Efficient Resource Planning Research Articles

Air Pollution Forecasting in a Regional Context for Sustainable Management

The aim of this research was to develop and apply a statistical model that can be used to forecast long-term daily maximum particulate matter with a diameter of less than 2.5 microns (PM2.5) concentrations. In order to predict the daily maximum PM2.5 concentrations in the northeastern region of Thailand, the extreme value theory was analyzed, and an appropriate distribution model was identified by employing the Generalized Pareto distribution (GPD). The data of daily maximum PM2.5 concentrations during the years 2021–2023 obtained from six stations was used. These stations are located in Khon Kaen, Loei, Nakhon Ratchasima, Nong Khai, Nakhon Phanom, and Ubon Ratchathani provinces. The results of this study reveal that the GPD is appropriate based on the results of Kolmogorov-Smirnov Statistics Test. Estimating the return levels during the following return periods: 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years showed that the area in the upper northeastern region, particularly Loei and Nakhon Phanom, has daily maximum PM2.5 concentrations above 500 micrograms per cubic meter. These results can also be used as information to support decision-making when conducting response planning in high-risk areas, which can be helpful for efficient resource planning and prevention actions. Doi: 10.28991/ESJ-2024-08-05-024 Full Text: PDF

Accurate short-term GHI forecasting using a novel temporal convolutional network model

Global energy demand is on the rise, driven by factors such as population growth and economic development.Utilizing renewable energy is crucial to meeting this energy demand in light of global warming and the depletion of fossil resources.One of the renewable energy sources that is extensively utilized in multiple countries worldwide is photovoltaic energy.While integrating photovoltaic (PV) energy into the grid offers substantial economic and environmental advantages, its intermittent nature presents challenges to maintaining grid stability at high penetration levels. Accurate solar energy estimations are essential for photovoltaic (PV) based energy facilities to enable early participation in energy markets and efficient resource planning. This study proposes and evaluates a Temporal Convolutional Network (TCN) model that can rapidly predict global horizontal irradiance (GHI) using univariate and multivariate approaches. To our knowledge, this is the first study to employ a TCN model for GHI forecasting. The univariate model solely considers GHI, while the multivariate time series models incorporate a combination of six variables: global horizontal irradiance, active power of a module plant, PV module temperature, wind speed, air temperature, and air pressure. The proposed model’s effectiveness is validated by comparing its performance to benchmark models, including MLP, RNN, GRU, and LSTM. The prediction models’ performance is evaluated using root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results demonstrate that the effectiveness of univariate TCN models is evident in their performance for 5, 10, 15, and 20 min ahead short-term GHI forecasting, its mean absolute error (MAE) values achieved are 22.935 W/m2 33.47 W/m2, 36.9994 W/m2, and 41.9946 W/m2, respectively. The multivariate model, incorporating additional variables, yielded higher MAE values : 37.928 W/m2, 55.88 W/m2, 66.61 W/m2, and 74.82 W/m2, respectively. These results suggest that the TCN model’s capability to capture both long-term and short-term dependencies within the data contributes to its accuracy for GHI forecasting compared to other models.

An explainable multiscale LSTM model with wavelet transform and layer-wise relevance propagation for daily streamflow forecasting

Developing an accurate and reliable daily streamflow forecasting model is important for facilitating the efficient resource planning and management of hydrological systems. In this study, an explainable multiscale long short-term memory (XM-LSTM) model is proposed for effective daily streamflow forecasting by integrating the à trous wavelet transform (ATWT) for decomposing data, the Boruta algorithm for identifying model inputs, and the layer-wise relevance propagation (LRP) for explaining the prediction results. The proposed XM-LSTM is tested by performing multi-step-ahead forecasting of daily streamflow at four stations in the middle and lower reaches of the Yangtze River basin and compared with the X-LSTM. The X-LSTM is formed by coupling the long short-term memory (LSTM) with the LRP. For comparison, the inputs of these two models are identified by the Boruta selection algorithm. The results show that all models exhibit good ability to forecast daily streamflow, however, the prediction performance decreases as the lead time increases. The XM-LSTM provides a better forecasting performance than the X-LSTM, suggesting the ability of the ATWT to improve the LSTM for daily streamflow forecasting. Moreover, the correlation scores analysis by the LRP shows that the ATWT can extract useful information that influences the daily streamflow from the raw predictors, and the water level has the most significant contribution to streamflow prediction. Accordingly, the XM-LSTM model can be viewed as a potentially useful approach for increasing the accuracy and explainability of streamflow forecasting.

