Variable Demand Research Articles

Enhancing supply chain resilience with recovery strategies under electricity shortages

Electricity shortages triggered by extreme events have profoundly disrupted supply chains (SCs) in recent years. As SC operations heavily depend on electricity, simultaneous uncertainties may occur in supply and production capacities, demand, and transportation capacity if electric trucks are used. To enhance SC resilience, it is imperative to combine new recovery strategies (i.e. alternative energy sources and alternative transportation vehicles (ATVs)) with traditional ones to restore SC performance. However, these strategies have not been investigated quantitatively. Besides, only partial probability distribution information of uncertainties may be available under electricity shortages. Thus, this work improves SC resilience from a worst-case perspective, where the moment information is known. For the problem, a bi-objective distributionally robust chance-constrained optimisation model is constructed to balance the total cost and the service level. Then, an effective ϵ-model-based constructive heuristic (ϵ-MBCH) is developed. Compared with the ϵ-constraint method, the efficiency of ϵ-MBCH is improved by 99.81%. Based on stress tests and sensitivity analyses on a real case, key managerial insights include: (i) the ATV strategy is indispensable under varying severities of electricity shortages; (ii) the inventory management strategy helps reduce reliance on power generators and diesel trucks; and (iii) mitigating demand variability is essential for reducing costs.

Towards sustainable agricultural water management in Poland – How to meet water demand for supplemental irrigation?

The global challenge of water scarcity, particularly in agriculture, demands urgent attention due to the overexploitation of water resources and the escalating impacts of climate change. This study focuses on the unique challenges faced by Poland, experiencing increasing concerns related to droughts. It explores the utilization of supplemental irrigation, specifically in the context of Central Europe, where a distinctive approach known as supplemental irrigation is employed. The study emphasizes the need for sustainable water management practices and investigates the potential of small water retention measures (SWRMs), such as ponds and drainage water management, as solutions to enhance water availability in agriculture. A macro-scale water balance study is conducted using the Soil and Water Assessment Tool (SWAT) to estimate spatio-temporal variability of water demand for supplemental irrigation in Poland. The highest demand, approximately 2.5 billion m3 (for arable lands) and 1.3 billion m3 (for grasslands), occurred during the exceptionally dry year of 2015, characterized by severe agricultural drought effects. The study also assesses the efficiency of SWRMs in meeting irrigation demands at national level. The results highlight a paradox in their effectiveness during critical periods, specifically in dry years when water demands are the highest. The outcomes of the model experiment underscored concerns about the insufficiency of meeting the water needs of irrigated agriculture solely through the construction of small retention facilities during very dry years. The outcomes of this research contribute to a better understanding of irrigation water demands in temperate climate region, support evidence-based practices for sustainable water management, and inform policymakers and stakeholders involved in water governance.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

An assessment of Fuzzy n-Policy queues with infinite capacity

This study examines the application of fuzzy n-policy queues with infinite capacity to optimize queuing systems under uncertain and variable conditions. Traditional queuing models often assume precise data, but real-world scenarios frequently involve ambiguity in parameters such as arrival rates, service rates, and system thresholds. The fuzzy n-policy model offers a more flexible approach by allowing these parameters to be represented as fuzzy variables, capturing the inherent uncertainty in complex queuing environments. By analyzing infinite capacity queues within this framework, the study evaluates system performance metrics like expected queue length, waiting time, and server utilization under different fuzzy set assumptions. The results demonstrate that the fuzzy nnn-policy enhances system responsiveness by adjusting service initiation thresholds based on the degree of uncertainty in arrival and service processes. These findings contribute to the broader field of stochastic and fuzzy queuing systems, providing practical insights for industries where demand variability and service uncertainty are prevalent, such as telecommunications, healthcare, and logistics.

Zero-inventory plans, constant workforce, or hybrid approach? Analysing pure production strategies for enhancing factory resilience with demand variability

Historically the notion of resilience capability has primarily been conceptualised as a holistic construct in the domain of supply chain networks – referred to as SCRES. The field of SCRES has since evolved gaining prominence among scholars and practitioners. However, the existing SCRES literature inadequately delves into the embedded resilience harboured by their constituent components, practices, or internal routines, including the aggregate production planning (APP) process. Therefore, the purpose of this article is to examine the interplay between medium-term pure strategies for APP and the resilience of manufacturing facilities, assuming demand to be the sole source of uncertainty. To accomplish this, a realistic Monte-Carlo simulation model integrated with a robust heuristic procedure for pure strategies is merged with a factory resilience index-based Cobb–Douglas function. The results of this research suggest that manufacturing facilities achieve a higher level of resilience when certain ‘zero-inventory plans’ are implemented. Based on this key finding, production/demand planners might incorporate the resilience dimension alongside the customary manufacturing success factors. Thus, to the best of the authors’ knowledge, this study represents the first attempt to establish a clear linkage between the implementation of pure APP strategies and a quantitative measure of factory resilience.

