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<p>Effective safety resource allocation is crucial to operational resilience in complicated port environments. This study looks into the difficulties and optimises the distribution of safety resources. A mixed-methods approach was used, starting with a cross-sectional survey of 108 port safety specialists to evaluate present practices, difficulties, and inefficiencies. The empirical data influenced the creation of a novel multi-objective nonlinear optimisation model aimed at minimising operational risk, environmental noise, and total costs while adhering to major operational restrictions. These objectives were aggregated using a Fuzzy Weighted Goal Programming technique, with metaheuristic algorithms used to provide solutions. The findings revealed substantial challenges, such as financing limits and personnel shortages. The Genetic Algorithm outperformed the other solvers, resulting in an optimal fitness value. The results demonstrate how empirically informed optimisation of safety resources can support more sustainable port operations by balancing occupational risk reduction, environmental noise mitigation, and economic feasibility. The study proposes a proven decision-support framework for port authorities to improve safety outcomes through data-driven, optimal resource allocation, demonstrating a considerable improvement over current heuristic methods.</p>

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Optimising Safety Resource Allocation for Sustainable Port Safety by Integrating Empirical Insights With Computational Intelligence

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Kato A. Saidu,
  • Ojo S. I. Fayomi,
  • Desmond E. Ighravwe

摘要

Effective safety resource allocation is crucial to operational resilience in complicated port environments. This study looks into the difficulties and optimises the distribution of safety resources. A mixed-methods approach was used, starting with a cross-sectional survey of 108 port safety specialists to evaluate present practices, difficulties, and inefficiencies. The empirical data influenced the creation of a novel multi-objective nonlinear optimisation model aimed at minimising operational risk, environmental noise, and total costs while adhering to major operational restrictions. These objectives were aggregated using a Fuzzy Weighted Goal Programming technique, with metaheuristic algorithms used to provide solutions. The findings revealed substantial challenges, such as financing limits and personnel shortages. The Genetic Algorithm outperformed the other solvers, resulting in an optimal fitness value. The results demonstrate how empirically informed optimisation of safety resources can support more sustainable port operations by balancing occupational risk reduction, environmental noise mitigation, and economic feasibility. The study proposes a proven decision-support framework for port authorities to improve safety outcomes through data-driven, optimal resource allocation, demonstrating a considerable improvement over current heuristic methods.