74results about How to "Improve scheduling" patented technology

A method and system to select, schedule, purchase, confirm, invoice, bill and settle service orders, such as home services orders from customers. The method and system accept electronic service orders as well as customer leads for services, selects service providers and schedule services in real-time via a computer network, confirms scheduling of service delivery in real-time, handles administrative and business details for both customers and service providers, as well as fulfills, tracks and settle service orders. The method and system also provide customized information for services available via an electronic services network and dynamically schedule services using plural electronic appointment calendars from plural service providers. The method and system includes customer service order and customer lead information and enterprise-wide visibility and management of business processes. The method and system may also be used for goods.

Methods, systems, computer program products, and methods of doing business by improving the scheduling of execution of jobs for or by network-connected devices, thereby enabling the job execution process to scale more easily, efficiently, and effectively to support large numbers of devices and / or users. Examples of jobs include, but are not limited to, distribution of resources (including software, configuration information, images, and other types of content) to a device, fetching a device's inventory information, backing up a device's contents, and so forth. Jobs are programmatically scheduled based upon a specified time internal, according to a class of the requester. Only if an earliest start time after which the job may be executed for this requester has been reached will the job be executed, otherwise, the job execution is delayed. The disclosed techniques lessen the need for additional servers to handle spikes in processing load, reduce the likelihood of reaching system overload, and reduce the likelihood of cascading failures that may occur when systems are overloaded.

The invention discloses an integrated scheduling system for a small and medium radial flow type hydropower station group. Rain and water condition information acquired from a rain and water condition monitoring terminal, flow information acquired from a flow monitoring terminal, power station information, power station installing information and electrical network electricity demand acquired from a power station operation monitoring terminal are provided by a data collection platform for a scheduling plan compiling platform to generate a corresponding scheduling plan, related data is further acquired by an information issuing platform from the data collection platform to comprehensively display various types of information in a basin, through establishment of the integrated scheduling system for the small and medium radial flow type hydropower station group, integrated hydropower scheduling is carried out at county net factories, a traditional, single and rough hydropower station management mode is changed, electrical network scheduling management boundary is broken, management capacity of small and medium water power enterprises is improved integrally, and energy saving, emission reduction, water saving and power generation increase are realized.

Scheduling methods and apparatus for use with optical switches with hybrid architectures are provided. An exemplary distributed scheduling process achieves 100% throughput for any admissible Bernoulli arrival traffic. The exemplary distributed scheduling process may be easily adapted to work for any finite round trip time, without sacrificing any throughput. Simulation results also showed that this distributed scheduling process can provide very good delay performance for different traffic patterns and for different round trip times associated with current switches.

The invention discloses a multi-target cloud workflow scheduling method based on a reinforcement learning strategy. A reinforcement learning Agent is improved by using a pointer network to form an improved deep reinforcement learning algorithm so as to construct a workflow scheduling model based on a reinforcement learning strategy, so that the workflow scheduling model can be suitable for cloud workflow scheduling problems of different sizes and different types, and the generalization ability of the model is improved while relatively high timeliness is ensured.

The invention discloses gasholder load prediction equipment and a gasholder load prediction method for a gas system, and a gas scheduling system and a gas scheduling method. The gas system comprises a low-pressure gas generating device and a gasholder which are connected with each other. The method comprises the following steps of: (1) acquiring gasholder loads and gas output of the low-pressure gas generating device at different moments in a first preset time period; (2) selecting a model structure of a gasholder load prediction model, and establishing a gasholder load prediction model according to the selected model structure and the acquired gasholder loads and gas output of the low-pressure gas generating device at different moments in the first preset time period; and (3) predicting gasholder loads at different moments in a second preset time period by utilizing the established gasholder load prediction model, wherein the second preset time period is a time period after the first preset time period. The gasholder load prediction-based gas scheduling system and the gasholder load prediction-based gas scheduling method can radically solve the current problem that the gas balancing and scheduling are carried out through experiences, obviously improve the scheduling level of the gas system and prevent the gasholder loads from exceeding the upper and lower load limits of the gasholder.

