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Primary healthcare plays a pivotal role in enhancing health conditions. In Singapore, such services are predominantly manifested through the implementation of the Community Health Assistance Scheme (CHAS). CHAS is an initiative aimed at providing fundamental preventive and therapeutic services, especially for those seniors and low‐income adults with chronic diseases. In spite of considerable efforts in policy and research in this domain, there is a dearth of studies focusing on the spatial optimization of these primary healthcare services. In this study, an innovative multi‐objective medical service facility siting model has been developed based on coarse‐grained parallel genetic algorithm to address the intricate challenges associated with the optimization of locations for CHAS clinics. The proposed optimization model aims to simultaneously maximize accessibility, minimize inequity, and minimize the number of clinics. The successful application of this model in the siting of CHAS clinics in Singapore demonstrates its effectiveness in enhancing residents' access to healthcare services. Apart from its novel academic contributions to the field of spatial optimization of primary healthcare facilities in general, we have also discussed the inherent limitations and identified certain aspects as the future directions of this research. [ABSTRACT FROM AUTHOR]

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta -models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta -model provides reliable estimation with an average RMSE and MAE of 2.604 m m / m i n and 3.402 m m / m i n for TBM's penetration rate(O 1), 0.0211 and 0.0324 for over/under excavation ratio (O 2), and 15.512 k w h and 23.088 k w h for energy consumption (O 3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O 1 to O 3 , respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O 1 to O 3 , respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation. [ABSTRACT FROM AUTHOR]

This paper developed a novel online optimization deep learning framework for accurate estimation and control of tunnel boring machine's (TBM) attitude and position. The proposed spatio-temporal deep learning model integrates graph convolutional network (GCN) and long short-term memorial (LSTM) neural network which can provide an accurate estimation of TBM's trajectory deviations. This model is later utilized as the meta-model and integrated with an online non-dominated sorting genetic algorithm II (NSGA-II) method to optimize and minimize the trajectory deviations in real-time. A tunnel project from Singapore is utilized as a case study for demonstration purposes. TBM's key operational parameters, articulation displacements, and TBM's trajectory deviations are processed and utilized for real-time prediction. The results indicate that (1) The developed deep learning models can predict TBM's trajectory deviations with high accuracy. (2) TBM's trajectory deviations can be effectively reduced by the proposed optimization method. (3) The proposed spatio-temporal deep learning methods outperform the state-of-the-art methods. (4) The proposed online optimization method is more practical and creates better overall improvement than the conventional multi-objective optimization (MOO) approach. The novelty of this paper includes (a) developing a hybrid framework for accurate trajectory deviation estimation and TBM operating optimization, and (b) investigating the influence of time and distance lag between the TBM operation and position control by considering an online optimization. • A novel online multi-objective optimization (MOO) method is proposed. • Deep learning model is established for reliable estimation on TBM's trajectory deviations. • The developed model is utilized as meta-models and hybrids with online NSGA-II method. • A tunnel project from Singapore is utilized as a case study • TBM's overall trajectory deviations can be reduced by 46.4% by the proposed method. [ABSTRACT FROM AUTHOR]