The number of people affected by natural disasters or displaced by conflict, persecution, violence or human rights violations has been steadily increasing, doubling in a decade and reaching 130.5 million in 2016. Fortunately, such trends have been accompanied by a growing research interest in the field of humanitarian logistics that investigates mechanisms which can improve assistance to disaster-affected communities and thus minimize human suffering. In spite of acknowledging a major difference between such an objective and the priorities of business logistics, many authors still adopt disaster relief problem formulations that aim to minimize costs. In this paper, we list a number of issues with the cost-minimizing approach, placing emphasis on the significant challenge of determining the controversial economic value of human suffering that is usually a part of such formulations. In order to circumvent these issues, we formulate an alternative mathematical model that maximizes response directly. The discussion about the cost-minimizing and our alternative approach is illustrated with the problem of increasing emergency preparedness by pre-positioning relief items at strategic locations. We evaluate the two formulations of the pre-positioning problem using a number of randomly generated instances and a case study focused on hurricane threat in the Gulf Coast area of the United States. The optimal solution of our model always meets at least the same percentage of demand as the cost-minimizing model, and is obtained in comparable computation time. Our study therefore suggests that putting a price on human life can and ergo should be avoided.

Topological data analysis is a recent and fast growing field that approaches the analysis of datasets using techniques from (algebraic) topology. Its main tool, persistent homology (PH), has seen a notable increase in applications in the last decade. Often cited as the most favourable property of PH and the main reason for practical success are the stability theorems that give theoretical results about noise robustness, since real data is typically contaminated with noise or measurement errors. However, little attention has been paid to what these stability theorems mean in practice. To gain some insight into this question, we evaluate the noise robustness of PH on the MNIST dataset of greyscale images. More precisely, we investigate to what extent PH changes under typical forms of image noise, and quantify the loss of performance in classifying the MNIST handwritten digits when noise is added to the data. The results show that the sensitivity to noise of PH is influenced by the choice of filtrations and persistence signatures (respectively the input and output of PH), and in particular, that PH features are often not robust to noise in a classification task.

Purpose Despite a growing body of research on the problem of increasing disaster preparedness by pre-positioning relief supplies at strategic locations, there is a lack of a benchmark set of problem instances that hinders thorough hypotheses testing, sensitivity analysis, model validation or solution procedure evaluation. The purpose of this paper is to address this issue by constructing a public library of diverse pre-positioning problem instances. Design/methodology/approach By carefully manipulating some of the instance parameters, the authors generated 30 case studies that were inspired by four instances collected from the literature that focus on disasters of different type and scale that occurred in different parts of the world. In addition, the authors developed a tool to algorithmically generate arbitrarily many diverse random instances of any size. Findings For many purposes, the problem library can eliminate or reduce the time-consuming process of data collection, conversion, digitization, calibration and validation, while simultaneously increasing the statistical significance of research results and allowing comparison with different works in the literature. Research limitations/implications The case studies are inspired by only four disasters, and some of the instance parameters are defined in a reasonable, albeit arbitrary way. The instances are also limited by the underlying problem assumptions. Practical implications The instances provide a more comprehensive and balanced experimental setting (compared to a single case study) that can be used to study the pre-positioning and related problems, or derive managerial implications that can directly benefit the practitioners. Social implications The instances can be used to derive practical guidelines that humanitarian workers can use on the ground to better plan their pre-positioning strategies and therefore minimize human suffering. Originality/value The case studies and the random instance generator are made publicly available to foster further research on the problem of pre-positioning relief supplies and humanitarian logistics in general.

Research on metaheuristics has focused almost exclusively on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield very little generalizable knowledge. The main goal of this paper is to promote meta-analysis-a systematic statistical examination that combines the results of several independent studies-as a more suitable way to obtain problem-and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scienti c domains, most notably the medical sciences (e.g., to establish the e cacy of a certain treatment). To the best of our knowledge this is the rst meta-analysis in the eld of metaheuristics.

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