CN115456268A - Guide roller manufacturing resource optimal allocation method, device, equipment and medium - Google Patents
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Abstract
The application discloses a method, a device, equipment and a medium for optimally configuring manufacturing resources of a guide roller, wherein the method comprises the following steps: acquiring guide roller manufacturing task information and guide roller manufacturing resource information; establishing a guide roller cooperative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information, and taking the minimum processing time and the production cost as objective functions; determining manufacturing constraints of the collaborative manufacturing chain model; and calculating the optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution. According to the method, the guide roller cooperative manufacturing chain model is established according to the actual guide roller production manufacturing condition, the mathematical model is used for describing the guide roller cooperative manufacturing resource allocation problem, so that the mathematical model is more in line with the production practice, the optimal solution is calculated by using the preset optimal allocation algorithm, the formulating speed of the allocation strategy is improved, and the formulating of the allocation scheme is more efficient.
Description
Technical Field
The invention relates to the technical field of printing machine part manufacturing, in particular to a method and a device for optimally configuring manufacturing resources of a guide roller, electronic equipment and a computer-readable storage medium.
Background
The printing industry is used as an important component of the solid economy in China, the intelligent process of the printing industry is promoted, and the intelligent printing industry has important significance for accelerating the construction of the printing strong country and leading the high-quality development of the industry. The printing machine is complex in structure, precise in manufacturing and high in installation difficulty, the guide roller is used as one of key parts of the printing machine, the processing quality requirement is extremely high, in order to guarantee the processing efficiency and quality stability of the guide roller and consider the processing economy, the guide roller manufacturing resources need to be integrated into the product period for organization and management, and the manufacturing resources are reasonably and optimally configured.
The optimized configuration of the manufacturing resources of the guide roll is similar to the scheduling problem of a mixed flow shop, for the NP (Non-deterministic Polynomial) problem, at present, a heuristic algorithm and a meta-heuristic algorithm are mostly adopted for solving, for example, an ILS algorithm is improved, global search and local search are balanced by introducing a taboo table, and three targets of shop energy consumption, completion time and machine load are optimized; as proposed by scholars, the IG algorithm assigns workpieces to the best positions for each factory by performing a plurality of insert operations. However, the existing research mostly focuses on theory and application under a specific background, and a model and an algorithm constructed in the prior art are not suitable for an actual manufacturing workshop of the guide roller, so that the manufacturing and production of the guide roller cannot be effectively optimized.
Therefore, it is necessary to establish a guide roller collaborative manufacturing chain model more fitting the reality according to the real situation of a printing machinery guide roller manufacturing workshop, and to find the optimal solution of the model by adopting an intelligent optimization algorithm, so as to realize the collaborative optimization of the guide roller production efficiency and the production cost.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a device and a medium for optimizing and allocating manufacturing resources of a guide roller, so as to solve the problem that the existing method for optimizing manufacturing resources of a guide roller of a printing machine cannot meet the actual situation of an actual guide roller manufacturing workshop, and cannot effectively optimize manufacturing and production of the guide roller.
In order to solve the above problems, the present invention provides a method for optimally allocating manufacturing resources of a guide roll, comprising:
acquiring guide roll manufacturing task information and guide roll manufacturing resource information;
establishing a guide roller cooperative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information so as to minimize the processing time and the production cost as an objective function of the cooperative manufacturing chain model;
determining manufacturing constraints of the collaborative manufacturing chain model;
and calculating the optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution.
Further, the guide roll manufacturing task information comprises a set of process-level collaborative manufacturing subtasks with time sequence; the guide roll manufacturing resource information includes a set of manufacturing resources for a guide roll manufacturing plant.
Further, in the objective function of the collaborative manufacturing chain model, the minimizing the processing time includes minimizing a maximum completion time of the processing of the workpiece; minimizing production costs includes minimizing the total cost of workpiece processing;
wherein the maximum finishing time of the workpiece processing is the processing finishing time of the last manufacturing subtask arranged in time sequence; the total cost of the workpiece processing includes manufacturing resource processing costs and workpiece warehousing and transportation costs during the manufacturing process.
Further, the manufacturing constraints include: guide roll processing technology constraints, manufacturing processing time constraints, and manufacturing resource status constraints.
Further, calculating an optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, including:
generating an initial solution set of the integrated scheduling model under the constraint condition;
calculating the fitness of the initial solution set, and determining an optimal individual and a worst individual according to the fitness;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set;
and determining the optimal individual of the candidate solution set by using a preset local search method, and taking the optimal individual as the optimal solution of the objective function under the manufacturing constraint condition when a preset termination condition is met.
