Intelligent production unit layout and energy consumption optimization method and system

Abstract

The application discloses a kind of intelligent production unit layout and energy consumption optimization method, it is related to intelligent production unit scheduling optimization technical field, including: with the maximum production capacity and minimization AGV energy consumption as target, construct mathematical model and objective function;According to mathematical model, establish intelligent production unit simulation model;Randomly generate initial population using improved NSGA-Ⅱ algorithm and carry out iteration, call simulation model to obtain individual fitness value in process, after iteration is completed, output non-dominated sorting result, obtain the optimal solution of the equipment layout information and AGV speed information that make objective function satisfy constraint condition, complete intelligent production unit layout and energy consumption optimization.The application is by establishing the multi-objective mathematical model and simulation model of equipment layout and AGV energy consumption, effectively retain the randomness of real production scene, find the production capacity and energy saving balance point to obtain overall optimal solution by solving equipment layout information and AGV speed information using improved NSGA-Ⅱ algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent production unit scheduling optimization, and more particularly to an intelligent production unit layout and energy consumption optimization method and system. BACKGROUND

[0002] Facility Layout Planning (FLP) involves scientifically configuring all production elements (such as equipment, buffer zone, personnel, etc.) that constitute a production system within a given space (such as a workshop, warehouse), by adjusting their relative positions and arrangement, to achieve optimal performance of the overall system, which is crucial to achieving the agility, energy saving and economy of the manufacturing system. The layout planning problem mainly includes layout logic, layout modeling and model solving. The device layout of the intelligent manufacturing factory should have high flexibility, modularity and ease of reconfigurability, so it pays more attention to unit layout, robust layout and other layout logic, and has higher requirements for layout modeling and solving algorithm. Layout models mainly include graph theory models (such as queuing theory), mathematical analysis models (such as nonlinear programming models, mixed integer programming models) and simulation models, and solving methods can use exact algorithms to find the optimal solution, or use heuristic algorithms or meta-heuristic algorithms to find the sub-optimal solution.

[0003] The adoption of optimization theory and methods for in-depth and systematic research on the two core problems of production unit construction and unit auxiliary resource allocation can provide key theoretical and methodological guidance for the implementation of intelligent unit production methods in discrete manufacturing enterprises. Intelligent Manufacturing Cell Layout Planning (IMCLP) relies on the efficient execution of material handling tasks by handling robots within the unit to achieve a compact device layout within the unit, and to solve the two key problems of intelligent production unit layout and intelligent logistics storage and transportation resource allocation. Therefore, the intelligent production unit layout problem considering the intelligent material storage and transportation system not only includes the FLP problem, but also involves the path network design and vehicle path planning problem.

[0004] In recent years, related research still adopts the classic device layout logistics distance and logistics intensity distribution to think about the problem, and intelligent manufacturing needs to form flexible and green production, and also needs to consider the coupling relationship between the logistics speed of handling equipment (AGV) and the intelligent production unit layout. At the same time, in recent years, the price of land-based production factors has grown rapidly, and considering the compact and coordinated intelligent production unit layout has great research value in reducing layout costs and reducing operating energy consumption, therefore, the present application provides an intelligent production unit layout and energy consumption optimization method and system. SUMMARY

[0005] The purpose of the present invention is to solve the problem of coordinated optimization of the ring layout and energy consumption of an intelligent production unit with AGV transportation in the prior art, to provide a method and system for optimizing the layout and energy consumption of an intelligent production unit, and to find the overall optimal solution for the production capacity and energy consumption of the intelligent production unit.

[0006] To achieve the above purpose, the technical method of the present invention is as follows:

[0007] Taking maximizing the production capacity of the intelligent unit and minimizing the energy consumption of the AGV as the joint optimization goals, a production capacity-energy consumption mathematical model and its objective function are constructed;

[0008] Based on the production capacity-energy consumption mathematical model, a simulation model of an intelligent production unit with AGV circular transportation is established using modeling software;

[0009] The preset improved NSGA-II genetic algorithm is used to randomly generate an initial population and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness values ​​of individuals in the population. After the iteration is completed, the final non-dominated sorting result, i.e., the approximate solution set of the Pareto optimal frontier, is output;

[0010] Based on the approximate solution set of the Pareto optimal frontier, the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraints are obtained. According to the equipment layout information and AGV speed information, the optimization of the intelligent production unit layout and energy consumption is completed.

[0011] Preferably, the objective function of the joint optimization objective is:

[0012] maxΘ(X,v)

[0013] minE(X,v)

[0014] in, The total number of workpieces num produced by the intelligent generation unit under the influence of variables X and v is divided by the simulation time t to obtain the production capacity Θ. E is the energy consumption of the AGV, X is the equipment layout information, and v is the AGV cruising speed. The 0-1 decision variable X, that is, the equipment layout information X, is:

[0015]

[0016] Among them, X is used to determine the relative position of the equipment and the loading and unloading ports. If the problem scale is n, X is an n×n dimensional 0-1 matrix, where x i,j If it is 1, it means that the facility node i is in the jth position L j On, otherwise 0;

[0017] The calculation process of energy consumption E is as follows:

[0018]

[0019] E s =E am +E um +E a

[0020]

[0021] E a =P a t ij

[0022] F t =m total a ac +F f

[0023] F f =fm total g

[0024] m total =m1+c1m2

[0025] l z =l ij -l ac -l de

[0026]

