79 results about "Variable neighborhood search" patented technology

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention provides a ship lockage appointment scheduling optimization method, system, equipment and medium, and belongs to the field of shipping traffic scheduling, and the method comprises the steps: based on an arrival time prediction model, according to the planned arrival time, historical actual arrival time and ship attribute data of at least two appointment ships, obtaining a ship lockage appointment scheduling optimization model; generating a mean value and a standard deviation of the estimated arrival time corresponding to each reserved ship; based on the mean value and the standard deviation of the estimated arrival time, constructing a multi-target optimization model meeting a preset optimization target and a preset constraint condition; calculating corresponding scheduling reference time according to the mean value and the standard deviation of the estimated arrival time, and creating an initial scheduling scheme population according to the scheduling reference time; and based on a non-dominated sorting genetic algorithm and a variable neighborhood search algorithm, solving the multi-objective optimization model according to the initial scheduling scheme population to obtain a final scheduling scheme. According to the invention, the traffic jam of the hub is effectively relieved, and the lockage efficiency of the ship is improved.

A reservoir basin dam line site selection method based on an improved adaptive variable neighborhood search algorithm is realized based on an established dam line site selection optimization target mathematical model, and comprises the following steps: obtaining area DEM data needing site selection, and dividing the area DEM data into four identical sub-areas R1-R4; obtaining an initial parameter of each sub-region boundary, initializing the initial parameter of each sub-region boundary, and generating an initial solution Sol0; the initial solution Sol0 is used as a starting point, an IAVNS algorithm is adopted to solve the optimization target mathematical model, and the optimal solution Solbest of each sub-region is obtained; and outputting a global optimal solution Solbest fullness of the site selection area by using an SHP file, and carrying out visual display on the global optimal solution Solbest fullness. By proposing an IAVNS algorithm and adopting a double-layer collaborative optimization architecture, a dynamic hierarchical variable neighborhood search strategy and a rapid evaluation method based on terrain elevation variance analysis, global exploration, fine local search and multi-target dynamic balance optimization are realized, and fine optimization requirements under complex terrains are met.

A two-dimensional layout and cutting path combination method based on digital twinning comprises the following steps that S100, two problems of two-dimensional layout optimization and two-dimensional cutting path are simultaneously established by introducing two weights alpha and beta, and a simultaneous problem model is established; s200, solving an optimal solution of the joint problem according to a designed variable neighborhood search algorithm; s300, establishing a digital twin model to simulate production, and collecting data of production parameters and external conditions from the simulated production process; s400, the collected data of the production parameters and external conditions are fed back to a digital twin system, weight parameters alpha and beta are adjusted according to the real-time data and the external conditions, dynamic adjustment is conducted according to changes of alpha and beta in an algorithm, the layout efficiency and the cutting length are balanced, and a layout and cutting scheme is optimized; and S500, finally, taking a weight meeting the actual expectation of the producer, and calculating an optimal solution. According to the method, cutting paths are reduced in the stock layout process, idle stroke and cutting time are reduced, and cutting efficiency and quality are improved.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce job scheduling optimization method. Comprising the following steps: initializing Bayesian genetic algorithm operation parameters; forming an initial population; the current population executes variable neighborhood search to generate a new solution, and the population is updated; constructing a Bayesian probability model; generating a new solution through Bayesian probability sampling and genetic manipulation, and updating the population; updating a global optimal solution; and judging whether the time limit is reached or not, if so, outputting a globally optimal solution and the corresponding maximum completion time, and if not, continuing iteration. The application of the method has the positive effects of minimizing the maximum completion time of the operation and improving the robustness of the algorithm and the scheduling efficiency.

The embodiment of the invention provides a different-address machine production and distribution cooperative scheduling optimization method and system and a storage medium, and belongs to the technical field of scheduling optimization. Comprising the following steps: constructing an objective function of service time of a parallel machine and a constraint function of the objective function, and generating an initial solution meeting the constraint function through a sorting scoring function based on variable machine lease cost; intelligently selecting a disturbance operator based on a deep reinforcement learning algorithm, and disturbing the initial solution in a variable neighborhood search mode based on the disturbance operator to generate a disturbance solution; performing local optimization based on the disturbance solution through a local search mode to determine a current optimal solution and a current optimal solution of the target function; and performing iterative optimization on the current optimal solution in an iterative search mode to determine a global optimal solution of the target function. According to the method, production and distribution are collaboratively optimized while the cost constraint existing in actual production is considered. The convergence efficiency is high, and the probability of falling into local optimum can be reduced.

