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CN114881446B - A collaborative scheduling method for trial production and testing of high-end equipment considering process uncertainty - Google Patents

A collaborative scheduling method for trial production and testing of high-end equipment considering process uncertainty Download PDF

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CN114881446B
CN114881446B CN202210466556.4A CN202210466556A CN114881446B CN 114881446 B CN114881446 B CN 114881446B CN 202210466556 A CN202210466556 A CN 202210466556A CN 114881446 B CN114881446 B CN 114881446B
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裴军
陈翔
周娅
严平
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Hefei University of Technology
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Abstract

The invention provides a high-end equipment trial-production and test collaborative scheduling method considering process uncertainty, and relates to the technical field of task scheduling. The planned completion time of the trial contemplated by the present invention is a time interval, i.e., the process time is indeterminate. Under the condition of uncertain working procedure time, the high-end equipment trial production and test process are cooperatively scheduled, the process requirements of parts and the processing sequence of the parts are comprehensively considered, the approximate optimal solution of the problem is dynamically and accurately obtained, and under the condition of uncertain working procedure considered, the resources of the high-end equipment trial production and the high-end equipment in two stages are forcefully cooperatively optimized and configured, so that the investment cost of the trial production and the test process is furthest reduced, and the resource utilization efficiency and the cooperation efficiency of high-end equipment enterprises are improved.

Description

High-end equipment trial-production and test collaborative scheduling method considering process uncertainty
Technical Field
The invention relates to the technical field of task scheduling, in particular to a high-end equipment trial-production and test collaborative scheduling method and system considering process uncertainty.
Background
After the design of the high-end equipment is finished, a series of technical tests are required to check the structure, bearing capacity, safety and the like of the equipment, so that the high-end equipment can be formally put into use or mass production. Before performing the test, it is necessary to make a part of the sample according to the design scheme and then perform the test. In the trial production stage, different tasks to be produced are assigned, and after the production is completed, the trial is carried out according to the requirements of different trial parts. In the actual trial production and test process, the trial production and test execution of part parts are often carried out synchronously, and the test execution period is often difficult to accurately estimate due to the characteristics of complex structure, strong specialization and the like of high-end equipment.
The existing trial-and-test two-stage optimization problem is mainly solved by virtue of a simulation method, a heuristic method or an artificial intelligent algorithm, the considered constraint is mostly concentrated on the production time of parts or the sequence of test tasks, and less attention is paid to other conditions in the actual process, so that the trial-and-test process of high-end equipment cannot be accurately guided, and various resources are occupied or wasted.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a high-end equipment trial-production and test cooperative scheduling method and system considering the uncertainty of a procedure, and solves the technical problem of cooperative scheduling optimization of the high-end equipment trial-production and test process considering the situation of the uncertainty of the procedure.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a high-end equipment trial-production and test collaborative scheduling method considering process uncertainty, including:
S1, setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in a high-end equipment trial production process, factory data, task data in a test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2,…,cs } of each part, a first part machining period of the part, and a second part machining period of the part A subsequent processing period d i;
S2, acquiring a global optimal solution according to the input parameters and a mixed genetic algorithm based on neighborhood search, acquiring a part set distributed to each factory in a trial production stage according to the global optimal solution, processing sequence of the parts in the factory and execution sequence of subsequent test tasks, and assigning technicians to each test task.
Preferably, the obtaining a global optimal solution according to the input parameter and a hybrid genetic algorithm based on a neighborhood search includes:
S21, setting execution parameters of a hybrid genetic algorithm based on neighborhood search;
s22, generating an initial population, wherein individuals in the initial population are represented by two-stage codes PS-FS and TS-PN, decoding the two-stage codes to obtain an individual fitness value, and obtaining and recording a current optimal solution;
S23, executing selection operation by adopting a roulette mode based on the fitness value of each individual in the population;
S24, performing cross operation on the population individuals selected in the S23 based on the cross probability to obtain a new population with the same scale as the initial population;
S25, decoding individuals in the current population to obtain an adaptability value C max of each individual, marking the best individual as pi, selecting a neighborhood structure NS k based on a roulette mode, and generating a neighborhood solution pi' of pi according to the neighborhood structure NS k;
S26, carrying out local search on a solution pi ' by using a neighborhood structure NS K to obtain a local optimal solution pi ', comparing the local optimal solution pi ' with a variable neighborhood search algorithm optimal solution pi, if pi ' is superior to pi, letting pi=pi ', recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure, updating the selection probability of the neighborhood structure NS K, judging whether gen 2≤Gen2 is met, if so, assigning gen 2 +1 to gen 2, returning to the step S25, otherwise, executing the step S27;
s27, comparing the optimal solution pi of the variable neighborhood search algorithm with worst individuals in the initial population X 0, replacing the solution with pi if pi is better than the worst individuals in the worst initial population, comparing pi with a global optimal solution pi best, and letting pi best =pi if pi is better than pi best;
S28, judging whether the gen 1≤Gen1 is met, if yes, assigning gen 1 +1 to gen 1, taking the updated population as input of the next iteration, returning to the step S23, and if not, outputting a global optimal solution pi best.
