Prediction of the Logistics Demand Based on an Innovative Mixed Model: an Empirical Case from Nanping City,China

The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy development of urban logistics industry.It constructs a comprehensive index system composed of freight volume and other eight relevant economic indices to form the foundation for the model construction.Combining forecasting models of principal component regression and GM(1,1)together,it makes mathematical calculation to predict the logistics demand of Nanping City from the years 2018 to 2022.The research makes systematical analyses of the indices influencing the precise prediction of logistics demand from a new perspective,which offers an innovative and practical option for urban logistics prediction.In line with the prediction,it offers some suggestions for the improvement of demand prediction and some strategies for the better development of the logistics industry in Nanping City.

Prediction on Cold Chain Logistics Demand of Urban Residents in Jiangsu Province during the Twelfth Five-Year Plan Period——Based on Estimates of GM(1,1) Model

This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,dairy,and medicine.Through selecting the consumption data of urban residents on transported products via cold chain in Jiangsu Province from 2005 to 2000 as sample,this paper establishes grey prediction model GM(1,1) of cold chain logistics demand and uses DPS7.05 software for test,to predict the cold chain logistics demand of urban residents in Jiangsu Province during the Twelfth Five-Year Plan period.The results show that in the period 2010-2015,the cold chain logistics demand of urban residents in Jiangsu Province is 1 151.589 1,1 185.136 6,1 219.661 3,1 255.191 8,1 291.757 3,1 329.388 1 t respectively;in the period 2005-2010,the cold chain logistics demand of urban residents in Jiangsu Province increases at annual growth rate of 3.9%;in the period 2011-2015,the growth rate declines to some extent,increasing slowly at rate of 2.9%.

Primary logistics planning of oil products under the imbalance of supply and demand

This paper intends to complete the primary logistics planning of oil products under the imbalance of supply and demand. An integrated mathematical programming model is developed to simultaneously find the balance between supply and demand, and optimize the logistics scheme. The model takes minimum logistics cost and resource adjustment cost as the objective function, and takes supply and demand capacity, transportation capacity, mass balance, and resource adjustment rules as constraints.Three adjustment rules are considered in the model, including resource adjustment within oil suppliers,within oil consumers, and between oil consumers. The model is tested on a large-scale primary logistics of a state-owned petroleum enterprise, involving 37 affiliated refineries, 31 procurement departments,286 market depots and dedicated consumers. After the unified optimization, the supply and demand imbalance is eased by 97% and the total cost is saved by 7%, which proves the effectiveness and applicability of the proposed model.

Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.

Research on Logistics Demand Forecast in Southeast Asia

This article predicts Southeast Asia’s logistics needs from a Southeast Asian logistics development perspective. This is not only an important prerequisite for supporting Southeast Asia’s trade policy, but also promoting the development of Southeast Asia’s logistics industry, building logistics infrastructure and improving the level of logistics services. Due to differences in economic development levels, trade structures, infrastructure construction and logistics development levels of Southeast Asian countries. Therefore, considering the actual situation of Southeast Asian countries, this article selected 21 cities in Southeast Asia as the research object. Use L-OD logistics demand forecasting method to forecast logistics demand in Southeast Asia. Obtain the amount of logistics occurrence and attraction in 21 cities in Southeast Asia in the future. And construct a double constrained gravity model to predict logistics distribution in Southeast Asia. The forecast results provide scientific data support for future logistics development planning in Southeast Asia.

Regional Logistics Demand Forecast Based on Least Square and Radial Basis Function

Regional logistics demand forecast is the basis for government departments to make logistics planning and logistics related policies.It has the characteristics of a small amount of data and being nonlinear,so the traditional prediction method can not guarantee the accuracy of prediction.Taking Xiamen City as an example,this paper selects the primary industry,the secondary industry,the tertiary industry,the total amount of investment in fixed assets,total import and export volume,per capita consumption expenditure,and the total retail sales of social consumer goods as the influencing factors,and uses a combining model least square and radial basis function(LS-RBF)neural network to analyze the related data from years 2000 to 2019,so as to predict the logistics demand from years 2020 to 2024.The model can well fit the training data,and the experimental results obtained from the comparison between the predicted value and the actual value in 2019 show that the error rate is very small.Therefore,the prediction results are reasonable and reliable.This method has high prediction accuracy,and it is suitable for irregular regional logistics demand forecast.

Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.