Further, calculating the fitness of the initial solution set, and determining the best individual and the worst individual according to the fitness, including:
calculating the non-dominant grade and the crowdedness degree of the initial solution set;
the higher the non-dominance level is, the better the individual is; individuals are more preferred when they have the same level of non-dominance, with higher crowdedness.
Further, the preset iteration rule is as follows: carrying out exchange transformation and shift transformation on individuals in the population to obtain transformed individuals;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set, wherein the candidate solution set comprises:
updating the transformed individuals according to the optimal individuals, the worst individuals and a Jaya algorithm iterative formula to obtain updated individuals;
judging whether the updated individual is superior to the transformed individual before updating or not according to the fitness of the updated individual; if yes, taking the updated individual as a reserved individual; if not, taking the transformed individual as a reserved individual;
and obtaining a candidate solution set according to the reserved individuals.
The invention also provides a guide roll manufacturing resource optimal configuration device, which comprises:
the information acquisition module is used for acquiring the guide roller manufacturing task information and the guide roller manufacturing resource information;
the target establishing module is used for establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information so as to minimize the processing time and the production cost as an objective function of the collaborative manufacturing chain model;
a constraint condition determination module for determining manufacturing constraint conditions of the collaborative manufacturing chain model;
and the configuration scheme output module is used for calculating the optimal solution of the target function under the manufacturing constraint by using a preset optimal configuration algorithm and obtaining a resource optimal configuration scheme manufactured by the guide roll according to the optimal solution.
The invention further provides an electronic device, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the method for optimally configuring the manufacturing resources of the guide roller is realized.
The invention further provides a computer-readable storage medium, which stores computer instructions, and the instructions are executed by a processor to implement a method for optimally configuring manufacturing resources of guide rollers according to any one of the above technical solutions.
Compared with the prior art, the invention has the beneficial effects that: firstly, acquiring guide roller manufacturing task information and guide roller manufacturing resource information; secondly, establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information, and determining an objective function and a manufacturing constraint condition of the model; and finally, calculating an optimal solution of the objective function under the manufacturing constraint part by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution. The invention aims at the processing process of the guide roller of the printing machine, researches the collaborative manufacturing resource allocation problem of the guide roller production workshop, establishes a guide roller collaborative manufacturing chain model which is more suitable for the actual production situation, summarizes the manufacturing resource allocation problem of the guide roller production workshop into a multi-objective optimization problem, designs an optimization allocation algorithm to solve the model problem, can quickly solve the collaborative manufacturing chain model, improves the formulation speed of the allocation strategy, and enables the formulation of the allocation scheme to be more efficient.
Drawings
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for optimally allocating manufacturing resources of a guide roll according to the present invention;
FIG. 2 is a schematic view of an embodiment of a guide roll co-manufacturing chain model provided by the present invention;
FIG. 3 is a diagram illustrating an embodiment of individual crowdedness according to the present invention;
FIG. 4 is a transformation diagram of an embodiment of a switch transformation provided by the present invention;
FIG. 5 is a transformation diagram of an embodiment of a shift transformation provided by the present invention;
FIG. 6 is a schematic diagram of an alternative embodiment of an insertion operation provided by the present invention;
FIG. 7 is a transformation diagram of one embodiment of a symmetric transformation operation provided by the present invention;
FIG. 8 is a schematic flow chart diagram of one embodiment of the hybrid Jaya algorithm provided by the present invention;
FIG. 9 is a schematic view of an embodiment of a guide roll line layout according to the present invention;
FIG. 10 (a) shows a schematic view of a guide roll manufacturing resource configured in accordance with a conventional mode for processing completion time Gantt of an embodiment of the present invention;
FIG. 10 (b) shows a Gantt chart of the processing completion time of one embodiment of the guide roll manufacturing resource configuration provided by the present invention according to the GA algorithm;
FIG. 10 (c) is a plot of the time Gantt of process completion for one embodiment of a guide roll manufacturing resource configuration provided by the present invention in accordance with the hybrid Jaya algorithm;
FIG. 11 is a schematic structural diagram illustrating an embodiment of an apparatus for optimally allocating manufacturing resources of a guide roll according to the present invention;
fig. 12 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the invention, taken in conjunction with the accompanying drawings, which form a part hereof, illustrate, and together with the embodiments of the invention, the principles of the invention and are not intended to limit the scope of the invention.
