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文章建立基于博弈策略的网格化应急资源调度模型,三个目标函数分别为:(1)完成任务花费的时间最小;(2)整个任务花费的费用最低;(3)任务的生存性。在建立模型之后,结合传统的网格化调度算法,运用基于静态贝叶斯博弈的多目标进化算法(SBG-MOEA)求解模型,得出Pareto最优解集,并针对模型结果将SBG-MOEA算法和经典的NSGA-∏算法进行了比较测试,发现算法SBG-MOEA在收敛性Pareto非支配解的分布性上都表现优异。决策者可以根据实际情况从最优解中选取最符合条件的解。
This paper establishes grid-based emergency resource scheduling model based on game strategy. The three objective functions are as follows: (1) the minimum time spent on completing the task; (2) the lowest cost of the whole task; and (3) the survivability of the task. After the model is established, the Pareto optimal solution set is obtained by combining the traditional gridding scheduling algorithm with the multi-objective evolutionary algorithm (SBG-MOEA) based on static Bayesian game, and the SBG-MOEA The algorithm is compared with the classical NSGA-Π algorithm. It is found that the algorithm SBG-MOEA performs well on the distribution of unconstrained convergence Pareto solutions. Decision makers can choose the best solution from the optimal solution based on the actual situation.