乡村旅游消费需求影响因素的多元顺序Logistics回归分析

随着国家乡村振兴战略的深入实施,乡村旅游作为带动乡村产业发展、推进精准扶贫和美丽乡村建设等工作的突破口,迎来了新的政策机遇与发展契机。然而,乡村旅游还远不能达到消费者预期的消费需求。以乡村旅游消费者为主要研究对象,运用问卷调查法,以消费者平均每年乡村旅游的次数为因变量,建立多元顺序logistics回归模型,对影响消费者乡村旅游消费需求的主要因素展开实证分析。结果显示,女性和受教育水平越高的消费者的乡村旅游意愿度更高;不了解乡村旅游模式、迫切想要去乡村旅游和日常运动量较少的消费者是主要潜在消费群体;乡村旅游的最佳时长是2~3 d,消费者更愿意结伴而行;消费者获取信息的方式主要是通过亲人、朋友宣传或在社交平台的相互交流。最后,基于分析结论,有针对性地提出了促进乡村旅游发展的建议。

基于BA-SVR混合模型的果蔬生鲜物流需求预测模型研究

本文通过构建BA-SVR混合模型对果蔬生鲜物流需求进行预测研究。首先通过互联网大数据搜索技术构建果蔬生鲜需求指数相关网络关键词词库,进而采用皮尔森(Pearson)相关分析和逐步回归选择预测因子。其次,结合果蔬自身特点以及物流市场变动因素,提出了果蔬生鲜物流指数(Fruit&Vegetable Logistic Index, FVLI)概念,分析了FVLI变动的影响变量,使其成为反映物流市场信息变动的重要指标。再次,利用蝙蝠算法(Bat Algorithm, BA)自动更新迭代参数的优势,将其引入到支持向量回归(Support Vector Regression, SVR)模型中,用于优化SVR模型中自由参数值,进而构建BA-SVR混合模型对北京市果蔬生鲜需求变化趋势进行模拟仿真及实证预测。最后根据构建的性能预测指标,通过确立的基准模型与其进行对比,评估BA-SVR混合模型性能的优劣,从而提出一种可以用于果蔬生鲜物流信息短期预测的改进方法。

航空旅客出行需求强度异质性研究

为了研究旅客出行需求强度问题,采用问卷调查的方式采集数据,对回收的问卷进行数据整理,构建了潜在类别模型,并用软件mplus对出行人群的出行需求强度进行分类,得到结果如下:可通过旅客属性特征调查,将旅客分为4类人群,分别是高需求人群、中高需求人群、中低需求人群、低需求人群。对分类结果采用多元logistic回归分析,结果表示需求强度人群划分影响因素主要为职业、出行目的、票价提高是否出行等。根据回归结果分析可以将4类需求强度异质性做具体的分析。

面向物流需求场景的智慧公路功能设计

推动智慧公路建设是提升交通安全与服务水平、提高通行效率的重要手段之一。本文梳理了中国智慧公路功能设计方面的研究现状,分类汇总了中国智慧公路现有功能及相关应用案例。同时,基于高速公路和城市道路的行车、停车场景以及政府行业监管需求,总结了中国公路物流行业运营痛点和需求场景,提出了面向物流场景需求的智慧公路功能设计思路,为中国智慧公路建设提供了新的理论参考和设计思路。

基于Adaboost回归算法的安徽省物流需求短期预测研究

【目的】物流需求预测有助于调整物流资源的分配,对促进物流业发展具有重要意义。【方法】选取安徽省1995—2022年与物流需求相关的指标数据为原始样本数据,用货运量来表征物流需求。通过XGBoost特征选择算法筛选出6个用于预测的指标。在此基础上,使用3种方法分别构建模型,并对这些模型进行对比分析。最终,选择精度最高的Adaboost回归算法来预测安徽省短期物流需求。【结果】2023—2026年,安徽省的物流需求预测值分别为402 942.428万t、369 877.222万t、380 884.375万t、382 319.5万t。【结论】未来四年,安徽省物流的货运量呈不稳定发展态势。根据安徽省的区位优势及疫情的全面开放,安徽省物流业表现出较大的发展潜力。

基于改进灰色预测模型的港口物流需求预测研究——以上海港为例

随着疫情防控政策的调整,我国外贸行业呈现快速增长态势。文章基于改进的灰色预测NGMG 1,(N)模型,运用MATLAB软件,以上海港2013至2022年的集装箱吞吐量为原始数据,预测未来五年上海港的集装箱吞吐量,并对改进的灰色NGMG 1,(N)模型预测结果进行精度检验,结果显示该模型预测精度较高。最后基于预测结果,分析上海港未来五年物流需求趋势向好的主要原因并得出结论。