Before describing the embodiments, the actual conditions of the production workshop of the guide roll of the current printing machine are described.
The finished product guide roller needs to sequentially pass through the processes of blanking, rough turning, semi-finish turning, hot charging, finish turning, oxidation, dynamic balance and the like, wherein the rough turning comprises turning a steel shaft, turning an aluminum plug and turning a roller. For the coordinated manufacture of the guide rolls, the manufacturing sequence of the guide rolls to be processed and the allocation of the processing equipment between the processing subtasks need to be considered at the same time.
According to the invention, according to the actual guide roller workshop resources and task conditions, with the goals of minimizing the processing time and minimizing the production cost, a collaborative manufacturing chain model of the guide roller manufacturing resources and the manufacturing tasks is constructed, the guide roller manufacturing resource allocation is summarized into a multi-objective optimization problem, and an optimization allocation algorithm is designed, the model is solved under the constraint condition of actual production, the optimal allocation of the guide roller workshop manufacturing resources is obtained, and the collaborative optimization of the production efficiency and the production cost is achieved.
An embodiment of the present invention provides a method for optimally configuring manufacturing resources of a guide roller, and fig. 1 is a flowchart illustrating an embodiment of the method for optimally configuring manufacturing resources of the guide roller, which includes:
step S101: acquiring guide roll manufacturing task information and guide roll manufacturing resource information;
step S102: establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information so as to minimize the processing time and the production cost as an objective function of the collaborative manufacturing chain model;
step S103: determining manufacturing constraints of the collaborative manufacturing chain model;
step S104: and calculating an optimal solution of the objective function under the manufacturing constraint piece by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution.
According to the method for optimally configuring the manufacturing resources of the guide roll, firstly, manufacturing task information and manufacturing resource information of the guide roll are obtained; secondly, establishing a guide roller cooperative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information, and determining a target function and a manufacturing constraint condition of the model; and finally, calculating the optimal solution of the objective function under the manufacturing constraint part by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution. The embodiment is oriented to the processing process of the guide roller of the printing machine, researches the collaborative manufacturing resource allocation problem of the guide roller production workshop, establishes a guide roller collaborative manufacturing chain model which is more suitable for the actual production situation, resolves the guide roller production workshop manufacturing resource allocation problem into a multi-objective optimization problem, and designs an optimized allocation algorithm to solve the model problem, so that the collaborative manufacturing chain model can be rapidly solved, the establishment speed of the allocation strategy is improved, and the establishment of the allocation scheme is more efficient.
As a preferred embodiment, in step S101, the guide roll manufacturing task information includes a set of process-level co-manufacturing subtasks having a time sequence; the guide roll manufacturing resource information includes a set of manufacturing resources for a guide roll manufacturing plant.
As a specific example, the guide roll manufacturing task is decomposed into sequential process-level collaborative manufacturing subtasks, and the set of all manufacturing subtasks can be expressed as: MS = { MS 1 ，MS 2 ，…，MS i ，…，MS n MS in the formula i For the ith co-manufacturing subtask, n is the total number of manufacturing subtasks.
The guide roll manufacturing resources comprise equipment such as a single-tool-rest turning center, a double-end-face numerical control lathe and a dynamic balancing machine, and all manufacturing resources of a guide roll production workshop are integrated into a collaborative manufacturing resource set: MR = { MR = { [ MR ] 1 ，MR 2 ，…，MR j ，…，MR k In the formula, MR j For the jth co-manufactured resource, k is the total number of manufactured resources.
And establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information as shown in FIG. 2.
As a preferred embodiment, in step S102, in the objective function of the collaborative manufacturing chain model, the minimizing the processing time includes minimizing a maximum completion time of the processing of the workpiece; the minimizing production costs includes minimizing the total cost of workpiece processing;
wherein the maximum finishing time of the workpiece processing is the processing finishing time of the last manufacturing subtask arranged in time sequence; the total cost of the workpiece processing includes manufacturing resource processing costs and workpiece warehousing and transportation costs during the manufacturing process.
