International Business Machines Corporation

Hongxia Yang

Markus Ettl

Young Min Lee

Rui Zhang

Pawan Raghunath Chowdhary

Zhengliang Xue

Roger Douglas Lederman

Tim Nonner

Ulrich B Schimpel

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.

Training a machine to automate spot pricing of logistics services in a large-scale network

An algebra and differential equations model of a physical system is constructed based on available training data and physical system characteristics. A hybrid calibration process is carried out to iteratively calibrate both time-insensitive and time-sensitive parameters of the algebra and differential equations model so as to obtain parameter vectors. Vector auto-regression is applied to the parameter vectors to predict values of the parameters for a future time period.

Building thermal control techniques

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Anaheim, CA, United States of America

Hongxia Yang