The invention provides an unmanned aerial vehicle terminal distribution method and system based on a public transport network, and belongs to the technical field of logistics distribution, and the method comprises the steps: obtaining customer information, public transport network information and unmanned aerial vehicle information; establishing a multi-target optimization model for cooperative distribution of the bus and the unmanned aerial vehicle by using the customer information, the public transportation network information and the unmanned aerial vehicle information; the target optimization module takes the minimum cost as a target function and considers time difference constraint, route constraint, load constraint and energy constraint; adopting a non-dominated transformer neighborhood search algorithm of a simulated annealing algorithm acceptance criterion to solve the multi-objective optimization model to obtain an approximate Pareto optimal solution set; the optimal solution set is an unmanned aerial vehicle tail end distribution scheme considering cost, time and risk. Based on the method, the invention further provides an unmanned aerial vehicle tail end distribution system based on the public transport network. According to the invention, through combination of unmanned aerial vehicle electric driving and public transportation network cooperation, multi-objective optimized logistics distribution is realized.

The invention relates to the technical field of shared bicycle scheduling methods, in particular to a shared bicycle multi-target dynamic scheduling method based on double-matrix variable neighborhood search. The system comprises four core components, namely a scheduling problem modeling module, a double-matrix coding module, an improved genetic algorithm module and a dynamic scheduling decision module. Constructing a multi-objective optimization model containing site topological data, operation constraint parameters and demand prediction data through a scheduling problem modeling module; the dual-matrix coding module adopts a dual coding structure of a path matrix and a scheduling quantity matrix, and respectively represents vehicle path planning and station loading and unloading quantity distribution; the improved genetic algorithm module implements a collaborative optimization strategy of hybrid crossover operation and variable neighborhood search; and the dynamic scheduling decision module finally outputs an optimal scheduling scheme meeting multi-target balance. According to the invention, through a double-matrix decoupling optimization architecture and an intelligent search mechanism, the problems of complex constraint and multi-target optimization in dynamic scheduling of shared bicycles are effectively solved.

The application aims at providing a multi-stage multi-level assembly job shop energy-saving scheduling multi-objective optimization method, relates to the technical field of assembly shop scheduling, and establishes a mathematical model of the problem in stages, and designs a problem-driven energy-saving strategy triggering mechanism according to the individual state, so that the quality and search efficiency of the solution are improved. Secondly, two heuristic rules and a random generation method are used to construct an initialization population that takes into account high quality and diversity, and a dynamic self-adaptive adjustment strategy for the assimilation operator parameters is realized through Q-learning, so that the convergence speed is improved while the population diversity is ensured, so that the exploration and mining ability of the algorithm is better balanced. A revolutionary operation guided by a hyper-heuristic variable neighborhood search is designed, and a high-quality colony found is searched in detail. The joint empire invasion operation is used to replace the competition, realizes the cooperative evolution and information interaction sharing of multiple empires, and thus finds non-dominated solutions with more uniform distribution.

The invention provides a coal machine equipment production plan optimization method based on a variable neighborhood search algorithm, and relates to the technical field of engineering scheduling. The method comprises the following steps: 1, initializing algorithm parameters; 2, constructing a priority ranking set and a total cost function; 3, setting and optimizing an initial solution; 4, generating a neighborhood solution set and updating the neighborhood solution set to obtain a candidate solution set; 5, selecting a neighborhood structure; 6, searching a neighborhood to obtain a local optimal solution; 7, updating the optimal solution; 8, optimizing the optimal solution; and 9, outputting a globally optimal solution after the execution of the number of iterations is completed. According to the method, the initial solution and the neighborhood structure of the variable neighborhood search algorithm are improved, the quality of the initial solution is optimized, premature falling into local optimum is avoided, the approximate optimal solution can be obtained for the coal machine equipment production plan optimization problem, more effective decision support is provided for decision makers, and therefore the efficiency and benefits of equipment batch production are improved.