Preferably, the generating an initial population, calculating an fitness value of each individual to obtain and record a current optimal solution, includes:
Based on a genetic and variable neighborhood search hybrid algorithm, parts and factory data in the high-end equipment trial process and task and technician data in the trial process are encoded to obtain an initial population X 0,X0 formed by PopSize initial solutions, each solution is formed by two sequence combinations of PS-FS and TS-PN, wherein the PS-FS encoding represents the corresponding relation and sequence of the parts and the factory in the trial stage, the TS-PN represents the assignment sequence of the trial task and the technician in the trial stage, each initial solution in the initial population is decoded to obtain an adaptation value C max of population individuals, the value represents the total time span of the trial and trial process, the best individual is recorded as a current global optimal solution pi best, the initial iteration number of the hybrid optimization algorithm is set to be g 1 =1, and the initial iteration number of the variable neighborhood search is set to be 2 =1.
Preferably, the hybrid genetic algorithm based on the neighborhood search encodes parts and factory data in the process of testing the high-end equipment and tasks and technician data in the process of testing, including:
based on the characteristics of two key stages of part trial production and combined test, the solution X consists of a solution X 1 of the trial production stage and a solution X 2 of the test stage, wherein the solutions of all stages are expressed by two-dimensional integer arrays, the solution X 1 comprises two parts, a part processing sequence PS and a factory list FS, and the length of the sequence or the list is The number in PS represents the number of parts, the number of times of appearance of the number represents the number of parts, FS represents the number of factories, each column in the array represents the parts with the corresponding number and is assigned to the corresponding factories for trial production, solution X 2 consists of a test execution sequence TS and a personnel list PN, the length of the sequence or list is k, TS and PN respectively consist of test tasks and technician numbers, and each column in the array represents the test tasks with the corresponding number and are executed by the corresponding technicians.
Preferably, the selecting operation is performed by adopting a roulette mode based on the fitness value of each individual in the population, and the selecting operation comprises the following steps:
S2301, decoding to obtain total span time of trial and test process of each initial solution, marking the value as f (X), namely making f (X) =C max, and calculating probability of each individual X i in initial population X 0 inheriting to next generation population
S2302, calculating cumulative probability of each individual in X 0
S2303, defining a variable Q, generating a random number in a (0, 1) interval, assigning the random number to the variable Q, judging the interval to which the variable Q belongs, and if Qx i-1＜Q＜Qxi, selecting an individual X i in an initial population X 0;
S2304, repeatedly executing the step S2303 until PopSize population individuals are selected.
Preferably, the method includes the steps of performing local search on a solution pi' by using a neighborhood structure NS K to obtain a local optimal solution pi ", comparing the local optimal solution pi" with a variable neighborhood search algorithm optimal solution pi, if pi "is better than pi, letting pi=pi", recording the success number of the neighborhood structure searching for the local optimal solution, otherwise, increasing the failure number of the neighborhood structure searching for the local optimal solution, and updating the selection probability of the neighborhood structure NS K, including:
S2601, a neighborhood structure NS K(K＝1,2,…,Kmax of a variable neighborhood search algorithm), and setting initial selection probability SP K of each neighborhood structure to be the same in the stage of setting algorithm parameters, wherein
S2602, in the local search process of the variable neighborhood search algorithm, recording the times S K of successful search of the local optimal solution and the times L K of failure of each neighborhood structure, and updating the selection probability of each neighborhood according to the following formula: Wherein SUC K represents the local search success rate of the neighborhood structure NS K, and the calculation formula is Delta is a sufficiently small positive number;
S2603, defining a variable Q ', generating a random number in the (0, 1) interval, assigning the random number to the variable Q ', judging the interval to which the variable Q ' belongs, and if SUC K-1＜Q'＜SUCK, selecting a neighborhood structure NS K as the type of neighborhood search.
In a second aspect, the present invention provides a high-end equipment trial and experiment co-scheduling system taking process uncertainty into account, comprising:
An input parameter acquisition module for setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in the high-end equipment trial production process, factory data, task data in the test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2,…,cs } of each part, a first part machining period of the part A subsequent processing period d i;
The solving module is used for obtaining a global optimal solution according to the input parameters and the mixed genetic algorithm based on the neighborhood search, obtaining a part set distributed to each factory in a trial production stage according to the global optimal solution, the processing sequence of the part in the factory and the execution sequence of the subsequent test tasks, and assigning technicians to each test task.
