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Updated: Sep 4, 2025

Orienteering as a Tool for Cognitive Research: An Implementation Guide
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A clustering metaheuristic for large orienteering problems.

Almiqdad Elzein1, Gianni A Di Caro1

  • 1Carnegie Mellon University in Qatar, Doha, Qatar.

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Summary
This summary is machine-generated.

Solving large Orienteering Problems efficiently is now possible with a new clustering-based metaheuristic. This approach significantly reduces computation time for routing and scheduling tasks, enabling real-time applications.

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Area of Science:

  • Operations Research
  • Computer Science
  • Algorithm Design

Background:

  • The Orienteering Problem (OP) is an NP-hard routing challenge crucial for logistics and scheduling.
  • Existing algorithms struggle with large-scale OPs, hindering real-time, adaptive planning in dynamic environments.

Purpose of the Study:

  • To develop a computationally efficient metaheuristic for solving large Orienteering Problems.
  • To enable effective solutions for online applications requiring iterative plan computation and adaptation.

Main Methods:

  • A multi-stage, clustering-based metaheuristic is proposed.
  • The method decomposes large OPs into smaller, manageable sub-problems.
  • Solutions are merged, optimized, and processed for feasibility.

Main Results:

  • The metaheuristic significantly improves computation time for OP algorithms.
  • Solution quality is maintained, especially for large problem instances.
  • Computational experiments confirm the benefits over standalone algorithms.

Conclusions:

  • The proposed metaheuristic effectively tackles large Orienteering Problems within practical time constraints.
  • It enhances the applicability of OP algorithms in online and dynamic scenarios.
  • This approach is particularly beneficial for real-world logistics and robotic applications.