[0027] Where E is the energy consumption of AGV; n is the problem scale, which is the number of locations where equipment can be arranged in the intelligent unit, including the loading port buffer and the unloading port buffer; E s : Total energy consumption of AGV for a single task; E am : Energy consumption during AGV acceleration phase; E um : Energy consumption of AGV during cruising; E a : Additional energy consumed by other energy-consuming components; P a : Power of energy-consuming components; h m : The total number of steps for workpiece m, p = 1, ..., m, q = 1, ..., h m ;t idle : AGV idle time; t ac1 : The acceleration time when the AGV cannot reach the cruising speed v when moving from position i to position j; t ac2 : the time required to accelerate from 0 to cruising speed; t de1 : The braking time when the AGV cannot reach the cruising speed v when moving from position i to position j; t de2 : the time required to decelerate from the cruising speed v to 0; t ij: The total time required for the AGV to move from position i to position j; c1: When the AGV loads the workpiece, c1 is 1, otherwise c1 is 0; c2: When the AGV cannot reach the cruising speed v during the process of moving from position i to position j, c2 = 1, otherwise c2 = 0; l z : moving distance during cruising; l ij : The distance AGV moves from position i to position j; l ac : Moving distance of AGV when accelerating; l de : Moving distance of AGV when decelerating; η: Energy efficiency factor; F t : driving force; F f : friction force; f: rolling friction coefficient; g: acceleration due to gravity; a ac : acceleration; a de : Braking acceleration, v ac :v ac <v,机器人从位置i前往位置j的过程中无法达到巡航速度v时的最大速度。

[0028] Preferably, the constraints of the objective function include:

[0029]

[0030] The above two expressions together express that only one device or facility node can be deployed at each location, and each device or facility node can only be deployed at one location;

[0031] v min ≤v≤v max

[0032] Among them, v min : The minimum AGV moving speed that can be set; v max : The maximum AGV moving speed that can be set.

[0033] Preferably, before performing population iteration, the improved NSGA-II genetic algorithm needs to design encoding rules, which are as follows:

[0034] The equipment layout information is encoded on the left side of the gene, and the length of the encoding depends on the problem scale n. That is, if there are n positions on the periphery of the circular track of the simulation model, the equipment layout information encoding is represented as an n-dimensional array, which can accommodate the loading port (P), the unloading port (D) and n-2 workstations. The set of workstations is {S1, S2, ..., S n-2};

[0035] The AGV speed information is encoded on the right side of the gene. Currently, the AGV speed in the workshop is generally set at 0-3m / s. To ensure the accuracy of the experiment, three decimal places are retained. Therefore, 14-bit binary encoding is used. After conversion to decimal, the value range is [0,8192]. Dividing by 1000, the AGV moving speed value is obtained.

[0036] The equipment layout information encoding and AGV speed information encoding are merged to form an individual code. The code length depends on the problem scale n. In addition, the merged code carefully adopts crossover and mutation operations to ensure the correctness of the information expressed by the code.

[0037] Preferably, the iterative process of the improved NSGA-II genetic algorithm includes:

[0038] Population initialization, randomly generate the initial population;

[0039] Performing a crossover operation on the genetic codes of the individuals of the initialized population in sections;

[0040] After performing single-point mutation operation on the individuals after crossover and passing the correctness test;

[0041] After the correctness test is passed, the child and parent populations are merged;

[0042] Traversing the merged population, identifying isomorphic individuals, marking one of each isomorphic individual, and obtaining the fitness value of one of each isomorphic individual and the fitness values ​​of other individuals for which no isomorphic individual exists through the simulation model, sharing the fitness value of one of each isomorphic individual with other isomorphic individuals, completing the task of removing isomorphic individuals, reducing the number of calls to the simulation model, and compressing the solution time;

[0043] The fast non-dominated sorting rule is used to construct a non-dominated set, and the other individuals outside the set are sorted to obtain the non-dominated sorting result;

[0044] Select elite individuals from the non-dominated sorting results to enter the next generation population;

[0045] Determine whether the iteration termination condition is met. If the iteration termination condition is met, output the final non-dominated sorting result. Otherwise, continue to iterate and execute the steps starting from the crossover operation.

[0046] Preferably, the crossover operation is performed using a partial matching crossover operator, and on the basis of the traditional two-point crossover partial matching operator, an intersection point is added to the AGV speed information encoding part, that is, the first and second intersection points are randomly generated in the equipment layout information encoding part, and the third intersection point is randomly generated in the AGV speed information encoding part, and the codes between the first and second intersection points of different individuals and the codes after the third intersection point are exchanged. After the exchange is completed, a uniqueness check is performed to ensure that the encoded information is correctly expressed.

[0047] Preferably, for the single-point mutation operation, when the mutation point is in the device layout information encoding, a uniqueness check needs to be performed after the mutation to ensure that the encoded information is correctly expressed.

[0048] Preferably, the specific process of constructing a non-dominated set using a fast non-dominated sorting rule and sorting other individuals outside the set includes two parts;

[0049] The first part consists of finding the non-dominated set, i.e. individuals of level 1, as follows:

[0050] For each individual in the population from i 1 to the population size and each other individual in the population from j + 1 to the population size, set the i-th individual in the two individuals currently being compared to be p and the j-th individual to be q; if p dominates q, add q to the domination set of p, the domination set of p represents the individuals dominated by p, and the number of times q is dominated is increased by 1; if q dominates p, add p to the domination set of q, the domination set of q represents the individuals dominated by q, and the number of times p is dominated is increased by 1; if the number of times the i-th individual is dominated is 0, that is, no other individual dominates it, then the individual is classified as level 1 and added to the first set of the non-dominated set F, and the level of the individual is set to 1;

[0051] The second part consists of classifying individuals other than those in Class 1 as follows:

[0052] Initialize an empty set Q to temporarily store individuals of new levels; for each individual in the non-dominated set F whose current level is r=1 and for all other individuals dominated by this individual, reduce the number of times these individuals are dominated. If the number of times an individual is dominated becomes 0, that is, it is now not dominated by any other individual, then set the level of this individual to r+1 and add it to Q; if Q is empty, it means that all individuals have been assigned levels, and exit the loop. Otherwise, add the individuals in Q as individuals of the new level r+1 to the next set of the non-dominated set F, increase the value of r, and continue to process individuals of the next level.

