Patient Bed Assignment (PBA) consists of assigning patients to hospital beds according to specific requirements such as patient diagnosis, equipment requirements, age and gender policies, among others. We worked in conjunction with the Hospital Universitario San Ignacio (HUSI) with the goal of designing an application to support decision-making during the bed assignment process. We introduced a mathematical model for the PBA. We used Analytic Hierarchy Process (AHP) to determine the weights attributed to each part of the objective function. Due to the long execution time required, we used a Greedy Algorithm and Tabu Search (TS) to optimize the match between the patient’s requirements and the characteristics of the assigned bed. To test the algorithms, we created 15 test instances of various sizes. The results showed that the gap between the value of the objective function resulting from using the Greedy/TS in comparison with the optimal solution is on average 6.2%. Also, the TS takes 84% less time than the MILP for medium and large instances. We collected data from real life instances and compared the actual method with the designed metaheuristic. On average, the value of the objective function resulting from using the proposed Greedy/Tabu algorithm is 8.6% higher.

The aim of the present study is to find the relative importance of factors influencing the stu-dents’ decisions to adopt Massive Open Online Courses (MOOCs). Eight sub-factors catego-rized under three main factors namely “benefits of MOOCs”, “MOOCs features” and “social recognition”, are considered for prioritization. The analytic hierarchy process (AHP) method-ology is also employed to prioritize the factors. The primary data pertaining to pair-wise com-parisons of various factors and sub-factors have been obtained from 250 students by using convenience sampling. The results indicate that academic recognition, followed by openness, autonomy and cost effectiveness of MOOCs are the most important aspects which students consider while deciding to learn through MOOCs.

Nowadays, selecting the most appropriate location for hub is one of the most significant issues not only in road, rail and air transportations, but also in maritime. Transshipment is the fastest growing segment of the marine container market; it increases traffic flow of marine container and scope of this type of marine carriage, accordingly. In this way, determining a movement loop for the voyages of a shipping company, probes identification of container hub ports by considering different operational factors including distance to the destinations. The focus of this paper is to locate the best location for container transshipment hub in southern seas of Iran. In this paper, an MCDM model is proposed for evaluating and selecting the marine container transshipment hub port. Finally, the utilization of the proposed model is demonstrated with a real case study of Iranian main ports. The results show that the MCDM model can be used to explain the evaluation and decision-making procedures of a proper marine container hub location selection.