Establishing best practices for modeling multi-day energy storage in deeply decarbonized energy systems

Abstract Multi-day energy storage (MDS, a subset of long-duration energy storage or LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: 1) modeling MDS with linked representative periods is crucial to capturing its full value, 2) MDS value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

Energetic Analysis of Passive Solar Strategies for Residential Buildings with Extreme Summer Conditions

This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace 3D Plus v6.00.106 software, typical residential buildings for each country were simulated to assess various passive solutions, such as building orientation, wall and roof modifications, glazing optimization options, window-to-wall ratio (WTWR) reduction, shading, and natural ventilation. The findings highlight that Brazil experienced the higher discomfort temperatures compared to Mediterranean climates, with indoor air temperatures exceeding 28 ºC all year round and remaining between 34 ºC and 37 ºC for nearly 40% of the time. Building orientation had a minimal impact near the equator, while Mediterranean climates benefited from an up to 10% variation in energy demand. Thermal insulation combined with white exterior paint resulted in Şanlıurfa experiencing annual energy savings of up to 26%. Optimal roof solutions yielded a 19% demand reduction in Évora, while WTWR reduction and double-colored glazing achieved up to a 35% reduction in Évora and 19% in other regions. Combined strategies achieved energy demand reductions of 44% for Évora, 40% for Şanlıurfa, and 32% for Teresina. The study emphasizes the need for integrated, climate-specific passive solutions, showing their potential to enhance both energy efficiency and the thermal environment in residential buildings across diverse hot climates.

Optimizing ticket booking strategies using Binomial Option Pricing Models

A flexible financial instrument that is frequently used to assess the fair value of options is the Binomial Option Pricing Model (BOPM). This study examines the innovative use of the BOPM in booking tickets, highlighting how it might improve decision-making in the quickly changing ticketing sector. The option pricing model is used in the study to solve the problem of cancelation fees in ticket purchases. The aim is to provide a solution that enhances the company’s reputation while also being in line with client pleasure. This study probably considers the dynamic nature of ticket prices, variations in demand, and industry uncertainty by using the BOPM. The model’s adaptability is used to customize a response that considers consumer preferences and expectations regarding cancelation fees. The utilization of the BOPM in the ticketing industry highlights the tool’s capacity to enhance decision-making procedures by offering a more sophisticated and customer-focused method for setting prices and developing policies.

Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Optimal Design of an Off-Grid Solar Energy System Integrated with a Diesel Generator for Urban Areas in Pakistan

The growing energy demand in Pakistan, coupled with the challenges posed by reliance on imported fossil fuels, necessitates the exploration of alternative energy solutions. This study presents the design and techno-economic analysis of an off-grid hybrid energy system tailored for a residential neighborhood in Karachi, Pakistan. The system integrates individual solar energy solutions for seven housles, supplemented by a shared diesel generator to ensure a reliable power supply, particularly during load-shedding and grid outages. The study utilizes HOMER Pro software to model and optimize various configurations, taking into account local solar insolation levels and seasonal variability in energy demands. The optimized system includes photovoltaic (PV) panels, battery storage, and a shared diesel generator with individual connections and meters, with each house receiving a customized solution based on its specific energy requirements, ranging from 15 kWh/day to 45 kWh/day. The shared diesel generator is designed to reduce the need for large battery banks, thereby minimizing both capital and operational costs. The results demonstrate a cost-effective solution with a Levelized Cost of Energy (LCOE) of $0.2959/kWh and a Net Present Cost (NPC) of $15,562.55 over a 25-year period. This research highlights the potential for implementing such systems in urban areas of Pakistan, offering a sustainable, reliable, and economically viable alternative to conventional energy sources.

Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA)

We study the data-integrated, price-setting newsvendor problem in which the price–demand relationship is described by some parametric model with unknown parameters. We develop the operational data analytics (ODA) formulation of this problem that features a data-integration model and a validation model. The data-integration model consists of a class of functions called the operational statistics. Each operational statistic maps the available data to the ordering decision. The validation model finds, among the set of candidate operational statistics, the ordering decision that leads to the highest actual profit, which is unknown because of the unknown demand parameters. This ODA framework leads to a consistent estimate of the profit function with which we optimize the pricing decision. The derived quantity and price decisions demonstrate robust profit performance even when the sample size is very small in relation to the demand variability. Compared with the conventional approach with which the unknown parameters are estimated and then the decisions are optimized, the ODA framework produces significantly superior performance in the mean, standard deviation, and minimum of the profit, suggesting the robustness of the ODA solution especially in the small-sample regime. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02227 .

