Predictive Scheduling Research Articles

Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering the railway execution strategy, which defined that the starting time of each operation cannot be earlier than its predictive starting time. Robustness is defined as the expected relative deviation between the realized makespan and the predictive makespan. In view of the time-consuming characteristic of simulation-based robustness measure (RMsim), this paper puts forward new SMs and investigates their performance through simulations. By utilizing the structure of schedule and the available information of stochastic processing times, two SMs on the basis of minimizing the robustness degradation on the critical path and the non-critical path are suggested. For this purpose, a hybrid estimation of distribution algorithm (HEDA) is adopted to conduct the simulations. To analyze the performance of the presented SMs, two computational experiments are carried out. Specifically, the correlation analysis is firstly conducted by comparing the coefficient of determination between the presented SMs and the corresponding simulation-based robustness values with those of the existing SMs. Secondly, the effectiveness and the performance of the presented SMs are further validated by comparing with the simulation-based robustness measure under different uncertainty levels. The experimental results demonstrate that the presented SMs are not only effective for assessing the robustness of SJSSP no matter the uncertainty levels, but also require a tremendously lower computational burden than the simulation-based robustness measure.

Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns

In real-world industrial environments, unplanned events and unforeseen incidents can happen at any time. Scheduling under uncertainty allows these unexpected disruptions to be taken into account. This work presents the study of the flexible job shop scheduling problems (FJSP) under machine breakdowns. The objective is to solve the problem such that the lowest makespan is obtained and also robust and stable schedules are guaranteed. A two-stage particle swarm optimization (2S-PSO) is proposed to solve the problem assuming that there is only one breakdown. Various benchmark data taken from the literature, varying from Partial FJSP to Total FJSP, are tested. Computational results prove that the developed algorithm is effective and efficient enough compared to literature approaches providing better robustness and stability. Statistical analyses are given to confirm this performance.

QoS-Aware Energy-Efficient Downlink Predictive Scheduler for OFDMA-Based Cellular Devices

We propose a predictive energy-efficient scheduling scheme that optimizes the user equipment (UE)'s bits/joule metric subject to quality-of-service (QoS) constraints in downlink orthogonal frequency-division multiple access (OFDMA) systems. This is achieved by minimizing the number of wake-up transmission time intervals (TTIs), where the UE receiver circuit is on , in a longer time horizon than studied before. The proposed predictive scheduler is supported by a ray-tracing (RT) engine that increases the scheduler's knowledge by long-term information about the users' propagation characteristics. A convex multiobjective binary-integer-programming formulation for the problem is presented to optimize both the UE's energy efficiency (EE) and QoS. The multiobjective formulation is then used for benchmarking of a low-complexity and computationally efficient heuristic scheduler. The results show that the proposed schedulers have significantly improved the UE's EE and the overall capacity of the system, compared with a recently published EE scheduling scheme while maintaining target QoS.

A proactive scheduling approach to steel rolling process with stochastic machine breakdown

We address a proactive scheduling problem with stochastic machine breakdown, controllable processing time and deterioration effect considerations arising from steel production. The problem is to determine the pre-compression amount of each job’s processing time and the job sequence for the rolling process so as to achieve a robust predictive schedule in response to machine breakdown. Both robustness and stability of the predictive schedule are considered, in correspondence with the mean and variance of rescheduling cost that consists of match-up time cost and additional resource cost. Since the scenario-based approach to robustness evaluation of a predictive schedule is cursed with high computational burden, a surrogate-assisted multi-objective evolutionary algorithm based on Elitist non-dominated sorting genetic algorithm is proposed to solve the proactive scheduling problem under consideration. Support vector regression model is introduced to approximate the robustness of the each alternative schedule which surrogates the time-consuming simulation-based fitness evaluation process and saves more time for solution space search. In addition, a probabilistic sequencing strategy which takes advantage of each job’s ability to absorb disruption at low cost is introduced to guide the evolutionary search. Computational experiments of numerical and practical data indicate that the proposed proactive scheduling approach performs well in response to stochastic machine breakdown. The support vector regression model and the probabilistic sequencing strategy improve the performance of the proposed algorithm with respect to the convergence and diversity of the obtained Pareto front.

SNR based Predictive Packet Scheduling in LTE with Varied CQI

SNR based Predictive Packet Scheduling in LTE with Varied CQI

Performance Modeling and Predictive Scheduling for Distributed Stream Data Processing

In a distributed stream data processing system, an application is usually modeled using a directed graph, in which each vertex corresponds to a data source or a processing unit, and edges indicate data flow. In this paper, we propose a novel predictive scheduling framework to enable fast and distributed stream data processing, which features topology-aware modeling for performance prediction and predictive scheduling. For prediction, we present a topology-aware method to accurately predict the average tuple processing time of an application for a given scheduling solution, according to the topology of the application graph and runtime statistics. For scheduling, we present an effective algorithm to assign threads to machines under the guidance of prediction results. To validate and evaluate the proposed framework, we implemented it based on a highly-regarded distributed stream data processing platform, Storm, and tested it with 3 representative applications: word count (stream version), log stream processing and continuous query. Extensive experimental results show 1) The topology-aware prediction method offers an average accuracy of 84.2 percent. 2) The predictive scheduling framework reduces the average tuple processing time by 24.9 percent on average, compared to Storm's default scheduler.

