Scheduling System Research Articles

Research on High-performance Computing Network Search System based on Computer Big Data

An efficient computing system for massively parallel systems is established. The stochastic Petri net abstracts and models the high-performance computer work scheduling system. The IB switch is modeled. Stochastic Petri nets are used for performance analysis. Finally, the proposed method is combined with InfiniBand interconnection architecture to evaluate the system's delay. The experimental results prove the feasibility of this algorithm.

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

A novel deviation measurement for scheduled intelligent transportation system via comparative spatial-temporal path networks

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.85 and a relative distance of 3.47. Furthermore, case analyses are invested in regions of China's Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

Comparative analysis in selecting best irrigation method to maximize tomato yield from various irrigation approaches in water scarce regions

It is now essential for farmers to evaluate and compare different irrigation scheduling strategies in order to operate their irrigation systems in accordance with their resources and achievements. For tomato crops, a rigorous comparison was made between the yield of soil moisture and irrigation scheduling systems based on evapotranspiration. The experimental setup used a randomized complete block design (RCBD) with three replications, with water requirements of 100%, 75%, and 50%. Using a ReplogleBos-Clemmens (RBC) flume with an input rate of 1.62 l/s, water was applied to the furrows. The total applied amount in the Soil Moisture (SM) and Evapotranspiration (ET) based methods was 229.1 mm, and 280 mm, respectively. The collected data were assessed using ANOVA at a 5% significance level. For every technique, yield values, yield attributes, and agronomic traits were computed. The result indicates that there is no appreciable variation among the factors. The net yield advantage was highest at 75% SM and lowest at 25% SM. Furthermore, compared to the ET-based approach, it was demonstrated that the SM-based method conserved approximately 18.2% of irrigation water. As a result, when producing crops that require a lot of water, like tomatoes, in areas with limited water resources, SM-based irrigation scheduling is preferable to ET-based irrigation scheduling.

Smart timetable display for students’ subject allocation in classroom using IOT

This essay describes the development of School bells have played a major role in all of our childhoods. But even with the advent of technology, a lot of things have gone digital, and the school bell is still a manual, antiquated bell. The first break occurs after two periods, and the second break occurs after the next two periods. After the fifth period, school is out. You can select a topic and duration for each period from a list of options in the project.The six days of the week that the user can set the schedule for are Monday through Saturday. The user can also alter the duration of each period. Our college's bell is set to ring continuously for ten seconds at a time in each of its blocks. This is accomplished by using data transmitted via the Internet of Things to notify teachers and students about the concurrent conclusion of a particular session in each block. In addition, a voice module is utilized to read aloud the data shown on the LCD module while a buzzer beeps to indicate an alarm. Three main parts were used in this Arduino college bell circuit: an Arduino Uno board, an IC RTCDS1307, and 16x2 LCD modules.The Arduino is a reading device.To develops this system, an STM32 controller, a Bluetooth module, a school bell buzzer, a 16 x 32-inch LED display, buttons, basic electronics components, and a PCB board are used. The STM 32 controller communicates with the user via the display. Both running and settings modes are available. The user can set the system's current schedule with timings using the Android app.

Automated Schedule System for Universities under the Bologna Education Process

Introduction. Countries that have joined the Bologna Process in higher education assume certain obligations. One of them is the transition to a credit education system, which is based on the personal participation of each student in the formation of their curriculum. Each student has the opportunity to structure the learning process, choose subjects and the sequence of their study within the framework of the curriculum for the chosen specialty, create their own individual schedule in accordance with their interests, capabilities and needs. The credit training system is asynchronous. Naturally, software applications for scheduling classes, developed for the classical education system, do not work in this case. Purpose. Development of an algorithm and software for a class scheduling system designed for the credit education system and taking into account its features, and also meets all the “hard” restrictions and most of the “soft” requirements for the class schedule. Results. The algorithm and software of the interactive system of scheduling classes for universities that have joined the Bologna process, which takes into account the features of credit-modular system of training, have been developed. Conclusions. The developed system covers the full cycle of including students in groups to study selected subjects and teachers, starting from scheduling teachers, registering students and ending with the formation of individual schedules for each student; The proposed iterative process of scheduling makes it possible to make changes to the schedule of teachers and re-register students in the case of teachers who have not formed groups, or a significant number of students who were unable to enroll in subjects of interest to them; The interactive mode of operation with the system combines an automated process of scheduling with active participation of the user responsible for scheduling; The system guarantees the preparation of permissible timetables for teachers and students, taking into account the implementation of all "hard" restrictions, and significantly reduces the time required for scheduling; The system ensures to the maximum extent the quality of teachers’ schedules, namely, taking into account “soft” requirements with the active participation of the user, who has the opportunity to choose an option that reduces the value of the function proposed in the article that evaluates the quality of the schedule; The developed system ensures the effective use of the classroom fund in accordance with its capacity, specialization and affiliation. Keywords: Bologna process of education, credit system, asynchronous learning system, class schedule, scheduling requirements, computer system.

