Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Pakistan’s Energy Demand Forecasting for Various Sector through Long Range Alternative Planning System

Energy forecasting and policy development needs a detailed evaluation of energy assets and long-term demand estimation. The demand forecast of electricity is an essential portion of energy management, particularly in the formation of electricity. It is necessary to predict electricity needs to avoid the energy deficits or a destabilization between energy demand and supply. In this article, long-range energy alternative planning (LEAP) is used for the modeling of energy and various sectors in Pakistan as a case study. The simulated model comprises three different scenarios, a strong economy, a weak economy, and a medium economy as a reference scenario. The base year is 2015 and the outlook year is 2040. Electricity demands are almost more than four times those of the outlook year, increasing from 7.71 million tons of oil equivalent (MTOE) in 2015 to 29.77 MTOE by the end of 2040.

Design of risk sharing and coordination mechanism in supply chain under demand and supply uncertainty

Perishable and short-life products can be seen everywhere in life. Due to the particularity of these products, they are more complicated in supply chain management. This paper studies whether the two-part tariff and ZRS contract can achieve the purpose of reducing risks and coordinating supply chain. We assume that market demand and supplier yield are uncertain, and we use game theory and probability distribution for research. The research results show that when the information is asymmetric, the manufacturer always ignore the demand forecast information provided by the retailer under the wholesale price contract. When the demand is uncertain, regardless of whether the information is symmetric or asymmetric, the two-part tariff contract and the ZRS contract can coordinate the supply chain and achieve maximum profit. When the retailer's degree of risk aversion is high, the ZRS contract is better than the two-part tariff, which can reduce the risk of retailers and achieve the purpose of coordinating the supply chain. When the supply is uncertain, the manufacturer can provide the supplier with a risk-sharing contract, including the return price and the sharing ratio that meet certain constraints. Such a contract can effectively reduce the supplier's risk and realize supply chain coordination.

Forecasting public bicycle rental demand using an optimized eXtreme Gradient Boosting model

The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011–2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles.

Optimal Green Operation and Information Leakage Decisions under Government Subsidy and Supply Uncertainty

This study investigated optimal green operation and information leakage decisions in a green supply chain system. The system consists of one supplier, one leader retailer 1, one follower retailer 2, and the government. In this system, the government subsidizes each retailer based on the selling price of the product. The supplier is subject to a yield uncertainty process. The suppler decides whether to leak leader retailer 1′s order quantity to follower retailer 2 or not. In this study, we first built a Stackelberg game to address the equilibrium green operation decisions, when the supplier has and has not information leakage behavior, respectively. Subsequently, we identify the supplier’s information leakage equilibrium and how such behavior affects retailers’ ex ante profits, consumer surplus, and social welfare through a numerical study. Interestingly, we obtained the following results: (1) Supplier leaks are the unique equilibrium of the supplier. The product’s green degree and wholesale price at supplier’s equilibrium are higher under information leakage than under no information leakage. (2) The supplier’s information leakage behavior is good for leader retailer 1 and bad for follower retailer 2. (3) Information leakage behavior increases both consumer surplus and social welfare under certain conditions. (4) In general, key system parameters (e.g., the subsidy rate, supply uncertainty, supply correlation, and forecast accuracies) positively correlate with consumer surplus and social welfare in the same direction, while they affect retailer 1′s and retailer 2′s ex ante profit in the opposite direction. These findings provide useful insights for businesses to manage demand forecast information and make decisions on the green level of the product in green supply chain management.

