Peer-to-Peer Scheduling System with Scalable Information Sharing Protocol

The demand for high-quality education delivery in increasingly dynamic and competitive educational markets has intensified the need for intelligent and adaptive scheduling systems. Manual classroom scheduling methods, which rely on human decision-making, often fail to optimize critical resources such as classrooms, teachers, and student time, leading to inefficiencies and economic losses. This paper proposes a comprehensive Smart Classroom Scheduling and Optimization System (LMSOPT) that leverages Artificial Intelligence (AI), advanced time-series forecasting, constraint-based multi-objective optimization, and real-time data integration. The proposed system employs Long Short-Term Memory (LSTM) neural networks for highly accurate demand forecasting, alongside heuristic and metaheuristic optimization algorithms such as Constraint Programming (CP), Genetic Algorithms (GA), and Tabu Search. The system aims to dynamically balance multiple conflicting objectives: maximizing classroom occupancy rates, minimizing student waiting times, and aligning teacher availability with student preferences. The expected contributions are multifold: significant operational cost savings, measurable improvements in resource utilization, increased student satisfaction, and the creation of an extensible research framework for AI applications in education management. The study aligns with national strategies for digital transformation and supports the vision of data-driven decision-making in educational administration. Empirical results and comparative analyses are presented to validate the system’s effectiveness and demonstrate its replicability for institutions of various scales.

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

As a potential alternative energy source, oil shale has richer reserves than traditional fossil fuels. Its comprehensive utilisation has been extended to many fields, improving resource utilisation, and reducing costs and environmental pollution. By analysing the resource characteristics, development technology, utilisation mode, policy measures, and environmental impacts of oil shale, this paper summarises the current status of the development of China’s oil shale industry and the existing problems and puts forward development prospects and suggestions. The study found that oil shale development faces challenges such as complex geological conditions, immature technology, imperfect policies, and high environmental impacts. However, through technological innovation and policy support, resource utilisation can be effectively improved, costs reduced and environmental pollution reduced. This paper is expected to provide reasonable suggestions for the rational development and efficient use of oil shale resources in China. The strategic and effective exploitation of oil shale resources in China has the potential to decrease reliance on conventional oil and gas sources, bolster energy self-sufficiency and stability, and significantly contribute to the restructuring of the global energy landscape, contributing to the realisation of energy diversification and sustainable development.

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.

Multi-objective Optimization of Shared Logistics and Express Delivery Platforms: an Algorithm Framework for Balancing Cost, Time, and Resource Utilization

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

With the advent of Industrial Internet-of-things (IIoT), flexible manufacturing has gained increasing attention. Continuous changes in market demand, real-time data collection and device interconnectivity have made the production process more dynamic and unpredictable, rendering traditional manufacturing scheduling methods inadequate in addressing these changes. This issue can be framed as a Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which seeks to accommodate ever-evolving production scenarios and requirements by making real-time modifications and optimizing resource allocation. Traditional scheduling algorithms designed for static, single-environment scenarios are increasingly inadequate for handling the growing complexity of production environments. In this context, there is a pressing need for efficient and real-time scheduling algorithms. We propose a Multi-Agent Reinforcement Learning (MARL) algorithm to solve DFJSP, where each device is associated with a corresponding agent, allowing the algorithm to scale flexibly. The complexity and dynamics of scheduling problems introduce additional challenges and complexities in state representation and decision-making. We propose two solutions to alleviate this problem. First, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the relational dependencies between tasks and extract state features. Through multiple feature updates, each agent is enabled to make decisions based on global information. Furthermore, leveraging the inherent Permutation Invariance (PI) and Permutation Equivariance (PE) features of tasks in the waiting queue, we apply hypernetwork techniques to address the issue of dimensionality explosion caused by the excessive state space in scheduling environments. Experiments conducted under various scenario settings demonstrate that our scheduling method can significantly reduce task latency and improve resource utilization.

