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The concept of community resilience receives much attention in studies and applications due to its ability to provide preparedness against hazards, to protect our life against risks, and to recover to stable living conditions. Nevertheless, community resilience is complex, contextual, multifaceted, and therefore hard to define, recognise, and operationalise. An essential advantage of having a complete process for community resilience is the capacity to be aware of and respond appropriately in times of adversity. A three-step process constituting of modelling, measurement, and visualisation is crucial to determine components, to assess value, and to represent information of community resilience, respectively. The goal of this review is to offer a general overview of multiple perspectives for modelling, measuring, and visualising community resilience derived from related and emerging studies, projects, and tools. By engaging throughout the entire process, which involves three sequential steps as we mentioned above, communities can discover important components of resilience, optimise available local and natural resources, and mitigate the impact of impairments effectively and efficiently. To this end, we conduct a systematic review of 77 different literature records published from 2000 to 2020, concentrating on five research questions. We believe that researchers, practitioners, and policymakers can utilise this paper as a potential reference and a starting point to surpass current hindrances as well as to sharpen their future research directions.

Disasters pose a serious threat to people' lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the information of trapped people and rescue teams, and the amount of information will continue to increase as the rescue proceeds. At present, most of the rescue plans are manually made based on previous rescue experience. But obviously these plans might be the not optimal one. Considering the real-time location data of trapped people, this paper develops a Mixed Integer Non-linear Programming (MINLP) model to find the highest efficient rescue plan To solve the model accurately and efficiently, a bi-level decomposition (BLD) algorithm is presented to iteratively solve a discretized Mixed Integer Linear Programming (MILP) model and its nonconvex Non-linear Programming (NLP) model until a converged solution is obtained. In addition, since more trapped people could be found over time, the built rescue units should also be considered when making a rescue plan for a new stage. To further improve the solving efficiency, an accelerated bi-level decomposition (ABLD) algorithm is also proposed. Finally, a real-world disaster rescue is given to validate the superiority of the proposed ABLD algorithm relative to particle swarm optimization (PSO) algorithm and BLD algorithm. setting, project design, project selection, organization implementation, and feedback modification (Lei, Wu, Xu, & Fujita, 2017). The process can be simplified into three stages according to the promotion of rescue

In this paper, we propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Integer Linear Programming, we then determine the optimal amount and locations of temporal rescue centers. Furthermore, our disaster relief system identifies an efficient distribution of supplies between hospitals and rescue centers and rescue demand points. By leveraging the temporal dimension of the social media data, our framework manages to iteratively optimize the disaster relief distribution.

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Nature has remedies for almost all problems. Though biological systems exhibits organised, complex and intelligent behaviour, they comprise of simple elements and governed by simple rules. Hence, mimicking such systems has been the attraction of researchers in the fields of computer science, neuroscience and biology for a long time. Generating complex behaviour from small agents working locally following simple rules is a highly cost-effective solution of the real life problem. Bio-inspired computing can be achieved through different models such as stochastic, ad hoc or discrete models; new paradigm inspired from nature like evolutionary approach and immune systems; and new platform, novel architecture and specially designed material such as artificial fuel cell. The consortium of bio-inspired computing are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing and quantum computing, etc. This article discusses consortium of bio-inspired computing along with applications and research scope. In spite of having advantages offered by partial simulation of natural intelligence, there are some limitations of the bio-inspired computing that need to be addressed. These challenges include creation of new model, techniques and platforms for bio-inspired computing. This article concludes with the challenges to be explored in the field.

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