Machine learning based simulation optimisation for urban routing problems

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

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Comparison of Machine Learning Algorithms for Vehicle Routing Problems

IOT with Smart Systems - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-3945-6_77 ◽

2022 ◽

pp. 785-793

Author(s):

V. S. Vamsi Krishna Munjuluri ◽

Yashwanth Reddy Telukuntla ◽

Parimi Sanath Kumar ◽

Aravind Mohan ◽

Georg Gutjahr

Keyword(s):

Machine Learning ◽

Vehicle Routing ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Vehicle Routing Problems ◽

Routing Problems

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Machine Learning based Simulation Optimisation for Trailer Management

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) ◽

10.1109/smc.2019.8914329 ◽

2019 ◽

Author(s):

Dylan Rijnen ◽

Jason Rhuggenaath ◽

Paulo Roberto de Oliveira da Costa ◽

Yingqian Zhang

Keyword(s):

Machine Learning ◽

Simulation Optimisation

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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