摘要
指派问题是一类典型的NP难问题,针对不同员工完成不同任务所产生的利润和时间不确定的情况,同时结合不确定理论的特点,以获得最大利润和耗费最少时间为优化目标,建立指派问题的机会约束多目标规划模型,并设计随机模拟、神经网络和非支配排序遗传算法融合的求解算法。通过具体的实例分析,对模型和算法的合理性进行检验。实例结果表明,与其他求解算法相比,该混合智能算法充分发挥了遗传算法搜索速度快的优势,在求解多目标组合优化问题方面更加优异。
Assignment problem is a typical NP-hard problem.Aiming at the uncertainty of profit and time caused by different employees completing different tasks,this paper establishes the multi-objective programming model with opportunity constraint for assignment problem by combing with the characteristics of uncertainty theory and taking the maximum profit and the least consumption time as the optimization goal.We designed a solution algorithm combining stochastic simulation,neural network and non dominated sorting genetic algorithm.Through the analysis of specific examples,the rationality of the model and algorithm was tested.The results show that compared with other algorithms,the hybrid intelligence algorithm gives full play to the advantage of fast searching speed of genetic algorithm,and it is better in solving multi-objective optimization problems.
作者
王文璨
巩梨
刘林忠
Wang Wencan;Gong Li;Liu Linzhong(School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)
出处
《计算机应用与软件》
北大核心
2022年第6期269-272,308,共5页
Computer Applications and Software
基金
国家自然科学基金项目(71671079,71361018)。
关键词
不确定理论
多目标优化问题
混合智能算法
Uncertainty theory
Multi-objective optimization problem
Hybrid intelligence algorithm