[0053] Preferably, in order to speed up the non-dominated sorting process and ensure the distribution and diversity of the population, the clustering distance of individuals is used as a supplementary rule for sorting between individuals, and individuals with large clustering distances are preferentially retained. The calculation formula of the clustering distance is as follows:

[0054]

[0055] The aggregation distance formula indicates that the aggregation distance of the i-th individual is equal to the sum of the differences between the i-1th individual and the i+1th individual in all objective function values. After adding the aggregation distance rule, the non-dominated sorting process includes the following: first, construct a non-dominated set and calculate the aggregation distance of each individual, then sort them in ascending order according to the level divided by the non-dominated set, and sort the individuals in the same level in descending order according to the size of the aggregation distance.

[0056] To achieve the purpose of the invention, the present invention further provides an intelligent production unit layout and energy consumption optimization system, which applies the intelligent production unit layout and energy consumption optimization method described in the above technical solution, and the system includes:

[0057] A digital model building unit is used to build a capacity-energy consumption mathematical model and its objective function with the joint optimization goal of maximizing the capacity of the intelligent unit and minimizing the energy consumption of the AGV;

[0058] A simulation unit, configured to establish a simulation model of an intelligent production unit with AGV circular transportation using modeling software based on the production capacity-energy consumption mathematical model;

[0059] An optimal solution solving unit is used to randomly generate an initial population using a preset improved NSGA-II genetic algorithm and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness values ​​of individuals in the population. After the iteration is completed, a Pareto optimal solution set for constructing the production capacity-energy consumption mathematical model is output;

[0060] The layout and energy consumption optimization unit is used to obtain the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraint conditions based on the Pareto optimal solution set, and complete the optimization of the intelligent production unit layout and energy consumption according to the equipment layout information and AGV speed information.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention effectively retains the randomness of real production scenarios by establishing a multi-objective mathematical model and simulation model of equipment layout and AGV energy consumption in intelligent production cells. The improved NSGA-II genetic algorithm is used to solve the equipment layout information and AGV speed information, finding a balance point between the two conflicting goals of production capacity and energy saving to obtain the overall optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a method for intelligent production unit layout and energy consumption optimization according to Example 1 of the present application;

[0064] Figure 2 This is a schematic diagram of the equipment layout of an intelligent production unit according to Example 1 of the present application;

[0065] Figure 3 This is a schematic diagram of individual coding of Example 1 of the present application;

[0066] Figure 4 This is a schematic diagram of a simulation model of an intelligent production unit with AGV circular transportation according to Example 1 of the present application;

[0067] Figure 5 Schematic diagram of a crossover operation including a partial matching crossover operator according to Example 1 of the present application;

[0068] Figure 6 Schematic diagram of the selection operation of the NSGA-II genetic algorithm of Example 2 of the present application;

[0069] Figure 7 This is a flow chart of the improved NSGA-Ⅱ genetic algorithm based on the simulation model of Example 2 of the present application. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0072] Example 1

[0073] See also Figure 1 , Example 1 of the present application provides a method for intelligent production unit layout and energy consumption optimization, comprising the following steps:

[0074] S1: Taking maximizing the production capacity of the intelligent unit and minimizing the energy consumption of the AGV as the joint optimization goals, a production capacity-energy consumption mathematical model and its objective function are constructed.

[0075] In this embodiment 1, the intelligent production unit is an organizational form of the production unit of the intelligent factory, which more compactly includes the two key production functions of material processing and material storage and transportation. The equipment in the unit is generally arranged around the outer side of the circular track for the robot to move, such as Figure 2 The figure shows a schematic diagram of the equipment layout of an intelligent production cell. Specifically, the material processing subsystem comprises workstations such as multiple stamping machines, machining centers, CNC machine tools, and quality inspection machines, while the material storage and transportation subsystem comprises automated loading and unloading robots and storage buffers. The intelligent production cell, comprised of these two subsystems, enables automated processing, material transportation, fixture and tool replacement, and monitoring and inspection.

[0076] The operations within the intelligent production cell are coordinated by three types of resources: workstations, handling robots, and buffers. These resources are subject to resource coordination constraints. Insufficient capacity in one resource type will result in system congestion and reduced efficiency. The handling robots respond to service requests on a first-come, first-served basis, operating in three states: idle, empty-load movement, and loaded movement. If a handling robot's load arrives at a workstation with a full buffer, it will wait for unloading, leading to a deadlock in the intelligent production cell.

[0077] The intelligent production unit meets the following conditions:

[0078] (1) The process of transporting the workpiece from the external area to the unit loading port is not considered, that is, the workpiece has been prepared at the beginning and can be directly selected from the unit loading port;

[0079] (2) After the materials processed outside the unit arrive, they are temporarily stored in the loading port buffer area, waiting for the AGV to transfer them to the workstation for the first process;

[0080] (3) Each workstation has only one machine and can only process one workpiece at a time. When the material arrives when the workstation is busy, it can only wait in the front buffer area;

[0081] (4) After the material is processed at the processing station, it is temporarily stored in the back buffer area of ​​the workstation, waiting for the AGV to transfer it to the workstation of the next process. After all the processes of the material are completed, the finished product will be transported by the AGV to the unloading port buffer area of ​​the unit and then leave the unit;

[0082] (5) The capacity of the AGV is 1, and it can only carry one workpiece at a time. The AGV is considered to be moving straight when cruising on the track;

[0083] (6) All caches can store multiple artifacts;

[0084] (7) Sudden production disturbances such as machine failures and workpiece repairs are not considered.

[0085] Battery-powered AGVs often start and stop frequently during transport tasks on the shop floor. Their actual energy consumption is inevitably higher than that of a vehicle traveling at a constant speed. It's inappropriate to estimate the overall energy consumption of an AGV using only the energy consumed at a constant speed. Therefore, AGV transport energy consumption includes both loaded and unloaded transport energy.

