13 results about "Constraint programming model" patented technology

The invention discloses a multi-element source-load coupled distributed wind power access capacity calculation method, which belongs to the technical field of power systems, and comprises the following steps: constructing a multi-element source-load probability model, and quantifying the probability distribution of wind power, photovoltaic and flexible loads; a dynamic vine Copula correlation model is constructed, and wind power-photovoltaic-flexible load joint probability distribution is obtained; generating a multi-element source load operation simulation scene set based on wind power-photovoltaic-flexible load joint probability distribution, and reducing simulation scenes; constructing a distributed wind power access capacity optimization model objective function; and solving the objective function by adopting a Benders decomposition algorithm to obtain an optimal distributed wind power capacity configuration scheme. By adopting the method, the nonlinear and time-varying correlation between the photovoltaic load and the flexible load can be accurately captured, meanwhile, the stochastic chance constraint programming model considering the uncertainty of the source load is constructed, the economic optimization is realized, and the calculation efficiency is greatly improved.

The invention discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: collecting production related parameters through an Apriso manufacturing operation management platform, building a database, calling a Weibull distribution equipment maintenance model to calculate the equipment maintenance priority, building a multi-target random opportunity constraint planning model based on the priority, and carrying out the optimization of a multi-target production plan. A multi-agent cooperation algorithm based on the game theory is introduced to obtain a preliminary production plan, and a final scheme is determined and issued after platform simulation operation evaluation; the system comprises six units. According to the method and the system, multi-model deep collaboration and platform efficient interaction are realized, multiple targets of production efficiency, cost and energy consumption are considered, parameter accuracy and plan feasibility are improved, a dynamic multi-constraint environment of a digital factory is adapted, and plan optimization requirements of a complex production scene are met.

The application discloses a truss environment-oriented two-legged robot crawling landing point sequence planning method and belongs to the technical field of robots; the method first constructs a truss undirected graph topology model based on the spatial connection relationship of truss joints and members, takes nodes to represent the truss joints, takes edges to represent the members and takes the member length as the edge weight; the A star algorithm is adopted on the topology model to search for the node sequence and the member sequence of the robot from the starting member to the target member; subsequently, the axial position of the member is described through a normalized member parameter model, and the non-connected feasible landing area set of the robot foot end is represented in combination with One-Hot coding; on this basis, the least number of steps required for the robot to reach the target position is taken as an optimization target, a mixed integer quadratic constraint programming model is established, the landing point sequence is solved, and the optimal landing point sequence satisfying the truss topology constraint and the robot motion constraint is obtained. The application can realize the landing point sequence planning of a two-legged truss crawling robot on a complex truss structure.

The application provides a planning method and system for a simultaneous pickup and delivery vehicle routing problem with multiple time windows under uncertain pickup demand, comprising the following steps: obtaining distribution information of customer points to be served; constructing a fuzzy chance-constrained programming model for the simultaneous pickup and delivery vehicle routing problem with multiple time windows based on credibility measure theory; solving the model by using a genetic and harmony search algorithm to obtain a pre-optimized distribution scheme; scheduling a vehicle to travel according to the pre-optimized distribution scheme to determine the pickup demand of each customer; judging whether there is a failure point with a vehicle residual loading capacity less than the actual pickup demand of the customer, if there is a failure point, adopting a failure point service strategy to re-optimize the path planning for the failure point within the remaining serviceable time window; if there is no failure point, storing the determined distribution information of the customer points and outputting an optimal distribution scheme. The application makes full use of the loading capacity of the vehicle and maximizes the economicization, efficiency and greenization of urban logistics distribution.

The invention relates to the technical field of supply chain order allocation, and discloses a supply chain order allocation method based on multi-stage cardinality constraint planning, which comprises the following steps: screening supplier subsets meeting cardinality constraints from all suppliers through a relationship awareness depth Q network; based on the screened supplier subsets, converting the multi-stage cardinality constraint programming model into an order allocation model only containing linear constraints, and solving to obtain an optimal order allocation weight; and repeatedly executing the two stages in a set decision period to minimize the total target cost to obtain supplier selection and order allocation results. According to the method, the optimal order allocation weight can be quickly calculated, and the rationality and high efficiency of resource allocation are ensured.

The invention provides a garment production line ranking method and system based on constrained programming and electronic equipment. The method comprises the steps of obtaining a process list, an employee list and configuration parameters of a garment production line; based on the configuration parameters, dividing the garment production line ranking problem into a plurality of solvable constraint planning sub-problems, and based on the constraint planning sub-problems, constructing an initial constraint planning model; based on the process list and the employee list, instantiating each initial constraint planning model to obtain a corresponding target constraint planning model; on the basis of constraint conditions, solving the target constraint planning model by taking maximization of staff skill matching scores and minimization of the process splitting number as targets to obtain a model solving result; and based on a model solving result, generating a clothing production line ranking result containing a station-process-employee-equipment mapping relation. According to the invention, by introducing the constraint planning method, the precision and efficiency of the arrangement result of the garment production line are improved, the bottleneck of the production line is relieved, and the line balance rate is optimized.

A warehouse management system uses a constraint programming model and associated search algorithm for determining packing of items into minimal or otherwise moderated sets of heterogeneous bins (e.g., cartons or other types of containers). In some embodiments, a constraint solver of the warehouse management system minimizes or otherwise moderates the number of shipping containers required to package and ship a group of items. Packaging configurations may be based on multi-dimensional considerations of item properties, bin capacities, such as weight and volume capacities, ordering and grouping rules, and packaging restrictions. Given a set of items, each with a given weight and volume, and a set of containers, each with a specified weight and volume capacity, the goal is to compute carton placement for each item. Container selection may be performed such that the overall number of cartons required for packaging is minimized or otherwise moderated. Optionally, containers chosen for each group of packaged items may be chosen to minimize or otherwise moderate wasted space, while respecting item ordering and grouping.

