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Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management. [ABSTRACT FROM AUTHOR]

Background: In the Australian maternity system, general practitioners play a vital role in advising and directing prospective parents to maternity models of care. Optimising model of care discussions and the decision-making process avoids misaligning women with over or under specialised care, reduces the potential for disruptive care transitions and unnecessary healthcare costs, and is critical in ensuring consumer satisfaction. Current literature overwhelmingly focusses on women's decision-making around model of care discussions and neglects the gatekeeping role of the General Practitioner (GP). This study aimed to explore and describe the factors influencing Australian GPs decision-making when referring pregnant women to maternity models of care. Methods: This study used a qualitative descriptive approach. General practitioners (N = 12) with experience referring women to maternity models of care in Australia participated in a semi-structured interview. Interviews occurred between October and November 2021 by telephone or videoconference. Reflexive thematic analysis was facilitated by NVivo-12 data management software to codify and interpret themes from the data. Findings: Two broad themes were interpreted from the data. The first theme entitled 'GP Factors', incorporated three associated sub-themes including '1) GPs Previous Model of Care Experience', '2) Gaps in GP Knowledge' and '3) GP Perception of Models of Care'. The second theme, entitled 'Woman's Factors', encapsulated two associated sub-themes including the '4) Woman's Preferences' and '5) Access to Models'. Conclusions: This study provides novel evidence regarding general practitioner perspectives of the factors influencing model of care decision-making and referral. Predominant findings suggest that gaps in GP knowledge regarding the available models of care are present and are largely informed by prior personal and professional experience. Most GPs described referring to models of care they perceive positively and centring their model of care discussions on the woman's preferences and accessibility. The exploration and description of factors influencing model of care decisions provide unique insight into the ways that all stakeholders can experience access to a broader range of models of care including midwifery-led continuity of care models aligned with consumer-demand. In addition, the role of national primary health networks is outlined as a means to achieving this. [ABSTRACT FROM AUTHOR]

Basketball involves frequent high-intensity movements requiring optimal aerobic power. Altitude training can enhance physiological adaptations, but research examining its effects in basketball is limited. This study aimed to characterize the internal/external workload of professional basketball players during preseason and evaluate the effects of altitude and playing position. Twelve top-tier professional male basketball players (Liga Endesa, ACB; guards: n = 3, forwards: n = 5, and centers: n = 4) participated in a crossover study design composed of two training camps with nine sessions over 6 days under two different conditions: high altitude (2320 m) and sea level (10 m). Internal loads (heart rate, %HRMAX) and external loads (total distances covered across speed thresholds, accelerations/decelerations, impacts, and jumps) were quantified via wearable tracking and heart rate telemetry. Repeated-measures MANOVA tested the altitude x playing position effects. Altitude increased the total distance (+10%), lower-speed running distances (+10–39%), accelerations/decelerations (+25–30%), average heart rate (+6%), time in higher-intensity HR zones (+23–63%), and jumps (+13%) across all positions (p < 0.05). Positional differences existed, with guards accruing more high-speed running and centers exhibiting greater cardiovascular demands (p < 0.05). In conclusion, a 6-day altitude block effectively overloads training, providing a stimulus to enhance fitness capacities when structured appropriately. Monitoring workloads and individualizing training by playing position are important when implementing altitude training, given the varied responses. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

This study examines the effects of the rising live streaming e-commerce on the 3DP supply chain, employing system dynamics to develop separate models for pure polymer and polymer-metal mixed printing. The analysis focuses on optimizing the 3DP supply chain configuration. Results indicate that, based solely on printing time, cost, and quality metrics, Corporate-live-3DP services are optimal for live commerce scenarios. However, despite this, Private-live-3DP maintains a substantial consumer base in practice, as evidenced by literature data and case studies. Both models pose significant challenges to conventional supply chains, necessitating adaptation. For Corporate-live-3DP, optimization strategies may include technology advancements, digital transformation, agile manufacturing, global network optimization, innovative management, collaborative R&D, fine-tuned inventory control, quality system upgrades, talent development, and organizational restructuring. Conversely, Private-live-3DP can be optimized through consolidation of private 3D printing resources, demand prediction and order optimization, supply chain collaboration platforms, quality management extensions, inventory strategy adjustments, increased transparency, regulatory compliance, and risk mitigation measures. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy. [ABSTRACT FROM AUTHOR]

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development. [ABSTRACT FROM AUTHOR]

It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy's 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability. [ABSTRACT FROM AUTHOR]

