Supporting Humanitarian Relief Distribution Decision-Making under Deep Uncertainty : A System Design Approach

With respect to copyright, all the papers were excluded from the dissertation.Disasters threaten society with widespread destruction of infrastructure and livelihood. For their survival, affected inhabitants depend on immediate humanitarian assistance from diverse organizations. During quick responses, humanitarian decision- makers (HDMs) act rapidly to distribute necessary relief goods, despite the deep, prevailing uncertainty that arises from scarce, conflicting, and uncertain information. To support HDMs in humanitarian relief distribution (HRD) decision-making, humanitarian logistics (HL) researchers have developed various mathematical models. These models are, however, specific to disaster scenarios, and most of them are detached from the realities of the field since end-users (mainly practitioners) have been absent in the development process. When tested, these decision-making models were found to be capable of producing good results, but they have not been implemented in practice because of operational inconsistency or complexity (i.e., lack of user-friendliness). Therefore, humanitarian responders are still in need of support systems to assist them in determining effective HRD. A computer-based decision support system (DSS) can fill this need by providing necessary recommendations and suggesting decision alternatives. Hence, developing such DSSs is always the priority in HL.publishedVersio

Making decisions for effective humanitarian actions: a conceptual framework for relief distribution

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Android App Store (Google Play) Mining and Analysis

The aim of mining and analysis of Apps in Google Play, the largest Android app store, is to provide in-depth insight on the hidden properties of the repository to app developers or app market contributors. This approach can help them to view the current circumstances of the market and make valuable decisions before releasing products. To perform this analysis, all available features (descriptions of the app, app developer information, app version, updating date, category, number of download, app size, user rating, number of participants in rating, price, user reviews and security policies) are collected for the repositoryand stored in structured prole for each app. This scientic study is mainly divided into two approaches: measuring pair-wise correlations between extracted features and clustering the dataset into number of groups with functionally similar apps. Two distinct datasets are exploited to perform the study, one of which is collected from Google Play (in 2012) and another one from Android Market, the former version of Google Play (in 2011). As soon as experiments and analysis is successfully conducted, signicant levels of pair-wise correlations are identied between some features for both datasets, which are further compared to achieve a generalized conclusion. Finally, cluster analysis is done to provide a similarity based recommendation system through probabilistic topic modeling method that can resolve Google Play's deciency upon app similarity

Supporting Humanitarian Relief Distribution Decision-Making under Deep Uncertainty : A System Design Approach

Disasters threaten society with widespread destruction of infrastructure and livelihood. For their survival, affected inhabitants depend on immediate humanitarian assistance from diverse organizations. During quick responses, humanitarian decision- makers (HDMs) act rapidly to distribute necessary relief goods, despite the deep, prevailing uncertainty that arises from scarce, conflicting, and uncertain information. To support HDMs in humanitarian relief distribution (HRD) decision-making, humanitarian logistics (HL) researchers have developed various mathematical models. These models are, however, specific to disaster scenarios, and most of them are detached from the realities of the field since end-users (mainly practitioners) have been absent in the development process. When tested, these decision-making models were found to be capable of producing good results, but they have not been implemented in practice because of operational inconsistency or complexity (i.e., lack of user-friendliness). Therefore, humanitarian responders are still in need of support systems to assist them in determining effective HRD. A computer-based decision support system (DSS) can fill this need by providing necessary recommendations and suggesting decision alternatives. Hence, developing such DSSs is always the priority in HL

Making decisions for effective humanitarian actions: a conceptual framework for relief distribution

Abstract Responding to a disaster encompasses a myriad of humanitarian actions; the ultimate and crucial is immediate relief distribution. Making effective decisions in chaotic disaster environment is always complex and challenging. Decisions made here are heavily influenced by the decisions made in several related problem areas such as facility locations, relief supply chain, transportation, scheduling, and inventory management. While each of these problem areas has its own set of decision factors, several of these factors are also common in multiple problem areas. These common decision factors offer both an opportunity and a challenge. The challenge is to balance the relative importance of a factor that is common between one or more problem areas—one factor that is considered vital in one area may have a lower priority in another area. The opportunity here is to develop a common framework that can help all problem areas to work together to achieve the main objective of effectively distributing essential relief goods among affected people. While the literature has studied individual problem areas and their decision factors, an integrated view showing the linkages between multiple problem areas is missing. In this paper, we propose such an integrative framework. Based on a systematic review of the literature, we first identified problem areas that are linked to relief distribution and then identified the linkages between these areas. We synthesized the findings into a conceptual framework and validated it through a panel of experienced field experts who work in relief distribution. We framed our refined framework as an information ecosystem of humanitarian actions where relief distribution resides at the core. Such a conceptualization will not only enrich the in-depth understanding of humanitarian domain, but also offer insights for developing computer-based decision support systems for relief distribution