The application discloses a kind of fuzzy garbage production under domestic waste clean-up planning method, device and equipment, the method includes: defining the fuzzy variable of the garbage quantity to be cleaned of each garbage generation point;Introduce fuzzy opportunity constraint based on credibility measure theory to handle the fuzziness of garbage production;The objective function of fuzzy opportunity constraint programming model is constructed to minimize the cost of cleaning and transporting, the negative effect of transfer station and the additional cost generated by cleaning and transporting failure;According to fuzzy variable, fuzzy opportunity constraint and objective function, construct fuzzy opportunity constraint model based on credibility measure theory;Based on the C-W saving algorithm of fuzzy simulation and the improved NSGA-II algorithm, the fuzzy opportunity constraint programming model is solved, and the transfer station position of each garbage generation point best garbage operation and cleaning and transporting car scheduling scheme are obtained.The application can plan the transfer station position of each garbage generation point best garbage operation and cleaning and transporting car scheduling scheme under the condition that garbage production is uncertain.

The application discloses a time-varying modulation opportunity array radar pattern synthesis method and belongs to the opportunity array radar field. The method comprises the following steps: constructing an opportunity array radar array pattern target function with the minimum side lobe peak level and first-order sideband level as the target, constructing a fuzzy opportunity constraint condition with a subarray position state, a subarray quantity and an array element time sequence in each subarray, obtaining a fuzzy opportunity constraint programming model of the opportunity array radar pattern synthesis, and optimizing and solving the fuzzy opportunity constraint programming model based on an adaptive differential evolution algorithm under the chaos elite and center guide thought to obtain the best array element switch time sequence and position. The trapezoidal pulse modulation is adopted in the application, and compared with the traditional rectangular pulse, the frequency spectrum energy distribution is more efficient; and through the fuzzy opportunity constraint programming model, the direction pattern synthesis can be completed by using 30%-70% of the subarray quantity in the confidence water time, and resource waste is reduced.

The invention relates to a resource-constrained flexible job shop scheduling method and system integrated with an advanced production organization, and the method comprises the steps: obtaining production data, building a scheduling model through the production data, and setting a corresponding key constraint; the scheduling model comprises an RCFJSP-SST model for integrating separable preparation time, an RCFJSP-SSTOBP model for integrating process batch processing, an RCFJSP-SSTPST model for integrating parallel sequential transmission, and an RCFJSP-SSTOBPPST model; and modeling the scheduling model by adopting a constraint programming model, and solving by utilizing a constraint programming solver to obtain an optimized scheduling scheme. According to the method, through collaborative integration and optimization of three advanced production organization modes, the manufacturing period is remarkably shortened, the equipment and resource utilization rate is increased, and an efficient and feasible scheduling scheme is provided for a multi-variety small-batch production scene.

The invention discloses a data security management and control method and system based on an evolutionary algorithm and opportunity-constrained programming, and belongs to the technical field of data security. The method comprises the following steps: acquiring a to-be-encrypted data set, and extracting attribute features of each data in the data set; based on the attribute characteristics of the data, an evolutionary algorithm is adopted to carry out encryption category division on all the data of the to-be-encrypted data set, based on the encryption categories after data division, encryption algorithms are selected for different categories of to-be-encrypted data, and a secret key meeting a chance constraint programming model is solved for each category of to-be-encrypted data in an iterative optimization mode; and based on the selected encryption algorithm and the generated key, encrypting each file of each type of data to be encrypted. According to the invention, refined classification can be carried out on the data intelligently and automatically, and differentiated security policies and encryption schemes are dynamically generated and customized for different types of data.

This invention belongs to the field of distributed replacement flow shop scheduling technology in intelligent manufacturing, and particularly relates to a distributed replacement flow shop scheduling optimization method with preventive maintenance. This invention divides the population into two co-evolutionary populations of explorers and developers. Through Q-learning, state evaluation and search action decisions are completed based on the population improvement rate and diversity. A multi-armed slot machine model is used to adaptively select eight neighborhood perturbation operators across three categories: factory layer, workpiece layer, and local layer. After iterative optimization through reproduction and diffusion, local search, competition elimination, and population migration, an elite pool is constructed to complete a deep search. Finally, the global optimal solution of a heuristic algorithm is used as the hot start point, and the final optimization is completed through a constrained programming (CP) model. This invention significantly improves the solution accuracy and efficiency of this type of scheduling problem, effectively balancing the algorithm's global exploration and local development capabilities.

The invention discloses a mechanical arm optimal motion control method and system based on jerk constraint, belongs to the technical field of robot kinematics, and solves the problems that an existing method depends on piecewise polynomial interpolation or a simplified dynamical model, time is consumed, and global optimality is difficult to consider. The method comprises the steps that modeling is conducted on the overall dynamic process of the mechanical arm, a continuous time domain path is converted into a parameter domain discrete path point, a linear programming parameter domain solution of a general linear programming model is determined, a jerk constraint programming model constrained by a nonlinear objective function is constructed, and a final convex optimization programming parameter domain solution is solved. Converting the parameter domain track into a time domain track; according to the method, on the premise of strictly meeting the dynamics and kinematics constraints of the robot, the jerk is introduced into the convex optimization framework as an independent optimization variable, so that the time for generating the trajectory is shortened, the trajectory has high-order smoothness, and the impact and residual vibration in the motion process are effectively inhibited.