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window. [ABSTRACT FROM AUTHOR]

The current situation of the Romanian economy must be understood beginning from the analysis of the main measures of fiscal-budgetary policy applied over the last years by the public authority. In general, the Romanian fiscal policy (before and after accession) was procyclical. However, we continue by presenting some of its characteristics for the past years when we underwent the last ascending phase of the economic cycle. Actually, Romania's GDP exceeded constantly the potential level, and the demand surplus became predominant, generating inflationary pressures. Maintaining the expansionist level of the fiscal policy, in the conditions of a positive deviation of GDP, as of 2017, and opting-out regarding the structural deficit target contributed to affecting the stability of public finances, on short-and medium-term. Romania entered into an extremely difficult economic context, generated by the pandemic, with an extremely narrow fiscal space which limited a lot the possibilities of combating the effects of the pandemic. In this paper we analyzed a period limited to the year 2020, because we consider this time as marking the end of an economic cycle in a period of peace and economic calm, as another is about to begin based on the new realities. [ABSTRACT FROM AUTHOR]

In order to achieve the hydrogen economy and respond to initial hydrogen demand appropriately, a hydrogen production and operation methodology is required to secure the economic feasibility of long-term on-site HRS. This study proposes a novel investment strategy for on-site hydrogen production to meet future hydrogen demand. The optimal investment strategy based on the dual-modal mode of combined autothermal reforming (ATR) and steam methane reforming (SMR) is proposed for hydrogen production using natural gas (NG) as a raw material. To predict hydrogen demand from 2020 to 2030, the machine learning (ML) technique was adopted. R 2 and MSE as result using ML were 0.9936 and 6.88 × 10 − 5 , respectively. In addition, the ATR-SMR hydrogen strategy (ASHS) process was analyzed and compared with the SMR-SMR and ATR-ATR hydrogen strategy (SSHS and AAHS) processes in terms of optimal operation rate, storage tank management, economics, and environmental impacts. The operation rate of proposed hydrogen production processes was determined by the hydrogen demand and storage tank level, and the optimal investment plan to install additional hydrogen process depends on the total amount of hydrogen production. In this study, these results were observed due to the effective combination of the strengths of ATR and SMR. Consequently, the ASHS had the best cost-effectiveness (LCOH at $5.63/kg H2) and environmental friendliness (unit CO2eq emissions at 10.21 kg CO2eq/kg H2 and 1.73 kg CO2eq/kg H2 with CCS). This study includes sensitivity analysis and a comparison of CO2 taxes by the country for three proposed hydrogen production processes. It could contribute to the optimal operation of the on-site hydrogen production system in preparation for future hydrogen demand. [ABSTRACT FROM AUTHOR]

Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and machine learning techniques for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariable approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach appears to be sufficient. We also comment on the approach to choose predictors for the multivariable models. [ABSTRACT FROM AUTHOR]

Background: Defining healthcare facility catchment areas is a key step in predicting future healthcare demand in epidemic settings. Forecasts of hospitalisations can be informed by leading indicators measured at the community level. However, this relies on the definition of so-called catchment areas or the geographies whose populations make up the patients admitted to a given hospital, which are often not well-defined. Little work has been done to quantify the impact of hospital catchment area definitions on healthcare demand forecasting. Methods: We made forecasts of local-level hospital admissions using a scaled convolution of local cases (as defined by the hospital catchment area) and delay distribution. Hospital catchment area definitions were derived from either simple heuristics (in which people are admitted to their nearest hospital or any nearby hospital) or historical admissions data (all emergency or elective admissions in 2019, or COVID-19 admissions), plus a marginal baseline definition based on the distribution of all hospital admissions. We evaluated predictive performance using each hospital catchment area definition using the weighted interval score and considered how this changed by the length of the predictive horizon, the date on which the forecast was made, and by location. We also considered the change, if any, in the relative performance of each definition in retrospective vs. real-time settings, or at different spatial scales. Results: The choice of hospital catchment area definition affected the accuracy of hospital admission forecasts. The definition based on COVID-19 admissions data resulted in the most accurate forecasts at both a 7- and 14-day horizon and was one of the top two best-performing definitions across forecast dates and locations. The "nearby" heuristic also performed well, but less consistently than the COVID-19 data definition. The marginal distribution baseline, which did not include any spatial information, was the lowest-ranked definition. The relative performance of the definitions was larger when using case forecasts compared to future observed cases. All results were consistent across spatial scales of the catchment area definitions. Conclusions: Using catchment area definitions derived from context-specific data can improve local-level hospital admission forecasts. Where context-specific data is not available, using catchment areas defined by carefully chosen heuristics is a sufficiently good substitute. There is clear value in understanding what drives local admissions patterns, and further research is needed to understand the impact of different catchment area definitions on forecast performance where case trends are more heterogeneous. [ABSTRACT FROM AUTHOR]

