Reliable Scheduling Research Articles

3D ergonomics-based motion-level productivity analysis for intelligent manufacturing in industrialized construction

In industrialized construction, accurate estimations of labour productivity and associated cycle times of repetitive operations are crucial for reliable production planning and scheduling. However, the reliability of the standardized productivity and cycle time analyses is hindered by their dependence on various factors that change with the increasing complexity of the working environment, as well as by inherent challenges involved in acquiring accurate measurements. Thus, this study proposes an automated 3D ergonomics-based motion-level productivity analysis method for intelligent manufacturing in industrialized construction that combines an existing predetermined motion–time system with ergonomic risk assessments and 3D modelling techniques. The actual and simulated cycle times and ergonomic risks of preliminary cases in the experiment are analyzed to demonstrate the feasibility and reliability of the proposed method. The proposed method can be implemented to improve occupational safety, increase the reliability of planning and scheduling, and boost the overall productivity of industrialized construction.

Dynamic Multi-Bus Dispatching Strategy With Boarding and Holding Control for Passenger Delay Alleviation and Schedule Reliability: A Combined Dispatching-Operation System

The continuing increase of the on-road private cars is contributing to a deterioration of the urban traffic system. Public transportation is widely used to tackle this issue due to its large ridership. In this paper, we propose a multi-bus dispatching strategy combined with the boarding and holding control (MBDBH) to improve bus utilization and further decrease the passenger excess delay. Dispatching adjustments and operation control are taken into account in the system. At the dispatching level, on the one hand, either a bus platoon or a single bus can be dispatched for each trip to provide adaptive bus capacity to match the highly-fluctuated stop demands, on the other hand, we adjust the bus dispatching time based on the existing timetable to minimize passenger excess waiting time to a large extent. Meanwhile, the operation level incorporates both holding strategy and boarding limit strategy to bring more flexible adjustments in improving bus service. Besides the efficiency, we also minimize the headway variation in order to maintain a high system reliability. The problem is formulated as a Mixed Integer Nonlinear Programming (MINP) problem, which is solved by the commercial solver Gurobi. With the computational complexity as a concern, we propose a distributed algorithm to implement dual decomposition based on the partial Lagrangian relaxation. Finally, numerical examples are investigated to illustrate the significant time reduction of distributed algorithm and the efficiency of our proposed strategy: The proposed MBDBH model can reduce roughly 50% and 30% of remaining passenger volumes when compared with the timetable-based fixed schedule and the optimized single-bus dispatching schedule, respectively.

Operational and strategic decision making in the perioperative setting: Meeting budgetary challenges and quality of care goals.

Efficient operating room (OR) management is a constant balancing act between optimal OR capacity, allocation of ORs to surgeons, assignment of staff, ordering of materials, and reliable scheduling, while according the highest priority to patient safety. We provide an overview of common concepts in OR management, specifically addressing the areas of strategic, tactical, and operational decision making (DM), and parameters to measure OR efficiency. For optimal OR productivity, a surgical suite needs to define its main stakeholders, identify and create strategies to meet their needs, and ensure staff and patient satisfaction. OR planning should be based on real-life data at every stage and should apply newly developed algorithms.

An Effective Channel Selection Solution for Reliable Scheduling in Industrial IoT Networks

An Effective Channel Selection Solution for Reliable Scheduling in Industrial IoT Networks

A Time and Reliability Optimization Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing System