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue.

Biofuel has gained significant attention as a potential source to meet fuel demands instead of fossil fuel. The price of biofuel and alternative fuel have a considerable impact on biofuel demand. Thus, it is important to design a biofuel supply chain network that incorporates the biofuel price into an elastic demand. More precisely, a variable demand, including customer importance level (to the environment), biofuel price, and substituted fuel price, is considered in this research. Furthermore, this research presents a bi-objective mixed-integer quadratic formulation that aims to maximize the total profit of the supply chain and carbon absorption in harvesting areas. The problem is solved by the ε-constraint and lagrangian relaxation methods due to its complexity. Moreover, substituted fuel price uncertainty is addressed by two-stage stochastic programming. Finally, a real case study utilizing the data envelopment analysis approach is applied to assess the efficiency and currency of the addressed model. Several consequences are illustrated in the case study, such as rich areas for exporting algae, suggesting hub locations for biofuel production, etc.

Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals. Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.

Analysis of Ground Water Level and Ground Water Pollution Using Machine Learning Algorithms

Groundwater plays a crucial role in sustaining human life, agriculture, and ecosystems. However, due to increasing demand, pollution, and climatic variations, monitoring and analyzing groundwater levels and quality has become imperative. In this paper, we explore the use of machine learning algorithms to analyze groundwater levels and groundwater pollution, aiming to develop predictive models for sustainable groundwater management. The study investigates the application of supervised and unsupervised learning techniques to predict groundwater levels, identify pollution sources, and assess contamination risks. By integrating environmental data, including rainfall patterns, land use, and pollution levels, the paper demonstrates how machine learning can provide more accurate, scalable, and real-time analysis for groundwater management.

Coordinated planning for offshore wind and electricity–hydrogen system based on SDDP: A case study of coastal provinces in China

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

A stochastic sustainable Closed-Loop Supply Chain Networks for used solar photovoltaic systems: Meta-heuristic comparison and real case study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

DYNAMIC DEMAND FORECASTING IN SUPPLY CHAINSUSING HYBRID ARIMA-LSTM ARCHITECTURES

Demand forecasting in supply chains is essential for efficient inventory management, production planning, and cost optimization. Traditional time series models, such as Autoregressive Integrated Moving Average (ARIMA), are effective in modeling linear relationships but often fall short in handling the nonlinear complexities characteristic of volatile demand patterns. Conversely, deep learning models like Long Short-Term Memory (LSTM) networks have proven adept at capturing intricate temporal dependencies, though they are computationally intensive and require substantial data. This study introduces a hybrid ARIMA-LSTM framework that leverages the linearity strengths of ARIMA in conjunction with LSTMs capacity for learning non-stationary, nonlinear patterns. The hybrid model decomposes the demand series, modeling linear trends with ARIMA and residual nonlinear components with LSTM. Empirical evaluations of real-world supply chain data reveal that this integrated architecture outperforms standalone ARIMA and LSTM models, achieving superior predictive accuracy and robustness in handling demand variability. Our findings underscore the hybrid models potential as an advanced predictive solution for dynamic, data-driven supply chain management.

Intra-articular knee injections in patients with primary osteoarthritis in a tertiary clinical setting: a best practice implementation project.

This project aimed to improve physicians' adherence to evidence-based practices regarding the selection and administration of intra-articular knee injections for patients with osteoarthritis. The project also aimed to empower patients by increasing their awareness of these evidence-based practices. In the management of knee osteoarthritis, intra-articular injections are commonly used when initial treatments prove inadequate. However, issues such as patients' demand for rapid relief and variability in physicians' familiarity with optimal practices often lead to suboptimal treatment decisions and potential adverse outcomes. This project followed the JBI Evidence Implementation Framework, which is grounded in an audit, feedback, and re-audit process. Initially, physicians were surveyed on the topic under consideration, and hospital records were reviewed. Subsequently, targeted interventions were implemented, including briefings and the distribution of informative flyers. The baseline audit indicated low adherence with all audit criteria. Various parameters, such as adherence to best clinical practices, informed decision-making by patients, and the recommendation of alternative treatment modalities, were significantly lacking. Following the interventions, substantial improvements were observed, including higher rates of adherence to best clinical practices by physicians for multiple criteria, such as stepwise referral to conservative therapy options and the content of intra-articular injections. Adherence to best clinical practices can be improved through educational briefings for physicians on the most current evidence-based treatment practices for injectable substances in knee osteoarthritis. The active engagement of patients, facilitated by informative flyers and physician assistance, contributed to better involvement in their treatment choices. However, the participation of health care institutions and providers as well as lifelong education practices may be necessary for more comprehensive and sustainable changes. http://links.lww.com/IJEBH/A277.