In nowadays manufacturing industries there is usually a lack of knowledge in decision makers and stakeholders to optimize production and logistics flows considering the shop floor layout. Decision support system through Simulation-Based Multi-Objective Optimization can be a significant approach for production and logistics system improvement. The main benefits are optimized solutions with an increased efficiency of the production, internal logistics and required space on the shop floor layout. Usually at a lower cost than consultancy companies, obtaining just the best optimal solutions and with the possibility of analyzing them without interrupting the system. The application of research with Simulation-based Optimization in manufacturing leads to significant improvements of complex systems as demonstrated in major manufacturing companies working together with universities and research centers.
Facility layout is considered a complex procedure due to the large number of variables involved, conflicting objectives, and a high level of stochasticity in the systems related to it. It is considered one of the major problems in the design and improvement process of complex systems. This complexity and variability make simulation and optimization tools well suited for the facility layout design and continuous improvement due to the need of expansions, redesigns, and adaptations of the systems in question.
The aim of this thesis is to investigate how simulation and optimization, and their combination – called simulation-based optimization – can support the design and improvement of facility layouts. A literature review of the state of the art of simulation and optimization for design and improvement of facility layouts indicates a gap of knowledge when it comes to optimizing the layout with consideration to the combination of the production system itself and the internal logistics system related to it. The thesis aims to bridge this gap by proposing a framework based on empirical studies. This framework can be used as a guideline by manufacturing systems to support facility layout design and improvement. The framework is designed to allow detailed simulation models of specific production and logistics sub-systems to be combined with more aggregated simulation models of the entire layout at a higher strategic level. The goal of this combination is giving an optimized facility layout and insights in its potential limitations.
The framework is evaluated using different the real-world empirical case studies within manufacturing, and shows to be valuable in optimizing both new designed and existing facility layouts. The results of the thesis include the identification of the key parameters for the optimization of the layout i and the key challenges limiting uncertainty reduction in layout designs with simulation and optimization. These results can serve managers and stakeholders to support strategic decisions regarding complex-system layout design and improvement.