Method For Demand Forecasting Research Articles

A Case Study on Demand Forecasting Method by Voting Systems and Their Dissemination

A Case Study on Demand Forecasting Method by Voting Systems and Their Dissemination

Improving deep-learning methods for area-based traffic demand prediction via hierarchical reconciliation

Mobility services require accurate demand prediction in both space and time to effectively manage fleet rebalancing, provide quick on-demand responses, and enable advanced ride-sharing with minimum fleet size. Although the optimization of mobility services is a widely studied topic, the demand prediction side has received relatively less attention. In this paper, we aim to develop an efficient method for traffic demand forecasting by means of deep learning and hierarchical reconciliation approaches. The concepts, as well as the theories behind the proposed approach, are founded on Hierarchical Time Series (HTS), which also adopts Long Short-term Memory (LSTM) as a special kind of Recurrent Neural Network (RNN) to learn from the associated time series and produce reliable demand predictions. The proposed approach relies on the proper design of the HTS structure to get accurate forecasts for the number of trip departures and their associated uncertainty within predefined zones and aggregated collections of these zones. Moreover, the error analysis is essential for accomplishing the reconciliation in the HTS structure optimally. The proposed approach, consisting of deep learning, error analysis, and optimal reconciliation stages, has a remarkable ability to predict demand and control the forecasting quality at all levels of the hierarchical structure. We evaluate the proposed approach using a large-scale GPS tracking dataset from Lyon, France. The proposed method reduces the root mean square error (RMSE) by 13.92% and 14.77% for predefined and aggregated zones, respectively, compared with the LSTM using the historical demand and external features of time at the quarter-hour time step. Similarly, the corresponding improvements in mean absolute percentage error (MAPE) are 14.87% and 19.23%, respectively.

An applied study of a technique incorporating machine learning algorithms to optimize water demand prediction

Abstract In water resource management, accurate water demand prediction is essential for developing effective water supply strategies and optimizing resource allocation. This study aims to investigate machine learning algorithms, particularly echo state network (ESN) models, to improve the accuracy and efficiency of water demand prediction. ESN models are selected for their excellent nonlinear time series processing capabilities, which address the challenges of traditional prediction methods when dealing with complex water resource systems. By optimizing the parameters of the ESN model, the study hopes to provide a more scientific and efficient method for residential domestic and agricultural water demand forecasting, thus supporting more refined water resources planning and management decisions. Residential water demand prediction and crop water demand prediction are the two parts of this study. In the prediction of residential water demand, based on the actual data of City Z, the optimized ESN model predicts the water demand in 2025, and the total water demand in the baseline scenario is 790.9 million m3, and the expected values of water demand in different scenarios combined with the economic growth rate and the change of water price range from 659.4708 million m3 to 730.448 million m3. The article’s accuracy analysis of crop water demand prediction indicates that the model’s relative errors in predicting the water demand of the three major crops are limited to 10%. The ESN model optimized using the machine learning algorithm in this paper has good potential for water demand prediction and is an efficient and accurate prediction tool for managing water resources.

Forecasting daily tourism demand with multiple factors

Various factors have contributed to forecasting tourism demand. Although deep learning methods can achieve accurate results, they haven't considered the temporal heterogeneity of multiple factors and lack interpretability. This study proposes a novel deep learning method for daily tourism demand forecasting. Benefiting from the encoder-decoder architecture, our method adequately exploits the temporal heterogeneity of multiple factors. Based on the attentional mechanism, our method provides an interpretation of tourism demand from both factors and temporal persistence patterns. The effectiveness of our method is verified through an empirical study of two tourist attractions before and during COVID-19. Our method compensates for the uninterpretability of deep learning models, which allows tourism managers to obtain deeper insights.