As a specific example, with the goal of minimizing all guide roll processing time and production costs, the objective function for modeling the guide roll co-manufacturing chain can be expressed mathematically as:
f＝{min T max ,min C ost } (1)
T max ＝FT nj (2)
equation (1) represents minimizing the maximum completion time and minimizing the processing cost, wherein T max Represents the maximum completion time of workpiece machining, C ost Represents the total cost of workpiece processing;
the formula (2) represents that the maximum finishing time of the workpiece processing is the processing finishing time of the last manufacturing subtask; wherein FT ij Representing the processing end time of the ith collaborative manufacturing subtask on the manufacturing resource j, wherein n represents the total number of collaborative manufacturing subtasks;
formula (3) represents that the total processing cost of the guide roll workpiece is the sum of the processing cost of the device and the storage and transportation cost, wherein the processing cost of the manufacturing resource is the product of the processing time and the processing cost of the manufacturing resource in unit time, and the storage and transportation cost is the product of the storage and transportation time and the storage and transportation cost in unit time; c m Represents the manufacturing resource processing cost, C r Representing the storage and transportation cost of the workpieces in the manufacturing process; y is ij Indicates that the ith manufacturing sub-task is on the th1 when j manufacturing resources are processed, or 0; t is t ij Representing a processing time of the ith collaborative manufacturing task on manufacturing resource j; f ij Representing the processing cost of the ith collaborative manufacturing task in the manufacturing resource j; l is a radical of an alcohol ij The storage and transportation time (including the transportation and waiting time of workpieces) of the ith collaborative manufacturing task on the manufacturing resource j is shown; l ij Table i workpiece cost per unit time for the ith co-manufacturing task on manufacturing resource j.
As a preferred embodiment, in step S103, the manufacturing constraints include: guide roll processing technology constraints, manufacturing processing time constraints, and manufacturing resource status constraints.
As a specific example, the constraint is expressed mathematically as follows:
1. the guide roller processing technology constraint comprises the following steps:
the formula (4) shows that all guide rollers to be processed need to pass through all manufacturing tasks, and each manufacturing task can be processed on one manufacturing resource; wherein Y is ij ∈{0,1}，Y ij Indicating that the ith manufacturing sub-task is 1 when processed on the jth manufacturing resource, otherwise is 0; n represents the total number of collaborative manufacturing subtasks; k represents the total number of manufacturing resources.
2. The manufacturing process time constraints include:
the equation (5) represents the relation between the processing start time and the processing end time of the guide roller, namely the continuity of the processing of the workpiece;
the formula (6) represents the difference between the preparation time of the guide roll and the processing time of the manufacturing resource; t is t ij Representing the processing time of the ith collaborative manufacturing task on manufacturing resource j; t is i Indicating the processing completion time of the ith co-manufacturing subtask;
the formula (7) shows that any guide roller to be machined is released at zero time to wait for machining; f ij Represents the processing cost per unit time for the ith co-manufacturing task to process resource j.
3. The manufacturing constraints include:
equation (8) represents the chain constraint of a manufacturing sub-job, i.e. the processing of a next sub-job can only be started when a certain sub-job of the guide rolls is completed.
As a preferred embodiment, in step S104, calculating an optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, includes:
generating an initial solution set of the integrated scheduling model under the constraint condition;
calculating the fitness of the initial solution set, and determining an optimal individual and a worst individual according to the fitness;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set;
and determining the optimal individual of the candidate solution set by using a preset local search method, and taking the optimal individual as the optimal solution of the objective function under the manufacturing constraint condition when a preset termination condition is met.
As a preferred embodiment, calculating the fitness of the initial solution set, and determining the optimal individual and the worst individual according to the fitness includes:
calculating the non-dominant grade and the crowdedness degree of the initial solution set;
the higher the non-dominance level is, the better the individual is; individuals are more preferred when they have the same level of non-dominance, with higher crowdedness.
As a preferred embodiment, the preset iteration rule is: carrying out exchange transformation and shift transformation on individuals in the population to obtain transformed individuals;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set, wherein the candidate solution set comprises:
updating the transformed individual according to the optimal individual, the worst individual and a Jaya algorithm iterative formula to obtain an updated individual;
judging whether the updated individual is superior to the transformed individual before updating or not according to the fitness of the updated individual; if yes, the updating individual is used as a reserved individual; if not, taking the transformed individual as a reserved individual;
and obtaining a candidate solution set according to the reserved individuals.
The above-mentioned optimal configuration algorithm is described in detail below with reference to specific embodiments.
The embodiment adopts an operation-based integer coding mode, and the specific rule is that the coding length is only related to the total number Z of workpieces to be processed and the number n of cooperative manufacturing subtasks, the coding length is Z x n, and the coding content is an integer value between 1,2,3,4,5 \8230andZ x n.
Taking the total number of the workpieces to be processed Z =4 and the number of the cooperative manufacturing subtasks n =3 as an example, the code and operation O can be obtained ji The correspondence between them is shown in table 1.
