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基于斯塔克尔伯格博弈的港口拖船调度多目标模糊规划模型

A multi-objective fuzzy programming model for port tugboat scheduling based on the Stackelberg game.

作者信息

Ren Yangjun, Chen Qiong, Lau Yui-Yip, Dulebenets Maxim A, Li Botang, Li Mengchi

机构信息

School of Economics and Trade, Changzhou Vocational Institute of Textile and Garment, Changzhou, 213164, China.

Navigation College, Jimei University, Xiamen, 361021, China.

出版信息

Sci Rep. 2024 Oct 23;14(1):25057. doi: 10.1038/s41598-024-76898-6.

DOI:10.1038/s41598-024-76898-6
PMID:39443651
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11499605/
Abstract

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model's uncertain parameters.

摘要

为解决不确定环境下拖船协助船舶进出港调度的优化问题，本研究探讨了在动态任务到达和拖船作业时间模糊的场景下，拖船操作员和港口调度员的决策对拖船调度的影响。考虑到最短距离拖船原则、最先可用拖船原则以及各拖船任务量公平性原则的特点，拖船公司旨在将拖船作业的每日总燃料消耗降至最低，将动态任务的总缓冲时间最大化，并将总完成时间降至最低作为目标函数。由于港口船舶靠泊和离港的限制，以及引航或调遣拖船的分配标准，本研究提出了一种基于Stackelberg博弈的港口拖船调度模糊模型，其中拖船操作员和港口调度员分别作为上层和下层的决策者。设计了一种基于优先级编码和遗传算子的海鸥优化算法作为求解方法。利用CPLEX、遗传算法、标准海鸥优化算法和模拟退火算法对从广州港实际数据生成的45个问题案例的求解结果进行比较分析。结果验证了所提出的基于优先级编码和遗传算子的海鸥优化算法的有效性。此外，还进行了额外的实验，以评估公平系数、不确定参数相关系数和目标函数相关系数的变化，以证明模糊规划模型的实用性。该分析涉及相对于模型的不确定参数将置信水平从0逐步调整到100%。

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