44 results about "Branch and bound" patented technology

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The invention provides a book box with variable partition plates and a boxing method. The method comprises the steps that a book list and an initial box body space are obtained; based on a branch and bound-greedy hybrid optimization algorithm, carrying out space state circulation processing to obtain an optimal space allocation scheme; based on the optimal space distribution scheme, re-planning the space layout, updating the optimal position of the partition plate, and obtaining the sizes of the four divided spaces; and on the basis of the sizes of the four divided spaces, book placement information is obtained, and books are placed in sequence. According to the method, the optimal space distribution scheme of the four partition spaces is obtained through the branch and limit-greedy hybrid optimization algorithm, so that the book placement direction and position are selected, and the residual space is minimum.

A ship deployment and refueling joint optimization method considering multiple uncertainties comprises the steps that firstly, a nonlinear mixed integer programming deterministic model with the total cost minimization as the target is constructed, then random oil price and berthing time are introduced, a robust optimization model containing an uncertainty set is constructed, and the nonlinear mixed integer programming deterministic model and the robust optimization model are integrated to form a two-stage robust optimization model; iterative solution is carried out through a Benders decomposition algorithm with embedded branches and delimits, so that a robust optimal ship deployment scheme and a fuel oil filling strategy are obtained; according to the method, an integrated joint decision framework is constructed, ship deployment and fuel oil management are optimized in a unified mathematical model at the same time to capture and utilize the internal relation between different decisions, potential huge cost brought by separated decision is avoided, the uncertainty influence of oil price and port berthing time is fully considered, and the method has the advantages of being high in reliability and high in reliability. Therefore, the decision has a good hedging risk characteristic, the scientificity and economy of the decision are fundamentally improved, and the comprehensive cost of operation is effectively reduced.

The invention provides a rotational symmetry point cloud registration method and device based on fuzzy clustering and a storage medium, and the method comprises the steps: extracting inner points through employing an algorithm combining the minimum median square and random sampling consistency, and estimating a rotating main axis through principal component analysis; realizing point cloud global coarse registration based on a rotating main shaft; fuzzy clustering is carried out on the fixed point cloud to construct local geometric description, a progressive constraint mechanism and a directional search strategy based on a main shaft are introduced, and fine registration is completed in combination with branch and bound and gradient descent algorithms. According to the embodiment of the invention, the registration precision and efficiency of point clouds with rotational symmetry, such as a computer-aided design (CAD) model, can be effectively improved.

The application discloses a ship deployment and refueling joint optimization method considering multiple uncertainties. The method firstly constructs a nonlinear mixed integer programming deterministic model with the minimum total cost as the target, then introduces random oil prices and berthing times, constructs a robust optimization model containing an uncertainty set, and integrates the two to form a two-stage robust optimization model. The robust optimal ship deployment scheme and fuel injection strategy are obtained by iteratively solving the model through a Benders decomposition algorithm embedded with a branch and bound algorithm. The application constructs an integrated joint decision framework, simultaneously optimizes the ship deployment and fuel management in a unified mathematical model, captures and utilizes the internal relationship between different decisions, avoids the potential huge cost caused by separate decisions, fully considers the influence of the uncertainty of oil prices and port berthing times, makes the decisions have good risk hedging characteristics, fundamentally improves the scientificity and economy of the decisions, and effectively reduces the comprehensive cost of operation.

The invention provides an off-grid ReP2A integrated system topology capacity collaborative planning method, and relates to the technical field of renewable energy source hydrogen production and synthesis ammonia, and the method comprises the steps: constructing a mixed integer second-order cone programming model covering a power network, energy storage, hydrogen production, hydrogen storage, hydrogen transmission and synthesis ammonia full-chain coupling constraint; wherein the mixed integer second-order cone programming model takes the minimum annual average comprehensive cost of the system as a target to collaboratively optimize equipment capacity configuration, power transmission and hydrogen transmission network topology and a system operation strategy; and solving the mixed integer second-order cone programming model by adopting a branch and bound Benders decomposition algorithm, and outputting a programming construction scheme. The scheme can solve the problems of high comprehensive cost and low operation efficiency of the system.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention discloses a cut plane selection method and system in mixed integer programming, and the method comprises the steps: carrying out the coding to obtain the global structure information of a whole branch and bound tree, inputting the global structure information into a multi-layer graph neural network, and enabling the graph neural network to carry out the local and global connection of a node neighborhood, dynamically calculating the feature representation of each variable node, constraint node and cut plane node, and outputting a feature vector representing the global state of the whole branch and bound tree; and carrying out joint modeling and inference on the candidate cutting plane set based on the feature vector, determining an optimal cutting plane selection strategy by adopting a reinforcement learning mode, and carrying out cutting plane selection based on the optimal cutting plane selection strategy. According to the method, the accumulation and cooperative influence of the cut plane among different nodes in the whole solving process can be accurately captured, the overall collaboration of cut plane allocation is remarkably improved, and the computing resources and time overhead required by high-dimensional information coding and model reasoning are reduced.

