Wireless Sensor Networks are gaining more and more importance in various application fields. Often, energy autonomy on the node level is an essential nonfunctional constraint to be met. Therefore, when simulating such networks, the energy consumption on the node level has to be included into the simulation. To make this time consuming task feasible, an overall simulation speedup on the network level is desirable. In this paper, we propose to use techniques similar to those used in Transaction Level Models of Bus Systems. 1
Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operation plans to control the shop floor. In the literature, there are two major approaches for tackling the simulation-based scheduling problems, namely dispatching rules and using meta-heuristic search algorithms. The purpose of this paper is to illustrate that there are advantages when these two approaches are combined. More precisely, this paper introduces a novel hybrid genetic representation as a combination of both a partially completed schedule (direct) and the optimal dispatching rules (indirect), for setting the schedules for some critical stages (e.g. bottlenecks) and other non-critical stages respectively. When applied to an industrial case study, this hybrid method has been found to outperform the two common approaches, in terms of finding reasonably good solutions within a shorter time period for most of the complex scheduling scenarios.
Traditionally, a simulation-based optimization (SO) system is designed as a black-box in which the internal details of the optimization process is hidden from the user and only the final optimization solutions are presented. As the complexity of the SO systems and the optimization problems to be solved increases, instrumentation -a technique for monitoring and controlling the SO processes -is becoming more important. This paper proposes a white-box approach by advocating the use of instrumentation components in SO systems, based on a component-based architecture. This paper argues that a number of advantages, including efficiency enhancement, gaining insight from the optimization trajectories and higher controllability of the SO processes, can be brought out by an on-line instrumentation approach. This argument is supported by the illustration of an instrumentation component developed for a SO system designed for solving real-world multi-objective operation scheduling problems.
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