Bi-objective facility location in the presence of uncertainty

Multiple and usually conflicting objectives subject to data uncertainty are main features in many real-world problems. Consequently, in practice, decision-makers need to understand the trade-off between the objectives, considering different levels of uncertainty in order to choose a suitable solution. In this paper, we consider a two-stage bi-objective single source capacitated model as a base formulation for designing a last-mile network in disaster relief where one of the objectives is subject to demand uncertainty. We analyze scenario-based two-stage risk-neutral stochastic programming, adaptive (two-stage) robust optimization, and a two-stage risk-averse stochastic approach using conditional value-at-risk (CVaR). To cope with the bi-objective nature of the problem, we embed these concepts into two criterion space search frameworks, the $\epsilon$-constraint method and the balanced box method, to determine the Pareto frontier. Additionally, a matheuristic technique is developed to obtain high-quality approximations of the Pareto frontier for large-size instances. In an extensive computational experiment, we evaluate and compare the performance of the applied approaches based on real-world data from a Thies drought case, Senegal

Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms

BACKGROUND: The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. METHODS: To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. RESULTS: The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men. CONCLUSIONS: Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption

The economic costs of alcohol consumption in Thailand, 2006

<p>Abstract</p> <p>Background</p> <p>There is evidence that the adverse consequences of alcohol impose a substantial economic burden on societies worldwide. Given the lack of generalizability of study results across different settings, many attempts have been made to estimate the economic costs of alcohol for various settings; however, these have mostly been confined to industrialized countries. To our knowledge, there are a very limited number of well-designed studies which estimate the economic costs of alcohol consumption in developing countries, including Thailand. Therefore, this study aims to estimate these economic costs, in Thailand, 2006.</p> <p>Methods</p> <p>This is a prevalence-based, cost-of-illness study. The estimated costs in this study included both direct and indirect costs. Direct costs included health care costs, costs of law enforcement, and costs of property damage due to road-traffic accidents. Indirect costs included costs of productivity loss due to premature mortality, and costs of reduced productivity due to absenteeism and presenteeism (reduced on-the-job productivity).</p> <p>Results</p> <p>The total economic cost of alcohol consumption in Thailand in 2006 was estimated at 156,105.4 million baht (9,627 million US$PPP) or about 1.99$ PPP), followed by cost of productivity loss due to reduced productivity (45,464.6 million baht/2,804 million US$PPP), health care cost (5,491.2 million baht/339 million US$ PPP), cost of property damage as a result of road traffic accidents (779.4 million baht/48 million US$PPP), and cost of law enforcement (242.4 million baht/15 million US$ PPP), respectively. The results from the sensitivity analysis revealed that the cost ranges from 115,160.4 million baht to 214,053.0 million baht (7,102.1 - 13,201 million US$ PPP) depending on the methods and assumptions employed.</p> <p>Conclusions</p> <p>Alcohol imposes a substantial economic burden on Thai society, and according to these findings, the Thai government needs to pay significantly more attention to implementing more effective alcohol policies/interventions in order to reduce the negative consequences associated with alcohol.</p

Hormonal and transcriptional profiles highlight common and differential host responses to arbuscular mycorrhizal fungi and the regulation of the oxylipin pathway

Arbuscular mycorrhizal (AM) symbioses are mutualistic associations between soil fungi and most vascular plants. The symbiosis significantly affects the host physiology in terms of nutrition and stress resistance. Despite the lack of host range specificity of the interaction, functional diversity between AM fungal species exists. The interaction is finely regulated according to plant and fungal characters, and plant hormones are believed to orchestrate the modifications in the host plant. Using tomato as a model, an integrative analysis of the host response to different mycorrhizal fungi was performed combining multiple hormone determination and transcriptional profiling. Analysis of ethylene-, abscisic acid-, salicylic acid-, and jasmonate-related compounds evidenced common and divergent responses of tomato roots to Glomus mosseae and Glomus intraradices, two fungi differing in their colonization abilities and impact on the host. Both hormonal and transcriptional analyses revealed, among others, regulation of the oxylipin pathway during the AM symbiosis and point to a key regulatory role for jasmonates. In addition, the results suggest that specific responses to particular fungi underlie the differential impact of individual AM fungi on plant physiology, and particularly on its ability to cope with biotic stresses

Partition testing vs. random testing: the influence of uncertainty

Abstract—This paper compares partition testing and random testing on the assumption that program failure rates are not known with certainty before testing and are, therefore, modeled by random variables. It is shown that under uncertainty, partition testing compares more favorably to random testing than suggested by prior investigations concerning the deterministic case: The restriction to failure rates that are known with certainty systematically favors random testing. In particular, we generalize a result by Weyuker and Jeng stating equal fault detection probabilities for partition testing and random testing in the case where the failure rates in the subdomains defined by the partition are equal. It turns out that for independent random failure rates with equal expectation, the case above is a boundary case (the worst case for partition testing), and the fault detection probability of partition testing can be up to k times higher than that of random testing, where k is the number of subdomains. Also in a related model for dependent failure rates, partition testing turns out to be consistently better than random testing. The dominance can also be verified for the expected (weighted) number of detected faults as an alternative comparison criterion. Index Terms—Decisions under uncertainty, fault detection, partition testing, program testing, random testing, software testing.