In project management, a schedule is considered as a list a project's milestones, activities, and deliverables, normally with some start and finish time schedule, which are estimated by some information incorporated in the project schedule including resource allocation, budget, task duration, and linkages of dependencies and scheduled events. This paper presents a comprehensive review of the studies associated with project scheduling. The study uses Scopus database as a primary search engine and covers 3370 records over the period 1963-2019. The records are statistically analyzed and categorized in terms of different criteria. Based on the survey, "decision support systems" is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in Germany have received the highest citations (9084), followed by United States (7058) and Belgium with 4853 citations.

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.

Electrical Energy is an essential commodity which significantly contributes to the economic development of any country. Many non-linear factors contribute to the final output of electrical energy demand. In order to efficiently predict electrical energy demand, many time-series analysis and multivariate techniques have been suggested. In order for these methods to accurately work, an enormous quantity of historical dataset is essential which sometimes are not available, inadequate and inaccurate. To overcome some of these challenges, this paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.

The increased role of wind turbine systems makes it important for its operational states to be con-tinuously monitored and optimized. This goal can be achieved using existing methods, which re-lies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10^-3, 4.2 ×10-3, and 4.0 × 10^-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

Supply chain management in oil and gas industry plays an important role for the success of these companies in most countries. A reliable supply chain helps on time delivery of goods and services and leads to better performance of the firms and yields higher profitability. This paper presents an empirical investigation to measure the relative efficiency of different oil distribution companies in Iran. The proposed study uses a five-stage Supply-Chain Operations Reference (SCOR) technique to measure the relative efficiencies of 40 distribution oil companies. The study designs a questionnaire based on four balanced scorecard perspectives and distributes it among various experts who were familiar with supply chain issues. The results indicate that the network performed relatively efficient since the study did not detect any unit with low performance and most of them maintained relatively high scores.

Supply chain provides continual turbulence with unpredictable disruptions potential for enterprises with complex network infrastructure. Supply chain in enterprises needs to be resilient and sustain-able to provide effective response, removing vulnerabilities and minimizing the impact of negative disturbances. This review article is based on evaluating the role of supply chain management in automobile sector of India. The overall findings derived from review of refined articles state that, in countries like India, automobile industry needs to have appropriate management commitment for sustainability and resilience of supply chain rather than rules and regulations of government. The review article examined 914 articles related to evaluating supply chain management practices in automobile sector of India. Through the analysis of the collected articles, it is evident that DEMATEL is effective technique for providing inter-relationship in automobile industry in India. Rather than economic influence in automobile sector, managerial activity is a major concern for effective sustainable and resilience practices in automobile industry of India.

In real world multi criteria decision making (MCDM) problem, it is tough to solve a decision matrix with vague and imprecise data. The degree of impreciseness depends on the kind of data avail-able. For interval valued data this impreciseness is less and interval-valued MCDM methods can be effectively used to solve the problem. A flexible manufacturing system (FMS) selection prob-lem was taken into consideration to find the best FMS among available alternatives. An interval extension of CODAS method is proposed in this paper which was used to solve the problem along with two other interval-valued decision-making methods i.e. interval-valued TOPSIS, interval-valued EDAS. All the three methods are distance-based approaches and it was found that the interval-valued CODAS method gave the exact same ranking with that of interval-valued TOPSIS and interval-valued EDAS.