The application provides a kind of optimization method for frame out of warehouse in forklift dense storage system, belongs to the technical field of warehouse management system, comprising: collecting the basic information of stacking area, obtaining the retrieval order of frame and initializing the stacking state of stacking area;Using heuristic method to determine the upper bound of the number of times of turning over and the turning over scheme;Using the lower bound estimation method of the number of times of turning over to determine the lower bound of the number of times of turning over and the turning over scheme;If the upper bound of the number of times of turning over is equal to the lower bound of the number of times of turning over, the turning over scheme determined by heuristic method is the optimal turning over scheme, otherwise, the upper bound of the number of times of turning over is the initial optimal upper bound, the lower bound of the number of times of turning over is the initial optimal lower bound, and the branch and bound algorithm is used for deepening iterative search to obtain the optimal turning over scheme.The application provides an optimization method for frame out of warehouse in forklift dense storage system, which can optimize the out of warehouse turning over scheme and improve the out of warehouse efficiency of dense storage system.

This invention discloses a production scheduling method for mixed continuous and discrete processes, comprising: acquiring historical, static, and dynamic data of continuous and discrete production lines; determining standard time granules based on the acquired historical data, and calculating batch consumption times for continuous and discrete production lines based on the standard time granules; constructing scheduling constraints based on static data, dynamic data, and batch consumption times, and building a scheduling model with a scheduling objective function; solving the scheduling model based on an improved branch-and-bound algorithm and preset inventory verification constraints to obtain a production line scheduling plan for mixed continuous and discrete processes; discretizing the continuous production line through continuous feature analysis and extraction of the process route to solve the problem of inconsistent time dimensions between continuous and discrete production lines in data modeling; establishing a global constraint model for mixed continuous and discrete production lines to achieve global constraint optimization of scheduling; and using a branch-and-bound algorithm for batch continuous time occupancy processing to achieve high-efficiency solution.

The application discloses a kind of subway train timetable and passenger flow OD collaborative optimization method, comprising: obtaining the expected arrival time period and origin-destination point of all passengers, obtain the minimum train interval, train capacity, line station and length;Construct subway train timetable and the collaborative optimization model of passenger flow OD, this is a mixed integer programming model, constraint conditions include departure interval constraint, delay passenger quantity constraint and passenger service constraint;Design adaptive branch and bound algorithm, calculate the above collaborative optimization model, output train departure scheme, the successful boarding time period and origin-destination point of all passengers.The application can reduce congestion and passenger delay time effectively while optimizing cost, improve the travel efficiency and experience of passengers.On this basis, based on the customization improvement of branch and bound algorithm on the basis of the theoretical characteristics of collaborative optimization model, can provide certain reference for solving large-scale subway network calculation efficiency problem.

The present application relates to a kind of micro energy network planning optimization method based on economic-environmental comprehensive evaluation, comprising the following steps: establishing micro energy network system optimization configuration model, the objective function of micro energy network system optimization configuration model is comprehensive evaluation index objective function, wherein, the comprehensive evaluation index objective function is obtained by the weighted processing of economic cost objective function and environmental protection cost objective function based on random weighting method, and economic cost objective function is determined by annual average fixed cost, annual operating cost, annual maintenance cost and annual average carbon trading income;Establish optimization scheduling constraint condition;Micro energy network system optimization configuration model is solved based on linear / nonlinear-branch and bound algorithm optimization, and the optimal configuration scheme is obtained.Compared with prior art, the present application introduces carbon trading income index, has the advantages that the conflict of economic cost and environmental cost is considered, the planning is more reasonable, etc.

The application discloses a GPU-based ARX type block cipher optimal differential characteristic search method, which first selects a high-probability differential transition to split the whole search process into forward search and reverse search; differential transition construction and probability calculation are taken as kernel functions, a GPU-based parallel scheme is designed, and the differential transition probability of different states in each round is calculated in parallel; the GPU parallel calculation result and the set probability threshold are taken as the determination condition of pruning in the search process, the optimal differential characteristic is searched round by round according to the branch and bound strategy, the forward and reverse searches are completed, and the optimal differential characteristics of the two parts are obtained; finally, the optimal differential characteristics of the two parts are spliced to obtain the optimal differential characteristics of the complete rounds. The search method can effectively reduce the recursion depth, improve the search efficiency through parallel calculation, shorten the calculation time and improve the search speed of the ARX type block cipher optimal differential characteristic.

