1.5.1. Resilience and Sustainable Development Framework for Decision-Making
A program delegate must be developed to encourage the creation of decision-making resources for the resiliency, healthy community and to promote the rating of decision-making options in line with the information required, compatible with priorities and goals and conforming to potential requirements. The following introduces a structure representation paradigm, which fits closely Faber et al. [58].
• Analysis system representation of hierarchical decisions
In order to help decisions about the management of processes, it is essential to create structure representations that regularly chart potential alternative options for decision-makers and the stakeholders involved in achieving their priorities. This assumes that the nature of the structures is decided by the policy makers, stakeholders and their choice, time-boundary and spatial limits, the functional features and functionality of the systems and their impact on system efficiency, and feasible and appropriate decision-making alternatives.
In other governance contexts, such as private organizations, or industrial practices, the overarching concept which underlies the hierarchical governance system seen in Figure 1.3 may be extended.
Theoretically, it is important for decisions to be rated in accordance with their anticipated worth (or benefit) in accordance with the Bavarian Decision Analytics and the axioms to be made in order to automate the design and/or the management of engineering systems subject to complexity and inadequate information in a normative decision sense.
The structure as outlined in Figure 1.4 incorporates not just threats in terms of potential negative value in various applicable indicators (e.g. negative in life, disruption to environmental values and financial losses) but also gains linked to decision-making options—the key goal of optimized structures—as opposed to more traditional risk-informed solutions to decision-making. The expansion supported the way Section 4 discusses durability and longevity as a framework for evaluation for stability outlined by Linkov et al. [59], thus accurately correcting typical risk modeling limitations. Specific decision alternatives to designed device architecture and management in accordance with the predicted utility benefit or any particular metrical requirements can be assessed and classified according to the device modeling paradigm as outlined in Figure 1.5.
Figure 1.3 Decision making resilience and sustainable development framework.
Figure 1.4 Bavarian decision analytics.
Figure 1.5 Framework system modeling.
1.5.2. Exposures and Disturbance Events
As seen in Figure 1.5, exposure incidents (disturbances) are considered to reflect, in theory, all future occurrences that may have implications. Resiliency, ecological models and analyses can include exposures.
Type-1 Hazards: The related threats are manageable in broad enough time and room, rendering their management far simpler. Geohazards such as earthquakes, flooding, waves, etc. are common manifestations of this form of hazards [37, 41, 43, 44].
Type-2 Hazards: They may be correlated with catastrophic combined effects on adequate time and space scales. Furthermore, their cumulative effects may cause the same characteristics as the hazards of type 3 to have more disastrous consequences. Typical cases include biological pollution, misuse of land, plant destruction, ineffective or poor management, insufficient financial planning, human mistakes, etc.
Type-3 Hazards: Very unusual and possibly catastrophic occurrences, also in broad sections of time and space, that are unforeseen and about which little evidence is practically available. The cumulative effects of type 2 hazards may be triggered. Examples include volcano eruptions, meteor collisions, solar storms of extreme severity, rapid temperature change as well as significant terrorist activities.
1.5.3 Quantification of Resilience
The literature includes a fairly wide number of ideas for modeling and quantifying network durability, e.g. Cimellaro et al. [60], Linkov et al. [59], Sharma et al. [61] and Tamvakis and Xenedis [62]. The proposed models are more commonly aimed at the short-term reflection of the system’s capacity to withstand and rebound from disruptions, without major output loss and without outside assistance, usually, the emphasis on the portrayal of resilience models.
For impact on service delivery of the stated perturbations and on recovery characteristics in relation to service grade recovered against period and overall service failure, see Figure 1.6.
Until recently only the modeling of processes to rebound from disruptions has been granted tacit attention. Neither the functional failure nor rather the production of capability that is critical to the productive, yet quick reorganization, change, yet recovery following disruptions and danger events will take account of processes flexibility providing a life cycle gain in the flexibility model described in Faber and Qin [57]. See Figure 1.6.
Figure 1.6 Quantification of resilience.
1.5.4 Quantification of Sustainability
Addressing biodiversity includes a shared analysis of the implications of inter-generational and intra-generational inequality on the environment, public safety and wellbeing, financial circumstances and extension of natural capital [45, 46, 48, 49]. In relation to the consequences currently discussed in resilience models, the emphasis is on whether changes on the ecosystem should be taken into consideration.
The theory behind this is to extend the Planetary Boundaries principle as a way to reflect the Earth‘s capabilities which are essential to continuing social growth, as we know it today. The Planet Life Support System (ELSS) is the following features of the Earth system. It is often believed that device states and the associated effects linked to the effect on the environmental quality, which put strain on the ELSS, may be attributed to every alternate decision concerning the configuration and the management of an integrated system. This relationship may be built in the sense of product production following Hauschild [63], by Life Cycle Analysis, as implemented in support of QSAs. Figure 1.7 demonstrates the definition.
Another important point of this article is that due to lack of knowledge and inherent natural variability the resilience and sustainability of engineered systems can only be proven and probabilistically modeled in a meaningful way. As a result, resilience and sustainability criteria need to be described in terms, for example, of appropriate annual resilience probabilities and sustainability failures. It quickly becomes apparent from this point of view that tradeoffs occur.
