Real-time Scheduling Model Research Articles

Enhanced Stabilization of Discrete-Time Takagi–Sugeno Fuzzy Systems Based on a Comprehensive Real-Time Scheduling Model

The problem of enhanced stabilization of discrete-time Takagi–Sugeno fuzzy system is investigated in depth via proposing a new way to employ much more information implied in various time-varying differences of multi-instant normalized fuzzy weighting functions. Benefit from the given comprehensive real-time scheduling model, these time-varying differences on time vertical dimension and two different transverse dimensions are for the first time integrated into fuzzy stabilization and two key alterable weights are also introduced. Consequently, much considerable freedom can be produced and, thus, it brings much less conservative results than the reported ones in recent literature. More importantly, a very simple workable way has been provided to online recognize the specific enabled mode at each sampling instant so that those existing time-consuming online operations in referred literature can be avoided to ease online implementation costs. Indeed, this feature is very beneficial to the practical application of our theoretical results. Finally, the superiority of our developed method over previous ones is validated adequately via two benchmark simulations.

Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns

The utilization of real-time information in production scheduling decisions becomes possible with the help of new developments in Information Technology and Industrial Informatics, such as Industry 4.0. Regardless of the beliefs that the availability of such information will enhance scheduling decisions, several questions and concerns have been reported. One such question is to what extent can the availability of real-time information enhance scheduling decisions? Another concern is how can such information be utilized to advance scheduling decisions and when should it be used? Moreover, there is a general assumption that continuous rescheduling using real-time system updates is beneficial to some extent. However, this general assumption has not been extensively investigated in complex manufacturing systems, such as flexible job shops. Therefore, in this paper, our objective is to study the above-mentioned research questions by developing real-time scheduling (RTS) models for the flexible job-shop scheduling problem (FJSP) with unexpected new job arrivals and machine random breakdowns. We investigate how real-time updates on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations can be utilized to generate new schedules (i.e., rescheduling). The performance of the developed RTS models is also investigated under different settings for shop-floor events, different rescheduling strategies, rescheduling policies, and scheduling methods. Lastly, results, conclusions, and several promising research avenues are provided.

Analysis of the Day-ahead Deviation Plan and Research on the Real-time Scheduling of Photovoltaic Greenhouses Based on Exergy Theory

For the correction problem of day-ahead plan deviation caused by energy prediction deviation in day-ahead scheduling stage of photovoltaic greenhouses, an exergy analysis method is used to propose the deviation model of heat required for photovoltaic greenhouses. Based on the deviation model, a real-time optimization scheduling model is established. The deviation model not only considers the non-negligible exergy loss during heating process of pipes, but also considers the difference between heat and thermal exergy affected by the actual indoor temperature. The goal of the real-time scheduling model is to minimize the absolute value of the difference between the energy supply and demand prediction deviation to be corrected and the adjustment of multi-form energy storage and electric loads, so that develop the real-time adjustment plan of energy storage and electric loads. The analysis results of the actual photovoltaic greenhouse show that of the heat required by a greenhouse based on the exergy theory calculation, the exergy loss of the heating process accounts for about 10%–20% of the total thermal exergy required and it cannot be ignored, so the calculation results can reflect the actual heat required more accurately and the greenhouse temperature is more suitable for plant growth. Moreover, the proposed real-time scheduling model can correct the deviation of the day-ahead plan and improve local consumption. The promotion ratio can reach 7%. Finally, the farmers’ electricity purchases cost is reduced. Thereby the effectiveness of the proposed heat deviation model and real-time scheduling model is verified.

Study on a Real Time Scheduling Optimization Model for Multi-source Heterogeneous Distribution Network

Real-time scheduling for distributed power consumption can be heterogeneous distribution network problems, in this paper, an optimization model based on real-time scheduling based on the day before the scheduling model, the power supply capacity of the model based on load forecasting to make full use of distributed power and energy storage equipment, reduces in the peak period of the main power supply rely on, and the imperialist competitive algorithm based on the optimization model was solved. Through the simulation test, the power supply capacity of the real-time scheduling model can effectively utilize the distributed power and energy storage devices to reduce the peak power grid based on the running cost of distribution network is reduced by 2.59%.