In the heterogeneous distributed computing systems, efficient task scheduling is necessary for high performance. System reliability and completion time are generally the two important metrics. In this paper, we proposed an algorithm named Merging and Duplication for Makespan and Reliability (MDMR) which combines task merging and task duplication to achieve single-objective constrained optimization of workflow reliability and completion time. The MDMR algorithm is divided into three phases: task merging, entry task duplication and reliability detection with nonentry task duplication. In the task merging phase, two tasks with a sole parent–child relationship are merged into a single task, then this process is iterated until no tasks satisfy the merging condition, thus simplifying the directed acyclic graph. In the entry task duplication phase, the entry task with the lowest reliability is duplicated on the processors, which are available at that moment to increase the reliability while reducing the workflow completion time. In the reliability detection and nonentry task duplication phase, the current workflow scheduling reliability is first calculated, then the result is directly output if the reliability is higher than the target value, otherwise the nonentry task duplication is executed with the aim of increasing the reliability until the value surpasses the target. According to the experimental results, MDMR dominates the compared algorithms and ensures that the entire workflow achieves the target reliability and shortens the workflow makespan as much as possible.

Multi-objective optimization for scientific workflow scheduling based on Performance-to-Power Ratio in fog–cloud environments

The ever-expanding processing of large-scale scientific workflow applications has increased power consumption and reliability costs. However, considering the complexity of computational data processing in fog–cloud computing, existing workflow scheduling techniques conceived for traditional distributed systems is limited and powerless. Therefore, for profit-driven cloud service providers, minimizing power consumption and maximizing scheduling reliability of scientific workflows is a pressing concern. In this paper, we model the scientific workflow scheduling as a multi-objective optimization problem to compromise the conflicting energy consumption and scheduling reliability solution in two steps. The first step places the tasks with lower computational requirements on fog resources and computationally complex tasks on cloud resources, avoiding resources with high failure rates. In the second step, we adapt the Reliability-Aware stepwise Performance-to-Power Ratio procedure to further reduce energy consumption by sampling the machine’s utilization levels with a distinguished Performance-to-Power Ratio (PPR). The PPR is defined as the calculation of the number of transactions performed at a specific time divided by the active power consumed. Simulation results under synthesized and real-world workflow applications demonstrated that dis-RMEE achieved considerably higher reliability values under the minimized energy constraint compared with some opponent workflow mapping algorithms.

Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling

In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. The 3 D reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on BIM (building information model). Second, the construction process of the 4dbim model is described, and the construction method is introduced. The construction of the model provides data information for the construction schedule reliability control system. Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the S-curve comparison method, and the work of the system is described through case simulation. The early warning result is from June 7 to June 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled. On June 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload. The project progress was out of date, so it needs to be closely tracked. On June 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload. The forecast result is the line forecast of the actual cumulative completion percentage on June 17. After calculation, the forecast result on June 17 is 84.311%. The progress deviation on June 17 was 5.21%. If no timely delay is taken on June 15, the delay will get worse. In addition, the system can predict the completion period of the project. When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed. Therefore, we can calculate the completion period of the three-storey project, and the construction period is about June 29 or 30. When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high. Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction. This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises. Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system. The results show that this technology can effectively improve the efficiency of the building construction schedule management. Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use. It also promotes the application of artificial intelligence technology in the construction phase.

Bus Predictive-Control Method considering the Impact of Traffic Lights

Amid the COVID-19 pandemic, many travelers have switched from public transit to other modes. How to maintain the stability and service quality of the bus system under regular pandemic prevention and control, so as to maintain the attractiveness of the bus, is an important research direction. Predicting operation states and adopting appropriate control measures for running buses are effective means of improving the bus system’s schedule reliability and service quality. Focusing on the impacts of intersection traffic lights on the link’s travel time durations, we establish a probabilistic prediction model for bus headways, classifying the bus headways into three states: bunching, stable, and big gap states. Based on the prediction of bus headways, the most suitable control strategy is selected by the proposed method from the plan set, such as holding control, speed-adjusting control, and stop-skipping control to minimize the bus headway deviation. Simulation experiments were employed to verify the effectiveness of the proposed method. Compared with the no-control situation, the expected headway variation, average passenger waiting time, and bus bunching frequency for 100 simulations by the proposed method are reduced by 77.73%, 41.66%, and 87.11%, respectively. Compared with some control methods without prediction, the proposed method is more robust, maintains good control performance, and reduces bus bunching despite significant variations in environmental parameters. In addition, the model still performs well when considering the execution errors of bus drivers.

