Nowadays, projects have become so widespread in the world that individuals and organizations are always involved in a variety of them. Recent advances in technology and fundamental chang-es in most scientific disciplines have had an essential impact on projects, and have made the na-ture and environmental conditions governing them to become more complex than before. With increasing complexity, the amount of information needed for project management increases. In general, the increasing complexity of projects is a growing source of project risks. It has been recognized that complexity affects the performance of a project and will be effective in its suc-cess. In this context, the traditional principles and practices of project management are no longer able to control the emerging complexity of projects. In addition, one of the key factors for the success of the projects is the appropriateness of the project manager's assignment. Many studies have been carried out in identifying the suitability criteria of the project manager and the methods of selecting the project manager. In most of these studies, the amount and type of complexity of the project are mentioned as factors influencing the design of an appropriate project manager. However, there has not yet been a specific approach for selecting the project manager with re-gard to the complexity of the project. Therefore, in this research, we try to investigate the rela-tionship between the complexity of the project and the merits of the project manager by applying a structural equation modeling approach.

This paper considers the dynamic variant of single processor scheduling problem of minimizing total tardiness. In practice, it occurs when minimizing tardiness penalty. The problem is NP-hard; thus two heuristics were proposed. The utility of the proposed models was demonstrated through computational experiments and comparative analyses against existing solution methods and the Branch and Bound (BB) method. The results show that the proposed models yield effi-cient solutions and in most cases perform effectively better than the existing heuristics in the lit-erature.

Construction sites are accident-prone locations and therefore safety management plays an im-portant role in these workplaces. This study presents an adaptive algorithm for performance as-sessment of project management with respect to resilience engineering and job security in a large construction site. The required data are collected using questionnaires in a large construction site. The presented algorithm is composed of radial basis function (RBF), artificial neural networks multi-layer perceptron (ANN-MLP), and statistical tests. The results indicate that preparedness, fault-tolerance, and flexibility are the most effective factors on overall efficiency. Moreover, job security and resilience engineering have similar statistical impacts on overall system efficiency. The results are verified and validated by the proposed algorithm.

Use of Automated guided vehicles (AGVs) is highly significant in Flexible Manufacturing Sys-tem (FMS) in which material handling in form of jobs is performed from one work center to an-other work center. A multifold increase in through put of FMS can be observed by application of multi load AGVs. In this paper, Particle Swarm Optimization (PSO) integrated with Memetic Algorithm (MA) named as Modified Memetic Particle Swarm Optimization Algorithm (MMP-SO) is applied to yield initial feasible solutions for scheduling of multi load AGVs for minimum travel and waiting time in the FMS. The proposed MMPSO algorithm exhibits balanced explora-tion and exploitation for global search method of standard Particle Swarm Optimization (PSO) algorithm and local search method of Memetic Algorithm (MA) which further results into yield of efficient and effective initial feasible solutions for the multi load AGVs scheduling problem.