52 results about "Column generation algorithm" patented technology

The invention discloses a dynamic shared energy storage distribution method and device based on power demand response, and belongs to the technical field of power systems, and the method comprises the steps: constructing a collective demand response prediction model of a virtual power plant; based on the collective demand response prediction model, taking maximization of collective demand response as a target to construct a target function, and based on the energy storage equipment, constructing constraint conditions of the target function; generating a main problem and a sub-problem for the target function by adopting a column generation algorithm, and carrying out iterative solution to obtain a scheduling strategy of the energy storage equipment; acquiring historical load data of the user, performing clustering analysis on the historical load data by adopting a load clustering method based on deep learning and adaptive clustering, and identifying a load mode of the user; and distributing the energy storage equipment to the corresponding users according to the load modes of the users and the scheduling strategies of the energy storage equipment. The method can help the aggregator to improve the utilization efficiency of the energy storage system, and enhance the scheduling flexibility and response capability of the virtual power plant.

The invention discloses a distribution network and micro network double-layer collaborative optimization scheduling method and system based on an active power aggregation domain, and the method comprises the steps: obtaining the operation parameters of a distribution network, and constructing the operation constraint of the distribution network; obtaining operation parameters of the micro-grid, and constructing operation constraints of the micro-grid; constructing a mathematical model of a micro-grid active power aggregation domain based on micro-grid operation constraints; solving the mathematical model by using a rank generation algorithm to obtain an operation boundary of the active power aggregation domain of the micro-grid; and submitting the active power aggregation domain of the micro-grid to the distribution network for scheduling, judging whether the active power aggregation domain of the micro-grid meets a distribution network loss minimization criterion or not in combination with the operation constraint of the distribution network, if so, controlling power output at a micro-gateway port by using the operation boundary, otherwise, adjusting the operation boundary of the active power aggregation domain of the micro-grid. And updating the mathematical model of the aggregation domain and solving until the operation boundary meets the target of minimum distribution network loss. According to the method, the scheduling model of the medium-voltage distribution network can be simplified and the scheduling efficiency can be improved under the condition that information interaction is limited.

The invention relates to a capacity expansion optimization method, system and equipment based on network performance marginal gain, and belongs to the technical field of network communication. The method comprises the following steps: constructing an undirected graph of communication network topology; defining a service requirement for each pair of nodes in the undirected graph, endowing each edge with a capacity value, and constructing to obtain an MCFP model; on the basis of the Lagrange duality theory, a duality model of the MCFP model is constructed, and a column generation algorithm is adopted to carry out double-model synchronous solution to obtain a maximum concurrency factor, optimal path flow and capacity constraint optimal duality variable; performing network key bottleneck identification according to the capacity constraint optimal dual variable and generating a plurality of capacity expansion candidate schemes; and comprehensively evaluating the capacity expansion candidate scheme by combining the network performance marginal gain, the influence degree of the link capacity change and the implementation cost, and selecting the optimal capacity expansion scheme for implementation. According to the invention, accurate load evaluation, bottleneck quantitative identification and efficient capacity expansion optimization of the communication network can be realized.

The invention discloses an active fluctuation collaborative stabilizing method and system for a high-proportion distributed new energy power grid, and relates to the technical field of power grid dispatching. According to the method, a physically consistent weather prediction model is established through multi-source meteorological data fusion, and a regional fluctuation propagation rule is accurately captured; identifying a high-risk fluctuation cluster based on dynamic time warping and spectral clustering, simulating a fluctuation propagation path by using a digital twin platform, and quantifying resource requirements; energy storage resource configuration is optimized by adopting mixed integer programming and a column generation algorithm, and multi-dimensional stability verification is carried out through a digital twin environment; a self-adaptive optimization mechanism based on reinforcement learning is established, continuous evolution of the system is realized, the technical bottlenecks of a traditional method in the aspects of fluctuation perception, resource allocation, system self-adaption and the like are solved, a collaborative stabilization mechanism with accurate prediction, intelligent recognition and decision optimization is formed, and a complete solution is provided for safe and stable operation of a high-proportion new energy power grid.