A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating a state space for the set of time series datasets using bi-partite graph representations for a mixed integer program (MIP), applying a transition function on the state space to obtain state-to-state transition function, performing a graph embedding on the state-to-state transition function to obtain historical transition data, applying a transformer multi-head attention function on the historical transition data to obtain positional encodings of observation history and feature representation, and perform an agent deployment on an order processing system based on finalized state-to-state transition functions, wherein the finalized state-to-state transition functions are based on the positional encodings.

The invention discloses an elastic power distribution network planning method and digital system considering wind and rain microtopography coupling, and relates to the technical field of power system disaster prevention and planning, and the method comprises the steps: constructing a typhoon wind field model considering microtopography correction, recognizing ridge features based on a ridge recognition algorithm, and outputting a wind speed acceleration ratio; simulating spatial and temporal distribution of rainfall in combination with a corrected wind field and a terrain uplift effect, constructing a two-dimensional hydrodynamic rainfall flood waterlogging model, and outputting a ponding evolution process of each grid region; a combined failure model and a node failure probability function are constructed for an overhead line, an underground cable and power distribution node equipment, a typical disaster scene is generated and reduced after fusion, and a two-stage mixed integer stochastic programming model is constructed; and solving the two-stage mixed integer stochastic programming model by adopting an improved dual decomposition algorithm, and solving dual gap global optimum by combining branch and bound processing. According to the method, the expected power failure loss under the extreme event is reduced, and the toughness support under the extreme environment is improved.

A hybrid integer optimal control optimization problem is solved by using a branch and bound (B&B) optimization that searches for a global optimal solution within a search space to control a system. The B&B optimization iteratively partitions the search space into a region-nested tree and prunes at least one region from the region-nested tree before finding a local optimal solution for each region when a dual objective value of a projection of a suboptimal dual solution estimate for each region in a dual feasible space is greater than an upper bound of a global optimal solution maintained by the B&B optimization or less than a lower bound of the global optimal solution.

The invention belongs to the technical field of circuit fault diagnosis, and particularly relates to a solving method and device based on multiple observation aggregation fault diagnosis and a medium. In order to solve the problems of one-sided single observation information, redundant diagnosis results and the like, multiple times of fault observation are performed on a circuit system, and multiple independent diagnosis results are generated and integrated into a diagnosis set; establishing a mapping relation between a diagnosis set and a unique keyword by using a map function of the STL, and converting the component combination into an integer identifier to simplify operation; calling a BWSS algorithm to perform collision calculation on the mapped keywords to obtain a candidate solution set covering all observation scenes; after the keyword solution set is converted to an original diagnosis form, deleting a superset according to a redundancy rule to form a simplified intermediate diagnosis solution set; and finally, through a branch and bound minimization algorithm, according to potential classification and ascending order arrangement of the diagnosis sets, high-order supersets are deleted layer by layer by adopting a heuristic strategy, and finally, a minimum diagnosis solution set of all faults is output.

The invention provides an optimal design method and system for a material preparation formula and a medium. The optimization design method of the material preparation formula comprises the following steps: acquiring experimental data including different formula combinations and corresponding performance indexes thereof; performing formula variable space design on the basis of the category and value rule of formula variables in the experimental data and formula constraint conditions to determine a formula search boundary comprising discrete variables and continuous variables; based on a target acquisition function, performing search optimization in the formula search boundary by adopting particle swarm optimization in combination with a branch and bound method so as to determine a plurality of candidate formula combinations, the target acquisition function being defined based on performance prediction values and prediction uncertainty of the candidate formula combinations determined by a support vector regression model; and in response to a termination condition meeting search optimization, determining the candidate formula combination with the optimal target acquisition function value as a formula design scheme.

The application discloses a kind of security constrained unit's mixed integer programming joint optimization method, system and equipment, it is related to power system unit combination optimization field, method includes: to the security constrained unit combination optimization model of pre-constructed model processing, obtain mixed integer linear programming model;According to prior rule, a plurality of variables in mixed integer linear programming model is fixed based on two-stage multivariate branch method;Artificial intelligence method is used to obtain solution information in the branch and bound solution process of the multivariate branch method, and the rule of the value of optimal solution and relevant parameter is learned, to predict the value of part of variables of optimal solution.The application is based on artificial intelligence method and large-scale integer programming solution interaction, obtain certain solution information in the branch and bound solution process, and learn the rule of the value of optimal solution and relevant parameter, to predict the value of part of variables of optimal solution, can reduce solution scale, and improve solution efficiency.

