Synchronous Scheduling Research Articles

Adapting an Interdisciplinary, Course-Based Undergraduate Research Experience (CURE) Course to an Online Format

August 2020 marked the inaugural CURE Science Bootcamp course. Due to COVID-19 restrictions, the entire bootcamp was taught virtually on a synchronous schedule using the learning management system Blackboard Collaborate Ultra. STEM transfer students completed all key elements of the traditional Science Bootcamp, with slight modifications due to the compressed schedule. In topic selection, students were nudged toward viable methodologies, given that in-person data collection was prohibited.

Perceptions of nutrition education classes offered in conjunction with a community-supported agriculture intervention among low-income families.

To examine participants' experiences with nutrition education classes that were implemented with and designed to complement a cost-offset community-supported agriculture (CSA) programme. Qualitative analysis of data from twenty-eight focus groups with ninety-six participants enrolled in Farm Fresh Foods for Healthy Kids (F3HK). Transcribed data were coded and analysed by a priori and emergent themes. Rural and micropolitan communities in New York, North Carolina, Vermont and Washington (USA). Ninety-six F3HK participants. Participants found recipes and class activities helpful and reported improvements in nutrition knowledge, food preservation skills and home cooking behaviours for themselves and their children; they also reported that classes promoted a sense of community. Some educators better incorporated CSA produce into lessons, which participants reported as beneficial. Other obligations and class logistics were barriers to attendance; participants recommended that lessons be offered multiple times weekly at different times of day. Other suggestions included lengthening class duration to encourage social engagement; emphasising recipes to incorporate that week's CSA produce and pantry staples and offering additional strategies to incorporate children in classes. Complementing a cost-offset CSA with nutrition education may enhance programme benefits to low-income families by improving nutrition knowledge and cooking behaviours. However, future interventions will benefit from ongoing coordination between educators and local growing trajectories to maximise timely coverage of unfamiliar produce in lessons; synchronous scheduling of CSA pick-up and classes for participant convenience and creative strategies to engage children and/or provide childcare.

A decision support framework for home health care transportation with simultaneous multi-vehicle routing and staff scheduling synchronization

Due to the ageing population and the prevalence of chronic diseases, Home Health Care (HHC) practices are significantly increasing in developed countries to provide coordinated health related services to patients at their homes. Accordingly, the scope of HHC services is also expanding from typical nursing and postoperative care at home to cover all types of needs of elderly patients (e.g., personal care, drug delivery and meal services). This paper aims to address the pressing demand for HHC services and develop a novel and effective mathematical model and solution methodology for supporting health care service delivery decisions. Our decision support framework captures the real needs of HHC services, including the challenges of creating simultaneous schedules and route plans for a set of HHC staff and Home Delivery Vehicles (HDVs) under the requirements of synchronization between HHC staff and HDVs visits, multiple visits to patients, multiple routes of HDVs and pickup/delivery visits related precedence for HDVs. A Mixed Integer Linear Programming (MILP) model is developed to characterize the optimization problem. Considering the computational complexity of the problem, a Hybrid Genetic Algorithm (HGA) is proposed to suggest HHC planning decisions. The model formulation and proposed HGA are examined on real-life instances for demonstrating its practicality and randomly generated test instances for assessing the scalability of the proposed approach. The results show the effectiveness and efficiency of our solution methodology. Experimental results indicate that the proposed algorithm provided a good performance even with an increasing number of required synchronized services, whereas the heuristic tactics facilitate the HGA to produce better-quality solutions in a significantly shorter time. Our framework is expected to contribute to an important aspect of shared healthcare mobility.

Scheduling synchronization in urban rail transit networks: Trade-offs between transfer passenger and last train operation

The scheduling synchronization problem in this paper is to obtain an optimal schedule by optimizing running time, departure time, dwell time and arrival time of the last train in urban rail transit networks. Operators are often faced with multiple conflicting requirements simultaneously such as high passenger’s service quality and less cost, smooth transfer events and less passenger’s travel time, and high accessibility and less operation time. Thus, researchers in this industry concentrate on utilizing operations and management decisions to solve the scheduling problem while balancing the service trade-offs. In this paper, we formulate a mixed integer programming approach for the last train schedule planning, and passengers benefit from smoother transfer in the form of maximizing the transfer synchronization events, while operators simultaneously can benefit with lower operation costs by minimizing the worst big difference between last trains. We propose an improved non-dominated sorting approach embedded in a genetic algorithm to obtain close-to-optimal solutions in a much shorter time for a sophisticated, real-world and large-scale Beijing subway network. Results demonstrate that significant service performance gains (76.33% for Just-missed, 32.01% for successful transfer, 15.39% for non-equity for last trains and 45.25% for variance indicators, etc.), which indicate the effectiveness of the proposed modeling framework and solution algorithm. The operator formulates an efficient schedule for the actual operation of the urban rail transit network by the proposed application method, and it also would be applicable to solving schedule problems among a large-scale network in other industries.