The invention discloses a spatiotemporal chaos sequence generation algorithm based on a dynamic lattice point state vector, which is characterized by comprising the following steps: S1, generating a random sequence; s2, initializing a system; initializing related parameters and initial conditions of the system; and S3, a coupling iteration process: after relevant parameters and initial conditions of the system are initialized, generating a chaotic sequence. The invention relates to the technical field of information security, in particular to a spatiotemporal chaos sequence generation algorithm based on dynamic grid point state vectors. The technical problem to be solved by the invention is to provide a spatiotemporal chaos sequence generation algorithm based on a dynamic lattice point state vector, aims to generate an efficient and safe pseudo-random sequence, and can be widely applied to the fields of image encryption, information hiding and other information security.

The invention discloses an electric vehicle freight planning system based on a charging and battery replacement hybrid strategy. The electric vehicle freight planning system comprises a network construction module, an optimization model module and an algorithm solving module. The network construction module conducts modeling through a space-time convolution network, time is dispersed into a plurality of time points, space is defined as freight station nodes, and a charging arc, a service arc and a convolution arc are constructed. And the optimization model module establishes a mixed integer programming model based on the network, wherein a target function minimizes the total transportation cost, and constraint conditions comprise commodity flow balance, transport capacity limitation, purchase budget and power conversion capacity constraint. The algorithm solving module adopts a column generation algorithm to generate a path ring, and solves a sub-problem in combination with multi-dimensional dynamic programming; the dynamic planning transmits and updates the electric quantity state through hierarchical labels, and applies a dominating rule to screen a non-inferior solution path. The system intelligently triggers battery replacement or charging according to the electric quantity state, optimizes an electric truck energy complementing strategy and a freight path, improves the transportation efficiency, reduces the energy consumption cost, reduces the unloaded driving time, and improves the utilization rate of a motorcade.

The invention relates to a high-speed rail freight train operation optimization method, device and equipment. The high-speed rail freight train operation optimization method comprises the following steps: establishing an operation optimization double-target nonlinear model of a high-speed rail freight train; decomposing into a train alternative set limitation main model, a freight train alternative set generation sub-model, a first single-target optimization model and a second single-target optimization model according to different modes; solving a train alternative set limitation main model and a freight train alternative set generation sub-model according to the high-speed rail freight train alternative set to obtain a current optimal freight train alternative set taking the initial freight train alternative set as a starting point; substituting into the first single-target optimization model and the second single-target optimization model to solve and obtain an optimal target value vector; and solving the optimal target value vector by adopting a genetic algorithm to obtain an optimal chromosome, and outputting the optimal chromosome as a final optimal solution. According to the method, the genetic algorithm and the column generation algorithm are combined, economical efficiency and transport capacity requirements are considered, matching of cargo flow and train flow is achieved, and a feasible high-speed rail freight train operation optimization scheme is obtained.

The invention relates to the technical field of path planning, and discloses a path optimization method for multi-vehicle circulating goods taking three-dimensional boxing, a terminal and a medium. The method comprises the following steps: acquiring related data, uniformly mapping part requirements of suppliers into a box body set taking a packaging box as a unit, and determining a unit residual space opportunity cost coefficient; constructing an original model according to the related data, wherein a target function of the original model comprises vehicle driving cost, vehicle starting fixed cost and compartment remaining space opportunity cost; a Dantzigzag-Wolfe decomposition method is adopted to decompose the original model into a limiting main problem and a pricing sub-problem; iteratively solving a limitation main problem and a pricing sub-problem through a column generation algorithm; and if the solution for limiting the main problem contains a non-integer, entering a branch and bound process, repeatedly executing a column generation algorithm on branch nodes until a preset termination condition is met, and outputting an optimal circulating goods taking scheme including vehicle path planning and carriage three-dimensional boxing layout. According to the invention, the transportation efficiency of circulating goods taking can be improved.

