CN114881504A - Electronic precision part full-automatic flexible production line scheduling method based on order selection - Google Patents
Electronic precision part full-automatic flexible production line scheduling method based on order selection Download PDFInfo
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Abstract
The invention discloses a full-automatic flexible production line scheduling method for electronic precision parts based on order selection, and relates to the research field of flexible production scheduling. The invention provides an electronic precision piece full-automatic flexible production line scheduling method based on order selection, aiming at the defects of the existing electronic precision piece full-automatic flexible production line scheduling. The method firstly aims at the problems that the electronic precision part production line cannot correctly determine the scheduling order, inter-unit transportation interval constraint, equipment switching constraint and the like are considered, a scheduling method based on order selection is established by combining a grey correlation method and an improved competitive scheduling data group algorithm, an algorithm with fast convergence is designed, and the scheduling efficiency is improved.
Description
Technical Field
The invention relates to the research field of flexible production line scheduling, in particular to a full-automatic flexible production line scheduling method for selecting a scheduling order based on the order urgency degree.
Background
With the gradual focus of China on the manufacture of advanced, precise and top-end products, the demands of the market on the quantity and the types of complex and precise parts of electronic products are gradually improved, so that the products of complex and precise part manufacturing enterprises of electronic products are various in types and large in order quantity, and most enterprises plan flexible production lines to improve the production efficiency. However, in actual production, due to the large number of orders, planning personnel often incorrectly estimate the capacity of the production line when receiving the orders, thereby obtaining wrong scheduling. Meanwhile, the full-automatic flexible production line is divided into a plurality of production units, and each unit is internally provided with a plurality of parallel devices, so that the flexible processing characteristics of the devices are presented; the production units have buffer areas of products, and an AGV trolley is used for transporting semi-finished products; the product switching is completed by full automation of the equipment, so that the switching time of the equipment is different according to different product types connected in front and back, and the scheduling complexity and difficulty of the flexible production line are further increased.
At present, scheduling research aiming at an electronic precision flexible production line is less, related constraints such as orders, transportation and switching of the electronic precision full-automatic flexible production line are mostly not considered, the speed of a scheduling algorithm is not emphasized, and the practicability of a scheduling result is poor.
The invention provides an electronic precision piece full-automatic flexible production line scheduling method based on order selection.
Disclosure of Invention
The invention provides an electronic precision piece full-automatic flexible production line scheduling method based on order selection, aiming at the defects of the existing electronic precision piece full-automatic flexible production line scheduling. The method firstly designs an order emergency degree determining method and an improved competitive scheduling data group solving algorithm considering transportation and switching constraints aiming at the problems of emergency scheduling order selection, transportation interval, switching and the like in the full-automatic flexible production line scheduling of the electronic precision parts.
The technical scheme of the invention is a full-automatic flexible production line scheduling method of electronic precision parts based on order selection, which comprises the following steps:
step 1: establishing a full-automatic flexible production line scheduling model of the electronic precision part;
step 1.1: determining a constraint model;
according to a scene layout drawing and a processing information table of a full-automatic flexible production line of an electronic precision part, determining a mathematical model based on a scheduling problem of a part of flexible job workshops and constraint conditions based on inter-process transportation intervals and switching time, wherein the mathematical model comprises the processes of the production line, the number of the processes and the positions of the processes;
step 1.2: determining an optimization objective and an objective function;
maximizing the on-time delivery quantity of orders:
minimize order pull-off time:
wherein G is an order, G represents the total order amount, σ g For order priority representative values, CT g Actual delivery time for order g, DT g A scheduled delivery time for order g;
setting f 1 Setting f for a power factor value of the sum of values to which the priority of a completed order belongs 2 For the efficacy coefficient value of the sum of the pull-out periods of all orders, the calculation formula is as follows:
while setting the objective function as follows:
f＝λ 1 f 1 +λ 2 f 2
in the formula, theta min And theta max Are each set f 2 Unsatisfactory value and satisfactory value, lambda 1 、λ 2 A weight coefficient for each sub-target;
step 2: determining the urgency of the order;
step 2.1: determining an evaluation index and an ideal emergency order index;
the evaluation indexes of the order emergency degree are respectively as follows: order priority r 1 Remaining lead time r 2 Total time r required for order 3 (ii) a Wherein r is 1 The priority is reduced in turn by four grades of SSI, SI, I and N, r 2 For the difference between the scheduled delivery time and the scheduled time, r 3 The calculation formula is shown below;
in the formula t nkm Machining time of k process at equipment m for workpiece N, N g Is the maximum number of workpieces, K, of order g n The maximum number of processes of the workpiece n;