Binary Galois field based asynchronous scheduling protocol for delay tolerant networks

Neighbour discovery plays a crucial role for communication in sparsely dense mobile networks, especially in delay tolerant networks, where neighbour discovery latency is generally much higher than the node contact duration. Hence, energy efficient neighbour discovery is an essential aspect of this type of network. Synchronous wake-up scheduling of the nodes can be used to decrease the neighbour discovery latency, but such scheduling cannot be applied to delay tolerant networks where the essence of the communication lies in it’s decentralization. Additionally, the synchronous process needs a global clock for node synchronization. Using a global clock is an energy hungry process. Hence, to avoid this energy hungriness, over the years, many asynchronous protocols based on wake-up scheduling have been developed in order to timely wake-up the nodes to ease the neighbour discovery process in an energy efficient manner. However, asynchronous protocols have reduced message delivery and are not fine-grained enough to support high delivery ratio. In this paper, we have used a binary Galois field based technique for designing asynchronous wake-up scheduling. A thorough analysis has been performed to demonstrate the performance of this asynchronous protocol when compared to the current state of the art in the adaptive and the non-adaptive mode. Results show that the proposed protocol in non-adaptive mode increases message delivery probability by 10% and reduces message delivery latency approximately by 7% while keeping the energy consumption constant when compared to the other existing asynchronous protocols.

Research on AGV Scheduling Optimization Based on Hungarian Algorithm

Aiming at some problems in the mode of intelligent warehouse multi-selection and order outbound, this paper proposes to optimize the picking station assignment order task and order task assignment AGV in a “serial” or “parallel” manner to realize the synchronous scheduling of order tasks and AGV. The improved Hungarian algorithm is used to solve the optimal assignment solution. Simulation proves that this strategy can effectively improve the order picking efficiency and avoid buffer blockage, thereby improving the efficiency of the dispatching system.

Research on Two-Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer

This paper proposes a two-stage joint optimization problem of green manufacturing and maintenance for semiconductor wafer (TSGMM-SW) considering manufacturing stage, inspection, and repair stage simultaneously, which is a typical NP-hard problem with practical research significance and value. Aiming at this problem, a green scheduling model with the objective of minimizing makespan, total carbon emissions, and total preventive maintenance (PM) costs is constructed, and an improved hybrid multiobjective multiverse optimization (IHMMVO) algorithm is proposed in this paper. The joint optimization of green manufacturing and maintenance is realized by designing synchronous scheduling and maintenance strategy for wafer manufacturing and equipment PM. The diversity of the population is expanded and the optimization performance of IHMMVO is improved by designing the initial population fusion strategy and subpopulation evolution strategy. In the experimental phase, we perform the simulation experiments of 900 test cases randomly generated from 90 parameter combinations. The IHMMVO algorithm is compared with other existing algorithms to verify the effectiveness and feasibility for TSGMM-SW.

МОДЕЛІ СИСТЕМИ АНАЛІЗУ ТА РОЗПІЗНАВАННЯ МУЗИЧНИХ КОМПОЗИЦІЙ

The article discusses the models of the recognition system for musical compositions in the system of synthesis and analysis of musical sounds, aimed at increasing the identification capabilities of an automated system. The recognition module for musical compositions is oriented to the server part of the system, which, regardless of the client, contains a database with fingerprints of musical compositions. With the help of melody recognition algorithms with a given argument in the form of a fingerprint, the server returns a list of musical compositions that most satisfy the search conditions. The client part interacts with the server part using the developed application programming interface, which, in addition to the well-known functional approach to the REST network protocol architecture based on the HTTP protocol, where the client uses requests only in the format defined by the server part specification, also provides for the implementation of the capabilities of the network architecture approach interactions using the GraphQL query language, which allows to build query parameters on the client side. The local database contains a set of fingerprints and metadata about musical compositions to speed up the recognition process, since it allows to cover most application scenarios with the most popular musical compositions with the ability to quickly return results after local data matching without the need for client-server interaction delays. The database synchronization module is responsible for the timely updating of the local database with new fingerprints from the server side and for the general synchronization of the client and server parts of the system. The synchronization scheduler provides a synchronization schedule for the local and server databases, as well as implements strategies for optimizing battery usage and ensures work with a low level of Internet connection. The features of storing the fingerprint database of compositions and strategies for working with devices based on mobile platforms, in particular, for the Android operating system in Doze mode, which prohibits the background operation of the device at rest, except for short periods of time, the so-called support windows, which are determined by the operating system work process and allow to perform short-term background operations. Performed the optimization of the process of using the battery energy of a mobile device while synchronizing the metadata of musical compositions between the client and server components of the system.