The present application relates to the field of intelligent manufacturing, and more particularly to a distributed workshop scheduling method for additive and subtractive hybrid manufacturing. The present application provides a two-stage algorithm, including a batching stage and a scheduling stage. In the batching stage, a balanced height distribution greedy algorithm with two-dimensional interval constraints and space block left-down placement strategy is designed to batch all the workpieces to be produced, determine the workpiece batches, and take the obtained batches as the coding information of the scheduling stage algorithm; in the scheduling stage, a non-dominated sorting genetic algorithm-II with excellent degree strategy driven variable neighborhood search is designed to perform additive and subtractive hybrid manufacturing scheduling, so as to achieve the optimization goal of minimizing the maximum completion time and the total production cost. The present application can realize additive and subtractive hybrid manufacturing scheduling, better utilize resources, maximize production efficiency, and reduce production cost through collaborative work of multiple workshops and load dispersion.

A stack area layout optimization method of an intelligent forklift intensive warehousing system relates to the technical field of logistics warehousing, and comprises the following steps: collecting operation information of a target warehousing area, generating an initial population taking stack area layout as individuals, processing the initial population, screening initial population individuals for crossover and variation, and obtaining a stack area layout optimization result; performing rapid non-dominated sorting on the varied population, then performing variable neighborhood search on the obtained population to obtain a potential layout individual with a smaller target function limit value, performing heap distribution on the potential layout individual population in sequence, and solving a heap distribution result through rapid non-dominated sorting and variable neighborhood search. Obtaining a Pareto leading edge of the potential layout, and outputting an optimal result; according to the container stacking area distribution scheme and the warehouse layout optimization method based on multi-objective optimization, the genetic algorithm and the variable neighborhood search algorithm are combined, the problems of low operation efficiency and unreasonable layout in a warehousing system are solved, the storage efficiency is further improved, and the cost is reduced.

This invention provides a three-dimensional path planning method for a drone used for fig seedling patrol, relating to the field of agricultural production and operation management technology. The method includes: acquiring planning parameters; establishing a three-dimensional path planning model for the patrol drone based on the planning parameters; the three-dimensional path planning model being a problem model with the objective of finding the set of drone service paths that minimizes the total cost of the patrol management system; and solving the three-dimensional path planning model using a variable neighborhood search algorithm with a utilization rate evaluation mechanism to obtain the final set of drone service paths. The utilization rate evaluation mechanism is used to calculate the utilization rate index of each candidate drone service path generated by the variable neighborhood search algorithm, and to filter and guide neighborhood movement operations for the generated candidate drone service paths based on the utilization rate index. This improves the feasibility, safety, and economic benefits of patrol management in fig planting environments.

The invention discloses a hotel employee scheduling method and device based on a hybrid heuristic algorithm, and relates to the field of task scheduling, and the method comprises the steps: constructing and training a hotel room demand prediction model based on LSTM, and obtaining a trained hotel room demand prediction model; inputting historical guest room demand data before the target period into the trained hotel guest room demand prediction model, and predicting to obtain guest room demand data in the target period; constructing a target function and a constraint condition of hotel employee scheduling by combining the guest room demand data in the target period; and solving the target function under the constraint condition by adopting a hybrid heuristic algorithm fusing ant colony optimization and variable neighborhood search to obtain a hotel employee scheduling table. According to the method, the problems of serious dependence on a path of manual experience scheduling, disjunction of demand prediction and scheduling optimization, lack of human-based scheduling consideration and the like are solved.

The invention provides an unmanned aerial vehicle cluster load resource planning method based on multi-attribute coding and parallel variable neighborhood search, which belongs to the technical field of unmanned aerial vehicle resource planning, and comprises the following steps: firstly, determining target characteristics and task requirements, and determining unmanned platforms and available resource attributes; calling a load configuration algorithm to generate a configuration scheme, adopting multi-attribute coding to represent the configuration scheme, preferentially constructing an initial scheme based on task requirements, then adopting a self-adaptive variable neighborhood multi-population parallel evolution method to solve, generating a task configuration scheme meeting an optimization target and constraint conditions, and uploading the task configuration scheme; determining whether to approve to use the scheme or not by using expert experience or a decision support system according to the feasibility and efficiency of the scheme and the integrating degree of the scheme and a target; starting from the rationality and economy of the load resource allocation method for the unmanned aerial vehicle cluster, a higher cost-effectiveness ratio is realized on the premise of ensuring the task efficiency.

