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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. [ABSTRACT FROM AUTHOR]

This paper introduces a novel two-phase framework for designing a proactive–reactive scheduling model in the multi-mode resource-constrained project scheduling problem under disruptions. The proactive phase involves constructing a resilient baseline scheduling model using a mixed-integer linear programming model. This phase contributes to a multi-objective model that minimizes the project completion time and total project cost while maximizing resilience criteria. In this context, resilience refers to allocating float time to project activities to protect their start and finish times against future disruptions as much as possible. The reactive phase involves a bi-objective mathematical model that mitigates the impact of disruptions through preempt-repeat, preempt-resume, and activity-crashing strategies. Real-world projects involve many uncertain parameters that can negatively affect the optimization of rescheduling problems if overlooked. Therefore, for the first time, a scenario-based robust optimization approach is proposed to cope with the uncertainty of the reactive phase. Additionally, a novel hybrid multi-objective method based on goal programming is introduced to solve the proposed multi-objective model. Finally, to demonstrate the capability of the proposed approach, an oil and gas project in Iran is regarded as a real case study. The results indicate that the negative impact of disruptions on the makespan and total cost of the project can be largely mitigated by considering resilience criteria in the proactive phase and preempt-repeat, preempt-resume, and activity-crashing strategies in the reactive phase. [ABSTRACT FROM AUTHOR]

Every year natural and man-made disasters cause considerable human and economic losses. It is essential to prepare for different relief operations to prevent and reduce these losses. In this paper, we propose an integrated evacuation and distribution logistic system to obtain simultaneous routing and scheduling of vehicles to evacuate people from affected areas to shelters and provide them with necessary relief commodities. We assume that shelters and vehicles have limited capacity and the demand of each affected area and distribution center could be fulfilled by more than one vehicle (split delivery). The proposed problem is formulated as a Mixed-Integer Linear Programming model with the objective of minimization of the sum of arrival times of the vehicles at affected areas, shelters, and distribution centers. We also propose a Memetic Algorithm (MA) to solve this integrated model on large-scale problems efficiently after tuning the MA parameters using the Taguchi method. The proposed model and algorithm are used to solve a case study in Tehran, the capital of Iran. The evaluation of the results shows the effectiveness of the proposed disaster relief logistic system in minimizing the total waiting time of evacuees and delivery time of supplies. The results also show that the number of relief vehicles and capacity of shelters can considerably affect the total relief time in disaster relief operations. [ABSTRACT FROM AUTHOR]