Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants

Multi-period maximal covering location problem with capacitated facilities and modules for natural disaster relief services

The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds-(LR1) and (LR2)-and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process

The Effect of Cognitive-Behavioral and Existential Methods on Reducing Test Anxiety in Nursing Students

Introduction: Test anxiety is now one of the most common types of anxiety. The purpose of the present study was to examine the effect of cognitive‐behavioral and existential methods on reducing test anxiety in nursing students. Methods: This quasi-experimental study employed a pretest-posttest design with control group. Statistical population consisted of all nursing students of Ardabil University of Medical Sciences in 2016. Sarason’s test anxiety questionnaire (pretest) was first administered to 500 nursing students. Of those found to have test anxiety, 45 students were selected by random sampling and randomly assigned to three groups (two experimental groups and one control group). The first group received cognitive‐behavioral therapy and the second group received existential psychotherapy for six 50-minute sessions. The control group received no intervention. Sarason’s test anxiety questionnaire was administered once again (posttest) to all of the participants, and the collected data were analyzed by means of descriptive statistical tests and ANCOVA. Results: The mean and standard deviation of anxiety scores were 16.20±2.30 (pretest) and 13.33±10.3 (posttest) in the cognitive-behavioral therapy group, 17.06±2.63 (pretest) and 14.73±34.3 (posttest) in the existential psychotherapy group, and 16.68+54.7 (pretest) and 17.33±41.3 (posttest) in the control group. Analysis of covariance showed that the posttest anxiety mean scores (F=10.17, p=0.001) were significantly different in the three groups. Conclusion: Results of the study suggest that proper planning and timely implementation of cognitive-behavioral and existential therapies can prevent the consequences of anxiety in students

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population