The ever-increasing demands for surgeries and the limited resources force hospitals to have efficient management of resources, especially the expensive ones like operating rooms (ORs). Scheduling surgeries including sequencing them, assigning resources to them and determining their start times is a complicated task for hospital managers. Surgery referrals usually include elective surgeries that are admitted before the planning horizon of the schedule and emergency surgeries that arrive during this horizon and require fast services. In this paper, we presented a mathematical model for scheduling electives and emergencies. In our model, we considered surgeries as projects with multi-activities. We implemented the Break-in-Moments (BIMs) technique in this structure, which to our best knowledge has not been implemented in the literature before. We examined this method with real data from a medium-sized Norwegian hospital and observed that this method reduces the waiting time of emergencies to be inserted into the schedule without dedicating any OR merely to emergencies. In such a way, this method counterbalances between efficient OR usage and responsiveness for emergency surgeries.

The rapid growth of the population has resulted in an increasing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title for problems that consists of the patient selection and sequencing of the surgeries at the operational level, setting their start times, and assigning the resources. Hospital managers usually encounter elective surgeries that can be delayed slightly and emergency surgeries whose arrivals are unexpected, and most of them need quick access to operating rooms. Reserving operating room capacity for handling incoming emergency surgeries is expensive.Moreover, emergency surgeries cannot afford long waiting times. This paper deals with the problem of surgery scheduling in the presence of emergency surgeries with a focus on balancing the efficient use of operating room capacity and responsiveness to emergency surgeries. We proposed a new algorithm for surgery scheduling with a specific operating room capacity planning and analyzed it through a simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite.

The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, we combine the GPA and averaged mapping approach to propose implicit and explicit composite iterative schemes for finding a common solution of an equilibrium problem and a constrained convex minimization problem. Then, we prove some strong convergence theorems which improve and extend some recent results.

In this paper, two-echelon newsvendor problem is considered. Many real-life situations like fashion, food industries, and healthcare services match newsvendor problem. Our problem is determining levels of inventory in order to optimize the profit and service level in selling a product. This product is made up of several raw materials. Only the distribution of demand is known, and the hot season of selling the product is just a short period and after that, the price of the product drops dramatically. The storage space and initial budget are limited. We modeled and solved the problem as an unconstrained nonlinear optimization problem using two nonlinear techniques, the sequential unconstrained minimization technique (SUMT) and steepest descent (SD).