Abstract
In this paper, a model-based decision-making framework for the design of localized networked production systems under largescale disruptions is developed. The framework consists of optimization and agent-based simulation models that run successively in an iterative manner, gradually improving the performance of the perceived system. The framework integrates uncertainty, provides decisions at different decision-making levels and embeds an algorithm that allows for communication between demand nodes and production sites once inventory shortages occur. The framework has been applied on a case study for the design of localized production and distribution networks, powered by additive manufacturing (AM), in South East England during the early stages of the COVID-19 pandemic outbreak. Results revealed that implementing the framework indeed results in performance improvements to AM-powered production networks, particularly with regards to inventory shortages and lead time.
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