Enhancing resource assignment efficiency in service industry: A predict-then-optimize approach with XGBoost

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63596658" target="_blank" >RIV/70883521:28140/25:63596658 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1877050925001346?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050925001346?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.01.126" target="_blank" >10.1016/j.procs.2025.01.126</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing resource assignment efficiency in service industry: A predict-then-optimize approach with XGBoost

Popis výsledku v původním jazyce

This paper addresses the critical aspect of resource planning in a service context through an integrated predictive and prescriptive approach. Utilizing real-world data from a company providing repair and maintenance services, we demonstrate the use of an XGBoost model to forecast ad-hoc service demands and subsequently optimize resource assignment using a mathematical model. Our findings show that the prediction evaluation metrics significantly improve, highlighting the superiority of complex machine learning models over baseline models such as Linear Regression. Furthermore, the integration of the prediction into the decision-making process resulted in a 26.4% lower decision error compared to the baseline model. Our research also found that the deviations in prediction and optimal objective function values are not aligned. While the average error for MAE % in prediction is 22.2%, the error for the optimal objective function is much lower, reducing to 5.3%. However, although true for our case, this might not be generalizable. Furthermore, when comparing the baseline model with these results, it is also shown that an improvement in prediction accuracy also improves decision making error. Our results indicate that a combined predict-then-optimize approach outperforms the existing methods in both predictive and prescriptive performance, demonstrating its applicability in real-world scenarios.

Název v anglickém jazyce

Enhancing resource assignment efficiency in service industry: A predict-then-optimize approach with XGBoost

Popis výsledku anglicky

This paper addresses the critical aspect of resource planning in a service context through an integrated predictive and prescriptive approach. Utilizing real-world data from a company providing repair and maintenance services, we demonstrate the use of an XGBoost model to forecast ad-hoc service demands and subsequently optimize resource assignment using a mathematical model. Our findings show that the prediction evaluation metrics significantly improve, highlighting the superiority of complex machine learning models over baseline models such as Linear Regression. Furthermore, the integration of the prediction into the decision-making process resulted in a 26.4% lower decision error compared to the baseline model. Our research also found that the deviations in prediction and optimal objective function values are not aligned. While the average error for MAE % in prediction is 22.2%, the error for the optimal objective function is much lower, reducing to 5.3%. However, although true for our case, this might not be generalizable. Furthermore, when comparing the baseline model with these results, it is also shown that an improvement in prediction accuracy also improves decision making error. Our results indicate that a combined predict-then-optimize approach outperforms the existing methods in both predictive and prescriptive performance, demonstrating its applicability in real-world scenarios.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Computer Science

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

10

Strana od-do

644-653

Název nakladatele

Elsevier B.V.

Místo vydání

Amsterdam

Místo konání akce

Praha

Datum konání akce

13. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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