106 results about "Variable neighborhood search" patented technology

The invention discloses an improved particle swarm optimization (PSO) algorithm of solving the zero-waiting flow shop scheduling problem. Firstly, parameter initialization and population initialization are carried out, wherein initial workpiece sequences are generated, then a factorial encoding method is used to map all permutations to integers to form an initial population, and finally, a feasible initial velocity set is randomly generated; particles are moved; the population is updated through an original PSO population updating strategy, a new population is mapped to corresponding workpiecesequences, and work completion time of each new workpiece sequence is evaluated; an improved variable neighborhood search (VNS) algorithm is used for a local search, and results obtained by the search are used for replacement; a population adaption (PA) operator is used to increase diversity of the population; and checking of a termination condition is carried out, if the termination condition ismet, a process is stopped, and values of variables and corresponding sequences are returned to be used as a final solution, and otherwise, particle velocity is continuously updated. The method has the advantages of improving a particle swarm optimization algorithm, improving global search capability, and avoiding too early convergence.

The invention discloses a heterogeneous hazardous chemical substance transportation path planning method with a time window. According to the method, a dynamic load transportation risk assessment model considering the vehicle type and the parking time is provided by fully considering the transportation vehicle type, the transportation load, the transportation road information, the population distribution and the hazardous chemical substance information, and a multi-objective optimization model for planning a heterogeneous hazardous chemical substance transportation path with a time window is constructed. According to model characteristics, a hybrid multi-objective evolutionary algorithm based on variable neighborhood search is designed to solve the problem, and finally a heterogeneous hazardous chemical substance vehicle path planning method with a time window is determined. According to the invention, based on a traditional vehicle path planning method, hazardous chemical substance transportation characteristics are combined; risk factors of hazardous chemical substance transportation are considered, a heterogeneous hazardous chemical substance transportation path multi-objectiveoptimization model with a time window closer to the actual situation of hazardous chemical substance transportation is constructed, and finally a mixed multi-objective algorithm based on variable neighborhood search is designed to solve the model.

The invention provides an electric vehicle battery swap station site selection and logistics distribution combined scheduling method, which comprises the following steps: establishing a mathematical model of electric vehicle battery swap station site selection and logistics distribution combined scheduling, and setting constraint conditions of the mathematical model; Constructing an initial solution for the mathematical model, and optimizing the initial solution by using a local search algorithm; And then adopting an adaptive variable neighborhood search algorithm, adaptively adjusting a search process based on the existing search performance according to the characteristics of the battery replacement network design, and guiding the algorithm to quickly converge to a globally optimal solution. According to the invention, the site selection position of the electric vehicle battery replacement station and the distribution route of the electric vehicle can be reasonably arranged, the efficiency of electric vehicle cargo distribution service is improved, the distribution time is shortened, the use of energy is reduced, and the pollution of carbon emission to the environment is furtherreduced.

The invention applies to the field of vehicle routing planning and provides a vehicle routing planning method and planning system. The method comprises steps as follows: an initial solution of a vehicle routing scheme is established on the basis of a minimum cost insertion method based on user demands; initial parameters of the initial solution are set; whether the initial solution meets the end condition of a stage is judged, the optimal solution is updated according to a judgement result, corresponding initial parameters of the optimal solution are updated, and the optimal solution is updated according to the judgement result as follows: if the initial solution doesn't meet the end condition, the optimal solution is acquired with a variable neighborhood search algorithm, and if the initial solution meets the end condition, the initial solution is the optimal solution, and restarting is performed. Through implementation of the embodiment of the invention, the routing planning procedure can be simplified.

A method and a system for scheduling parallel machines based on hybrid shuffled frog leaping algorithm and variable neighborhood search algorithm are provided to solve collaborative production and processing of jobs on a plurality of unrelated batch processing machines. The jobs are distributed to machines based on the normal processing time and deterioration situation of the jobs on different machines and are arranged. An effective multi-machine heuristic rule is designed according to the structural properties of an optimal solution for the single-machine problem, and the improved rule is applied to the improved shuffled frog leaping algorithm to solve this problem. The improvement strategy for the traditional shuffled frog leaping algorithm is to improve the local search procedure of the traditional frog leaping algorithm by introducing the variable neighborhood search algorithm. The convergence rate and optimization capacity of the original algorithm are thus improved.

