Method For Demand Forecasting Research Articles

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Energy Demand Estimation in Turkey According to Road and Rail Transportation: Walrus Optimizer and White Shark Optimizer Algorithm-Based Model Development and Application

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey’s energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements.

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

Comparative Analysis of Demand Forecasting Methods to Optimize Supply Chain Efficiency in PharmaHealth Group

Comparative Analysis of Demand Forecasting Methods to Optimize Supply Chain Efficiency in PharmaHealth Group

Development of the Demand Forecasting and Product Recommendation Method to Support the Small and Medium Distribution Companies based on the Product Recategorization

Development of the Demand Forecasting and Product Recommendation Method to Support the Small and Medium Distribution Companies based on the Product Recategorization

Implementasi Metode EOQ dan ROP dalam Manajemen Persediaan Bahan Shoe Piston dengan Dukungan Teknologi RFID: Studi Kasus pada PT XYZ

PT XYZ is a manufacturing company in the automotive sector, with six production departments producing different types of spare parts. This research aims to address the stock shortages experienced by PT XYZ to prevent recurrence in the future. The methods used include linear regression for demand forecasting, EOQ and ROP methods for inventory management and determining reorder timing. The research findings indicate that linear regression is suitable for demand forecasting as it shows an increasing trend, indicating a positive relationship between time and demand. The combination of EOQ and ROP can save costs up to Rp 23,651,327,- from the initial costs as per company policy, and reduce ordering frequency to twice a year. The recommended safety stock for materials is 14 coils per month, with reorder required when stock reaches 140 coils. This research is expected to encourage the implementation of linear regression, EOQ, and ROP methods in the company, and propose the use of RFID technology for inventory monitoring in both raw material and finished goods warehouses

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.

Solving the forecasting demand for specialists problem using fuzzy logic methods

The article deals with the actual problem of one of the tasks of realization of personnel provision of a large industrial enterprise. At the beginning of the article the comparative analysis of methods of forecasting of dynamics of demand for specialists of the enterprise is given. On the basis of this analysis the possible approach to the solution of the problem of forecasting of personnel needs of an industrial enterprise is suggested. A possible formulation of the problem of forecasting the demand for specialists is given, the proposed formulation is based on the long-term experience of the authors of the article in educational institutions in Russia, as well as on the analysis of the data obtained when the authors communicate with employees of industrial enterprises. The urgency of solving the proposed problem is substantiated and the necessity of using fuzzy logic methods is argued. The method of demand forecasting is offered, the advantages of which are simplicity of use, sufficient efficiency in the presence of a sufficient number of statistics and convenience of realization in the form of a software product. The paper concludes with a brief assessment of the effectiveness and usability of the proposed method.

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.