Background: Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. Objective: This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. Methods: A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. Results: Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. Conclusion: The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies. [ABSTRACT FROM AUTHOR]

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model's performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape. [ABSTRACT FROM AUTHOR]

Risk analysis (including resilience analysis) of infrastructure requires models that describe the connection of components and subsequent flow dynamics. However, the detailed information needed to define these models may not be available, especially for small‐scale infrastructure that connect to every building. In this paper, we generate location‐specific small‐scale networks using detailed data that should always be available. We propose a general framework where we generate the network topology, we estimate the resource demand at each building, and we design the network components to meet the demands. This general framework is applicable to all types of infrastructure, but many procedures are specific to the type of network being generated. This paper develops the necessary procedures to generate sewer networks and illustrates the usage for an example network in a small study area in Seaside, Oregon. The proposed sewer network generator produces realistic sewer networks as compared to the real network of Seaside. [ABSTRACT FROM AUTHOR]

As an emerging force in the travel industry, online car-hailing (OCH) has been relying on digital and intelligent technologies for business innovation since its birth. Three hybrid deep learning models combining multi-factor external and internal features are proposed to predict the OCH demand. Convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM) are selected to extract features. Attention mechanisms are used to combine the global parts so that the importance of feature sequences at different times can be distinguished. The effectiveness of the proposed models considering all factors is proved by comparative experiments. Then, ablation experiments are performed to analyze the effects of attention module, external and internal factors. The results showed that the hybrid models performed better than the existing models under different factors. Various factors had different impacts on the departure and arrival flows and the hybrid models. [ABSTRACT FROM AUTHOR]

Kansei Engineering (KE) is a product design method that aims to develop products to meet users' emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user's multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user's multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. [ABSTRACT FROM AUTHOR]

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, existing non-stationary techniques will often aim to address individual non-stationary features in isolation, leaving aside the exploration of concurrent multiple non-stationary effects. Our overarching objective here is the formulation of a framework to systematically model and forecast non-stationary electricity-price time series, encompassing the broader scope of non-stationary behavior. For this purpose, we develop a data-driven model that combines an N-dimensional Langevin equation (LE) with a neural-ordinary differential equation (NODE). The LE captures fine-grained details of the electricity-price behavior in stationary regimes but is inadequate for non-stationary conditions. To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE. By learning this difference, the NODE reconstructs the non-stationary components of the time series that the LE is not able to capture. We exemplify the effectiveness of our framework using the Spanish electricity day-ahead market as a prototypical case study. Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior. The framework's dependability and robustness is demonstrated through different non-stationary scenarios by comparing it against a range of basic naïve methods. [ABSTRACT FROM AUTHOR]

Grey forecasting models have found extensive applications across various domains, but the connection between their theory and practice has not yet been fully revealed. This paper seeks to discuss the modelling mechanism of grey forecasting models from the perspective of dynamic system modelling and illustrate how to establish grey forecasting models to address real-world challenges. Firstly, we outline the grey forecasting models under the traditional and direct frameworks. Then, the problem description and model assumptions of grey forecasting models are discussed by incorporating model characteristics and Prof. Deng's original concepts. The complete process of model establishment as well as the purpose and tasks of each step is elaborated in detail. Ultimately, taking the inventory of perishable products as a case study, this article discusses the utilization of grey forecasting models in inventory management and elucidates the application process using citrus as a specific example. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade‐offs between simple climate‐independent econometric‐based models and complex climate‐sensitive data‐driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. The climate‐independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate‐sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. −27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate‐independent model. The climate‐sensitive workflow increased model accuracy and characterized climate‐demand interactions, demonstrating a novel tool to enhance water system management. [ABSTRACT FROM AUTHOR]

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings. [ABSTRACT FROM AUTHOR]