Preferably, the obtaining a global optimal solution according to the input parameter and a hybrid genetic algorithm based on a neighborhood search includes:
S21, setting execution parameters of a hybrid genetic algorithm based on neighborhood search;
s22, generating an initial population, wherein individuals in the initial population are represented by two-stage codes PS-FS and TS-PN, decoding the two-stage codes to obtain an individual fitness value, and obtaining and recording a current optimal solution;
S23, executing selection operation by adopting a roulette mode based on the fitness value of each individual in the population;
S24, performing cross operation on the population individuals selected in the S23 based on the cross probability to obtain a new population with the same scale as the initial population;
S25, decoding individuals in the current population to obtain an adaptability value C max of each individual, marking the best individual as pi, selecting a neighborhood structure NS k based on a roulette mode, and generating a neighborhood solution pi' of pi according to the neighborhood structure NS k;
S26, carrying out local search on a solution pi ' by using a neighborhood structure NS K to obtain a local optimal solution pi ', comparing the local optimal solution pi ' with a variable neighborhood search algorithm optimal solution pi, if pi ' is superior to pi, letting pi=pi ', recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure, updating the selection probability of the neighborhood structure NS K, judging whether gen 2≤Gen2 is met, if so, assigning gen 2 +1 to gen 2, returning to the step S25, otherwise, executing the step S27;
s27, comparing the optimal solution pi of the variable neighborhood search algorithm with worst individuals in the initial population X 0, replacing the solution with pi if pi is better than the worst individuals in the worst initial population, comparing pi with a global optimal solution pi best, and letting pi best =pi if pi is better than pi best;
S28, judging whether the gen 1≤Gen1 is met, if yes, assigning gen 1 +1 to gen 1, taking the updated population as input of the next iteration, returning to the step S23, and if not, outputting a global optimal solution pi best.
(III) beneficial effects
The invention provides a high-end equipment trial-production and test collaborative scheduling method and system considering process uncertainty. Compared with the prior art, the method has the following beneficial effects:
The invention considers that the planned completion time of the test is a time interval, i.e. the process time is uncertain. Under the condition of uncertain working procedure time, the high-end equipment trial production and test process is cooperatively scheduled, the part process requirements and the processing sequence of the parts are comprehensively considered, the approximate optimal solution of the problem is dynamically and accurately obtained, and the resources of the high-end equipment in two stages of trial production and test can be effectively cooperatively optimized and configured, so that the investment cost of the trial production and test process is reduced to the greatest extent, and the resource utilization efficiency and the cooperation efficiency of high-end equipment enterprises are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a high-end equipment trial and test co-scheduling method that accounts for process uncertainty in an embodiment of the present invention;
FIG. 2 is a flow chart of a high-end equipment trial and test co-scheduling method taking process uncertainty into account in an embodiment of the invention;
Fig. 3 is a schematic diagram of encoding in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the application realizes the collaborative optimization and configuration of resources at two stages of high-end equipment trial production and test by providing the high-end equipment trial production and test collaborative scheduling method and the system considering the process uncertainty, thereby improving the resource utilization efficiency and the collaborative efficiency of high-end equipment enterprises.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
the existing trial-and-test two-stage optimization problem is mainly solved by means of a simulation method, a heuristic method or an artificial intelligent algorithm, the process requirements in the actual process, the processing sequence of parts and the uncertainty of test time are ignored, and the trial production of high-end equipment and the test process cannot be accurately and efficiently guided, so that various resources are occupied or wasted. According to the embodiment of the invention, when the process time uncertainty is considered and the cooperative scheduling is carried out on the trial production and test process of the high-end equipment, the process requirements of the parts and the processing sequence of the parts are comprehensively considered, the approximate optimal solution of the problem is dynamically and accurately obtained, and the cooperative optimization and configuration can be carried out on the resources of the trial production and test of the high-end equipment, so that the investment cost of the trial production and test process is reduced to the greatest extent, and the resource utilization efficiency and the cooperative efficiency of high-end equipment enterprises are improved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a high-end equipment trial-production and test collaborative scheduling method considering process uncertainty, which is shown in fig. 1 and comprises the following steps:
S1, setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in a high-end equipment trial production process, factory data, task data in a test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2,…,cs } of each part, a first part machining period of the part, and a second part machining period of the part A subsequent processing period d i;
S2, acquiring a global optimal solution according to the input parameters and a mixed genetic algorithm based on neighborhood search, acquiring a part set distributed to each factory in a trial production stage according to the global optimal solution, processing sequence of the parts in the factory and execution sequence of subsequent test tasks, and assigning technicians to each test task.