The present application relates to the technical field of intelligent decision-making, in particular to a subway scheduling decision-making intelligent generation method based on column generation algorithm, comprising: abstracting the subway scheduling problem as a path problem; designing a mathematical heuristic method to obtain a high-quality initial solution; dividing the problem into a main problem and a sub-problem, and constructing linear mathematical models corresponding to the main problem and the sub-problem; automatically generating an intelligent decision-making scheme according to the input solving time iterative calculation, which can quickly and accurately generate an intelligent decision-making scheme for the subway scheduling problem, reduce decision-making errors, and greatly improve the efficiency of the subway scheduling plan.

The invention relates to the technical field of satellite network communication, and particularly discloses a multi-task planning method and system for a low-orbit communication-remote integrated satellite network, and the method comprises the steps: constructing a system model which comprises a communication-remote integrated satellite, a data relay satellite, a ground station and a task set, and carrying out the multi-task planning of the low-orbit communication-remote integrated satellite network based on the system model. Establishing an optimization problem aiming at maximizing the weighted task completion amount; solving the task acquisition planning sub-problem by using a positive semidefinite relaxation algorithm, and solving the task transmission planning sub-problem by using a column generation algorithm; and feeding back a solving result of the task transmission planning sub-problem to the task acquisition planning sub-problem to update a task weight, and iteratively executing the step of solving the task acquisition planning sub-problem and the task transmission planning sub-problem until a convergence condition is met, and aiming at a complex scene in which heterogeneous businesses coexist, solving the task transmission planning sub-problem and solving the task transmission planning sub-problem. An efficient global optimization solution is provided, the task income is maximized, a dynamic feedback iteration mechanism is introduced, and the overall collaboration and adaptability of the system are enhanced.

The present application proposes a multi-time scale based micro-grid multi-element energy storage collaborative optimization method and system, which takes the lowest life cycle cost as the core optimization target, considers the flexible adjustment ability of hydrogen energy storage system, electrochemical energy storage system and demand response, and constructs a multi-time scale micro-grid multi-element energy storage collaborative optimization configuration model; the first stage and the second stage constraint conditions of the micro-grid multi-element energy storage collaborative optimization configuration model are constructed; the improved column generation algorithm is used to solve the micro-grid multi-element energy storage collaborative optimization configuration model, and the optimal capacity configuration scheme and real-time operation optimization strategy of the micro-grid are obtained. Through multi-time scale modeling and two-stage collaborative optimization solution, the present application quantifies the internal correlation between multi-element energy storage configuration and micro-grid energy balance, significantly reduces the system operation cost, and takes into account the effectiveness and economy of the configuration scheme, thereby providing technical support for stable and efficient operation of the micro-grid.

The method for fine-grained computing task division and scheduling of multi-intelligent application of the application divides the interrupted intelligent application into five types of subtasks, including data preparation, model preparation, hardware resource preparation, inference and result post-processing. According to the subtask attribute inheritance principle, the attribute five-tuple of each subtask is determined. All the interrupted intelligent application subtasks are input into the subtask scheduling sequence generation algorithm, the subtask application scheduling sequence is calculated, and the subtask scheduling sequence is added to the application scheduling sequence. Through the task division and subtask scheduling method, the problems of low resource utilization and high multi-task execution delay in the airborne environment are optimized. The multi-intelligent application collaborative operation process is optimized based on the intelligent application scheduling algorithm and the subtask scheduling algorithm.

The application discloses a satellite-ground cooperative task scheduling method considering energy consumption and experience quality fairness, and belongs to the technical field of edge computing, comprising the following steps: a task scheduling model is constructed based on a mobile edge computing architecture, time delay cost, energy consumption cost and experience quality fairness indexes of each to-be-scheduled task under different scheduling paths are calculated, and a satellite-ground cooperative task scheduling optimization problem with task time delay and resource capacity as constraints is established; for a scenario with a constellation scale within a preset range, a mixed integer nonlinear programming task scheduling method is adopted to solve the satellite-ground cooperative task scheduling optimization problem; otherwise, a fairness-aware hybrid column generation algorithm is adopted to solve the satellite-ground cooperative task scheduling optimization problem, so as to obtain an optimal scheduling scheme of the satellite-ground cooperative task. The application solves the problem that in the existing satellite edge computing system, energy consumption and experience quality fairness cannot be considered while meeting the time delay constraint, and the satellite-ground cooperative task cannot be jointly scheduled and unloaded.