The invention discloses a multi-unmanned aerial vehicle task scheduling method and system and a storage medium, the first stage is a multi-unmanned aerial vehicle task allocation stage, a multi-unmanned aerial vehicle task scheduling problem is divided into a plurality of single-unmanned aerial vehicle scheduling sub-problems, and a simulated annealing algorithm embedded with a tabu table is proposed to realize multi-unmanned aerial vehicle task allocation; and the second stage is a single unmanned aerial vehicle task scheduling stage, and a variable neighborhood search descent algorithm is designed according to the task allocation scheme in the first stage by considering the observation capability of the unmanned aerial vehicle platform and the requirements of the tasks so as to provide an effective and feasible task scheduling scheme. And in the first stage, according to a feedback result in the second stage, combining the tabu factor, the transfer factor and the exchange factor to iteratively adjust and update the task allocation scheme until a stop criterion is met. In conclusion, a two-stage iterative optimization method is provided for solving the multi-unmanned aerial vehicle cooperative task scheduling problem. Simulation experiments verify the superiority and efficiency of the method.

The continuous casting machine fault problem involves the casting characteristics of casting times, so that the conventional rescheduling method of steelmaking-continuous casting production for processing equipment faults is difficult to apply. The invention provides a steelmaking-continuous casting rescheduling method for solving a continuous casting machine fault, specifically comprising the steps of: establishing a rescheduling optimization model according to the difference of rescheduling strategies of different heat processes treatment after the continuous casting machine fault on the rescheduling process; and designing a new hybrid algorithm having the advantages of global searching ability of a comprehensive genetic algorithm and local searching ability of a variable neighborhood searching algorithm to perform iterative optimization of an optimal solution. The algorithm has the main characteristics: initial solutions of chromosomes are quickly obtained by using a decoding heuristic algorithm on the premise of complex constraint at the slack; and the quality of each initial solution is improved by using the variable neighborhood searching algorithm before population crossover mutation, wherein different neighborhood structures are designed for non-feasible solutions and feasible solutions. The method can effectively solve the rescheduling problem under the continuous casting machine fault.

The invention discloses a variable neighborhood search method and system on a cloud computing platform. The which are variable neighborhood search method and system on the cloud computing platform used for overcoming the defects, of excessively-quick convergence and being weakened in the capacity of fleeing a local extreme value, existing in conducted variable neighborhood search based on a single living example. The method includes the steps of presetting a data collection and a plurality of initial solutions and storing the initial solutions to the data collection;, using a plurality of living examples and being based on at least one of the initial solutions to conduct neighborhood search on a solving space; for any living example, when a locally optimal solution which is obtained in a searching mode is superior to a worst solution in the data collection, using the locally optimal solution to update the worst solution, wherein the worst solution is the solution of a minimum value among initial solutions and/or locally optimal solution in the data collection. The living examples are adopted to simultaneously conduct search and results can be searched mutually among the living examples, therefore, solving space can be effectively expanded, the possibility of fleeing locally optimal possibility is improved, and furthermore a better overall optimal solution can be obtained.

The invention discloses a nurse scheduling method based on a random variable neighborhood search algorithm. The method comprises the steps that a constraint condition needing to be met by nurse scheduling is set, and an objective function is established; a random initial solution is generated and used as a current solution and a historical optimal solution; the random variable neighborhood search algorithm is adopted to perform iterative search on a current optimal solution, and an iteration termination condition is set; in the iteration process, a random selection exchange operator or a mobile operation operator is used to execute operation on a temporary solution; when an objective function value corresponding to the temporary search solution is smaller than or equal to an objective function value corresponding to the current solution, the temporary search solution is used as a new current solution; and conversely, if the number of iterations meets a determined threshold value, the historical optimal solution is set to be the current solution, and a disturbance operation operator is executed to obtain a new current solution. By the adoption of the technical scheme, nurses can be scheduled quickly and efficiently.

