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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Algorithmic solutions for warehouse site selection using complex pythagorean fuzzy soft sets in supply chain

Abaker A Hassaballa1,2, Ali Asghar3, Tasadduq Niaz4

  • 1Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.

Scientific Reports
|August 29, 2025
PubMed
Summary

This study introduces a new algorithm using Complex Pythagorean Fuzzy Soft Sets (CPFSS) to improve warehouse site selection. The method effectively handles data uncertainty for better supply chain management and faster deliveries.

Keywords:
Decision-makingDistance measuresPythagorean fuzzy soft setSite selectionSupply chain managementUncertainty

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Decision Science

Background:

  • Global trade relies on efficient supply chain management and strategic distributor placement.
  • Increasingly diverse customer demands necessitate prompt and reliable delivery services.
  • Strategic warehouse site selection is crucial for businesses, especially e-commerce, to meet delivery expectations.

Purpose of the Study:

  • To develop a novel algorithmic approach for distribution center site selection.
  • To address data uncertainties and fuzziness inherent in the site selection process.
  • To enhance the efficiency and reliability of supply chain operations through informed decision-making.

Main Methods:

  • Formulation of distance measures within the Complex Pythagorean Fuzzy Soft Set (CPFSS) framework.
  • Development of a new algorithm based on these CPFSS distance measures.
  • Application of the algorithm to a real-life case study for warehouse site selection.

Main Results:

  • The proposed CPFSS-based algorithm effectively handles uncertainties in site selection data.
  • The algorithm was successfully applied to a case study for HCRFT's handicraft warehouse.
  • Validation through comparison with existing models demonstrated the algorithm's effectiveness.

Conclusions:

  • The novel CPFSS approach provides a robust framework for complex decision-making problems like warehouse site selection.
  • The developed algorithm offers a practical and effective tool for optimizing supply chain logistics.
  • The study highlights the importance of advanced mathematical tools in managing real-world business challenges.