Optimization of Wireless Sensor Networks using PPSS Algorithm

In the development of various large-scale sensor systems, a particularly challenging problem is how to dynamically organize the sensors into a wireless communication network and route sensed information from the field sensors to a target system. The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through a new energy-efficient dynamic optimization-based sleep scheduling and target prediction technique for large-scale sensor networks. We present a probability-based prediction and optimization-based sleep scheduling protocol (PPSS) to improve energy efficiency of proactive wake up. A cluster-based scheme is exploited for optimization-based sleep scheduling. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a non myopic rule, which is based on not only the target state prediction but also its future tendency. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art protocols in terms of tracking accuracy, inter node communication, and computation complexity . Index Terms—Energy efficiency, target prediction, sleep scheduling, target tracking, sensor networks. In a large-scale sensor network, hundreds or thousands of tiny sensor nodes are randomly deployed into a monitoring field to gather data. The complexity of computation and communication increases with the number of active sensor nodes tracking the target. The amount of energy used in the network is proportional to the number of active sensor nodes. It is best for sensor nodes to be arranged into collaborative m groups. Group collaboration should be limited to a tracking area around the target so that the communication and computation will be independent of the size of the network. Multiple nodes surrounding the target may collaborate and gather information. The tracking accuracy and performance is limited to the information in those sensors. In a large-scale sensor network, it is important to locate the target with high accuracy while consuming the least amount of energy. Some of the existing studies have focused on energy efficient methods to track mobile targets. Our objective is to propose a simple routing metric that is composed of the energy expenditure and battery power of a node. Therefore, the cluster activation phase has a great importance not only in minimizing energy consumption but also improve the optimized tracking accuracy.

Understanding Eating Events: Snacks and Meal Patterns in Great Britain

Sociological analysis of eating has for a long time tracked the fate of a popularly endorsed, idealized form of the “family” or “proper” meal. “Snacking” has often been constructed as its symbolic opposite, as irregular, anomic and unhealthy. This paper, based on data from a survey of eating patterns conducted in Great Britain in 2012 (n = 2,784), analyzes eating occasions which respondents stated were snacks rather than meals, focusing on their frequency, scheduling, contents, duration and social context. It finds that “snacks” are taken across all socio-demographic groups and take place in accordance with a common and predictable schedule. Snacks are smaller and less structured than most meals but mostly do not comprise what is generically described as “snack foods.” Snacks are shorter in duration, and less sociable than other eating events, reflecting their size, simplicity and informality. However, most are eaten in company, are planned for, and are wholesome. Common negative evaluations of eating between and outside meals have failed to appreciate the range and character of adjunct eating events, and the degree to which they form part of aggregate patterns and individual routines.

A reliable power management scheme for consistent hashing based distributed key value storage systems

Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%.

A hybrid multi-objective immune algorithm for predictive and reactive scheduling

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

Virtual Rounds Offer a Glimpse into the Future of Healthcare

Virtual Rounds Offer a Glimpse into the Future of Healthcare

Distributed Late-binding Scheduling and Cooperative Data Caching

Pull-based overlays are used in some of today’s largest computational grids. Job agents are submitted to resources with the duty of retrieving real workload from a central queue at runtime and executing it. This model helps overcome the problems of direct job submission in the highly complex grid environments, namely, heterogeneity, imprecise status information, relatively high failure rates and slow adaptation to changes of grid conditions or user priorities. This article presents a distributed scheduling architecture for such late-binding overlays. In this architecture, execution nodes share a distributed hash table and cooperatively perform job assignment. As our experiments prove, scalability problems of centralized matching are avoided, achieving low and predictable scheduling overheads even for execution of large workflows, and total turnaround times are improved. This is in line with the predictions of a theoretical model of grid workflow execution that the article also discusses. Scalability makes fine-grained scheduling possible and enables new functionalities, like a distributed data cache shared by the execution nodes, which helps alleviate the commonly congested storage services. In addition, we show that our system is more resilient to problems like communication breakdowns between computation centres. Moreover, the new architecture is better prepared to deal with demanding scenarios like intense demand of popular data files or remote data processing.

When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction.

The comparison of predictive scheduling algorithms for different sizes of job shop scheduling problems

In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper [1], where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.

Walking Behavior of Zoo Elephants: Associations between GPS-Measured Daily Walking Distances and Environmental Factors, Social Factors, and Welfare Indicators.