The embodiment of the invention considers that the planned completion time of the test is a time interval, namely the working procedure time is uncertain. Under the condition of uncertain working procedure time, the high-end equipment trial production and test process is cooperatively scheduled, the part process requirements and the processing sequence of the parts are comprehensively considered, the approximate optimal solution of the problem is dynamically and accurately obtained, and the resources of the high-end equipment in two stages of trial production and test can be effectively cooperatively optimized and configured, so that the investment cost of the trial production and test process is reduced to the greatest extent, and the resource utilization efficiency and the cooperation efficiency of high-end equipment enterprises are improved.
The steps are described in detail below, as shown in fig. 2:
In step S1, input parameters of a hybrid genetic algorithm based on neighborhood search are set according to part data in the high-end equipment trial production process, factory data, task data in the test process and technician data in the test process. The specific implementation process is as follows:
Part data and factory data of the high-end equipment in the trial production stage are acquired through manual input and the like, test task data and research personnel data of the test stage are acquired, and input parameters of a genetic and variable neighborhood search hybrid algorithm are set based on the acquired data.
Input parameters of the hybrid genetic algorithm based on the neighborhood search comprise a high-end equipment part set J= {1,2, & gt, n }, a part manufacturing process C= { C 1,c2,…,cs }, s≤n, each part corresponds to one manufacturing process, and each process category C r(cr epsilon C) needs to be in a specific factoryThe manufacturing factory set of trial manufacturing stage M= {1, 2..once., M }, the number of each part required N i, the first part machining cycle of the workpieceThe method comprises the following steps of processing time d i in the same factory, test task set T= {1, 2..once, k } in a test production stage, planned completion time interval e t of a test, technician set P= {1, 2..once, l } in the test production stage, and working efficiency w p of the technician P, wherein 0<w p is less than or equal to 1.
In step S2, a global optimal solution is obtained according to the input parameters and the hybrid genetic algorithm based on the neighborhood search, a part set distributed to each factory in a trial production stage is obtained according to the global optimal solution, a processing sequence of the part in the factory, and an execution sequence of a subsequent test task, and technicians are assigned to each test task. The specific implementation process is as follows:
S21, setting execution parameters of a hybrid genetic algorithm based on neighborhood search. The method specifically comprises the following steps:
Maximum iteration number Gen 1 of the hybrid genetic algorithm based on neighborhood search;
population size PopSize of the algorithm;
Cross probability Pc;
Maximum number of iterations of local search Gen 2;
The initial selection probability SP K =1/6 for each neighborhood structure NS K in the neighborhood set { NS 1,NS2,…,NS6 }.
S22, generating an initial population, wherein individuals in the initial population are represented by two-stage codes PS-FS and TS-PN, decoding the two-stage codes to obtain the fitness value of the individuals, and obtaining and recording the current optimal solution. The method specifically comprises the following steps:
And (3) coding parts and factory data in the high-end equipment trial production process and task and technician data in the test process based on a genetic and variable neighborhood search hybrid algorithm to obtain an initial population X 0,X0 formed by PopSize initial solutions, wherein each solution in the initial population X 0,X0 is formed by combining two sequences of PS-FS and TS-PN, the PS-FS codes represent the corresponding relation and sequence of the parts and the factory in the trial production stage, and the TS-PN codes represent the assignment sequence of the test task and the technician in the test stage. Decoding each initial solution in the initial population to obtain an adaptability value C max of population individuals, wherein the adaptability value C max represents the total time span of trial production and test processes, marking the best individual as the current global optimal solution pi best, setting the initial iteration times gen 1 =1 of a hybrid optimization algorithm, and setting the initial iteration times gen 2 =1 of variable neighborhood search. The specific implementation process is as follows:
The encoding process is as follows:
S2201, based on features of two key stages of part trial production and combined test, a solution X consists of a solution X 1 of the trial production stage and a solution X 2 of the test stage, and solutions of all stages are expressed by two-dimensional integer arrays. Solution X 1 contains two parts, part-processing sequence PS and factory list FS, the length of the sequence or list being The numbers in PS represent the number of parts and the number of times the number appears represents the number of parts. FS represents the number of the factory and each column in the array represents the assignment of the corresponding numbered part to the corresponding factory for trial production. Solution X 2 consists of a trial execution order sequence TS and a person list PN, the length of the sequence or list being k. TS and PN are respectively composed of test tasks and technician numbers, and each column in the array represents the test task of the corresponding number to be executed by the corresponding technician. In particular, the order in TS is determined according to the following rule:
taking expected values according to planned completion time intervals of all test tasks And calculating the completion time of each test task to obtain the earliest start time ES k of each test, and then arranging the test tasks in sequence from small to large according to the ES k value. If the earliest starting time of the test tasks is the same, the part nesting time is calculated according to X 1, and the test tasks of the part nesting first are executed preferentially. The encoding scheme is shown in fig. 3.
