Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Impacts of Increasing Model Resolutions and Shortening Forecast Lead Times on QPFs in South China During the Rainy Season

This study investigated the impacts of increasing model resolutions and shortening forecast lead times on the quantitative precipitation forecast(QPF)for heavy-rainfall events over south China during the rainy seasons in 2013-2020.The control experiment,where the analysis-forecast cycles run with model resolutions of about 3 km,was compared to a lower-resolution experiment with model resolutions of about 9 km,and a longer-term experiment activated 12 hours earlier.Rainfall forecasting in the presummer rainy season was significantly improved by improving model resolutions,with more improvements in cases with stronger synoptic-scale forcings.This is partially attributed to the improved initial conditions(ICs)and subsequent forecasts for low-level jets(LLJs).Forecasts of heavy rainfall induced by landfalling tropical cyclones(TCs)benefited from increasing model resolutions in the first 6 hours.Forecast improvements in rainfall due to shortening forecast lead times were more significant at earlier(1-6 h)and later(7-12 h)lead times for cases with stronger and weaker synoptic-scale forcings,respectively,due to the area-and case-dependent improvements in ICs for nonprecipitation variables.Specifically,significant improvements mainly presented over the northern South China Sea for low-level onshore wind of weak-forcing cases but over south China for LLJs of strong-forcing cases during the presummer rainy season,and over south China for all the nonprecipitation variables above the surface during the TC season.However,some disadvantages of higher-resolution and shorter-term forecasts in QPFs highlight the importance of developing ensemble forecasting with proper IC perturbations,which include the complementary advantages of lower-resolution and longer-term forecasts.

Infinitesimal dividing modeling method for dual suppliers inventory model with random lead times

As one of the basic inventory cost models, the （Q, τ）inventory cost model of dual suppliers with random procurement lead time is mostly formulated by using the concepts of ＂effective lead time＂ and ＂lead time demand＂, which may lead to an imprecise inventory cost. Through the real-time statistic of the inventory quantities, this paper considers the precise （Q, τ） inventory cost model of dual supplier procurement by using an infinitesimal dividing method. The traditional modeling method of the inventory cost for dual supplier procurement includes complex procedures. To reduce the complexity effectively, the presented method investigates the statistics properties in real-time of the inventory quantities with the application of the infinitesimal dividing method. It is proved that the optimal holding and shortage costs of dual supplier procurement are less than those of single supplier procurement respectively. With the assumption that both suppliers have the same distribution of lead times, the convexity of the cost function per unit time is proved. So the optimal solution can be easily obtained by applying the classical convex optimization methods. The numerical examples are given to verify the main conclusions.

ANALYSIS AND IMPROVEMENT OF LEAD TIME FOR JOB SHOP UNDER MIXED PRODUCTION SYSTEM

Firstly an overview of the potential impact on work-in-process （WIP） and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes. Simple performance examples are compared to those from a shop with one-piece transfer lots. Next, a mathematical programming model for minimizing lead time in the mixed-model job shop is presented, in which one-piece transfer lots are used. Key factors affecting lead time are found by analyzing the sum of the longest setup time of individual items among the shared processes （SLST） and the longest processing time of individual items among processes （LPT）. And lead time can be minimized by cutting down the SLST and LPT. Reduction of the SLST is described as a traveling salesman problem （TSP）, and the minimum of the SLST is solved through job shop scheduling. Removing the bottleneck and leveling the production line optimize the LPT. If the number of items produced is small, the routings are relatively short, and items and facilities are changed infrequently, the optimal schedule will remain valid. Finally a brief example serves to illustrate the method.

Two-echelon inventory model with service level constraint and controllable lead time sensitive to order quantity

The decision-making and optimization of two-echelon inventory coordination were analyzed with service level constraint and controllable lead time sensitive to order quantity.First,the basic model of this problem was established and based on relevant analysis,the original model could be transformed by minimax method.Then,the optimal order quantity and production quantity influenced by service level constraint were analyzed and the boundary of optimal order quantity and production quantity was given.According to this boundary,the effective method and tactics were put forward to solve the transformed model.In case analysis,the optimal expected total cost of two-echelon inventory can be obtained and it was analyzed how service level constraint and safety factor influence the optimal expected total cost of two-echelon inventory.The results show that the optimal expected total cost of two-echelon inventory is constrained by the higher constraint between service level constraint and safety factor.

PLANNED LEAD TIME OPTIMIZATION IN MATERIAL REQUIREMENT PLANNING ENVIRONMENT FOR MULTILEVEL PRODUCTION SYSTEMS

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.