Real-Time Optimal Scheduling Model for Transit System with Flexible Bus Line Length

We propose a new structured transit system with flexible bus line length based on the real-time requests of passengers. This operation mode can meet the travel demands of passengers (lower travel-time cost) and reduce the number of unnecessary stops (without boarding or alighting activities). We then compare the performance of the proposed transit system with that of fixed-route transit service and show the pros and cons of each system for several passenger demand levels. It is found that at low-to-moderate demand levels, the proposed flexible-route system tends to provide the lowest total travel time. In comparing a real-world deployment of the system in Guangzhou with comparable alternative system states, the optimization of the former is evident in idle and tidal modes, in which the total travel times reduced by more than 10%. Moreover, this operating system can achieve enhanced performance with the development of automated vehicle technology in the future.

An industrial view on the common academic understanding of mixed-criticality systems

With the rapid evolution of commercial hardware platforms, in most application domains, the industry has shown a growing interest in integrating and running independently-developed applications of different “criticalities” in the same multi-core platform, with the objective of improving the performance/cost ratio of the system. Such integrated systems are commonly referred to as mixed-criticality systems (MCS). Most of the MCS-related research published in the state-of-the-art cite the safety-related standards associated to each application domain (e.g. aeronautics, space, railway, automotive). However, those standards are not, in most cases, freely available, and do not always clearly and explicitly specify the requirements for mixed-criticality systems. This paper addresses the important challenge of presenting the relevant information available in some of the safety-related standards, such that the mixed-criticality concept is understood from an industrialist’s perspective. In addition, the paper evaluates state-of-the-art mixed-criticality real-time scheduling models and algorithms against the safety-related standards.

Timed Coloured Petri Nets for Modelling and Managing Processes and Projects

The Resource Constrained Scheduling Project (RCSP) is one of the most complex tasks in the sector of project management. Many researchers devoted effort during the last years to solve this criticality. Moreover, interesting studies analyse the problem regarding mathematical formulation. In so doing, the exact and/or heuristic solutions tools allow extending the analysis to a broad range of Operation Management problems.The proposed article describes a new approach for project/process modelling based on Timed Coloured Petri Nets (TCPNs), to simplify the resource allocation in a resource-constrained problem. In particular, its utilisation provides a robust formalism to represent and analyse parallel systems. The TCPNs utilisation allows analysing interdependencies, criticality, substitution, conflicting resource priorities and variations in the availability of resources. This paper proposes a new model and highlights the usefulness of the model for real-time activity scheduling in a resource-constrained project environment. A case study for an industrial project is described to provide a systematic analysis of the proposed tool.

Real-time scheduling optimization considering the unexpected events in home health care

Home health care, a new kind of health services that can be given in home for special populations (elderly, disabled, youth, etc.), is usually less expensive, more convenient and more efficient. Based on the face-to-face (in-person) interviews, we find that scheduling arrangements of home health care are often affected by some unexpected events such as cancellation of services, demand for emergency care and medical device failures. These events may lead to medical scheduling conflicts and therefore might decrease patient satisfaction due to the delayed service. Considering the emergencies in the home health care, this study takes the home health care mode under the unexpected events as the research object and focuses on the time window constraints involved in the real-time scheduling problem. In order to obtain an optimal medical dispatch program and ensure patients’ golden period for treatment, we establish an effective real-time scheduling model to minimize the total required time of scheduling, and propose an improved memetic algorithm to optimize the model. Empirical analysis was then adopted to verify the rationality of the model. Finally, we analyze the practical effect of dispatching decision and put forward the recommendations for future research directions.

Multi-Time Scale Coordinated Scheduling Strategy with Distributed Power Flow Controllers for Minimizing Wind Power Spillage

The inherent variability and randomness of large-scale wind power integration have brought great challenges to power flow control and dispatch. The distributed power flow controller (DPFC) has the higher flexibility and capacity in power flow control in the system with wind generation. This paper proposes a multi-time scale coordinated scheduling model with DPFC to minimize wind power spillage. Configuration of DPFCs is initially determined by stochastic method. Afterward, two sequential procedures containing day-head and real-time scales are applied for determining maximum schedulable wind sources, optimal outputs of generating units and operation setting of DPFCs. The generating plan is obtained initially in day-ahead scheduling stage and modified in real-time scheduling model, while considering the uncertainty of wind power and fast operation of DPFC. Numerical simulation results in IEEE-RTS79 system illustrate that wind power is maximum scheduled with the optimal deployment and operation of DPFC, which confirms the applicability and effectiveness of the proposed method.