TABLE 1 coding base mapping table
In the decoding process, the manufacturing sequence of the guide rollers to be processed and the distribution of the processing devices among the subtasks are considered at the same time. In the embodiment, the guide roller is processed firstly in a certain subtask, and the processing is preferentially carried out in the next manufacturing subtask; prioritizing processing at the device that is first idle for a given subtask; and when a plurality of guide rollers complete a certain subtask simultaneously, a Random Rule (RR) is adopted to solve.
For the problem of too long waiting time of manufacturing resources, the present embodiment provides an insertion mechanism based on ascending order of idle time: if the processing time of a certain guide roller is shorter than the idle time of a certain manufacturing device, the guide roller is processed preferentially and is directly inserted into the idle time of the device to complete the processing.
When the population is initialized, in order to ensure the global diversity and the problem of optimized configuration of manufacturing resources of a guide roller production workshop, only the processing sequencing and the resource allocation need to be considered, so that the coding scheme uses a completely random generation strategy according to the coding length.
In order to effectively balance the characteristics between the processing efficiency and the processing cost, in the application, the individual fitness evaluation strategy is as follows: and (4) utilizing a mechanism of combining non-dominant level ordering and individual crowding degree by pareto optimization to select individuals. The specific rule is that,
min f(X 1 )＝min{f 1 (X 1 )，f 2 (X 1 )，…f n (X 1 )}，X 1 ∈M，
wherein f is n (X 1 ) Representing the nth objective function, X, with minimization as the target 1 One solution of the objective function is represented and M represents the solution set space. If there is another solution X 2 E.g. M, for
All satisfy f i (X 1 )≤f i (X 2 ) And is and
satisfy f i (X 1 )<f i (X 2 ) Then there is X 1 f X 2 I.e. X 1 Dominating X 2 (iv) resolving X 1 With a higher rating.
A higher ranked solution may be considered a more excellent solution; if two solutions have the same rank, the solution with the higher crowding distance may be considered superior to the other solution.
Wherein the degree of congestion D s The formula (c) is shown as follows:
in the formula (9), n is the number of objective functions, f i max And f i min Respectively the maximum value and the minimum value of the ith target function value in the population, f i (s + 1) and f i And (s-1) respectively obtaining the ith objective function value of two adjacent positions to be solved.
Fig. 3 is a schematic diagram of the crowdedness of individuals s in a certain boundary set. For the boundary solution (0 th and r-th individuals), the boundary distance is set to infinity.
In this embodiment, an iteration rule is preset, and the initial solution set is updated, specifically: for the population individual X k Two transformation factors are implemented, and the new individual after transformation and the original individual form an iterative candidate set ISet k X 'is obtained from the candidate set by a selection strategy for roulette' k Participate in Jaya iteration for producing a candidate solution of X' k The two transforms of (2) include a swap transform and a shift transform.
The switching transformation is specifically: two sequence points are randomly selected in a processing sequence, and their positions are exchanged to obtain a new processing sequence, as shown in fig. 4.
The shift transformation is specifically as follows: two sequence points are randomly selected in a processing sequence, the first sequence point is sequentially exchanged with the next sequence point until the position is exchanged with the randomly selected second sequence point, and a new sequence is obtained, as shown in fig. 5.
In the Jaya iteration process, the optimal solution is the solution with the highest non-dominant level, if the non-dominant levels of the solutions are the same, the optimal solution is the solution with a large crowdedness distance, and the selection of the worst solution is opposite to the selection of the solution. The Jaya algorithm iterative formula is:
A(i+1，j，k)＝A(i，j，k)+r(i，j，1)(A(i，j，b)-|A(i，j，k)|) -r(i，j，2)(A(i，j，b)-|A(i，j，w)|) (10)
wherein i, j and k respectively represent iterative algebra, individual variables and individuals in a population. For example, a (i, j, k) represents the jth variable of the kth individual in the population at the iteration round; a (i, j, b) and A (i, j, w) respectively represent the j (th) variable of the optimal individual and the worst individual in the population at the generation i; and r (i, j, 1) and r (i, j, 2) respectively control the scaling size and take values between [0,1 ]. And updating the individuals through a formula (10) to obtain updated individuals.
In order to further improve the searching performance of the algorithm, the embodiment adopts Variable Neighbor Search (VNS) to locally optimize the late excellent individuals, so as to improve the convergence rate of the hybrid algorithm and increase the probability of jumping out of the local optimum.
