Michael-Sam Vidza , Marcin Budka , Wei Koong Chai , Mark Thrush , Mickaël Teixeira Alves
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Supply network disruption: A framework for assessing vulnerability and implementing resilience strategies
Disruptions to food supply chains can have significant impacts on food security and economic stability. This study investigates the resilience of supply networks to such disruptions, focusing on the distribution of live fish between farms in England and Wales as a case study. A decision support framework is developed to assess network vulnerability and ensure operational continuity in the face of disruptions to the supply and demand balance. The framework incorporates a novel rewiring algorithm that dynamically reconfigures network connections to maintain the flow of goods. The algorithm predicts supply-demand pairs and adjusts connections to preserve functionality during disruptions. To evaluate the performance of the framework and algorithm, a combination of topological metrics, such as connectivity and redundancy, and operational measures, including supply fulfilment and distribution efficiency, is utilised. Through simulations of random and targeted node removals, the rewiring algorithm is shown to effectively mitigate the impact of disruptions, preserve network functionality, and help ensure a consistent supply of live fish. These findings offer valuable insights for managing disruptions in aquaculture supply chains and highlight the broader applicability of the framework to enhance the resilience of other supply networks.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.