Research with humans and other animals suggests that walking benefits physical health. Perhaps because these links have been demonstrated in other species, it has been suggested that walking is important to elephant welfare, and that zoo elephant exhibits should be designed to allow for more walking. Our study is the first to address this suggestion empirically by measuring the mean daily walking distance of elephants in North American zoos, determining the factors that are associated with variations in walking distance, and testing for associations between walking and welfare indicators. We used anklets equipped with GPS data loggers to measure outdoor daily walking distance in 56 adult female African (n = 33) and Asian (n = 23) elephants housed in 30 North American zoos. We collected 259 days of data and determined associations between distance walked and social, housing, management, and demographic factors. Elephants walked an average of 5.3 km/day with no significant difference between species. In our multivariable model, more diverse feeding regimens were correlated with increased walking, and elephants who were fed on a temporally unpredictable feeding schedule walked 1.29 km/day more than elephants fed on a predictable schedule. Distance walked was also positively correlated with an increase in the number of social groupings and negatively correlated with age. We found a small but significant negative correlation between distance walked and nighttime Space Experience, but no other associations between walking distances and exhibit size were found. Finally, distance walked was not related to health or behavioral outcomes including foot health, joint health, body condition, and the performance of stereotypic behavior, suggesting that more research is necessary to determine explicitly how differences in walking may impact elephant welfare.

Enhanced Load Balance to Predict Fast Data Stream using E-Tree MSI Method on Cloud

Cloud computing implements virtualization processing of data service in the internet, where it delivers the conceptual, scalable platforms and applications as on data services. The important problem arises in Cloud infrastructure in storing a very large amount of data and processing on the computational load on the cloud. It is a big challenge to overcome computation complexity on cloud. An effectively predict the data streams process with load factors of ensemble model and data stream are implemented to overcome in cloud. Data stream processes on the cloud infrastructure runs with continuously varying load factors. In this work, we propose an architecture with a load balancing framework for cloud infrastructure by using the Ensemble Tree Metric Space Indexing (E-tree MSI) technique. We developed three techniques to construct our E-tree MSI technique: Fast Predictive Look-ahead Scheduling approach (FPLS) where the scheduling of Spatio-temporal data stream files takes place; Parallel Ensemble Tree Classification (PETC) which performs the process of classification operations on cloud data stream; and a Bilinear quadrilateral Mapping process which adds efficient implementation of cloud infrastructure. We have done an experimental evolution using Cloud Sim, from which it is achieved that the performance of load balancing factor is increased, the accuracy rate of classification is better and it reduced the execution time for mapping.

A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures

Nowadays, manufacturing systems are seeking control architectures that offer efficient production performance and reactivity to disruptive events. Dynamic hybrid control architectures are a promising approach as they are not only able to switch dynamically between hierarchical, heterarchical and semi-heterarchical structures, they can also switch the level of coupling between predictive scheduling and reactive control techniques. However, few approaches address an efficient switching process in terms of structure and coupling. This paper presents a switching mechanism framework in dynamic hybrid control architectures, which exploits the advantages of hierarchical manufacturing scheduling systems and heterarchical manufacturing execution systems, and also mitigates the respective reactivity and optimality drawbacks. The main feature in this framework is that it monitors the system dynamics online and shifts between different operating modes to attain the most suitable production control strategy. The experiments were carried out in an emulation of a real manufacturing system to illustrate the benefits of including a switching mechanism in simulated scenarios. The results show that the switching mechanism improves response to disruptions in a global performance indicator as it permits to select the best alternative from several operating modes.

On-line fault diagnosis of FMS based on flows analysis

Any flexible manufacturing system (FMS) may face fault which may disrupt the production and cause delays. Thus, the identification of the source of failure is very important to intervene rapidly. This paper aims to develop an indirect and incremental diagnostic approach to identify the root cause of the observed delay in the context of a single fault occurrence. In this study the observation is done only at the output of the system to measure the output dates of each part and to detect the eventual delay. For this purpose, a mathematical model is developed to model the proposed diagnostic approach of FMS under cyclic scheduling. This cyclic approach provides intermediary reference points to detect any discrepancy with regard the predictive scheduling. These intermediary reference points correspond to the end of each cycle defined by the scheduling. To solve this problem, the constraint programming technique is used. Finally, the performance of the proposed approach is evaluated with respect to the literature. The major merit of this study is to prove the capacity to diagnose efficiently the progressive faults of a plant without the necessity to add sensors dedicated to its monitoring.

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probability-based prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting.

A two-stage method for predicting and scheduling energy in an oxygen/nitrogen system of the steel industry

As essential energy resources in steel industry, oxygen and nitrogen are massively utilized in many production procedures, such as iron-making by blast furnaces, steel-making by converters, etc. The trends of the energy generation/consumption flows along with the related scheduling works play a pivotal role on the energy management of steel enterprises. Aiming at an oxygen/nitrogen system of a steel plant in China, a two-stage predictive scheduling method is proposed in this study for resolving the optimal energy decision-making problem. Given the high cost of time consuming on the load change of air separation units (ASU) of the oxygen/nitrogen system, a Granular-Computing (GrC)-based prediction model is firstly established at the stage of prediction, which extends the predicting length to even a day based on data segment rather than generic point-wise mode. At the stage of optimal scheduling, a mixed-integer program model is constructed on the basis of constraining the number of adjustable energy units, which considers not only the actual capacity of the energy devices, but the practical energy conversion procedure as well. The experiments employing the real data coming from this plant also involve two stages, the long-term prediction and the energy scheduling, and the experimental results exhibit both satisfactory accuracy and practicability. Furthermore, the results of system application also indicate the effectiveness of the proposed method.