Simulation Optimization Models for Reducing Inequities in Ambulance Coverage
Integrated Science, ISSN: 2662-947X, Vol: 27, Page: 413-435
2024
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
InequityInequities in access to ambulanceAmbulance services is a well-recognized issue in the literature. Most studies have focused on the efficiency objective of maximizing ambulanceAmbulance coverage, paying little attention to the equitable distribution of this coverage among the served population. This chapter proposes coverage models that seek both to maximize overall coverage and distribute it equitably across the areas of the studied territory. To this end, several multi-objective simulation optimization (SO) models are developed and compared using data from the Fez-Meknes region in Morocco. These SO models were built using the weighted sum method and the ϵ-constraint method and were solved by deploying the OptQuest engine. The simulation results show that efficiency and equityEquity objectives are conflicting. Additionally, SO models developed using the weighted sum method achieved better results than those developed using the ϵ-constraint method in terms of both coverage and equity.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85219588009&origin=inward; http://dx.doi.org/10.1007/978-3-031-70292-1_20; https://link.springer.com/10.1007/978-3-031-70292-1_20; https://dx.doi.org/10.1007/978-3-031-70292-1_20; https://link.springer.com/chapter/10.1007/978-3-031-70292-1_20
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know