Dynamic Demand Research Articles

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

The Biases in Applying Static Demand Models Under Dynamic Demand

This article investigates the mechanisms that underlie the biases in price elasticities of demand in applying static demand models under dynamic demand, which have been pointed out by previous empirical studies. It studies three sources of biases: disregard of state variables (affecting short-run elasticity); inconsistent utility parameter estimates; and changing expectations of consumers (affecting long-run elasticity). Disregard of state variables, such as durable product holdings, which is not negligible but not paid much attention to in the literature, leads to an overestimate of short-run own elasticities. Inconsistent utility parameter estimates arises due to the failure to account for consumers’ future expectations and unobserved state variables. Changing expectations of consumers are not explicitly specified in the static model, and this also leads to biased results when applying static models. Regarding the magnitude of the biases, the first and the third sources of biases might induce large biases in price elasticities, especially when the focus is on the large conditional choice probability products. Possible remedies for the use of static demand models are also discussed.

Optimizing Cold Chain Distribution Routes Considering Dynamic Demand: A Low-Emission Perspective

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs.

An Efficient Real-Time Algorithm for Placing Electronic Components on Panel

In this paper, we present an industrial challenge faced by electronic component manufacturers. From each order, the components need to be placed on panels for shipping. Panels incur significant costs. To control expenses, companies are compelled to optimize the placement of components on panels. Every day, thousands of orders have to be shipped. For each order, they have to pack between a hundred and a thousand rectangular electronic components of different sizes on panels with specifications such as free component orientation and tight time constraints. This packaging must be optimized to minimize the number of panels used and associated costs. The industrial challenge lies not only in space optimization but also in the speed of the process, with solutions needing to be found in less than a minute to meet the dynamic and continuous demands of production and shipping.

High-Precision Printing of Flexible MXene Patterns for Dynamically Tunable Electromagnetic Interference Shielding Performance.

Smart electromagnetic interference (EMI) shielding materials are of great significance in coping with the dynamic performance demands of cutting-edge electronic devices. However, smart EMI shielding materials are still in their infancy and face a variety of challenges (e.g., large thickness, limited tunable range, poor reversibility, and unclear mechanisms). Here, we report a method for controllable shielding electromagnetic (EM) waves through subwavelength structure changes regulated by the customized structure via a direct printing route. The highly conductive MXene ink is regulated with metal ions (Al3+ ions), giving superb metallic conductivity (∼5000 S cm-1) for the printed lines without an annealing treatment. The reversible tunability of EMI shielding effectiveness (SE) ranging from 8.2 dB ("off" state) to 34 dB ("on" state) is realized through the controllable modulation of subwavelength structure driven by stress. This work provides a feasible strategy to develop intelligent shielding materials and EM devices.

Reviewing the role of AI and machine learning in supply chain analytics

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.

Evaluating the impact of precision engineering education on industry standards and practices

Precision engineering plays a critical role in various industries, including manufacturing, aerospace, automotive, and healthcare. The evolution of precision engineering education is essential to meet the dynamic demands of these industries. This review outlines a comprehensive evaluation of how precision engineering education influences industry standards and practices. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews with industry professionals, educators, and students. Through a structured questionnaire, data is collected on the perceived effectiveness of precision engineering education in preparing graduates for the industry. Additionally, in-depth interviews provide nuanced insights into specific skills, knowledge areas, and competencies deemed essential by practitioners. Initial findings indicate a positive correlation between precision engineering education and industry standards. Graduates equipped with a strong foundation in precision engineering principles demonstrate a higher level of proficiency in meeting industry requirements. Notably, the integration of hands-on training, simulation exercises, and real-world projects within the curriculum significantly enhances students' readiness for practical applications. Moreover, the study delves into the adaptability of precision engineering programs in addressing emerging technological trends and advancements. As industries evolve with the adoption of automation, artificial intelligence, and additive manufacturing, the curriculum's responsiveness to these changes becomes paramount. Evaluating the alignment between educational content and contemporary industry practices sheds light on areas necessitating refinement or enhancement. Furthermore, the research explores the role of industry-academic partnerships in shaping precision engineering education. Collaborative initiatives such as internships, co-op programs, and joint research projects facilitate knowledge exchange and bridge the gap between academia and industry. By fostering mutually beneficial relationships, these partnerships contribute to curriculum development that remains abreast of evolving industry needs. This study underscores the pivotal influence of precision engineering education on industry standards and practices. By continually evaluating and refining educational approaches, stakeholders can ensure that graduates possess the requisite skills and knowledge to thrive in an ever-changing industrial landscape.

