A two-step stochastic approach for operating rooms scheduling in multi-resource environment

Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97 of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Production Scheduling of a Large-Scale Steelmaking Continuous Casting Process via Unit-Specific Event-Based Continuous-Time Models: Short-Term and Medium-Term Scheduling

The scheduling of steelmaking continuous-casting (SCC) processes is of major importance in iron and steel operations, since it is often a bottleneck in iron and steel production. Optimal scheduling of SCC processes can increase profit, minimize production cost, reduce material and energy consumption, and improve customer satisfaction. Scheduling of SCC processes is challenging, because of its combinatorial nature, complex practical constraints, and strict requirements on material continuity and flow time, as well as the technological requirements to ensure practical feasibility of the resulting scheduling. In this paper, we first develop a novel unit-specific event-based continuous-time mixed-integer linear optimization (MILP) model for this problem and incorporate several realistic operational features. Then, we extend the rolling horizon approach proposed by Lin et al. [Lin et al. Ind. Eng. Chem. Res. 2002, 41, 3884-3906] and Janak et al. [Janak et al. Ind. Eng. Chem. Res. 2006, 45, 8234-8252] to solve this large-scale and complex optimization problem. Four large-scale industrial problems are used to illustrate the efficiency and effectiveness of the proposed formulation and rolling horizon approach. The computational results show that the extended rolling horizon approach successfully solves the large-scale case studies and results in the same or better integer solution than that obtained from directly solving the entire scheduling model