And for the neighborhood structure of the local search strategy, selecting an insertion operation and a symmetric transformation operation, wherein in the search process, the two mixed structures respectively have the probability of 50 percent to be selected to participate in iteration.
The specific rule of the insertion operation is as follows: one element is randomly selected to be inserted into the position of the beginning of the sequence, and other sub-batches are sequentially backed off, as shown in fig. 6.
The specific rule of the symmetric transformation operation is as follows: two sequence pairs with continuous positions are randomly generated in one sequence, sequence points in each sequence are reversed, and then the sequence pairs are exchanged for positions to obtain a new sequence, as shown in FIG. 7.
A complete flowchart of the above process is shown in fig. 8, and the specific steps include:
step 1: initializing the population and setting the maximum iteration times as termination conditions.
Step 2: finding X best And X worst (ii) a And calculating the non-dominant grade and the crowding degree of all individuals, and determining a relatively optimal solution and a worst solution.
And 3, step 3: implementing a transformation factor; for each individual X k Performing exchange and shift transformation, and forming a candidate iteration set ISet with the original individual k 。
And 4, step 4: selecting iterative candidate solution X' k (ii) a Selecting solutions in the iteration candidate set by a turn-by-turn gambling method, and determining candidate solutions X 'participating in the iteration' k 。
And 5: updating the population; and updating all candidate solutions according to an iterative formula.
Step 6: judging whether to replace or not; and calculating the fitness of the new solution, replacing the old solution if the fitness of the new solution is better than that of the old solution, and otherwise, keeping the old solution to enter the next iteration.
And 7: performing a local search; a variable neighborhood search was initiated on the first 15% of individuals in the population.
And 8: selecting an optimal solution; and updating the optimal individual according to the fitness evaluation rule.
And step 9: judging whether a termination condition is reached; if so, jump to step 9, otherwise jump back to step 3 and follow the subsequent loop.
Step 10: and outputting the result and ending.
In order to analyze and compare the optimization configuration method of the present application with a commonly used optimization algorithm, the present embodiment selects a common genetic algorithm and the optimization configuration algorithm of the present application (hereinafter, referred to as a hybrid Jaya algorithm for short) to perform simulation comparative analysis, which illustrates the practicability and superiority of the algorithm of the present application.
Taking an actual order of a certain printing machine guide roll production workshop as an example, research on guide roll-oriented collaborative manufacturing resource optimization configuration problem is carried out, and comparison is carried out with the current processing mode and other resource optimization configuration algorithms, and the guide roll production line layout is shown in fig. 9.
With 12 guide roll processing tasks in a certain time period as an example, a collaborative manufacturing chain formed by six procedures of rough turning, semi-finish turning, hot charging, finish turning, oxidation and dynamic balance in the guide roll manufacturing process is optimized, 12 guide roll blanks to be processed need to complete the above 6 manufacturing subtasks in sequence, wherein the manufacturing resource numbers of the rough turning, the semi-finish turning, the hot charging, the finish turning, the oxidation and the dynamic balance are respectively: 1. 2,3, 1,2, eachManufacturing Subtasks (MS) of workpieces i ) At different Manufacturing Resources (MR) i ) The processing time and the processing cost per unit time in (2) are shown in table 3, and the storage and transportation cost is 30 yuan per hour.
TABLE 2 manufacturing task of each workpiece different manufacturing resources processing time table (unit: min)
TABLE 3 MRi processing costs per manufacturing resource per unit time (unit: yuan/min)
| MR 1 | MR 2 | MR 3 | MR 4 | MR 5 | MR 6 |
| 2 | 3 | 2.5 | 1.5 | 1.5 | 5 |
| MR 7 | MR 8 | MR 9 | MR 10 | MR 11 | |
| 4.5 | 4 | 1 | 2 | 2.5 |
And (3) taking the parameter combination with the optimal performance of the mixed Jaya algorithm through a complete experimental design method: popsize was 100, gmax was 400. Meanwhile, model solution was performed using a hybrid Jaya algorithm and a Genetic Algorithm (GA) according to guide roll processing data shown in tables 2 and 3, respectively, and compared with a conventional production method, and the results are shown in fig. 10 (a), 10 (b), 10 (c), and 4. In FIGS. 10 (a), (b), and (c), a bar frame [ z MS ] is shown i ]The ith manufacturing subtask representing the z-th blank to be processed on the wire guide roll performs processing on the corresponding vertical coordinate manufacturing resource,
as can be seen from fig. 10 (a), 10 (b) and 10 (c), the idle waiting time of the manufacturing resources is too long due to the conventional processing mode, and compared with the length of the bar frame, it can be found that when parallel manufacturing resources exist, the conventional mode cannot select a better resource route, and the processing mode adopting the result of the mixed Jaya algorithm can well select the manufacturing resources, and the total completion time is shortened, thereby improving the production efficiency of the workshop.
