13 results about "Objective programming" patented technology

The invention relates to an elastic balance area multi-objective planning method considering source-network load interaction, and the method comprises the steps: analyzing the real-time operation data of a power distribution network and the topological graph data of the power distribution network, and obtaining a source load feature matrix, an interaction mode matrix, and a final constraint set; constructing an optimization objective function set and a coordination mechanism set according to the source load feature matrix, the interaction mode matrix and the final constraint set; and establishing a comprehensive optimization model according to the optimization objective function set, the coordination mechanism set and the final constraint set to obtain a complete optimization model and a hierarchical structure matrix, and solving based on the complete optimization model and the hierarchical structure matrix to obtain a final optimization solution and a performance index matrix. According to the method, the uncertain influence caused by the random fluctuation power supply in the active power distribution network can be better processed.

This invention discloses an agricultural water resource optimization method based on crop water requirement and multi-objective optimization. The method includes: S1 Constructing a multi-objective programming model that maximizes crop yield, irrigation water productivity, and minimizes planting water costs; using the Zimmerman method to transform the multi-objective programming model into a deterministic single-objective model; S2 Reading monitoring data collected in the current monitoring period and monitoring data from the previous monitoring period corresponding to periods not collected in the current monitoring period; the monitoring period includes a complete planting cycle for corn and wheat; S3 Inputting all monitoring data into the deterministic single-objective model and solving the single-objective model using Lingo programming to obtain the irrigation amount for corn and wheat at each growth stage within the current monitoring period; S4 Using the irrigation amount obtained in step S3 for the corresponding growth stage of corn and wheat as the guiding irrigation amount for the current growth stage of corn and wheat, based on their current growth stage.

This invention belongs to the field of power grid investment optimization technology, and relates to a regional power grid investment optimization method based on a multi-objective programming model. It includes the following steps: Step S1: Considering the decoupling requirements between regional power grid investment and electricity generation, a multi-dimensional parameter system covering investment, electricity generation, safety, and green / low-carbon parameters is constructed; Step S2: Based on the multi-dimensional parameter system, a multi-objective programming model is constructed to maximize the net present value of investment, maximize the power grid safety margin, and maximize the investment-electricity decoupling degree; Step S3: An improved multi-objective particle swarm optimization algorithm is used to solve the model, and the Pareto optimal solution set is output to obtain the optimal investment scheme. This invention quantifies the decoupling relationship between investment and electricity generation through a multi-dimensional parameter system, and optimizes the balance between economic efficiency, power supply safety, and resource allocation efficiency using a multi-objective model, solving the problems of excessive coupling between investment and electricity generation and insufficient functional value mining in existing planning.

A method that includes obtaining a minimum deactivation time for each processing device (203) of a processing system configured to produce one or more materials and operate in accordance with a deactivation schedule. The deactivation schedule includes a calendared deactivation period for each processing device (203). The method further includes obtaining a maximum turnaround time for the processing system, obtaining a set of conditions (311) for the deactivation schedule and determining, subject to the set of conditions (311), an optimum deactivation schedule (317) by optimizing a production objective. The conditions require that the calendared deactivation period of each processing device lasts at least the minimum deactivation time, and a total duration of the deactivation schedule does not last longer than the maximum turnaround time. The conditions also seek to reduce overlap between calendared deactivation periods. The method further includes performing a system maintenance (319) of the processing system according to the optimum deactivation schedule (317).

The invention relates to an express delivery vehicle scheduling planning method and device based on hybrid multi-target planning, computer equipment and a storage medium. The method comprises the following steps: acquiring a historical data set of a target cargo collection and distribution center and performing preprocessing; through an MLP neural network and a particle swarm algorithm, predicting the arrival quantity of the target cargo collection and distribution center in a specified time period; establishing constraint conditions according to the arrival quantity of the target cargo collection and distribution center in the specified time period and a pre-acquired constraint condition information set; constructing a hybrid multi-objective planning function based on the own vehicle turnover rate, the vehicle average package number and the total cost; and through a greedy algorithm and a dynamic planning method, solving the mixed multi-objective planning function based on constraint conditions, and outputting an express vehicle scheduling planning scheme. By adopting the method, the problems of inaccurate cargo volume prediction, unreasonable vehicle scheduling and the like can be solved, the logistics transportation efficiency is remarkably improved, and the cost is reduced.

The invention discloses a dynamic constraint multi-target planning method and system for unmanned aerial vehicle data acquisition, and relates to the technical field of low-altitude intelligent networks, and the method comprises the steps: randomly generating an initial population based on a first period of a target task, and carrying out the iterative optimization, obtaining an initial optimal solution set, and obtaining an initial optimal solution as an unmanned aerial vehicle execution scheme of a first period according to the target function value and the constraint violation value; in each planning period after the first period, performing random replacement and constraint judgment repair based on the optimal solution set of the previous period to obtain an initial new population, and obtaining an objective function value and a constraint violation value of each individual of the initial new population to jointly form a repaired new population; performing iterative optimization based on the repaired new population, and obtaining a current optimal solution as an unmanned aerial vehicle execution scheme of the current period according to the target function value and the constraint violation value; and circularly executing the process, and executing the dynamic re-planning of the new planning cycle. And the airspace resource utilization efficiency and the overall task efficiency in the low-altitude intelligent network are improved.

