Sustainable Logistics Planning in Modular Integrated Construction Using Multimethod Simulation and Taguchi Approach

Abstract

This study examined logistics planning in the modular integrated construction supply chain (MiC-SC). Existing models overlook two important characteristics of the MiC-SC—the dynamic behavior of its stakeholders, and the uncertain interactions between its activities and resources—making their applicability limited in real practice. The literature also lacks an understanding of which logistics and construction decisions significantly impact the MiC-SC key performance indicators (KPIs) (e.g., project duration, MiC-SC costs, and emissions). Moreover, optimizing logistics and construction decisions to achieve a trade-off between these KPIs is yet to be addressed. This study contributes to the MiC-SC knowledge by (1) developing a multimethod simulation model that captures the dynamic behavior of MiC-SC stakeholders through agent-based modeling and the uncertain interactions between their internal activities and resources using discrete-event simulation and considering more realistic features such as traffic condition, site layout, heterogeneous truck types, and uncertainty of activities duration; (2) integrating the developed model with the Taguchi approach (TA) to identify the significant logistics and construction decisions that impact five KPIs (project duration, construction costs, construction emissions, logistics costs, and logistics emissions); and (3) applying a simulation-optimization (SO) approach to find the near-optimum logistics and construction decisions that maximize the MiC-SC sustainability through minimizing the project duration, total costs, and carbon emissions. The model’s applicability was demonstrated using a case study of a high-rise MiC project. The TA analysis highlighted some interactions between logistics and construction decisions, calling for collaborative decision-making between stakeholders. The proposed SO approach could provide them with near-optimum decisions that minimize the project duration, MiC-SC costs, and emissions.