A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows

By Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Christian Prins. Computers & Operations Research, 2013

545 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/0eb8e4978dabc46c6b5a8b86361c5e178318c7df

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In Automotive Engineering

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Citations in the corpus

Routing a mixed fleet of electric and conventional vehicles (2015, unknown): “Note that for all Solomon instances the solution found by NBD corresponds to the best-known solution in the literature (Vidal et al. 2013).”
The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations (2016, unknown): “According to the categorization introduced by Vidal et al. (2013a), the E-FSMFTW consists of several EVAL attributes, i.e., capacity, distance, and state of charge, as well as a single ASSIGN attribute: the vehicle type.”
A unified solution framework for multi-attribute vehicle routing problems (2014, methodology): “Table 4 also includes the results from previous HGSADC applications on the PVRP, MDVRP, CVRP, and VRTDSP with European Union regulations (Vidal et al. 2013a, 2012a, Goel and Vidal 2013) since UHGS works identically when instantiated on these problems.”
Rich vehicle routing problems: From a taxonomy to a definition (2015, background): “A first step in this direction has been made by Røpke and Pisinger (2006), Subramanian et al. (2011), Derigs and Vogel (2014), and Vidal et al. (2013, 2014).”
The vehicle routing problem: State of the art classification and review (2016, background): “In our classification, only a few articles (e.g., Cordeau & Maischberger, 2012; Subramanian, Uchoa, & Ochi, 2013; Vidal, Crainic, Gendreau, & Prins, 2013a, 2014a) propose a “general” algorithm that can solve multiple variants of the VRP.”
On the Min-cost Traveling Salesman Problem with Drone (2015, methodology): “Other split procedures are now used widely in state-of-the-art metaheuristics such as [20], [21], [22], [23] to solve many variants of VRPs.”