step 2.2: determining an index weight based on an analytic hierarchy process;
according to the analytic hierarchy process, consistency test is carried out on the maximum characteristic root of the judgment matrix X, and a weight vector Z Z is obtained after the test is passed 1 z 2 z 3 H, wherein the judgment matrix X is calculated as follows;
in the formula a ij Representing the importance of each index i to the index j;
step 2.3: determining the order priority of an ideal emergency order as SSI, the remaining delivery time as 1 unit time, the total man-hour required by the order as the total man-hour of full load of all equipment in 1 working day, and calculating the grey correlation degree of the order;
in the formula of j The degree of association, δ, of the j-th order representing a reference ideal order j ∈(0,1]The larger the value, the more urgent the order, k ij Is the value of the ith row and the j column in the grey correlation matrix;
and step 3: designing a solving step based on an improved competitive scheduling data group, taking the objective function of the step 1 as a fitness function, and introducing an order selection mechanism, transportation interval constraint and switching constraint of a flexible production line to solve;
step 3.1: carrying out order scheduling based on the order urgency degree;
selecting the emergency degree value greater than the set threshold value delta x The orders enter a dispatching queue and are dynamically adjusted until a dispatching result shows a set average Load rate and an average Load rate Load avg The calculation is as follows, and the execution flow is as shown in FIG. 3;
step 3.2: encoding and initializing scheduling data;
the scheduling data comprises procedure sequencing codes, workpiece numbers and equipment selection values; three-section coding is adopted: the first section is a procedure sequencing code, the second section is a workpiece number, and the third section is an equipment selection value; the procedure sequencing code is initialized to H random numbers randomly generated within the range of [0,100] and arranged according to the increasing sequence; the device selection value is initialized to M random integers generated within the range [0, maxMC ]; the workpiece number is initially a positive integer c with different values of H randomly generated in a range [1, H ], and is calculated according to the following formula;
wherein M is the total number of workpieces, H is the length of each segment code, and maxMC represents the maximum usable number of the working proceduresNumber of devices, Code n Coding the workpiece number;
step 3.3: a decoding method determined according to the transport interval and the workpiece switching;
during decoding, according to the workpiece number code of the second segment code and the equipment number corresponding to the equipment selection value of the third segment, processing events are sequentially arranged according to the occurrence sequence, the occurrence frequency of the same workpiece number represents the work number of the workpiece, and the formula of the equipment selection value corresponding to the equipment number is as follows;
wherein m represents the equipment number, Code machine Selecting a value for the device of the third section;
meanwhile, considering the transportation interval and the constraint of the workpieces, the following conditions are required to be met when the processing event is arranged;
ST n(u+1) ≥ET nu +YT u(u+1)
E my +s ijm ＝B m(y+1)
in the formula ST nu Represents the starting time, ET, of the machining of the workpiece n in the u unit nu Represents the end time of the machining of the workpiece n in the unit u, YT u(u+1) Representing the transit time, Gap, between unit u and unit u +1 avg Representing the transport interval of the AGV cars, B my End time of event No. y of device m, E my Start time, s, of event number y representing device m ijm Represents the switching time of different kinds of workpieces i and j on the equipment m;
step 3.4: calculating the fitness and executing competition;
obtaining the optimized target value in the step 1.2 according to the decoded scheduling data, calculating the fitness of the scheduling result according to the target function in the step 1.2, meanwhile, dividing the scheduling data into two groups, respectively taking one scheduling result from each group for comparison, determining the result with high fitness as a winner, reserving the winner to the next generation, executing the following updating formula on the first section of code and the third section of code of the loser, and executing the step 3.5;
X id ＝X id +V id
in the formula V id Update speed, L, of dimension d representing i group of losers id Position parameter, W, of dimension d representing i group of losers id Represents the d-th dimension position of the ith group of winners,
representing the average position of the d-th dimension of all scheduling data, wherein w is an inertia weight, R is a random number from 0 to 1, and C is a social learning factor;
step 3.5: the loser executes a simulated annealing probability receiving process;
if the fitness of the loser is higher than that of the previous generation, receiving scheduling data, otherwise, executing a probability receiving mechanism as shown in the following;
wherein e is a natural number, T is an annealing temperature, and T is max Is the maximum value of all generation temperatures, and p is the reception probability;
step 3.6: updating the annealing temperature and the inertia weight;
in the formula g best Fitness value, g, for the current generation of optimal scheduling results avg The average value of all the current scheduling result fitness is obtained, and (a) and (b) are the value ranges of the inertia weight;
step 3.7: a termination condition;
when the algorithm meets the iteration frequency condition, terminating the iteration;
N cur ≥N itmax
N cur for the current number of iterations, N max Is the maximum iteration number;
step 3.8, scheduling is executed;
and entering an execution stage after the construction of the scheduling model is finished, inputting a target value boundary, workpiece processing related information, equipment related information and the like, and outputting a scheduled Gantt chart.