Enhancing emergency vehicle access in dense settlements of Mumbai using high-resolution satellite imagery: A deep learning approach

Unplanned urbanization in developing cities has led to haphazard development, increasing disaster risks in densely populated areas. Despite being more disaster-prone, these dense settlements often lack essential emergency services. Delivering emergency services requires a high-resolution geodatabase of minor roads and their attributes, such as road width. Such information can be a crucial input for quantifying emergency vehicles (EVs) access in an area. To the best of our knowledge, no research has addressed this problem statement, making this paper the first to propose quantifying road width and using it to develop EVs accessibility maps. We propose a two-stage process to achieve these goals. In the first stage, we designed a deep neural network (DNN) to accurately organize built-up features, including minor roads using true-colour high-resolution satellite imagery of Pleiades-1A for Mumbai. In the second stage, we propose an algorithm to calculate road width and then compare it with EVs dimension (width) details to develop high-resolution accessibility maps. The model classified features with around 91.88% accuracy along with 90% Kappa coefficient, while the classification accuracy of minor roads in informal settlements was about 85.80%. We found that informal settlements have much less access to various EVs compared to formal settlements due to mixed signatures. The outcomes presented in this paper can be used as a decision-making tool to develop a geodatabase of road widths and EVs accessibility maps for efficient resource planning, which can ultimately lead to the development of disaster-resilient cities.

Impact of Resource Planning and Information Management Services based on Sustainable Development Goals (SDGs) - The Eco-Environmental and Economic Behavior for the Chemical Industries in China

The Chinese economy and the global supply chain both heavily rely on the chemical industry. These services strive to optimize resource utilization, decrease environmental contamination, and support environmentally friendly and commercially successful operations in the chemical sector by incorporating sustainable practices and utilizing technical breakthroughs .Sustainable development goals (SDGs) are a necessity that has only lately come to light on a global scale, but they are a dynamic phenomenon that calls for ongoing analysis and is the subject of current study and policy. As more people become aware of environmental issues, businesses are under pressure to manage their supply chains in an environmentally sustainable manner. This quantitative study analyses the effects of resource planning and information management services on eco-environmental and economic behavior in China's chemical industries with a focus on the Sustainable Development Goals (SDGs). The study used a longitudinal research design with data collected at two separate time points due to low response rates. The study employs a 5-point Likert scale questionnaire for data collection, and SPSS Macros is used for statistical analysis. The author has also investigated the moderating effects of resource availability and the use of resource theory. The findings of this study contribute valuable insights to the field of sustainable development and industrial practices. The implications of these research findings are twofold. First, they provide valuable guidance for the chemical industries in China to enhance their eco-environmental and economic performance through efficient resource planning and data-driven information management. Second, the study's identification of key sustainable development factors can assist policymakers in formulating targeted strategies to advance the SDGs within the chemical sector. The findings support sustainable behavior in the chemical industry by offering useful insights into how to include SDGs into resource planning and information management systems.

Orthogonal projection for anomaly detection in networking datasets

In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efficient resource planning in advance involves a lot of labor-intensive efforts. Consequently, network usage analysis is a very useful tool that allows network administrators to find patterns and anomalies. Whilst pattern detection provides administrators the ability to define the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering different locations in the Milan metropolitan area, differentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques.