The invention discloses a multi-target goods allocation optimization method based on a variable neighborhood NSGA-II algorithm. According to the method, firstly, on the basis of a traditional storageallocation strategy with warehouse-in and warehouse-out efficiency, goods shelf stability and similar goods nearby storage as the center, goods allocation is performed by considering workload balanceof a roadway stacking machine to create a multi-target goods allocation optimization mathematical model; secondly, in order to prevent the NSGA-II algorithm from falling into local optimum during solving, an NSGA-II algorithm based on variable neighborhood search is proposed, and neighborhood operation is performed on part of individuals subjected to genetic operation by applying three different neighborhood structures so as to enhance the local search capability of the individuals. According to a goods allocation scheme obtained by the invention, the workload of each roadway stacking machineis more balanced, and the goods distribution is more reasonable.

The invention discloses an intelligent optimization method and system for ecological operation of cascade hydropower stations, belonging to the technical field of efficient utilization of water resources and optimal operation of hydropower systems. The method comprises the following steps: establishing a cascade hydropower station reservoir operation model which takes the minimum ecological watershortage as an objective function and considers a complex constraint set; Knowledge rules are used to dynamically generate the initial scheduling process, and the initial population is generated by random disturbance in its neighborhood. All individuals are modified to feasible search space in turn, individual fitness value is calculated by penalty function method, individual position and velocityare updated dynamically, and neighborhood search is carried out by adaptive mutation strategy. The search process described above is repeated until the termination condition is met. The invention hasthe advantages of clear and simple principle, fast convergence speed and the like, and the obtained result is reasonable and feasible, and can provide strong technical support for rational allocationof hydropower resources.

The invention discloses a high-efficiency scheduling optimization method. According to the invention, a swarm intelligence optimization method based on a variable neighborhood search algorithm is employed for solving a multi-processing-task mixed flow work shop scheduling problem, and the variable neighborhood search algorithm which comprises five neighborhood structures is introduced to a fireflyalgorithm, thereby improving the diversity of populations, improving the local search capability of the algorithm, and improving the search precision of the algorithm. Moreover, a work adjustment rule is proposed, thereby increasing the convergence rate of the algorithm, and enabling the method to achieve the optimal scheduling at high efficiency. The method can effectively shorten the idle timeof a processing machine, improves the production efficiency, and improves the economic benefits after scheduling.

The invention provides an urban crowdsourcing distribution task optimization scheduling method based on path planning. The method comprises the following steps: constructing a crowdsourcing distribution network graph; acquiring crowdsourcing riders and crowdsourcing distribution task information; constructing a crowdsourcing distribution task optimization scheduling model based on path planning; solving the initial crowdsourcing task scheduling scheme based on a greedy strategy; and carrying out optimal scheduling on the crowdsourcing distribution task based on variable neighborhood search. According to the invention, according to position information of riders, merchants and clients, an optimized task scheduling scheme including a distribution task set and a shortest distribution path sequence of each rider is formulated according to the time constraint of each task and the real-time load constraint of each rider, so that the tasks can be reasonably distributed, the length of a totaldistribution path can be reduced, and the crowdsourcing distribution cost can be reduced.

The invention discloses a robot path planning method based on spider colony algorithm, comprising of: modeling the map environment by the grid method; initializing the basic parameters of the spider colony algorithm; randomly assigning the initial spider colony; the spider colony completing the collaborative process according to the learning strategy of the spider colony algorithm; outputting fitness value and determining whether the fitness value is less than the set minimum fitness value; and outputting a shortest path and a length. The invention optimizes the neighborhood search and the global search by introducing the inertia weight and the learning factor, and increases the interaction between the inertia weight and the learning factor to balance the global search and local development capabilities of the method. The improved method has more stable convergence results, improves convergence speed and search accuracy, and palns the best shortest path.