THE ROLE OF LEADERSHIP STYLES IN ENHANCING WORKPLACE EFFECTIVENESS: CASE STUDY OF NIGERIAN MANUFACTURING FIRMS

Nigeria’s diverse manufacturing sector, characterized by varying organizational structures, product portfolios, workforce demographics, and market scope, necessitates nuanced leadership strategies. This study delves into the multifaceted environment of Nigerian manufacturing, emphasizing the importance of adaptive leadership practices amid dynamic industrial demands. This study aims to assess the prevalent leadership styles in Nigerian manufacturing firms, evaluate the impact of leadership styles on workplace effectiveness, and recommend context-specific leadership strategies. A mixed-method approach was employed by combining quantitative and qualitative methods. A structured questionnaire was administered to 320 participants using stratified random sampling. Statistical analyses included chi-square tests and ANOVA. Thematic analysis was applied to the qualitative responses. The results show that there are significant differences in the prevalence of leadership styles, affirming the need for diverse approaches, which significantly impact workplace effectiveness across various dimensions, emphasizing their critical role. Context-specific leadership strategies were identified through thematic analysis, highlighting the importance of tailored approaches. This study reveals the dynamic landscape of leadership styles in Nigerian manufacturing. These findings underscore the necessity of adaptive leadership to optimize workplace effectiveness. Practical recommendations offer valuable insights for leaders and policymakers and promote organizational success in this sector.

Factors influencing Digital Transformation Success: A Study on a Sri Lankan Commercial Bank

The purpose of this research was to identify the most influencing factors to the success of Digital Transformation (DT) journey of a commercial bank in meeting the digital savvy, dynamic customer demand in the banking business. The study has focused on the predominant stages of Digital Transformation, various theoretical models related to DT, the influence of the DT over Banking Industry, Benefits, Challenges and Barriers of DT along with various internal factors influencing Success of DT. The expansion of the DT phenomenon at national level over the years with the regulatory influence has urged Commercial Banks to digitally transform to be in the competition. The low success rate reported consistently for a considerable period in the selected commercial bank was identified as the Research problem for the study. The respondents have been selected from the population of managerial level employees and the scaling down of DT success had been concentrated on the empirically used “Digital Maturity Model”. The conceptual framework consists of four independent variables contributing to DT success most cautiously selected by researchers from the previous literature on the higher frequency in simultaneous research. The Author recommends the strategies and measures to increase the low success rate reported in the DT initiatives of the Banks such as (1) Recognizing Change and urgent response (2) Strategic Alignment (3) Reinvigoration of Culture (4) Training and upskilling workforce (5) Business perspective Technology disruption (6) Significance on Data Culture and (7) Digital Product Development. The research significantly covers the theoretical gap in the DT discipline in local context and by assessing the internal factors subject to inside-out approach anticipating making a reference to support sustainable future growth in the Digital Business in the Banking Industry despite the limitations of individuals researching on a typical discipline like Digital Transformation. Keywords: Digital Business, Digital Maturity Model, Digital Savvy, Digital Transformation.

Converting passenger aircraft into cargo planes under volatile market demand

The COVID-19 pandemic has severely affected airlines’ passenger businesses, while the air cargo sector has been more resilient. To reduce the financial pressure and tap into the profitable cargo business, airlines around the world are actively converting passenger aircraft into cargo planes (i.e., preighters). This paper develops an integrated economic model to examine airlines’ aircraft conversion strategies when facing dynamic market demand due to the pandemic. We consider the possibility of temporary conversion, i.e., passenger aircraft that have been converted into preighters during the pandemic can be converted back into passenger aircraft after the pandemic. We find that when the air cargo transport price is exogenous, the socially optimal conversion rates are lower than the profit-maximizing level for airlines in both the pandemic and the post-pandemic periods, and a government subsidy reduces social welfare. Competition in the passenger market or the cargo market increases conversion rates under both the airline optimum and social optimum, and widens the gap between them. However, if the airlines can influence the air cargo transport price, conversion rates may be higher under the social optimum than under the airline optimum. Competition in the passenger market and the cargo market has opposite effects on the two optimal conversion rates. When the expected cargo market size is small (large), the conversion rates in the social optimum are lower (higher) than the airline optimums, and a government subsidy worsens (enhances) social welfare. Finally, we extend our model by incorporating more real-world factors, such as the lower efficiency of preighters than that of freighters, the freighter leasing market and airlines’ lay-up of idle passenger aircraft. Most of our analytical results remain valid.

Traffic Signs and Real-World Driver Interaction

The purpose of this field study was to compare nighttime traffic sign legibility under static (parked) and dynamic (driving) conditions using bottom-up legibility as a measure of performance. Drivers need more traffic sign luminance to recognize words on signs while driving than they need when sitting still (parked). The primary reason for this increase in luminance demand is that the driver must periodically shift visual attention away from the sign to attend to the driving task. Legibility performance was assessed with N = 18 observers driving at an assigned speed of 48 km/h (30 mph). Participants ranged in age from 55 to 77 with a mean of 66 years. At a distance of 36 m (120 ft) there was a 117% increase from the static to the dynamic demand luminance. At a longer distance of 55 m (180 ft), there was a 256% increase, and at a distance of 57 m (187 ft), the increase was 556%, and beyond that distance, we could no longer generate a luminance that was sufficient to meet the dynamic demand.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

A Simulation-Based Bayesian Network Approach to the Joint Decision of the International Transportation Mode and Safety Inventory Policy