Concurrent 12. Presentation for: Shuts, turns and roundabouts

Presented on Wednesday 18 May: Session 12 The outcomes of operational shutdowns and maintenance events, ‘Shuts and Turns’, can significantly affect a company’s performance and reputation. These are typically highly complex operations that demand significant resources and must deliver results. Shuts and Turns are expensive activities, not only in terms of the direct cost of labour plant and materials but also the production deferral. Leading operators recognise that they also pose significant risk to future production which is why significant time and effort goes into robust management framework. Safety performance is paramount, with schedule adherence also key. Operators spend considerable time planning to ensure successful Shuts and Turns have: an established management framework; effective scope management; thorough front-end loading; robust controls and change management; an assessment of the risks of emerging work; efficient resource planning; and rigorous safety and QA/QC processes. This paper provides: an overview of the different types of Shuts and Turns used by Australian operators; the benefits of a robust shutdown governance approach; an overview of key success areas; importance of lessons learned; and a detailed case study based on our work with a major downstream facility. The paper will appeal to oil and gas operators, major contractors and service providers. To access the presentation click the link on the right. To read the full paper click here

Shuts, turns and roundabouts

The outcomes of operational shutdowns and maintenance events, ‘Shuts and Turns’, can significantly affect a company’s performance and reputation. These are typically highly complex operations that demand significant resources and must deliver results. Shuts and Turns are expensive activities, not only in terms of the direct cost of labour plant and materials but also the production deferral. Leading operators recognise that they also pose significant risk to future production which is why significant time and effort goes into robust management framework. Safety performance is paramount, with schedule adherence also key. Operators spend considerable time planning to ensure successful Shuts and Turns have: an established management framework; effective scope management; thorough front-end loading; robust controls and change management; an assessment of the risks of emerging work; efficient resource planning; and rigorous safety and QA/QC processes. This paper provides: an overview of the different types of Shuts and Turns used by Australian operators; the benefits of a robust shutdown governance approach; an overview of key success areas; importance of lessons learned; and a detailed case study based on our work with a major downstream facility. The paper will appeal to oil and gas operators, major contractors and service providers.

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.

A First Route Second Assign decomposition to enforce continuity of care in home health care

Home health care (HHC) is a very popular service that plays an important role in reducing hospitalization costs and improving the quality of life for patients. Human resource planning is one of the most important processes in HHC facilities, and service providers must deal with several operational problems, e.g., the assignment of nurses to patients and the nurse routing. These two problems in HHC are intrinsically related. In the literature, they are solved simultaneously or sequentially, by exploiting First Assign Second Route (FASR) decomposition approaches in which the assignment problem is solved first and the routing problem is solved second. However, on the one hand, the simultaneous approach focuses primarily on the routing component of the problem but fails to offer continuity of care to the patients. On the other hand, FASR is more adequate to enforce the continuity of care constraint but is less effective toward the routing part. In this paper, we propose a novel decomposition approach that combines the advantages of each these approaches, which is based on the First Route Second Assign (FRSA) paradigm. To validate our FRSA approach and compare with a benchmark FASR decomposition, we also develop an instance generator that is inspired by real HHC settings with different sizes and travel time ratios. Experiments show the effectiveness of the FRSA decomposition and improvements with respect to the classical FASR, especially when travel times constitute a relevant part of the workload and the routing component of the problem is predominant; moreover, continuity of care is fully respected. Thus, FRSA can be effectively implemented by HHC providers for an efficient planning of resources and visits, especially where patients are spread in a vast territory.

Predictive analytics for hospital inpatient flow determination

Currently, the efficient planning of resources in hospitals present a responsibility of extreme importance in the management of the various clinical units. In Intensive Care Unit (ICU), a hospital service where patients require constant observation and control, considering the high costs incurred with hospitalized patients, the optimization of these factors assumes an extremely important role. Given its unpredictability, this study focused on a characterization of this unit, identifying existing patterns, during a 5-year period, 2017 to 2021, at the Centro Hospitalar do Tâmega e Sousa (CHTS), providing a set of useful information crucial for decision making. Additionally, a prediction of future ICU admissions is performed using time series and Machine Learning (ML) models. However, the models did not reveal a predictive ability with an adequate level of reliability.

Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models

Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.

Patient-specific COVID-19 resource utilization prediction using fusion AI model

The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.

Using efficiency frontier to inform operational decision-making

In this study, we introduce Efficiency Frontier, an economic assessment framework for business operations. The proposed framework emphasizes efficiency and sustainability. It aims to help organizations translate a complex metric system into a value index, conduct performance evaluation, and determine efficient resource planning strategies. Unlike the traditional methods that majorly focus on the achievement of performance targets, the proposed framework also accounts for the amount of resource that is invested in the achievement, which encourages managers to implement sustainable strategies that aim for long-term success. To validate the new framework, we systematically study the case of the City of Chicago’s 311 contact center with a base case analysis and a sensitivity analysis. In the base case, an Efficiency Frontier architected by a set of efficient solutions from 2013 to 2016 is used to identify outperforming scenarios given resource constraints. In the sensitivity analysis, we demonstrate the application of Efficiency Frontier in determining optimal resource planning strategies in conservative, moderate, and aggressive business investment scenarios. The base case Efficiency Frontier reveals that the marginal return on investment in improving the performance may diminish as investment increases. The sensitivity study suggests that optimal resource planning strategies should be adjusted according to value propositions. This work builds a framework for business leaders to economically assess their operational decision-making.Article highlightsEfficient strategies can be identified by exploring the maximum value increase per unit of resource given cost scenariosThe fund of a strategy should be reduced when the marginal return of the strategy begins to diminish.The framework’s ability to suggest cost-saving strategies is especially informative in resource-limited settings.

Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

Accurate forecasts of daily runoff are essential for facilitating efficient resource planning and management of a hydrological system. In practice, daily runoff is needed for various practical applications and can be predicted using precipitation and evapotranspiration data. To this end, a long short-term memory (LSTM) under a cascade framework (C-LSTM) approach is proposed for forecasting daily runoff. This C-LSTM model is composed of a 2-level forecasting process. (1) In the first level, an LSTM is established to learn the relationship between the precipitation and evapotranspiration at present and to learn several meteorological variables one day in advance. (2) In the second level, an LSTM is constructed to forecast the daily runoff using the historical and simulated precipitation and evapotranspiration data produced by the first LSTM. Through cascade modeling, the complex features of the numerous targets in the different stages can be sufficiently extracted and learned by multiple models in a single framework. In order to evaluate the performance of the C-LSTM approach, four mesoscale sub-catchments of the Ljubljanica River in Slovenia were investigated. The results indicate that based on the root-mean-square error, the Pearson correlation coefficient, and the Nash-Sutcliffe model efficiency coefficient, the proposed model yields better results than two other tested models, including the normal LSTM and other neural network approaches. Based on the results of this study, we conclude that the LSTM under the cascade architecture is a valuable approach and can be regarded as a promising model for forecasting daily runoff.

A data-driven methodology for supporting resource planning of health services

In recent years, healthcare systems have been forced to better organize their services in the final attempt to maximize both care effectiveness and efficiency. In particular, emergent trends are prompting hospitals to pay more attention to the effective and efficient planning of resources and to the creation of patient-centred services, in which current activities and resources are reorganized around patients.This paper proposes a process mining based methodology to systematically support the resource planning of health services. Specifically, combining Time-Driven Activity Based Costing and process mining approaches, it automatically identifies the patient flow and analytically evaluates activities, service times, and resource consumptions for a specific class (-es) of patients (e.g., a DRG, patients with specific medical condition, etc.). Thus, it allows to reliably estimate the expected resource consumptions for the patient group under investigation. Thanks to process mining, the method overcomes the limitations of existing quantitative approaches that are often time-consuming, based on subjective observations, and too case specific. The method was applied to a real case study of lung cancer patients in an Italian hospital.