The invention relates to a logistics path intelligent optimization method and system based on coal quality detection, and the method comprises the following steps: collecting the quality data of coal on a transport vehicle in real time, carrying out the preprocessing, inputting a trained coal quality detection model, and obtaining a coal quality detection result, the quality data comprises physical and chemical attribute data and spectral data of the coal, and the physical and chemical attribute data comprises moisture, ash content and sulfur content of the coal; according to the coal quality detection result, whether the coal on the transport vehicle has a flammable risk or not is judged, if yes, a column generation algorithm is adopted to solve a distribution optimization model, the advancing path of the transport vehicle is adjusted according to the solving result, and the distribution optimization model takes the minimum total ton kilometer price as the target, and the distribution optimization model is used for optimizing the total ton kilometer price. And vehicle operation constraints and time window constraints are used as constraint conditions. Compared with the prior art, the logistics path can be adjusted according to the change of the coal quality, and the coal quality is prevented from being reduced in the transportation process.

The invention relates to the technical field of satellites, and particularly discloses an optimized deployment method and system for a low-orbit large-scale satellite constellation, and the method comprises the steps: S1, constructing a constellation deployment network model according to the low-orbit large-scale constellation, satellite parameters and vehicle parameters; s2, establishing a multivariable mixed integer optimization objective function according to information contained in nodes and edges in the constellation deployment network model; s3, determining a limited main problem RMP and a sub-problem of the target function, and solving the RMP through a column generation algorithm to obtain a target value and a dual variable; s4, when any sub-problem has a new column, and the new column is added into the RMP to reduce the target value, adding the new column into the RMP to update the target value and the dual variable; and S5, directly outputting a satellite launching deployment scheme or outputting the satellite launching deployment scheme after solving the target value of an integer according to a branch and bound method when all the sub-problems do not have new columns or all the new columns are added into the RMP so that the target value cannot be reduced.

The application discloses a satellite-ground cooperative task scheduling method considering energy consumption and experience quality fairness, and belongs to the technical field of edge computing, comprising the following steps: a task scheduling model is constructed based on a mobile edge computing architecture, time delay cost, energy consumption cost and experience quality fairness indexes of each to-be-scheduled task under different scheduling paths are calculated, and a satellite-ground cooperative task scheduling optimization problem with task time delay and resource capacity as constraints is established; for a scenario with a constellation scale within a preset range, a mixed integer nonlinear programming task scheduling method is adopted to solve the satellite-ground cooperative task scheduling optimization problem; otherwise, a fairness-aware hybrid column generation algorithm is adopted to solve the satellite-ground cooperative task scheduling optimization problem, so as to obtain an optimal scheduling scheme of the satellite-ground cooperative task. The application solves the problem that in the existing satellite edge computing system, energy consumption and experience quality fairness cannot be considered while meeting the time delay constraint, and the satellite-ground cooperative task cannot be jointly scheduled and unloaded.

The invention relates to the technical field of plate-type product production, in particular to a production scheduling-batch-layout combined optimization method and system for plate-type products, and the method comprises the steps: constructing a production scheduling model based on a column generation algorithm, carrying out the decomposition of a main problem and a sub-problem, and carrying out the iterative mining of a negative reduction cost scheme column, generating a production scheduling result meeting operation coverage and productivity constraints; on the basis of the time slice of the production scheduling result, order multi-dimensional feature vectors are constructed and normalized, similarity is measured by adopting weighted Euclidean distance, and a batch grouping result compatible with layout is generated through multi-constraint clustering and legality screening; based on the grouping result, adopting a double iterative search mechanism and a variable domain search algorithm, and combining with a post-processing strategy to generate a layout scheme with a minimum non-placement area; and meanwhile, information sharing and bidirectional adjustment among three stages are realized in the optimization process. According to the method, the bottleneck of segmentation solution and difficulty in integration in the prior art can be solved, and the execution efficiency and the resource utilization level of the plate-type product manufacturing process are improved.

