A review of approaches to modeling applied vehicle routing problems
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Abstract (arXiv)
Due to the practical importance of vehicle routing problems (VRP), there exists an ever-growing body of research in algorithms and (meta)heuristics for solving such problems. However, the diversity of VRP domains creates the separate problem of modeling such problems -- describing the domain entities (and, in particular, the planning decisions), the set of valid planning decisions, and the preferences between different plans. In this paper, we review the approaches for modeling vehicle routing problems. To make the comparison more straightforward, we formulate several criteria for evaluating modeling methods reflecting the practical requirements of the development of optimization algorithms for such problems. Finally, as a result of this comparison, we discuss several future research avenues in the field of modeling VRP domains.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 55 references that are in the map. First posted on arXiv 23 May 2021.
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7 of 55 references.
References · 7
- 2018Attention, Learn to Solve Routing Problems!cited by 656
- 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
- 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
- 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
- 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
- 2020Learn to Design the Heuristics for Vehicle Routing Problemcited by 24
- 2016Type Inference for Static Compilation of JavaScript (Extended Version)cited by 2
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