Sustainable Supply Chain Strategies for Modular-Integrated Construction Using a Hybrid Multi-Agent–Deep Learning Approach

Ali Attajer, Boubakeur Mecheri, Imane Hadbi, Solomon N. Amoo and Anass Bouchnita ()
Additional contact information
Ali Attajer: Institut de Recherche de la Construction (IRC), ESTP, 28 Avenue du Président Wilson, F-94230 Cachan, France
Boubakeur Mecheri: Institut de Recherche de la Construction (IRC), ESTP, 28 Avenue du Président Wilson, F-94230 Cachan, France
Imane Hadbi: Institut de Recherche de la Construction (IRC), ESTP, 28 Avenue du Président Wilson, F-94230 Cachan, France
Solomon N. Amoo: Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
Anass Bouchnita: Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA

Sustainability, 2025, vol. 17, issue 12, 1-31

Abstract: Modular integrated construction (MiC) is a cutting-edge approach to construction that significantly improves efficiency and reduces project timelines by prefabricating entire building modules off-site. Despite the operational benefits of MiC, the carbon footprint of its extensive supply chain remains understudied. This study develops a hybrid approach that combines multi-agent simulation (MAS) with deep learning to provide scenario-based estimations of CO 2 emissions, costs, and schedule performance for MiC supply chain. First, we build an MAS model of the MiC supply chain in AnyLogic, representing suppliers, the prefabrication plant, road transport fleets, and the destination site as autonomous agents. Each agent incorporates activity data and emission factors specific to the process. This enables us to translate each movement, including prefabricated components of construction deliveries, module transfers, and module assembly, into kilograms of CO 2 equivalent. We generate 23,000 scenarios for vehicle allocations using the multi-agent model and estimate three key performance indicators (KPIs): cumulative carbon footprint, logistics cost, and project completion time. Then, we train artificial neural network and statistical regression machine learning algorithms to captures the non-linear interactions between fleet allocation decisions and project outcomes. Once trained, the models are used to determine optimal fleet allocation strategies that minimize the carbon footprint, the completion time, and the total cost. The approach can be readily adapted to different MiC configurations and can be extended to include supply chain, production, and assembly disruptions.

Keywords: modular integrated construction (MiC); carbon footprint; multi-agent simulation; supply chain; AnyLogic; neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/12/5434/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/12/5434/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:12:p:5434-:d:1677715

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().