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

A self-learning framework combining association rules and mathematical models to solve production scheduling programs

ABSTRACT Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

Validation of farmers’ on-farm irrigation scheduling for optimal water utilization in Tigray Region, Ethiopia

Unwise use of irrigation water in water-scarce areas exacerbates water scarcity, diminishes crop yield, and leads to resource wastage. In the Tigray region of Ethiopia, where water is scarce and a determinant resource, no regional-level validation of on-farm irrigation scheduling had been conducted until now. The objectives of this study were to (i) validate farmers’ irrigation scheduling practices and (ii) optimize water consumption for increased irrigated area and number of farmers. Eight different irrigation schemes were purposively selected from the Tigray region. Primary data, acquired through field measurements, observations, and discussions, were supplemented with secondary data. Farmers' irrigation scheduling practices were predominantly of the "Fixed" type, characterized by constant irrigation intervals and amounts. Unfortunately, these practices were non-optimal, with 55% over-irrigation and 45% under-irrigation. Over-irrigation instances ranged from 1350 m3in a garlic plot within the Mesima scheme to a maximum of 1,327,067 m3in a maize plot within the Serenta scheme. The excess water could potentially be utilized to irrigate an additional area of 2 to 148 hectares, thereby benefiting 7 to 296 more farmers, respectively. Conversely, instances of under-irrigation in the Fre Lekatit scheme resulted in yield reductions of 10,445 kg for potatoes and 138,499 kg for maize. In the Tigray region, most of the schemes are semi-arid, emphasizing the imperative need for water conservation. Renovating regional-level irrigation scheduling by integrating performance assessment and enhancing water productivity at the field level, and establishing a remote-sensing-based 'Real-Time Irrigation Scheduling System' is deemed necessary to sustain dryland irrigated agriculture.

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

A dynamic traffic signal scheduling system based on improved greedy algorithm.

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.

Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age. The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval. The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.

Holistic Energy Awareness and Robustness for Intelligent Drones

Drones represent a significant technological shift at the convergence of on-demand cyber-physical systems and edge intelligence. However, realizing their full potential necessitates managing the limited energy resources carefully. Prior work looks at factors such as battery characteristics, intelligent edge sensing considerations, planning, and robustness in isolation. But a global view of energy awareness that considers these factors and looks at various tradeoffs is essential. To this end, we present results from our detailed empirical study of battery charge-discharge characteristics and the impact of altitude and lighting on edge inference accuracy. Our energy models, derived from these observations, predict energy usage while performing various manoeuvres with an error of 5.6%, a 2.5X improvement over the state-of-the-art. Furthermore, we propose a holistic energy-aware multi-drone scheduling system that decreases the energy consumed by 21.14% and the mission times by 46.91% over state-of-the-art baselines. To achieve system robustness in the event of link or drone failure, we observe trends in Packet Delivery Ratio to propose a methodology to establish reliable communication between nodes. We release an open-source implementation of our system. Finally, we tie all of these pieces together using a people-counting case study.

High-Performance Multi-GPU Analytic RI-MP2 Energy Gradients.

This article presents a novel algorithm for the calculation of analytic energy gradients from second-order Møller-Plesset perturbation theory within the Resolution-of-the-Identity approximation (RI-MP2), which is designed to achieve high performance on clusters with multiple graphical processing units (GPUs). The algorithm uses GPUs for all major steps of the calculation, including integral generation, formation of all required intermediate tensors, solution of the Z-vector equation and gradient accumulation. The implementation in the EXtreme Scale Electronic Structure System (EXESS) software package includes a tailored, highly efficient, multistream scheduling system to hide CPU-GPU data transfer latencies and allows nodes with 8 A100 GPUs to operate at over 80% of theoretical peak floating-point performance. Comparative performance analysis shows a significant reduction in computational time relative to traditional multicore CPU-based methods, with our approach achieving up to a 95-fold speedup over the single-node performance of established software such as Q-Chem and ORCA. Additionally, we demonstrate that pairing our implementation with the molecular fragmentation framework in EXESS can drastically lower the computational scaling of RI-MP2 gradient calculations from quintic to subquadratic, enabling further substantial savings in runtime while retaining high numerical accuracy in the resulting gradients.