[0086] This Example 1 uses production capacity and energy consumption as optimization objectives to solve the optimal combination of equipment layout schemes and AGV speed configurations. Equipment layout directly determines the upper limit of production capacity. At the same time, a solution can be selected based on maintaining production capacity to shorten the AGV travel distance and time, thereby improving production efficiency. When the distance is the same, a faster AGV speed may improve work efficiency, but it will also increase energy consumption. The coupled relationship between production capacity and energy consumption poses challenges to problem modeling and solution. The equipment layout optimization problem is a typical combinatorial optimization problem. The search space grows exponentially with the increase in the number of equipment. Combined with the AGV speed optimization problem, its multi-objective functions, namely production capacity and energy consumption, are highly nonlinear and non-convex. Although a single-objective model can be constructed and another objective used as a constraint to reduce the solution difficulty and shorten the solution time, multi-objective optimization can consider multiple objectives simultaneously to reduce excessive reliance on a single objective. By finding a "Pareto-Optimal Set", the robustness and stability of the optimization results are enhanced. Therefore, this Example 1 takes maximizing the production capacity of the intelligent unit and minimizing the energy consumption of the AGV as the joint optimization objectives.

[0087] Establish a mathematical model and its objective function:

[0088] The Intelligent Manufacturing Cell Loop Layout Problem (IMCLLP) with AGV transport involves arranging several intelligent devices and buffers within a limited space outside a circular track. The relative positions of the devices and the loading and unloading buffers are determined to minimize overall energy consumption, material flow costs, layout floor space, and maximize production capacity. To facilitate the description of the IMCLLP studied in Example 1 of the present invention, the following parameters and variables are defined:

[0089] X: equipment layout information; v: AGV cruising speed; v min : The minimum AGV moving speed that can be set; v max : The maximum robot movement speed that can be set; v ac :v ac<v,机器人从位置i前往位置j的过程中无法达到巡航速度时的最大速度;Θ:智能单元的产能,p=1,...,m;E:AGV的能耗;n:问题规模,智能单元内的可布置设备的位置个数,并包含上料口缓冲区和下料口缓冲区;E s : The total energy consumption of the robot in a single task; E am : Energy consumption of the robot during acceleration; E um : Energy consumption of the robot during cruising; E a : Additional energy consumed by other energy-consuming components; P a : Power of energy-consuming components; h m : The total number of steps for workpiece m, q = 1, ..., h m ;t idle : AGV idle time; t ac1 : The acceleration time when the robot cannot reach the cruising speed when moving from position i to position j; t ac2 : The time required to accelerate from 0 to cruising speed; t de1 : The braking time when the robot cannot reach the cruising speed when moving from position i to position j; t de2 : the time required to decelerate from cruising speed to 0; t ij : The total time required for the robot to move from position i to position j; c1: When the AGV load workpiece moves, c1 is 1, otherwise c1 is 0; c2: When the robot cannot reach v during the process of moving from position i to position j, c2 = 1, otherwise c2 = 0; l z : Moving distance during cruising; l ij : The distance the robot moves from position i to position j; l ac : The moving distance of the robot when accelerating; l de : The moving distance of the robot when it decelerates; η: Energy efficiency factor; F t : driving force; F f : friction force; f: rolling friction coefficient; g: acceleration due to gravity; a ac : acceleration; a de : Braking acceleration.

[0090] The objective function is as follows:

[0091] maxΘ(X,v)(1)

[0092] minE(X,v)(2)

[0093]

[0094] Where Θ represents the production capacity obtained by dividing the total number of workpieces num produced by the intelligent generation unit under the influence of variables X and v by the simulation time t.

[0095] Calculation process of energy consumption E:

[0096]

[0097] E s =E am +E um +E a (5)

[0098]

[0099] E a =P a t ij (8)

[0100] F t =m total a ac +F f (9)

[0101] F f =fm total g (10)

[0102] m total =m1+c1m2 (11)

[0103] l z =l ij -l ac -l de (12)

[0104]

[0105] The 0-1 decision variables are as follows:

[0106]

[0107] The constraints are as follows:

[0108]

[0109] v min ≤v≤v max (18)

[0110] Among them, v min : The minimum AGV moving speed that can be set; v max: The maximum AGV moving speed that can be set. Formula (1) represents the maximization of production capacity Θ, and formula (2) represents the minimization of energy consumption E; Formula (3) represents the calculation of production capacity, and the total number of workpieces produced by the system under the influence of variables X and v is obtained through simulation, and then divided by the simulation time; Formula (4) represents the total energy consumption of AGV; Formula (5) represents the energy consumption of AGV single movement, which is composed of energy consumption in the acceleration stage, energy consumption in the cruising stage, and energy consumption of other energy-consuming components; Formulas (6) to (8) respectively represent the calculation methods of energy consumption in the acceleration stage, energy consumption in the cruising stage, and energy consumption of other energy-consuming components; Formulas (9) and (10) respectively represent the calculation methods of driving force and friction force; Formula (11) represents the calculation method of total weight; Formulas (12) to (14) represent the calculation methods of the moving distance of AGV in each stage; In addition to the AGV moving speed v, the decision variables in the model also introduce 0-1 decision variables X, as shown in Formula (15), to determine the relative positions of equipment or facility nodes and loading and unloading ports. If the problem scale is n, X is an n×n dimensional 0-1 matrix, where x i,j If it is 1, it means that the equipment or facility node i is in the jth position L j Otherwise, it is 0; Equations (16) and (17) together express that only one equipment or facility node can be arranged at each location, and each equipment or facility node can only be arranged at one location; Equation (18) represents the settable range of AGV moving speed.

[0111] S2: Based on the capacity-energy consumption mathematical model, use modeling software to establish a simulation model of an intelligent production unit with AGV circular transportation.

