TRAINING A MACHINE TO AUTOMATE SPOT PRICING OF LOGISTICS SERVICES IN A LARGE-SCALE NETWORK

A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subg...

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Bibliographic Details
Main Authors Ettl, Markus R, Xue, Zhengliang, Chowdhary, Pawan R, Yang, Hongxia, Nonner, Tim, Lederman, Roger D, Schimpel, Ulrich B
Format Patent
LanguageEnglish
Published 03.05.2018
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.
Bibliography:Application Number: US201615340548