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A novel parameterized neutrosophic score function and its application in genetic algorithm.

Yi Zhao1, Fangwei Zhang2, Bing Han3

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Summary
This summary is machine-generated.

This study introduces a new parameterized neutrosophic objective-proportionate genetic algorithm (PNO-PGA) to optimize warehouse operations. The PNO-PGA model enhances efficiency, safety, and cost management, outperforming traditional genetic algorithms (GA).

Keywords:
Aggregating operatorGenetic algorithmMulti-objective optimizationNeutrosophic fuzzy setWarehouse operation

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Artificial Intelligence

Background:

  • Warehouse operations are evaluated by efficiency, safety, and cost.
  • Uncertainty in these factors can cause significant economic losses and resource waste.

Purpose of the Study:

  • To propose a novel parameterized neutrosophic objective-proportionate genetic algorithm (PNO-PGA) for optimizing warehouse efficiency, safety, and cost.
  • To address the inherent uncertainties in these critical operational metrics.

Main Methods:

  • Development of a new score function for neutrosophic sets (NSs) to integrate fuzziness in efficiency, safety, and cost evaluations.
  • Application of a novel proportionate genetic algorithm for adaptive iteration and inheritance.
  • Introduction of two parameters for flexible adaptation to diverse environments and problems.

Main Results:

  • The proposed PNO-PGA model effectively integrates fuzzy evaluation metrics.
  • The algorithm demonstrates adaptive iteration and inheritance capabilities.
  • PNO-PGA shows superior problem-solving ability in warehouse operations compared to the standard genetic algorithm (GA).

Conclusions:

  • The PNO-PGA model offers a robust approach to optimizing complex warehouse operations.
  • The method effectively handles uncertainty and fuzziness in evaluation criteria.
  • This research provides a valuable tool for improving warehouse performance and resource management.