[0112] Energy consumption and production capacity of smart cells with AGV transportation are conflicting goals, and it is necessary to identify the key factors affecting both in order to find a balance point for the optimal solution. The factors that affect the performance indicators of smart cells mainly include: equipment layout, AGV movement speed, work intensity within the cell, buffer area capacity, etc. Due to the randomness rife within smart cells, such as uncertainty in order arrival time, uncertainty in the processing procedures and requirements of arriving workpieces, and uncertainty in AGV transportation cycles, its performance indicators are difficult to calculate using closed-form mathematical formulas, that is, the calculation results are difficult to accurately restore the actual production scenario. Therefore, it is necessary to adopt simulation technology with strong simulation detail capabilities, combined with scientific experimental design, to provide more reliable system performance data and verify the key factors affecting system performance.

[0113] In Example 1 of the present invention, Tecnomatix Plant Simulation 15.0 software is used to build a simulation model of an intelligent production unit with AGV circular transportation. The operating system environment is PC Windows 10, and the hardware environment is Intel (R) CPU 2.50GHz, 12.0GB RAM. Figure 4The simulation model of the intelligent production unit with AGV circular transportation is shown. This simulation model is used to calculate the fitness value. The fitness function is the system production capacity and AGV energy consumption, that is, formula (1) and formula (2), and the function value is calculated by the simulation model.

[0114]

[0115]

[0116] Table 1 Figure 4 Entity and object mapping table

[0117] (1) Source object Figure 4 (left side of Buf_0 in the figure). Since the simulation model in this paper only simulates the production within the intelligent unit system, the process of external workpieces arriving at the system is realized by the Source object generating a corresponding number of MUs (to simulate the workpieces in the production process) according to the time interval set by the user.

[0118] (2) Drain object Figure 4 Similarly, the process of a workpiece that has completed all processing steps leaving the intelligent unit is realized by recycling the MU by the Drain object;

[0119] (3)Method object Figure 4 The object with the letter M in the middle is used to write code programs so that the actual production logic can be better simulated in the simulation model. At the same time, the statistics and output of certain performance indicators also rely on the implementation of the program written in the Method object;

[0120] (4)EventController object Figure 4 The simulation time in the simulation model is controlled and the simulation speed is adjusted. In addition, the operation of triggering a certain behavior at a specific time node is also realized by relying on this object.

[0121] (5) Table object Figure 4 It is used to record the data that the user needs to record during the simulation or to store intermediate values.

[0122] S3: The preset improved NSGA-Ⅱ genetic algorithm is used to randomly generate the initial population and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness values ​​of individuals in the population. After the iteration is completed, the final non-dominated sorting result, that is, the approximate solution set of the Pareto optimal frontier, is output.

[0123] Improved NSGA-Ⅱ genetic algorithm design:

[0124] The improved NSGA-II genetic algorithm first needs to design coding rules. The individual coding of Example 1 of the present invention is composed of the equipment layout information coding and the AGV speed information coding. Figure 3 The following is a schematic diagram of the individual encoding of Example 1. The equipment layout information is encoded on the left side of the gene. The problem scale is the encoding length. That is, if there are n positions on the periphery of the circular track, the equipment layout information encoding is represented as an n-dimensional array that can accommodate the loading port (P), the unloading port (D) and n-2 workstations. The set of workstations is {S1, S2, ..., S n-2 For example, for a problem size of n = 6, six positions need to be allocated to four workstations and a pair of PD ports. The relational array can be represented as [S3, S1, D, S4, P, S2]. This array indicates that in this layout, position 1 houses workstation S3, position 2 houses S1, position 3 houses the loading port, and so on. For ease of calculation, the loading port is denoted as 0, the unloading port as n-1, and the workstations are coded from 1 to n-2. The equipment layout code for this layout is [3, 1, 5, 4, 0, 2]. AGV speed information is encoded on the right side of the gene. Currently, the speed of workshop transfer AGVs is generally set between 0 and 3 m / s. To ensure accuracy, three decimal places are retained, such as 2.965 m / s. Therefore, a 14-bit binary code is used, and when converted to decimal, the value range is [0, 8192]. For example, the binary number 00101110010101 converts to decimal as 2965, which, when divided by 1000, gives the AGV's speed. In summary, the equipment layout information encoding and the AGV speed information encoding are combined to form a separate code. The length of the code depends on the scale of the problem. Furthermore, the combined code requires careful crossover and mutation operations to ensure the correctness of the information expressed in the code.

[0125] See also Figure 7 The iterative process of the improved NSGA-II genetic algorithm in this embodiment 1 includes:

[0126] S11: Population initialization, randomly generating the initial population, that is, randomly generating the equipment layout information code and AGV speed code respectively, and then splicing them into individual codes;

[0127] S12: Perform crossover operations on the genetic codes of individuals in the initialized population in segments;

[0128] S13: Perform single-point mutation operation on the individuals after crossover and perform correctness test after passing;

[0129] S14: After the correctness test is passed, the child and parent populations are merged;

[0130] S15: traversing the merged population, judging isomorphic individuals, marking one of each isomorphic individual, and obtaining the fitness value of one individual in each isomorphic individual and the fitness value of an individual without isomorphic individuals by the simulation model, sharing the fitness value of one individual in each isomorphic individual to other isomorphic individuals, completing the task of removing isomorphic individuals, reducing the number of calls of the simulation model, and compressing the time consumption of solving;

[0131] S16: constructing a non-dominated set by using a fast non-dominated sorting rule, and sorting other individuals outside the set to obtain a non-dominated sorting result;

[0132] S17: selecting elite individuals in the non-dominated sorting result into the next generation population;

[0133] S18: judging whether an iteration termination condition is met, outputting a final non-dominated sorting result if the iteration termination condition is met, or continuing to iteratively execute steps S12-S18.

