Hybrid scheduling model and analysis of performance for switched industrial Ethernet

The fact that outburst traffic in industrial Ethemet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed in which the outburst data was given the highest priority. Some properties of the self-similar outburst data were proved by network calculus, and its service curve scheduled by the switch was gained. And then the performance of the scheduling algorithm was obtained. The simulation results are close to those calculated by using network calculus model. Some results are of actual significance to the construction of switched industrial Ethernet.

Interruptible Load Scheduling Model Based on an Improved Chicken Swarm Optimization Algorithm

With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.

Dynamic scheduling model of computing resource based on MAS cooperation mechanism

Allocation of grid resources aims at improving resource utility and grid application performance. Currently, the algorithms proposed for this purpose do not fit well the autonomic, dynamic, distributive and heterogeneous features of the grid environment. According to MAS （multi-agent system） cooperation mechanism and market bidding game rules, a model of allocating allocation of grid resources based on market economy is introduced to reveal the relationship between supply and demand. This model can make good use of the studying and negotiating ability of consumers＇ agent and takes full consideration of the consumer＇s behavior, thus rendering the application and allocation of resource of the consumers rational and valid. In the meantime, the utility function of consumer is given; the existence and the uniqueness of Nash equilibrium point in the resource allocation game and the Nash equilibrium solution are discussed. A dynamic game algorithm of allocating grid resources is designed. Experimental results demonstrate that this algorithm diminishes effectively the unnecessary latency, improves significantly the smoothness of response time, the ratio of throughput and resource utility, thus rendering the supply and demand of the whole grid resource reasonable and the overall grid load balanceable.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

Uncertain optimal model and solving method to platform scheduling problem in battlefield

The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

Try and error-based scheduling algorithm for cluster tools of wafer fabrications with residency time constraints

To improve the productivity of cluster tools in semiconductor fabrications,on the basis of stating scheduling problems,a try and error-based scheduling algorithm was proposed with residency time constraints and an objective of minimizing Makespan for the wafer jobs in cluster tools.Firstly,mathematical formulations of scheduling problems were presented by using assumptions and definitions of a scheduling domain.Resource conflicts were analyzed in the built scheduling model,and policies to solve resource conflicts were built.A scheduling algorithm was developed.Finally,the performances of the proposed algorithm were evaluated and compared with those of other methods by simulations.Experiment results indicate that the proposed algorithm is effective and practical in solving the scheduling problem of the cluster tools.

Scheduling Step-Deteriorating Jobs on Parallel Machines by Mixed Integer Programming

Production scheduling has a major impact on the productivity of the manufacturing process. Recently, scheduling problems with deteriorating jobs have attracted increasing attentions from researchers. In many practical situations,it is found that some jobs fail to be processed prior to the pre-specified thresholds,and they often consume extra deteriorating time for successful accomplishment. Their processing times can be characterized by a step-wise function. Such kinds of jobs are called step-deteriorating jobs. In this paper,parallel machine scheduling problem with stepdeteriorating jobs( PMSD) is considered. Due to its intractability,four different mixed integer programming( MIP) models are formulated for solving the problem under consideration. The study aims to investigate the performance of these models and find promising optimization formulation to solve the largest possible problem instances. The proposed four models are solved by commercial software CPLEX. Moreover,the near-optimal solutions can be obtained by black-box local-search solver LocalS olver with the fourth one. The computational results show that the efficiencies of different MIP models depend on the distribution intervals of deteriorating thresholds, and the performance of LocalS olver is clearly better than that of CPLEX in terms of the quality of the solutions and the computational time.

Global Optimization of Nonlinear Blend-Scheduling Problems

The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation （PMCR） and normalized multiparametric disaggregation tech-nique （NMDT） to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches.

KDD-based rolling schedule model for plate mill

A novel method for generating a rolling schedule is presented, which is fundamentally different from the existing ones. KDD (knowledge discovery in database) techniques are applied for discovering association rules between rolling parameters in a large database of rolling operation, and based on these rules, the schedule for the crucial last six finishing passes is generated. Operational evaluation shows that the schedule generated by the new method outperforms that generated by existing methods. It also shows how in this application the human's domain knowledge is applied to speed up the KDD process and to ensure the validity of the knowledge discovered.

