Optimal assignment of infrastructure construction workers

Haoqing Wang; Wen Yi; Yannick Liu; Haoqing Wang; Wen Yi; Yannick Liu

doi:10.3934/era.2022211

Electronic Research Archive

2022, Volume 30, Issue 11: 4178-4190. doi: 10.3934/era.2022211

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Optimal assignment of infrastructure construction workers

1.
Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
2.
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Received: 07 July 2022 Revised: 30 August 2022 Accepted: 08 September 2022 Published: 21 September 2022

Worker assignment is a classic topic in infrastructure construction. In this study, we developed an integer optimization model to help decision-makers make optimal worker assignment plans while maximizing the daily productivity of all workers. Our proposed model considers the professional skills and physical fitness of workers. Using a real-world dataset, we adopted a machine learning method to estimate the maximum working tolerance time for different workers to carry out different jobs. The real-world dataset also demonstrates the effectiveness of our optimization model. Our work can help project managers achieve efficient management and save labor costs.
- infrastructure management,
- worker assignment,
- integer programming,
- machine learning
Citation: Haoqing Wang, Wen Yi, Yannick Liu. Optimal assignment of infrastructure construction workers[J]. Electronic Research Archive, 2022, 30(11): 4178-4190. doi: 10.3934/era.2022211

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Abstract

Worker assignment is a classic topic in infrastructure construction. In this study, we developed an integer optimization model to help decision-makers make optimal worker assignment plans while maximizing the daily productivity of all workers. Our proposed model considers the professional skills and physical fitness of workers. Using a real-world dataset, we adopted a machine learning method to estimate the maximum working tolerance time for different workers to carry out different jobs. The real-world dataset also demonstrates the effectiveness of our optimization model. Our work can help project managers achieve efficient management and save labor costs.

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