This application provides a method, apparatus, electronic device, and storage medium for accelerating the solution of set covering problems, belonging to the technical field of data processing. The method includes: decomposing the main problem of a graph-based set covering problem into a restricted main problem and complete subproblems according to a column generation algorithm; determining the probability of all edges in the underlying graph corresponding to the complete subproblems being selected in the final solution; removing edges with probabilities less than a preset threshold to obtain a simplified underlying graph and simplified subproblems corresponding to the simplified underlying graph; iteratively solving the restricted main problem and simplified subproblems; until there are no feasible columns in the simplified subproblems that can be added to the restricted main problem, taking the solution of the restricted main problem as the final relaxed solution of the main problem; and obtaining the final solution of the main problem based on the final relaxed solution. This application aims to accelerate the computational speed when solving large-scale set covering problems.

The present invention relates to the field of satellite technology, and specifically discloses an optimization deployment method and system for a low-earth orbit large-scale satellite constellation. The method includes: S1, constructing a constellation deployment network model according to the low-earth orbit large-scale constellation, satellite parameters and carrier vehicle parameters; S2, establishing an objective function of multivariable mixed-integer optimization based on the information contained in the nodes and edges of the constellation deployment network model; S3, determining the restricted master problem RMP and sub-problems of the objective function, and solving the RMP through the column generation algorithm to obtain the objective value and dual variables; S4, when there is a new column in any sub-problem, and adding the new column to the RMP makes the objective value decrease, adding the new column to the RMP to update the objective value and dual variables; S5, when there are no new columns in all sub-problems, or adding all new columns to the RMP cannot make the objective value decrease, directly outputting the satellite launch deployment plan or outputting the satellite launch deployment plan after solving for the integer objective value according to the branch and bound method.

The invention discloses a sea-air cooperative emergency material delivery optimization method in a remote group island scene, and the method comprises the steps: obtaining the coordinate and material demand of each island, and the weight, speed and loading and unloading speed data of a rescue ship and a helicopter, and building a sea-air cooperative emergency material delivery optimization model based on the data; solving the sea-air cooperation emergency material delivery optimization model through a customized column generation algorithm for embedding variable neighborhood search of a fusion column generation framework and a variable neighborhood search framework, and generating a preliminary sea-air cooperation path; and adopting an improved genetic algorithm to optimize the sea-air cooperation path, feeding back an optimization result to the column generation framework, and finally obtaining an optimal sea-air cooperation path. According to the invention, the material delivery efficiency and the resource cooperative utilization level of remote group island emergency rescue are effectively improved.

The invention provides a traffic flow path optimization method and system based on a column generation algorithm, and relates to the technical field of traffic flow path optimization, and the method comprises the steps: obtaining map data, transportation cost and transportation capability of a target region, and determining a plurality of basic parameters; generating an initial tree entering scheme; obtaining a path optimization model; determining an optimal tree entering scheme; and determining a corresponding optimal traffic flow path scheme with the target path point as an end point based on the optimal tree entering scheme. According to the method, the initial tree entering scheme based on the target path point is constructed, and continuous adjustment and optimization are carried out by utilizing the path optimization model and the column generation solution algorithm, so that the transportation cost can be effectively reduced, the traffic flow transportation capacity can be improved, the transportation path selection can be optimized, and the flow balance and the cost minimization in the transportation process can be ensured. According to the method, rapid and accurate path optimization can be carried out in a complex traffic network, and an accurate and operable scheme is provided for transportation management, so that the transportation efficiency is improved, and the logistics cost is reduced.

