CN115641049A - Multi-station multi-material task allocation and path planning method based on genetic algorithm - Google Patents
Multi-station multi-material task allocation and path planning method based on genetic algorithm Download PDFInfo
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- CN115641049A CN115641049A CN202211354338.8A CN202211354338A CN115641049A CN 115641049 A CN115641049 A CN 115641049A CN 202211354338 A CN202211354338 A CN 202211354338A CN 115641049 A CN115641049 A CN 115641049A
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Abstract
The invention provides a multi-station multi-material task allocation and path planning method based on a genetic algorithm, which comprises the following steps of S1, preprocessing material distribution; the step S1 further includes the steps of: s11: modular design; s12: clustering materials; s13: screening key materials; s14: taking the station requirements into consideration, and carrying out batch staggered-time distribution; s2: establishing a mathematical model by taking the shortest completion time as an optimization target; s3: and solving the mathematical model by adopting a genetic algorithm according to the constraint conditions. According to the multi-station multi-material task allocation and path planning method based on the genetic algorithm, aiming at the problems that the multi-station multi-material distribution of a discrete assembly workshop requires long time and is complicated, the completion of assembly tasks is restricted and the like, a plurality of stations can obtain correct material types in correct time windows, the material distribution time is shortest as much as possible on the basis of the completion of the material distribution, and the workshop efficiency is improved.
Description
Technical Field
The invention relates to the technical field of workshop material distribution path optimization methods, in particular to a multi-station multi-material task allocation and path planning method based on a genetic algorithm.
Background
(1) The patent document CN113344487A proposes an assembly line material distribution method and system based on a line-edge integrated supermarket, and the main technical scheme is to adopt an improved harmony search algorithm to determine a material distribution scheduling scheme according to an objective function for constructing a minimum employment cost and a minimum feeding cost of a feeder, wherein the material distribution scheduling scheme comprises a responsible station of the feeder, time of each feeding action, a station and a material amount distributed for each station. Thereby ensuring that the employment cost of the feeder and the feeding cost are minimized, and simultaneously completing the material distribution task efficiently and reliably. (2) Patent document CN113947310A proposes a method for optimizing a material distribution path in a workshop based on an improved ant colony algorithm, and the main technical scheme is to improve an ant colony state transition rule on the basis of a traditional ant colony algorithm, so that the ant colony considers the emergency degree of station material demand and carbon emission when selecting the next point, and better conforms to the actual production situation. (3) Patent document CN113159687A proposes a method and a system for planning a material delivery path in cooperation with a plant AGV-UAV, which adopts a main technical scheme that an improved genetic algorithm is used to solve an optimization model for path planning to improve delivery efficiency and the like.
The prior art has the following disadvantages:
(1) The assembly line material distribution method disclosed in patent document CN113344487A requires a feeder to complete material sorting and assembly in an online integrated supermarket according to a material distribution scheduling scheme in advance, and store the materials in the material boxes, but when the material needs to be distributed for a work station, the feeder takes out a plurality of material boxes and places the material boxes in each work station in sequence. The disadvantage is that the timeliness and accuracy of material distribution need to be ensured by the feeding workers. (2) Patent document CN113947310A constructs a relevant mathematical model with the minimum number of vehicles and the shortest vehicle travel distance as optimization objectives. However, in actual production, most production workshops guarantee that production quality is the shortest time, so that the mathematical model is separated from actual production to some extent. (3) The patent document CN113159687A takes the energy consumption of the vehicle distribution process as an optimization target, and the actual production is not taken as a single optimization target, so the actual reference meaning is small. Simultaneously to the complicated region of workshop topography propose artifical unmanned aerial vehicle cooperation of using and carry out the material delivery, increased the complexity.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-station multi-material task allocation and path planning method based on a genetic algorithm, aiming at the problems that the multi-station distribution of multi-material required in a discrete assembly workshop has long time and is complicated, the completion of the assembly task is restricted and the like, so that a plurality of stations can obtain correct material types in correct time windows, the material distribution time is shortest as far as possible on the basis of the completion of the material distribution, and the workshop efficiency is improved.
