Lacking the understanding of the first principles leads to the apparent black box attributes of complex industrial processes. How to understand complex industrial processes from data and guiding industrial decision-making has become an urgent problem to solve. However, the existing data-driven models are also black boxes, focusing only on the correlation relationships between data without reflecting causal relationships. Therefore, this study addresses the challenge of double black boxes in complex industrial decision-making, proposing a research framework of "causal analysis → performance prediction → process optimization". Firstly, nonparametric copula entropy, network deconvolution , and information geometric causal inference are integrated to construct the causal relations network. Also, the observability and controllability of complex industrial processes are analyzed to provide valuable insights for improving the dataset. Then, drawing inspiration from the transformational machine learning idea, an explainable predictive model is constructed for predicting key performance indicators . Lastly, taking this predictive model as the process surrogate model , the optimal process parameters are solved using the particle swarm optimization algorithm. Moreover, the dataset of 16600 samples from a real-world injection molding process is used for application validation. The research results show that by reconstructing the causal relations network from data, the proposed framework can support the analysis, prediction, and optimization of complex industrial processes, achieving the decision-making goals of safety, robustness, improving quality and efficiency.

Chronic disease patients often require revisits for long-term care. Online medical services shift revisits to online, which can improve the access to chronic care and reduce the burden on offline medical services. However, whether Internet healthcare can truly match the medical supply and demand, one of the critical issues is the efficient advance scheduling of the integrated online and offline systems. This study investigates the advance scheduling problem for the first visit and revisit patients in chronic care. The uncertainty of revisit status (i.e., online or offline) and heterogeneity of online and offline revisits (i.e., revisit interval, continuity of care violation penalty) are considered. A stochastic mixed-integer programming model is formulated for assigning patients to a specific physician on a specific day over the course of a finite planning period. The aim is to minimize the expected sum of three cost components related to offline and online services: overtime and idle time, continuity of care violation penalty, and fixed setup. This study proposes a modified progressive hedging algorithm and applies a sequential decision-making framework to obtain rolling time advance schedules. Results of the numerical analysis demonstrate the effectiveness of our algorithm compared to both the published state-of-the-art Lagrangian decomposition embedded with surrogate subgradient method and the commercial solver Gurobi. The insight obtained from the experiments is that a capacity allocation scheme with all physicians assigned with both offline and online capacities would be a good choice for considerable cost savings.Note to Practitioners—Internet healthcare is becoming increasingly popular. Operation and management issues have arisen in the integrated online and offline appointment systems. A sequential decision-making method embedded with a stochastic programming model and a modified PHA is proposed to help decision-makers generate the first visit and revisit advance schedules for chronic care. The performance of this approach and the system is thoroughly verified. Results show that the developed decision technique can lessen the operational cost generated by scheduling and realize the goal of continuity of care. This study offers a useful tool to help with intelligent patient advance scheduling in an integrated management system of online and offline chronic care.