Measures to Improve the Accuracy and Reliability of Clock Synchronization in Time-Sensitive Networking

Most of the time-sensitive networking standards are based on a high precise and reliability time reference, which is accomplished by IEEE 802.1 AS. However, the accuracy and reliability of clock synchronization could be affected in practice. To conquer the inaccuracy resulting from the collision between different packets, an algorithm that combines time-slot-based synchronization and priority scheduling is proposed. To improve the reliability, an abnormal recovery strategy of the master clock is proposed. The proposed algorithms are implemented on the OMNet++ simulation platform with an typical in-vehicle network topology. The simulation results show that the clock synchronization accuracy could achieve sub-microsecond level even if there are interference flows, and the abnormal recovery mechanism can maintain the stable state of the clock.

Integrated Simulation and Optimization of Scheduling Yard Crane and Yard Truck in Loading Operation

To further decrease turnaround time of container vessels, synchronous scheduling of yard crane (YC) and yard truck (YT) with the impact of quay crane (QC) is mainly considered. On this basis, the interference between YCs, YT pool strategy and other practical constraints, are also taken into consideration. Firstly, this problem formulated as a mixed integer programming model to minimise the makespan of loading operation. Due to the computational intractability, the multi-layers genetic algorithm is introduced to solve the problem with a simulation model, then an improved algorithm and an algorithm accelerating strategy are also designed to further improve the optimising efficiency. Finally, two sets of experiments with over 300 tests are conducted by eight elastic compute services on Alibaba Cloud to validate necessity of considering the impact of QC and the solution quality and effectiveness of the proposed methods.

Multipoint Synchronization for Fog-Controlled Internet of Things

This paper presents a fog-resident controller architecture for synchronizing the operations of large collections of Internet of Things (IoT), such as drones, Internet of Vehicles, etc. Synchronization in IoT is grouped into different classes, use cases identified and multipoint synchronous scheduling algorithms are developed to schedule tasks with varying timing requirements; strict (synchronous) and relaxed (asynchronous and local) onto a bunch of worker nodes that are coordinated by a fog resident controller in the presence of disconnections and worker failures. The algorithms use time-based or component-based redundancy to cope with failures and embed a publish-subscribe message update scheme to reduce the message overhead at the controller as the number of workers increase. The performance of the algorithms are evaluated using trace-driven experiments and practicability is shown by implementing the time-based redundancy synchronous scheduling algorithm in JAMScript—a polyglot programming platform for Cloud of Things and report initial findings.

Convergence Analysis of Gaussian Belief Propagation Under High-Order Factorization and Asynchronous Scheduling

It is well known that the convergence of Gaussian belief propagation (BP) is not guaranteed in loopy graphs. The classical convergence conditions, including diagonal dominance, walk-summability, and convex decomposition, are derived under pairwise factorizations of the joint Gaussian distribution. However, many applications run Gaussian BP under high-order factorizations, making the classical results not applicable. In this paper, the convergence of Gaussian BP under high-order factorization and asynchronous scheduling is investigated. In particular, three classes of asynchronous scheduling are considered. The first one is the totally asynchronous scheduling, and a sufficient convergence condition is derived. Since the totally asynchronous scheduling represents a broad class of asynchronous scheduling, the derived convergence condition might not be tight for a particular asynchronous schedule. Consequently, the second class of asynchronous scheduling, called quasi-asynchronous scheduling, is considered. Being a subclass of the totally asynchronous scheduling, quasi-asynchronous scheduling possesses a simpler structure, which facilitates the derivation of the necessary and sufficient convergence condition. To get a deeper insight into the quasi-asynchronous scheduling, a third class of asynchronous scheduling, named independent and identically distributed (i.i.d.) quasi-asynchronous scheduling, is further proposed, and the convergence is analyzed in the probabilistic sense. Compared to the synchronous scheduling, it is found that Gaussian BP under the i.i.d. quasi-asynchronous scheduling demonstrates better convergence. Numerical examples and applications are presented to corroborate the newly established theoretical results.