In order to achieve the aim, the invention provides a multi-station multi-material task allocation and path planning method based on a genetic algorithm, which comprises the following steps:
s1, a pretreatment step of material distribution;
the step S1 further includes the steps of:
s11: modular design;
s12: clustering materials;
s13: screening key materials;
s14: taking the station requirements into consideration, and carrying out batch staggered-time distribution;
s2: establishing a mathematical model by taking the shortest completion time as an optimization target;
s3: and solving the mathematical model by adopting a genetic algorithm according to the constraint conditions.
Preferably, in the step S11: according to different requirements and common requirements of different customers, the work of the whole mechanical equipment is divided into four modules during design work: the system comprises a basic module, an optional module, a customization module and an auxiliary module.
Preferably, in the step S12: according to the fixed assembly process of mechanical equipment, the characteristics that time windows of stations for the material demands are different and the material demands in each stage are fixed are used as main classification indexes, the materials are divided into four categories according to different time periods, and the categories comprise: basic, matching, customization and assistance.
Preferably, in the step S13: and setting evaluation indexes with different weights to screen key materials, and comprehensively considering the economy of the materials, the volume of the materials and the module in which the materials are positioned.
Preferably, in the step S14: describing the sequence and the emergency degree of the materials required by the stations by using the time window, assigning an initial value to each transport vehicle by combining the distribution serial number of the module where the materials are located, searching the next more emergency station or the more emergency material for distribution, achieving the optimal path with the shortest indexes, and completing tasks by a plurality of distribution sub-paths together; the metrics include range, latency, and penalty times.
Preferably, in the step S2: and adopting partial simplification treatment on the mathematical model according to the actual condition of the delivered materials.
Preferably, the S3 step further comprises the steps of:
s31: encoding and decoding the mathematical model by adopting a multilayer encoding mode based on natural numbers;
s32: taking the reciprocal of the total completion time of the distribution scheme represented by each chromosome as the fitness value of the genetic algorithm;
s33: randomly generating an initial population, and selecting the initial population by adopting a selection method combining a roulette method and an elite retention strategy;
s34: carrying out crossover operation and mutation operation by randomly exchanging gene positions by adopting a consistent crossover operator;
s35: and (4) eliminating individuals with low fitness, and obtaining an optimal solution through multiple rounds of evolution.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
firstly, clustering all materials into four modules according to a modular working mode adopted by the design work of mechanical equipment by improving the traditional ABC material classification rule through a K-means algorithm; selecting key materials by using three screening indexes to obtain key concerns; and finally, solving the established mathematical model taking the shortest distribution time as a main optimization target by adopting a genetic algorithm to obtain a double distribution path with a station sequence and material types. The problem of the many materials of multistation delivery in discrete assembly shop is solved. Compared with the prior art, the material distribution solution is added, and the practicability of actual production is also considered.
Drawings
Fig. 1 is a flowchart of a multi-station multi-material task allocation and path planning method based on a genetic algorithm according to an embodiment of the present invention.
Detailed Description
The following description of the preferred embodiment of the present invention, with reference to the accompanying drawings and fig. 1, will provide a better understanding of the function and features of the invention.
Referring to fig. 1, in the method for multi-station and multi-material task allocation and path planning based on the genetic algorithm according to the embodiment of the present invention, all materials are clustered into four modules by improving the conventional ABC material classification criterion through the K-means algorithm according to the modular working mode adopted by the design work of the mechanical equipment; selecting key materials by using three screening indexes to obtain key concerns; finally, solving the established mathematical model taking the shortest distribution time as a main optimization target by adopting a genetic algorithm to obtain a distribution path with station sequence and material types; the method comprises the following steps:
s1, a pretreatment step of material distribution;
the method is most important for the assembly work of large and medium-sized fluid mechanical equipment, and the key is to make the materials arrive accurately and timely. Therefore, pretreatment is formed by three works before material distribution, namely modular design, material clustering and key material screening, and batch staggered-time distribution is carried out by considering station requirements.