The manufacturing cycle times and certification costs for the three processing schemes are compared by table 4: based on the processing mode of the mixed Jaya algorithm result provided by the application, the processing time is about 33 hours, the total processing cost is about 21000 yuan, and compared with 2176min required by the processing mode according to the genetic algorithm result, the total processing cost is 22836 yuan, the processing time can be saved by 9% and the processing cost can be saved by 7%. Under the condition of finishing the same workload, the traditional processing mode is adopted, the time is about 35 hours, the total processing cost is about 23000 yuan, the processing time can be saved by 4.6 percent by adopting the algorithm result mode provided by the invention for processing, and the processing cost can be greatly saved by 7.9 percent.
TABLE 4 comparison of the results of different mode processing of the guide rolls
| Mode of processing | Device processing cost | Cost of storage and transportation | Total time of completion | Total processing cost |
| Legacy mode | 13915 | 9101 | 2087 | 23016 |
| Genetic algorithm | 13084 | 9752 | 2176 | 22836 |
| Hybrid Jaya algorithm | 12600 | 8755 | 1995 | 21335 |
Therefore, the guide roller collaborative manufacturing resource optimization configuration scheme adopting the hybrid Jaya algorithm achieves collaborative optimization of manufacturing efficiency and manufacturing cost, and compared with other intelligent algorithms, the hybrid Jaya algorithm has more advantages in solving the problems.
The present invention also provides a guiding roller manufacturing resource optimal allocation device, a structural block diagram of which is shown in fig. 11, and the guiding roller manufacturing resource optimal allocation device 1100 includes:
an information acquisition module 1101 for acquiring guide roll manufacturing task information and guide roll manufacturing resource information;
a target establishing module 1102, configured to establish a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information, so as to minimize processing time and production cost as an objective function of the collaborative manufacturing chain model;
a constraint condition determining module 1103, configured to determine manufacturing constraint conditions of the collaborative manufacturing chain model;
and the configuration scheme output module 1104 is configured to calculate an optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, and obtain a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution.
As shown in fig. 12, the present invention further provides an electronic device 1200, which may be a mobile terminal, a desktop computer, a notebook, a palmtop computer, a server, or other computing devices. The electronic device includes a processor 1201, a memory 1202, and a display 1203.
The storage 1202 may be, in some embodiments, an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 1202 may also be an external storage device of the computer device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device. Further, memory 1202 may also include both internal storage units of a computer device and external storage devices. The memory 1202 is used for storing application software installed in the computer device and various data, such as program codes for installing the computer device. Memory 1202 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 1202 stores a guide roll manufacturing resource optimization configuration method program 1204, and the guide roll manufacturing resource optimization configuration method program 1204 can be executed by the processor 1201 to implement a guide roll manufacturing resource optimization configuration method according to various embodiments of the present invention.
The display 1203 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 1203 is used for displaying information on the computer device and for displaying a visual user interface. The components 1201-1203 of the computer device communicate with each other via a system bus.
The embodiment further provides a computer-readable storage medium, on which a program of a method for optimally allocating manufacturing resources of a guide roller is stored, and when the program is executed by a processor, the method for optimally allocating manufacturing resources of a guide roller according to any one of the above technical solutions is implemented.
According to the computer-readable storage medium and the computing device provided by the above embodiments of the present invention, the content specifically described for implementing the above-mentioned method for optimally configuring manufacturing resources of the guide roller according to the present invention can be referred to, and the method has similar beneficial effects to the above-mentioned method for optimally configuring manufacturing resources of the guide roller, and will not be described again here.
The invention discloses a method, a device, equipment and a medium for optimally configuring manufacturing resources of a guide roller, wherein firstly, manufacturing task information of the guide roller and manufacturing resource information of the guide roller are obtained; secondly, establishing a guide roller cooperative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information, and determining a target function and a manufacturing constraint condition of the model; and finally, calculating the optimal solution of the objective function under the manufacturing constraint part by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution.
