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Related Concept Videos

Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Supplier selection based on a neural network model using genetic algorithm.

Davood Golmohammadi1, Robert C Creese, Haleh Valian

  • 1Management Science and Information Systems,University of Massachusetts Boston, Boston, MA 02125, USA. davood.golmohammadi@umb.edu

IEEE Transactions on Neural Networks
|August 22, 2009
PubMed
Summary

This study introduces a novel decision-making model for selecting vendors using neural networks (NNs) and genetic algorithms (GA). The model enhances supplier selection by integrating historical performance data and managerial insights.

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07:35

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Published on: October 11, 2018

Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Supply Chain Management

Background:

  • Effective supplier selection is crucial for business success.
  • Traditional methods often struggle to integrate diverse data types and managerial expertise.
  • Developing advanced decision-support systems is essential for optimizing procurement processes.

Purpose of the Study:

  • To develop and validate a novel decision-making model for supplier selection.
  • To integrate neural networks (NNs) with genetic algorithms (GA) for enhanced model performance.
  • To incorporate historical performance data and managerial judgments into a unified selection framework.

Main Methods:

  • A decision-making model utilizing neural networks (NNs) was developed.
  • Historical supplier performance data was used as input.
  • Managerial judgments were simulated using a pairwise comparisons matrix for NN output.
  • Genetic algorithms (GA) were applied for optimizing NN architecture and initial weights.
  • Supplier database information was designed for dynamic updates.

Main Results:

  • The developed NN-GA model effectively simulates managerial judgments for supplier evaluation.
  • The model provides a dynamic scoring system for suppliers based on updated performance data.
  • A case study demonstrated the practical applicability of the model in real-world supplier selection scenarios.

Conclusions:

  • The integrated NN-GA model offers a robust and adaptable approach to supplier selection.
  • This methodology enhances decision-making accuracy by combining quantitative data with qualitative insights.
  • The model's flexibility allows for continuous improvement and adaptation to changing business environments.