[0134] In this embodiment 1, the population size is set to 70, the crossover operation adopts three-point crossover, the proportion is set to 0.8, the mutation operation adopts single-point mutation, and the proportion is 0.2.

[0135] The crossover operation and the mutation operation are specifically as follows:

[0136] Please refer to Figure 5 , Figure 5 The crossover operation in this embodiment 1 is shown in FIG. 1, and the uniqueness of the genes in the chromosome needs to be ensured, that is, the gene appears only once in the chromosome. The partially-matched crossover (PMX) operator is more suitable, but the chromosome of the AGV speed information coding part does not need to follow the uniqueness principle. The operation process of PMX is shown in Figure 5 , Figure 5 Step I is to record the mapping relationship according to the positions of the two crossover points, and exchange the codes between the two crossover points; Step II is the situation of the two individuals after the crossover is completed; and Step III shows the de-duplication process of the repeated points in the individual. According to the mapping relationship, the repeated points are corrected. This step is called "uniqueness check". Therefore, based on the traditional two-point crossover PMX operator, one crossover point is added to the AGV speed information coding part in this embodiment 1. That is, two crossover points (the first and second crossover points) are randomly generated in the device layout information coding part, one crossover point (the third crossover point) is randomly generated in the AGV speed information coding part, the codes between the first and second crossover points and the codes after the third crossover point of the chromosomes of different individuals are exchanged, and the uniqueness check is performed after the exchange is completed.

[0137] Similarly, for mutation operations, when the mutation point is encoded in the device layout information, a uniqueness check is also required after the mutation to ensure that the encoded information is correctly expressed.

[0138] S4: Based on the approximate solution set of the Pareto optimal frontier, the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraints are obtained. According to the equipment layout information and AGV speed information, the layout and energy consumption of the intelligent production unit are optimized.

[0139] Example 2

[0140] This embodiment 2 is based on embodiment 1 and further illustrates the fast non-dominated sorting and selection operations in embodiment 1.

[0141] Fast non-dominated sort:

[0142] In the IMCLLP problem, production system performance indicators such as production capacity and energy consumption are difficult to express using closed-form mathematical formulas. Therefore, simulation models are used to calculate these values ​​and use them as individual fitness values. Due to the presence of multiple conflicting fitness values, genetic algorithms cannot directly compare and select them. NSGA-II addresses this problem by introducing a non-dominated sorting rule. The non-dominated sorting rule constructs a set of non-dominated solutions based on the dominance relationships between solutions, then ranks the remaining solutions, selecting individuals with the highest rankings to advance to the next generation. However, when there are too many individuals of a certain level, it is difficult to obtain elite individuals through random selection, resulting in a long search time. Therefore, to accelerate the selection process of non-dominated sorting, this Example 1 introduces clustering distance as an elite individual screening rule, embedded within the non-dominated sorting rule to shorten the algorithm's search time.

[0143] Dominate relations are used to compare the pros and cons of two solutions in multi-objective optimization. When a solution A is said to dominate another solution B, denoted as A < B, it means that A is not inferior to B in all objectives and is superior to B in at least one objective, that is, it satisfies two conditions: 1) For all sub-objectives, A is not worse than B, that is, f k (A)≤f k (B)(k=1,2,…,r); 2) There is at least one sub-goal that makes A better than B, that is , so that f l (A) <f l (B); where r is the number of sub-goals.

[0144] At this time, A is called non-dominated, or non-inferior or superior, and B is dominated. If there are multiple solutions without mutual domination, the comprehensive performance of these solutions is relatively good, forming a non-dominated set. Before the selection operation, the non-dominated set needs to be constructed, and the other individuals outside the set are sorted, including two parts:

[0145] The first part includes finding the non-dominated set, i.e. the first level individuals, as follows:

[0146] For each individual i in the population from 1 to the population size and each other individual j in the population from i+1 to the population size, set the i-th individual in the two individuals being compared as p and the j-th individual as q; if p dominates q, add q to the dominance set of p, the dominance set of p represents the individuals dominated by p, and the dominated number of q is incremented by 1; if q dominates p, add p to the dominance set of q, the dominance set of q represents the individuals dominated by q, and the dominated number of p is incremented by 1; if the dominated number of the i-th individual is 0, i.e. it is not dominated by any other individual, the individual is classified as the first level and added to the first set of the non-dominated set F, and the level of the individual is set to 1;

[0147] The second part includes dividing the individuals other than the first level into levels, as follows:

[0148] Initialize an empty set Q for temporary storage of individuals of new levels; for each individual in the non-dominated set F with the current level r=1 and for all other individuals dominated by the individual, reduce the dominated number of these individuals, if the dominated number of a certain individual becomes 0, i.e. it is not dominated by any other individual, set the level of the individual to r+1 and add it to Q; if Q is empty, it means that all individuals have been assigned levels, exit the loop, otherwise, the individuals in Q are added to the next set of the non-dominated set F as individuals of the new level r+1, and the value of r is increased to continue processing the individuals of the next level.

[0149] The pseudo code of the above process is as follows:

[0150]

[0151]

[0152]

[0153] To speed up the process of non-dominated sorting and ensure the distribution and diversity of the population, the aggregation distance of individuals is used as a supplementary rule for sorting between individuals, and individuals with larger aggregation distances are preferentially retained. The calculation formula of the aggregation distance is as follows:

[0154]

[0155] The aggregation distance formula indicates that the aggregation distance of the i-th individual is equal to the sum of the differences between the i-1th individual and the i+1th individual in all objective function values. After adding the aggregation distance rule, the non-dominated sorting process includes the following: first, construct a non-dominated set and calculate the aggregation distance of each individual, then sort them in ascending order according to the level divided by the non-dominated set, and sort the individuals in the same level in descending order according to the size of the aggregation distance.

