Simulation-based optimization or parameter tuning of discrete stochastic models becomes necessary, when no analytic expression for the goal function is available. Sensitivity analysis on the other hand is used to determine the required degree of detail that is needed when building a model. Both of these tasks are similar in the sense that one needs to repeatedly simulate a model with only sligh...

Many real-world manufacturing problems are too complex to be modeled analytically and in these settings simulation-based optimization is a highly valuable tool. It has shown to be a useful technique for systems improvement and has been applied to address a wide range of industrial optimization problems. Simulation runs are, however, often computationally expensive and to enhance the efficiency ...

In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this pr...

Many city governments are under pressure to optimize the utilization of their resources to respond to fire, rescue and medical emergencies. In this paper we describe a simulation-based optimization software called SOFER that learns from a history of emergency requests to optimize the placement of resources and response policies. We describe a two-level random-restart hill climbing approach that...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

To deal with severe drought when water supply is insufficient hedging rule, based on hedging rule curve, is proposed. In general, in discrete hedging rules, the rationing factors have changed from a zone to another zone at once. Accordingly, this paper is an attempt to improve the conventional hedging rule to control the changes of rationing factors. In this regard, the simulation model has emp...

Abstract This article presents a novel approach for the automated 3D-layout planning of multi-station assembly lines. The method is based on comprehensive model used production resources, including their geometry, kinematic properties, and general characteristics. Different resource types can be included in system. A genetic algorithm generates optimizes possible layouts line. optimization aims...

We discuss algorithms for multidisciplinary simulation and optimization that efficiently couple existing single-discipline codes. The algorithms are based on a strategy in which unknown data at the interfaces is determined through an optimization process. The strategy allows for the user to select the data type at the interfaces for each discipline, so that the method can be tailored to existin...