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.

Quantity-based shipment consolidation and stock replenishment policy with lead time in vendor-managed inventory system

In order to let the supplier make more reasonable supply decisions,an integrated continuous replenishment policy for the vendor-managed inventory system is presented,which considers the quantity-based shipment consolidation and stock replenishment with lead time.Then the system cost is analyzed and a mathematical model is built.Since the model is rather complex,the bounds of the optimal policy are first attained,then the problem is solved by a heuristic algorithm.Through experiments the relationship between the order lead time and the corresponding integrated policy is discussed,and the influence on the system cost is also analyzed.The results reveal that the lead time's influence on the system is more serious with the increase of the order lead time,the integrated policy with the order lead time is more reasonable and the optimal policy can minimize the total system cost.Finally,the parameter sensitivity of the model is analyzed.

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.

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.

Design of cost allocation rule for joint replenishment with controllable lead time

To encourage retailers to form cooperative alliances to jointly replenish inventory,considering that the supplier provides a flexible lead time and quantity discount to retailers,a model of average total cost per unit time of periodic joint replenishment is constructed,and an approximate algorithm,which can satisfy the requirement of any given precision,is given.The cost allocation rule in the core of the joint replenishment game is designed based on the cooperative game theory.The numerical experiment results show that the proposed algorithm can quickly solve the joint replenishment problem when the item number is not greater than 640.The retailer's cost saving rate is always greater than 0,and it increases with the increase in quantity discount and fixed cost after adopting the given cost allocation rule.With the increase in the safety stock level,the retailer's cost saving rate increases first and then decreases;and the retailer's cost saving rate increases with the increase in the size of the alliance,but it decreases as the number of product category increases.The proposed cost allocation rule can reduce the retailer's cost up to 20%,which is conducive to forming a cooperative coalition.

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.

Study on Lead Time and Price Discount in Supply Chain Management

The mathematical model of the demander versus supplier has been presented by adopting optimization theories, the economical order quantity （EOQ） and economic production quantity （EPQ） has further been studied. Then under the consideration of Pareto optimization, a joint decision model of price and lead time discount and lot size has been presented. Further more the sensitive analysis of price and lead time discount are analyzed with an empirical example.

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XGBoost的震后物资动态需求预测

地震作为突发性自然灾害,常常造成严重人员伤亡,应急物资需求预测研究是灾后应急救援的重要组成部分,是物资统筹调配的前提,合理科学的物资需求预测可以提高救援效率。针对震后应急物资需求预测分为两步,首先,对震灾总伤亡人数进行预计,再利用经验函数对地震动态伤亡人数进行初始估计;旨在根据前期时间段内的伤亡人数,实现对后续伤亡人数有更为精准的预测,将以前序时间内的伤亡人数为依据,引入基于XGBoost的地震动态伤亡人数预测模型,以提高预测的准确性和可靠性。其次,利用物资需求量与动态伤亡人数的线性关系,根据具有提前期概念的动态需求估计模型计算出每日所需物资,实现动态需求预测。最后,运用所提方法对“汶川地震”伤亡人数、受伤人数、死亡人数进行动态预测,并估算了物资动态需求量,为灾区应急物资供应提供参考。

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.

考虑记忆时间的LSTM模型在赣江流域径流预报中的应用

在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景分析:模型的有效预见期与不同流域平均产汇流时间之间的关系,有效预见期内LSTM径流预测模型精度与记忆时间之间的关系,不同长度的预见期与模型最佳记忆时间之间的关系,同时探讨LSTM径流预测所需的记忆时间与流域面积的关系。结果表明:(1)综合考虑降水和前期径流情景下的径流预测效果最好,当预见期为1 d时,外洲、峡江、栋背站的纳什效率系数(NSE)分别可达0.98、0.96以及0.90;且其有效预见期与仅考虑降水信息的有效预见期相同,均与流域平均产汇流时间相近。(2)随着预见期的延长,不同情景下的预测精度均有不同程度的下降,其中仅考虑前期径流情景的下降率最大,说明降水信息较前期径流对径流预测效果的提升更重要。同时,随着流域面积的增加,相同预见期内径流预测精度均有所提升。(3)当预见期相同时,随记忆时间的延长,不同径流预测模型的预测精度均先上升至最高,接着具有下降趋势,最后逐渐趋于稳定。且在有效预见期内,随着预见期的延长,最佳记忆时间均有增大趋势,当达到最长的有效预见期时,对应的最佳记忆时间均为14 d。此外,在赣江流域的模拟结果表明,随着流域面积的增大,LSTM的最佳记忆时间减小。研究结果可为赣江流域的径流预报提供参考,同时有助于推求其他流域采用机器学习进行径流预测所需的最佳记忆时间。

基于LSTM、RF、SVR三种机器学习方法的径流预测研究

为探究不同预报方案对机器学习模型径流预测的影响,以淮河王家坝~蒋家集~润河集区间流域为例,设计了七种径流预测方案,采用LSTM(长短期记忆神经网络)、RF(随机森林)以及SVR(支持向量回归)三种机器学习模型进行径流预测。研究结果表明:(1)三种机器学习模型对降雨信息的敏感程度不同,且采用同时考虑径流影响因素以及前期历史径流的方案预测效果最佳,但随着预见期的延长,前期历史径流的重要性逐渐降低;(2)三种机器学习模型在不同预见期的径流预测表现有所差异:三种机器学习模型在预见期为1 d时预测精度均较高;当预见期为2~4 d时,SVR模型的预测效果较好;RF模型在预见期为5~7 d时预测精度较高。研究可为后续基于机器学习的径流预测提供参考。