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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A three-phase algorithm for the pollution traveling Salesman problem.

Karen García-Vasquez1, Rodrigo Linfati2, John Willmer Escobar3

  • 1School of Industrial Engineering, Universidad del Bío-Bío, Concepción, 4030000, Chile.

Heliyon
|May 2, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a new algorithm for the Pollution Traveling Salesman Problem (PTSP), optimizing fuel and emissions. The three-phase method effectively finds high-quality solutions for this complex logistics challenge.

Area of Science:

  • Operations Research
  • Logistics Optimization
  • Environmental Management

Background:

  • The Pollution Traveling Salesman Problem (PTSP) is an NP-Hard problem generalizing the TSP.
  • Minimizing fuel consumption and pollution emissions is crucial in modern logistics.
  • Existing methods may not adequately address the multi-objective nature of PTSP.

Purpose of the Study:

  • To develop and evaluate a novel three-phase algorithm for the Pollution Traveling Salesman Problem with Considerations for fuel consumption and emissions (PTSPC).
  • To minimize a complex objective function incorporating speed, vehicle mass, load mass, and distance.
  • To provide a computationally efficient method for solving PTSPC instances.

Main Methods:

  • A three-phase algorithm combining exact TSP solvers, heuristic Nearest Neighborhood Search, and commercial software.
Keywords:
HeuristicMatheheuristic algorithmPollution Traveling SalesmanSimulated annealingTabu searchVNS

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  • Metaheuristic algorithms including Variable Neighborhood Search (VNS), Tabu Search (TS), and Simulated Annealing (SA) for solution enhancement.
  • A mathematical model for final solution refinement, considering heuristic outputs.

    • Main Results:

    • The proposed algorithm successfully adapts instances from the Pollution Routing Problem (PRP) to PTSPC.
    • Experimental validation demonstrates the algorithm's ability to yield high-quality solutions.
    • The method achieves these results within acceptable computational timeframes.

    Conclusions:

    • The developed three-phase algorithm is effective for solving the PTSPC.
    • The approach balances solution quality with computational efficiency.
    • This work contributes a valuable tool for optimizing delivery routes with environmental considerations.