Naval Engineers JournalVolume 117, Issue 2 p. 67-76 Keynote Address — Wednesday Dr. Clark Graham, Dr. Clark Graham DR. CLARK GRAHAM was appointed to the position of Senior Vice President of Advanced Ship Technology for Northrop Grumman Ship Systems on 15 October 2002, bringing with him his distinguished record in advanced design, development and ship systems engineering for the Navy in critical leadership positions. In September 2003, Dr. Graham was appointed to the position of Technical Director of the DD(X)Program concurrent with his duties as Senior Vice President, Advanced Ship Technology. Dr. Graham previously was the Senior Vice President, Operations, where he assumed sector level management responsibilities for the consolidated Ingalls and Avondale businesses. Dr. Graham joined the company February 1, 2001, as Vice President, Resource Utilization and Productivity Improvement starting up the sector's Lean Process Improvement Program.Search for more papers by this author Dr. Clark Graham, Dr. Clark Graham DR. CLARK GRAHAM was appointed to the position of Senior Vice President of Advanced Ship Technology for Northrop Grumman Ship Systems on 15 October 2002, bringing with him his distinguished record in advanced design, development and ship systems engineering for the Navy in critical leadership positions. In September 2003, Dr. Graham was appointed to the position of Technical Director of the DD(X)Program concurrent with his duties as Senior Vice President, Advanced Ship Technology. Dr. Graham previously was the Senior Vice President, Operations, where he assumed sector level management responsibilities for the consolidated Ingalls and Avondale businesses. Dr. Graham joined the company February 1, 2001, as Vice President, Resource Utilization and Productivity Improvement starting up the sector's Lean Process Improvement Program.Search for more papers by this author First published: 29 October 2008 https://doi.org/10.1111/j.1559-3584.2005.tb00339.x AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume117, Issue2April 2005Pages 67-76 RelatedInformation

Over 90% of injury deaths occur in low-income countries. Evaluating the impact of focused trauma courses in these settings is challenging. We hypothesized that implementation of a focused trauma education initiative in a low-income country would result in measurable differences in injury-related outcomes and resource utilization. Two 3-day trauma education courses were conducted in the Rwandan capital over a one-month period (October-November, 2011). An ATLS provider demonstration course was delivered to 24 faculty surgeons and 15 Rwandan trauma nurse auditors, and a Canadian Network for International Surgery Trauma Team Training (TTT) course was delivered to 25 faculty, residents, and nurses. Trauma registry data over the 6 months prior to the courses were compared to the 6 months afterward with emergency department (ED) mortality as the primary endpoint. Secondary endpoints included radiology utilization and early procedural interventions. Univariate analyses were conducted using χ(2) and Fisher's exact test. A total of 798 and 575 patients were prospectively studied during the pre-intervention and post-intervention periods, respectively. Overall mortality of injured patients decreased after education implementation from 8.8 to 6.3%, but was not statistically significant (p = 0.09). Patients with an initial Glasgow Coma Score (GCS) of 3-8 had the highest injury-related mortality, which significantly decreased from 58.5% (n = 55) to 37.1% (n = 23), (p = 0.009, OR 0.42, 95% CI 0.22-0.81). There was no statistical difference in the rates of early intubation, cervical collar use, imaging studies, or transfusion in the overall cohort or the head injury subset. When further stratified by GCS, patients with an initial GCS of 3-5 in the post-intervention period had higher utilization of head CT scans and chest X-rays. The mortality of severely injured patients decreased after initiation of focused trauma education courses, but no significant increase in resource utilization was observed. The explanation may be complex and multi-factorial. Long-term multidisciplinary efforts that pair training with changes in resources and mentorship may be needed to produce broad and lasting changes in the overall care system.

In order to provide users timely and reliable services, resources and facilities in cloud data centers are often more than actual needs, which leads to low resource utilization and high operating costs. However, controling the quantity of resources and facilities may cause low throughput. Therefore, many researches are devoted to increasing resource utilization, improving jobs throughput and reducing operating costs through effective cloud resource management. The cloud resource management in cloud data center can be regarded as a multi-resource demand job allocation problem. This paper proposed an allocation algorithm of jobs based on deep reinforcement learning. The algorithm allocates waiting jobs to clusters respectively, so as to optimize throughput and improve resource utilization. The algorithm is implemented in cloud data center simulation model CloudSim. The experimental results show that the method based on Deep Reinforcement Learning is better than traditional method in multi-resource job allocation problem.

The increasing adoption of digital health care solutions offers a transformative opportunity to improve patient access while addressing sustainability and cost-efficiency challenges. Our study evaluates a virtual remote care system (VRCS) that has been implemented at our clinic in northern Sweden. By combining patient-centered outcomes, environmental metrics, and health economic models, this study examines the impact of a VRCS on patient satisfaction, resource utilization, and environmental sustainability. Eighty patients with chronic pain, treated with spinal cord or dorsal root ganglion stimulation, participated in this study. All accessed the NeuroSphere Virtual Clinic platform for remote consultations and troubleshooting. A patient satisfaction survey assessed usability, trust, and preference for digital vs in-person consultations. A health economic model analyzed travel costs, CO2 emissions, and time burdens for in-clinic consultations vs a mixed care pathway across three scenarios of VRCS adoption (20%, 50%, and 100%). The survey results indicated high levels of patient satisfaction, with an average score of 9.1 out of 10, and strong trust in the platform, reported by 98.75% of participants. The introduction of digital consultations led to substantial reductions in travel-related burdens, including a 75% decrease in costs, CO2 emissions, and travel time during the first year, stabilizing at a 70% reduction in subsequent years. Patients traveling over 500 km experienced the greatest benefits, with annual savings of €525, a reduction of 231 kg in CO2 emissions, and 19 fewer hours of travel time. The mixed care pathways enhanced accessibility, reducing cancellations and no-shows by up to 59%. Despite increased troubleshooting consultations, hospital resource utilization remained stable, with only marginal increases in workload and space utilization. The VRCS aligns with key health care priorities, including improving access, enhancing patient experience, and promoting sustainability. It addresses regional challenges, such as long travel distances and harsh winters, while reducing the ecological footprint of health care delivery. The platform's ability to decrease travel burdens and CO2 emissions aligns with global sustainability goals. However, barriers such as digital literacy and data privacy require further attention to ensure equitable access. The VRCS demonstrates significant benefits, including high patient satisfaction, reduced travel burdens, and improved resource utilization. By aligning with sustainability objectives, it offers a scalable model for digital transformation in health care, underscoring the importance of continued investment in remote care technologies.

