Robotic conveyor line for precast-concrete building production: units, layout optimisation, and parallel scheduling
Abstract
Abstract Precast plants face labor shortages, tight delivery windows, and increasingly complex internal logistics, yet decision-level methods that couple robot-level processing times with factory layout–routing–scheduling remain limited. This paper proposes an integrated workflow for designing or retrofitting an Optimized Robotic Conveyor for Precast Concrete (ORC-PC) line and demonstrates it on a six-story reference building. Elements are grouped into product families and mapped to robotic operational units ( $$U_0$$ U 0 – $$U_{12}$$ U 12 ), each representing a defined workcell or resource in the ORC-PC line, in a Rhino3D factory model consistent with KUKA workspaces, safety envelopes, and formwork geometry. Family-dependent cycle times from KUKA.sim drive a Particle swarm optimisation layout model that minimizes flow-weighted conveyor length under non-overlap and clearance constraints, yielding a compact layout of approximately $$43\times 29$$ 43 × 29 m with a loop length of about 96.6 m and implied internal transport times on the order of 10–15 min per element. A Resource-Constrained Project Scheduling Problem then embeds transport and processing times to identify bottlenecks and quantify parallelisation strategies. Results show that curing time and curing-capacity limits dominate throughput, with cage fabrication and downstream drilling acting as secondary bottlenecks under nominal parameters. Overall, under the assumed curing-capacity and scheduling parameters, the ORC-PC line approaches a production rate on the order of one story per day for the structural frame of the reference building (footprint $$\approx 320~\text {m}^2$$ ≈ 320 m 2 ), providing a transferable, data-driven workflow to link robot cycle times to layout decisions and capacity-planning recommendations.
Fields of Study & Subjects
Innovations in Concrete and Construction Materials
BIM and Construction Integration
Advanced Manufacturing and Logistics Optimization
Publication details
- Journal / source
- Construction Robotics
- Publisher
- Springer Science and Business Media LLC
- Volume
- 10
- Issue
- 2
- Publication date
- 17 July 2026
- Language
- EN