Schedule Model for Project Portfolio Based on Design Structure Matrix

To improve the enterprise resource utilization and shorten the cycle of the whole project portfolio, a scheduling model based on Design Structure Matrix （DSM） is built. By setting the project activity weight index system and calculating the activity weight for the project portfolio, the constraint relationship between project portfolio information and resource utilization, as the two dimensions of the DSM, are fully reflected in the sched- ule model to determine the order of these activities of project portfolio. A project portfolio example is given to il- lustrate the applicability and effectiveness of the schedule model.

Root length density distribution and associated soil water dynamics for tomato plants under furrow irrigation in a solar greenhouse

Furrow irrigation is a traditional widely-used irrigation method in the world. Understanding the dynamics of soil water distribution is essential to developing effective furrow irrigation strategies, especially in water-limited regions. The objectives of this study are to analyze root length density distribution and to explore soil water dynamics by simulating soil water content using a HYDRUS-2D model with consideration of root water uptake for furrow irrigated tomato plants in a solar greenhouse in Northwest China. Soil water contents were also in-situ observed by the ECH_2O sensors from 4 June to 19 June and from 21 June to 4 July, 2012. Results showed that the root length density of tomato plants was concentrated in the 0–50 cm soil layers, and radiated 0–18 cm toward the furrow and 0–30 cm along the bed axis. Soil water content values simulated by the HYDRUS-2D model agreed well with those observed by the ECH_2O sensors, with regression coefficient of 0.988, coefficient of determination of 0.89, and index of agreement of 0.97. The HYDRUS-2D model with the calibrated parameters was then applied to explore the optimal irrigation scheduling. Infrequent irrigation with a large amount of water for each irrigation event could result in 10%–18% of the irrigation water losses. Thus we recommend high irrigation frequency with a low amount of water for each irrigation event in greenhouses for arid region. The maximum high irrigation amount and the suitable irrigation interval required to avoid plant water stress and drainage water were 34 mm and 6 days, respectively, for given daily average transpiration rate of 4.0 mm/d. To sum up, the HYDRUS-2D model with consideration of root water uptake can be used to improve irrigation scheduling for furrow irrigated tomato plants in greenhouses in arid regions.

Optimization based accurate scheduling for generation and reserve of power system

With the improvement of electricity markets,the gradual aggravation of energy shortage and the environment pollution,it is urgent to formulate a new model to precisely satisfy the system demand for energy and reserve.Currently,power system opti-mization dispatching is always formulated as a discrete-time scheduling model.In this paper,we first demonstrate through an example that the upper and lower bounds of spinning reserve offered by a unit,given in the discrete-time model framework as constraints,is unreachable.This causes the problem that the reserve delivery obtained by the discrete-time scheduling model cannot be carried out precisely.From the detailed analysis of the ramp rate constraints,it is proved that the reachable upper and lower bounds of spinning reserve in every period can be expressed as functions of two variables,i.e.,generation level of unit at the start and end of this period.Thus,a new method is provided to calculate the upper and lower bounds of spinning reserve which are reachable in average.Furthermore,a new model based on this proposed method for joint scheduling of generation and reserve is presented,which considers the ability to realize the scheduled energy and reserve delivery.It converts the opti-mization based accurate scheduling for generation and reserve of power system from a continuous-time optimal control prob-lem to a nonlinear programming problem.Therefore,the proposed model can avoid the difficulties in solving a continu-ous-time optimal control problem.Based on the sequential quadratic programming method,numerical experiments for sched-uling electric power production systems are performed to evaluate the model and the results show that the new model is highly effective.