Smart grid (SG) is proposed as a solution to the problems of production, distribution, monitoring, and control of the electricity in traditional power grids. Smart grid networks place Internet of Things (IoT) sensor nodes at various grid lines and collect large volume of data about power flow, usage, etc. The collected data are analyzed for various applications like demand forecasting, fault diagnosis and fault prediction, etc. The sensor nodes and the communication links can be compromised affecting the privacy of consumers. False data can be propagated with malicious intentions. This work proposes a secure and privacy-preserving framework for smart grid IoT networks to secure the data and decision at sensor nodes and communication links. The privacy-preserving framework proposes a novel secure compressive sensing technique to secure the data and integrated blockchain with interplanetary file system (IPFS) authenticated decision rules for storage and retrieval. Blockchain is a distributed-ledger technology that records transactions in chronological sequence and does not allow for any modifications. The technology is popular because of its robust features. Use of blockchain provides irrefutable tamper proof storage for the transactions. IPFS is used due to its higher computation time for storing the data. Through experimental study, it was discovered that the suggested method offers greater resilience against data security assaults at comparative savings of 12.4% on computing, 15% on communication, and 19.9% on storage. Forecasting using altered data in the suggested approach only slightly differed in accuracy from forecasting using original data by 1.08%. [ABSTRACT FROM AUTHOR]

Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness of user behavior, which makes the distribution of bikes unbalanced. These bring great challenges to bike-sharing demand prediction. In this study, a usage pattern similarity-based dual-network for bike-sharing demand prediction, called FF-STGCN, is proposed. Inter-station flow features and similar usage pattern features are fully considered. The model includes three modules: multi-scale spatio-temporal feature fusion module, bike usage pattern similarity learning module, and bike-sharing demand prediction module. In particular, we design a multi-scale spatio-temporal feature fusion module to address limitations in multi-scale spatio-temporal accuracy. Then, a bike usage pattern similarity learning module is constructed to capture the underlying correlated features among stations. Finally, we employ a dual network structure to integrate inter-station flow features and similar usage pattern features in the bike-sharing demand prediction module to realize the final prediction. Experiments on the Citi Bike dataset have demonstrated the effectiveness of our proposed model. The ablation experiments further confirm the indispensability of each module in the proposed model. [ABSTRACT FROM AUTHOR]

The principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago’s O’Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high vacancy rate and unbalanced demand distribution in the taxi industry,through in-depth research on the taxi travel region, this paper proposes a demand forecasting model TCGODE (temporal correlation graphs-ordinary differential equation) combining temporal correlation dynamic Graphs and Ordinary Differential Equations. Firstly, the model used ordinary differential equations (ODE) to model the differential equations of graph convolutional networks (GCN), and abstracted the stacked local feature information into a dynamic graph. Advancing the local node state based on the timing characteristics of nodes. Then, a distillation scheme based on the attention score adjustment sampling strategy is designed to improve the adaptation effect to the multi-layer sparse graph, so as to more stably represent the complex spatio-temporal characteristics, and finally realize the prediction of inter-regional taxi demand. Experimental results conducted on real taxi order datasets demonstrate that the TCG-ODE model outperforms both benchmark models and the pre-improvement model in terms of demand prediction accuracy. By accurately forecasting taxi demand among different regions, this model provides decision support information for taxi drivers and passengers, thereby optimizing the supply-demand relationship. [ABSTRACT FROM AUTHOR]

The relationship between economy, transportation, and tourism has yet to be investigated for small heritage sites. In addition to more research into the elements mentioned above, there is still a need to establish an integrated model that answers the need for economic augmentation in such tourism destinations. The model uses a 'choice freedom index' with financial health and transportation infrastructure to forecast positive tourism supply and demand. This article combines 'economy,' 'transportation,' and 'tourism' in a constructed model that employs dynamic panel data regression, econometric modelling, and regression analysis, resulting in a user interface software that identifies future investment requirements to enhance tourism and applies it to Bishnupur, a city in the Indian subcontinent to address the Sustainable Development Goals – promoting economic growth and job creation (Goal 8), increase innovation, promote inclusive and sustainable industrialization, and create resilient infrastructure (Goal 9), sustainable cities (Goal 11), as well as sustainable production and consumption (Goal 12). • Numerous tourism flow models present but lack of a universal tourism flow model. • Lack of transportation, economy and conservation integration in present models. • Choice freedom index unintroduced in any present models studied. • Transport modal variance dispute hindering travel behaviour integration. • Indian subcontinents lagging behind in tourism flow model inclusion. [ABSTRACT FROM AUTHOR]

