assembly line balancing. This entails optimizing critical factors such as cycle time, the number of workstations, and resource allocation. The ALBP, central to this study, encompasses various forms, including the Mixed-Model Assembly Line Balancing Problem (MiMALBP) and the Robotic Assembly Line Balancing Problem (RALBP), each presenting unique complexities. Moreover, as the industry diversifies and production requirements evolve, the ALBP encounters novel characteristics and constraints, underscoring the need for innovative solutions. Drawing on meta-heuristic approaches powered by Artificial Intelligence, this thesis explores the efficient resolution of ALBP, as well as its variants. The research also tackles the challenge of energy optimization in assembly lines designed for the mixed production of multiple product models. Ultimately, this thesis navigates the ever-evolving industrial landscape, offering insights into addressing the complexities of assembly line balancing while harnessing the potential of advanced technologies and metaheuristic methods to optimize resources effectively.