# Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty
## 5 Conclusions  
This study proposed a proactive scheduling model for PC production under due date uncertainty, which finds an optimal schedule insensitive to a priori anticipated contactor schedule uncertainties regarding tardiness. The proposed model comprises a PC production simulation module, which simulates and evaluates the total tardiness of the schedule under contractor schedule uncertainty, and a GA-based production schedule optimization module, which finds the optimal schedule through iterative generation and evaluation using the PC production simulation module. The proposed model was validated through the experimental study. As a result, the proposed model had the lowest tardiness of 303.8 h at the confidence level of 100%, followed by NEHedd 324.7 h, basic GA 328.5 h, and EDD 335.8 h. The results show that the proposed model can find a schedule with minimum tardiness given the uncertainty of the due date.  
The proposed model can efficiently find an optimal schedule by simulating a vast number of scenarios under due date uncertainty, something that would be highly impractical to test in the real world without this approach. The optimal schedule created before production is robust to due-date changes without requiring production schedule changes that incur additional costs and time. Moreover, the proposed model can be used in other uncertain environments, such as equipment breakdown, failure of process operations, or processing time variation. Finally, the proposed model would contribute to the successful completion of the PC construction project by ensuring that the PC components are delivered to the contractor within their due date under due date uncertainty.  
Even though we scientifically tested the promising potential of the proactive scheduling approach for PC production, there are some notable limitations. When simulating PC production considering the uncertain environment, we assumed the due date uncertainty distribution, which must be known in advance. Future research will examine the distribution of uncertain factors such as due dates, processing times, and equipment breakdowns. Additionally, the proactive scheduling cycle for PC production influences both overall performance and optimization runtime. In this study, 12 PC components were used for the experiments, and an optimal solution was achieved within 30 s through 1000 iterations of the GA. However, the longer the proactive scheduling cycle, the longer the optimization run time and the worse the performance of the established robust schedule can be. Of course, the optimization runtime or the performance of the robust schedule will not entirely dominate the scheduling cycle. In practice, the optimal scheduling cycle should consider various conditions in the production environment, such as resource planning cycles and lead times. Therefore, we recommend that future studies analyze the proactive and reactive scheduling cycles to optimize schedule performance and runtime.  
Finally, while this study focused on due date uncertainty and tardiness to develop a robust schedule, PC construction projects may encounter various other uncertainties, as well as objectives such as makespan or earliness. The future research will explore scheduling models that optimize multiple objectives within multi-uncertainty environments.  
Conceptualization, T.K. and Y.-W.K.; methodology, Y.-W.K.; validation, T.K.; formal analysis, T.K. and Y.-W.K.; investigation, T.K.; resources, T.K.; data curation, T.K.; writing--original draft preparation, T.K. and Y.-W.K.; writing--review and editing, T.K. and Y.-W.K.; visualization, T.K.; supervision, Y.-W.K.; project administration, Y.-W.K.; funding acquisition, T.K. and Y.-W.K. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by Seoul National University of Science and Technology.  
Not applicable.  
The data present in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.