Improved Giza pyramids construction algorithm for Modify the deep neural network-based method for energy demand forecasting

Accurate prediction of energy demand is crucial for improving services, reducing costs, and optimizing operations in energy systems. Deep neural networks (DNNs) have emerged as a popular method for energy demand forecasting. However, the performance of DNNs can be affected by data quality and hyperparameter selection. To address these concerns, this study proposes a novel energy demand forecasting technique that combines DNNs with an enhanced Giza pyramid construction methodology. The aim of this study is to provide a more reliable and effective approach for forecasting energy demand. The DNNs are employed to capture the complex relationships between input and output variables, while the Giza pyramids algorithm is utilized to optimal selection of hyperparameters of the network. Real-world energy demand data is used to evaluate the proposed approach, comparing it with state-of-the-art baseline models. The research methodology involves assessing the suggested approach using real-world energy demand information and conducting a comparative analysis with cutting-edge baseline models, including modified BP neural network (MBPNN), Neural Network based Genetic Algorithm (NNGA), and reinforcement learning and deep neural network (RLDNN). The IGPCA/CNN method outperforms other methods in energy prediction accuracy across short-term, medium-term, and long-term time scales. It achieves an MSE score of 0.564, lower than MBPNN, NNGA, and RLDNN. In medium-term prediction, it achieves an MSE score of 0.587, better than MBPNN, NNGA, and RLDNN. In long-term prediction, it achieves an MSE score of 0.629, lower than MBPNN and RLDNN. Further analysis and validation experiments are needed to ensure robustness and generalizability. Comparing the method with other state-of-the-art approaches can provide a comprehensive understanding of its superiority. The performance of the models is evaluated based on reliability and effectiveness in energy demand forecasting. The major conclusion of this study is that the proposed approach outperforms the initial models in accurately forecasting energy demand. The combination of DNNs and the improved Giza pyramid construction methodology results in enhanced performance, demonstrating superior reliability and effectiveness compared to other models. The study highlights the significance of accurate energy demand prediction for optimizing energy systems and reducing costs.

Extension of Fuzzy ELECTRE I for Evaluating Demand Forecasting Methods in Sustainable Manufacturing

The selection of a demand forecasting method is critical for companies aiming to avoid manufacturing overproduction or shortages in pursuit of sustainable development. Various qualitative and quantitative criteria with different weights must be considered during the evaluation of a forecasting method. The qualitative criteria and criteria weights are usually assessed in linguistic terms. Aggregating these various criteria and linguistic weights for evaluating and selecting demand forecasting methods in sustainable manufacturing is a major challenge. This paper proposes an extension of fuzzy elimination and choice translating reality (ELECTRE) I to resolve this problem. In the proposed method, fuzzy weighted ratings are defuzzified with the signed distance to develop a crisp ELECTRE I model. Moreover, an extension to ELECTRE I is developed by suggesting an extended modified discordance matrix and a closeness coefficient for ranking alternatives. The proposed extension can overcome the problem of information loss, which can lead to incorrect ranking results when using the Hadamard product to combine concordance and modified discordance matrices. A comparison is conducted to show the advantage of the proposed extension. Finally, a numerical example is used to demonstrate the feasibility of the proposed method. Furthermore, a numerical comparison is made to display the advantage of the proposed method.

"Efficiency at Every Slice: Domino\'s Inventory Management System”

Abstract: For firms functioning in the competitive and quickly-paced food market, effective inventory management is essential. This abstract outline the salient features of Domino's inventory management system, demonstrating its strategy for increasing operational effectiveness, lowering waste, and optimizing inventory levels. Domino's inventory management method is based on precise forecasting, ongoing observation, and efficient logistics. Domino's makes sure that its franchisees maintain ideal supply levels while minimizing excess inventory and stockouts by utilizing cuttingedge technologies and data-driven decision-making. The key elements of Domino's inventory management system are examined in the abstract, including methods for demand forecasting, inventory control, and supplier cooperation. It explains the company's focus on utilizing technology, such as integrated ordering systems and inventory management software, to increase visibility, automate procedures, and improve inventory accuracy. The summary also highlights Domino's initiatives to cut waste through efficient supply chain procedures, optimized routing and delivery, and smart perishable inventory management. It highlights the business' dedication to environmental protection and sustainability.

Overview of Logistics Demand Forecasting Methods

Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.

Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.

