S. H. Iranmanesh, M. Shakhsi-Niaei, H. Rastegar,
Volume 9, Issue 2 (4-2019)
Abstract
Existing project selection models do not consider the complexity of projects as a selection criterion, while their complexity may prolong the project duration and even result in its failure. In addition, existing models cannot formulate the aggregate complexity of the selected projects. The aggregated complexity is not always equal to summation of complexity of projects because of possible synergies or conflicts between them may increase or decrease the total complexity. In this paper, a model is proposed for measuring the aggregate complexity in the selection of project portfolios. A case study is presented to show the usefulness of the model and its applicability in practice. Moreover, several large-sized numerical examples have been tested showing the capability of the model to solve such problems in logical computational time.
M. Rastegar Moghaddam,
Volume 16, Issue 2 (4-2026)
Abstract
Decision-making in the selection of sustainable building components remains one of the most persistent challenges in the construction industry. Projects involve numerous conflicting objectives and highly interdependent variables, yet the rich semantic and relational data embedded in IFC-based BIM models is rarely fully exploited for advanced analytical support. Existing approaches typically suffer from fragmented workflows, inefficient data extraction, and poor integration between modelling, optimisation, and decision-making processes. This study proposes a comprehensive, integrated data-driven decision-support framework that directly addresses these limitations. The framework transforms IFC-based BIM data into a scalable graph database using Neo4j and connects it seamlessly with multi-objective optimisation, Data Envelopment Analysis (DEA), and multi-criteria decision-making (MCDM) within a single coherent pipeline. The framework was implemented and validated on a residential building case study, considering four key sustainability objectives. Results demonstrate that the graph-based representation improves data accessibility and efficient retrieval, while the integrated pipeline effectively reduces the solution space and delivers transparent, high-quality recommendations that balance technical performance with stakeholder preferences. Compared with conventional fragmented methods, the proposed framework offers a more coherent, practical, and potentially scalable solution for complex multi-criteria decision-making problems across the Architecture, Engineering, and Construction (AEC) industry.
