C
Christian Prins
Researcher at University of Technology of Troyes
Publications - 170
Citations - 10455
Christian Prins is an academic researcher from University of Technology of Troyes. The author has contributed to research in topics: Vehicle routing problem & Metaheuristic. The author has an hindex of 52, co-authored 168 publications receiving 9363 citations. Previous affiliations of Christian Prins include École des mines de Nantes & Team Losi.
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A simple and effective evolutionary algorithm for the vehicle routing problem
TL;DR: A GA without trip delimiters, hybridized with a local search procedure is proposed, which outperforms most published TS heuristics on the 14 classical Christofides instances and becomes the best solution method for the 20 large-scale instances generated by Golden et al.
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A survey of recent research on location-routing problems
Caroline Prodhon,Christian Prins +1 more
TL;DR: This paper analyzes the recent literature on the standard LRP and new extensions such as several distribution echelons, multiple objectives or uncertain data and results of state-of-the-art metaheuristics are compared on standard sets of instances for the classical LRP, the two-echelon L RP and the truck and trailer problem.
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A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows
TL;DR: The paper presents an efficient Hybrid Genetic Search with Advanced Diversity Control for a large class of time-constrained vehicle routing problems, introducing several new features to manage the temporal dimension.
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Heuristics for multi-attribute vehicle routing problems: A survey and synthesis
TL;DR: This article takes a closer look at the concepts of 64 remarkable meta-heuristics, selected objectively for their outstanding performance on 15 classic MAVRP with different attributes, and leads to the identification of “winning strategies” in designing effective heuristics forMAVRP.
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A unified solution framework for multi-attribute vehicle routing problems ☆
TL;DR: The proposed Unified Hybrid Genetic Search metaheuristic relies on problem-independent unified local search, genetic operators, and advanced diversity management methods and shows remarkable performance, which matches or outperforms the current state-of-the-art problem-tailored algorithms.