The emergence of Large Language Models (LLMs) in Multi-Agent Systems (MAS) has opened new possibilities for artificial intelligence, yet current implementations face significant challenges in resource management, task coordination, and system efficiency. While existing frameworks demonstrate the potential of LLM-based agents in collaborative problem-solving, they often lack sophisticated mechanisms for parallel execution and dynamic task management. This paper introduces DynTaskMAS, a novel framework that orchestrates asynchronous and parallel operations in LLM-based MAS through dynamic task graphs. The framework features four key innovations: (1) a Dynamic Task Graph Generator that intelligently decomposes complex tasks while maintaining logical dependencies, (2) an Asynchronous Parallel Execution Engine that optimizes resource utilization through efficient task scheduling, (3) a Semantic-Aware Context Management System that enables efficient information sharing among agents, and (4) an Adaptive Workflow Manager that dynamically optimizes system performance. Experimental evaluations demonstrate that DynTaskMAS achieves significant improvements over traditional approaches: a 21-33% reduction in execution time across task complexities (with higher gains for more complex tasks), a 35.4% improvement in resource utilization (from 65% to 88%), and near-linear throughput scaling up to 16 concurrent agents (3.47× improvement for 4× agents). Our framework establishes a foundation for building scalable, high-performance LLM-based multi-agent systems capable of handling complex, dynamic tasks efficiently.

Field Programmable Gate Arrays (FPGAs) accelerate the expansion of the Internet of Things by enabling seamless device integration, energy efficiency, low latency, and high determinism, thanks to their reconfigurability feature. However, FPGAs must be protected against various security threats to guarantee intended operation. Physically Unclonable Functions (PUFs), which utilize a device’s intrinsic physical properties, have become a robust security primitive that overcomes the limitations of conventional security solutions. This work proposes a lightweight, platform-independent Ring Oscillator (RO) PUF that adapts the cross-platform RO PUF (CPRO PUF) technique. Its main goal is to minimize the hardware footprint and improve resource efficiency by optimizing the use of various logic components. The proposed PUF has been implemented and validated on Xilinx Artix-7 (28 nm) and Spartan-6 (45 nm) FPGAs. In comparison with earlier studies, the obtained results show improvements in resource utilization, reliability, uniqueness, and uniformity, while ensuring the intended security.

Chapter 8 - Local Resource Consumption Shaping: A Case for MapReduce

Traditional rule-based industrial automation in Electric Vehicle (EV) and Industry 4.0 manufacturing creates critical bottlenecks in efficiency, adaptability, and quality control, resulting in substantial operational losses. To address this rigidity, we conducted a systematic analysis of 1,247 publications and 342 manufacturing use cases to establish a taxonomy of agentic systems. Empirical validation was performed through longitudinal case studies with a tier-1 EV manufacturer across NA, EMEA, and Asia, where we compared traditional automation, purely autonomous systems, and Human-AI Collaborative approaches. Our analysis reveals a critical fragmentation in agentic toolkits and a significant operational disconnect between AI governance theory and operational deployment. In response, we developed the Integrated Agentic Foundry (IAF) framework, which integrates adaptive learning and multi-agent coordination. This framework showed remarkable improvements, including a 34% reduction in unplanned downtime (p<0.01), a 28% decrease in quality defects, and a 41% improvement in resource utilization. Notably, human-AI collaborative approaches consistently outperformed purely autonomous systems, proving large effect sizes (Cohen's d=0.72−1.14). This study redefines modern manufacturing intelligence by demonstrating that the future of smart manufacturing lies in human augmentation, not replacement. The IAF framework provides a comprehensive roadmap for transitioning manufacturing ecosystems from rigid automation to adaptive intelligence, enabling unprecedented operational excellence.