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The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the Republic of Korea Army’s third generation main battle tanks. Data about the spare part consumption of 1,053,422 transactional data points stored in a military logistics management system were obtained. Demand forecasting classification models were developed to exploit machine learning, stacked generalization, and time series as baseline methods. Additionally, various stacked generalizations were established in spare part demand forecasting. The results demonstrated that a suitable selection of methods could help enhance the performance of the forecasting models in this domain.

Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

Since accurate demand forecasts are a key input to any airline revenue management system, it is reasonable to assume that an improvement in demand forecast accuracy would lead to increased revenues. However, this relationship has often been called into question. Past work has not conclusively proven that more accurate demand forecasts lead to higher revenue, causing researchers and practitioners to debate whether the concept of demand forecast accuracy itself is “myth or reality.” In this paper, we demonstrate that it is possible to consistently link demand forecast accuracy to airline revenue. After discussing why traditional demand forecast error metrics have struggled to demonstrate this relationship, we evaluate a novel conditional demand forecast error metric which compares demand forecasts to historical bookings conditional on the set of fare classes that were open at the time of booking. We prove under some mild assumptions that minimizing conditional demand forecast error will maximize revenue under any fare structure and customer choice behavior. These theoretical findings are supported by simulations in both a simple, single-leg model and in a complex multiple-airline network in the Passenger Origin–Destination Simulator. We find that price elasticity parameter bias of ± 10% can reduce revenues by up to about 1%, while price elasticity parameter bias of ± 20% can reduce revenues by up to 4%. We close by discussing the implications of the findings for revenue management practitioners.

Platinum and palladium are used in small but essential quantities in a variety of advanced industrial sectors. Platinum and palladium are used as catalysts in various industrial sectors, especially in the car industry. However, their sources are typically concentrated in South Africa and Russia, and there are concerns about supply security. In terms of resource security, it is important to verify domestic platinum and palladium consumption trends and future demand. In order to understand the domestic platinum and palladium consumption trends in Japan, we tracked the historical platinum and palladium consumption structures from 2001 to 2013, applying a bottom-up approach, and illustrated recent domestic platinum and palladium flow by using a substance flow analysis. The results showed that catalytic converters (9.1–12.8 t) and jewelry (5.3–15.5 t) for platinum, and catalytic converters (14.2–20.0 t) and dental use (9.5–16.4 t) for palladium, have marked the biggest consumption sectors during 2001–2013, where the total consumption of platinum and palladium have fluctuated by 18.4–31.6 t for platinum and from 33.0–46.3 t for palladium. We also forecasted the demand for each end-use of both up to the year 2025 using multiple regression analysis. Our results suggest that platinum demand could decrease from 18.9 t in 2013 to 11.9 t in 2025 and palladium demand could slightly decrease from 33.0 t in 2013 to 13.8 t in 2025.

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.

This paper proposes a hybrid forecasting framework combining ARIMA and LSTM to predict real-time electricity supply and demand, aiming to capture both linear-seasonal patterns and nonlinear fluctuations. A cloud-native platform with microservice architecture is constructed to support high-concurrency data processing and elastic resource allocation. Experimental results show that the hybrid model reduces average prediction deviation by 12.5% compared to traditional methods, with 92.3% accuracy. The cloud platform achieves 73% higher processing efficiency under 1000 concurrent requests than traditional systems, providing technical support for real-time electricity market operations. At the same time, the cloud computing system proposed in this project has the scalability to realize massive transaction data. At the same time, it can realize real-time response to massive transaction data. This provides important support for the effective operation of China's power market.

In this paper we consider a dynamic forecast-inventory model with forecast updates, based on the martingale model of forecast evolution. Two types of updates are considered, additive and multiplicative. The formulation of the model results in a dynamic program with multidimensional state space. We derive some characteristics of optimal policies and also develop a computational approach to obtain approximate solutions. The approach is based on simulation and function approximation.

In today’s environment, organizations utilise Machine Learning based-models to keep stocks depending on the demand for a particular type of product. This article develops an inventory model considering the imperfect deteriorating product in a fuzzy environment. The shortages are allowed and partially backlogged. Since the deterioration rate and defective percentage in quantity in the received lot may not be predicted precisely because it depends on many uncertain situations, therefore both are considered fuzzy variables. This study aims to determine the optimal ordering quantity and replenishment period to optimize (minimize) the average overall cost with carbon emission cost. The defuzzification process is done using the sign distance approach method. A methodology based on Machine Learning is used to demand forecast seasonally. Some numerical examples are taken to validate the proposed mathematical model. The findings demonstrate the generation of direct month-wise predicted demand for deteriorating products based on the input of the month value, enabling organizations to optimize their inventory management according to forecasted demand. A comparative analysis is conducted between fixed and month-wise forecasted demand by highlighting the advantages of machine learning-based forecasting approaches. Sensitivity analysis performs to examine the behaviour of several parameters on an optimal solution and provides some managerial insights.

Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions.

Urban online ride-hailing demand forecasting is an important component of smart city transportation systems. An accurate online ride-hailing demand prediction model can help cities allocate online ride-hailing resources reasonably, reduce energy waste, and reduce traffic congestion. With the massive popularity of online ride-hailing, we can collect a large amount of order data, and how to use deep learning models for improving order prediction accuracy has become a hot research topic. Most of the urban online taxi demand forecasting methods do not sufficiently consider the influencing factors and cannot model the complex nonlinear spatio-temporal relationships. Therefore, we propose a multi-view deep spatio-temporal network framework (MVDSTN) architecture to obtain the spatio-temporal relationships for online ride-hailing demand prediction. Our proposed model includes five views,up-passenger order view, down-passenger order view, POI view, spatial GCN view, POI view and weather view, applies LSTM with attention mechanism to achieve demand prediction for urban online taxi bodies. Experiments Haikou Didi Taxi datasets and Wuhan Taxi datasets prove that our model has good robustness and the prediction method outperforms current methods.