On Predicting the Work Load for Service Contractors

Himadri Sikhar Khargharia, Siddhartha Shakya, Sara Sharif, Russell Ainslie, Gilbert Owusu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Service Industries rely on resource planning and service optimisation to improve operational efficiency. Forecasting the demand for the service with high accuracy plays a significant role in proactively planning the resources to support the expected demand. With the evolution of the Internet Of Things (IoT), the service contractors use different types of devices connected to the internet to capture the demand and monitor the historical pattern. In this work, we analyse the arrival pattern tracked using different IoT devices of personnel employed by a contractor at different zones for providing service. This arrival pattern at a specific zone is considered the service demand. We document this analysis and forecast the future arrival pattern of personnel at different zones. We compare different regression models based on their accuracy to select the best fit model and report the results. The best fit model is used for forecasting the arrival pattern by a real-life application.

Original languageBritish English
Title of host publicationArtificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings
EditorsMax Bramer, Frederic Stahl
PublisherSpringer Science and Business Media Deutschland GmbH
Pages223-237
Number of pages15
ISBN (Print)9783031214400
DOIs
StatePublished - 2022
Event42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 - Cambridge, United Kingdom
Duration: 13 Dec 202215 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13652 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022
Country/TerritoryUnited Kingdom
CityCambridge
Period13/12/2215/12/22

Keywords

  • Decision tree regressor
  • ElasticNet
  • Forecasting
  • Gradient boosting regressor
  • KNN regressor
  • Neural network
  • Random forest regressor
  • SVR
  • Work load prediction

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