Genetic Programming for Vehicle Subset Selection in Ambulance Dispatching

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{MacLachlan:2022:CEC,

• author = "Jordan MacLachlan and Yi Mei and Fangfang Zhang and Mengjie Zhang",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Genetic Programming for Vehicle Subset Selection in Ambulance Dispatching",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• keywords = "genetic algorithms, genetic programming, Decision making, Semantics, Medical services, Evolutionary computation, Dispatching, Real-time systems, Hyper Heuristic, Ambulance Dispatch, Evolutionary Computation",
• isbn13 = "978-1-6654-6708-7",
• URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/master/files/2022-GP_for_Vehicle_Subset_Selection_in_Ambulance_Dispatching.pdf",
• DOI = "doi:10.1109/CEC55065.2022.9870323",
• size = "8 pages",
• abstract = "Assigning ambulances to emergencies in real-time, ensuring both that patients receive adequate care and that the fleet remains capable of responding to any potential new emergency, is a critical component of any ambulance service. Thus far, most techniques to manage this problem are as convoluted as the problem itself. As such, many real-world medical services resort to using the naive closest-idle rule, whereby the nearest available vehicles are dispatched to serve each new call. This paper explores the feasibility of using a genetic programming hyper heuristic (GPHH) in order to generate intelligible rules of thumb to select which vehicles should attend any given emergency. Such rules, either manually or automatically designed, are evaluated within a novel solution construction procedure which constructs solutions to the ambulance dispatching problem given the parameters of the simulation environment. Experimental results suggest that GPHH is a promising technique to use when approaching the ambulance dispatching problem. Further, a GPHH-evolved rule interpretability allows for detailed semantic analysis into which features of the environment are valuable to the decision making process, allowing for human dispatching agents to make more informed decisions in practice.",
• notes = "Also known as \cite{9870323}",

}

Genetic Programming entries for Jordan MacLachlan Yi Mei Fangfang Zhang Mengjie Zhang

Citations