Research on comprehensive recovery of liner schedule and container flow with hard time windows constraints

COVID-19 has had a huge impact on the global container market. Many liner companies have adopted a blank sailing for some voyages to adjust capacity, and vessel schedule reliability continues to be sluggish. From the perspective of the container liner company, this paper studies the integrated recovery of liner schedule and container flow under the background of suspension of shipping service. With the goal of minimizing the total cost of the liner company, the hard time window constraints of the container flow on the suspended routes are set to construct the integrated recovery problem.The increased carbon emission cost during the restoration of the container flow is taken into account.A mixed integer nonlinear programming model is established, and the adaptive mutation particle swarm optimization (AMPSO) is used to solve the model. The results show that the total cost of the model is reduced by 10.66% compared with the total cost of the shipping schedule recovery model that did not consider the recovery of container flow.

The reliability of photovoltaic power generation scheduling in seventeen European countries

As photovoltaic (PV) electricity generation is becoming ubiquitous, the ability to forecast solar power becomes crucial for such aspects as economical dispatch, optimal unit commitment and the stability of the grid. Our research investigated the discrepancies between day-ahead and intraday country-specific PV power generation forecasts and the real generation data in the member states of the European Network of Transmission System Operators for Electricity for the year 2021. The goal of the research was, firstly, to offer an insight into the schedule accuracy achieved in the examined countries, secondly, to present the potentials for improvement in each of them (plausible even by the practical application of the methods available in 2021), and thirdly, to show the amount of regulation need resulting from PV electricity production forecasting error, the knowledge of which can help with the estimation of the size of the necessary storage capacities. This is the first work that analyzes the schedule accuracy from a practical perspective by examining the accuracy of the day-ahead and intraday PV forecasts of seventeen European countries, following the latest recommendations for verification. The results found significant differences between the forecast accuracy of different countries, which indicates a huge potential for imbalance reduction in most countries by improving their own algorithms or creating a uniform European PV power generation forecasting system. Overall, the intraday forecasts are less skillful than the day-ahead forecasts in all but one of the countries, which highlights the significance of further application-related studies in the intraday horizon. The aggregate regulatory need resulting from the discrepancies between the forecasted and actually produced amounts of energy of all studied countries is 4 TWh in the positive and 3 TWh in the negative direction, which calls for a large amount of energy storage capacity to provide carbon-free balancing power and reduce the regulation challenges of the increasing PV penetration.

Understanding Stakeholders' impact on design workflow dynamics using agent-based modeling

Inadequate management of the design process can affect the process performance as well as the final product value. While several studies targeted the management of design over BIM, correlating the social dynamics of design stakeholders to the dynamics of the BIM process is not thoroughly investigated. This study uses agent-based modeling to study the impact of various stakeholders' characteristics on the performance of BIM-based design. Results show that the characteristics of involved stakeholders and the setup of the design process can affect the performance of design projects in terms of project's duration, value, rework and project's risks. This paper is significant to design managers and researchers to comprehensively understand the effects of stakeholders' characteristics and related social dynamics on design process performance. Also, the paper opens the door to future research that targets the employment of social-based parameters to enhance the reliability of design planning and scheduling.

English

This study was conducted in three tehsils, Gujranwala, Nowshera Virkan and Wazirabad of district Gujranwala. The major objective of the study was to explore the causes and consequences of the different conflicts faced by the rural people. A total 384 respondents were chosen at random. Data were collected through face-to-face interview technique on a structured, validated and reliable interview schedule from randomly selected 384 respondents. Collected data were analyzed with the help of Statistical Package for Social Sciences (SPSS). Findings indicated that, conflicts between two groups; conflicts over access to power, feeling of self-superiority, dispute over access to agricultural resources, mutual farming, disturbance in family relationship and theft of farm produce were the major causes of the conflicts as perceived by respondents. The conflicts had adverse impacts on the availability of labor, inputs, and farm assets and more importantly on adoption of innovations among farmers in particular. In result, decrease in farm production and income of farmers was more likely. Multiple linear regression analysis confirmed that, age, education, income level, land size and farming experience of respondents had statistically significant relationship (P