Further, the grey correlation value of the dispatch order of step 2.3 with respect to the ideal emergency order is calculated in a specific manner:
hypothesis vector r 0 ＝(r 00 ,r 01 ...r 0R ) Is an ideal order factor vector, r i ＝(r i0 ,r i1 ...r iR ) For the factor vector of order i, i is 1,2,3., G, j represents the factor serial number in this section, j is 1,2,3., R, where R represents the maximum number of factors, then the original matrix is:
A＝[r ij ] G×R
(1) vector standardization processing to obtain a standardized matrix B ═ B ij ] G×R ；
(2) Combining the index weight proportion determined by the hierarchical analysis to obtain a weighted canonical matrix C ═ C ij ] G×R ；
c ij ＝z i ×b ij
(3) Matrix normalization, normalizing all values of the weighted normalized matrix to [0, 1%]In the interval of (1), wherein maxc ij And minc ij Respectively representing the maximum value and the minimum value of the j-th factor of the matrix C;
(4) solving the gray correlation matrix D ═ k ij ] G×R Wherein the original vector r 0j The vector normalization and the hierarchical weighting processing are also required to be carried out together with the factor vector, and become r 0j * ；
(5) Calculating the degree of correlation δ of the grays j ，δ j Degree of association, δ, of order j representing a reference ideal order j ∈(0,1]The larger the value, the more urgent the order;
the invention establishes a scheduling method based on order selection for the scheduling of the electronic precision full-automatic flexible production line, designs an algorithm with rapid convergence, and improves the scheduling efficiency.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention.
Fig. 2 is a scene schematic diagram of a full-automatic flexible production line of an electronic precision part.
FIG. 3 is a flow chart of selection based on order urgency.
Fig. 4 is a gantt chart of an exemplary scheduling result.
FIG. 5 is a flow chart of the method for solving the scheduling algorithm of the full-automatic flexible production line of the electronic precision part.
FIG. 6 is a final output Gantt chart of an exemplary input of the present invention.
Detailed Description
The following is a detailed description of the implementation routine of the present invention (fig. 1), and the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation and a specific operation procedure are given, but the scope of the present invention is not limited to the implementation routine described below.
The implementation routine can be mainly divided into the following steps:
step 1: establishing a full-automatic flexible production line scheduling model of the electronic precision part;
step 1.1: determining a constraint model;
the full-automatic digital production line of the electronic precision part consists of connected full-automatic processing equipment, the production line is divided into different production units due to the fact that the process and the processing sequence of the product are determined, the production units comprise a micro-gap welding unit, a thermo-sonic welding unit and an automatic pasting unit, each production unit comprises a plurality of automatic processing equipment connected through a conveying belt, semi-finished products among the units are conveyed by an AGV trolley, and the layout of a workshop is shown in fig. 2. The customer places a production task in the form of an order, and the order comprises task information, order priority and delivery time limit, wherein the task information comprises the type and the quantity of workpieces required to be processed by the order.
