Genetic Programming Hyper-Heuristic for the Dynamic Electric Dial-a-Ride Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{DBLP:conf/cec/HuangMRZTLZR25,

• author = "William Huang and Yi Mei and Guenther R. Raidl and Fangfang Zhang and Laurenz Tomandl and Steffen Limmer and Mengjie Zhang and Tobias Rodemann",
• title = "Genetic Programming Hyper-Heuristic for the Dynamic Electric Dial-a-Ride Problem",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Training, Shared transport, Dynamic scheduling, Real-time systems, Dispatching, Planning, Resource management, Vehicle dynamics, Testing",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Wed, 02 Jul 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/HuangMRZTLZR25.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11042942",
• DOI = "10.1109/CEC65147.2025.11042942",
• abstract = "This paper studies the Dynamic Electric Dial-A-Ride Problem (DEDARP), which is a combinatorial optimisation problem that has applications in real-world ridesharing services with electric vehicles. In addition to the challenges from classical scheduling and route planning, we consider here the extra challenge of making real-time dispatching decisions in dynamic environments with new requests arriving over time and selecting proper times for the vehicles to recharge. To solve DEDARP effectively, we propose a Genetic Programming Hyper-Heuristic (GPHH) that evolves heuristics/policies to dispatch vehicles in real time. We have developed a simulation process that generates a solution for any given instance by two policies, one for vehicle allocation and the other for request allocation, and design fitness evaluations based on the simulation. Moreover, we propose a multi-tree GP to evolve these two policies simultaneously, which makes use of advanced terminals to comprehensively represent the state. Experimental results on a wide range of instances show that GPHH can evolve effective policies that make significantly better real-time dispatching decisions than human-designed policies based on prior knowledge.",
• notes = "also known as \cite{huang:2025:CEC} \cite{11042942}",

}

Genetic Programming entries for William Huang Yi Mei Gunther R Raidl Fangfang Zhang Laurenz Tomandl Steffen Limmer Mengjie Zhang Tobias Rodemann

Citations