The invention provides an orthogonal successive approximation method based on orthogonal experimental design and variable metric neighborhood search. The orthogonal successive approximation method comprises the following steps of setting an initialization parameter, and selecting a proper orthogonal table according to solved problem dimensions and discrete level numbers; conducting orthogonal experiments within a feasible region by beginning from an initial point x0, and calculating each experimental scheme through evaluation by adopting a penalty function method; selecting a new iteration point from the experimental schemes; if x1 is superior to x0, then allowing x0 to be equal to x1, and enlarging the step size in search to enhance global search at the same time; otherwise, shrinking search space to enhance local search; repeating the steps, and repeatedly iterating to successively approximate a global optimal solution until convergence conditions are met. The invention provides the orthogonal successive approximation method for solving a global optimization problem, has the advantages of simple principle, fewer calculating parameters, high convergence speed and the like and can be used for rapidly acquiring the optimal solution or the approximate solution of the global optimization problem.

The invention discloses a cloud resource distribution method based on a variable neighborhood searching strategy. A mode of renting according to demands is normally adopted by cloud computing resources, the demands of each service on the cloud resources are different for SOA applications formed by cloud services, and a calling relationship map among services is established to reasonably distributethe cloud resources to each cloud service; the service quality and deployment schemes of SOA mode cloud applications are defined; a multi-target cloud resource optimization distribution problem modelis established; a multi-target genetic algorithm based on the variable neighborhood searching strategy is adopted to solve a cloud resource optimized distribution problem. The invention provides thecloud resource distribution method based on the variable neighborhood searching strategy, the local searching capability of variable neighborhood searching and the global searching capability of the genetic algorithm are combined, the algorithm efficiency and the quality of resolutions are improved, and therefore the optimal cloud resource distribution problem that service performance is ensured and the cost is the lowest when the SOA applications are deployed is solved.

The invention discloses an urban two-stage distribution and scheduling method with a mobile distribution station, aiming to improve the urban distribution efficiency. According to the two-stage distribution mode, goods are transferred to a distribution station located in an urban area from a distribution center located in a suburb through a truck, and then tail-end distribution is completed through the distribution station by means of a light-weight vehicle. Existing distribution stations are fixed in position while the requirements of urban customers are changed every day and the rent of an urban center is expensive, so that a distribution route is not optimal every day, and the cost is high. According to the urban two-stage distribution mode with the mobile distribution stations, trucksare used as the mobile distribution stations, and the positions of the mobile distribution stations dynamically change according to the requirements of customers every day, so that the distribution paths every day are optimal. For the distribution mode, a clustering-based variable neighborhood search scheduling algorithm is designed, and the clustering algorithm is used for determining the position of a mobile distribution station, and variable neighborhood search optimizes a distribution path, and a selection strategy in a non-dominated sorting genetic algorithm III is adopted to select a multi-target elite solution. Meanwhile, a truck direct distribution strategy is designed in the algorithm, so that the efficiency is further improved and the cost is reduced.

The embodiment of the invention discloses an operating room scheduling method and device based on mixed grey wolf-variable neighborhood search algorithm. The method comprises the following steps: firstly, setting algorithm parameters according to the mixed grey wolf-variable neighborhood search algorithm, randomly generating an initial solution, then calculating a fitness value, and determining three best solutions; Performing variable neighborhood search transformation on the three best solutions to improve the quality of the solutions; Performing cross operation on the population based on the fitness of the solution, continuously updating the population through repeated iteration, and finally obtaining an optimal solution. In the embodiment of the invention, mixed grey wolf-wolf is used;The variable neighborhood search algorithm has good convergence speed and convergence result, the high-complexity operating room scheduling problem is solved, and the operating room scheduling efficiency of hospitals is improved.

The invention relates to an industrial process optimization control method for steelmaking and continuous casting. Firstly, a heuristic algorithm is used to solve the initial heat planning and cast planning; and then the initial plan is used as the starting point of the solution space, and the same width selection and steel species order are used as the neighborhood structure. In each neighborhoodstructure, exchange and insertion of short plates and long plates, exchange of slabs in the short plates, tail slab deletion of the long plates or optimization of the production operation are carriedout; by a variable neighborhood search method, local optimum heat planning and cast planning are solved, the neighborhood structure is replaced, the search range is widened, and the next local optimum heating planning and cast planning are solved; and when all neighborhood structures find local optimum heat planning and cast planning, the highest KPI index value is taken as the final heat and cast planning. By the method of the invention, the optimization level of the steel-making and continuous casting plans can be raised to ensure that the iron and steel enterprises obtain the maximum profit.

