Application of optimization to the production planning of construction prefabrication supply chains

Prefabrication and modularization are a growing trend in the construction industry. Efficiencies of the operation of construction prefabrication and modularization (CP&M) supply chains result in benefits for construction projects. Although the variable delivery time of construction schedules impacts the operation efficiencies of CP&M supply chains, few studies have investigated this problem by addressing uncertainties in CP&M schedules. On the other hand, the growing interest in CP&M motivates many prefabrication companies to open multiple fabrication shops and allocate jobs between these shops based on various factors. Nonetheless, few quantitative models are available to facilitate this allocation, and research on the CP&M supply chain with multiple shops is scarce. This study aims to fill this gap by investigating the application of optimization to facilitate the production planning of the CP&M supply chain with multiple fabrication shops. This study starts with a literature review covering 110 CP&M papers to understand common improvement strategies. Seven strategies were identified including using building information modelling, focusing on product design, using advanced technologies, applying lean principles, utilizing optimizations in production planning, utilizing optimizations in design and using simulation. Then, an industry survey with 10 fabricators is performed to understand current practices and impacts of uncertainty. After that, two optimization models for job allocations and production planning are developed: one is a deterministic model and the other is a stochastic programing model with multi objectives. Each optimization model is demonstrated through an example problem. The first model generates an optimal solution for the example problem that saves 2. 5% of total cost compared to the Early Due Date method. The second model allows to develop a robust set of optimal schedules subject to uncertainty and flexible in balancing cost and time reduction objectives. Its example shows that variable delivery time causes up to 2. 93% of total cost increase. Moreover, a sensitivity analysis is introduced to quantify impacts of parameters sensitive to supply chain performance and identify improvement opportunities. This research is expected to contribute the knowledge on production planning of CP&M supply chain under uncertainty and enhance CP&M supply chain performance, therefore benefit construction projects and the construction industry.