Mansooreh Mollaghasemi

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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.
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)
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)
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)
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)
The single-machine early/tardy (E/T) scheduling problem is addressed in this research. The objective of this problem is to minimize the total amount of earliness and tardiness. Earliness and tardiness are weighted equally and the due date is common and large (unrestricted) for all jobs. Machine setup time is included and is considered sequence-dependent.(More)
Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation(More)
Most simulation models output multiple responses. Yet little research has been done in the area of multicriteria optimi=tion of simulation models. This paper suggests a framework for the multicriteria optimization of simulation models by, first, discussing the unique difficulties of this problem area along with important problem characteristics, and,(More)