It should be understood that the number of parts to be processed in the trial production stage is actually calculated according to the requirements of the subsequent tests on the types and the number of the parts and the multiplexing times of the parts, each test needs different parts to be combined, and in order to avoid resource waste, in the actual situation, the high-end equipment manufacturing enterprises can put the multiplexed parts into the execution process of the next test after completing a certain test. In addition, technicians in the test stage can choose to ensure the normal performance of the test, and the actual task is often larger than the number of people, which requires that the technicians wait or immediately throw in the next test process after completing a certain test, and according to the difference of the working efficiency of each technician, when a certain test task t is distributed to the technicians p, the actual completion time of the test is calculated according to the formula ofWherein the method comprises the steps ofRepresenting the expected value of the planned completion time of the test, w p represents the efficiency of the technician, typically 0<w p≤1.
The decoding process is as follows:
S2202, the decoding process is to calculate the obtained feasible solution according to the actual situation to obtain the total span time of the whole trial production and test process. The method mainly comprises the following operations:
S2202a, generating a part set M j distributed to the factory j for machining by the solution X 1. Let h=1, define the variable f h =0, representing the processing completion time of the h-th part in the sequence PS.
S2202b deriving from M j a set of preamble processes for the h-th part in the assigned plant
S2202c, calculating the latest completion time of the h part precursor processing setAnd judgeIf the h-th part is included, the step S2202d is executed, and if not, the step S2202e is executed.
S2202d、H+1 is assigned to h.
S2202e、H+1 is assigned to h.
S2202f, judgmentIf so, a variable t=1 is defined, and step S2202g is executed, otherwise, step S2202b is returned.
S2202g, assigning the completion time of the T-th test immediately preceding task set T t to a variable F t *, assigning the part alignment time to a variable C t, and assigning the latest completion time of the corresponding technician' S preceding test task in PN to a variable P t.
S2202h, definition variable S t,St represents the earliest start time of the t-th test, let S t＝max(Ft *,Ct,Pt).
S2202i, assigning t+1 to t. Whether t > k is true or not is determined, if yes, step S2202j is executed, and if not, the procedure returns to the previous step S2202h.
S2202j, selecting the maximum value according to the earliest starting time of each test task obtained in the steps S2202g to S2202i, and assigning the maximum value to S max.
S2202k, outputting C max according to the formula C max≥Smax+at, wherein a t is the actual completion time of the corresponding test task of S max.
S23, executing selection operation by adopting a roulette mode based on the fitness value of each individual in the population. The method specifically comprises the following steps:
S2301, decoding to obtain total span time of trial and test process of each initial solution, marking the value as f (X), namely making f (X) =C max, and calculating probability of each individual X i in initial population X 0 inheriting to next generation population
S2302, calculating cumulative probability of each individual in X 0
S2303, defining a variable Q, generating a random number in the (0, 1) interval, assigning the random number to the variable Q, judging the interval to which the variable Q belongs, and if Qx i-1＜Q＜Qxi, selecting an individual X i in the initial population X 0.
S2304, repeatedly executing the step S2303 until PopSize population individuals are selected.
S24, performing cross operation on the population individuals selected in the S23 based on the cross probability to obtain a new population with the same scale as the initial population. The specific implementation process is as follows:
The crossover operation is to reproduce and spread the excellent genes in the parent through crossover combination of two individuals, thereby generating new excellent individuals, and enabling the resulting solution to evolve toward a better direction. In order to maintain the population size, the crossover operation needs to be carried out PopSize/2 times, and each time of operation, whether random (1) > Pc is satisfied or not is judged, if so, the parents are crossed, and otherwise, the two parents are reselected. The specific steps of the crossover operation are described as:
Selecting two individuals Pa 1 and Pa 2 from the parent, and selecting two individuals Pa 1 and Pa 2 from the parent Randomly generates two integers Cut 1、Cut2 as crossing points, whereinThe PS-FS sequence in the offspring 1 replicates the codes of the parent Pa 2 that the PS-FS is located between Cut 1 and Cut 2, the other codes replicate the codes of the parent Pa 2 that are not replicated in sequence, two integers Cut 1、Cut2 are randomly generated in the interval (1, k) as crossing points, wherein 1< Cut 1＜Cut2 < k, the sequence PN in the offspring 1 replicates the codes of the parent Pa 2 that the PN is located between Cut 1 and Cut 2, the other codes replicate the codes of the other positions in the parent Pa 2 directly, and the offspring 2 is generated in the same way.