The invention aims at the processing process of the guide roller of the printing machine, researches the collaborative manufacturing resource allocation problem of the guide roller production workshop, establishes a guide roller collaborative manufacturing chain model which is more suitable for the actual production situation, summarizes the manufacturing resource allocation problem of the guide roller production workshop into a multi-objective optimization problem, designs an optimized allocation algorithm to solve the model problem, can quickly solve the collaborative manufacturing chain model, improves the establishment speed of the allocation strategy, and enables the establishment of the allocation scheme to be more efficient.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention should be covered within the scope of the present invention.
Claims (10)
1. A guide roll manufacturing resource optimal allocation method is characterized by comprising the following steps:
acquiring guide roller manufacturing task information and guide roller manufacturing resource information;
establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information so as to minimize the processing time and the production cost as an objective function of the collaborative manufacturing chain model;
determining manufacturing constraints of the collaborative manufacturing chain model;
and calculating the optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm, and obtaining a resource optimal configuration scheme for manufacturing the guide roller according to the optimal solution.
2. The method for optimally allocating guide roll manufacturing resources as recited in claim 1, wherein said guide roll manufacturing task information comprises a set of process-level co-manufacturing subtasks having a time sequence; the guide roll manufacturing resource information includes a set of manufacturing resources for a guide roll manufacturing plant.
3. The method of claim 2, wherein the minimizing the processing time in the objective function of the collaborative manufacturing chain model comprises minimizing a maximum completion time of the processing of the workpiece; minimizing production costs includes minimizing the total cost of workpiece processing;
wherein the maximum finishing time of the workpiece processing is the processing finishing time of the last manufacturing subtask arranged in time sequence; the total cost of the workpiece processing includes manufacturing resource processing costs and workpiece storage and transportation costs during the manufacturing process.
4. The guide roll manufacturing resource optimal allocation method according to claim 2, wherein the manufacturing constraints include: guide roll processing technology constraints, manufacturing processing time constraints, and manufacturing resource status constraints.
5. The method of optimally allocating guide roll manufacturing resources as recited in claim 2, wherein calculating an optimal solution for said objective function under said manufacturing constraints using a predetermined optimal allocation algorithm comprises:
generating an initial solution set of the integrated scheduling model under the constraint condition;
calculating the fitness of the initial solution set, and determining an optimal individual and a worst individual according to the fitness;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set;
and determining the optimal individual of the candidate solution set by using a preset local search method, and taking the optimal individual as the optimal solution of the objective function under the manufacturing constraint when a preset termination condition is met.
6. The method for optimally configuring manufacturing resources of guide rollers according to claim 5, wherein the step of calculating the fitness of the initial solution set and determining the optimal individual and the worst individual according to the fitness comprises the following steps:
calculating the non-dominant grade and the crowdedness degree of the initial solution set;
the higher the non-dominance level is, the better the individual is; individuals are better when they have the same level of non-dominance, with higher crowdedness.
7. The method for optimally configuring manufacturing resources of guide rolls according to claim 5, wherein the preset iteration rule is as follows: carrying out exchange transformation and shift transformation on individuals in the population to obtain transformed individuals;
updating the initial solution set according to the optimal individual, the worst individual and a preset iteration rule to obtain a candidate solution set, wherein the candidate solution set comprises:
updating the transformed individual according to the optimal individual, the worst individual and a Jaya algorithm iterative formula to obtain an updated individual;
judging whether the updated individual is superior to the transformed individual before updating or not according to the fitness of the updated individual; if yes, taking the updated individual as a reserved individual; if not, taking the transformed individual as a reserved individual;
and obtaining a candidate solution set according to the reserved individuals.
8. A guide roll manufacturing resource optimal allocation device is characterized by comprising:
the information acquisition module is used for acquiring the guide roller manufacturing task information and the guide roller manufacturing resource information;
the target establishing module is used for establishing a guide roller collaborative manufacturing chain model according to the guide roller manufacturing task information and the guide roller manufacturing resource information so as to minimize the processing time and the production cost as an objective function of the collaborative manufacturing chain model;
a constraint determining module for determining manufacturing constraints of the collaborative manufacturing chain model;
and the configuration scheme output module is used for calculating the optimal solution of the objective function under the manufacturing constraint by using a preset optimal configuration algorithm and obtaining a resource optimal configuration scheme manufactured by the guide roller according to the optimal solution.
9. An electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements a method for optimally configuring manufacturing resources for a guide roll according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for execution by a processor of a method for optimally configuring a manufacturing resource for a guide roll according to any one of claims 1 to 7.
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