Incorporating A New Class of Uncertainty in Disaster Relief Logistics Planning

In recent years, there has been a growing interest among emergency managers in using Social data in disaster response planning. However, the trustworthiness and reliability of posted information are two of the most significant concerns, because much of the user-generated data is initially not verified. Therefore, a key tradeoff exists for emergency managers when considering whether to incorporate Social data in disaster planning efforts. By considering Social data, a larger number of needs can be identified in a shorter amount of time, potentially enabling a faster response and satisfying a class of demand that might not otherwise be discovered. However, some critical resources can be allocated to inaccurate demands in this manner. This dissertation research is dedicated to evaluating this tradeoff by creating routing plans while considering two separate streams of information: (i) unverified data describing demand that is not known with certainty, obtained from Social media platforms and (ii) verified data describing demand known with certainty, obtained from trusted traditional sources (i.e. on the ground assessment teams). These projects extend previous models in the disaster relief routing literature that address uncertainty in demand. More broadly, this research contributes to the body of literature that addresses questions surrounding the usefulness of Social data for response planning

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges

Application of Image Analytics for Disaster Response in Smart Cities

Post-disaster, city planners need to effectively plan response activities and assign rescue teams to specific disaster zones quickly. We address the problem of lack of accurate information of the disaster zones and existence of human survivors in debris using image analytics from smart city data. Innovative usage of smart city infrastructure is proposed as a potential solution to this issue. We collected images from earthquake-hit smart urban environments and implemented a CNN model for classification of these images to identify human body parts out of the debris. TensorFlow backend (using Keras) was utilized for this classification. We were able to achieve 83.2% accuracy from our model. The novel application of image data from smart city infrastructure and the resultant findings from our model has significant implications for effective disaster response operations, especially in smart cities

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly

Online Optimisation of Casualty Processing in Major Incident Response

Recent emergency response operations to Mass Casualty Incidents (MCIs) have been criticised for a lack of coordination, implying that there is clear potential for response operations to be improved and for corresponding benefits in terms of the health and well-being of those affected by such incidents. In this thesis, the use of mathematical modelling, and in particular optimisation, is considered as a means with which to help improve the coordination of MCI response. Upon reviewing the nature of decision making in MCIs and other disaster response operations in practice, this work demonstrates through an in-depth review of the available academic literature that an important problem has yet to be modelled and solved using an optimisation methodology. This thesis involves the development of such a model, identifying an appropriate task scheduling formulation of the decision problem and a number of objective functions corresponding to the goals of the MCI response decision makers. Efficient solution methodologies are developed to allow for solutions to the model, and therefore to the MCI response operation, to be found in a timely manner. Following on from the development of the optimisation model, the dynamic and uncertain nature of the MCI response environment is considered in detail. Highlighting the lack of relevant research considering this important aspect of the problem, the optimisation model is extended to allow for its use in real-time. In order to allow for the utility of the model to be thoroughly examined, a complementary simulation is developed and an interface allowing for its communication with the optimisation model specified. Extensive computational experiments are reported, demonstrating both the danger of developing and applying optimisation models under a set of unrealistic assumptions, and the potential for the model developed in this work to deliver improvements in MCI response operations

Social media in operations and supply chain management: state-of-the-art and research directions

Recently, industrial and academic communities in the operations and supply chain management (OSCM) field are paying increasing attention to social media. However, the value of social media in OSCM is quite unclear, and more investigations are still needed. To pave the way for a directed future research, this paper systematically reviewed and synthesised 152 peer-review journal papers to identify research focus and gaps in this area, supported by an appropriate conceptual framework. The result reveals that the research interests in this area have increased dramatically within the last decade across various industries and regions. Different companies’ OSCM activities, such as sourcing and delivery, can benefit from employment of social media. This paper also indicates that future research can explore the value of social media in sourcing, delivery, product return and reverse logistics activities, forecasting and inventory management, and product development and production

Business model canvas for humanitarian operations of logistics service providers

YesFor years, humankind has been facing various disasters of which logistics has a crucial role for alleviating sufferings of vulnerable people, who are isolated and in need of basic supplies. Owing to the increasing importance of logistics in humanitarian operations, logistics service providers (LSPs) have recently become more prominent. Yet, only a few LSPs have the capabilities and mechanisms to offer operational solutions for humanitarian relief. Also, the conducted extensive literature review makes evident that the existence of a limited number of normative research reveals a barrier about what LSPs can bring into the humanitarian field. Accordingly, why LSPs are particularly important in the humanitarian supply chain and how LSPs manage their activities and resources in humanitarian operations become the main questions to be addressed. Thus, this study seeks to explore humanitarian operations of LSPs from different dimensions, enabled by Business Model Canvas (BMC). In this sense, the obtained findings clarify both similar and different viewpoints of diverse LSPs when mapped against the BMC. Consequently, the categorised interrelated information presented through the cross-case synthesis provide novelty to advance insights both on strategic missions of LSPs in humanitarian relief operations and on the usage of BMC beyond its common commercial implementations

Evaluating a Web-Based Interface for Internet Telemedicine

The objective is to introduce the usability engineering methodology, heuristic evaluation, to the design and development of a web-based telemedicine system. Using a set of usability criteria, or heuristics, one evaluator examined the Spacebridge to Russia web-site for usability problems. Thirty-four usability problems were found in this preliminary study and all were assigned a severity rating. The value of heuristic analysis in the iterative design of a system is shown because the problems can be fixed before deployment of a system and the problems are of a different nature than those found by actual users of the system. It was therefore determined that there is potential value of heuristic evaluation paired with user testing as a strategy for optimal system performance design