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An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem.

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This study introduces an improved tugboat scheduling model and algorithm (IGWO) to optimize port operations. The new approach enhances efficiency and reduces costs by better aligning with real-world maritime logistics.

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Area of Science:

  • Maritime Logistics
  • Operations Research
  • Optimization Algorithms

Background:

  • Global maritime transport growth necessitates efficient port operations.
  • Current tugboat scheduling models lack real-world applicability and accuracy.
  • Existing methods struggle with complex optimization problems and integer programming.

Purpose of the Study:

  • To develop a novel multi-objective tugboat scheduling model that addresses practical limitations.
  • To introduce an Improved Grey Wolf Optimization (IGWO) algorithm for enhanced scheduling accuracy.
  • To validate the proposed model and algorithm in a real-world port setting.

Main Methods:

  • Formulation of a multi-objective tugboat scheduling model.
  • Development and enhancement of the Grey Wolf Optimization algorithm (IGWO).
  • Simulation experiments comparing IGWO with other intelligent algorithms on scaled port instances.

Main Results:

  • The IGWO algorithm demonstrates superior convergence and accuracy for tugboat scheduling.
  • The model effectively optimizes tugboat deployment, improving port operational efficiency.
  • Validation in Huanghua Port confirms the practical applicability and cost-saving potential.

Conclusions:

  • The proposed multi-objective model and IGWO algorithm offer a significant advancement in tugboat scheduling.
  • Optimized scheduling leads to reduced operational costs and enhanced maritime transportation sustainability.
  • The findings provide valuable insights for port management and operational strategies.