Developing Project Duration Models in Software Engineering

Based on the empirical analysis of data contained in the International Software Benchmarking Standards Group （ISBSG） repository, this paper presents software engineering project duration models based on project effort. Duration models are built for the entire dataset and for subsets of projects developed for personal computer, mid-range and mainframe platforms. Duration models are also constructed for projects requiring fewer than 400 person-hours of effort and for projects requiring more than 400 person-hours of effort. The usefulness of adding the maximum number of assigned resources as a second independent variable to explain duration is also analyzed. The opportunity to build duration models directly from project functional size in function points is investigated as well.

Decision Support System for emergency scheduling of raw water supply systems with multiple sources

A hydraulic model-based emergency schedul- ing Decision Support System （DSS） is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reason- able strategies including technical measures. Otherwise, a Genetic Algorithm （GA）-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.

Approach of Synthesizing Model Predictive Control and Its Applications for Rotary Kiln Calcination Process

An actual control demand of rotary kiln is taken as background. By analyzing and improving approach of MPC （synthesizing model predictive control）, an effective strategy which applies complex S-MPC in actual industrial process is designed. Firstly, after analyzing the main components technology and calcination reaction mechanism in detail, the calcining belt state-space model of rotary kiln is built using PO-Moesp （past-output multivariable output error state space model identification） method. Then, calcining belt temperature predictive control system is de signed. The control system combines time-delay gain scheduled, output-tracking, recursive subspace adaptive and other methods, and forms the off-line/on-line predictive controller of rotary kiln. At last, MATLAB is applied for simulation, experiments run in constant value tracking and servo tracking situation. Simulation results show its ef- fectiveness and feasibility.

Review of conceptual and systematic progress of precision irrigation

Precision irrigation,defined as accurate and appropriate agricultural techniques characterized by optimal management and best collaboration of various irrigation factors,attracts great attention and obtains wide employments in different irrigation conditions or cultivation processes.Moreover,it becomes well-established in major areas of agricultural researches and across the broad spectrum of agricultural techniques especially in specific sectors of scientific frontiers,including soil quality,irrigation scheduling,water resource distribution,crop productivity,tillage management,climate adaptation,and environment monitoring,etc.This paper reviews the research developments and integrated applications of precision irrigation in typical domains of mechanism and performance,covering key aspects such as process optimization,schedule modelling,and effectiveness evaluation,indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil;Current schedule modelling techniques provide a set of instructive demonstrations and heuristic descriptions for the working principles of precision irrigation and the quantitative assessments of irrigation productivity;The novel investigation on effectiveness evaluation is extremely significant to obtain higher infiltration efficiency,simultaneously to achieve the optimized irrigation qualities for water balance condition,soil water redistribution,and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably.It is concluded that precision irrigation owns an outstanding collaborating capability and possesses much better working advancement in typical calibration indexes of cultivation accuracy and infiltration efficiency,meanwhile,a high agreement between the predicted and actual irrigation effectiveness could be expected.This novel irrigation review concentrating on the conceptual and systematic progress should be promoted constructively to improve the quality uniformity for precision irrigation and its constructive influences in different applications,and to facilitate the integrated management of agricultural production by higher irrigation efficiency consequently.

Micro ball-end milling of freeform titanium parts

Micro machining has growing number of applications in various industries such as biomedical, automotive, aerospace, micro-sensor, micro-actuator and jewelry industries. Small-sized freeform titanium parts are frequently needed in the biomedical applications, especially in the implantations such as mini-blood pumps and mini left-ventricular assist devices, finger joint replacements and small bone implants. Most of the small-sized titanium parts with freeform geometries are machined using micro ball-end milling before polishing and other surface treatments. Decreasing the cycle time of the machining parts is important for the productivity. In order to reduce the cycle time of the roughing process in the micro ball-end milling, this paper investigates the imple- mentation of a previously developed force-based feedrate scheduling （FFS） technique on micro milling of freeform titanium parts. After briefly introducing the instantaneous micro milling forces in micro ball-end milling of titanium parts with freeform surfaces, the FFS technique is implemented in the rough machining of a freeform titanium surface to demonstrate the cycle time reduction potentials via virtual micro milling simulations.