The role of concurrent engineering in resilient critical infrastructures during disasters

Abstract

The world has complex mega-cities and interdependent infrastructures. This complication in infrastructure relations makes it sensitive to disasters and failures. Cascading failure causes blackouts for the whole system of infrastructures during disasters and the lack of performance of the emergency management stakeholders is clear during a disaster due to the complexity of the system. This research aimed to develop a new concurrent engineering model following the total recovery effort. The objectives of this research were to identify the clustered intervention utilized in the field of resilience and developing a cross-functional intervention network to enhance the resilience of societies during a disaster. Content analysis was employed to classify and categorize the intervention in the main divisions and sub-divisions and the grouping of stakeholders. The transposing system was employed to develop an integrated model. The result of this research showed that the operations division achieved the highest weight of information interchange during the response to improve the resilience of the system. The committee of logistics and the committee of rescue and relief needed the widest bandwidth of information flow in the concurrent engineering (CE) model. The contributed CE model helped the stakeholders provide a resilient response system. The final model and the relative share value of exchanging information for each workgroup can speed up recovery actions. This research found that concurrent engineering (CE) is a viable concept to be implemented as a strategy for emergency management. The result of this research can help policymakers achieve a collaborative teamwork environment and to improve resilience factors during emergency circumstances for critical infrastructures.

Evaluation of relative weight of balanced personnel index in Korean Public Offices through AHP

“…2) Establishing a hierarchy To obtain an optimal alternative using AHP analysis, we first define the problem accurately and identify the relationship between the objectives of decisionmaking, evaluation criteria, and alternatives to solve the problem. It then constructs a hierarchy that encompasses the objective of the highest-level problem, the selection and placement of intermediate-level large classification items, and the comparison of small classification items at the lowest level (Nekooie, 2021). AHP hierarchy separation is shown in Figure 1.…”