Resilience is commonly understood as the capacity for a system to maintain a desirable state while undergoing adversity or to return to a desirable state as quickly as possible after being impacted. In this paper, we focus on resilience for complex sociotechnical systems (STS), specifically those where safety is an important aspect. Two main desiderata for safety-critical STS to be resilient are adaptive capacity and adaptation. Formal studies integrating human cognition and social aspects are needed to quantify the capacity to adapt and the effects of adaptation. We propose a conceptual framework to elaborate on the concept of resilience of safety-critical STS, based on adaptive capacity and adaptation and how this can be formalized. A set of mechanisms is identified that is necessary for STS to have the capacity to adapt. Mechanisms belonging to adaptive capacity include situation awareness, sensemaking, monitoring, decision-making, coordination, and learning. It is posited that the two mechanisms required to perform adaptation are anticipation and responding. This framework attempts to coherently integrate the key components of the multifaceted concept of STS adaptive resilience. This can then be used to pursue the formal representation of adaptive resilience, its modeling, and its operationalization in real-world safety-critical STS.

Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in complex systems, especially during disturbances. In contrast to the centralised approaches, distributed planning is performed by several agents, which coordinate plans with each other. This research evaluates the contribution of agent-based distributed planning and coordination to the resilience of airport surface movement operations when runway reconfigurations occur. An autonomous Multi-Agent System (MAS) model was created based on the layout and airport surface movement operations of Schiphol Airport in the Netherlands. Within the MAS model, three distributed planning and coordination mechanisms were incorporated, based on the Conflict-Based Search (CBS) Multi-Agent Path Finding (MAPF) algorithm and adaptive highways. MAS simulations were run based on eight days of real-world operational data from Schiphol Airport and the results of the autonomous MAS simulations were compared to the performance of the real-world human operated system. The MAS results show that the distributed planning and coordination mechanisms were effective in contributing to the resilient behaviour of the airport surface movement operations, closely following the real-world behaviour, and sometimes even surpassing it. In particular, the mechanisms were found to contribute to more resilient behaviour than the real-world when considering the taxi time after runway reconfiguration events. Finally, the highway included distributed planning and coordination mechanisms contributed to the most resilient behaviour of the airport surface movement operations.

This meta-review systematically examines 88 review papers from the scientific literature, focusing on the diverse ways scholars define and conceptualise the resilience of complex sociotechnical systems (STS). Among the 484 different conceptualisations identified in the reviews, we observe recurring patterns based on their semantics. In particular, four constructs are predominant: some positive elements, some negative events, specific actions, and some constraints on these actions. Our analysis involves a meticulous categorisation and synthesis of these findings, revealing underlying convergences in the academic discourse on STS resilience. Despite what seemed to be apparent disagreements among scholars in the last decade, our study shows that many differing viewpoints are actually complementary, representing varied expressions of similar underlying principles converging towards a large consensus. This comprehensive synthesis offers a unique perspective on the field of STS resilience, demonstrating the feasibility of moving from diverse meta-theoretical paradigms towards a more unified paradigmatic approach.

The severity of natural disasters is increasing every year, having an impact on many people’s lives. During the response phase of disasters, airports are important hubs where relief aid arrives while people need to be evacuated to safety. However, the airport often forms a bottleneck in these relief operations because of the sudden need for increased capacity. Limited research is carried out on the operational side of airport disaster management. Experts identify the main problems as first the asymmetry of information between the airport and the incoming flights, and second the lack of resources. The goal of this research is to gain understanding of the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of the cargo handling operations in an airport after a natural disaster event. An agent-based model is created, where realistic offloading strategies with different degrees of information uncertainty are implemented. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which can be split into offloading time, boarding time and the cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, the waiting times and other inefficiencies rapidly increase with the more unplanned aircraft arriving.

The severity of natural disasters is increasing every year, impacting many people's lives. During the response phase of disasters, airports are important hubs where relief aid arrives and people need to be evacuated. However, the airport often forms a bottleneck in these relief operations due to the sudden need for increased capacity. Limited research has been done on the operational side of airport disaster management. Experts identify the main problems as, first, the asymmetry of information between the airport and incoming flights, and second, the lack of resources. The goal of this research is to understand the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of cargo handling operations at an airport after a natural disaster. An agent-based model is created, implementing realistic offloading strategies with different degrees of information uncertainty. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which is divided into offloading time, boarding time, and cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, all waiting times increase with more arriving unplanned aircraft.