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Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP

Authors Jayanta Mandi , Rocsildes Canoy , Víctor Bucarey , Tias Guns

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Jayanta Mandi
  • Data Analytics Laboratory, Vrije Universiteit Brussel, Belgium
Rocsildes Canoy
  • Data Analytics Laboratory, Vrije Universiteit Brussel, Belgium
Víctor Bucarey
  • Institute of Engineering Sciences, Universidad de O'Higgins, Rancagua, Chile
Tias Guns
  • Data Analytics Laboratory, Vrije Universiteit Brussel, Belgium
  • Department of Computer Science, KU Leuven, Belgium

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Jayanta Mandi, Rocsildes Canoy, Víctor Bucarey, and Tias Guns. Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 42:1-42:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption. Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning and scheduling
  • Vehicle routing
  • Neural network
  • Preference learning


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