A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

选择性集成算法分类与比较

选择性集成是当前机器学习领域的研究热点之一。由于选择性集成属于NP"难"问题,人们多利用启发式方法将选择性集成转化为其他问题来求得近似最优解,因为各种算法的出发点和描述角度各不相同,现有的大量选择性集成算法显得繁杂而没有规律。为便于研究人员迅速了解和应用本领域的最新进展,本文根据选择过程中核心策略的特征将选择性集成算法分为四类,即迭代优化法、排名法、分簇法、模式挖掘法;然后利用UCI数据库的20个常用数据集,从预测性能、选择时间、结果集成分类器大小三个方面对这些典型算法进行了实验比较;最后总结了各类方法的优缺点,并展望了选择性集成的未来研究重点。     

基于种群多样性的自适应PSO算法求解VRPSPD问题

为有效求解逆向物流车辆路径(VRPSPD)模型,本文提出一种基于种群多样性的自适应PSO算法(SDAPSO)。在SDAPSO运行时,根据种群多样性,自适应地对种群中运行较差的粒子进行扰动操作,提升这些粒子向最优解收敛的能力;同时,对全局最优粒子进行概率扰动,以增加种群的多样性。标准检测函数的仿真结果表明SDAPSO算法是对基本PSO算法的有效改进。在对VRPSPD模型求解中,通过与其它粒子群算法相比,表明SDAPSO是求解该类问题的一种有效方法。     

低成本钢尾渣-矿渣基矿山充填料的优化开发

 为实现多固废协同利用、降低充填成本,在矿渣基全粒级细尾砂胶结充填料基础上,以流动性和抗压强度为表征,利用热闷钢渣磁选尾渣(钢尾渣)替代部分矿渣作为胶凝材料,脱硫灰和水泥熟料替代部分专用添加剂作为外加剂,采用正交试验探寻掺量规律,优化固体填充料配比,开发钢尾渣-矿渣基软性矿山充填料,并研究了外加剂与胶材比、灰砂比等因素的影响。对比分析了矿渣基准组、钢尾渣-矿渣基准组(B1)、强度最优外加剂组分钢尾渣-矿渣组(B7)等3组充填料的微观形貌及XRD图谱以探究其水化机理。结果表明,钢尾渣替代矿渣量增加、外加剂与胶材比减小,充填料浆流动性改善,但充填体抗压强度下降。强度正交试验结果表明,钢尾渣掺量大小决定强度低高,脱硫灰掺量宜高于水泥熟料。进一步调整外加剂组分配比,在灰砂比为1∶6、钢尾渣替代矿渣为20%条件下,找出B7组外加剂组分为脱硫灰、水泥熟料分别替代30%、20%专用添加剂,B7组料浆扩展度为143 mm,充填体形貌为富铁绿泥石胶结假方体钙硅灰石,28 d抗压强度达2.13 MPa,较基准组低0.19 MPa,较B1提高0.26 MPa。该替代方案满足现场充填C2级强度的要求,改善流动性并显著降低了充填成本。优化的外加剂组分配比在灰砂比为1∶4条件下同样具有强度优化作用,但较灰砂比为1∶6条件下低。     

内燃式热风炉燃烧器结构优化

针对内燃式热风炉在燃烧期烟气中CO含量超标问题开展研究工作,提出一种改进型的矩形燃烧器结构,在煤气通道中加入挡板来改变高炉煤气的流动方向。以某公司3号高炉热风炉为研究对象,建立了内燃式热风炉矩形燃烧器和燃烧室的三维模型。利用CFD模拟技术对矩形燃烧器的原始结构和改进后的结构进行燃烧模拟,在矩形燃烧器中加入的煤气挡板分别采用45°、60°、75° 3种倾斜角度放置,分析在不同倾斜角度下的温度场和CO浓度场。与原始结构的结果进行对比,结果表明,优化结构之后燃烧室出口截面的温度场中高温区范围有所扩大,两端眼角处的CO平均体积分数有一定程度减少。当煤气挡板的倾斜角度为60°时出口截面平均温度上升最大,平均温度从1 669 K上升到1 676 K,出口烟气中的CO平均体积分数下降最多,CO平均体积分数从0.007 028%下降到0.005 678%。     

Multidisciplinary optimization of gas-quenching process

Distortion as a result of the quenching process is predominantly due to the thermal gradient and phase transformations within the component. Compared with traditional liquid quenching, the thermal boundary conditions during gas quenching are relatively simple to control. By adjusting the gas-quenching furnace pressure, the flow speed, or the spray nozzle configuration, the heat-transfer coefficients can be designed in terms of both the component geometry and the quenching time. The purpose of this research is to apply the optimization methodology to design the gas-quenching process. The design objective is to minimize the distortion caused by quenching. Constraints on the average surface hardness, and its distribution and residual stress are imposed. The heat-transfer coefficients are used as design variables. DEFORM-HT is used to predict material response during quenching. The response surface method is used to obtain the analytical models of the objective function and constraints in terms of the design variables. Once the response surfaces of the objective and constraints are obtained, they are used to search for the optimum heat-transfer coefficients. This process is then used instead of the finite-element analysis. A one-gear blank case study is used to demonstrate the optimization scheme.     

复吹转炉“留渣 双渣”脱磷工艺试验

摘要：在国内某转炉钢厂采用“留渣 双渣”工艺技术进行脱磷工艺试验。结果表明：随着转炉前期脱磷率不断升高,终点脱磷率不断提高。铁水硅含量对前期脱磷率的影响最大。根据铁水成分,在冶炼前期适当降低供氧强度、降低气固氧比、加入适量石灰及烧结矿,均有利于前期脱磷率的提高。在一倒时每吨钢液加入4~8kg石灰,不影响出钢温度,可提高一倒-终点阶段脱磷率,同时可提高终点脱磷率。从终点的控制效果可知,终点炉渣碱度应保持不小于3.0,炉渣中FeO质量分数在16%~20%,并适当降低终点出钢温度在1610~1630℃,有利于终点脱磷率的提高。通过加强熔池搅拌,促进钢渣反应趋于平衡,有利于终点磷分配比提高,从而可进一步提高终点脱磷率。     

水性聚氨酯材料配方设计技巧

介绍了优选法的0.618法和分数法,对其进行改进创新和原理证明,并应用于生产涂料配方设计中。