Data‐driven next destination prediction and ETA improvement for urban delivery fleets

In an urban logistics system, predictions of the next destination and estimated time of arrival (ETA) are of paramount importance for efficient resource planning of delivery fleets and for providing a satisfactory client experience. The quality of prediction is limited by the information accessible to individual logistics business entities, and further complicated by the complex urban road system. Data collection under the auspices of smart city initiatives worldwide provides exciting new opportunities to overcome these limitations. In this study, the authors identify two areas of improvement through data-driven approaches, including a next destination predictor, based on the delivery fleet's historical global positioning system trajectory data using a non-linear autoregressive neural network, and a road incident detector for real-time ETA improvement. By comparing a range of machine learning classification algorithms for incident detection, XGBoost has been found to be the most practical choice, due to its performance and efficiency. The proposed framework can be utilised by government authorities who possess such data for better urban planning and providing advanced infrastructure, so as to improve the operational efficiency of the logistics industry.

A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

Accurate forecasts of solar energy are important for photovoltaic (PV) based energy plants to facilitate an early participation in energy auction markets and efficient resource planning. The study concentrates on Long Short Term Memory (LSTM), a novel forecasting method from the family of deep neural networks, and compares its forecasting accuracy to alternative methods with a proven track record in solar energy forecasting. To provide a comprehensive and reliable assessment of LSTM, the study employs remote-sensing data for testing predictive accuracy at 21 locations, 16 of which are in mainland Europe and 5 in the US. To that end, a novel framework to conduct empirical forecasting comparisons is introduced, which includes the generation of virtual PV plants. The framework enables richer comparisons with higher coverage of geographical regions. Empirical results suggest that LSTM outperforms a large number of alternative methods with substantial margin and an average forecast skill of 52.2% over the persistence model. An implication for energy management practice is that LSTM is a promising technique, which deserves a place in forecasters’ toolbox. From an academic point of view, LSTM and the proposed framework for experimental design provide a valuable environment for future studies that assess new forecasting technology.

Efficient Resource Planning of Intermodal Terminals under Uncertainty

This paper presents a decision support tool for the efficient resource planning and management of intermodal terminals under uncertainty, allowing to address the planning issue under imprecise or uncertain data (e.g., estimates on flows, resource utilization, operating conditions). The procedure consists of three steps: 1) the definition of a Timed Petri Net model of the terminal; 2) the computation of suitable performance indices to evaluate whether the current configuration is able to cope with a foreseen increase in the freight flows; 3) in the case of not satisfactory values of the indices at the previous step, the simulation of alternative planning solutions and the detection of the most efficient one via a cross-efficiency fuzzy Data Envelopment Analysis technique. In order to test its effectiveness, the procedure is applied to a real case study.

Resource optimisation for an automobile chassis manufacturer through value stream mapping enhanced with simulation technique and constraint programming

Incorporation of lean thinking has become an essential part of any manufacturing system, as reducing waste leads to cost savings and hence competitiveness of the company. Value stream mapping (VSM) which leads to cost savings is a part of the portfolio of tools to achieve lean orientation in manufacturing systems. Lean implementation starts with the application of VSM to an assembly line; however, it has various limitations. VSM being a pencil-and-paper technique is a static tool. It does not provide a dynamic view of the situation. There is need to enhance the VSM through discrete-event simulation (DES) technique (using Arena® simulation software) and constraint programming (using CPLEX® optimisation software) for an assembly line balancing problem (ALBP). This study suggests the usage of a simulation tool to gain meaningful insight into an assembly line to minimise waste. Constraint programming using combinatorial optimisation helps in efficient resource planning of an automotive chassis assembly line. Although the primary focus of this paper is manpower optimisation, but methodology discussed in this paper can be used for optimisation of machine resources as well. This study is expected to enhance the lean optimisation of the assembly lines.