The application relates to the technical field of satellite network communication, and particularly discloses a low-orbit integrated satellite network multi-task planning method and system, which comprises the following steps: a system model containing an integrated satellite, a data relay satellite, a ground station and a task set is constructed; based on the system model, an optimization problem with the target of maximizing the weighted task completion amount is established; a semi-positive definite relaxation algorithm is used to solve the task collection planning sub-problem, and a column generation algorithm is used to solve the task transmission planning sub-problem; the solving result of the task transmission planning sub-problem is fed back to the task collection planning sub-problem to update the task weight, and the steps of solving the task collection planning sub-problem and the task transmission planning sub-problem are iteratively executed until the convergence condition is met; for a complex scene where heterogeneous services coexist, an efficient global optimization solution is provided, the task benefit is maximized, a dynamic feedback iteration mechanism is introduced, and the overall coordination and adaptability of the system are enhanced.

The invention discloses a metro crew scheduling optimization method based on a column generation algorithm. The metro crew scheduling optimization method comprises the following steps: 1, constructing a metro crew scheduling space-time network based on a point-arc; 2, establishing a subway crew scheduling model based on the on-duty path; 3, solving the subway crew scheduling model by using a column generation algorithm to obtain an optimal scheduling scheme; and 4, continuously optimizing the optimal scheduling scheme by using a greedy insertion heuristic algorithm to obtain the optimal scheduling scheme considering fairness. According to the method, the subway crew scheduling model based on the value multiplication path is established by utilizing the construction of the point-arc space-time network, and the subway crew scheduling is solved and optimized by adopting an accurate column generation algorithm by taking the balance between efficient and fair distribution of the subway crew scheduling as a purpose, so that a high-quality optimal scheduling scheme is obtained, and the working efficiency of the subway crew scheduling is improved. And on the basis, a greedy insertion heuristic algorithm is used for adjustment to ensure that the scheduling scheme is more fair and reasonable, so that the scheduling efficiency and the working quality of subway crew members are effectively improved.

The invention discloses a unit commitment optimization model construction and production optimization method and system of a multi-source system, and relates to the technical field of production resource regulation and control. The operation parameters and the operation constraints of the pumped storage power station are subjected to linearization processing, so that interference of nonlinear integer variables in the pumped storage power station is weakened; meanwhile, the renewable energy sources are modeled through the uncertain set, a large number of scenes do not need to be generated, and the solving efficiency of solving the uncertain unit commitment optimization problem is improved; according to the method, two-stage robust optimization and a rank generation algorithm are used for conversion, so that variables and constraints in the unit commitment optimization problem of the multi-source system meet the strong duality required by solution, the unit commitment optimization problem is conveniently solved, the mapping relation between the variables and the constraints in the unit commitment optimization problem is simplified, and the optimization efficiency of the unit commitment optimization problem is improved. The calculation speed for solving the unit commitment optimization problem of the multi-source system is increased, and an accurate calculation result is obtained.

The present invention relates to a high-speed rail freight train operation optimization method, device, and equipment, comprising: establishing a dual-objective nonlinear model for high-speed rail freight train operation optimization; decomposing it into a train candidate set restriction main model and a freight train candidate set generation submodel in different ways, and a first single-objective optimization model and a second single-objective optimization model; solving the train candidate set restriction main model and the freight train candidate set generation submodel based on the high-speed rail freight train candidate set to obtain the current optimal freight train candidate set with the initial freight train candidate set as the starting point; substituting the first single-objective optimization model and the second single-objective optimization model into the solution to obtain an optimal target value vector; and using a genetic algorithm to solve the optimal target value vector to obtain an optimal chromosome as the final optimal solution output. The present invention combines the genetic algorithm with a column generation algorithm, taking into account both economic efficiency and transportation capacity requirements, achieving matching of freight flow and train flow, and obtaining a feasible high-speed rail freight train operation optimization solution.