Therefore, the existing U production units of the full-automatic flexible production line are set to totally comprise M devices, the existing G orders comprise X-type workpieces, the processing needs to be carried out on the devices of the three production units in sequence, and each workpiece needs to go through K processes; the order priority is sequentially divided into SR, R and LR from emergency to mild, an order task table is shown in a table 1, a workpiece processing information table is shown in a table 2, wherein DT stands for residual delivery time, L stands for processing quantity, and P stands for priority;
TABLE 1 order task Table
Table 2 workpiece processing information table
Because the microwave assembly digital production line is produced by full-automatic equipment, the microwave assembly is precise and small in volume, fixed working hours are consumed when product types are switched, and the switching of the automatic equipment is determined according to the types of workpieces processed before and after, for example, the switching time is shown in an example table 3, wherein s is m,i,j Represents the time required for switching between the workpiece type i and the workpiece type j of the m-th apparatus.
TABLE 3 example table of switching times
The workpiece processing of the full-automatic flexible production line of the electronic precision part is set to have the following constraints:
(1) the starting time of the initial process is 0 time;
ST nk ＝0
(2) after the working procedure operation of the current unit is finished, the workpiece n goes to the next unit for processing, and the transportation interval time between the units needs to be waited;
ST n(u+1) ≥ET nu +YT u(u+1)
(3) when the equipment m switches the types of the workpieces to process, different switching time exists;
E my +s ijkm ≤B m(y+1)
(4) the start and end times of the process and the event start and end times of the equipment correspond to each other;
in the formula e nkmy For a decision variable whose value set is {0, 1}, Q is minus infinity.
Step 1.2: determining an optimization objective and an objective function;
the constraints of the electronic precision flexible production line order comprise two parts of priority and delivery time appointed by a client. Because the number of orders for the enterprise is large, order stalls often occur, and one of the optimization objectives should be to maximize the number of orders delivered on time, as shown below.
Secondly, some orders contain urgent finished product requirements, which do not allow for pull-out, orders with higher priority need to be guaranteed to be completed within the appointed time, and in order to combine order priority and minimize pull-out time, the following objectives are listed.
In the iterative process of the heuristic algorithm, the reasonable adaptive value can reflect the quality degree of the individual, and has positive leading effect on the population optimizing process. Setting f according to the improved power factor method 1 Setting f for a power factor value of the sum of values to which the priority of a completed order belongs 2 The efficiency coefficient value of the sum of the pull-out time lengths of all orders is calculated according to the following formula:
while setting the objective function as follows:
f＝λ 1 ×f 1 +λ 2 ×f 2
step 2: determining the urgency of the order;
step 2.1: determining an evaluation index and an ideal emergency order index;
the evaluation indexes of the order emergency degree are respectively as follows: order priority r 1 Remaining lead time r 2 Total time r required for order 3 . Wherein r is 1 The priority is reduced sequentially by four levels of SSI, SI, I and N, r 2 For the difference between the scheduled delivery time and the scheduled time, r 3 The calculation formula is shown below;
meanwhile, determining the order priority of an ideal emergency order as SSI, the remaining delivery time as 1 unit time, and the total working hours required by the order as the total working hours of full load of all equipment in 1 working day;
step 2.2: determining an index weight based on an analytic hierarchy process;
the gray correlation analysis method is widely used in defining the emergency degree of an order, but the order is provided with a priority factor P, the influence of the priority factor P on the emergency degree of the order is too large compared with other factors, and the weight proportion of each factor needs to be further considered when the gray correlation analysis method is applied, so the influence weight of each factor needs to be determined according to the analytic hierarchy process;
firstly, scoring each target according to the scale of an analytic hierarchy process, and establishing a judgment matrix X;
then, carrying out geometric averaging on each row vector of the matrix X by using a mean square root method, then normalizing to obtain each evaluation index weight and a characteristic vector Z, and calculating a formula of a maximum characteristic root as shown in the following;
and finally, carrying out consistency check on the maximum characteristic root of the judgment matrix, and obtaining a weight vector Z after the check is passed:
Z＝{z 1 z 2 z 3 }
step 2.3: calculating gray correlation degree delta of order j ；