Vehicle Scheduling Optimization considering the Passenger Waiting Cost

In the operational planning process of public transport, the time a passenger spends on waiting is a very critical element for judging passenger service. Schedule synchronization is a useful strategy for reducing bus waiting time and improving service connectivity. This paper develops an extended vehicle scheduling model, taking into account the interests of passengers and operators in attaining optimization of timetable synchronization integrated with vehicle scheduling and considering the passenger waiting cost. Deficit functions at terminals are formulated. Deadheading (DH), shifting departure time (SDT), and network flow technique are used for vehicle scheduling with the consideration of passenger waiting times. An experimental study in Beijing is conducted and three important bus lines are selected as a regional bus network to demonstrate the methodology developed. Results show that both the fleet size of bus operators and the waiting cost of passengers are minimized. For example, the minimum fleet size can be reduced from 28 vehicles to 24 ones while the passenger times are less than 20 minutes in this multidepot network.

Practicing interprofessional communication competencies with health profession learners in a palliative care virtual simulation: A curricular short report

Abstract This 6-week learning activity used an online virtual reality environment through Second Life® to advance interprofessional education with a convenience sample of undergraduate and graduate health professions learners through a simulated palliative and end-of-life care conference. The activity was implemented in four phases: scheduling, pre-learning, virtual simulation engagement, and a guided reflection discussion for closure. Four learning objectives guided the activity and addressed learner ability to: critique individual team communication performance using selected sections of the Interprofessional Collaborator Assessment Rubric (ICAR)1, demonstrate at least two specific communication skills while participating in the activity, share a list of the patient/family goals for end-of-life care, and propose at least three different strategies that could be used in their future practice to facilitate effective interprofessional care delivery. Faculty used a grading rubric to assess learner achievement of the four learning objectives. A mixed methods approach evaluated the effectiveness of the activity and effect on self-reported learner attitudes and future practice. This educational resource was an effective, accessible, and low-risk learning modality for advancing interprofessional competencies in education for practice. Teaching strategies that emphasize situated learning and reflective learning support interprofessional competency development. Use of virtual reality technologies engages learners in realistic scenarios in a safe environment free from the risk of harm to patients and families, allowing small groups of learners to meet synchronously online, and providing an alternative to face-to-face synchronous scheduling of multiple health professions disciplines, thus reducing strain on program budgets and scheduling logistics. Materials are shared and may be adapted to prepare learners for other contexts of practice.

A self-stabilizing algorithm for b-matching

We present the first self-stabilizing algorithm for finding a maximal b-matching in arbitrary networks and under distributed unfair (adversarial) schedulers. The previous self-stabilizing b-matching algorithm presented by Goddard et al. in 2003 assumes central scheduler. We give proof for the correctness of our new algorithm for both unfair and fair schedulers, as well as the synchronous scheduler. Our algorithm stabilizes in O(Bm) steps under unfair scheduler and in O(n) rounds under distributed fair and synchronous schedulers, where n, m and B are the number of processors, the number of edges and the maximum capacity in the graph, respectively. The time complexity of distributed fair version of our algorithm is better than that of the transformation of Goddard et al.'s sequential self-stabilizing algorithm to the distributed fair one with the conflict manager of Gradinariu and Tixeuil (2007).

The generalized rollon-rolloff vehicle routing problem and savings-based algorithm