Optimal operation method of integrated energy system considering thermal inertia

This paper proposes a comprehensive energy system operation optimization method considering the thermal inertia of the heating system. Firstly, based on the energy network of the integrated energy system, establish the thermal inertia and heat storage model of the heating pipeline; secondly, the goal is to minimize the total operating cost of the electricity-heat-gas integrated energy system. Considering constraints such as power balance and safe operation of equipment, an integrated energy system operation optimization model based on thermal inertia is established; the results show that the model can better balance scheduling cost and reliability.

A Reliable Self-Adaptive Scheduling Control Protocol for Cellular V2X Mode 4

A Reliable Self-Adaptive Scheduling Control Protocol for Cellular V2X Mode 4

How to Design Scheduling Solutions for Smart Manufacturing Environments Using RAMI 4.0?

The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case.

Hierarchical Distributed Frequency Regulation Strategy of Electric Vehicle Cluster Considering Demand Charging Load Optimization

In view of the problems that the disorderly demand charging load increases the power supply pressure and the control of large-scale cluster is difficult, which will appear during the process of large-scale electric vehicle cluster participating in frequency regulation, this article proposes a hierarchical distributed frequency regulation strategy of electric vehicle cluster considering the optimization of demand charging load. First, the demand charging plans of electric vehicles are optimized to suppress the fluctuation of wind power generation and system load, and the reserve capacity of electric vehicle cluster is predicted in real time. Then, in order to improve the control performance of large-scale cluster, a hierarchically distributed control framework is established, which enables the traditional unit to assist the electric vehicle through the real-time assignment of system frequency regulation task, and realizes the individual-level orderly scheduling of electric vehicles based on the distributed sorting. The simulation results of two-area interconnected system show that the proposed frequency regulation strategy can not only adjust automatically for ensuring expected frequency regulation effect but also reduce the power supply pressure on the grid side, as well as achieve efficient and reliable scheduling of electric vehicle cluster.

Research on decentralized operation scheduling strategy of integrated energy system based on energy blockchain

With the increasing proportion of renewable energy generation connected to grid, the architecture, control method, and operation mode of the energy system begin to change. The energy system has some problems, such as difficult to maintain the privacy of energy suppliers, difficult to distinguish the authenticity of data, and low security and reliability. The introduction of blockchain technology into energy system to form an energy blockchain network is conducive to solving the information security and other issues. Aiming at the integrated energy system (IES) with renewable energy generation, the practical Byzantine fault tolerance (PBFT) algorithm consensus mechanism suitable for IES is proposed to realize the two-stage robust optimal scheduling model, which uses decentralized scheduling strategy and blockchain technology. In the first stage, the technology of decoupling heat power is used to form electrical/thermal iteration chain, and the decentralized scheduling model based on Lagrange multiplier method is built. For the data consistency of decentralized scheduling, the undirected unicom structure is used to establish the communication network between energy suppliers. The thermal/electrical iterative chain and the terminal chain apply the consistency protocol algorithm and Byzantine fault tolerance consensus mechanism to solve the prescheduling scheme based on the decentralized algorithm. In the second stage, the blockchain technology is used to obtain historical data, and a data-driven renewable generation power uncertain set is established to solve the Regulation scheme. This constraint set can exclude some extreme scenarios to reduce the conservatism of the model. In the two-stage optimization process, the verification function of Byzantine fault tolerance consensus mechanism is used to discard the information tampered by malicious attack and enhance the system fault tolerance capability. The example verification results show that the proposed architecture can operate effectively and save total cost of scheduling solution 5.71%, prevent the energy suppliers from tampering with information and weaken the impact of server downtime and network errors, and improve the privacy, security, and reliability of operation scheduling.