Energy and Reserve under Distributed Energy Resources Management—Day-Ahead, Hour-Ahead and Real-Time

The increasing penetration of distributed energy resources based on renewable energy sources in distribution systems leads to a more complex management of power systems. Consequently, ancillary services become even more important to maintain the system security and reliability. This paper proposes and evaluates a generic model for day-ahead, intraday (hour-ahead) and real-time scheduling, considering the joint optimization of energy and reserve in the scope of the virtual power player concept. The model aims to minimize the operation costs in the point of view of one aggregator agent taking into account the balance of the distribution system. For each scheduling stage, previous scheduling results and updated forecasts are considered. An illustrative test case of a distribution network with 33 buses, considering a large penetration of distribution energy resources allows demonstrating the benefits of the proposed model.

A Real-Time Multistep Optimization-Based Model for Scheduling of Storage-Based Large-Scale Electricity Consumers in a Wholesale Market

A new real-time optimal scheduling model is proposed and analyzed in this paper to aggregate storage benefits for a large-scale electricity consumer. The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed. A real-time load forecaster is incorporated into the optimal scheduling algorithm using soft constraints, slack variables, and penalizing mechanisms. The application of the proposed model to a real-world large-scale electricity consumer is examined and compared with previous models. It is demonstrated that the proposed model outperforms prior models by generating higher profitability of investment in storage, lower storage operating expenditure, and an extended life of the storage plant.

A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.

Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment

The lack of timely feedback shopfloor information during manufacturing execution stage leads to significant difficulties in achieving real-time production scheduling. To address this problem, an overall architecture of multi-agent based real-time production scheduling is presented to close the loop of production planning and control. Several contributions are significant. Firstly, wireless devices such as radio frequency identification (RFID) are deployed into value-adding points in a ubiquitous shopfloor environment to form Machine Agent for the collection and processing of real-time shopfloor data. Secondly, Capability Evaluation Agent is designed to optimally assign the tasks to the involved machines at the process planning stage based on the real-time utilization ration of each machine. The third contribution is a Real-time Scheduling Agent for manufacturing tasks scheduling/re-scheduling strategy and methods according to the real-time feedback. Fourthly, a Process Monitor Agent model is designed for tracking and tracing the manufacturing execution based on a critical event structure. Finally, a case is used to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

Towards dynamic real-time scheduling for multiple earth observation satellites

Currently, most tasks submitted to earth observation satellites (EOSs) are with deadlines, to satisfy users' timing requirements. Besides, tasks are normally submitted dynamically, with uncertainties of tasks number and submission times. In this paper, we establish a multi-objective mathematic programming model for dynamic real-time scheduling of EOSs. To improve user's revenue and resource utilization, a task dynamic merging strategy is proposed. With the dynamic merging in place, plus consideration of retracting and rearranging some scheduled tasks to make room for incorporating the newly arrived tasks, we present a novel dynamic real-time scheduling algorithm—DMTRH. To demonstrate the superiority of our DMTRH, we conduct extensive experiments by simulations to compare DMTRH with two algorithms—RHTSS and STI. The experimental results indicate that DMTRH improves the scheduling quality of others and is suitable for dynamic real-time scheduling.

Practical Approach to Water System Optimal Operation

Optimal pump scheduling is a major consideration when dealing with minimizing operational costs of a water distribution system. Pump operation must balance between three factors. Water balance constraints, including consumer demand and water tank volumes. Hydraulic constraints determining water pump operating point. Electrical tariff rate effecting energy cost.Optimization models may assume linear or discrete pump operation, depending on type and accuracy of the model in use. Linear operation assumes the pump may operate during part of the time step while discrete operation requires the pump to be either on or off during the entire time step. Linear optimization models commonly have short solution times, but cannot contain non-linear constraints such as hydraulic headloss. By such, linear model results may be difficult to implement in a real water system as the hydraulic behavior of the system may render the optimal solution impractical. Likewise, if the pump operation partially uses the time step the pump may be forced to come in and out of duty often causing mechanical ware and tare.Discrete operation provides smooth pump operation and may contain non-linear hydraulic constraint to calculate a more realistic working point for the pump. Discrete models have long solution times due the vast amount of pump operating combinations, which must be explored. Heuristic techniques may be used to shorten solution times but these do not assure global minimization of the solution.The goal of the research is to create a minimum cost optimal operation water distribution system model that utilizes the short solution time of a linear model but also includes non-linear hydraulic constraints effecting pump energy consumption and discrete pump operation. The motivation is to use the model for real-time pump scheduling and for water system design.