The invention discloses a park project collaboration assessment method based on an STIRPAT model and multi-objective planning, and belongs to the technical field of project collaboration assessment, and the method comprises the following steps: S1, data collection and standardization processing, S2, building of a collaborative quantitative assessment model, S3, generation of a collaboration index, and S4, output of an assessment report. The collaboration of the newly-built and stock projects of the industrial park is comprehensively and accurately evaluated, and powerful support is provided for sustainable development of the park. The accuracy and consistency of the data can be ensured in the data acquisition and processing links, and the collaborative effect of the project can be effectively predicted and optimized by constructing the collaborative quantitative evaluation model. The generated collaboration index evaluates the project from multiple dimensions, a clear quantitative index is provided for decision making, a finally output evaluation report provides detailed guidance for project improvement and resource configuration, a three-level early warning mechanism is set, the whole method covers the whole life cycle of the project, and dynamic effectiveness of evaluation is ensured.

The application provides a method and system for collaborative optimization of a commercial model of an energy storage power station based on multi-objective programming, relates to the technical field of energy storage power station operation, and comprises the following steps: obtaining a commercial model feature set by performing space-time dimension decoupling on operation data of the energy storage power station; establishing a multi-objective programming constraint system; obtaining a commercial model combination scheme based on a Pareto front search strategy; generating a dispatch execution instruction according to the commercial model combination scheme; and identifying and adjusting the capacity allocation of the commercial model in conflict. The application realizes dynamic collaborative optimization of multiple commercial models of the energy storage power station, and improves economic benefits and operation safety.

The application discloses a lexicographic-based fuzzy Stackelberg game strategy generation method, and the method comprises the following steps: determining the players of the game, letting the party that makes the action first be a leader, and letting the party that makes the action later be a follower; determining the income of the leader and the follower in the fuzzy Stackelberg game model under each strategy profile through expert decision or experience evaluation, thereby obtaining the fuzzy income matrix of the leader and the follower; using the solution concept of strong Stackelberg equilibrium to convert the solution of the fuzzy Stackelberg game model into the solution of a multi-round multi-objective programming problem; using the lexicographic method to convert the multi-round multi-objective programming problem into a multi-round two-stage single-objective programming problem, and then obtaining the mixed strategy Nash equilibrium solution, that is, the strategy optimization result of the leader and the follower.

The application relates to the technical field of power system management, and discloses a power consumption management peak-shaving method based on multi-target programming, which comprises the following steps: constructing a heterogeneous graph network structure of a power system, defining an uncertainty representation model on the graph structure, constructing an uncertainty propagation dynamics model, realizing an uncertainty propagation algorithm with graph structure perception, constructing a robust optimization framework with graph structure perception, realizing a multi-level risk partition management strategy, and discovering and utilizing the uncertainty complementary effect; the application effectively solves the problem that a traditional method ignores the influence of a system topological structure, balances the contradiction between the calculation complexity and the modeling accuracy, realizes differentiated processing of different levels of uncertainty, can discover and utilize the uncertainty complementary effect, and improves the effectiveness of power grid peak-shaving management, system adaptability and resource utilization efficiency.

The invention provides an energy storage power station business mode collaborative optimization method and system based on multi-objective planning, and relates to the technical field of energy storage power station operation, and the method comprises the steps: carrying out the space-time dimension decoupling of the operation data of an energy storage power station, and obtaining a business mode feature set; establishing a multi-objective planning constraint system; obtaining a business mode combination scheme based on a Pareto frontier search strategy; generating a scheduling execution instruction according to the business mode combination scheme; and identifying business modes with conflicts and carrying out capacity allocation adjustment. According to the method, dynamic collaborative optimization of multiple business modes of the energy storage power station is realized, and the economic benefit and the operation safety are improved.

The invention is applicable to the technical field of airport regulation and control, and provides an emergency multi-airport flight cooperative release method based on dual fairness, comprising the following steps: acquiring data; the airport fairness aims at minimizing the total delay push-out time of all flights which are not canceled, minimizing the total loss of all flights which are canceled, and maximizing the delay push-out time deviation of all flights of each airport; a mathematical model aiming at maximizing airline fairness of canceled flight loss deviation of all flights of each airline; converting the four mathematical models into target planning models; and solving the solution of the problem based on a solver. According to the method, the coupling relationship among the scheduling cost, the airport fairness and the airline fairness is sought, the multi-airport cooperative release efficiency is improved, and technical support is provided for daily intelligent emergency release of a plurality of associated airports.