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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Most altruistic behavior—in which one animal helps another at a cost to themselves—occurs between relatives. Scientists think these altruistic behaviors evolved because they increase the inclusive fitness of the animal providing help.
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An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem.

Yasser M Ayid1, Mohammad Zakaraia2, Mohamed Meselhy Eltoukhy3

  • 1Mathematics Department, Faculty of Sciences and Arts, Al-Kamil University of Jeddah, Saudi Arabia.

Methodsx
|October 9, 2024
PubMed
Summary

This study introduces a fuzzy model for optimizing distribution center selection under uncertain plant demands. It presents hybridized artificial bee colony algorithms and benchmark problems to enhance logistic planning and decision-making.

Keywords:
An Artificial Bee Colony Optimization Algorithm for Solving Fuzzy Capacitated Logistic Distribution Center ProblemArtificial bee colony optimizationCapacitated logistic distribution centerDesign of experimentsFuzzy sets

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Area of Science:

  • Operations Research
  • Logistics Management
  • Fuzzy Set Theory

Background:

  • The capacitated logistic distribution center problem involves selecting optimal distribution centers to meet plant demands.
  • Existing models often struggle with uncertain demand, necessitating robust approaches for real-world applications.
  • Fuzzy logic offers a framework to model and manage uncertainty in demand forecasting and network design.

Purpose of the Study:

  • To develop a methodological approach for solving the fuzzy capacitated logistic distribution center problem.
  • To optimize the selection of distribution centers considering fixed costs, capacities, and fuzzy plant demands.
  • To provide a benchmark dataset and advanced algorithms for research and practical application in logistic distribution.

Main Methods:

  • Mathematical formulation of the fuzzy capacitated logistic distribution center problem by converting fuzzy demands into crisp values.
  • Hybridization of three distinct artificial bee colony algorithm variants with a heuristic approach.
  • Application of Taguchi's orthogonal arrays for systematic optimization of algorithm parameters.

Main Results:

  • A fuzzy model was developed for distribution center selection under uncertain demands.
  • Twenty benchmark problems were generated to facilitate research and comparative analysis.
  • Hybridized artificial bee colony algorithms demonstrated effectiveness in addressing the problem's complexities.

Conclusions:

  • The proposed methodological approach effectively addresses the fuzzy capacitated logistic distribution center problem.
  • The developed algorithms and benchmark problems contribute to advancing research in uncertain logistic planning.
  • The study provides a comprehensive toolkit for optimizing distribution center selection with uncertain demands.