Proposed model for predicting economic growth based on the Keynesian aggregate demand equation: Case of Ecuador 1965-2018

It is important for researchers and economic policy makers to forecast GDP but sometimes it is complicated or expensive to access the information of the five components of the equation, so this research proposed to validate a model as parsimonious as possible that would make reliable predictions of GDP. Through an iterative process they were estimated and validated, using multiple linear regression and based on the Expenditure Method equation, equations to whichnon-significant and / or less explanatory regressors were eliminated seeking maximum parsimony, to then prove the predictive power of valid equations. As a result, a statistically valid estimator with high predictive power was found, but it includes the five regressors of the original equation. Keywords: GDP, aggregate demand, forecast, parsimony. References [1]E. López Fernández de Lascoiti, «CRACK DE 1929: Causas, desarrollo y consecuencias.,» Revista Internacional del Mundo Económico y del Derecho, vol. I, pp. 1 - 16, 2009. [2]J. Montano, «Gran Depresión: Causas, Características y Consecuencias,» 2019. [Online]. Available: https://www.lifeder.com/gran-depresion/. [3]M. Rapoport, «La crisis de 1929, la teoría económica y el New Deal,» 2008. [Online]. Available: https://www.pagina12.com.ar/diario/economia/subnotas/111712-35315-2008-09-17.html. [4]J. Ros, «La Teoría General de Keynes y la macroeconomía moderna,» 2012. [Online]. Available: https://www.redalyc.org/pdf/601/60123307002.pdf. [5]M. Kiziryan, «Demanda agregada,» 2019. [Online]. Available: https://economipedia.com/definiciones/demanda-agregada.html. [6]G. Mankiw, Macroeconomía, 6ta. Ed., España: Antoni Bosch, editor, S.A., 2006. [7]L. Gastón Lorente, «Cómo calcular el PIB: Tres métodos,» 2019. [Online]. Available: https://www.bbva.com/es/bbva-patrocina-el-almuerzo-inaugural-de-la-cumbre-del-clima-disenado-por-los-hermanos-roca/. [8]R. Dornbusch, S. Fischer and R. Startz, Macroeconomía, 10ma. Ed., México D. F.: McGraw-Hill/Interamericana Editores, S.A. de C.V., 2009. [9]A. B. Abel and B. S. Bernanke, Macroeconomía, 4ta. Ed., Madrid: Pearson Educación S.A., 2004. [10]S. Jahan, A. Saber Mahmud and C. Papageorgiou, «¿Qué es la economía keynesiana?,» 09 2014. [Online]. Available: https://www.imf.org/external/pubs/ft/fandd/spa/2014/09/pdf/basics.pdf. [11]D. A. Lind, W. G. Marchal and S. A. Wathen, Estadística aplicada a los negocios y la economía. 15ta. Ed., México D.F.: McGraw Hill/Interamericana Editores S.A. de C.V., 2012. [12]R. S. Pindick and D. L. Rubinfeld, Econometría: Modelos y pronósticos, 4ta. Ed., México D.F.: McGraw-Hill Interamericana, 2001. [13]D. R. Anderson, D. J. Sweeney and T. A. Williams, Estadística para administración y economía. 10ma. Ed., México D.F.: Cengage Learning Editores, S.A., 2008. [14]Banco Central del Ecuador, «Información Económica-Estadísticas del sector real,» 2020. [Online]. Available: https://contenido.bce.fin.ec/documentos/Administracion/CuentasNacionalesAnuales.html. [15]D. N. Gujarati and D. C. Porter, Econometría. 5ta. Ed., México, D. F.: McGraw Hill Educación, 2010. [16]E. Court and E. Williams, Estadísticas y econometría financiera, 1ra. Ed., Buenos Aires: Cengage Learning Argentina, 2011. [17]R. Montero Granados, «Modelos de regresión lineal múltiple,» Documentos de Trabajo en Economía Aplicada. Universidad de Granada. España, 2016. [18]R. A. Fernández Montt, «Regresión lineal. Multicolinealidad perfecta,» 2006. [Online]. Available: http://www.eumed.net/cursecon/medir/rfm-multico.htm. [19]C. . H. Achen, Interpreting and Using Regression, Beverly Hills: Sage, 1982. [20]J. M. Wooldridge, Introducción a la econometría. Un enfoque moderno. 4ta. Ed., México, D. F.: Cengage Learning, 2010. [21]R. Geary, «Some Results about Relations Between Stochastic Variables: A Discussion Document,» Review of International Statistical Institute, vol. 31, pp. 163-181, 1963.