The basic idea of determining the order urgency degree by using the gray correlation analysis method is to draw a series of factor values influencing the order urgency degree into a two-dimensional curve, and if the curves of the two orders are more similar, the gray correlation degree of the two orders is higher. If a series factor vector set of an optimal emergency order is assumed, the higher the relevance of the vector sets of the rest orders to the ideal set, the more emergency the order is represented. Hypothesis vector r 0 ＝(r 00 ,r 01 ...r 0R ) Is an ideal order factor vector, r i ＝(r i0 ,r i1 ...r iR ) For the factor vector of order i, i is 1,2,3., G, j represents the factor serial number in this section, j is 1,2,3., R, where R represents the maximum number of factors, then the original matrix is:
A＝[r ij ] G×R
(1) vector standardization processing to obtain a standardized matrix B ═ B ij ] G×R ；
(2) Combining the index weight proportion determined by the hierarchical analysis to obtain a weighted canonical matrix C ═ C ij ] G×R ；
c ij ＝z i ×b ij
(3) Matrix normalizationProcessing, normalizing all values of the weighted canonical matrix to [0, 1%]In the interval of (1), wherein maxc ij And minc ij Respectively representing the maximum value and the minimum value of the j-th factor of the matrix C;
(4) solving the gray correlation matrix D ═ k ij ] G×R Wherein the original vector r 0j The vector normalization and the hierarchical weighting processing are also required to be carried out together with the factor vector, and become r 0j * ；
(5) Calculating the degree of correlation δ of the grays j ，δ j The degree of association, δ, of the j-th order representing a reference ideal order j ∈(0,1]The larger the value, the more urgent the order;
and step 3: designing a solving step based on an improved competitive scheduling data group, taking the objective function of the step 1 as a fitness function, and introducing an order selection mechanism, transportation interval constraint and switching constraint of a flexible production line to solve;
step 3.1: scheduling order selection based on order urgency;
selecting a certain threshold value delta with the emergency degree value larger than the set value x And (4) entering the dispatching queue, and dynamically adjusting the dispatching queue until the dispatching result shows a relatively proper average load rate. Wherein the average Load rate Load avg The calculation is as follows, and the execution flow is as shown in FIG. 3;
step 3.2: coding and initializing;
designing a three-segment coding: the first section is a process sequence priority value, the second section is a workpiece number, and the third section is an equipment selection value. The process sequencing code is initialized to H random numbers randomly generated within the range [0,100] and arranged in increasing order. The device selection values are initialized to M random integers generated within the range 0, maxMC. The workpiece number is initially a positive integer c with different values of H randomly generated in a range [1, H ], and is calculated according to the following formula;
example initialization certain initialization scheduling data are shown in table 4;
table 4 exemplary initial scheduling data
Step 3.3: decoding design considering transport interval and workpiece switching;
and during decoding, sequentially arranging the processing events according to the appearance sequence according to the workpiece number of the second segment code and the equipment number corresponding to the third segment equipment selection value. The frequency of the same workpiece number represents the work number of the workpiece, and the formula of the equipment number corresponding to the equipment selection value is as follows;
meanwhile, considering the transportation interval and the constraint of the workpieces, the following conditions are required to be met when the processing event is arranged;
ST n(u+1) ≥ET nu +YT u(u+1)
E my +s ijm ＝B m(y+1)
exemplary scheduling data decoding details, e.g., step 3.2, are shown in fig. 4;
step 3.4: calculating the fitness and executing competition;
and obtaining the optimized target value in the step 1.2 according to the decoded scheduling data, and calculating the fitness of the scheduling result according to the target function in the step 1.2. Meanwhile, the scheduling data are divided into two groups, each group takes one scheduling result for comparison, the scheduling result with high fitness is a winner, the winner is reserved to the next generation, the first section of coding and the third section of coding of a loser execute the following updating formula, and the step 3.5 is executed;
X id ＝X id +V id ×1
step 3.5: the loser executes a simulated annealing probability receiving process;
if the fitness of the loser is higher than that of the previous generation, receiving scheduling data, otherwise, executing a probability receiving mechanism as shown in the following;
wherein e is a natural number, T is an annealing temperature, and T is max Is the maximum value of all generations of temperature, and p is the probability of reception.
Step 3.6: updating annealing temperature and inertia weight parameters;
in the formula g best Fitness value, g, for the current generation of optimal scheduling results avg The value range (a, b) is the value range of the inertia weight.
Step 3.7: a termination condition;
and when the algorithm meets the iteration number condition, terminating the iteration.
N cur ≥N itmax
In the formula N cur For the current number of iterations, N max Is the maximum number of iterations.