The invention relates to the technical field of power systems, and discloses a branch cutting method and device for accelerating power system security constraint unit commitment solution. In the branch and bound process, if the relaxation problem of a target node has no solution, simple single-point pruning is not carried out any more, but a network constraint minimum illegal model is constructed, and if no solution is caused by network constraint, global feasible cut is generated through a Lagrange multiplier at an optimal solution, so that the network constraint minimum illegal model is constructed. The global feasible cut is a constraint condition of a specific unit commitment mode which causes a relaxation problem to have no solution, and the global feasible cut is added to a branch and bound cut pool, so that a subsequent infeasible solution similar to the specific unit commitment mode can be cut off, a large amount of repeated invalid exploration in a branch and bound process is avoided, solution is accelerated, and the problem is solved. The safety and the economical efficiency of power grid dispatching are ensured; the global feasible cut is strictly based on the strong duality theory, it is ensured that the feasible solution or the optimal solution cannot be cut by mistake, and the feasibility and optimality of the security constraint unit commitment solving result are fundamentally guaranteed.

A method and system for configuring a product is disclosed. The method includes obtaining, by a processor (102) of a PDM system (100), a plurality of options defined for a product and one or more constraints from a product database (116), wherein each of the options has one or more variations. Further, an ordered logical group is generated from the obtained plurality of options based on a weight associated with each of the options. Further, a branch and bound algorithm is used to determine one or more buildable product configurations based on the ordered logical group and one or more of the constraints applicable to the options, wherein the applicability of the one or more constraints is determined using a solver based on a satisfiability modulo theories. The one or more buildable product configurations are also output on an output device (110).

The embodiment of the invention provides a boiler heating surface crack detection radar side cloud collaborative task scheduling method and system. The method comprises the following steps: firstly, deconstructing a millimeter wave radar detection process into a plurality of calculation components with a data stream dependency relationship; further constructing a mixed integer nonlinear programming model with the goal of minimizing the total delay and the total energy consumption of the system, wherein model decision variables comprise task allocation positions, edges and cloud computing resources; solving by adopting an optimization algorithm integrating problem relaxation, Monte Carlo robust adjustment, branch and bound and genetic algorithms to obtain an optimal task scheduling and resource allocation strategy; and finally, dynamically deploying each component to an edge or a cloud node for execution according to the strategy. Unification of high precision, low delay and high energy efficiency of a detection task in a complex industrial environment is realized, and the system resource utilization rate and the environmental adaptability are improved.

The embodiment of the application discloses a kind of electric cold and hot gas comprehensive energy optimization configuration method, system and device, the method includes the following steps: carbon price and renewable energy output power after considering carbon price and renewable energy output power uncertainty are calculated, and the typical scenario of carbon price and renewable energy output power is generated;Multi-energy flow model is established and carbon trading cost model is established by combining carbon price after considering carbon price uncertainty;On the basis of the typical scenario, an optimization configuration model is established, the optimization configuration model is converted into a mixed integer linear programming model using smoothing method, the optimization configuration model is solved using branch and bound algorithm, and the planning scheme under a given confidence level is obtained.The present application aims to introduce carbon price and renewable energy output power uncertainty into the planning total cost fluctuation risk modeling, in addition, due to the strong power fluctuation of renewable energy, the negative carbonization is possible.

The invention relates to an integer programming-based berth optimal allocation method and device, equipment and a medium. The method comprises the steps of collecting multi-source scoring indexes related to ship supply priorities in real time and performing normalization processing; constructing a ship supply priority real-time scoring model based on multi-source scoring index weight fusion, introducing a reinforcement learning algorithm to optimize an optimal weight distribution strategy, and outputting the supply priority of each ship; according to the supply priority of each ship, constructing a port berth allocation model based on integer programming, including target functions and constraint conditions for minimizing operation cost, minimizing ship waiting cost and maximizing ship supply priority response efficiency; and solving the port berth distribution model in combination with a greedy algorithm and a branch and bound algorithm to obtain a berth distribution result meeting preset precision, and performing ship supply scheduling according to the berth distribution result. According to the method, efficient and scientific configuration of berth resources in a complex and changeable environment can be realized.

