h. haleh

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.

In this paper,a novel bi-objectivemathematical model is proposed to designa four-dimensional (i.e.,part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators’ educational costs, and 2) maximizing skill level of operators. The developedmodel is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available inthe literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate theperformance of the proposedMOVDOalgorithm for solving various instances of the developed model. A Taguchi method is employed to calibrate the main parameters ofthese fouralgorithms. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that theproposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.

Rail lines consist of a big fleet of different kinds of locomotives. Allocating the locomotives to the trains is one of the operational purposes in railways. Considering the network development and some out of service locomotives, the improved operation of locomotives is of special importance. Regarding the traditional allocation, there is no appropriate utilization of the locomotives presently, which is one of the reasons for the inappropriate allocation of locomotives to the trains. In recent studies, the items like different traction powers of each locomotive, the possibility for combination of different kinds of locos and utilization of passenger and freight locomotives have not been considered. Therefore in this research, a new mathematical model for locomotives allocation to trains has been presented, which is more compatible with RAI’s structure. The presented model is solved using GAMS software by solver Cplex 7.0 and shows that by improved allocation of the locomotives to the trains, in the existing conditions, nearly %20 can be saved by operation of locomotives.