Step 3.8, scheduling is executed;
and entering an execution stage after the construction of the scheduling model is finished, inputting a target value boundary, workpiece processing related information, equipment related information and the like, and outputting a scheduled Gantt chart. For example, the input task set shown in table 5, the final output data result is shown in table 6, and the gantt chart is shown in fig. 6;
TABLE 5 input task set
TABLE 6 data results of input task set
The invention establishes a scheduling method based on order selection for the scheduling of the electronic precision full-automatic flexible production line, designs an algorithm with rapid convergence, and improves the scheduling efficiency.
Claims (2)
1. A full-automatic flexible production line scheduling method of electronic precision parts based on order selection comprises the following steps:
step 1: establishing a full-automatic flexible production line scheduling model of the electronic precision part;
step 1.1: determining a constraint model;
according to a scene layout drawing and a processing information table of a full-automatic flexible production line of an electronic precision part, determining a mathematical model based on a scheduling problem of a part of flexible job workshops and constraint conditions based on inter-process transportation intervals and switching time, wherein the mathematical model comprises the processes of the production line, the number of the processes and the positions of the processes;
step 1.2: determining an optimization objective and an objective function;
maximizing the on-time delivery quantity of orders:
minimize order pull-off time:
wherein G is an order, G represents the total order amount, σ g For order priority representative values, CT g Actual delivery time for order g, DT g A scheduled delivery time for order g;
setting f 1 Setting f for a power factor value of the sum of values to which the priority of a completed order belongs 2 For the efficacy coefficient value of the sum of the pull-out periods of all orders, the calculation formula is as follows:
while setting the objective function as follows:
f＝λ 1 f 1 +λ 2 f 2
in the formula, theta min And theta max Are each set f 2 Unsatisfactory value and satisfactory value, lambda 1 、λ 2 A weight coefficient for each sub-target;
step 2: determining the urgency of the order;
step 2.1: determining an evaluation index and an ideal emergency order index;
the evaluation indexes of the order emergency degree are respectively as follows: order priority r 1 Remaining lead time r 2 Total time r required for order 3 (ii) a Wherein r is 1 The priority is reduced in turn by four grades of SSI, SI, I and N, r 2 For the difference between the scheduled delivery time and the scheduled time, r 3 The calculation formula is shown below;
in the formula t nkm Machining time of k process at equipment m for workpiece N, N g Is the maximum number of workpieces, K, of order g n The maximum number of processes of the workpiece n;
step 2.2: determining an index weight based on an analytic hierarchy process;
according to an analytic hierarchy process, consistency test is carried out on the maximum characteristic root of the judgment matrix X, and a weight vector ZZ ═ z is obtained after the test is passed 1 z 2 z 3 H, wherein the judgment matrix X is calculated as follows;
in the formula a ij Representing the importance of each index i to the index j;
step 2.3: determining the order priority of an ideal emergency order as SSI, the remaining delivery time as 1 unit time, the total man-hour required by the order as the total man-hour of full load of all equipment in 1 working day, and calculating the grey correlation degree of the order;
in the formula of j The degree of association, δ, of the j-th order representing a reference ideal order j ∈(0,1]The larger the value, the more urgent the order, k ij Is the value of the ith row and the j column in the grey correlation matrix;
and step 3: designing a solving step based on an improved competitive scheduling data group, taking the objective function of the step 1 as a fitness function, and introducing an order selection mechanism, transportation interval constraint and switching constraint of a flexible production line to solve;
step 3.1: carrying out order scheduling based on the order urgency degree;
selecting the emergency degree value greater than the set threshold value delta x The orders enter a dispatching queue and are dynamically adjusted until a dispatching result shows a set average Load rate and an average Load rate Load avg The calculation is as follows;
step 3.2: encoding and initializing scheduling data;
the scheduling data comprises procedure sequencing codes, workpiece numbers and equipment selection values; three-section coding is adopted: the first section is a procedure sequencing code, the second section is a workpiece number, and the third section is an equipment selection value; the procedure sequencing code is initialized to H random numbers randomly generated within the range of [0,100] and arranged according to the increasing sequence; the device selection value is initialized to M random integers generated within the range [0, maxMC ]; the workpiece number is initially a positive integer c with different values of H randomly generated in a range [1, H ], and is calculated according to the following formula;
