126 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 robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

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.

The invention discloses a multi-type farmland plant protection unmanned aerial vehicle and unmanned vehicle collaborative operation path optimization method. The method comprises the following steps: constructing a plant protection model and a farmland distribution rule according to a plant protection operation scene; the plant protection model is a time cost model of minimum total operation time consumption of cooperative operation of the plant protection unmanned aerial vehicle and the plant protection unmanned vehicle; a preset K-VNS algorithm constructed based on a clustering algorithm and a variable neighborhood search algorithm is utilized, the minimum total operation time consumption is taken as a target, the multi-unmanned-vehicle and unmanned-aerial-vehicle cooperative plant protection operation problem constructed based on a plant protection model and a farmland distribution rule is solved, and a multi-unmanned-vehicle and unmanned-aerial-vehicle cooperative plant protection operation scheme is obtained. Therefore, the farmland plant protection unmanned aerial vehicle and unmanned vehicle collaborative operation path optimization method which comprehensively considers plant protection operation scenes of the unmanned aerial vehicle and the unmanned vehicle and is high in calculation efficiency and simple in solution is realized.

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.

The invention discloses an air energy water heater control system based on remote monitoring of the Internet of Things, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing multi-source real-time data of an air energy water heater to generate a data set; the conditional variation auto-encoder prediction module is used for inputting the data set into a conditional variation auto-encoder model to generate prediction data; the control parameter optimization module is used for carrying out multi-neighborhood disturbance search by adopting a variable neighborhood search algorithm according to the prediction data and outputting an optimal control strategy; the control instruction issuing and executing module is used for issuing an optimal control strategy and controlling the terminal to execute heating, heat preservation and closing operations; and the operation feedback and model adaptive optimization module is used for collecting operation feedback data and realizing adaptive updating and closed-loop control of model parameters. Intelligent analysis, prediction and self-adaptive remote regulation and control of the air energy water heater are achieved, and the energy saving performance and the use comfort degree are improved.

The invention relates to the technical field of unmanned delivery, in particular to an unmanned delivery method based on joint delivery of heterogeneous facilities. The method comprises the following steps: constructing an unmanned distribution mathematical planning model based on the combination of an unmanned self-service cabinet and a multifunctional unmanned distribution station, and carrying out heterogeneous unmanned facility configuration, customer distribution and robot vehicle path integrated optimization according to individual requirements; based on an integrated optimization decision, designing a solution scheme characterization method fusing a distribution relation and path planning two-way quantity mapping coupling; and designing a variable neighborhood search algorithm fusing multiple advanced search strategies. According to the unmanned delivery method based on heterogeneous facility joint delivery provided by the invention, through improvement of the delivery method and route planning in delivery, efficient personalized delivery service of the last kilometer can be provided, and heterogeneous facility site selection-coverage-path oriented to personalized requirements can be solved; and therefore, the distribution requirements of large-scale distribution customers in the actual operation process can be 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 provides a truck and unmanned aerial vehicle cooperative distribution method based on hybrid eagle-variable neighborhood search, and relates to the field of vehicle and unmanned aerial vehicle cooperative scheduling. According to the method, firstly, truck group resources and customer point demands are acquired; secondly, taking minimization of the total operation cost as an optimization target, and constructing a truck and unmanned aerial vehicle collaborative distribution model without limiting the time sequence relation of arrival of the truck and the unmanned aerial vehicle at the convergence point; and finally, solving and constructing the model by adopting a hybrid eagle-variable neighborhood search algorithm to obtain a global optimal solution, and decoding to obtain an optimal truck group service customer point number, a service sequence of each customer point and total operation cost. In the modeling process, the time sequence relation of arrival of the trucks and the unmanned aerial vehicles at the confluence point is not limited, and the method is closer to the characteristic of a multi-truck unmanned aerial vehicle collaborative distribution problem. And reliable decision support can be provided for distribution enterprises in a complex real truck and unmanned aerial vehicle cooperative distribution environment.

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 invention discloses an electric freight train intelligent scheduling method based on ant colony optimization and variable neighborhood search, comprising the following steps: collecting historical data of a traffic network and a power network, combining the historical data with a virtual road simulation model, and constructing an electric freight train intelligent scheduling simulation model; constructing a directed graph, constructing an optimal path set, and calculating the total driving distance of the electric freight vehicle; and based on an ant colony algorithm and a variable neighborhood search algorithm, calculating a driving route of the electric freight vehicle by taking the minimum total driving distance of the electric freight vehicle as an optimization target, and obtaining a driving path scheme. According to the invention, the ant colony optimization and the variable neighborhood search algorithm are applied to the multi-order scheduling problem of the electric freight vehicle, and the method has a good scheduling effect and energy consumption control capability in an actual environment in which traffic network congestion and power resource limitation are confronted at the same time.

