Modular construction (MC) is a sustainable construction method; however, its extensive adoption is hindered by challenges in logistics planning and coordination. Recognizing this barrier, many governments are planning the deployment of modular construction hubs (MCHs) to provide contractors with storage buffers near construction sites to alleviate logistics-related challenges. Current MCH planning efforts, however, are largely government-led and focus on evaluating candidate sites, with limited consideration of contractors’ operational perspectives through optimization. To bridge this gap, this paper proposes a novel bilevel programming framework that models the strategic interactions between governments and contractors in MCH planning. The upper level formulates the government’s decisions on MCH site selection, MCH construction and expansion, and service pricing, aiming to maximize MCH utilization and cost efficiency. The lower level captures contractors’ supply chain decisions, aiming to minimize logistics costs following MCH deployment. To solve the model, a decomposition-based biobjective particle swarm optimization algorithm is developed to generate a diverse set of Pareto optimal solutions for trade-off analysis. A nondominated sorting genetic algorithm-II is also implemented as a benchmark for performance comparison. The proposed model and algorithms are validated using data from Hong Kong. Semistructured interviews are conducted to gain industry feedback on the derived planning outcomes. Finally, this research provides governments with an effective tool for designing optimal MCH planning, which can enhance the resilience of MC supply chains and facilitate broader adoption of MC technologies in the construction sector.
Yi et al. (Mon,) studied this question.