Leveraging machine learning to solve The vehicle Routing Problem with Time Windows
Name
1191849628-MIT.pdf
Size
2.19 MB
Format
Adobe PDF
Checksum (MD5)
0d10bea08ec484a67b193f495d70493d
Author(s)
Poullet, Julie(Julie M.)
Advisor(s)
Matthias Winkenbach.
Alternative Title
Leveraging machine learning to solve VRPTW
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
The Vehicle Routing Problem with Time Windows (VRPTW) has been widely studied in the Operations Research (OR) literature given its increasingly widespread applications, ranging from school bus scheduling to packages delivery. In the last decades, and in large part due to the surge in e-commerce and shortened promised lead times, the scale of the highly constrained VRPTW instances encountered in real-world applications has significantly increased. Simultaneously, various Machine Learning (ML) methods have been developed to tackle combinatorial problems and to leverage complex data structure, but little research has been done on applying these techniques to the VRPTW. In light of this research gap, our thesis develops a process to solve large-scale VRPTW without classical OR routing by proposing a two-stage algorithm. In the first stage, we design a clustering algorithm leveraging Optimal Classification Trees (OCT), which aims at dividing customers into smaller subsets. In the second stage, we present an actor-critic Reinforcement Learning (RL) approach to solve the VRPTW on these smaller customers clusters. Subsequently, we explore the interactions between ML and OR and develop a framework to overcome the difficulties linked to the differences between the train and test sets, as well as the adversity created by the OR algorithm. We also study the generalization limitations of RL methods. Results show that the clustering approach is competitive with regards to a k-means-based clustering, yielding improvements up to 5% in terms of number of vehicles, and that a RL approach can successfully solve medium-size VRPTW instances, providing optimality results similar to state-of-the-art industrial solvers.
Description
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-125).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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