Work

Applying deep reinforcement learning for construction labour dispatching in a union hall setting

Year: 2026

Type: article

Abstract: This paper addresses the gap in dynamic resource allocation in construction by formalizing union hall dispatching, a stochastic process with uncertain job arrivals, durations, and worker turnover, as a Markov Decision Process (MDP) and developing a Deep Reinforcement Learning (DRL) agent to automate labour allocation. The agent incorporates a waiting-queue-based fairness metric and, in simulation, achieves consistent fairness improvements while maintaining competitive job assignments against heuristic baselines. Methodologically, the study tailors DRL formalization through careful state representation and reward design, supported by a narrative analogy that frames the agent as a novice dispatcher to enhance interpretability. While serving as a proof-of-concept study with acknowledged simplifications, the work (1) establishes an MDP foundation for DRL in dynamic resource allocation, and (2) demystifies DRL for construction applications. These contributions lay the groundwork for dynamic resource allocation as an emerging research direction in construction, with applicability extending beyond the union context.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2026.106838

Authors: Xiaoteng Ge, Ming Lu, Jun Jin

Institutions: University of Alberta, Hong Kong Polytechnic University

Language: English

Topic: Construction Project Management and Performance

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Estimated APC paid: $5,490