A dynamic simulation framework based on hybrid modeling paradigm for parallel scheduling systems in warehouses

Efficient warehouse management is of crucial significance for the smooth operation of a company's supply chain. With the challenging nature of warehouse environment changes, research on warehouse operational issues is increasingly important. Moreover, system simulation has emerged as a prevalent means of investigating warehouse operational management. However, prior research on warehouse simulation either utilized singular paradigms or lacked a generalized modeling framework. This remains a challenge for modelers exploring diverse domains of warehouse-related issues using simulation techniques. In this context, this study presents an integrated control methodology(ICM) based on the state changes of dispatchers and logistics equipments, the discrimination of task scenarios, and the behaviors of dispatchers. This methodology is incorporated into the warehouse workflow model. The workflow model serves as a conceptual abstraction of a warehouse's parallel scheduling system, while the integrated control methodology (ICM) simulates the entire decision-making process of dispatchers to resolve potential deadlock issues during task execution. Subsequently, we utilize a steel slab warehouse in a case study, employing a dynamic simulation using a hybrid paradigm based on Discrete Event Simulation (DES) and Agent-Based Simulation (ABS) to replicate the historical scheduling process within the warehouse. This demonstration confirms the feasibility of the proposed framework. Finally, we devise multiple dimensions of validation metrics to confirm the model's effectiveness.

A study on constructing an efficient examination scheduling system

The objective of this study is to investigate the final exam scheduling process at the Ho Chi Minh City Open University and develop an automated exam scheduling application. Our primary objectives are to prevent students from having conflicting exam schedules and to ensure that no student has to take more than two exams on the same day. This research focuses on applying graph coloring algorithms to the problem of automatic exam scheduling. Our research findings indicate that the graph coloring algorithm is highly effective for automated exam scheduling. This study has the potential to expand and support the development of an automatic exam scheduling and management system, in line with our overall goals. We conduct the experiments on the practical data at HCMCOU and obtain promising results.

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Advanced Systems of Planning and Scheduling and the Limitations of their Implementation: A Study Case of Al kufa Old Cement Factory

The present study is based on research and investigation into planning and scheduling systems as a basic variable and the limitations of applying planning and scheduling systems as a dependent variable to investigate the most important obstacles that prevent their application due to the development of the technology in planning and scheduling systems in addition to the advances in the algorithms of optimization by using a computer to make most of the routine decisions and using interactivity to send the information and the real understanding of the efficiency of the planners. And by changing the competition between the manufacturing companies around the world, as well as the relationships between these companies, suppliers, and customers. Supply chain management has greatly reduced the planning problem, and the concepts of flexible production have contributed to changing the relationship between suppliers and consumers at every link in the supply chain. This was done through a questionnaire that was distributed to the company’s management to investigate the extent to which this factory benefits from these modern systems, the obstacles that curb applying these systems in this factory, and the extent to which this industrial factory seeks to obtain modern systems, to cope with development, and be able to improve their performance, which will contribute to increasing their efficiency and ability to compete in the labor market.

Distributed collaborative scheduling technology for random access in real-time

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

A scheduling optimization method for multiple automated guided vehicle systems

When constructing a multiple automated guided vehicle (multi-AGV) system, it is essential to calculate the potential values of the system’s feature parameters. This article presents two time-cost calculators and an improved simulation platform for scientifically estimating the cost of constructing a multi-AGV system. By inputting the parameters of the number of automated guided vehicles (AGVs), the velocity of the AGV, the number of tasks, scheduling interval and path length, the designed calculator can output the system’s time expense when all tasks are completed. The designed simulation platform can display a specific multi-AGV system’s time cost and how the entire progress works in a visual interface. An advanced scheduling method based on handover mode and A* algorithm is used to solve task scheduling problems in a multi-AGV system. The overall performance of the newly integrated scheduling system is compared with other scheduling systems to validate its superiority and shortcomings in different corresponding work scenarios. The final results present a robust solution to improve a multi-AGV system’s effectiveness.