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<p>This study addresses a routing and scheduling problem related to the transport of forest tractors between harvesting sites using ballast trucks. The problem is framed as a Pickup and Delivery Vehicle Routing Problem with Time Windows (PDVRPTW), and considers factors like road conditions, legal time limits, and a diverse fleet with specific compatibility rules. To address it, we developed a mixed-integer linear programming model aimed at minimizing travel distance, fleet usage, and delays. The model was implemented in CPLEX with a Branch-and-Bound approach and tested using real data from a Brazilian forestry company. Scenarios with varying demand levels–low, medium, and high–were solved optimally in under 3&#xa0;min. These results show that exact optimization methods can be practical for complex planning in forest logistics, offering valuable insights for future decision-support applications.</p>

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Optimization of forest tractor transport using operacional research

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Giovanni Correia Vieira,
  • Geraldo Regis Mauri,
  • Gilson Fernandes da Silva,
  • Adriano Ribeiro de Mendonça,
  • Nilton César Fiedler,
  • Eduardo da Silva Lopes,
  • Evandro Ferreira da Silva,
  • Gabriel Lessa Lavagnoli

摘要

This study addresses a routing and scheduling problem related to the transport of forest tractors between harvesting sites using ballast trucks. The problem is framed as a Pickup and Delivery Vehicle Routing Problem with Time Windows (PDVRPTW), and considers factors like road conditions, legal time limits, and a diverse fleet with specific compatibility rules. To address it, we developed a mixed-integer linear programming model aimed at minimizing travel distance, fleet usage, and delays. The model was implemented in CPLEX with a Branch-and-Bound approach and tested using real data from a Brazilian forestry company. Scenarios with varying demand levels–low, medium, and high–were solved optimally in under 3 min. These results show that exact optimization methods can be practical for complex planning in forest logistics, offering valuable insights for future decision-support applications.