The invention relates to a flexible job shop scheduling method of a co-fusion AGV. The method comprises the following steps that: a flexible job scheduling mathematical model containing AGV transfer is established; parameters are initialized, each individual in a population is encoded, and an encoding sequence is generated; and the neighborhood code of each individual is obtained through crossover, variation and sequence value exchange, and if the fitness value of the neighborhood code of a certain individual is smaller than the fitness value of an individual in a mutated population, the individual in the corresponding mutated population is replaced with the neighborhood code of the individual. According to the method, a three-layer coding strategy of a process, a machine and an AGV sequence is used, so that the relationship between a workpiece, a processing machine and the AGV can be effectively mapped; an adaptive weight factor dynamically adjusts crossover and mutation probabilities, good stability can be obtained; and the local search capability of an introduced variable neighborhood search based on a mobile and exchange process strategy is high.

The invention relates to a method for solving flexible job shop scheduling by using a particle swarm algorithm based on longicorn beard search, which utilizes longicorn beard search to improve the particle swarm algorithm and mixes a variable neighborhood search algorithm to realize flexible job shop scheduling. According to the method, the problems of insufficient depth and stability and the likein solving the flexible job shop scheduling problem are effectively solved. In order to balance the global search capability and the local search capability, the variable neighborhood algorithm is ingeniously fused into the hybrid particle swarm algorithm, and the local search capability of the algorithm is enhanced. The hybrid algorithm is applied to the field of flexible job shop scheduling forthe first time, and the quality of a scheduling scheme can be effectively improved. Considering the coding mode of chromosomes in a flexible job shop, the hybrid particle swarm algorithm needs to beproperly coded and improved to avoid generating infeasible solutions, and genetic manipulation is introduced to enhance the global search capability of the algorithm, so that the robustness of the algorithm is improved.

The invention provides a production scheduling method and system based on a hybrid parallel inheritance and variable neighborhood algorithm, a storage medium and electronic equipment, and relates to the field of production scheduling. The method includes: adopting a heuristic algorithm to obtain each workshop production scheduling scheme of each individual in an initialized population, and taking the individual with the highest fitness value as a global optimal solution; searching a new solution in a neighborhood structure; the updated global optimal solution is migrated to each sub-group; according to the updated fitness value of the individual in each sub-group, adopting a selection operator, a crossover operator and a mutation operator to obtain a next-generation sub-group; and selecting an individual with the highest fitness value in the current group, and updating the globally optimal solution. An approximate optimal solution is found through iteration of mixed coarse-grained parallel inheritance and a variable neighborhood search optimization algorithm, the premature phenomenon of a genetic algorithm is avoided, and the convergence degree of the algorithm is increased; the efficiency improvement caused by the machine processing deterioration effect and the resource investment is considered, and the problems of production scheduling decision and resource configuration decision are considered.

The invention provides a development resource integrated scheduling method for a high-end equipment complex hierarchical task network. The method includes: settinginput parameters of a variable neighborhood search algorithm based on task data of a task high-end equipment complex hierarchical hybrid development task; setting execution parameters of the algorithm, and initializing an initial solution of the algorithm; updating the initial solution in the Shaking neighborhood operation; carrying out local search in the local search domain structure obtained by the probability through the initialsolution, and calculating fitness values of individuals obtained in the local search; updating the initial solution, and updating the weight in the local search domain structure; and judging whether the algorithm meets a termination condition or not, if so, outputting a globally optimal solution, and otherwise, returning to update the initial solution in the Shaking neighborhood operation. According to the method, the approximately optimal solution can be obtained for the multi-type development resource collaborative integration scheduling problem with a complex hierarchical development task network, so that the resource utilization efficiency and the operation efficiency of a high-end equipment manufacturing enterprise are improved to the maximum extent.