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Simulation might be an effective decision support tool in supply chain management. The review of supply chain simulation modeling methodologies revealed some issues one of which is the practicability of simulation in the supply chain environment. The supply chain environment is dynamic, information intensive, geographically dispersed, and heterogeneous. In(More)
Simulation is a commonly used design tool in engineering. Once a prototype design is modeled and simulated however, many designers resort to either trial and error, univariate perturbation or local optimization methods to find a design which yields the optimum simulated results. The performance of the simulation as a function of the design parameters is(More)
A methodology for optimization of simulation models is presented. The methodology is based on a genetic algorithm in conjunction with an indifference-zone ranking and selection procedure under common random numbers. An application of this optimization algorithm to a stochastic mathematical model is provided in this paper.
On January 14, 2004 President George W. Bush announced a new Vision for Space Exploration. This vision called for NASA to complete the assembly of the International Space Station by 2010 and retire the Space Shuttle immediately thereafter. A discrete event simulation (DES) based tool has been built to assess the viability of NASA accomplishing all of the(More)
In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM),(More)
This paper focuses on the evolution of ARTMAP architectures, using genetic algorithms, with the objective of improving generalization performance and alleviating the ART category proliferation problem. We refer to the resulting architectures as GFAM, GEAM, and GGAM. We demonstrate through extensive experimentation that evolved ARTMAP architectures exhibit(More)
This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the ART category proliferation problem (the problem of creating more than necessary ART network categories to(More)
This paper presents an improved genetic algorithm approach, based on new ranking strategy, to conduct multi-objective optimization of simulation modeling problems. This approach integrates a simulation model with stochastic nondomination-based multiobjective optimization technique and genetic algorithms. New genetic operators are introduced to enhance the(More)