[0156] Select an action:

[0157] Fast non-dominated sorting assigns all individuals in the parent and offspring populations to corresponding levels of dominance and non-domination, and then selects a certain proportion of individuals to enter the next generation population. First, individuals with higher non-dominated levels are selected. When all individuals of a certain level cannot enter the next generation at the same time, individuals with larger aggregation distances are selected to enter the next generation. The selection operation is as follows: Figure 6 As shown in the figure, P t represents the parent generation, Q t represents the unselected offspring after the parent generation crossover mutation, F1, F2, F3, ... represent the various levels, P t+1 Represents a new population.

[0158] The other steps or technical details of this embodiment 2 are the same as those of embodiment 1 and will not be repeated here.

[0159] Example 3

[0160] This embodiment 3 is based on the embodiments 1 and 2, and applies the intelligent production unit layout and energy consumption optimization method in the embodiments 1 and 2 to provide an intelligent production unit layout and energy consumption optimization system, which includes:

[0161] A digital model building unit is used to build a capacity-energy consumption mathematical model and its objective function with the joint optimization goal of maximizing the capacity of the intelligent unit and minimizing the energy consumption of the AGV;

[0162] A simulation unit is used to establish a simulation model of an intelligent production unit with AGV circular transportation using modeling software based on a capacity-energy consumption mathematical model;

[0163] The optimal solution solving unit is used to randomly generate the initial population using the preset improved NSGA-Ⅱ genetic algorithm and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness value of the individuals in the population. After the iteration is completed, the Pareto optimal solution set for constructing the production capacity-energy consumption mathematical model is output;

[0164] The layout and energy consumption optimization unit is used to obtain the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraints based on the Pareto optimal solution set, and complete the optimization of the layout and energy consumption of the intelligent production unit based on the equipment layout information and AGV speed information.

[0165] The other steps of this embodiment 3 are the same as those of embodiments 1 and 2 and will not be repeated here.

[0166] In summary, the present invention establishes a multi-objective model by considering the equipment layout information and the energy consumption of the AGV. Through Plant Simulation, the randomness of the real production scene is effectively retained. The improved NSGA-II genetic algorithm is proposed to solve the equipment layout information and the AGV speed information, find a balance point for the two conflicting goals of production capacity and energy saving, and obtain the overall optimal solution. The speed of the AGV robot, the interaction between the equipment and the layout of the loading / unloading port of the production unit are reasonably set, and the timely delivery of materials is further guaranteed to stabilize the production efficiency of the intelligent unit and reduce the energy consumption of the intelligent unit.

[0167] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the layout and energy consumption of intelligent production units, characterized in that: The following steps are involved: Taking maximizing the production capacity of the intelligent unit and minimizing the energy consumption of the AGV as the joint optimization goals, a production capacity-energy consumption mathematical model and its objective function are constructed; Based on the production capacity-energy consumption mathematical model, a simulation model of an intelligent production unit with AGV circular transportation is established using modeling software; The preset improved NSGA-II genetic algorithm is used to randomly generate an initial population and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness values ​​of individuals in the population. After the iteration is completed, the final non-dominated sorting result, i.e., the approximate solution set of the Pareto optimal frontier, is output; Based on the approximate solution set of the Pareto optimal frontier, the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraint conditions are obtained, and the layout and energy consumption of the intelligent production unit are optimized according to the equipment layout information and AGV speed information; The iterative process of the improved NSGA-II genetic algorithm includes: Population initialization, randomly generate the initial population; Performing a crossover operation on the genetic codes of the individuals of the initial population in sections; Perform single-point mutation operation on the individuals after crossover and perform correctness test; After the correctness test is passed, the child and parent populations are merged; Traversing the merged population, identifying isomorphic individuals, marking one of each isomorphic individual, and obtaining the fitness value of one of each isomorphic individual and the fitness values ​​of other individuals for which no isomorphic individual exists through the simulation model, sharing the fitness value of one of each isomorphic individual with other isomorphic individuals, completing the task of removing isomorphic individuals, reducing the number of calls to the simulation model, and compressing the solution time; The fast non-dominated sorting rule is used to construct a non-dominated set, and the other individuals outside the set are sorted to obtain the non-dominated sorting result; Select elite individuals from the non-dominated sorting results to enter the next generation population; Determine whether the iteration termination condition is met. If the iteration termination condition is met, output the final non-dominated sorting result. Otherwise, continue to iterate and execute the steps starting from the crossover operation.

2. The method for intelligent production unit layout and energy consumption optimization according to claim 1, characterized in that: The objective function of the joint optimization objective is: in, , which means that in the variable and The total number of artifacts produced by the intelligent generation unit under the influence Divide by the simulation time Get production capacity , is the energy consumption of AGV, For device layout information, is the cruising speed of the AGV; 0-1 decision variable , that is, device layout information for: ， ， in, To determine the relative position of the equipment and loading and unloading ports, if the problem scale is , for dimension 0-1 matrix, where If 1, it indicates a facility node exist Locations On, otherwise 0; Energy consumption The calculation process is as follows: in, : Energy consumption of AGV; : Problem scale, the number of locations where equipment can be arranged within the intelligent unit, including the loading and unloading buffers; : Total energy consumption of AGV in a single mission; : Energy consumption during AGV acceleration phase; : Energy consumption of AGV during cruising; : Additional energy consumed by other energy-consuming components; : Power of energy-consuming components; :Workpiece The total number of processes, , ; : AGV idle time; : AGV from position Go to location Unable to reach cruising speed during Acceleration time : The time required to accelerate from 0 to cruising speed; : AGV from position Go to location Unable to reach cruising speed during Braking time; : From cruising speed The time required to decelerate to 0; : AGV from position Go to location Total time required; : When the AGV loads the workpiece and moves is 1, otherwise is 0; :When AGV moves from position Go to location Unable to reach cruising speed during hour, ,otherwise ; : Moving distance during cruising; : AGV from position To location The moving distance; : The distance the AGV moves when accelerating; : Moving distance of AGV when decelerating; : Energy efficiency factor; : driving force; : friction; : rolling friction coefficient; : acceleration due to gravity; : acceleration; : braking acceleration, : , the robot moves from position Go to location Unable to reach cruising speed during The maximum speed at .