Schedule reliability in liner shipping timetable design: A convex programming approach

Container liner shipping is the primary mode of moving manufactured products across continents. Partly due to inherent uncertainties at sea and ports, the liner shipping industry has long had a notorious reputation of schedule delays and unreliable on-time performance. This paper formulates a new approach to incorporate schedule reliability targets in liner shipping timetable design, to balance bunker consumption, time taken for the voyage, and schedule delays. We first model a surrogate problem using a copositive program through a moment decomposition approach and solve it as a convex semidefinite programming relaxation. We next incorporate schedule reliability targets implicitly by exploiting the optimality condition of this surrogate model.We use this approach to analyze the trade-offs between the bunker consumption and the schedule reliability targets for each port call. Furthermore, we derive the optimal speed of the vessel in each leg of the schedule to control for total bunker consumption. Surprisingly, the analytical analysis shows that, if a shipping line has the freedom to choose its preferred berthing times at all ports, it is optimal to set design sailing speeds equal at all legs to maximize schedule reliability.We validate our model using data from a Daily Maersk service, and demonstrate that our schedule can achieve an even higher schedule reliability than the innovative Daily Maersk service schedule, which attained more than 98% reliability in practice, with 11.4% lower bunker consumption. In comparison with a common schedule design heuristic, our model can design service schedules that improve the reliability performance by at least five percentage points, consuming the same amount of bunker. For the same reliability target (of 80%) in schedule design, our model can help reduce bunker consumption by about 11.6% for an 11-week schedule. The savings in bunker consumption can be much more substantial when an ocean carrier aims for higher schedule reliability.

Reliability aware green workflow scheduling using \u03b5-fuzzy dominance in cloud

The enormous energy consumed by cloud data centers (CDCs) increases the carbon footprints, operational cost and decreases the system reliability, so it becomes a great challenge for CDCs providers. Dynamic voltage and frequency scaling (DVFS) is an efficient approach for energy efficiency, which reduces the operating frequency, and supply voltage of the processor during the task’s execution. Recent research shows that scaling of the supply voltage and operating frequency has negative impact on the system’s reliability as it increases transient fault rate of the resources. Thus, the system’s reliability and the energy consumption are two prime concerns in a cloud computing environment that requires attention. Most workflow scheduling algorithms in literature do not consider energy and reliability simultaneously. In this paper, we proposed the ε-fuzzy dominance based reliable green workflow scheduling (FDRGS) algorithm, which optimizes the application’s reliability and energy consumption simultaneously using the ε-fuzzy dominance mechanism. The simulation results obtained using fast Fourier transform (FFT) and gaussian elimination (GE) task graphs manifest that our scheduling algorithm is more efficient in optimizing energy consumption and lifetime system’s reliability jointly than several widely used algorithms. The proposed algorithm will help scientists and engineers for further insight into future research in the area of cloud.

Selection of Efficient Parameter Estimation Method for Two-Parameter Weibull Distribution

Several studies have considered various scheduling methods and reliability functions to determine the optimum maintenance time. These methods and functions correspond to the lowest cost by using the maximum likelihood estimator to evaluate the model parameters. However, this paper aims to estimate the parameters of the two-parameter Weibull distribution (α, β). The maximum likelihood estimation method, modified linear exponential loss function, and Wyatt-based regression method are used for the estimation of the parameters. Minimum mean square error (MSE) criterion is used to evaluate the relative efficiency of the estimators. The comparison of the different parameter estimation methods is conducted, and the efficiency of these methods is observed, both mathematically and experimentally. The simulation study is conducted for comparison of samples sizes (10, 50, 100, 150) based on the mean square error (MSE). It is concluded that the maximum likelihood method was found to be the most efficient method for all sample sizes used in the research because it achieved the least MSE compared with other methods.