S25, decoding individuals in the current population to obtain an adaptability value C max of each individual, marking the best individual as pi, selecting a neighborhood structure NS k based on a roulette mode, and generating a neighborhood solution pi' of pi according to the neighborhood structure NS k. The specific implementation process is as follows:
s2501, decoding individuals in the current population to obtain fitness value C max of each individual, and marking the best individual as pi. The specific decoding process thereof refers to S2202, and is not described here again.
S2502, selecting a neighborhood structure NS k based on a roulette mode, and generating a neighborhood solution pi' of pi according to the neighborhood structure NS k. The method specifically comprises the following steps:
The feasible solution pi is transformed in the selected neighborhood structure NS K, so that the initial solution at the stage jumps to another solution in the feasible domain, thereby avoiding repeated iteration under the same solution and causing the algorithm to fall into local optimum. Considering the complexity of high-end equipment trial production and test process and the requirement of constraint relation, the following 6 neighborhood structures are designed for enabling solutions generated by each transformation to be feasible solutions:
neighborhood structure 1 defining variables a, b, in interval And randomly generates two integers, assigned to a and b, where a < b. The encoding between position a and position b in the sequences PS and FS is processed in reverse order. Two integers are randomly generated in the interval [1, k ], and assigned to the variables a, b, so that a < b, the coding of the sequence PN between the positions a, b is processed in reverse order.
Neighborhood structure 2 defining variables a, b, in intervalAnd randomly generates two integers, assigned to a and b, where a < b. The codes in the sequences PS and FS to the left of position a and to the right of position b are interchanged. Two integers are randomly generated in the interval [1, k ], which are assigned to the variables a, b such that a < b, the coding of the sequence PN to the left of position a and to the right of position b are interchanged.
And 3, defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning the coding positions to the variables a and b, enabling a to be smaller than b, and exchanging the codes at the position a and the position b in the sequence FS. Two integers are randomly generated in the interval [1, k ] and assigned to a, b, wherein a < b, the codes of position a and position b in PN are interchanged.
And the neighborhood structure 4 is formed by defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning the coding positions to the variables a and b, enabling a to be smaller than b, and exchanging the codes at the position a and the position b in the sequence PS. Two integers are randomly generated in the interval [1, k ] and assigned to a, b, wherein a < b, the codes of position a and position b in PN are interchanged.
Neighborhood structure 5 defining variables a, b, c, in intervalThree different integer values are randomly generated in the sequence PS and FS, assigned to the variables a, b, c such that a < b < c, the codes to the left of position a and a, b in the sequence PS and FS are interchanged, and the codes to the right of position c and b, c are interchanged. Three different integer values are randomly generated in the interval [1, k ] and assigned to a, b, c, wherein a < b < c, the codes between the left side of position a and a, b in the sequence PN are interchanged, and the codes between the right side of position c and b, c are interchanged.
Neighborhood structure 6 defining variables a, b, c, in intervalThree different integer values are randomly generated and assigned to variables a, b and c, so that a < b < c, the codes between the positions a, b and c in the sequences PS and FS are interchanged, and the codes on the left side of a and the right side of c are respectively processed in reverse order. Three different integer values are randomly generated in the interval [1, k ] and assigned to a, b and c, wherein a < b < c, codes between the positions a, b and b, c in the sequence PN are exchanged, and codes on the left side of a and the right side of c are respectively processed in reverse order.
S26, carrying out local search on a solution pi ' by using a neighborhood structure NS K to obtain a local optimal solution pi ', comparing the local optimal solution pi ' with a variable neighborhood search algorithm optimal solution pi, if pi ' is superior to pi, letting pi=pi ', recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure, updating the selection probability of the neighborhood structure NS K, judging whether gen 2≤Gen2 is met, if so, assigning gen 2 +1 to gen 2, returning to the step S25, and otherwise, executing the step S27. The specific implementation process is as follows:
the traditional variable neighborhood searching process has no requirement on the sequence or the selection weight of the neighborhood structure, and each neighborhood structure is equally searched, and different neighborhood structures can bring different optimizing performance. In order to select a neighborhood structure with better effect, thereby improving the optimizing efficiency of local search, the selection weight of the neighborhood structure is introduced into a variable neighborhood searching algorithm and is continuously updated in the iterative process, and the method comprises the following steps:
S2601, a neighborhood structure NS K(K＝1,2,…,Kmax of a variable neighborhood search algorithm), and setting initial selection probability SP K of each neighborhood structure to be the same in the stage of setting algorithm parameters, wherein
S2602, updating the selection probability of each neighborhood according to the adaptive selection mechanism, wherein the method specifically comprises the following steps:
In the local searching process of the variable neighborhood searching algorithm, recording the times S K of successful searching of the local optimal solution and the times L K of failure of each neighborhood structure, and updating the selection probability of each neighborhood according to the following formula: Wherein SUC K represents the local search success rate of the neighborhood structure NS K, and the calculation formula is Δ is a sufficiently small positive number to avoid creating a situation where the probability of selection of a certain neighborhood structure is 0.
