Crewing of Sea Vessels Taking into Account Project Risks and Technical Condition of Ship Equipment

Motivation: One of the main concepts in project management is the concept of “team†in the project, and in project management - the human resources management of the project, which includes the processes of planning, forming and creating a team, its development and support activities, transformation or disbandment of the team. Despite the great attention paid to the formation of project management teams, existing studies do not fully highlight the specifics and features of crew operations. Criteria for the quantitative optimization of the ship's crew should be consistent with the main objectives of the project.Novelty: The research paper proposes an approach that allows optimizing the quantitative composition of the crew of a ship by more accurately assessing the level of project risks and costs associated with the maintenance of ship equipment. The practical application of this approach will optimize the quantitative composition of the ship's crew, which will both satisfy the needs of managing the technical equipment and minimize the risks and costs of the shipowner.Methodology and Methods: Risk management tools were used to achieve the objective and test the hypotheses suggested in the research, namely: methodology for estimating the net present value of the project; the method of estimating internal rate of return for the project; the method of estimating the return on investment in the project; the method of estimation for the period of return on investment costs in the project; the method of estimating the discounted payback period for the project, as well as the tools of simulation modelling (Monte Carlo simulation method). The method of identification and grouping in the process of classification of project risks in the sphere of marine transportation, methods of systematization, grouping and logical generalization were also applied for systematization of information, drawing conclusions and making scientific suggestions in the research.Policy Considerations: Shipping plays an important role in the trade and tourism industry; human factor is the most important aspect that determines the efficiency of shipping development; maintaining of technical and technological processes of the ship puts certain requirements to the quantitative and qualitative composition of the team, deviation from which leads to the occurrence of certain risk events; formation of an effective model of ship's crew manning is the main link in ensuring effective shipping project management

Research on the Effect of DPSO in Team Selection Optimization under the Background of Big Data

Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data