非线性关系下绿色施工管理多目标权衡研究

绿色施工管理除去工期、费用、质量三大目标外,还考虑环境影响、社会影响,形成了更全面、复杂的目标管理体系。分析了绿色施工管理各单项工作综合费用、质量、环保水平、安全指数与持续时间之间的非线性关系,利用多属性效用函数理论及最优化理论,构建了非线性关系下的绿色施工管理多目标权衡模型。采用遗传算法求解,求得的最满意解为绿色施工管理的各目标控制要求。最后应用实例验证了模型的合理性,并讨论了实现绿色施工管理的一个困境。     

现代农业生物科技发展困境及解决之道

以转基因技术为代表的现代农业生物科技正面临两难发展困境,公众在对待农业生物科技的发展和选择上常常存在误判,主要表现在：对农业生物科技起源及进化的误识、对技术内涵及术语的误读、对技术风险属性的误解以及对技术路径选择的误判等,政府决策也因此而陷入二难困境。要破解这种认识与发展困境,可以从公众科学素养培育、科技术语表述分析、多元化信息交流、技术风险与利益测评等方面综合寻求解决之道。     

基于算法随机排列的安全增强模型

数据安全是技术管理中的1个重要因素,为此出现了各种加密方法并广泛应用于保护敏感信息。加密方法都是以算法为基础,算法无论多么复杂,因其遵循固有的单调性,易受到暴力攻击的威胁。在选择加密算法时引入1个随机函数,提出1个集成的复合加密算法,它由经密钥选取的若干个算法组成;引入算法库的概念,定义算法的混合、层次排列加密机制。     

模糊随机环境下再利用逆向物流网络设计

考虑模糊随机环境下,再利用逆向物流网络在数量方面的不确定性,并考虑了回收点与回收中心的回收周期不同步等复杂因素,提出了第三方构建再利用逆向物流网络的数学模型。该模型结合遗传算法、模糊随机模拟及数学规划的混合智能算法,并通过算例验证模型和算法的可行性,得到模糊随机环境下的最优选址结果和回收点最优回收周期。     

Learning and behavioral stability An economic interpretation of genetic algorithms

This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other hand, recent economic literature uses genetic algorithms as a metaphor for social learning. This paper will face the question of what an economist can learn from the mathematical branch of research, especially concerning the convergence and stability properties of the genetic algorithm. It is shown that genetic algorithm learning is a compound of three different learning schemes. First, each particular scheme is analyzed. Then it is shown that it is the combination of the three schemes that gives genetic algorithm learning its special flair: A kind of stability somewhere in between asymptotic convergence and explosion.     

基于家电回收的逆向物流网络模型研究

随着家电产品的增加,废旧家电产品的回收成为一个迫切需要解决的问题。在分析逆向物流网络模型的基础上,根据家电产品的特征,提出了家电回收的逆向物流网络模型;它包括了收集点、检测点、再制造工厂、最终处理点、产品分销中心和销售点。并运用遗传算法,以运输成本最小为目标,对家电产品回收的逆向物流网络的设施进行选址优化。最后,给出一个应用实例。     

创新型大企业的星云模型与选择子企业的遗传算法研究

通过分析创新型大企业的创新内涵和规模外延,建立了星云模型,针对构建中选择子企业的实际需要,提出改进的遗传算法,引入倒位算子代替交叉操作,建立了企业间协同系数矩阵和相应的适应度函数,提高了对此类多目标组合优化问题求解的有效性。     

关联规则中支持度变化的数据重挖掘算法研究

使用关联规则进行数据挖掘时,使用者为了达到一定的挖掘效果,经常需要不断地改变关联规则的支持度阈值(support)。文中提出了一种从大型数据库中挖掘关联规则的快速算法。该算法以经典的Apriori算法为基础,可以在提出新的支持度后,直接在首次挖掘的基础上进行再一次挖掘。结果表明,它较Apriori算法的实现速度有明显的提高。     

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.     

Integrated inventory-transportation model by synchronizing delivery and production cycles

This paper develops an integrated single-vendor multi-buyer inventory-transportation synchronized supply chain model. In this paper, the decisions of truck assignment and routing are also considered and as a result, a series of vehicle routing problems (VRP) are required to be solved. Due to the highly complicated objective function and the NP-hard VRP problems, the problem cannot be solved analytically. Hence, meta-heuristics are proposed. By means of the numerical examples and a case study, the meta-heuristics developed are shown to be very effective in solving such comprehensive supply chain models, and the results so obtained are promising.