The invention discloses an intelligent work order scheduling method and system based on multi-factor cost prediction, and belongs to the technical field of work order management. According to the method, a multi-factor dynamic cost model comprehensively considering in-transit time, skill matching degree, work order priority and service time limit SLA risk is constructed by acquiring work order and personnel states in real time, and a cost index is calculated for each potential scheduling scheme. According to the system, a variable neighborhood search VNS optimization algorithm is adopted, and global solution is carried out on the basis of a cost matrix so as to find an optimal task allocation scheme with the lowest total cost. According to the method, the weight can be dynamically adjusted and optimized according to operation states such as real-time traffic and personnel load, self-adaptive intelligent decision making is realized, and finally, an optimization scheme is automatically distributed to a personnel terminal, so that the work order scheduling efficiency, the resource utilization rate and the SLA fulfillment rate are comprehensively improved.

The invention discloses an electric bicycle battery changing cabinet locating and sizing method and system based on bilevel programming. The method comprises the following steps: firstly, acquiring required parameters, constructing each individual of an initial population in an upper-layer optimization model based on the required parameters, and coding candidate site selection schemes and capacity configuration of the power conversion cabinet; then, the initial population is transmitted to a lower-layer solving module, the lower-layer solving module generates a plurality of feasible scheduling paths of a scheduling vehicle in a random scene through the randomization requirement of an electric bicycle station, Monte Carlo sampling and a variable neighborhood search algorithm, and the comprehensive index of the individual is calculated according to the feasible scheduling paths. And then, feeding back the comprehensive index to an upper layer optimization model for correcting the initial population, and obtaining a convergent optimal solution through an iterative evolution process of the upper layer model. Through the double-layer interaction mechanism, collaborative optimization of battery changing cabinet site selection, capacity configuration and scheduling vehicle path planning is realized, and the operation efficiency and robustness of the whole system are improved.

The present application relates to the technical field of unmanned distribution, and particularly relates to an unmanned distribution method based on heterogeneous facility joint distribution. The method comprises the following steps: constructing an unmanned distribution mathematical programming model based on the joint of unmanned self-service cabinets and multifunctional unmanned distribution stations, carrying out heterogeneous unmanned facility configuration, customer allocation and robot vehicle path integrated optimization for personalized demand; based on the integrated optimization decision, designing a solution representation method which is coupled with two-way vector mapping of allocation relationship and path planning; designing a variable neighborhood search algorithm which is fused with multiple advanced search strategies. The unmanned distribution method based on heterogeneous facility joint distribution can provide efficient last kilometer personalized distribution service and solve the heterogeneous facility site selection-coverage-path for personalized demand by improving the distribution method and planning the route in the distribution, and further solve the distribution demand of large-scale distribution customers in the actual operation process.

This invention discloses a virtual unit scheduling method and system based on grouping technology and an improved CS algorithm, belonging to the field of virtual unit scheduling. It includes: receiving the division of workpiece groups, machine groups, and manufacturing units, where all components in the workpiece groups have similar processes, all machines in the machine groups process the same workpiece groups, and the manufacturing units correspond one-to-one with the machine groups; constructing the objective function of virtual unit scheduling as minimizing the maximum completion time of all workpiece processing steps; encoding the workpiece processing sequence, AGV allocation, and machine configuration within the unit on different machines; solving the encoded virtual unit scheduling problem using an improved CS algorithm, which introduces Levy flight with adaptive flight step size and variable neighborhood search with adaptive bird's nest discovery probability; decoding the optimal solution and outputting the virtual unit scheduling result. This invention can effectively solve the virtual unit scheduling problem and obtain a production plan with the shortest completion time as the objective.