Uncertainty in international transportation can have a significant impact on inventory costs and customer service levels in a global supply chain network. A very limited number of studies in the literature have addressed the international transportation mode and safety inventory policy (ITM-SIP) issues simultaneously. This study formulates the problem and proposes a simulation-based Bayesian network (SBN) approach for the joint decisions on ITM-SIP, where Bayesian networks are used to dynamically estimate international replenishment lead times for railway and maritime transportation modes. We implement our approaches in an international automotive spare parts supply chain experiencing fluctuations in its shipping supply market and with dynamic demand. For this joint decision problem, we build an international supply chain network simulation model in which the time-delay uncertainties of key bottleneck nodes in maritime and railway transportation routes are considered. The simulation experiment results show the performance improvement of the SBN approach in substantially reducing total costs and achieving service levels. We also provide insights into the value of decision tactics for responding to different fluctuation modes in the shipping supply market in the global supply chain network.

Developing a dynamic quality prediction model for limited samples target grade based on transfer learning

In response to the dynamic demands of the market, this study addresses the challenge of frequent operational adjustments in a production line to accommodate diverse product grades. The resulting scarcity of data in the new operating conditions (target grade) impedes the development of reliable soft-sensor prediction models. A two-step learning method, termed S2-LGMNSSM-TS-T, is proposed. This method employs a semi-supervised latent Gaussian mixture nonlinear state space model (S2-LGMNSSM-TS) trained on both target and source data, providing a dynamic, one-step-ahead predictive soft sensor. To overcome data scarcity in the target grade, insights from the source are utilized. With a Gaussian mixture prior distribution, S2-LGMNSSM-TS identifies dynamic behaviors in both target and source grades. The enhanced S2-LGMNSSM-TS-T configuration focuses on target-grade predictions, leveraging source knowledge and mitigating data scarcity issues. A numerical example and an industrial case study demonstrate the model's effectiveness in improving target-grade predictions through source knowledge utilization.

Bridging the Gap between Theoretical Learning and Practical Application: A Qualitative Study in the Italian Educational Context

In the contemporary educational landscape, there is a growing recognition of the transformative impact of practical experiences within traditional learning frameworks. This shift reflects a pedagogical evolution that values contextualized learning and the acquisition of practical skills together with theoretical knowledge. In the Italian educational context, School–Work Alternation (SWA) represents a proactive response to the evolving needs of the workforce and the imperative for educational institutions to prepare students for professional life. This study’s objectives include a deep exploration of students’ SWA experience, evaluating its impact on employability perceptions and the sense of agency, examining the influence of Self-Orientation, and contributing insights to the discourse on integrating practical experiences in education. Employing a mixed methodology and a bottom-up approach, 63 high school students of different Italian regions participated in an online in-depth interview and an ad hoc questionnaire designed to measure the experience of SWA in relation to variables of interest, utility, advantage, perception of support, quality of received mentoring, engagement, and satisfaction. The quantitative results indicate that personal choice significantly influences the perceived usefulness of and satisfaction with SWA, with those students guided by curiosity exhibiting higher utility and satisfaction. Qualitative analysis underscores both positive and negative aspects, with respondents viewing SWA as a useful experience bridging work, corporate, and school realms provided that students are key players in the SWA experience’s choice and that the SWA’s partners are motivated to guide them in this practical training. Moreover, results highlight SWA’s relevance in guiding academic and career paths, emphasizing its potential to offer valuable support to students. This study contributes nuanced insights into integrating practical experiences in education, offering recommendations for educators and policymakers to better prepare students for the dynamic demands of the modern job market.

A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

Pricing of Drinking Water under Dynamic Supply and Demand based on Government Role: A Game-Theoretic Approach

Pricing of Drinking Water under Dynamic Supply and Demand based on Government Role: A Game-Theoretic Approach

1-out-of-n standby systems with storages and activation moment-dependent component operation time limit

This paper pioneers the modeling and optimization of a 1-out-of-n standby system with a required mission time and non-identical components, whose operation times are limited and dependent on their activation time and dynamic resource supply and storage units. Each component contains a main operating unit characterized by certain time-to-failure distribution and mode-dependent resource demands, a resource generator characterized by a time-varying resource generation rate, and a storage unit characterized by an initial amount of resource and the maximum capacity. The resource generator and storage interact collaboratively to supply the dynamic resource demand of the main unit. Based on an event transition probabilistic model, a new numerical algorithm is proposed for assessing the mission success probability (MSP) of the considered standby system. An optimization problem is further formulated and solved, which determines the activation sequence of standby components maximizing the MSP. A detailed case study of a standby sensor system with the photo-voltaic power supply source and battery storage is provided to demonstrate the proposed model and examine the effects of several system parameters on the MSP and optimization solutions. Several useful findings related to the MSP improvement are revealed from the case study.

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Abstract Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and R 2 of 0.95, demonstrating adequate performance.

Dynamic Pricing Strategies and Demand Forecasting in Inventory Models for Deteriorating Items: A Theoretical Framework

Dynamic Pricing Strategies and Demand Forecasting in Inventory Models for Deteriorating Items: A Theoretical Framework