Mansooreh Mollaghasemi

Learn 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)
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)
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)
Analyzing systems by means of simulation is necessarily a time consuming process. This becomes even more pronounced when models of multiple systems must be compared. In general, and even more so in today’s fast-paced environment, competitive pressure does not allow for waiting on the results of a lengthy analysis. That competitive pressure also makes it(More)
Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent the knowledge by a simple but interpretable model that approximates the optimal classifier/predictor in the sense of expected value of accuracy. This model requires an important preset smoothing parameter, which is usually chosen by cross-validation or clustering. In(More)
Simulation modeling provides an effective and powerful approach for capturing and analyzing complex manufacturing systems. More and more decisions are based on computer generated data derived from simulation. The strength of these decisions is a direct function of the validity of this data. Thus the need for efficient and objective methods to verify and(More)