The invention discloses an electric vehicle battery swap station site selection optimization method considering multi-facility synchronous service, and relates to the technical field of traffic operation management, and the method comprises the steps: 1, constructing a battery swap station site selection model; 2, constructing a queuing system and a multi-layer perceptron neural network structure; step 3, generating a neighborhood through man-machine cooperation, and executing a variable neighborhood search algorithm for solving to obtain an optimal battery swap station site selection scheme; the invention provides an electric vehicle battery swap station site selection optimization method considering multi-facility synchronous service, and solves the problems of long waiting time, non-uniform distribution and the like of single-facility service in the prior art.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change and transportation tasks based on a Q-Learning model algorithm. The method comprises the following steps: S1, establishing a scheduling model taking total production completion time, total inventory time and total manpower cost as optimization targets; s2, providing a Q-Learning memetic algorithm to solve the scheduling model, designing a five-layer segmented mixed chromosome coding structure and a two-subgeneration chromosome updating method, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, adaptively adjusting the cross range of the chromosomes through Q-Learning to improve the algorithm efficiency; and S4, through comparison with other three algorithms, validity and robustness of the proposed algorithm are verified. Based on the scheduling method provided by the invention, the processing and assembling sequences of product parts can be optimized simultaneously in the scheduling process of multi-product mixed production and consideration of transportation tasks, and meanwhile, machines, workers and AGV resources are reasonably allocated, so that the production cycle of products is effectively shortened.

The application discloses a power grid metering material verification distribution collaborative optimization method and device and a storage medium. The collaborative optimization method comprises the following steps: setting a neighborhood structure, setting a maximum iteration number, an iteration solution quality improvement number, a current iteration parameter and a neighborhood index parameter, a neighborhood number upper limit and a feasible solution improvement number upper limit; inputting a randomly generated feasible solution, starting iteration, and if the iteration number reaches, terminating the search and outputting a final scheduling scheme; otherwise, continuing iteration. The application designs three types of neighborhood operators of vehicle scheduling, distribution time window and verification scheduling based on the problem structure characteristics, integrates the three types of neighborhood operators in a variable neighborhood search framework and fuses heuristic rules, effectively avoids falling into a local optimum in the algorithm iteration process and accelerates convergence; finally, through the neighborhood search and iteration optimization mechanism, the optimal distribution path, time arrangement and material scheduling plan of the verification center are obtained within a reasonable time.

The application discloses a kind of for ship section painting plan scheduling method, consider VOCs exhaust treatment equipment consumption electric energy and the carbon emission caused by LNG gas, consider the constraint of human resources and establish the multi-schedule plan scheduling model of two kinds of tasks including sand washing and spraying.A kind of improved artificial bee colony algorithm is proposed to effectively obtain approximate optimal solution within reasonable time, and a three-dimensional coding solution mechanism is designed based on the model.In the algorithm, the search efficiency of the algorithm is increased by mixing greedy random adaptive search algorithm and variable neighborhood search algorithm, which can be well used for ship section painting plan scheduling problem.

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 application provides a flexible job shop multi-objective scheduling method with a limited number of AGVs, and belongs to the technical field of multi-objective scheduling.The method comprises the following steps: improving the oryx optimization algorithm based on the Pareto optimality theory, initializing the population through a hybrid chaotic strategy, and guiding the population update by using an elite group matrix;discretizing the population update mode according to the characteristics of the FJSP-AGV problem, including the discrete update operation in the development stage and the exploration stage, and the discrete update operation of affecting the population by using the success rate of the predator; adding a variable neighborhood local search strategy to improve the utilization efficiency of the population and the possibility of the algorithm jumping out of the local optimum, designing an improved oryx multi-objective optimization algorithm based on the variable neighborhood search, and realizing the multi-objective scheduling of the flexible job shop. The effectiveness of the algorithm provided by the application is verified through the design experiment, and it is shown that the algorithm has a remarkable effect on solving the multi-objective FJSP-AGV problem, and the convergence and solution quality of the algorithm are greatly improved.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change based on a hybrid memetic algorithm, and the method comprises the following steps: S1, building a hybrid production flexible assembly job shop scheduling model with total production completion time, total inventory time and total labor cost as optimization targets under dual-resource constraint; a flexible assembly job shop scheduling mathematical model for multi-assembly sequence change mixed production is considered; s2, on the established mathematical model, providing a hybrid memetic algorithm to solve the provided problem, designing a four-layer segmented hybrid chromosome coding structure adapted to the problem and an updating method of two sub-generation chromosomes, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, proving the superiority of the proposed scheduling method through case research; and S4, through comparison with a flower pollination algorithm and a rapid non-dominated genetic algorithm, verifying the effectiveness and robustness of the proposed algorithm. According to the hybrid production flexible assembly job shop scheduling method considering the multi-assembly sequence change based on the hybrid memetic algorithm, the processing sequence and the assembly sequence of product parts can be optimized at the same time in the multi-product hybrid production scheduling process, and machine and worker resources are reasonably distributed at the same time; and the production cycle of products is effectively shortened.

