Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting time...Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.展开更多
Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting...Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospective profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the potential applications of these models.展开更多
研究随机环境下车流径路的选择问题,首先给出路网车流径路方案可靠性的定义,并在此基础上建立随机环境下车流径路选择问题的随机相关机会多目标规划模型。模型考虑了车流具有不同权重的情况,目标为极大化车流径路方案的可靠性及极小化...研究随机环境下车流径路的选择问题,首先给出路网车流径路方案可靠性的定义,并在此基础上建立随机环境下车流径路选择问题的随机相关机会多目标规划模型。模型考虑了车流具有不同权重的情况,目标为极大化车流径路方案的可靠性及极小化期望总费用。用C++语言编写K短路算法,并在Visual Studio 6.0上基于该算法开发了软件,用于计算网络上任意两点之间的K短路。以该软件计算出的K短路作为节点间的可选径路集,提出一种基于随机模拟的混合遗传算法。算例表明,在不同交叉和变异概率的条件下算法均可在给定进化代数内收敛至相同的最优解,有较强的适应性。展开更多
基金National Natural Science Foundations of China(Nos.71371141,71001080)
文摘Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.
基金the National Natural Science Foundation of China (No. 70601034)
文摘Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospective profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the potential applications of these models.
文摘研究随机环境下车流径路的选择问题,首先给出路网车流径路方案可靠性的定义,并在此基础上建立随机环境下车流径路选择问题的随机相关机会多目标规划模型。模型考虑了车流具有不同权重的情况,目标为极大化车流径路方案的可靠性及极小化期望总费用。用C++语言编写K短路算法,并在Visual Studio 6.0上基于该算法开发了软件,用于计算网络上任意两点之间的K短路。以该软件计算出的K短路作为节点间的可选径路集,提出一种基于随机模拟的混合遗传算法。算例表明,在不同交叉和变异概率的条件下算法均可在给定进化代数内收敛至相同的最优解,有较强的适应性。