Due to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting services comprise various modules for intra-day and day-ahead forecasts. This work specifically addresses day-ahead forecasts, utilizing specifications based on endogenous, historical measurements. These specifications are designed to be computationally efficient, requiring fewer input variables and less training data. Such weather-independent specifications serve as benchmarks against the more computationally demanding forecasts based on numerical weather predictions. A series of experiments, designed to simulate the real-world application of an online system, were conducted on sliding windows of back-contact photovoltaic (installed at KAUST, Saudi Arabia) output series, solar irradiance recorded in Hawaii, and simulated data. Our analysis evaluated 24 specifications, which are variants of (i) functional time series models (including two novel shrinkage procedures); (ii) time series nearest neighbor schemes; (iii) exponential smoothing procedures; (iv) autoregressive integrated moving average processes; (v) automatic techniques based on time series decomposition; and (vi) the persistence model. In addition to employing outlier-robust accuracy metrics, such as mean absolute error, our evaluation also prioritized prediction-interval accuracy, quantified by the mean scaled interval score. Our findings suggest that practitioners can achieve significant improvements over the persistence model by forecasting daily profiles using adaptive nonparametric or functional data analysis-based procedures. Moreover, applying shrinkage to nearest neighbor (NN) forecasts toward smooth, average daily profiles significantly enhances NN performance. Conversely, some popular, computationally intensive models fail to perform adequately to justify their additional cost. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

Faced with the cost of local management of IT infrastructure, many transportation operators have decided to use Cloud Computing services via its providers. The latter make resources available to transportation operators in the form of Virtual Machine (VM). Thus, transportation operators only use a limited number of virtual machines capable of satisfying their needs. This contributes to the reduction in the costs of the customer IT infrastructure. However, in DRT Demand-Responsive Transportation system and in order to avoid service overload and largely unused resources, it is important to anticipate the number of future passenger demands. Considering the importance of these challenges, this paper presents a Cloud-based Demand-Responsive Transportation system. Such a system is based on two principles: (I) forecasting passenger demand, in order to prepare the conditions for exploiting resources and achieving their optimal efficiency. For this purpose, actual passenger demand is predicted by applying an artificial neural network based on a Multilayer Feed-Forward Neural Network (MLFF). It constitutes a reference predicting the future passenger demands based on past demands and thus, obtaining an appropriate prediction of future loads. (II) Pre-allocate resources, to avoid overloading or over-provisioning their capacity to better manage peaks in demand, all based on an estimation process that will help predetermine resource requirements. System evaluation is performed by using OpenNebula middleware, and real passenger demand data have shown performance gains ranging up to a 25% margin for utilization rate applied to forecasting future demand. [ABSTRACT FROM AUTHOR]

Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

The article focuses on the inadequacy of Sales and Operations Planning (S&OP) in addressing extreme demand-supply uncertainties, particularly highlighted during the COVID-19 pandemic. Topics include the recommendation for 'Quick-Response' planning teams for managing uncertainties, 'Supply-in-Mind' planning teams for optimizing demand under supply constraints, and the need for specialized planning processes to supplement S&OP during volatile conditions.

The article focuses on the challenges and strategies in demand forecasting for new product launches. Topics include the complexity of identifying key drivers of demand, the difficulties in estimating the numerical impact of each driver, and the limitations of artificial intelligence in real-world applications for demand forecasting.

Indonesia is widely known as a country with rich biodiversity. Medicinal plants that thrive in Indonesia are utilized as traditional medicine locally known as "jamu". One of the islands famous for jamu production is Madura Island. As a well-known jamu producer, Madura Island are facing problems related to jamu production. Procurement of medicinal plants is not well controlled. There are no reports of spices procurement and production. When there is an increase in demand or sale of certain jamu, the stock of jamu is commonly inadequate/insufficient This may result in order cancellation. The solution to this problem is to create a production forecasting information system by using single exponential smoothing. The data used is a weekly report on the number of sales of 3 types of jamu from August to October 2024. Mean Absolute Percentage Error (MAPE) testing using an alpha value of 0.1 to 0.9 resulted in "high" accuracy and the forecasted values were close to the actual data values. [ABSTRACT FROM AUTHOR]

