Non-emergency patient transport services planning through genetic algorithms

Abstract

a b s t r a c tNon-emergency Patient Transport Services (PTS) are provided by ambulance companies for patients who do not require urgent and emergency transport. These patients require transport to or from a health facility like a hospital, but due to clinical requirements are unable to use private or public transport. This task is performed nowadays mainly by human operators, spending a high amount of time and resources to obtain solutions that are suboptimal in most cases. To overcome this limitation, in this paper we present NURA (Non-Urgent transport Routing Algorithm), a novel algorithm aimed at ambulance route planning. In particular, NURA relies on a genetic algorithm to explore the solution space, and it includes a scheduling algorithm to generate detailed routes for ambulances. Experimental results show that NURA is able to outperform human experts in several real scenarios, reducing the time spent by patients in ambulances during non-emergency transportations, increasing ambulance usage, while saving time and money for ambulance companies.

A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards

“…GA-based solutions have also increasingly been used at the molecular level in tasks such as handling and predicting transposon-derived piRNAs [8]. Yet the importance of GAbased solutions in the medical field is not limited to solving problems on the microscopic scope as applications have been developed to handle larger scale infrastructure and logistics that can be vital for entire health care systems [9,10]. Among the most frequent uses of GAs however, is their role in feature selection where they help to narrow down the possible features so that a complementary algorithm can achieve far greater performance [7,11,12,13,14].…”

“…Additionally, GAs have been used for imaging and visualizing applications both due to their importance in feature selection and their ability to combine representations of learned information such as known shapes, and relative position into a single framework that can be used in three-dimensional segmentation [17]. Finally GAs have been employed to handle logistics both in handling complex hospital supply chains [9] and in optimizing ambulance dispatches to non-emergency situations [10]. Therefore, it can be easily seen that bioinformatics research entails many problems that can be solved using machine learning tasks, and that GA is well-suited for such tasks.…”

Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

“…Inversely, Nikoo et al [2] propose a threeobjective interprogramming model to seek the path with best robustness by sacrificing the timeliness. Regarding the algorithm, genetic algorithm is frequently used in related researches, such as [26][27][28].…”

Semantic Framework of Internet of Things for Smart Cities: Case Studies

“…According to [ 11 ], there are three main challenges associated with urban computing: urban sensing and data acquisition, computing with heterogeneous data, and hybrid systems blending the physical and virtual worlds. In recent years, many applications have been proposed in different scenarios of smart cities including transportation [ 12 ], the environment [ 13 ], energy [ 14 ], social [ 15 ], the economy [ 16 ], and public safety and security [ 17 , 18 ]. Moreover, the Intelligent Transportation Systems (ITS) have rapidly developed in Europe.…”