In this paper, we develop a simulation optimization procedure for optimizing the urban arterial traffic signal timings including a bunch of sequential intersections. The system performance is estimated via a stochastic discrete-event meso-scopic traffic simulator, and a gradient-based search algorithm on stochastic approximation is applied to give the optimal signal timings. Simultaneous pertur...

computational fluid dynamics (cfd) is a computational technology that enables researchers to study the dynamics of things that flow. by using cfd, it is possible to build a computational model that represents a system under study. it not only predicts fluid flow behavior, but also the transfer of heat, mass, phase change, chemical reaction, mechanical movement, and stress or deformation of rela...

abstract one of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. so, simulation and forecasting models such as artificial neural networks (anns) are commonly used for simulation and predi...

in this paper, we focus on a continuously deteriorating critical equipment which its failure cannot be measured by cost criterion. for these types of systems like military systems, nuclear systems, etc it is extremely important to avoid failure during the actual operation of the system. in this paper we propose an approach which constrains failure probability to a pre-specified value. this valu...

differential steering of in-wheel electric vehicle provides the functions of both active steering and power assisted steering with the coupling control of force and displacement transfer characteristic of system. a collaborative optimization model of the differential power-assisted steering system of in-wheel electric vehicle is built, with steering economy as the main system optimization goal,...

This paper presents a new measurement-based optimization framework for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus ...

The main bottleneck in using optimization in industry is the way uncertainty is handled, which forms the subject of this series of two papers. The first part dealt with the characterization of the nominal solution and proposed an approach to separate the constraint-seeking and the compromise-seeking components of the inputs. This second part reviews various strategies for optimization under unc...

In this paper we focus on nonparametric estimation of a constrained regression function using penalized wavelet regression techniques. This results into a convex optimization problem under linear constraints. Necessary and sufficient conditions for existence of a unique solution are discussed. The estimator is easily obtained via the dual formulation of the optimization problem. In particular w...

JDMS, Volume 1, Issue 3, August 2004 Page 127-140 © 2004 The Society for Modeling and Simulation International How can computer-aided planning systems deal with the complexities, uncertainties, and rapidly shifting information needed to support air warfare operational planning? This paper uses a hierarchical decomposition of decision-making, coupled to a predictive simulation model that estimat...

Research on the optimization of stochastic systems via simulation often centers on the development of algorithms for which global convergence can be guaranteed. Applications of optimization via simulation, on the other hand, typically involve search heuristics that have been successful in deterministic settings. Search heuristics give up on global convergence in order to be more generally appli...