S2603, defining a variable Q ', generating a random number in the (0, 1) interval, assigning the random number to the variable Q ', judging the interval to which the variable Q ' belongs, and if SUC K-1＜Q'＜SUCK, selecting a neighborhood structure NS K as the type of neighborhood search.
S27, comparing the optimal solution pi of the variable neighborhood search algorithm with worst individuals in the initial population X 0, replacing the solution with pi if pi is better than the worst individuals in the worst initial population, comparing pi with a global optimal solution pi best, and letting pi best =pi if pi is better than pi best.
S28, judging whether the gen 1≤Gen1 is met, if yes, assigning gen 1 +1 to gen 1, taking the updated population as input of the next iteration, returning to the step S23, and if not, outputting a global optimal solution pi best. The method comprises the following steps:
Analyzing the PS-FS sequence and the TS-PN sequence in the solution according to the global optimal solution pi best output by the algorithm and the actual scene of the high-end equipment enterprise to obtain a part set distributed to each factory in a trial production stage and the processing sequence of the part in the factory, obtaining the execution sequence of the subsequent test tasks on the basis, and assigning corresponding technicians for each test task according to PN.
The embodiment of the invention also provides a high-end equipment trial-production and test collaborative scheduling system considering the process uncertainty, which comprises the following steps:
An input parameter acquisition module for setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in the high-end equipment trial production process, factory data, task data in the test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2,…,cs } of each part, a first part machining period of the part A subsequent processing period d i;
The solving module is used for obtaining a global optimal solution according to the input parameters and the mixed genetic algorithm based on the neighborhood search, obtaining a part set distributed to each factory in a trial production stage according to the global optimal solution, the processing sequence of the part in the factory and the execution sequence of the subsequent test tasks, and assigning technicians to each test task.
It may be understood that the high-end equipment trial and test co-scheduling system taking into account the process uncertainty provided by the embodiment of the invention corresponds to the high-end equipment trial and test co-scheduling method taking into account the process uncertainty, and the explanation, the examples, the beneficial effects and the like of the relevant content can refer to the corresponding content in the high-end equipment trial and test co-scheduling method taking into account the process uncertainty, which is not repeated here.
In summary, compared with the prior art, the method has the following beneficial effects:
1. The embodiment of the invention considers that the planned completion time of the test is a time interval, namely the working procedure time is uncertain. Under the condition of uncertain working procedure time, the high-end equipment trial production and test process is cooperatively scheduled, the part process requirements and the processing sequence of the parts are comprehensively considered, the approximate optimal solution of the problem is dynamically and accurately obtained, and the resources of the high-end equipment in two stages of trial production and test can be effectively cooperatively optimized and configured, so that the investment cost of the trial production and test process is reduced to the greatest extent, and the resource utilization efficiency and the cooperation efficiency of high-end equipment enterprises are improved.
2. The embodiment of the invention designs a hybrid genetic algorithm based on neighborhood search, combines the global optimization of the genetic algorithm with the local search capability of a variable neighborhood search algorithm, introduces a self-adaptive selection mechanism based on search results in the variable neighborhood search algorithm, designs a corresponding local search neighborhood structure, and effectively improves the optimization efficiency and the solution quality of the algorithm.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing embodiments are merely for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiments or equivalents may be substituted for parts of the technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solution of the embodiments of the present invention in essence.