The embodiment of the invention discloses an AGV cross-workshop distribution task scheduling method and device based on a particle swarm algorithm, and the method comprises the steps: S101, obtaining a cross-workshop task, decomposing the cross-workshop task into tasks of different workshops, merging the tasks with tasks in each workshop, and obtaining a to-be-executed task of each workshop; s102, a particle swarm is initialized, an initial optimal task allocation sequence of the particle swarm is updated according to the fitness function, and particles in the particle swarm represent a task allocation sequence in a single workshop; s103, updating each task allocation sequence according to a speed updating formula and a position updating formula, then performing variable neighborhood search, and updating an optimal task allocation sequence according to a fitness function after each operation; and S104, if the number of iterations is not met, executing the step S103, and if the number of iterations is met, outputting the optimal task allocation sequence of the current particle swarm, obtaining a single workshop AGV scheduling route map, and controlling the AGVs in the workshop to execute the distribution task according to the scheduling route map.

The invention is suitable for the technical field of workshop scheduling, and particularly relates to a dynamic flexible operation semiconductor workshop scheduling method of a knowledge-driven evolutionary algorithm, and the method comprises the steps: obtaining workshop working condition data which at least comprises workpiece data and machine data; constructing a workshop scheduling mathematical model based on the workshop working condition data; and solving the workshop scheduling data model based on a genetic algorithm, Q-learning and a variable neighborhood search algorithm based on knowledge driving to generate a workshop scheduling scheme. According to the method, workpiece transportation time, machine adjustment time and machine fault uncertainty factors are introduced on the basis of traditional flexible job shop scheduling, energy-saving scheduling is also considered, and the energy-saving purpose is achieved while the production requirement is met by reducing energy consumption. While the efficiency is improved, the energy consumption can be reduced, and the market competitiveness and sustainable development capability of enterprises are enhanced.

The invention provides a complex area unmanned aerial vehicle surveying and mapping mode decision and path planning method, and relates to the technical field of unmanned aerial vehicles. According to the method, aiming at the defect that traditional unmanned aerial vehicle surveying and mapping cannot automatically adapt to different terrains in a terrain with large height fluctuation, flight modes of an unmanned aerial vehicle at different terrain heights are decided based on a height fall threshold algorithm, and surveying and mapping sub-regions adopting different flight modes are generated; the complexity of manual flight mode setting and surveying and mapping area delimiting is avoided, and it is ensured that each surveying and mapping sub-area can adopt the optimal surveying and mapping path according to the topographic characteristics of the surveying and mapping sub-area. Performing path planning on each surveying and mapping sub-region, and recording coordinates of a driving-in / driving-out point of each surveying and mapping sub-region; a mathematical model of coverage path planning of multiple surveying and mapping sub-regions of the single unmanned aerial vehicle is constructed, and a path which passes through all task points in the to-be-surveyed and mapped region and has the shortest flight distance is obtained based on a variable neighborhood search VNS-genetic algorithm, so that the surveying and mapping efficiency and precision are improved.

The invention provides a cooperative scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle, and relates to the field of scheduling optimization, and the method comprises the steps: obtaining client data, and initializing algorithm parameters; constructing an initial feasible solution, calculating the total cost and the infection risk of the path of each firefly, and selecting the firefly path with the minimum objective function value as a current global optimal solution; selecting mutation operation to generate a neighborhood solution, calculating a corresponding target function value, and comparing the target function value with the current globally optimal solution to update the globally optimal solution; and updating the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, performing adjustment and feasibility test on the solution in combination with mutation operation in the position updating process, and performing iterative optimization until termination. According to the method, the firefly algorithm and the variable neighborhood search algorithm are combined to realize efficient path optimization and charging scheduling, so that balance between total cost minimization and infection risk minimization is achieved, the medical waste transfer efficiency is improved, and the operation cost is reduced.

The invention provides a multi-AGV flexible job shop active scheduling method based on an improved VNS-INSGA-II algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of the multi-AGV flexible job shop is constructed, and constraint conditions of the multi-objective optimization model are set; the VNS-INSGA-II algorithm is improved by adopting a multi-layer coding mode, a hybrid population initialization mode, a time-varying coefficient-based dual-strategy binary tournament selection strategy and a variable neighborhood search algorithm, and the improved VNS-INSGA-II algorithm is obtained; based on a set constraint condition of the multi-objective optimization model, an improved VNS-INSGA-II algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problems of low stability and low scheduling precision of the scheduling system are solved.

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 a ship steel plate storage yard ex-warehouse operation planning method for cutting operation, which comprises the following steps of: acquiring operation parameters of cutting, a storage yard and a travelling crane by utilizing a cutting task plan and a storage yard management system, and determining the number of barrier plates of a storage position; generating an initial cut steel plate ex-warehouse sequence by adopting a rule-based heuristic algorithm; updating and searching the ex-warehouse sequence of the cut steel plate by adopting a variable neighborhood search algorithm; on the basis of a given cut steel plate warehouse-out sequence, a mixed integer programming model is constructed, and a plate turnover scheme and total operation time are determined; and judging whether a stopping condition of the method is met or not, and outputting a storage yard steel plate ex-warehouse operation plan determined by the method. According to the method, a warehouse-out operation scheme can be planned according to different cutting operation task requirements, cutting steel plates are timely and efficiently provided for cutting lines, and the cutting waiting time is shortened.