The application discloses a truck-unmanned aerial vehicle cooperative path planning method and system. The method comprises the following steps: inputting parameter information of a vehicle path problem with an unmanned aerial vehicle considering an unmanned aerial vehicle exchange mode, calling a heuristic algorithm, and outputting an initial upper bound solution scheme; inputting the initial upper bound solution scheme, calling a column generation algorithm, solving a subproblem in the column generation algorithm by using a cooperative path label algorithm, finding a path column by using a heuristic algorithm, and outputting a linear solution scheme; inputting the linear solution scheme, calling a branching strategy and a cut constraint search strategy, adding a branch and a cut constraint after solving the column generation algorithm at each branching node, and outputting an integer solution scheme. According to the scheme, the unmanned aerial vehicle can be exchanged between different trucks without relying on an unmanned aerial vehicle parking point, and the solving efficiency of an accurate algorithm is improved.

The application discloses a microgrid and distribution network collaborative optimization scheduling method and system based on an active power aggregation domain. The method comprises the following steps: obtaining operation parameters of a distribution network and constructing operation constraints of the distribution network; obtaining operation parameters of a microgrid and constructing operation constraints of the microgrid; constructing a mathematical model of the active power aggregation domain of the microgrid based on the operation constraints of the microgrid; solving the mathematical model by using a row-column generation algorithm to obtain an operation boundary of the active power aggregation domain of the microgrid; submitting the active power aggregation domain of the microgrid to the distribution network for scheduling; combining the operation constraints of the distribution network to determine whether the active power aggregation domain of the microgrid satisfies a distribution network loss minimization criterion; if yes, controlling power output at a gateway of the microgrid by using the operation boundary; otherwise, adjusting the operation boundary of the active power aggregation domain of the microgrid, updating the mathematical model of the aggregation domain and solving the mathematical model until the operation boundary satisfies the target of the minimum distribution network loss. The application can simplify a scheduling model of a medium-voltage distribution network and improve scheduling efficiency under the condition that information interaction is limited.

The present invention discloses a method for collaborative optimization of multi-charging mode electric bus and driver scheduling, comprising the following steps: 1: extracting the shift data of the selected actual operating route based on the actual operating status of the selected actual operating route; 2: determining the relevant characteristic parameters of the electric bus on the selected actual operating route and the driver's scheduling rule parameters; 3: constructing a collaborative optimization model for vehicle and driver scheduling of the electric bus system with the goal of minimizing driver cost, vehicle cost, and charging cost; 4: using a tree enumeration-based labeling algorithm to enumerate all shift chains that meet the relevant constraints of the collaborative optimization model; and 5: constructing a set covering model and using a column generation algorithm to solve the constructed set covering problem. With the goal of minimizing cost, the present invention provides vehicle scheduling and personnel scheduling suggestions for electric bus operating routes, accurately serving users in need and improving operating revenue.

The invention discloses a wind storage system robust-random decision control method considering internal and external uncertain factors. The method comprises the following steps: firstly, determining an energy consumption objective function of the wind storage system according to uncertain factors and internal composition factors of the wind storage system, and establishing an energy consumption model of the wind storage system; then determining a clearing target function according to factors except the wind storage system and establishing a clearing model; establishing a wind storage system double-layer model according to the obtained energy consumption model and the obtained clearing model; and finally, optimizing and solving the double-layer model of the wind storage system by adopting an embedded Carlo demand-Kuhn-tuck condition method and an improved constraint and column generation algorithm in sequence to obtain the lowest total energy consumption of the wind storage system. The method is comprehensive in consideration and sufficient in coverage factor, so that the operation parameter evaluation is more practical, and the obtained minimum energy consumption can bring the maximum power storage value.

The application discloses a robust optimization model for container booking decision of sea shipping considering order uncertainty and an optimization solving method thereof, wherein the modeling process of the optimization model comprises the following steps: 1) determining a target function of a two-stage robust optimization model; and 2) determining constraint conditions contained in the model, including one-stage constraint conditions and two-stage constraint conditions. The container shipping booking problem considering customer order demand uncertainty is established as a two-stage robust optimization model. Since the uncertain set is a polyhedron, the model cannot be solved by simply enumerating all uncertain scenarios. The model is solved by using a row and column generation algorithm. The shipping container booking problem is abstracted as a mathematical model, and a row and column generation algorithm based on a main problem-subproblem framework is designed for the model. The algorithm can efficiently solve the problem and has high technical and economic value.