The application discloses a kind of rehabilitation resource collaborative scheduling method and system considering multidimensional space-time constraints, it is related to medical information management technical field.The method includes: response scheduling request and obtain basic data;Standardized pretreatment is carried out to data and the degree of matching of doctor and patient is calculated;Mixed integer linear programming mathematical model is constructed, including oxygenated block therapy time sequence continuity constraint, resource group sharing capacity constraint and personnel skill matching constraint, and multiple objective optimization function including treatment amount, matching degree and load balancing is set;Branch and bound algorithm is called to solve model, and global optimal scheduling scheme and exception analysis report are generated.The application effectively solves the long-term project continuity guarantee difficulty, shared resource conflict and low individual matching degree in rehabilitation scheduling by mathematical programming technology, and significantly improves the resource utilization and operation efficiency of rehabilitation center.

The application is an automated laboratory non-deadlock dynamic scheduling method based on Monte Carlo tree search, and belongs to the technical field of industrial intelligent scheduling. The method models the tasks of the automated laboratory as a directed acyclic graph, searches the task scheduling decision sequence for the graph by using the Monte Carlo tree search method, the state of the tree node includes the state of whether the task is executed and the state of whether the device is occupied, the action of the edge represents scheduling which task to execute on which device, the variables of the reward value, the access frequency and the number of deadlocks are set for the tree node, the improved action selection function is designed, the branch and bound strategy is used to perform the pruning operation on the expanded child nodes, etc.; the list scheduling mode is used for actual scheduling, and the task directed acyclic graph is reconstructed when a dynamic event occurs. The method can not only complete the optimization target of minimizing the maximum task completion time, but also effectively solve the scheduling deadlock problem caused by the blocking of the task to the device and the resource competition.

The application discloses a kind of based on main sub-problem dynamic interaction's power system unit commitment acceleration method and system, method includes: step 1, establish power system unit commitment mathematical model, obtain main problem;Step 2, using branch and bound method to solve power system unit commitment mathematical model, collect N slack solution of initial solution;Step 3, N slack solution is evaluated, predicts the unit start-stop state variable value;Step 4, the start-stop state of fixed part unit is constructed small-scale auxiliary unit commitment model, obtain sub-problem;Step 5, solve sub-problem and send the sub-optimal solution obtained to main problem solving process, accelerate branch and bound convergence process by updating global upper bound, finally obtain the optimal solution of main problem.The heuristic method based on the physical characteristics of power system is deeply embedded in the general solver solving process with branch and bound as the core algorithm, and customized efficient and accurate solution of power system unit commitment is realized.

The invention discloses a method and device for searching optimal difference and linear features of a similar SIMON password, and the method comprises the steps: traversing plaintext differences or masks of the similar SIMON password according to the sequence of Hamming weights from low to high, calculating the upper bound of the difference or linear probability, and completing the pruning in advance through the upper bound; the cyclic shift invariance of the SIMON-like password is utilized to exclude differential or linear features with equal probabilities; constructing an input difference or output mask set of a next round of round function according to the input difference or mask of the current round, and sorting according to the difference or linear probability to obtain a more accurate branch condition of the branch and bound algorithm; after all plaintext differences and masks are traversed, the maximum difference and the linear feature probability are obtained to serve as the maximum difference and the linear feature probability of the class SIMON password of the corresponding round number, the efficiency is improved through multiple optimization strategies, and the security boundary of anti-difference and linear analysis of the class SIMON password can be obtained in a very short time.

The invention discloses a multi-wave collaborative operation resource optimization method, system and device and a storage medium. The method comprises the steps that S1, multi-wave operation parameters are acquired, and a three-dimensional linkage mapping table of window time, resource states and spatial positions is generated; s2, constructing a comprehensive optimization function which takes minimization of global total job duration and wave collaborative redundancy time and maximization of window time utilization rate and multi-resource utilization rate as objective functions, and configuring multiple types of constraints to form a multi-objective optimization model; s3, binary and real number hybrid coding is adopted to code the model, an initial population is generated, and an optimal decision variable is output through elite retention and adaptive operation optimization; and S4, verifying the scheduling scheme by adopting a branch and bound method, if the verification is passed, outputting a final scheme, otherwise, adjusting the weight coefficient and the genetic parameter and returning to S3. According to the method, the window and resource decoupling problem is solved through three-dimensional linkage mapping, and global optimal scheduling of multi-wave collaborative operation is realized in combination with an improved genetic algorithm and branch and bound verification.