This paper focuses on the development of a new generation of interoperable simulators of micro activities in a logistics node; the proposed example focuses on a real-time full-scope virtual simulator of port activities able to simulate the activities of the whole port by having ships, cranes, trucks and containers interoperating in a federation. The technologies adopted in terms of architecture and installation were very effective in creating a mobile laboratory open to further extension by online interoperation with other simulators and with biomedical devices for assessing human capabilities within this framework. This approach allows us to face the existing challenges in extending the capabilities of a logistics node over their current capabilities limited by technological and human factors
The focus of the research work presented in this article is the design of an industrial plant devoted to produce different types of hazelnuts-based products. The industrial plant design is initially based on a traditional approach (production process flow charts analysis and plant layout study) and it is then supported by a simulation model mainly used to investigate the system behavior in different operative scenarios. The industrial plant simulation model (called HAZIMUT, HAZelnuts Industrial plant design and management based on advanced simulation Models Utilization) recreates the entire hazelnuts production process (based on nine different production lines). The HAZIMUT simulation model is used to evaluate the effects -caused by multiple changes in the production lines capacities -on multiple performance measures based on machines levels utilization, work in process and production system productivity. The simulation results show how variations in production lines capacities generate an over-reaction of the system with major changes in some of the performance measures therefore stressing the importance to use the HAZIMUT simulator to tune the system correctly to improve the overall plant performances.approaches allow to acquire knowledge about the system under study, the results cannot be transferred to the real system because of the restrictive assumptions and simplifications often required for the mathematical formulation of the problem. An alternative approach, that, over the years, has strongly proved to be useful both as decision support tool and as problem solving methodology for real-world complex systems design is based on the use of Modeling and Simulation, M&S. 1 In effect, starting from the '50s, thanks to the advances in computer hardware and software, M&S has been extensively used in industry. As stated by Wiendahl and Worbs 2 and Fowler and Rose 3 simulation plays a key role as practical methodology for understanding the high-level dynamics that usually characterize complex manufacturing and production systems. M&S offers the possibility to generate reliable output results, to describe and analyze the behavior of existing systems while changing initial conditions and operative scenarios. Furthermore, during the last 60 years, a number of different simulation software tools have been developed and proposed; an overview of simulation software tools used for computer aided production engineering can be found in Ref. 4. According to Banks, 1 different features must be considered in choosing a simulation software package including input features, processing features, output features, environment features and finally cost features. To this end, Cochran and Chen 5 propose an approach based on fuzzy set for simulation software packages selection while Nikoukaran and Ray 6 provide a survey about methodologies for simulation software selection, techniques for evaluating simulation software, criteria used in evaluation and recommendations. Important guidelines about simulation software selection can also ...
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