Abstract
The large number of real-world applications have shown that the use of computational method for distribution process planning produces substantial savings. Many of these applications lead to problem generally known as Vehicle Routing Problem. The real-world applications are highly computationally demanding for larger instances. This article aims to show the possibilities and benefits of using hyperparameter search for solving the Periodic Vehicle Routing Problem for exhausted oil collection by execution on the supercomputing infrastructure using HyperLoom platform. HyperLoom is an open source platform for defining and executing scientific pipelines in a distributed environment. This experiment was run on the supercomputer Salomon operated by IT4Innovations.
Highlights
The logistic industry heavily relies on optimal route planning as it represents a critical task
One of such variants is the Periodic Vehicle Routing Problem (PVRP) in which routes are constructed over a period of time
In [7] we considered a variant of the PVRP in which exhausted oil must be collected from customers
Summary
The logistic industry heavily relies on optimal route planning as it represents a critical task. The supercomputing infrastructure allows us to solve the complicated optimization problems and to use the optimization algorithms to find the optimal solutions Many of these algorithms depend on configuration settings that are typically hand-tuned in the course of evaluating the algorithm for a particular data set. HyperLoom has been successfully used at IT4Innovations for distributed hyperparameter search within machine learning applications for pharma industry [18] This application aims to be another use case proving its usability beyond the pharmaceutic domain. This article aims to show the possibilities and benefits of using hyperparameter search for solving the Periodic Vehicle Routing Problem for exhausted oil collection by execution on the supercomputing infrastructure using HyperLoom platform.
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