10 results about "Variable neighborhood search" patented technology

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 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.

This invention relates to an optimization scheduling method and system for an electric-hydrogen coupling system based on a multi-stage hybrid search algorithm. The method employs a high-precision nonlinear model and specifically designs a two-stage scheme that divides the scheduling solution into a multi-step variable neighborhood search algorithm and an adaptive simulated annealing algorithm. This satisfies the computational efficiency requirements of the solution, while leveraging the ability of adaptive perturbation, global optimization, and escaping local optima. It generates a high-precision optimal solution while breaking through local optima.

This invention discloses a reentrant hybrid flow shop scheduling method based on the IMOEA / D algorithm. Specifically, it first establishes a BPM-RHFSP multi-objective optimization mathematical model with the optimization objectives of minimizing maximum completion time, total delay time, and total equipment energy cost. This transforms the actual production job scheduling problem into a combinatorial optimization mathematical model problem. Then, based on IMOEA / D, it solves the problem model by designing a variable threshold batching strategy for batch processing job decoding; multi-region global search and random variable neighborhood search; and reinforcement search for individuals within the elite solution set. This invention can effectively coordinate the processing of multiple equipment, improve the rationality of resource allocation, shorten the workshop manufacturing cycle and product delay time, and reduce energy costs.

The application discloses a kind of Chinese construction automatic learning method based on correlation degree, which is divided into background statistical corpus, candidate construction generation corpus, MDL optimization corpus and determine the best correlation degree threshold corpus by corpus;Then the correlation degree of each information pair is calculated in the background statistical corpus, and the locally optimal correlation degree is searched on the small corpus for determining the best correlation degree threshold, so that the construction set obtained with the threshold has the best MDL evaluation index;Then the candidate construction set is generated using the locally optimal correlation degree on the larger corpus generated by candidate construction, and the correlation degree characteristics of candidate construction are calculated;Finally, the best MDL candidate construction set is searched by tabu search algorithm and variable neighborhood search algorithm on the MDL optimization corpus, and the Chinese construction set is obtained.The application can automatically learn to good quality Chinese construction, which can be directly applied to natural language processing technology, and has good application prospect.

This application provides a collaborative scheduling method for electric medical waste transport vehicles and mobile battery swapping vehicles, relating to the field of scheduling optimization. The method includes: acquiring customer data and initializing algorithm parameters; constructing an initial feasible solution, calculating the total cost and infection risk for each firefly's path, and selecting the firefly path with the minimum objective function value as the current global optimal solution; using a mutation operation to generate neighborhood solutions and calculating the corresponding objective function value, comparing it with the current global optimal solution to update the global optimal solution; updating the position of each firefly in the population according to the firefly algorithm's attraction mechanism, and during the position update process, adjusting and verifying the solution using mutation operations, and iteratively optimizing until termination. This application combines the firefly algorithm with a variable neighborhood search algorithm to achieve efficient path optimization and charging scheduling, achieving a balance between minimizing total cost and minimizing infection risk, thereby improving the transportation efficiency of medical waste and reducing operating costs.

The present application belongs to the technical field of workshop production scheduling and intelligent manufacturing, and discloses a scheduling optimization method and equipment for a thin plate planar segmented flow shop, wherein a scheduling model is constructed by considering three stages of a pre-processing process, an assembly processing stage and a post-assembly processing stage, taking unified constraints obtained by converting parallel equipment constraints of the pre-processing stage, multi-predecessor synchronization constraints of the assembly processing stage and process connection constraints of the post-assembly processing stage as model constraints, without omitting or weakening assembly constraints, and iteratively optimizing the scheduling scheme by using a variable neighborhood search mechanism, so that the maximum completion time can be effectively shortened under the premise of ensuring the feasibility of the scheduling scheme, the scheduling optimization efficiency is improved, and the feasibility and optimization effect of the scheduling result are further improved.

The application provides a kind of based on big data's forestry waste recycling method and system, obtains forestry waste generation point and candidate recycling point information;The planned area is gridded, determines the weight of building point, and accordingly generates initial population, individual coding includes point layout and initial scheduling path;Generate offspring by rotating center crossover operation: select a point as the center of circle, exchange the point and path gene segment in the two concentric ring regions of parent generation, and repair the path after crossover;Elite retention strategy is used to select individuals to form a new generation population;If the average fitness of the population stagnates continuously for several generations, perform variable neighborhood search on the current optimal individual: close one or two points, select the point closest to the center of gravity in the service point to open a new point, and optimize the affected path by two-point exchange and node insertion, replace the new individual back to the population;Repeat iteration until the maximum number of iterations is reached, output the recycling point layout and vehicle scheduling scheme corresponding to the optimal individual.

This invention discloses a multi-objective train scheduling method and system integrating Q-learning, aiming to solve the problems of low efficiency and insufficient multi-objective coordination in existing technologies for large-scale solutions. Its core is to transform train scheduling into a work-shop scheduling problem with congestion constraints, establishing a mixed-integer programming model with the objectives of minimizing delay penalties, passenger dissatisfaction, and the total number of operating vehicles. A two-layer vector encoding of scheduling ordering and vehicle allocation is adopted, combined with multi-block left insertion heuristic decoding adaptation constraints. A multi-objective evolutionary framework is constructed by improving the NSGA-II algorithm, incorporating integrated population selection and customized crossover operators. Q-learning is introduced to drive variable neighborhood search, and adaptive selection of neighborhood operators enhances search intelligence. Experimental verification shows that this method outperforms mainstream algorithms in both solution efficiency and solution quality in large-scale examples and real subway data. It can efficiently balance multiple objectives, adapt to dynamic train formations and passenger flow fluctuations, and has engineering application value.

The invention belongs to the technical field of steel production and steam collaborative optimization, and particularly discloses a steel plant production self-adaptive collaborative optimization scheduling method and system oriented to steam production and elimination balance. A steelmaking-continuous casting steel plant hybrid flow shop scheduling model considering steam supply and demand balance is constructed, and minimization of maximum completion time and steam operation cost are taken as double optimization objectives. A double-layer coding strategy is designed to be fused with a process sequence and time window adjustment, a feasible scheduling scheme is generated in combination with a heuristic decoding algorithm, a population initialization strategy based on a casting plan and steam productivity balance is adopted, the quality of an initial solution is improved, a target-oriented adaptive variable neighborhood search mechanism is introduced, and the scheduling efficiency is improved. And dynamically selecting a neighborhood structure for local optimization to realize collaborative scheduling of converter production and a steam system. According to the invention, the problem of energy waste caused by mismatching of steam production and elimination in steel production is effectively solved, and the production energy efficiency and the scheduling intelligent level are improved.