Taking the waste collection management as the practical background, the rollon-rolloff vehicle routing problem (RRVRP) involves tractors pulling large containers between customer locations and the disposal facility. Each used-tractor begins and ends its route at the depot. Customer demand includes emptying a full container, ordering an empty container or changing a container. The objective is to find tractor routes that feature the minimum amount of total nonproductive time. In the literature the RRVRP is formulated as the node routing problem, and the “trip” definition that is the complete transport service of a container is used. To relax some assumptions of the RRVRP to cater to practical desires, we present a variant called the generalized RRVRP (G-RRVRP). The G-RRVRP generalizes the practical background, the objective function and the demand flow, and considers specially container loading and unloading time constraints. The G-RRVRP classifies demand into loaded-container demand and cargo demand with time windows. On condition of respecting container loading/unloading time and customer time windows, the G-RRVRP can design tractor routes for the synchronous scheduling of loaded and empty containers so as to ensure the timeliness of transport service. The G-RRVRP aims to minimize the total running cost of used tractors, instead of the total nonproductive time of tractors adopted by the RRVRP. A mixed integer linear programming model for the G-RRVRP is proposed. The Benders decomposition algorithm involving Pareto-optimal cuts and Benders decomposition-callback implementation, and a two-stage heuristic involving the savings algorithm followed by a local search phase are provided. The mathematical formulation and the two-stage heuristic are tested by solving 40 small-scale instances and 20 benchmark instances. Small-scale instances can be solved directly by CPLEX through the Benders decomposition strategies to find exact solutions. The case study indicates the applicability of the G-RRVRP model and the two-stage heuristic to realistic-size problems abstracted from intercity linehaul systems. The computational experiments and case study indicate that the heuristic can solve various instances of the G-RRVRP such that the solution quality and the computation time are acceptable.

Process Improvement in Outpatient Installation RSUD dr. Soediran Mangun Sumarso Using Lean Hospital Approach

RSUD dr. Soediran Mangun Sumarso is a public hospital in Wonogiri district which has an outpatient installation service. However, the waiting time of some services in outpatient installations exceeds the standard time set by the health minister of the Republic of Indonesia. It is known from the data waiting time in the outpatient installation. The purpose of this study is to provide improvements using lean hospital approach. Proposed improvement is done by eliminating waste that occurs in outpatient installation service. The methodology used in this study consists of four stages. The first stage is describing the service system using a cross-functional flowchart. The second stage is identifying waste using value stream mapping, observation and interview. The third stage is to determine critical waste by borda method and pareto diagram. The last stage is to provide recommendation improvement using fishbone diagram and FMEA. The result of this research is proposed improvements. The proposed improvements are adding special register counters, implementing an online reservation system, doctors schedule synchronization, adding doctors in polyclinics, fixing queue numbers, applying visual management concepts, making connecting glass in pharmacies and adding multifunction shelves in polyclinics.

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and Earliest Deadline First (EDF) scheduling, shared resources, and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards.

An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

Genomic sequence alignment of varied species is one of the most sort of applications in bioinformatics. In future bioinformatics technologies are expected to produce genomic data of terabyte. Bioinformatics computation require super computer for sequence alignment computation which involves huge cost. Parallelization technique is a way forward in computing sequence alignment with limited cost and time. Cloud computing and MapReduce framework play an important role in bioinformatics intensive application to achieve parallelization since it provides a consistent performance over time and it also provides good fault tolerant mechanism. The existing gene sequencing methodologies are designed based on Hadoop-MapReduce framework which adopts a serial execution strategy which is an area of concern. This work introduces a Smith-Waterman Alignment on the Bulk synchronous Parallel Map Reduce (SW-BSPMR) cloud platform for bioinformatics gene sequence alignment. This work adopts a widely accepted and accurate SW algorithm for sequence alignment and parallel synchronous scheduler methodology of map and reduce framework process is considered. A customized MapReduce based on Microsoft Azure cloud platform is developed to overcome the issue in Hadoop-MapReduce framework. The experimental study presented in this work proves that the SW-BSPMR can accurately and effectively align bioinformatics genomic sequences of various read length.

One Handshake Can Achieve More: An Energy-Efficient, Practical Pipelined Data Collection for Duty-Cycled Sensor Networks

To alleviate long sleep latency due to duty-cycled radio operations, existing collection protocols adopted pipelined scheduling techniques, which stagger the sleep-wakeup schedules of nodes along forwarding paths, requiring accurate time synchronization as underlying support. They either ignored the synchronization issue or just assumed that a local synchronization scheme over non-duty-cycled radios could meet the requirement, however, which may lead to a significant synchronization issue in practice. In this paper, we propose a practical pipelined data collection (PDC) protocol for duty-cycled sensor networks. PDC adopts an inter-layer incorporation of network and media access control layers. It only relies on an RTS/CTS-like handshake with a set of the proposed algorithms, not only for data transmission as commonly utilized, but also for pipelined scheduling and schedule synchronization, data-gathering tree establishment, and network topology control and maintenance, all of which are naturally and seamlessly incorporated together and able to support each other. PDC has been implemented in the Contiki operating system. The testbed evaluations based on two hardware platforms (Z1 and MicaZ) and the compared results with a de facto standard for data collection based on the fully emulated Z1 in Cooja have demonstrated its practicality and efficacy.