A Benders Decomposition Approach for Appointment Scheduling of Unpunctual Patients in a MultiServer Setting Appointment patients usually arrive unpunctually, which significantly affects the daily operations of healthcare facilities. To mitigate the negative impact of unpunctuality, this paper attempts to optimize the appointment schedule by considering unpunctual arrivals and noshows in a multiserver setting. A twostage stochastic programming model is formulated with the objective of minimizing patient waiting cost and overtime cost. The appointment schedule is determined by a modified Benders decomposition with sample average approximation. The effectiveness of this algorithm is validated through the comparison with lower bounds. Extensive experiments show that the optimal appointment schedule exhibits a zigzag pattern rather than a dome pattern. The impacts of various factors are also explored by numerical experiments.

Healthcare service configuration based on project scheduling Abstract   Configuration has been recognized as an effective methodology to provide high product variety that caters to individual customerxe2x80x99s needs in the manufacturing industry. For healthcare service configuration, this decisionmaking process can be taken as service package creation and treated analogously as a project, which is defined as a collection of tasks. This research develops a decision support model to integrate individual patients into the healthcare service configuration process. The healthcare service configuration is formulated as a Resource Constrained Project Scheduling Problem (RCPSP), and a bilevel optimization algorithm based on Genetic Algorithm (GA) is developed for problem solving. The methodology and algorithm are implemented with a case study based on the data obtained from a general hospital in Singapore, which has demonstrated the applicability of healthcare service configuration.