Design of shared unit-dose drug distribution network using multi-level particle swarm optimization

Investigation of the pharmaceutical warehouse locations under COVID-19-A case study for Duzce, Turkey

Pharmaceutical warehouses are among the centers that play a critical role in the delivery of medicines from the producers to the consumers. Especially with the new drugs and vaccines added during the pandemic period to the supply chain, the importance of the regions they are located in has increased critically. Since the selection of pharmaceutical warehouse location is a strategic decision, it should be handled in detail and a comprehensive analysis should be made for the location selection process. Considering all these, in this study, a real-case application by taking the problem of selecting the best location for a pharmaceutical warehouse is carried out for a city that can be seen as critical in drug distribution in Turkey. For this aim, two effective multi-criteria decision-making (MCDM) methodologies, namely Analytic Hierarchy Process (AHP) and Evaluation based on Distance from Average Solution (EDAS), are integrated under spherical fuzzy environment to reflect fuzziness and indeterminacy better in the decision-making process and the pharmaceutical warehouse location selection problem is discussed by the proposed fuzzy integrated methodology for the first time. Finally, the best region is found for the pharmaceutical warehouse and the results are discussed under the determined criteria. A detailed robustness analysis is also conducted to measure the validity, sensibility and effectiveness of the proposed methodology. With this study, it can be claimed that literature has initiated to be revealed for the pharmaceutical warehouse location problem and a guide has been put forward for those who are willing to study this area.

Practice-enhancing publications about the medication-use process in 2019

PURPOSE

This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2019 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. Articles that evaluated one of the steps were gauged for their usefulness in promoting daily practice change.

SUMMARY

A PubMed search was conducted in January 2020 for calendar year 2019 using targeted Medical Subject Headings keywords; in addition, searches of the table of contents of selected pharmacy journals were conducted. A total of 4,317 articles were identified. A thorough review identified 66 potentially practice-enhancing articles: 17 for prescribing/transcribing, 17 for dispensing, 7 for administration, and 25 for monitoring. Ranking of the articles for importance by peers led to the selection of key articles from each category. The highest-ranked articles are briefly summarized, with a mention of why each article is important within health-system pharmacy. The other articles are listed for further review and evaluation.

CONCLUSION

It is important to routinely review the published literature and to incorporate significant findings into daily practice; this article assists in identifying and summarizing the most impactful recently published literature in this area. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of the significant published studies can assist in changing practice at the institutional level.

Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy

There is no definite conclusion about what the main variables that play a fundamental role in road safety are. Therefore, the identification of significant factors in road accidents has been a primary concern of the transportation safety research community for many years. Every accident is influenced by multiple variables that, in a given time interval, concur to cause a crash scenario. Information coming from crash reports is very useful in traffic safety research, and several reported crash variables can be analyzed with modern statistical methods to establish whether a classification or clustering of different crash variables is possible. Hence, this study aims to use stochastic techniques for evaluating the role of some variables in accidents with a clustering analysis. The variables that are considered in this paper are light conditions, weekday, average speed, annual average daily traffic, number of vehicles, and type of accident. For this purpose, a combination of particle swarm optimization (PSO) and the genetic algorithm (GA) with the k-means algorithm was used as the machine-learning technique to cluster and evaluate road safety data. According to a multiscale approach, based on a set of data from two years of crash records collected from rural and urban roads in the province of Cosenza, 154 accident cases were accurately investigated and selected for three categories of accident places, including straight, intersection, and other, in each urban and rural network. PSO had a superior performance, with 0.87% accuracy on urban and rural roads in comparison with GA, although the results of GA had an acceptable degree of accuracy. In addition, the results show that, on urban roads, social cost and type of accident had the most and least influence for all accident places, while, on rural roads, although the social cost was the most notable factor for all accident places, the type of accident had the least effect on the straight sections and curves, and the number of vehicles had the least influence at intersections.

Optimization of Power Consumption Associated with Surface Roughness in Ultrasonic Assisted Turning of Nimonic-90 Using Hybrid Particle Swarm-Simplex Method

These days, power consumption and energy related issues are very hot topics of research especially for machine tooling process industries because of the strict environmental regulations and policies. Hence, the present paper discusses the application of such an advanced machining process i.e., ultrasonic assisted turning (UAT) process with the collaboration of nature inspired algorithms to determine the ideal solution. The cutting speed, feed rate, depth of cut and frequency of cutting tool were considered as input variables and the machining performance of Nimonic-90 alloy in terms of surface roughness and power consumption has been investigated. Then, the experimentation was conducted as per the Taguchi L9 orthogonal array and the mono as well as bi-objective optimizations were performed with standard particle swarm and hybrid particle swarm with simplex methods (PSO-SM). Further, the statistical analysis was performed with well-known analysis of variance (ANOVA) test. After that, the regression equation along with selected boundary conditions was used for creation of fitness function in the subjected algorithms. The results showed that the UAT process was more preferable for the Nimconic-90 alloy as compared with conventional turning process. In addition, the hybrid PSO-SM gave the best results for obtaining the minimized values of selected responses.