scholarly journals Product Demand Forecasting and Dynamic Pricing considering Consumers’ Mental Accounting and Peak-End Reference Effects

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wenjie Bi ◽  
Mengqi Liu
Keyword(s):  
Steady State ◽  
Dynamic Pricing ◽  
Demand Forecasting ◽  
Mental Accounting ◽  
Product Demand ◽  
Proposed Model ◽  
Payment Schemes ◽  
Maximal Profit ◽  
Demand Forecasting Model ◽  
Double Entry

We introduce a demand forecasting model for a monopolistic company selling products to consumers with double-entry mental accounting, which means consumers experience pleasure when consuming goods or service and feel pains when paying for them. Moreover, as the monopolist changes prices, consumers form a reference price that adjusts an anchoring standard based on the lowest price that they perceived, namely, the peak-end anchoring. We obtain the steady state prices under three different payment schemes for two- and infinite-period. We also analyze the relationship between these steady prices and maximal profit and compare the steady state prices of different payment schemes by changing the double-entry mental accounting’s parameters through numerical examples. The proposed model is computationally tractable for demand forecasting of realistic size.

A Combined Power Demand Forecasting Model with Variable Weight

2013 ◽  
Vol 392 ◽  
pp. 618-621
Author(s):  
Zhi Gang Wang ◽  
Qing Jie Zhou ◽  
Xing Hua Zhou
Keyword(s):  
Demand Forecasting ◽  
Development Trend ◽  
Forecasting Model ◽  
Power Demand ◽  
Proposed Model ◽  
Variable Weight ◽  
The Difference ◽  
Demand Forecasting Model ◽  
Analysis Models ◽  
The Impact

A combined power demand forecasting model with variable weight considering both of the impact of the macroeconomic situation and the internal development trend is proposed. The proposed model consists of regression analysis models and the trend extrapolation models. The variable weight is determined by the difference of the prediction results between the two kinds of models . Beijing's power demand forecasting illustrates the usefulness and reliability of the combined model.

scholarly journals Shipbuilding capacity optimization using shipbuilding demand forecasting model

2021 ◽  
Author(s):  
Yujiro Wada ◽  
Kunihiro Hamada ◽  
Noritaka Hirata
Keyword(s):  
Supply And Demand ◽  
Demand Forecasting ◽  
Sustainable Growth ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Shipbuilding Industry ◽  
Complex Issue ◽  
Proposed Model ◽  
Demand Fluctuations ◽  
Demand Forecasting Model

AbstractThe shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

scholarly journals Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2480
Author(s):  
Xiaofei Ye ◽  
Qiming Ye ◽  
Xingchen Yan ◽  
Tao Wang ◽  
Jun Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Pricing ◽  
Demand Forecasting ◽  
Short Term ◽  
Real Time Scheduling ◽  
Attention Model ◽  
Time Scheduling ◽  
Temporal And Spatial ◽  
Demand Forecasting Model ◽  
Highly Correlated

The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

Modelling And Simulation of Topical Drug Diffusion in The Dermal Layer of Human Body

2021 ◽  
Vol 86 (2) ◽  
pp. 39-49
Author(s):  
Sudi Mungkasi
Keyword(s):  
Steady State ◽  
Human Body ◽  
Diffusion Problem ◽  
State Condition ◽  
Unsteady State ◽  
Steady State Condition ◽  
Drug Diffusion ◽  
Dermal Layer ◽  
Drug Concentrations ◽  
Proposed Model

We consider the problem of drug diffusion in the dermal layer of human body. Two existing mathematical models of the drug diffusion problem are recalled. We obtain that the existing models lead to inconsistent equations for the steady state condition. We also obtain that solutions to the existing models are unrealistic for some cases of the unsteady state condition, because negative drug concentrations occur due to the inappropriate assumption of the model. Therefore, in this paper, we propose a modified mathematical model, so that the model is consistent, and the solution is nonnegative for both steady and unsteady state conditions of the drug diffusion problem in the dermal layer of human body. For the steady state condition, the exact solution to the proposed model is given. For unsteady state condition, we use a finite difference method for solving the models numerically, where the discretisation is centred in space and forward in time. Simulation results confirm that our proposed model and method preserve the non-negativity of the solution to the problem, so the solution is more realistic than that of the old model.

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