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<p>To address the complex issue of efficient berth allocation for ships entering the port under conditions of uncertainty, we have adopted a credible fuzzy programming approach. This approach takes into account the significant impact of fluctuating water depths on berth assignments and constructs a linear integer programming model for berth allocation. The primary objective of this model is to minimize the total time cost. Additionally, two heuristic algorithms—the genetic algorithm and the simulated annealing algorithm—have been designed to accommodate the problem’s unique characteristics. The innovation of this paper lies in establishing a new model based on novel scenarios and designing a new genetic algorithm tailored for this problem, with results compared against those from a simulated annealing algorithm. The solution results of several test cases are compared and analyzed using CPLEX. The results verify the feasibility of the proposed algorithms, and an analysis of the variation in confidence levels is conducted based on test cases generated from actual data, demonstrating the applicability of the fuzzy programming model. When the confidence level increases from 0.5 to 1, the objective value rises by 12 units, yet the impact on berth allocation plans remains negligible, indicating the stability of the proposed model.</p>

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Research on a fuzzy programming model and algorithm for berth allocation considering time-varying water depth

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Di Liu,
  • Botang Li,
  • Mengchi Li,
  • Yushun Lei,
  • Pei Yu,
  • Chaofeng Tan

摘要

To address the complex issue of efficient berth allocation for ships entering the port under conditions of uncertainty, we have adopted a credible fuzzy programming approach. This approach takes into account the significant impact of fluctuating water depths on berth assignments and constructs a linear integer programming model for berth allocation. The primary objective of this model is to minimize the total time cost. Additionally, two heuristic algorithms—the genetic algorithm and the simulated annealing algorithm—have been designed to accommodate the problem’s unique characteristics. The innovation of this paper lies in establishing a new model based on novel scenarios and designing a new genetic algorithm tailored for this problem, with results compared against those from a simulated annealing algorithm. The solution results of several test cases are compared and analyzed using CPLEX. The results verify the feasibility of the proposed algorithms, and an analysis of the variation in confidence levels is conducted based on test cases generated from actual data, demonstrating the applicability of the fuzzy programming model. When the confidence level increases from 0.5 to 1, the objective value rises by 12 units, yet the impact on berth allocation plans remains negligible, indicating the stability of the proposed model.