104 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.

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 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 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 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 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.

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 present invention relates to a dynamic parking allocation method based on parking occupancy prediction under a cloud control system, and belongs to the field of smart parking technology. The method proposes a basic framework of an allocation method based on parking occupancy prediction, establishes a SARIMA prediction model for parking occupancy according to the characteristics of the parking lot occupancy time series, and then dynamically allocates the model according to the dynamic characteristics of the parking problem. For the established dynamic allocation model, a hybrid heuristic algorithm of an improved variable neighborhood search algorithm is designed to solve the model, an initial solution is generated by a greedy algorithm, and then an improved variable neighborhood search algorithm is used to search for the optimal solution, and a simulation experiment is set up in combination with a real data set. Based on the research on parking occupancy by the cloud control platform, the present invention establishes a SARIMA prediction model for parking occupancy to predict it, and uses the cloud control platform through a dynamic parking allocation method to provide parking users with an efficient, accurate and low-cost parking solution.

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.

The invention provides a multi-target goods allocation method and a related device, and belongs to the technical field of goods allocation. According to the method, an NSGA-II algorithm is improved by adopting adaptive crossover and mutation operation and a variable neighborhood search algorithm, and an AVNS-NSGA-II algorithm is obtained; based on the set constraint condition of the goods allocation model, an AVNS-NSGA-II algorithm is adopted to solve the target function of the established goods allocation model, and a Pareto leading edge is obtained; calculating subjective weights and objective weights of the maximized warehouse-in and warehouse-out efficiency objective function, the maximized goods shelf stability objective function and the maximized goods allocation equilibrium distribution objective function; performing coordinated distribution on the subjective weight and the objective weight to obtain a comprehensive weight; and evaluating and sorting each solution in the Pareto frontier based on the comprehensive weight to obtain a goods allocation result so as to complete multi-target goods allocation. According to the invention, the problem of low goods allocation accuracy is solved.

The invention provides a distributed heterogeneous factory cell-PACK cooperative scheduling method and system, and the method comprises the steps: initializing the parameters of a COA-VNS algorithm, including the number N of raccoon longnose, the maximum number of iterations Maxter, the number kmax of neighborhood structures, and the maximum number lmax of neighborhood searches; generating all initial positions of the raccoon by adopting ICMIC chaotic mapping; determining an optimal individual as an initial position of agama through fitness evaluation sorting, and realizing position updating in combination with a random falling strategy and a predator escape mechanism; screening a high-quality solution by using an elitism strategy, and introducing dynamic reverse learning to generate a reverse population for global expansion; two domain structures provided by the invention are used for performing variable neighborhood search on the position of the raccoon, and the maximum search frequency of each neighborhood in each iteration is lmax. According to the invention, the technical problem that the algorithm is easy to fall into local optimum due to deviation between theoretical application and actual application is solved.