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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Time-constrained maximal covering routing problem.

Afsaneh Amiri1, Majid Salari1

  • 1Department of Industrial Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

OR Spectrum : Quantitative Approaches in Management
|March 28, 2020
PubMed
Summary

We present the time-constrained maximal covering routing problem (TCMCRP) to maximize covered customers using p-length Hamiltonian cycles. Our methods, including ILS, TS, and VNS, effectively solve this complex routing challenge.

Keywords:
CoveringCovering salesman problemHeuristicsMixed integer linear programmingTransportation

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Area of Science:

  • Operations Research
  • Combinatorial Optimization
  • Logistics Management

Background:

  • The covering salesman problem (CSP) is a foundational routing problem.
  • Existing models often lack explicit time or length constraints for routing solutions.
  • Efficiently covering dispersed customers from a central depot is a critical logistical challenge.

Purpose of the Study:

  • Introduce the time-constrained maximal covering routing problem (TCMCRP) as a generalization of the CSP.
  • Develop and evaluate solution methodologies for the TCMCRP to maximize customer coverage within defined constraints.
  • Address the need for realistic routing solutions that incorporate service time or route length limitations.

Main Methods:

  • Formulated a mixed integer linear programming (MILP) model for the TCMCRP.
  • Developed three heuristic algorithms: Iterated Local Search (ILS), Tabu Search (TS), and Variable Neighborhood Search (VNS).
  • Conducted extensive computational experiments to assess algorithm performance on the TCMCRP and its variants.

Main Results:

  • The proposed MILP model provides a benchmark for solving the TCMCRP.
  • Heuristic algorithms (ILS, TS, VNS) demonstrate effectiveness in finding high-quality solutions.
  • Computational tests confirm the efficiency and scalability of the developed solution methods.

Conclusions:

  • The TCMCRP offers a more realistic framework for vehicle routing and facility location problems.
  • The ILS, TS, and VNS algorithms are effective and efficient for solving the TCMCRP.
  • This research contributes practical solution methods for optimizing customer coverage in logistics and service operations.