Bibliometric Analysis of Articles on Project Management Research

Project management, as a subsidiary of social science, is a vast and varied topic of the area of knowledge. In the past decades, many studies have compiled an immense amount of information for theoreticians and practitioners in this field. In this paper, traditional and novel methods of bibliometric analysis are introduced through a survey for analyzing the history of research in project management. This study focuses on the last four decades of publications on project management, from 1980 to 2019. In the survey, the number of publications, the countries of publication, the cooperating relations among those countries, and the top categories of publications are analyzed. The extraction of publication keywords and the investigation of knowledge seeds are also presented. In the survey, the examination of the network of top occurring keywords, keyword clustering, together with the keyword correlation matrix, were used to explore the main trends in project management. A novel indicator, called the ICCO ranking, is presented by using the degree, betweenness and cluster coefficient of the network of keywords. Using this indicator, the potential knowledge seeds in project management may be identified

A Simulated Annealing Algorithm for Multi Objective Flexible Job Shop Scheduling with Overlapping in Operations

In this paper, we considered solving approaches to flexible job shop problems. Makespan is not a good evaluation criterion with overlapping in operations assumption. Accordingly, in addition to makespan, we used total machine work loading time and critical machine work loading time as evaluation criteria. As overlapping in operations is a practical assumption in chemical, petrochemical, and glass industries, we used simulated annealing algorithm for multi-objective flexible job shop scheduling problem with overlapping in operations to find a suitable solution. To evaluate performance of the algorithm, we developed a mixed integer linear programming model, and solved it with the classical method (branch and bound). The results showed that in small size problems, the solutions of the proposed algorithm and the mathematical model were so close, and in medium size problems, they only had lower and upper bounds of solution and our proposed algorithm had a suitable solution. We used an experimental design for improving the proposed algorithm

Layout of Cellular Manufacturing System in Dynamic Condition

Cellular manufacturing system (CMS) is highly important in modern manufacturing methods. Given the ever increasing market competition in terms of time and cost of manufacturing, we need models to decrease the cost and time of manufacturing. In this study, CMS is considered in condition of dynamic demand in each period. The model is developed for facing dynamic demand that increases the cost of material flow. This model generates the cells and location facilities at the same time and it can move the machine(s) from one cell to another cell and can generate the new cells for each period. Cell formation is NP-Complete and when this problem is considered in dynamic condition, surly, it is strongly NP- Complete. In this study, genetic algorithm (GA) is used as a meta-heuristic algorithm for solving problems and evaluating the proposed algorithm, Branch and Bound (B & B) is used as a deterministic method for solving problems. Ultimately, the time and final solution of both algorithms are compared