Matheuristics Algorithm Guided by Machine Learning for Solving the Vehicle Routing Problem
Bachtiar Herdianto  1, 2@  , Marc Sevaux  2, 3@  , Romain Billot  2, 4  , Flavien Lucas  5  
1 : IMT Atlantique
IMT Atlantique - Brest : BachtiarHerdianto, IMT Atlantique - Brest : RomainBillot, Université de Bretagne Sud : MarcSevaux, IMT Nord Europe : FlavienLucas
2 : Equipe DECIDE
Laboratoire des sciences et techniques de l\'information, de la communication et de la connaissance : UMR6285, Laboratoire des sciences et techniques de l\'information, de la communication et de la connaissance : UMR6285, Laboratoire des sciences et techniques de l\'information, de la communication et de la connaissance : UMR6285
3 : Université de Bretagne Sud
Université de Bretagne Sud [UBS]
4 : IMT Atlantique
IMT Atlantique - Brest : BachtiarHerdianto, IMT Atlantique - Brest : RomainBillot, Université de Bretagne Sud : MarcSevaux, IMT Nord Europe : FlavienLucas
5 : Ecole nationale supérieure Mines-Télécom Lille Douai
Institut Mines-Télécom [Paris]

In recent days, the usage of learning algorithm to improve optimization methods have become increasingly interesting \cite{begio21,bogyrbayeva22}. For example, the Vehicle Routing Problem (VRP) that logistic companies might face daily. The main problem is arising whenever the delivery routes could not be optimal, which causes an increase in delivery costs. To reduce these costs, we need optimize the delivery route \cite{herdianto21}. However, most optimization algorithm still solves the problem from scratch, even for the same problem type, and nothing useful is extracted from prior solutions. Meanwhile, the historical data could be useful to gain solutions efficiently and effectively. In term of optimization algorithm, the use of artificial intelligence (AI) for solving VRP promise to learn from past solutions or in real-time and then to guide the algorithm to solve the problem. Moreover, the optimization algorithm could learn from its own decisions and adjust its behaviour accordingly to gain better behaviour. Therefore, the objective of this research is divided into two goals: (1) to get an understanding of the connection between the quality of the solutions, their features, and the associated problem instances, and (2) to construct an efficient learning process consolidated with a simple yet powerful optimization algorithm to solve the problems quickly and effectively.


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