A supply chain model for imperfect production system with stochastic lead time demand

This study deals an integrated manufacturer-buyer supply chain system for imperfect production under stochastic lead time demand.Here,defective rate has been followed as a function of production rate.Also,the produced units have been inspected in order to screen the defective units but screening rate is less than the production rate and greater than the demand rate.Buyer purchases the products from the manufacturer.Also,we assume that shortage during the lead time is permitted and demand during the shortage period is fully backordered.The objective is to derive the optimal production rate,ordering quantity and to maximize joint total profit.Basically,two different models for different probability distribution functions of stochastic lead time demand have been developed.Some numerical examples are provided to show the applicability of the proposed models comparing the optimum average profits.Finally,sensitive analysis,conclusion and future researches are presented.

Optimal structure of joint inventory-pricing management with dual suppliers and different lead times

We consider a joint inventory-pricing control problem in a single-product,periodic-review,dual-supplier inventory system.The two suppliers have different lead times.One expedited supplier offers instantaneous replenishment,and one regular supplier requires an L-period lead time for delivery.The supply quantity is stochastic and the demand is price-dependent.For the expedited inventory replenishment,we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting.To investigate the optimal regular inventory replenishment and pricing policy,we propose the notions of partially stochastic translation(PST)and increasing partially stochastic translation(IPST),which help in obtaining the antimultimodularity preservation in dynamic programming problems.We provide properties,sufficient conditions,and examples for PST and IPST functions.By applying PST and IPST,we obtain the antimultimodularity of the profit functions.The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities.Moreover,we reveal that as the time interval increases,the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing,respectively.PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level.However,the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities,as shown in the example.

Dynamic robust optimal reorder point with uncertain lead time and changeable demand distribution

In fixed order quantity systems,uncertainty in lead time is expressed as a set of scenarios with occurrence probabilities,and the mean and variance in demand distribution are supposed to be changeable according to a known pattern.A new concept of "dynamic robust optimal reorder point" is proposed in this paper and its value is calculated as a "robust optimal reorder point function with respect to reorder time".Two approaches were employed in determining the dynamic optimal reorder point.The first is a shortage rate satisfaction approach and the second is a backorder cost minimization approach.The former aims at finding the minimum value of reorder point at each reorder time which satisfies the condition that the cumulative distribution function (CDF) of shortage rate under a given set of scenarios in lead time is greater than or equal to a basic CDF of shortage rate predetermined by a decision-maker.In the latter approach,the CDF of closeness of reorder point is defined at each reorder time to express how close to the optimal reorder points under the set of scenarios,and the dynamic optimal reorder point is defined according to stochastic ordering.Some numerical examples demonstrate the features of these dynamic robust optimal reorder points.

A two-tier supply chain model under two distributions with MTTF,rework,variable production rate and lead time

This article considers the two-level supply chain model incorporating an imperfect production process under a variable lead time.The cost of producing a unit item is calculated as a function of the production rate.In addition,two alternative production functions(linear and quadratic functions)are used to relate process quality and production rate.Lead time demand follows two different distributions,based on which two mathematical formulations are described in this paper.In the first model,the lead time demand follows a normal distribution.In the second model,the lead time demand doesn’t follow any particular distribution,but the mean and the standard deviation are known.The lead time length is minimized by incorporating the lead time crashing cost.This research aims to analyze the optimized total cost of the supply chain under two different distributions.

Multi-Item EOQ Model with Both Demand-Dependent Unit Cost and Varying Leading Time via Geometric Programming

The objective of this paper is to derive the analytical solution of the EOQ model of multiple items with both demand-dependent unit cost and leading time using geometric programming approach. The varying purchase and leading time crashing costs are considered to be continuous functions of demand rate and leading time, respectively. The researchers deduce the optimal order quantity, the demand rate and the leading time as decision variables then the optimal total cost is obtained.

Long Lead-Time Streamflow Forecasting Using Oceanic-Atmospheric Oscillation Indices

Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the forecast opportunity for the streamflow. Prediction of future water availability accurately and reliably is a key step for successful water resource management in the arid regions. Four popular ocean-atmospheric indices were used in this study for annual streamflow volume prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven model based on Bayesian learning approach was used as a prediction model. The model was applied to four unimpaired stream gages in Utah that spatially covers the state from north to south. Different models were developed based on the combinations of oscillation indices in the input. A total of 60 years (1950-2009) of data were used for the analysis. The model was trained on 50 years of data (1950-1999) and tested on 10 years of data (2000-2009). The best combination of oscillation indices and the lead-time were identified for each gage which was used to develop the prediction model. The predicted flow had reasonable agreement with the actual annual flow volume. The sensitivity analysis shows that the PDO and ENSO have relatively stronger effect compared to other oscillation indices in Utah. The prediction results from the MVRVM were compared with the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM performed relatively better.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