wherein M is the total number of workpieces, H is the length of each segment of Code, and maxMC represents the maximum number of usable devices in the process, Code n Coding the workpiece number;
step 3.3: a decoding method determined according to the transport interval and the workpiece switching;
during decoding, according to the workpiece number code of the second segment code and the equipment number corresponding to the equipment selection value of the third segment, processing events are sequentially arranged according to the occurrence sequence, the occurrence frequency of the same workpiece number represents the work number of the workpiece, and the formula of the equipment selection value corresponding to the equipment number is as follows;
wherein m represents the equipment number, Code machine Selecting a value for the device of the third section;
meanwhile, considering the transportation interval and the constraint of the workpieces, the following conditions are required to be met when the processing event is arranged;
ST n(u+1) ≥ET nu +YT u(u+1)
E my +s ijm ＝B m(y+1)
in the formula ST nu Represents the starting time, ET, of the machining of the workpiece n in the u unit nu Represents the end time of the machining of the workpiece n in the unit u, YT u(u+1) Representing the transit time, Gap, between unit u and unit u +1 avg Representing the transport interval of the AGV cars, B my End time of event No. y of device m, E my Start time, s, of event number y representing device m ijm Represents the switching time of different kinds of workpieces i and j on the equipment m;
step 3.4: calculating the fitness and executing competition;
obtaining the optimized target value in the step 1.2 according to the decoded scheduling data, calculating the fitness of the scheduling result according to the target function in the step 1.2, meanwhile, dividing the scheduling data into two groups, respectively taking one scheduling result from each group for comparison, determining the result with high fitness as a winner, reserving the winner to the next generation, executing the following updating formula on the first section of code and the third section of code of the loser, and executing the step 3.5;
X id ＝X id +V id
in the formula V id Update speed, L, of dimension d representing i group of losers id Position parameter, W, of dimension d representing i group of losers id Represents the d-th dimension position of the ith group of winners,
representing the average position of the d-th dimension of all scheduling data, wherein w is an inertia weight, R is a random number from 0 to 1, and C is a social learning factor;
step 3.5: the loser executes a simulated annealing probability receiving flow;
if the fitness of the loser is higher than that of the previous generation, receiving scheduling data, otherwise, executing a probability receiving mechanism as shown in the following;
wherein e is a natural number, T is an annealing temperature, and T is max Is the maximum value of all generation temperatures, and p is the reception probability;
step 3.6: updating the annealing temperature and the inertia weight;
in the formula g best Fitness value, g, for the current generation of optimal scheduling results avg The average value of all the current scheduling result fitness is obtained, and (a) and (b) are the value ranges of the inertia weight;
step 3.7: a termination condition;
when the algorithm meets the iteration frequency condition, terminating the iteration;
N cur ≥N itmax
N cur for the current number of iterations, N max Is the maximum iteration number;
step 3.8, scheduling is executed;
and entering an execution stage after the construction of the scheduling model is finished, inputting a target value boundary, workpiece processing related information, equipment related information and the like, and outputting a scheduled Gantt chart.
2. The method for dispatching the electronic precision parts full-automatic flexible production line based on the order selection as claimed in claim 1, wherein the grey correlation value of the dispatching order of the step 2.3 with respect to the ideal emergency order is calculated by:
hypothesis vector r 0 ＝(r 00 ,r 01 ...r 0R ) Is an ideal order factor vector, r i ＝(r i0 ,r i1 ...r iR ) For the factor vector of order i, i is 1,2,3., G, j represents the factor serial number in this section, j is 1,2,3., R, where R represents the maximum number of factors, then the original matrix is:
A＝[r ij ] G×R
(1) vector standardization processing to obtain a standardized matrix B ═ B ij ] G×R ；
(2) Combining the index weight proportion determined by the hierarchical analysis to obtain a weighted canonical matrix C ═ C ij ] G×R ；
c ij ＝z i ×b ij
(3) Matrix normalization, normalizing all values of the weighted normalized matrix to [0, 1%]In the interval of (1), wherein maxc ij And minc ij Respectively representing the maximum value and the minimum value of the j-th factor of the matrix C;
(4) solving the gray correlation matrix D ═ k ij ] G×R Wherein the original vector r 0j The vector normalization and the hierarchical weighting processing are also required to be carried out together with the factor vector, and become r 0j * ；
(5) Calculating the degree of correlation δ of the grays j ，δ j The degree of association, δ, of the j-th order representing a reference ideal order j ∈(0,1]The larger the value, the more urgent the order;
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