基于组合预测法的云南省生鲜农产品冷链物流需求预测

冷链物流需求预测是区域冷链物流基础设施规划的基础工作,建立合适的预测模型,较为准确地预测生鲜农产品冷链物流需求量具有重要的现实意义。该文章在单一预测模型的基础上,通过熵值法赋予灰色预测GM(1,1)、时间序列ARIMA法和二次平滑指数法三种单一预测模型权重建立组合预测模型,利用云南省2009—2020年六类生鲜农产品生产量数据对2021—2030年相关物流量进行预测,预测结果表明组合预测法的预测精度要优于三种单一预测方法。该研究选用组合预测模型预测云南省冷链物流需求量,结果显示云南省生鲜农产品冷链物流需求保持一定幅度的线性增长趋势,年均增长率将达到2.94%。通过冷链物流基础设施对比得到云南省冷链物流基础设施和其他省份存在较大差距,难以满足生鲜农产品冷链物流需求日益增长的速度。本文从云南省政府、企业和农民的角度,提出加速云南省冷链物流发展的对策建议。

基于GM(1,1)模型的河南省物流需求组合预测

为了能够更加准确地预测河南省未来物流需求,文章首先采用灰色关联分析法对物流需求相关影响因素进行分析,进而确定文章所需要的指标,然后结合相关数据计算出多元线性回归方程,最后通过采用灰色预测和多元线性回归相结合的方式来预测未来5年河南省物流需求。结果表明,相较于单一的灰色预测方法,灰色预测和多元线性回归相结合的方式具有更高的精确度。除此之外,文章结合河南省物流发展状况提出了三点建议:完善物流服务体系、推动物流服务价值链延伸、提升应急物流水平。

无人机物流城市末端配送需求关键影响因素探索

无人机物流城市末端配送(DLULD)能有效解决传统末端配送存在的问题,更好地满足消费者需求。然而,DLULD需求受哪些因素影响,关键因素间的逻辑尚不清晰,直接限制了具体业务的推广与改进。通过文献研究构建需求影响因素指标体系,并首次将决策实验室-综合对抗解释结构模型引入DLULD研究,分析因素间关系与层级结构,找到提升DLULD服务的关键抓手,有助于物流业整体降本增效。结果表明:需求受到顶层、中间层和底层三层因素共同作用,并应重点关注服务可靠性、努力期望、绩效期望、交付风险和体积五个关键因素。据此提出,DLULD应从建立健全管理机制、把控运输风险,推动无人机技术持续发展、提高公众认知度,加强智能化水平建设、实现数字化管理等方面进行优化。

广东省水产预制菜冷链物流需求分析及预测

为预测广东省水产预制菜冷链物流的市场需求,从区域经济发展水平、市场供需水平、交通运输水平、冷链技术水平4个方面构建水产预制菜冷链物流需求评价指标,采用灰色关联分析法研究影响水产预制菜冷链物流需求变化的主要因素,分别采用灰色模型(grey model,GM)中的一阶一元微分方程GM(1,1)与长短时记忆(long short-term memory,LSTM)神经网络对比分析2015—2021年广东省水产预制菜冷链物流需求。结果表明:影响广东省水产预制菜冷链物流需求发展的主要影响因素为货物周转量和冷链冷藏水平;GM(1,1)、LSTM神经网络预测结果的平均相对误差分别为2.68%、0.22%,后者的预测准确度明显优于前者;采用LSTM神经网络预测2022—2024年广东省水产预制菜冷链物流需求,广东省水产预制菜需求呈上升趋势,预计2024年将达到509.09万t。广东省应立足冷链基础设施建设,确保贮藏、运输过程中水产预制菜温度稳定,加强水产预制菜食品监督,保证食品质量安全,不断促进水产预制菜冷链产业的发展。

基于BP神经网络的云南花卉物流需求预测

文章首先对云南花卉市场进行现状分析,在此基础上以近10年来云南花卉总产值为变量依据,来预测云南花卉的未来物流需求量情况。文章提出了可能会影响花卉物流需求量的8个因素,运用BP神经网络预测法并结合近10年的云南花卉总产值对未来3年的需求量进行预测。预测结果表明,未来几年,云南花卉市场对于物流的需求不降反增。而作为鲜活植物产品,花卉的运输又对冷链物流提出了更高的要求。因此,提高冷链物流的技术势在必行。

中国农户信贷需求及其影响因素分析——基于Logistic模型的实证研究

本文基于来自中国12个省(市)3051户农户家庭的实地调查数据,运用Logistic模型实证研究了农户信贷需求及其影响因素。研究结果表明,样本农户的年龄与信贷需求之间成倒"U"型关系;农户家庭生命周期、受教育程度会在一定程度上影响信贷需求;农户的家庭收入、家庭规模、家庭负担率以及是否购买养老保险对农户的信贷需求有显著的正向影响。另外,农户从事的经济活动类型也会对农户的信贷需求产生影响,以兼业农户为参照,纯农业农户对信贷需求有显著的正向影响,而非农业农户对信贷需求有显著的负向影响。