A BIM–IoT-Driven Digital Twin Framework for Uncertainty-Aware 4PL-Orchestrated Just-in-Time and Low-Carbon Material Delivery in Multi-Site Construction Projects

Karimi, Behnam; mohammadpour omran, M.; Sahebi, Hadi

doi:10.22068/ijiepr.37.3.2743

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‎ 10.22068/ijiepr.37.3.2743

A BIM–IoT-Driven Digital Twin Framework for Uncertainty-Aware 4PL-Orchestrated Just-in-Time and Low-Carbon Material Delivery in Multi-Site Construction Projects

Behnam Karimi¹

, M. Mohammadpour omran²

, Hadi Sahebi¹

1- Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
2- Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran , omran@iust.ac.ir

Abstract: (81 Views)

Construction material delivery in multi-site projects is challenged by uncertain demand and site readiness, limited logistics capacity, restricted storage space, and transportation-related emissions. This study develops an uncertainty-aware BIM–IoT-driven digital-twin framework for fourth-party logistics orchestration of just-in-time and low-carbon material delivery. The framework integrates BIM-based material and activity information with operational observations of project progress, shipment status, inventory, site readiness, and logistics-resource availability. A scenario-based multi-objective mixed-integer linear programming model is formulated within a rolling-horizon structure to minimize expected logistics cost, risk-adjusted delivery deviation, site congestion, and carbon emissions. Implemented decisions, immediate non-anticipative decisions, and future scenario-dependent recourse decisions are distinguished, while transportation uncertainty is represented by separating dispatch and arrival and incorporating conditional value at risk. For large-scale instances, an Event-Aware Rolling-Horizon NSGA-II is developed using scenario-linked encoding, Pareto warm starts, event-affected gene masks, adaptive operators, and hierarchical feasibility repair. Results from an Iranian steel-construction application and multi-size benchmarks show improved delivery, congestion, emission, hypervolume, and IGD+ performance relative to tested baseline policies and algorithms. The framework also yields quantitative decision rules for logistics capacity, site storage, data quality, consolidation, and carbon restrictions.

Keywords: Construction logistics, Digital twin, BIM–IoT, Fourth-party logistics, Conditional value at risk, Rolling-horizon optimization, Low-carbon delivery.

Full-Text [PDF 668 kb] (15 Downloads)

Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2026/06/3 | Accepted: 2026/08/16