JO
J.A. Ottjes
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This paper describes the complete modelling process for simulation, starting with the identification of agents and ending with a clear and unambiguous description of their behaviour that can be easily translated into each processoriented simulation platform. It focuses on the definition of agents, a subject which is described rarely. Agents usually "are there", emerging from the things one can see. Defining agents however, determines completely the range and content of a simulation and is not a trivial case. After a deliberate definition of the agents, a natural way to describe their behaviour is presented. These behaviour descriptions match the real process interaction approach. The described method is developed for educational reasons in order to teach discrete event simulation in a non-mathematical way, but is rather complete, coherent and well-defined.
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This paper describes the complete modelling process for simulation, starting with the identification of agents and ending with a clear and unambiguous description of their behaviour that can be easily translated into each processoriented simulation platform. It focuses on the definition of agents, a subject which is described rarely. Agents usually "are there", emerging from the things one can see. Defining agents however, determines completely the range and content of a simulation and is not a trivial case. After a deliberate definition of the agents, a natural way to describe their behaviour is presented. These behaviour descriptions match the real process interaction approach. The described method is developed for educational reasons in order to teach discrete event simulation in a non-mathematical way, but is rather complete, coherent and well-defined.
In this paper simulation is applied to design belt conveyor networks at dry bulk terminals. Stochastic variations in ship interarrival times, shiploads and equipment availabilities enforce the use of simulation. Parameters that affect belt conveyor network designs like the network connectivity, storage policy and stochastic distributions are evaluated in this paper. One of the main findings is that installing the maximum number of connections does not necessarily lead to better performances. Another finding is that redundancy of piles (a pile is in reach of two stockyard machines) is more efficient than increasing the number of connections. In a case study, designs for belt conveyor networks were formulated and assessed using the simulation model developed.
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In this paper simulation is applied to design belt conveyor networks at dry bulk terminals. Stochastic variations in ship interarrival times, shiploads and equipment availabilities enforce the use of simulation. Parameters that affect belt conveyor network designs like the network connectivity, storage policy and stochastic distributions are evaluated in this paper. One of the main findings is that installing the maximum number of connections does not necessarily lead to better performances. Another finding is that redundancy of piles (a pile is in reach of two stockyard machines) is more efficient than increasing the number of connections. In a case study, designs for belt conveyor networks were formulated and assessed using the simulation model developed.