In order to manage a project seamlessly, there is a need to establish effective communication be-tween different departments and identify the risks in the project, determine the affected or influ-encing stakeholders, provide timely resources and logistics, and manage the available resources to make a framework for project implementation. In order to successfully complete the project it is also necessary to focus on approved costs, project completion time and quality within the specified range. Project management is the coordination among different parts of the project to achieve the main goals of the project and the stakeholders’ expectations. To achieve this, there are several standards and one of the most recognized standards is the Project Management Knowledge Facility (PMBOK), which has come with the assistance of Project Managers for professional, targeted and comprehensive management. PMBOK is not just a guideline and a methodology for project management. Building Information Modeling (BIM), a project man-agement methodology has been adopted in recent years to design a project integrated as a 3-D information model, which adds all project information in the various phases of the project to a 3-D information model. The purpose of this research is to gather some date from experts using some questionnaires in the area of project management to build an information modeling. The study determines that each of the basic BIM capabilities had positive effects on different domains of PMBOK knowledge. Moreover, using SAW analysis, the study suggests that BIM had the greatest impact on project integration management, and finally, the BIM general process model is introduced to implement each of the areas of knowledge.

The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the effi-cient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced workload for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

This paper presents a review of human resource planning methods, related techniques, and their effects on cellular manufacturing systems (CMS). In-depth analysis has been conducted through a review of 43 dominant research papers available in the literature. The advantages, limitations, and drawbacks of material transferring methods have been discussed as well. The domains of the examined studies include some of the important problems in staff planning, such as worker assigning, hiring and firing, optimum number of workers, skilled workers, cross-functional experts, worker satisfaction and outsourcing. The results of this study can fill research gaps and clarify many related questions in CMS problems.

Cash flow models are one of the spotlights for evaluating a project. The actual data should be modeled then it could be used for the prediction process. In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. The predicted data for each point is achieved by using of previous points from the beginning. The model is based on artificial neural network, and it is trained by three algorithms which are Genet-ic Algorithm (GA), Estimation of Distribution Algorithm (EDA), and hybrid GA-EDA method. Two dynamic ratios are used which are dividing the population into two halves, and the other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in the hybrid algorithm, and then the hybrid algorithm could model the system more accurately. For each algorithm, three main errors are calculated which are mean absolute percentage error (MAPE), mean square error (MSE), and root means square error (RMSE). The best result is achieved for hybrid GA-EDA model without dividing the population and the MAPE, RMSE, and MSE values are %0.022, 28944.59 Dollars, and 837789503.79 Dollars, respectively.