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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Evaluation of Genetic Algorithm on the Multidrop Truck-Drone Logistic Problem
verfasst von : David Sánchez-Wells, Pedro L. González-R, Jose L. Andrade-Pineda
Verlag: Springer Nature Switzerland
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Abstract
This paper explores the use of genetic algorithms (GA) to optimize the route and schedule planning for multi-trip last-mile delivery operations using a fleet of trucks and drones. The research builds upon the work of (Murray and Chu, 2015) who proposed the use of truck-drone teams. The authors suggest that GA is a flexible procedure for defining the routing plan for urban last-mile delivery applications, capable of searching through a large search space to reach high-quality solutions. The authors conduct a literature review of twenty papers that use GA to solve the truck-drone delivery problem and identify two interesting approaches. They present the specifics of their coding structure and describe their GA framework for addressing the targeted problem. The findings demonstrate that the proposed GA approach outperforms other frequently employed optimization methods. The authors conclude that GA provides a powerful tool for decision-makers to optimize multi-destination last-mile delivery operations.