As the development of hydrogen refueling stations (HRS) is key to the hydrogen industry, it is necessary to study the investment feasibility for potential investors in order to stimulate its investment. We view the HRS investment as a two-stage decision-making process, distinguishing characteristics of off-site HRS from those of on-site HRS. Investors can first decide whether or when to invest in an off-site HRS, and then can accordingly decide whether or when to upgrade from an existing off-site HRS to an on-site HRS, by retrofitting off-site HRS with on-site hydrogen production device. We select natural gas reforming as the technology of on-site hydrogen production device, not only because natural gas reforming is cheaper and maturer, but also because this type of retrofit has been applied in a real case. Since the investment in HRS entails multiple uncertainties, we use a compound real options approach for conducting an appraisal of HRS investment. Accordingly, we use the Chinese case to present a new perspective to the sequential analysis of HRS investment decision-making. Four various scenarios are set for a case study, including one benchmark scenario reflecting the current Chinese hydrogen policies, and three comparative scenarios reflecting potentially improved policies, such as increasing operational subsidy, implementing preferential taxation and stimulating demand. The results show the current scenario is not beneficial to invest in HRS building on the proposed analytical framework and the data collected from the case of China. Furthermore, the increase in demand plays the leading role in improving the probability of HRS investment, followed by the implementation of preferential taxation and increasing operational subsidies. Additionally, the on-site HRS shows its superiority over off-site HRS, because all scenarios suggest upgrading to on-site HRS should be executed immediately once the initial off-site HRS is constructed. Therefore, in the context of Chinese hydrogen market and the selected on-site technology of natural gas reforming, it is necessary to create a more favorable business environment for the layout of on-site HRS. • Building hydrogen refueling station (HRS) is a two-stage decision-making process. • The appraisal of two-stage HRS investment is studied by compound real options. • Off-site HRS construction is the prerequisite to the subsequent on-site retrofit. • On-site HRS show its superiority over off-site HRS based on the case study. • Growth in demand is a better policy for increasing investment probabilities of HRS. [ABSTRACT FROM AUTHOR]

In this work, the short-term electric load demand forecasting problem is addressed, proposing a method inspired by the use of anchors in object detection methods. Specifically, a method named Anchored Input–Output Learning (AnIO) is proposed. AnIO proposes to define and use an anchor, reformulating the problem into offset prediction instead of actual load value prediction. Additionally, the use of anchor-encoded input features to match the encoded output is proposed. Extensive experiments were conducted, considering different anchors and model architectures on different datasets. Considering the Greek energy market, AnIO improves the performance from 2.914 to 2.251% in terms of MAPE. In conclusion, AnIO method achieves to improve the performance, considering time-series forecasting tasks. [ABSTRACT FROM AUTHOR]

This study systematically investigates the pivotal role of inventory management within the framework of "cloud supply chain" operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a "cloud supply chain" through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain. [ABSTRACT FROM AUTHOR]

Car-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal relationships. This paper introduces USTIN, the Unified Spatio-Temporal Inference Prediction Network, a novel neural network architecture for demand prediction. The model consists of three key components: a temporal feature unit, a spatial feature unit, and a spatio-temporal feature unit. The temporal unit utilizes historical demand data and comprises four layers, each corresponding to a different time scale (hourly, daily, weekly, and monthly). Meanwhile, the spatial unit incorporates contextual points of interest data to capture geographic demand factors around parking stations. Additionally, the spatio-temporal unit incorporates weather data to model the meteorological impacts across locations and time. We conducted extensive experiments on real-world car-sharing data. The proposed USTIN model demonstrated its ability to effectively learn intricate temporal, spatial, and spatiotemporal relationships, and outperformed existing state-of-the-art approaches. Moreover, we employed negative binomial regression with uncertainty to identify the most influential factors affecting car usage. [ABSTRACT FROM AUTHOR]

Many control algorithms have been applied to manage the flow of products in supply chains. However, in the era of thriving globalization, even a small disruption can be fatal for some companies. On the other hand, the rising environmental impact of a rapid industry is imposing limitations on energy usage and waste generation. Therefore, taking into account the mentioned perspectives, there is a need to explore the research directions that concern product perishability together with different demand patterns and their uncertain character. This study aims to propose a robust control approach that combines neural networks and optimal controller tuning with the use of both different demand patterns and fuzzy logic. Firstly, the demand forecast is generated, following which the parameters of the neural controller are optimized, taking into account the different demand patterns and uncertainty. As part of the verification of the designated controller, the sensitivity to parameter changes has been determined using the OAT method. It turns out that the proposed approach can provide significant waste reductions compared to the well-known POUT method while maintaining low stocks, a high fill rate, and providing lower sensitivity for parameter changes in most considered cases. The effectiveness of this approach is verified by using a dataset from a worldwide retailer. The simulation results show that the proposed approach can effectively improve the control of uncertain perishable inventories. [ABSTRACT FROM AUTHOR]