On Predicting the Work Load for Service Contractors

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

doi:10.1007/978-3-031-21441-7_16

On Predicting the Work Load for Service Contractors

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

Emirates ICT Innovation Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	British English
Title of host publication	Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings
Editors	Max Bramer, Frederic Stahl
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	223-237
Number of pages	15
ISBN (Print)	9783031214400
DOIs	https://doi.org/10.1007/978-3-031-21441-7_16
State	Published - 2022
Event	42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 - Cambridge, United Kingdom Duration: 13 Dec 2022 → 15 Dec 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13652 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022
Country/Territory	United Kingdom
City	Cambridge
Period	13/12/22 → 15/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

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

Access to Document

10.1007/978-3-031-21441-7_16

OpenUrl availability

Full text

Cite this

Khargharia, H. S., Shakya, S., Sharif, S., Ainslie, R., & Owusu, G. (2022). On Predicting the Work Load for Service Contractors. In M. Bramer, & F. Stahl (Eds.), Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings (pp. 223-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13652 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21441-7_16

Khargharia, Himadri Sikhar ; Shakya, Siddhartha ; Sharif, Sara et al. / On Predicting the Work Load for Service Contractors. Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings. editor / Max Bramer ; Frederic Stahl. Springer Science and Business Media Deutschland GmbH, 2022. pp. 223-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Khargharia, HS, Shakya, S, Sharif, S, Ainslie, R & Owusu, G 2022, On Predicting the Work Load for Service Contractors. in M Bramer & F Stahl (eds), Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13652 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 223-237, 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022, Cambridge, United Kingdom, 13/12/22. https://doi.org/10.1007/978-3-031-21441-7_16

On Predicting the Work Load for Service Contractors. / Khargharia, Himadri Sikhar; Shakya, Siddhartha; Sharif, Sara et al.
Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings. ed. / Max Bramer; Frederic Stahl. Springer Science and Business Media Deutschland GmbH, 2022. p. 223-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13652 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Khargharia HS, Shakya S, Sharif S, Ainslie R, Owusu G. On Predicting the Work Load for Service Contractors. In Bramer M, Stahl F, editors, Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 223-237. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-21441-7_16