The step S1 further comprises the steps of:
s11: modular design;
according to different requirements and common requirements of different customers, the work of the whole mechanical equipment is divided into four modules during design work: the system comprises a basic module, an optional module, a customization module and an auxiliary module.
S12: clustering materials;
according to the fixed assembly process of mechanical equipment, the characteristics that the time windows of the station for the material demands are different and the material demands at each stage are fixed are utilized as main classification indexes, the materials are divided into four categories according to different time periods, and the categories comprise: basic, matching, customizing and assisting.
S13: screening key materials;
not all materials need to be distributed by special persons, and meanwhile, the logistics resources of a workshop are limited and cannot cover all the materials. Therefore, evaluation indexes with different weights are set to screen key materials, and the economy of the materials, the volume of the materials and the modules where the materials are located are comprehensively considered.
S14: taking the station requirements into consideration, and carrying out batch staggered-time distribution;
considering that modules of materials required by stations are approximately the same and only have difference in types, describing the sequence and emergency degree of the materials required by the stations by using a time window, assigning an initial value for each transport vehicle by combining the distribution serial numbers of the modules where the materials are located, searching the next more emergency station or the more emergency material for distribution, achieving a plurality of optimal paths with the shortest indexes, and completing tasks by a plurality of distribution sub-paths together; the metrics include range, latency, and penalty times.
S2: establishing a mathematical model by taking the shortest completion time as an optimization target;
the material distribution problem of the workshop means that the material transportation trolley obtains the planning of the traveling route of the materials from the distribution center to each required station from the distribution center, so that the vehicle can distribute different materials to different stations according to the paths meeting the requirements and return to the distribution center, the purpose is to meet the station requirements while achieving the distribution target, and simultaneously meet certain condition constraints.
The specific implementation process of establishing the mathematical model by taking the shortest completion time as the optimization target is as follows:
the following conditions are satisfied in multi-station and multi-material distribution of a discrete workshop:
1. when all transport vehicles start from a material supermarket, the actual carrying capacity cannot exceed the maximum carrying capacity;
2. the distance from the material supermarket to any station and the distance from the station to the station are known;
3. during task distribution, the vehicle starts from the material supermarket to each station for material distribution, and when the vehicle returns to the material supermarket again, a material distribution path is considered to be finished;
4. one transport vehicle can carry out one-time or multi-time distribution on one station or a plurality of stations;
5. the time of awakening, waiting and the like of the transport vehicle is ignored, the running speed is constant, and the conditions of collision and the like are not considered;
parameters are as follows:
c is the maximum completion time; i/j is the number of the served work station; k is the transport vehicle number; t is t i The time of the transport vehicle reaching the station; w is a i The wait time necessary to service workstation i; s i Service time at station i; d ij The distance between the station i and the station j is calculated; v is the average speed of the vehicle;
model:
the objective function is as follows:
wherein:
∑d ij ≤L (8)；
meanwhile, according to the actual condition of the delivered materials, the model is subjected to partial simplification treatment as follows:
equation (1) is the objective function that minimizes the time to complete the delivery task. The method mainly comprises the vehicle running time, the waiting time in a material supermarket, the service time in a work station and the punishment time exceeding a set time window. Wherein α and β in formula (2) are penalty coefficients of arrival before and after the time window, respectively. Equation (3) ensures that the number of arriving vehicles and departing vehicles at each station is consistent. Equation (4) is the limit of the workstation time window. Formula (5) is that the distribution of all materials needs to be completed within a certain time. Equation (6) is the constraint on the service time of the vehicle from station i to station j. Equation (7) ensures that the vehicle load is within its maximum load. Equation (8) ensures the vehicle running state without exceeding the maximum running distance of the vehicle.
S3: and solving the mathematical model by adopting a genetic algorithm according to the constraint conditions.