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 discloses a zero-latency flow shop scheduling method based on a discrete coding red mouth blue magpie optimization algorithm, and belongs to the technical field of flow shop scheduling. According to the method, on the basis of a discrete coding Red-billed Blue-Tapie optimization algorithm (DRBMO), the zero-latency flow shop scheduling problem with the goal of minimizing the maximum completion time is solved. The method mainly comprises the following steps: proposing a method based on INEH, random and reverse learning to generate a high-quality initial population; dividing the population into a large group and a small group according to the fitness value; discretizing a continuous position updating formula of the algorithm, and updating the position of the individual by using the discretized updating formula; variable neighborhood search is carried out, an adaptive parameter q and two neighborhood structures are given, the global search capability in the earlier stage of iteration is high, and the local search capability in the later stage is high; substitution operation: reserving one of the same individuals in the population, and randomly generating other repeated individuals; and checking a termination condition, if the termination condition is satisfied, stopping iteration, and outputting an optimal individual as an optimal scheduling scheme, otherwise, re-dividing the population.

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 invention provides an equipment production scheduling multi-objective optimization method and system based on multi-chain collaboration, and relates to the field of production scheduling, and the method comprises the steps: setting input parameters according to workpiece data, agent data, factory data of a supplier production stage and factory data of a manufacturer reprocessing stage in an equipment production process; obtaining a global optimal solution according to the input parameters and a non-dominated sorting genetic algorithm based on variable neighborhood search; the globally optimal solution is decoded to determine a set of workpieces allocated to each machine at the supplier production stage, a machining order of the workpieces on each machine, and a machining order of the workpieces on a single machine at the manufacturer remachining stage. According to the method, collaborative optimization and configuration can be powerfully carried out on resources in two stages of supplier processing and manufacturer packaging, so that the operation efficiency and the integration capability of multi-chain collaborative production of equipment are remarkably improved.

The invention discloses a storage and processing collaborative optimization scheduling method and system for a flexible manufacturing unit, and relates to the technical field of scheduling in a workshop manufacturing process, and the method comprises the steps: constructing a storage and processing collaborative optimization scheduling model for the flexible intelligent manufacturing unit, the scheduling model considers flexible processing scheduling in the flexible intelligent manufacturing unit and scheduling of goods locations of workpieces in a warehouse at the same time, and the optimization target of the scheduling model is to minimize the maximum completion time; flexible operation related data and machine and storage goods location layout data in the flexible intelligent manufacturing unit are collected, then according to the data, a hybrid optimization algorithm combining a genetic algorithm and variable neighborhood search is adopted to carry out optimization solution on the scheduling model, and a processing and storage scheduling scheme of the flexible intelligent manufacturing unit is obtained. According to the method, the production scheduling and the storage scheduling in the flexible intelligent manufacturing unit containing the storage are optimized, the transportation time can be reasonably optimized, the production efficiency can be improved, the production cycle can be shortened, and great economic benefits are brought.

The invention relates to the field of logistics distribution, and particularly provides a shared electric bicycle battery distribution path planning method based on an improved hybrid genetic algorithm. In order to solve the problems of low distribution efficiency, high operation cost, unreasonable resource scheduling and the like of the existing shared electric bicycle system (EBSS) battery management, the invention constructs a battery distribution optimization model considering the demand urgency degree. According to the method, firstly, through data analysis and literature research, an evaluation index system of the battery demand urgency degree is established, and the evaluation index system comprises the number of power-shortage electric bicycles, the human traffic, the regional population density and the number of available vehicles at a station; and quantifying the demand urgency degree of each station by adopting an entropy weight TOPSIS method, and determining the battery distribution priority according to the demand urgency degree. In the aspect of optimization scheduling, the invention provides an improved hybrid genetic algorithm (SVGA), and by combining a simulated annealing criterion and a variable neighborhood search strategy, the global search capability of the algorithm is improved, and local optimum is avoided. Experimental results show that compared with a traditional genetic algorithm (GA) and a simulated annealing improved genetic algorithm (SAGA), the SVGA has remarkable advantages in the aspects of solution cost, calculation time and solution stability. According to the invention, intelligent optimization of shared electric bicycle battery distribution is realized, the resource utilization rate is improved, the operation cost is reduced, and an innovative solution is provided for efficient management of EBSS.

The invention provides a four-way shuttle vehicle warehouse-in and warehouse-out composite operation scheduling method in a cross-layer operation mode and a related device, and belongs to the technical field of warehouse scheduling. According to the method, a random initial population mode, a double-layer coding mode, a discrete integer decoding mode, a nonlinear convergence factor and a variable neighborhood search mechanism are adopted to improve a whale optimization algorithm, and a multi-strategy whale optimization algorithm is obtained; based on the set constraint conditions and decision variables of the four-way shuttle vehicle in-out warehouse compound operation scheduling optimization model, a multi-strategy whale optimization algorithm is adopted to solve the established target function of the four-way shuttle vehicle in-out warehouse compound operation scheduling optimization model, and an approximate optimal solution is obtained; and a scheduling result is obtained based on the approximate optimal solution, so that the four-way shuttle vehicle warehouse-in and warehouse-out composite operation scheduling is completed. The problem that the working efficiency of equipment is not high is 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.