Calculation model and application of ecological water demand of watercourses in low-carbon sponge urban areas based on DPSA algorithm

Abstract To improve the prediction ability of river ecological water requirement in sponge low-carbon urban areas, a prediction model of it based on big data analysis was proposed. A statistical analysis model of river ecological water requirement in sponge and low-carbon urban areas was established. Differential Particle Swarm Optimization Algorithm (DPSA) was used to calculate the characteristic quantity of river ecological water requirement in sponge and low-carbon urban areas. The detection model of ecosystem hydrothermal balance and lognormal distribution time series of groundwater level were constructed for the prediction of river ecological water requirement in sponge and low-carbon urban areas. Combined with the integrated control method of river ecological environment water demand forecasting and information exchange, a decentralized control model of river ecological water demand in low-carbon sponge urban areas was established. Through applicability analysis and model measurement analysis, combined with comprehensive index measurement, the prediction model of river ecological water requirement in low-carbon sponge urban areas was optimized. The empirical analysis results show that this method has good adaptability and high prediction accuracy for the prediction of river ecological water requirement in sponge low-carbon urban areas and improves the reliability of the prediction of river ecological water requirement and the matching level of urban ecological environment.

Review of Flight Demand Forecasting Methods

Review of Flight Demand Forecasting Methods

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period

During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.

A Human Resource Demand Forecasting Method Based on Improved BP Algorithm.

Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise's human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise's human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company's business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise's total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning.

Tourism demand forecasting with spatiotemporal features

Tourism demand forecasting is a crucial prerequisite for effective and efficient tourism management. This study develops a novel model based on deep learning methods for precise demand forecasting, namely, spatial-temporal fused graph convolutional network (ST-FGCN). ST-FGCN generates forecasts based on spatial effects extracted using graph convolutional network and temporal dependency captured through long short-term memory. A data-driven spatial matrix is used in our model to strengthen forecasting performance further. Two markedly different forecasting experiments verify the effectiveness of our model. Empirical results suggest that incorporating spatial effects can remarkably reduce forecasting errors. Furthermore, our model shows good applicability for data with different time granularity and different periods: before and during the COVID-19 pandemic.

Systematic Review of Deep Learning and Machine Learning for Building Energy

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANN-based techniques provided a medium robustness score.

Review and analysis of artificial intelligence methods for demand forecasting in supply chain management

The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.

Supply Chain Inventory Model with Markov Chain Demand

This study aims to develop a new one-vendor multiple-buyers integrated inventory model. We believe that our proposed model can forecast the demand of all buyers in the coming future by using data that have already existed and to minimize the total cost-for both buyers and vendors. In recent days, the Markov chain approach has played one of the critical methods of demand forecasting in the Supply Chain Management (SCM) field. The proposed model of this study is to analysis the demand of all buyers in one specific season that was impacted by the demand in the same season from last year. Finally, the results of this article discover the most optimal number of buyers and shipments, and the quantity of demand per period. Furthermore, a sensitivity analysis is also conducted to find out the sensitivity of the new model.

Forecasting the Cargo Throughput for Small and Medium-sized Ports: Multi-stage Approach with Reference to the Multi-port System

Purpose: The paper aims to elaborate on long-term throughput forecasts in Small and Medium-Sized ports (SMPs). The research problem relates to determining a method that is relevant for the long-term transhipment forecasting in SMPs. Design/Methodology/Approach: Research was applied to the Polish ports system consisting of three major ports (Gdansk, Gdynia, and Świnoujście) and the minor port of Szczecin. Forecasts for the cargo groups in the major ports were produced using regression models where the model parameters were estimated with the Ordinary Least Squares Method. The obtained forecasts of the throughput of cargo groups in major Polish seaports were converted into dynamics indices. The resulting matrices of the cargo throughput dynamics indices established for the leading Polish seaports were used to prepare cargo throughput forecasts for the port of Szczecin. Findings: Elaborated throughput forecasts indicate that, in the future, the port in Szczecin will retain its universal character, and a moderate increase in the cargo volumes confirms that it will serve as a complementary port to the major ports in the range. The forecasted modal split of hinterland transport indicates the increasing importance of road transport, and a decreased rail and inland waterway transport to/from the hinterland. That is likely due to a lower cargo throughput and difficulties in organizing the rail and inland waterway corridors based on smaller freight volumes. Practical implications: The study provides a practical tool for long-term throughput and hinterland traffic forecasting in SMPs. It is addressed to port authorities as the method may be used in decision-making on capacity expansion and academics exploring the phenomenon of predictions in maritime transport. Originality/Values: The novel method of demand forecasting in SMPs includes relations between major and minor ports in the range and multi-staged validation of results. Research advances studies on the dynamics of multi-port systems.

A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.