A decision support system for public logistics information service management and optimization

Transportation optimization usually aims at minimizing the empty load ratios (ELRs) of vehicles. Most Chinese vehicles for logistics are owned by individual entrepreneurs. Because China is very large, transport distances are typically long, and thus the ELR is very high. The ELR is the primary reason for high transport costs, considerable pollution, and high energy consumption. Many Chinese local governments try to build public transport information services that decrease the ELR. This work proposes a decision support system (DSS) for public logistics information service management and optimization (PLISMO) for vehicle drivers and owners, logistics customers and related logistics service providers and management institutes. The dynamic and real-time matching model between goods and vehicles, and the enabling technologies are important issues for the DSS for PLISMO. Therefore, intelligent positioning technologies are employed to acquire and manage the vehicle status. A model matching vehicles with goods is developed based on an assessment model of transport capability and service priority criteria. A multi-objective real-time scheduling model is devised to minimize the ELR. Based on the concepts and decision-making models for PLISMO, a DSS is created and the architecture of the system is investigated. The effectiveness of the DSS and decision-making models is demonstrated by a case of finished vehicle logistics (FVL). Analytical results show that the proposed DSS can reduce the ELR and logistics cost. This system helps governments construct DSSs for general PLISMO.

Fault-tolerant hierarchical real-time scheduling with backup partitions on single processor

The resource partitioning has been suggested to provide efficient composition of multi-threaded real-time applications. Partitioning can provide reliable and flexible software upgrade as partitions are strongly isolated in terms of resources. However, there are always possibility of experiencing software faults while operating on a real plant. To avoid entering a hazardous state due to a partition that is yet to be fully verified, we can deploy a backup partition that may implement inefficient algorithms or limited features but is verified with respect to reliability. The backup partition performs failover to carry out missions of the corresponding primary partition when a software fault is detected. There have been significant researches for fault-tolerant real-time scheduling but considerations for partitioned systems have not been studied. In this paper, we extend the resource model for hierarchical real-time scheduling to support primary and backup partitions. Our model can support context-dependent and context-independent tasks in the backup partition efficiently. In addition, we provide the schedulability analysis for suggested model.

Real-time scheduling of multi-stage flexible job shop floor

This paper first formulates a binary integer programming (BIP) model that minimises C max for a multi-stage flexible job shop floor with machine compatibility. Due to a computational limitation, the exact optimal model is then relaxed as a linear programming model. The output from the relaxation model then turns into the objective of the BIP-based real-time scheduling (RTS) heuristic model. The RTS heuristic requires an iteration to calculate the final C max. At each iteration, the RTS heuristic assigns just one job to the earliest available machines. Since the set of jobs and machines included in the RTS model is relatively small, RTS can be solved in a very short computational time. We evaluate an overall effectiveness (in terms of solution quality and run time) of the RTS heuristic by way of computer experiments.

Study on Optimal Real-Time Feedback Scheduling of Networked Collaborative Design Project under Uncertainty

Aimed at resource-restrained project scheduling problem under uncertainty which is a hot research spot, a kind of real-time feedback scheduling method is presented in this paper. Before a project is executed, the scheduling plan of all subtasks is made according to ascertain condition. The running procedure of the project is monitored by a workflow management system. When a situation, in which the finish time of the project will be delayed by changed running condition, is detected, the related information is fed back to a scheduling controller; And the controller calculates the controlling data according to optimal objective and then sends it to object scheduled; And then the old plan is rescheduled The concept model for real-time feedback scheduling of cooperative design project with resource-restrained is setup. When only the finish time of subtask is influenced and it is described as interval number, its corresponding mathematic model is setup and converted to an ascertain one by possibility-level, and solve it by GA(Genetic Algorithm). The structure of this GA is given, and a method of chromosome coding, that is the chromosome is segmented according to resources and gene is sorted in task-series, is presented in this paper. A computational example is studied in this paper, the results reveal that the method presented is valid.

FEL-H Robust Control Real-Time Scheduling

The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to improve real-time performance, and to allow the system to adapt to changes in the environment, the workload, or to changes in the system architecture due to failures. In this paper, we pursue this goal by formulating and simulating new real-time scheduling models that enable us to easily analyse feedback scheduling with various constraints, overload and disturbance, and by designing a robust, adaptive scheduler that responds gracefully to overload with robust H∞ and feedback error learning control.