Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

The role of time perspective in predicting Iranian farmers’ participatory-based water conservation attitude and behavior

ABSTRACT

In current perspective, farmers’ participatory behavior toward conservation of water resources (FPBCWR) is one of the most important strategies under water resource management in rural Iran. In this regard, understanding the predictors of farmers’ participatory-based water conservation behaviors and attitudes is gaining more importance than earlier. Among different dimensions of farmers’ participatory behavior, the potential of temporal frames was examined rarely. Thus, 322 Iranian farmers were investigated to examine the potential of their time perspectives in predicting their participatory-based water conservation behavior and attitude. According to the study results, the effects of present orientation on attitude and behavior were negatively significant, while the effects of future orientation on attitude and behavior were positively significant, whereas its effects (path coefficients) were stronger than present orientation effects. Past orientation did not have a significant effect on attitude, though attitude positively and significantly affected farmers’ participatory behavior. The results of causal analysis revealed that presented model accounted for 58% of variance in farmers’ ‘behavior’ and 42% of variance in “attitude.” In conclusion, a few demonstrable illustrations of policy implications are presented to enable utilizing the important findings and concluding results of this study that is linked further with water resource management domain.     

Demography,Education, and Democracy: Global Trends and the Case of Iran

Reconstructions and projections of populations by age, sex, and educational attainment for 120 countries since 1970 are used to assess the global relationship between improvements in human capital and democracy. Democracy is measured by the Freedom House indicator of political rights. Similar to an earlier study on the effects of improving educational attainment on economic growth, the greater age detail of this new dataset resolves earlier ambiguities about the effect of improving education as assessed using a global set of national time series. The results show consistently strong effects of improving overall levels of educational attainment, of a narrowing gender gap in education, and of fertility declines and the subsequent changes in age structure on improvements in the democracy indicator. This global relationship is then applied to the Islamic Republic of Iran. Over the past two decades Iran has experienced the world's most rapid fertility decline associated with massive increases in female education. The results show that based on the experience of 120 countries since 1970, Iran has a high chance of significant movement toward more democracy over the following two decades.     

Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.     

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Control chart is the most important statistical process control tool used to monitor changes in process location and dispersion. In this study, an EWMA control chart is proposed for efficient and robust monitoring of process dispersion. The proposed chart, namely the MDEWMA chart, is based on estimating the process standard deviation (σ) using the mean absolute deviations (MD), taken from the sample median. The performance of the proposed chart has been compared with the EWMASR chart (a dispersion EWMA chart based on sample range) and MD chart (a Shewhart-type dispersion chart based on MD), under the existence and violation of normality assumption. It has been observed that the proposed MDEWMA chart is more efficient and robust when compared with both EWMASR and MD charts in terms of run length (RL) characteristics such as average RL, median RL and standard deviation of the RL distribution.     

Enhanced adaptive CUSUM charts for process mean

A statistical quality control chart is an important tool of the statistical process control, which is widely used to control and monitor a production process. The CUSUM chart is designed to detect a specific shift, provided that the shift size is known in advance. In practice, however, shift sizes are rarely known. It is then customary to use an adaptive CUSUM chart, which can effectively detect a range of shift sizes. In this paper, we enhance the sensitivities of the improved adaptive CUSUM mean charts using an auxiliary-information-based (AIB) mean estimator. The run length performances of the proposed charts are compared with those of the AIB adaptive and non-adaptive CUSUM charts in terms of the average run length (ARL), extra quadratic loss, and integral relative ARL. These run length comparisons reveal that the proposed charts are more sensitive than the existing charts when detecting different kinds of shift in the process mean. An example is given to demonstrate the implementation of existing and proposed charts.     

Association between serum levels of testosterone and biomarkers of subclinical atherosclerosis

Objective: To investigate the association between serum levels of testosterone and biomarkers of subclinical atherosclerosis based on data from 119 middle-aged men of the general population.

Methods: Testosterone, Apolipoprotein A-1 (ApoA-1), Apolipoprotein B (ApoB), Apolipoprotein B-to-Apolipoprotein A-1 ratio (ApoB-to-ApoA-1), high-sensitive C-reactive protein (hsCRP), and fibrinogen levels were measured. Data were also gathered based on age, BMI, waist circumference, smoking, alcohol consumption, and family history of cardiovascular diseases. Men were classified into two groups based on testosterone levels: hypogonadal (testosterone ≤12?nmol/L) and eugonadal men (testosterone >12?nmol/L).

Results: When compared to eugonadal, the hypogonadal men were significantly older (56?years vs. 55?years, p?=?.03), had greater BMI (28?kg/cm² vs. 26?kg/cm², p?=?.01), and higher waist circumference (104?cm vs. 100?cm, p?=?.01). Moreover, ApoB, ApoB-to-ApoA-1 ratio, and hsCRP were significantly higher in hypogonadal men compared to eugonadal men (1.1?g/L vs. 1.0?g/L, p?=?.03), (0.8 vs. 0.7, p?=?.03), (3.3?mg/L vs. 2.0?mg/L, p?=?.01), respectively. On the other hand, ApoA-1 and fibrinogen levels did not differ significantly between groups (p?>?.05). In an adjusted multivariate regression analysis model, only ApoB showed a significant negative association with testosterone levels (β?=??0.01; 95% CI?=??0.02, ?1.50; p?=?.04).

Conclusion: Testosterone levels showed an inverse relation to ApoB, a biomarker implicated in subclinical atherosclerosis. These findings support the hypothesis that low testosterone levels play a role in atherosclerosis.     

Some bounds related to Harris family of distributions

As a lifetime distribution, Harris family of distributions are applied to the lifetime of a series system with random number of components. In this paper, properties of various ageing classes of mixtures of Harris family of distributions, where the tilt parameter of a Harris distribution is taken as a random variable, are studied. We obtain an upper bound for maximum error in evaluating its reliability function. Two bounds are also presented for survival function and expectation of the mixed Harris family. We also provide some interesting bounds for its residual survival function. Our results generalize several previous findings in this connection. Some illustrative examples are also provided.     

A solution to an open problem on lower against number in graphs

In Wang (Appl Anal Discret Math 4:207–218, 2010) the problem of finding a sharp lower bound on lower against number of a general graph is mentioned as an open question. We solve the problem by establishing a tight lower bound on lower against number of a general graph in terms of order and maximum degree.     

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.