The invention provides a medium-short term coordinated dispatching method for ultra-high voltage AC-DC hybrid power grid, and relates to the field of electrical power system power generation dispatching. When electricity is transmitted from west to east, feed-in scale is sharply increased in a low peak period, and load adjustment difficulty of a receiving power network is significantly increased, causing a power grid medium-short term start-up mode and running plan arrangement difficulty to be increased accordingly. The method comprises medium term plan formulation and short term plan formulation. The medium term plan formulation adopts a month as a decision cycle and a day as a calculation period, on the conditions of guaranteeing electrical power system load requirements and operational parameters of a power station and a machine set, monthly electricity is divided into days, and a daily start-up mode of the machine set is optimized; the short term plant forms a 96-point power output plan of daily electricity according to a short-term load prediction result; rolling amendment is conducted on an original medium-term power generating plan according to the latest prediction information of the load and machine set actual electricity completion situation; according to the technical scheme, frequent start-ups and stops of the power generating machine set are avoided, effective engagement and mutual coordination between a medium term and short term power generating plan are achieved.

The invention discloses a method for scheduling simulation resources based on an improved genetic algorithm. The method includes: S1: initializing and generating a population of simulation models and simulation resources; S2: calculating the individual fitness value of each individual in the population based on a fitness function; S3: Perform crossover operation on the population based on the single-point crossover method; S4: Perform mutation operation on the population based on the adaptive mutation probability; S5: Use the selection operator based on the cumulative probability improvement to select the population; S6: Repeat S1-S5 , and end after reaching the predetermined number of steps. The present invention optimizes the scheduling of simulation resources based on the genetic algorithm, improves the scheduling efficiency of high-throughput simulation resources, and improves the execution efficiency of simulation tasks and the throughput of the simulation system.

The invention discloses a resource-constrained project scheduling method based on a multi-agent evolutionary algorithm, belonging to the technical field of automatic control and information. According to the method, a multi-agent system is combined with evolutionary computation so as to solve the resource-constrained project scheduling problem. The method is characterized by comprising the following steps of: initializing each agent in an agent grid according to two algorithms; and designing a neighborhood competition operator, a neighborhood cross operator, a mutation operator and a self-learning operator for optimizing the agent. The verification result proves that the method has advantages in two aspects for evaluating the effectiveness of the method for solving the resource-constrained project scheduling problem, namely the solving of the optimal solution proportion and the solving of the average error deviating from the optimal solution, so that the method effectively solves the resource-constrained project scheduling problem, and the method can be expanded to solving other combined optimization problems with precedence relationship constraints.

A software solution for managing, sorting and ranking lists of tasks and integrating task and time management, such that tasks can be automatically or manually assigned to specified time blocks. Users can monitor the relationship between volume of tasks and available time in which to complete them. The solution and method can be applied to individual task lists as well as to the management of time across multiple projects and can be employed either by individuals or by collaborative groups. The solution and method employ multiple filters, sorts and handling rules to embody users' personal planning preferences. It can be used to manage task lists both with and without employing a scheduling component. The solution can interoperate with existing computer- and web-based calendar software and can use third-party calendar clients to display its output and to accept input from users.

The invention discloses a cloud manufacturing multi-task scheduling optimization method based on a game theory. The method comprises the steps of establishing a CMMS game model based on time, cost andreliability in combination with the game theory; initializing to obtain habitats, and representing each habitat by adopting a matrix; adopting a fitness function based on Nash equilibrium to calculate the habitat suitability index of each habitat, and determining an initial optimal solution according to the habitat suitability index; selecting a habitat to execute migration operation according tothe immigration rate and the emigration rate, and updating the optimal solution; selecting a corresponding habitat to perform mutation operation according to the mutation rate, and updating the optimal solution; updating the population by adopting an elitist replacement strategy; if the number of iterations is reached, outputting an optimal solution, namely an optimal scheduling scheme; otherwise, continuing iteration. According to the method, a new cloud manufacturing multi-task scheduling game model is provided based on the game theory, a better scheduling scheme can be obtained, and the defects of premature convergence and poor population diversity of a basic BBO algorithm are overcome.

The invention discloses an improved sparrow search optimization method for blocking a flow shop scheduling problem, and belongs to the technical field of shop scheduling. The method comprises the steps: employing an integer-real number hybrid coding mode to code a sparrow individual and workpiece production sequence; using a new collaborative initialization strategy to initialize the positions of the sparrows; dividing the sparrow population into a plurality of subgroups for mutual learning; obtaining a good workpiece production sequence solution set, namely a sparrow individual elite solution set, through an improved sparrow individual search strategy; and carrying out local enhancement on the obtained elite solution set, and obtaining an optimal sparrow individual, namely an optimal workpiece machining sequence. The method can solve the problems that the population diversity is reduced and the algorithm is easy to fall into local optimum in the later stage of iteration of the sparrow algorithm; and the processing efficiency of the blockage flow workshop can be effectively improved, and the economic benefits of factories are improved.