基于驻留时间控制的压气机叶片前缘砂带磨削研究

提出了一种面向叶片前缘廓形精准控制的机器人砂带磨削加工方法.以轴流压气机叶片为研究对象,结合半赫兹接触理论和有限元仿真获取了柔性磨具和叶片前缘的接触区域内的应力分布,基于Preston方程求解材料去除函数.遍历刀位点对控制点的磨削深度,建立全局材料去除矩阵,搭建驻留时间求解非线性方程组.采用带有阻尼因子的Tikhonov正则化消除大型稀疏病态矩阵对求解精度波动的影响,将所求驻留时间转换为对应刀位点的进给速度,生成机器人加工代码.磨削试验结果表明,基于驻留时间控制的机器人砂带磨削方法能够实现给定允差范围内叶片前缘廓形的精准加工,型面误差可以控制在0.02mm以内.

Probabilistic earthquake early warning times in Fujian Province

Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.

周转箱多模型系统的鲁棒切换策略

为了抑制提前期和不确定需求对周转箱利用率的影响,研究一种周转箱多模型系统的鲁棒切换策略。首先,基于周转箱的正向流动和逆向流动,设计一种包含制造商、运营商和客户的周转箱供应链系统;然后,构建不确定需求下含生产提前期、订购提前期和还箱提前期的周转箱供应链动态演变基本模型;再次,在设计制造商生产切换策略和运营商运营切换策略的基础上,将周转箱供应链系统表示为多模型系统。将上述周转箱供应链的基本模型、切换策略和多模型系统扩展为周转箱供应链网络后,对可描述周转箱多模型系统的Takagi-Sugeno模糊控制系统提出一种鲁棒控制策略。由周转箱的切换策略和鲁棒控制策略组成的周转箱多模型系统的鲁棒切换策略可以降低多种提前期和不确定需求对周转箱供应链和网络的影响,实现周转箱供应链和网络的低成本稳定运作。最后,通过某汽车零部件周转箱供应链和网络的仿真实验验证了所提周转箱多模型系统鲁棒切换策略的有效性。

反舰导弹大前置角下三维剩余飞行时间估计方法

为解决导弹开启末制导时初始前置角过大或前置角变化幅度较大的问题,设计适用于多种导弹制导律的前置角三维剩余飞行时间估计方法。将导弹速度和目标速度投影分解,在地面坐标系的Oxy面和Oxz面上建立弹目相对运动关系方程,以此为基础建立三维弹目相对运动模型。利用弹目相对运动关系方程和比例导引控制方程,建立1阶非线性微分方程,解析弹目距离与前置角的关系,并利用麦克劳林展开式求解方程。根据视线角速度变化选择合适的内框角,对前置角进行补偿,同时使用中位值平均滤波法降低前置角波动造成的误差。利用已建立的三维弹目相对运动模型进行仿真实验。仿真结果表明,在小角度假设不能成立时,尤其是因制导律进行角度控制而导致前置角变化较大时,前置角剩余飞行时间(Time-to-go,TGO)估计方法的估计值误差收敛时间和最大误差值要小于其他TGO估计方法,估计效果最佳。

基于滑动质心法的华南地区锋面和暖区降水ECMWF模式预报性能评估

应用空间滑动质心法对2018—2020年华南地区锋面和暖区中雨、大雨及暴雨量级降水ECMWF模式(简称EC模式)24~168 h时效的空间落区预报结果进行定量分析。结果表明:EC模式对华南暖区降水和锋面暴雨的空间落区预报存在系统性经纬向偏差,且以偏西北为主;对锋面中雨和大雨的空间落区预报存在系统性经向偏差,且以偏北为主,但纬向偏差不明显。模式对锋面和暖区降水空间落区预报的纬度偏差分别集中在1.0°和1.5°以内,经度偏差分别集中在1.5°和2.0°以内。随预报时效增加,EC模式的空间预报偏差增大,24~72 h时效的EC模式空间落区预报相对稳定,96~168 h时效的预报偏差明显增大。2022年华南地区两次锋面和暖区降水空间位置质心订正结果表明,订正后的模式降水TS评分明显提高。

电气设备交货周期优化的精益改善分析方法

【目的】在制造企业进行“智改数转”时需要以流程优化为前提的背景下,针对电气设备交货周期过长的情况,设计了一种系统的精益改善分析方法。【方法】首先,对全生产周期内不同阶段的耗时因素进行建模;其次,基于多色集合理论提出了一种影响因素权重矩阵与影响力评估方法,以找出关键影响因素;最后,结合企业现状和改善目标确定精益改善路径并实施。【结果】利用此方法对N公司旗下T产品的生产流程进行精益改善,使其交货周期从19~25 d缩短为16~20 d,满足了T产品在3周内完成交货的精益改善目标。【结论】该精益改善实例印证了所提出方法的实用性与可行性。