Heuristics for multi-attribute vehicle routing problems: A survey and synthesis

By Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Christian Prins. European Journal of Operational Research, 2013

448 citations recorded by the source corpus.

The attributes of vehicle routing problems are additional characteristics or constraints that aim to better take into account the specificities of real applications. The variants thus formed are supported by a well-developed literature, including a large variety of heuristics. This article first reviews the main classes of attributes, providing a survey of heuristics and meta-heuristics for Multi-Attribute Vehicle Routing Problems (MAVRP). It then 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. This cross-analysis leads to the identification of “winning strategies” in designing effective heuristics for MAVRP. This is an important step in the development of general and efficient solution methods for dealing with the large range of vehicle routing variants.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2e1704cb7cca0fed8b1d227620a6360eee5e56af

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Citations in the corpus

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 comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “adjust the sequence of visited machines in shop-floor), as well as the best delivery and pickup time windows (Vidal et al., 2012).”
A concise guide to existing and emerging vehicle routing problem variants (2019, background): “, the specific constraints, decision sets and objectives arising from real applications (Vidal et al. 2013).”
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.”
Big data analytics in logistics and supply chain management: Certain investigations for research and applications (2016, unknown): “The optimal sequence considers the distances between each pair of nodes, expected traffic volume, left turns, and other constraints placed on the routes, such as delivery and pickup time windows (Vidal et al., 2013).”