Based on a novel discrete-event zone-control model, in our previous papers [1,2], we presented a time-efficient traffic control for automated guided vehicle (AGV) systems to exclude inter-vehicle collisions and system deadlocks, together with a case study on container terminals. The traffic control allows each vehicle in an AGV system to freely choose its routes for any finite sequence of zone-tozone transportation tasks and the routes can be constructed in an online fashion. In this paper, we extended our previous results with two practical goals: (1) to increase the utilization of the workspace area by reducing the minimally allowed area of each zone; (2) to avoid vehicle collisions and deadlocks with the occurrence of vehicle breakdowns. To achieve the first goal, we include one extra vehicle event that allows each vehicle to probe further ahead while it is moving on the guide-path. This leads to an extension of our previous discrete-event model and traffic control rules, which are presented in the first part of the paper. The second part of the paper concerns the second goal, for which an emergency traffic control scheme is designed as supplementary to the normal traffic control rules. As in our previous papers, the improved model and traffic control are applied to a simulation of quayside container transshipment at container terminals; our simulation results are compared with those from two interesting works in the literature.
Understanding the driving forces in the markets of their products is a basic necessity for any business. Quantitative models are either aggregated over large market segments or restricted to utility models of an individual's buying decision. While the aggregate models acknowledge that customer interactions are important they do not model them and hence have no way to adjust their model to changing business environments. This paper bridges the gap between individual decisions and the overall market behavior using agent based simulations to model the sales of computer chips in the high-end gamers market. The simulation environment is dynamic and models the succession of 19 products introduced over a 40 month time horizon which includes the recession of 2008-2010. Simulated sales are compared to actual sales data and are used to adjust the parametrization of the agents and their environment. We found that only two agent parameters are sufficient to obtain a very reasonable fit between simulations and data: The amount of money 1350028-1 Advs. Complex Syst. 2013.16. Downloaded from www.worldscientific.com by AUSTRALIAN NATIONAL UNIVERSITY on 03/16/15. For personal use only. T. Adriaansen et al.available for the gaming hobby and a parameter related to the gaming success of the high-end gamers.
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