This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations ...This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.展开更多
Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day ...Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices.The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.Design/methodology/approach–A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine(QSOS-ELM)is proposed to forecast the next-day closing prices effectively.Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases.This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.Findings–Simulation is carried out on seven stock indices,and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error,mean absolute percentage error,accuracy and paired sample t-test.Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.Originality/value–The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices.The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.展开更多
为了找到汽轮机在不同负荷下的最优初压,利用改进的共生生物搜索(FSOS)算法和极限学习机(ELM)建立热耗率预测模型,并与BP神经网络、共生生物搜索(SOS)算法优化ELM和FSOS算法优化支持向量机(SVM)等进行了比较。然后,在该模型的基础上用F...为了找到汽轮机在不同负荷下的最优初压,利用改进的共生生物搜索(FSOS)算法和极限学习机(ELM)建立热耗率预测模型,并与BP神经网络、共生生物搜索(SOS)算法优化ELM和FSOS算法优化支持向量机(SVM)等进行了比较。然后,在该模型的基础上用FSOS算法对主蒸汽压力和主蒸汽流量进行优化,使其在各负荷下的热耗率最低。最后,通过优化后的主蒸汽压力拟合出一条最优初压曲线,并与厂家设计的滑压运行曲线进行对比。结果表明:按照最优初压曲线运行,热耗率平均下降约58. 51 k J·(k W·h)^(-1),提高了机组能量的转换效率,对汽轮机经济运行有着显著的效果。展开更多
文摘This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.
文摘Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices.The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.Design/methodology/approach–A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine(QSOS-ELM)is proposed to forecast the next-day closing prices effectively.Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases.This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.Findings–Simulation is carried out on seven stock indices,and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error,mean absolute percentage error,accuracy and paired sample t-test.Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.Originality/value–The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices.The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.
文摘为了找到汽轮机在不同负荷下的最优初压,利用改进的共生生物搜索(FSOS)算法和极限学习机(ELM)建立热耗率预测模型,并与BP神经网络、共生生物搜索(SOS)算法优化ELM和FSOS算法优化支持向量机(SVM)等进行了比较。然后,在该模型的基础上用FSOS算法对主蒸汽压力和主蒸汽流量进行优化,使其在各负荷下的热耗率最低。最后,通过优化后的主蒸汽压力拟合出一条最优初压曲线,并与厂家设计的滑压运行曲线进行对比。结果表明:按照最优初压曲线运行,热耗率平均下降约58. 51 k J·(k W·h)^(-1),提高了机组能量的转换效率,对汽轮机经济运行有着显著的效果。
文摘为解决位置指纹定位算法中指纹采集工作量大、定位精度低的问题,提出一种基于稀疏指纹采集和改进加权K最近邻(weighted k-nearest neighbor,WKNN)的定位算法。稀疏选定参考点并采集来自各接入点(access point,AP)的接收信号强度(received signal strength,RSS),根据容错四分位法对采集的RSS进行异常值预处理;利用经过预处理的指纹数据训练高斯过程回归(Gaussian process regression,GPR)模型,通过共栖生物搜索算法(symbiotic organisms search,SOS)求取模型最优超参数以提高模型的泛化能力,进而预测定位区域内非参考点的RSS;由有限参考点数据通过SOS-GPR模型的训练与预测生成密集位置指纹库,结合由卡方距离和AP加权改进的WKNN算法完成仿真验证。实验结果表明,在保证定位精度的前提下,稀疏指纹采集法较传统全采集法减少50%的采集工作量;与原WKNN算法和M-KWNN算法相比,提出的WKNN算法有效提高了定位精度。
