Scheduling System Research Articles

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.

Joint computational and optimized resource allocation in edge computing networks

Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined.

Improving Real-Time Performance of Micro-ROS with Priority-Driven Chain-Aware Scheduling

Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS.

Penentuan Jumlah Persediaan Sparepart Berdasarkan Waktu Maintennance Headtruck Giga di PT. Bima Site Berlian

PT. Berkah Industri Mesin Angkat (PT.BIMA) is a company engaged in the maintenance and supply of port equipment. In the service process there are five types of transport aircraft at PT BIMA. Loading and unloading equipment operates continuously, causing a decrease in the level of equipment reliability and causing frequent breakdowns and high downtime on the machines, especially the Headtruck equipment. To minimize breakdowns and downtime, it is necessary to have a good maintenance scheduling system to prevent machine damage. Mean Time to Failure (MTTF) is one of the company's provisions as a reference for determining an effective maintenance schedule. Therefore, appropriate corrective action is needed to ensure the number of spare parts available so that equipment downtime is not too long which results in decreased/unstable levels of availability and reliability.

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Preoperative cross-matching in major elective surgical procedures in children: An audit of department’s transfusion practices

Abstract: AIMS: To determine the utilization of cross-matched packed red blood cell (PRBC) concentrate in children posted for major elective operative procedures. To evaluate the efficacy of a simple preoperative “screening criteria” for ordering preoperative cross-match. MATERIALS AND METHODS: Data were retrieved from online surgery scheduling system prospectively over a 4-month period (January–April 2022). The number of units cross-matched and those utilized were assessed. A screening criterion was devised in consultation with stakeholders for ordering blood. The utilization of PRBCs was reassessed after implementation of these criteria and reaudited (July–October 2022). A subset analysis of A1 (tunneled catheter placements) and A2 (core pediatric surgical procedures) groups was done to analyze blood utilization. RESULTS: Initial audit showed poor PRBC utilization with 46 units unused of the 54 cross-matched units, and most of these were from A1 group (3 of 29 units and 5 out of 25 units utilized in A1 and A2 groups, respectively). Reaudit after introducing cutoff criteria showed sharp decline (79.3%) in the number of cross-matched units and the number of units returned (84.6% decrease) in A1 group and the number of units returned unused dropped by 50% in the A2 group. There was visible reversal of indicators of utilization (cross-match to transfusion ratio dropped from 6.75 to 1.73 and TI increased from 0.163 to 0.613), indicating optimal utilization. CONCLUSION: This pilot audit clearly demonstrates the gap between requisition and utilization of cross-matched PRBCs. Formulating an effective screening criterion avoids unnecessary cross-matching and wastage of resources and maximizes utilization.

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

A next-generation electrical power system known as the "Smart Grid" (SG) uses two-way communication to generate, use, and transport electrical energy. Demand Response (DR) is one of the SG's primary features (DR). Smart meters in DR transmit a pricing signal to the consumer, allowing them to adjust their demand in reaction to the corresponding price signals. Day-Ahead and Time-of-Use are the most widely used load scheduling schemes, however, they deviate from the Real time pricing scheme (RTP). Due to its erratic nature, integrating renewable energy sources like solar and wind is a difficult process in SG. Because of the fluctuations in both energy consumption patterns and power rates, the majority of current methods for managing demand are predicated either on day-ahead or time-of-use pricing rather than real-time pricing. This study describes a load scheduling system that uses a Genetic Algorithm (GA) to classify various users according to their energy use in a real-time pricing environment. Our load scheduling problem is formulated utilizing the knapsack mathematical formulation technique to reduce the electricity expenditure. In order to keep the grid stable and reduce costs, renewable energy is integrated with the grid's energy to lower the Peak-to-Average Ratio (PAR). The efficiency of the suggested algorithm in terms of electricity cost and PAR reduction is supported by simulation results.

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.