The step S3 further comprises the steps of:
s31: coding and decoding the mathematical model by adopting a multilayer coding mode based on natural numbers;
s32: taking the reciprocal of the total completion time of the distribution scheme represented by each chromosome as the fitness value of the genetic algorithm;
s33: randomly generating an initial population, and selecting the initial population by adopting a selection method combining a roulette method and an elite retention strategy;
s34: carrying out crossover operation by adopting a consistent crossover operator and carrying out mutation operation by randomly exchanging gene positions;
s35: and (4) eliminating individuals with low fitness, and obtaining an optimal solution through multiple rounds of evolution.
While the present invention has been described in detail and with reference to the embodiments thereof as shown in the accompanying drawings, it will be apparent to one skilled in the art that various changes and modifications can be made therein. Therefore, certain details of the embodiments are not to be interpreted as limiting, and the scope of the invention is to be determined by the appended claims.
Claims (7)
1. A multi-station multi-material task allocation and path planning method based on a genetic algorithm comprises the following steps:
s1, a pretreatment step of material distribution;
the step S1 further comprises the steps of:
s11: modular design;
s12: clustering materials;
s13: screening key materials;
s14: taking the station requirements into consideration, and carrying out batch staggered-time distribution;
s2: establishing a mathematical model by taking the shortest completion time as an optimization target;
s3: and solving the mathematical model by adopting a genetic algorithm according to the constraint conditions.
2. The method for multi-station and multi-material task allocation and path planning based on genetic algorithm as claimed in claim 1, wherein in the step S11: according to different requirements and common requirements of different customers, the work of the whole mechanical equipment is divided into four modules during design work: the system comprises a basic module, an optional module, a customization module and an auxiliary module.
3. The multi-station and multi-material task allocation and path planning method based on genetic algorithm according to claim 2, wherein in the step S12: according to the fixed assembly process of mechanical equipment, the characteristics that time windows of stations for the material demands are different and the material demands of each stage are fixed are used as main classification indexes, the materials are classified into four categories according to different time periods, and the categories comprise: basic, matching, customizing and assisting.
4. The method for multi-station and multi-material task allocation and path planning based on genetic algorithm as claimed in claim 3, wherein in the step S13: and setting evaluation indexes with different weights to screen key materials, and comprehensively considering the economy of the materials, the volume of the materials and the module in which the materials are positioned.
5. The multi-station and multi-material task allocation and path planning method based on genetic algorithm according to claim 4, wherein in the step S14: describing the sequence and the emergency degree of the materials required by the stations by using the time window, assigning an initial value to each transport vehicle by combining the distribution serial number of the module where the materials are located, searching the next more emergency station or the more emergency material for distribution, achieving the optimal path with the shortest indexes, and completing tasks by a plurality of distribution sub-paths together; the metrics include range, latency, and penalty times.
6. The multi-station multi-material task allocation and path planning method based on the genetic algorithm according to claim 5, wherein in the step S2: and adopting partial simplification treatment on the mathematical model according to the actual condition of the delivered materials.
7. The method for multi-station and multi-material task allocation and path planning based on genetic algorithm according to claim 6, wherein the S3 step further comprises the steps of:
s31: encoding and decoding the mathematical model by adopting a multilayer encoding mode based on natural numbers;
s32: taking the reciprocal of the total completion time of the distribution scheme represented by each chromosome as a fitness value of the genetic algorithm;
s33: randomly generating an initial population, and selecting the initial population by adopting a selection method combining a roulette method and an elite retention strategy;
s34: carrying out crossover operation and mutation operation by randomly exchanging gene positions by adopting a consistent crossover operator;
s35: and (4) eliminating individuals with low fitness, and obtaining an optimal solution through multiple rounds of evolution.
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| CN116167541A (en) * | 2023-04-19 | 2023-05-26 | 南京邮电大学 | Path planning method based on self-adaptive distribution strategy under emergency condition |
| CN116167541B (en) * | 2023-04-19 | 2023-09-29 | 南京邮电大学 | Path planning method based on self-adaptive distribution strategy under emergency condition |
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