3. The method for intelligent production unit layout and energy consumption optimization according to claim 2, characterized in that: The constraints of the objective function include: The above two expressions together express that only one device or facility node can be deployed at each location, and each device or facility node can only be deployed at one location; in, : The minimum AGV moving speed that can be set; : The maximum AGV moving speed that can be set.

4. The method for optimizing the layout and energy consumption of intelligent production units according to claim 1, characterized in that: Before the improved NSGA-II genetic algorithm performs population iteration, it is necessary to design coding rules, as follows: The device layout information is encoded on the left side of the gene, and the length of the encoding depends on the problem size. , that is, if the outer periphery of the circular track of the simulation model has Position, the device layout information encoding is represented as a dimensional array, accommodating the loading port (P), unloading port (D) and -2 workstations, the collection of workstations is ; The AGV speed information is encoded on the right side of the gene. Currently, the AGV speed in the workshop is generally set at 0~3m / s. To ensure the accuracy of the experiment, three decimal places are retained. Therefore, 14-bit binary encoding is used. After conversion to decimal, the value range is [0,8192]. Dividing by 1000, the AGV moving speed value is obtained. Combine the equipment layout information code and the AGV speed information code to form an individual code. The code length depends on the scale of the problem. ; In addition, the merged coding carefully adopts crossover and mutation operations to ensure the correctness of the information expressed by the coding.

5. The method for optimizing the layout and energy consumption of intelligent production units according to claim 4, characterized in that: The crossover operation is performed using a partial matching crossover operator, and on the basis of the traditional two-point crossover partial matching operator, an intersection point is added to the AGV speed information encoding part, that is, the first and second intersection points are randomly generated in the equipment layout information encoding part, and the third intersection point is randomly generated in the AGV speed information encoding part. The codes between the first and second intersection points of different individuals and the codes after the third intersection point are exchanged, and a uniqueness check is performed after the exchange is completed to ensure that the encoded information is correctly expressed.

6. The method for intelligent production unit layout and energy consumption optimization according to claim 4, characterized in that: For the single-point mutation operation, when the mutation point is encoded in the device layout information, a uniqueness check needs to be performed after the mutation to ensure that the encoded information is correctly expressed.

7. The method for optimizing the layout and energy consumption of intelligent production units according to claim 1, characterized in that: The specific process of constructing a non-dominated set using the fast non-dominated sorting rule and sorting the other individuals outside the set includes two parts: The first part consists of finding the non-dominated set, i.e. individuals of level 1, as follows: For each individual in the population from i 1 to the population size and for each other individual in the population from j i+1 to the population size, set the i-th individual in the two individuals currently being compared to p and the j-th individual to q; If p dominates q, add q to p's dominance set, which represents the individuals dominated by p, and the number of times q is dominated increases by 1; If q dominates p, add p to q's dominating set, which represents the individuals dominated by q, and the number of times p is dominated is increased by 1; if the number of times the i-th individual is dominated is 0, that is, no other individual dominates it, then the individual is classified as level 1 and added to the first set of the non-dominated set F, and the level of the individual is set to 1; The second part consists of classifying individuals other than those in Class 1 as follows: Initialize an empty set Q to temporarily store individuals of new ranks; for each individual in the non-dominated set F whose current rank is r=1 and for all other individuals dominated by this individual, reduce the number of times these individuals are dominated. If the number of times an individual is dominated becomes 0, that is, it is now not dominated by any other individual, then set the rank of this individual to r+1 and add it to Q; If Q is empty, it means that all individuals have been assigned levels and the loop exits. Otherwise, the individuals in Q are added to the next set of the non-dominated set F as individuals of the new level r+1, and the value of r is increased to continue processing the individuals of the next level.

8. The method for optimizing the layout and energy consumption of intelligent production units according to claim 7, characterized in that: In order to speed up the non-dominated sorting process and ensure the distribution and diversity of the population, the individual aggregation distance is used as a supplementary rule for sorting between individuals, and individuals with large aggregation distances are retained first. The calculation formula for aggregation distance is as follows: The aggregation distance formula indicates that the aggregation distance of the i-th individual is equal to the sum of the differences between the i-1th individual and the i+1th individual in all objective function values. After adding the aggregation distance rule, the non-dominated sorting process includes the following: first, construct a non-dominated set and calculate the aggregation distance of each individual, then sort them in ascending order according to the level divided by the non-dominated set, and sort the individuals in the same level in descending order according to the size of the aggregation distance.

9. An intelligent production unit layout and energy consumption optimization system, applying an intelligent production unit layout and energy consumption optimization method according to any one of claims 1 to 8, characterized in that: The system comprises: A digital model building unit is used to build a capacity-energy consumption mathematical model and its objective function with the joint optimization goal of maximizing the capacity of the intelligent unit and minimizing the energy consumption of the AGV; A simulation unit, configured to establish a simulation model of an intelligent production unit with AGV circular transportation using modeling software based on the production capacity-energy consumption mathematical model; An optimal solution solving unit is used to randomly generate an initial population using a preset improved NSGA-II genetic algorithm and perform population iteration. During the iteration process, the intelligent production unit simulation model is called to simulate to obtain the fitness values ​​of individuals in the population. After the iteration is completed, a Pareto optimal solution set for constructing the production capacity-energy consumption mathematical model is output; The layout and energy consumption optimization unit is used to obtain the equipment layout information and AGV speed information of the optimal solution that makes the objective function meet the constraint conditions based on the Pareto optimal solution set, and complete the optimization of the intelligent production unit layout and energy consumption according to the equipment layout information and AGV speed information.

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