A deferred acceptance mechanism for decentralized, fast, and fair childcare assignment

We design and implement a program-proposing deferred acceptance mechanism with ties (DAT) and apply it to childcare assignment in two German cities. The mechanism can accommodate complementarities in providers' preferences, is fast to terminate even in larger cities, is difficult to manipulate in practice, and produces stable allocations. It can be further sped up by introducing two new features. First, allowing for an arbitrary share of facilities who participate in a centralized manner by submitting a rank-order-list over applicants. Second, by breaking ties in applicants' rank-order-lists on a first-come-first-serve basis, which sets incentives for programs to propose faster. We provide and evaluate simulation results.

Scheduling and Synchronization Algorithms in Operating System: A Survey

An operating system is software that is designed to manage computer hardware and software resources. However, this management requires applying an ample number of techniques and algorithms which are called synchronization and scheduling. The scheduling algorithms are used to arrange the way that the CPU is assigned to the processes, while synchronization is utilized to indicate how to work with multi-processes at the same time. Therefore, they are related to each other. CPU scheduling is a vital phenomenon of an operating system. At present, numerous CPU scheduling algorithms exist as First Come First Serve) FCFS(, Shortest Job First (SJF), Shortest Remaining Time First (SRTF), Priority Scheduling, and Round Robin (RR). In this paper, a survey of the current synchronization and scheduling algorithms have been presented. An overview of each technique with the main algorithms have been described in detail with the advantages and the issues of each algorithm. Furthermore, this paper has dug deep into the real-time operating system scheduling issues, which is the current trend in operating system researches.

Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

Applying Task Scheduling For IOMT-Cloud Business Optimisation Using AI

The IoMT-cloud enables a surplus extent of customers to get disseminated, versatile, and virtualized gear just as programming structure over the Internet. The IoMT-cloud is one of the principal headway used recently, it grants customers to get cloud resources over the internet remotely. Hence, we need to complete a reasonable task scheduling estimation to tolerably and viably meet these requests. The scheduling of task issue is perhaps the most essential issue in the IoMT-cloud since cloud execution depends prevalently upon it. Capable task scheduling administration should meet customer's requirements and improve the resources used to overhaul the introduction of the IoMT-cloud framework. To deal with this issue, in this investigation, we attempt to show the two most notable static and one dynamic task scheduling execution separately, short job first (SJF), first come first serve (FCFS), and round-robin (RR). Likewise, it was advanced using the AI technique known as genetic algorithm (GA). The CloudSim simulation framework is used to measure their impact on total execution time (TET), algorithm complexity, throughput, resource utilization, total waiting time (TWT), availability of assets, total finish time (TFT), cost, and resource utilization. The model proposed is to improve the viability of task scheduling for the IoMT-cloud stage with the best execution rate of 32.47ms. The exploratory results show that GA cuts down the cost of planning and reduces the total time, which is a convincing computation for the IoMT-cloud task scheduling.

Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II

Effective appointment scheduling (EAS) is essential for the quality and patient satisfaction in hospital management. Healthcare schedulers typically refer patients to a suitable period of service before the admission call closes. The appointment date can no longer be adjusted. This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Based on these two algorithms, this paper has addressed the multi-criteria method in appointment scheduling. This paper computes WOA and NSGA with various hypotheses to meet the analysis and different factors related to patients in the hospital. In the last part of the model, this paper has analyzed NSGA and WOA with three cases. Fairness policy first come first serve (FCFS) considers the most priority factor to obtain from figure to strategies optimized solution for best satisfaction results. In the proposed NSGA, the FCFS approach and the WOA approach are contrasted. Numerical results indicate that both the FCFS and WOA approaches outperform the strategy optimized by the proposed algorithm.

ANALISA PERBANDINGAN PENJADWALAN PRODUKSI DENGAN METODE CDS (CAMPBELL DUDEK SMITH), EDD (EARLIEST DUE DATE) DAN FCFS (FIRST COME FIRST SERVE) PADA PT. SARI WARNA ASLI UNIT V

Scheduling is defined as the process of sequencing the manufacture of a product as a whole on several machines. All industries need proper scheduling to manage the allocation of resources so that the production system can run quickly and precisely as of it can produce optimal product. PT. Sari Warna Asli Unit V is one of the companies that implements a make to order production system with the FCFS system. Thus, scheduling the production process at this company is also known as job shop production scheduling. The methods used in this research are the CDS method, the EDD method and the FCFS method. The purpose of this research is to minimize the production time and determine the best method that can be applied to the company. The results of this research showed that the makespan obtained in the company's scheduling system with FCFS rules was 458 minutes, and the results of scheduling using the CDS method obtained a makespan value of 329 minutes, then the best production scheduling method that had the smallest makespan value was the CDS method.

Performance analysis of CPU scheduling algorithms – A problem solving approach

Scheduling algorithms plays a significant role in optimizing the CPU in operating system. Each scheduling algorithms [8] schedules the processes in the ready queue with its own algorithm design and its properties. In this paper, the performance analysis of First come First serve scheduling, non-pre-emptive scheduling, Pre-emptive scheduling, Shortest Job scheduling First (SJF) and Round Robin algorithm has been discussed with an example and the results has been analysed with the performance parameters such as minimum waiting time, minimum turnaround time and Response time. This will help the young researchers to analyse algorithms to develop a new optimized algorithm for CPU optimization.

Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment

It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.

PERANCANGAN APLIKASI PESAN TANDING FUTSAL DENGAN METODE WATERFALL

Futsal merupakan salah satu jenis olahraga beregu atau berkelompok dan masuk cabang atletik dengan jumlah pemain dalam satu kelompok 5 pemain. Dengan banyaknya minat masyarakat akan olahraga sepak bola (futsal), lebih dari 30 club/team futsal se kecamatan Sindang. Sebanyak 60% dari jumlah itu menjadi pelanggan tetap di Vianos Futsal. Dalam satu minggu lebih dari 20 team/club menyewa lapangan. Jadi, untuk memudahkan penjadwalan dan pengaturan dalam penggunaan lapangan futsal (booking sarana), penulis merancang sistem pengaturan pinjaman lapangan dengan metode First Come First Serve (FCFS) yaitu pertama daftar, maka pertama dilayani. Dalam perancangan sistem informasi pesan tanding futsal, penulis melakukan beberapa metode pengembangan sistem yaitu dengan menggunakan metodologi SDLC. SDLC has a similar set of four fundamental phases: planning, analysis, design, and implementation. Dari hasil penelitian ini diperoleh rancang bangun aplikasi berupa aplikasi login dan register akun, aplikasi penyewaan, aplikasi laporan pemesanan, dan aplikasi atur jadwal. Tentunya masih terdapat kekurangan dalam sistem ini sehingga perlu adanya penelitian berikutnya untuk pengembangan sistem yang lebih baik

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.