This invention relates to a multi-objective distributed hybrid flow shop scheduling method, belonging to the technical field of hybrid flow shop scheduling. The invention establishes a corresponding objective function with the simultaneous minimization of maximum completion time and maximum processing time in distributed hybrid flow shop scheduling. First, a vector-based evaluation genetic algorithm and a fitness function based on Pareto dominance and non-dominance relationships are used to divide the population into three meme groups. Then, PSO is used to perform a global search on each meme group, exploring solutions in multiple directions of the Pareto front to accelerate the convergence speed of the upper and lower edges and the central region of the Pareto front. Second, critical-factory insert and critical-factory swap multi-neighborhood search operators are used to perform local searches on individuals, enhancing the quality of solutions in the meme groups. Third, a Q-Learning variable neighborhood search strategy is used to further enhance the algorithm's search capability on the three meme groups, thereby improving the quality and diversity of solutions and preventing the algorithm from converging prematurely and failing to find better solutions.

The invention relates to an optimal control method and system for paper product lamination process parameters and a medium, and the method comprises the steps: S01, in the PE lamination process of a paper product, obtaining the data information of the historical lamination process parameters of the paper product, and collecting the data information of the transmission speed, lamination temperature and lamination pressure of the paper product at the same moment in real time; and S02, based on the data information of the transmission speed, the lamination temperature and the lamination pressure of the paper product at the same moment, predicting the lamination quality of the paper product by adopting a multivariable regression prediction algorithm based on an improved variable neighborhood search algorithm optimized long and short term memory neural network-attention mechanism. And the predicted data information of the lamination quality of the paper product is obtained. According to the method, the technical problems of strong parameter coupling, difficult modeling and slow response of the lamination process are solved, an intelligent process optimization normal form capable of being copied and popularized is formed, and the method has important demonstration value for promoting digital transformation of the papermaking industry.

The invention relates to an order process merging workshop scheduling method and system based on operation level constraints. The method comprises the steps of obtaining production data; establishing a mixed integer programming scheduling model taking the minimization of the maximum completion time and the minimization of the assembly time as targets; order combination constraints, process combination constraints and operation level constraints are integrated in the mixed integer programming scheduling model; and an improved grey wolf optimization algorithm is established to solve the mixed integer programming scheduling model, an optimal scheduling scheme is output, the improved grey wolf optimization algorithm combines a double-layer coding scheme of three constraints, a mixed initialization population strategy and an improved hunting mechanism, and a variable neighborhood search and simulated annealing dynamic acceptance mechanism is integrated, so that the optimal scheduling scheme of the hybrid integer programming scheduling model is obtained. Global exploration and local development capabilities are enhanced, so that an optimal scheduling scheme is obtained; and finally outputting the dispatching Gantt chart and issuing and executing. The scheduling problem caused by a complex assembly relation and too long preparation time in multi-variety small-batch production is effectively solved, and the production period is remarkably shortened.

The invention provides a train timetable and train bottom scheduling collaborative optimization method based on a flexible marshalling mode. The method comprises the steps of obtaining high-speed railway line conditions and passenger flow demand data in an operation period; taking minimization of total passenger travel time as a target, based on route conditions and passenger flow demand data, generating a current optimal solution through a demand-oriented greedy heuristic algorithm, the current optimal solution including an initial train timetable and an initial train bottom scheduling scheme; and performing iterative optimization on the current optimal solution through a variable neighborhood search algorithm within a preset iteration frequency range, and performing disturbance and local search operation on the current optimal solution so as to determine a target train timetable and a target train bottom scheduling scheme which meet the requirement of the optimal benefit of the railway system. According to the method, the optimal scheme for obtaining the time table and the vehicle bottom scheduling in a short time is realized, so that the comprehensive improvement of the high-speed railway operation in three dimensions of service quality, resource utilization rate and operation economy is realized.