Dynamic Integrated Online and Offline Advance Scheduling Under Multitype Revisit Demand Uncertainty

Internet healthcare provides a new access way for revisits through texts or videos, which can lighten the service load on offline hospitals. In this study, we investigate the dynamic integrated advance scheduling problem considering the complex coupling of stochastic and heterogeneous (e.g., online and offline, first visit and multitype revisit) demands. We formulate the problem as a multistage dynamic stochastic mixed-integer programming (SMIP) model. Dynamic decisions of matching service capacities with patients are made at each stage, which ensure that series revisits are scheduled on the most beneficial revisit day as much as possible under meeting the revisit interval restrictions. We reformulate the model into an equivalent formulation that can be solved directly and develop a Monte Carlo heuristic progressive hedging algorithm (MCH-PHA) to solve the model. Numerical results illustrate the superiority of our proposed approach in terms of computation time and solution quality compared with the other proposed benchmarks. In addition, to aid practitioners in applying the decision-making model more effectively, some managerial insights are also provided. For instance, to reduce operational costs, practitioners can directly extend the revisit interval, increase the permitted revisit interval duration, or choose appropriate maximum wait time targets for the first visit and revisit