Claims (5)

1. The high-end equipment trial production and test cooperative scheduling method taking process uncertainty into consideration is characterized by comprising the following steps of:
S1, setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in a high-end equipment trial production process, factory data, task data in a test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2,…,cs } of each part, a first part machining period of the part, and a second part machining period of the part A subsequent processing period d i;
S2, acquiring a global optimal solution according to the input parameters and a mixed genetic algorithm based on neighborhood search, acquiring a part set distributed to each factory in a trial production stage according to the global optimal solution, processing sequence of the parts in the factory and execution sequence of subsequent test tasks, and assigning technicians to each test task;
The obtaining a global optimal solution according to the input parameters and a hybrid genetic algorithm based on neighborhood search comprises the following steps:
S21, setting execution parameters of a hybrid genetic algorithm based on neighborhood search;
s22, generating an initial population, wherein individuals in the initial population are represented by two-stage codes PS-FS and TS-PN, decoding the two-stage codes to obtain an individual fitness value, and obtaining and recording a current optimal solution;
S23, executing selection operation by adopting a roulette mode based on the fitness value of each individual in the population;
S24, performing cross operation on the population individuals selected in the S23 based on the cross probability to obtain a new population with the same scale as the initial population;
S25, decoding individuals in the current population to obtain fitness values C max of the individuals, marking the best individuals as pi, selecting a neighborhood structure NS k based on a roulette mode, generating a neighborhood solution pi' of pi according to the neighborhood structure NS k, and designing the following 6 neighborhood structures in order to enable solutions generated by each transformation to be feasible in consideration of the requirements of complexity and constraint relation of high-end equipment trial production and test processes:
neighborhood structure 1 defining variables a, b, in interval The two integers are randomly generated in the interval [1, k ], and assigned to the variables a, b, so that a < b, the encoding of the sequence PN between the positions a, b are processed in reverse order;
Neighborhood structure 2 defining variables a, b, in interval The two integers are randomly generated in the interval [1, k ], and assigned to the variables a, b, so that a < b exchanges the codes on the left side of the position a and the right side of the position b in the sequence PN;
defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning coding positions to the variables a and b, so that a < b, exchanging the codes at the position a and the position b in the sequence FS, randomly generating two integers in the interval [1, k ], assigning the two integers to the a and the b, wherein a < b, and exchanging the codes at the position a and the position b in PN;
defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning coding positions to the variables a and b, so that a < b, exchanging the codes at the position a and the position b in the sequence PS, randomly generating two integers in the interval [1, k ], assigning the two integers to the a and the b, wherein a < b, and exchanging the codes at the position a and the position b in PN;
Neighborhood structure 5 defining variables a, b, c, in interval Three different integer values are randomly generated in the interval [1, k ], assigned to a, b, c, wherein a < b < c, the codes between the left side of the position a and a, b in the sequence PN and the codes between the right side of the position c and the codes between the position b and c are interchanged, and the codes between the left side of the position a and the codes between the position b and the codes between the position c are interchanged;
neighborhood structure 6 defining variables a, b, c, in interval Three different integer values are randomly generated in the interval [1, k ], and assigned to the variables a, b and c, so that a < b < c, the codes of the positions a, b and c in the sequence PS and FS are exchanged, and the codes on the left side of a and the right side of c are respectively processed in reverse order;
S26, carrying out local search on a solution pi ' by using a neighborhood structure NS K to obtain a local optimal solution pi ', comparing the local optimal solution pi ' with a variable neighborhood search algorithm optimal solution pi, if pi ' is superior to pi, letting pi=pi ', recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure, updating the selection probability of the neighborhood structure NS K, judging whether gen 2≤Gen2 is met, if so, assigning gen 2 +1 to gen 2, returning to the step S25, otherwise, executing the step S27;
s27, comparing the optimal solution pi of the variable neighborhood search algorithm with worst individuals in the initial population X 0, replacing the solution with pi if pi is better than the worst individuals in the worst initial population, comparing pi with a global optimal solution pi best, and letting pi best =pi if pi is better than pi best;
S28, judging whether the gen 1≤Gen1 is met, if so, assigning gen 1 +1 to gen 1, taking the updated population as input of the next iteration, returning to the step S23, otherwise, outputting a global optimal solution pi best;
the generation of the initial population, the calculation of the fitness value of each individual, the acquisition and the recording of the current optimal solution, comprises the following steps:
Based on a genetic and variable neighborhood search hybrid algorithm, parts and factory data in the high-end equipment trial process and task and technician data in the trial process are encoded to obtain an initial population X 0,X0 formed by PopSize initial solutions, each solution is formed by two sequence combinations of PS-FS and TS-PN, wherein the PS-FS encoding represents the corresponding relation and sequence of the parts and the factory in the trial stage, the TS-PN represents the assignment sequence of the trial task and the technician in the trial stage, each initial solution in the initial population is decoded to obtain an adaptation value C max of population individuals, the value represents the total time span of the trial and trial process, the best individual is recorded as a current global optimal solution pi best, the initial iteration number of the hybrid optimization algorithm is set to be g 1 =1, and the initial iteration number of the variable neighborhood search is set to be 2 =1.
2. The high-end equipment trial and experiment co-scheduling method considering process uncertainty as claimed in claim 1, wherein the neighborhood search-based hybrid genetic algorithm encodes parts and factory data in the high-end equipment trial process and tasks and technician data in the experiment process, comprising:
based on the characteristics of two key stages of part trial production and combined test, the solution X consists of a solution X 1 of the trial production stage and a solution X 2 of the test stage, wherein the solutions of all stages are expressed by two-dimensional integer arrays, the solution X 1 comprises two parts, a part processing sequence PS and a factory list FS, and the length of the sequence or the list is The number in PS represents the number of parts, the number of times of appearance of the number represents the number of parts, FS represents the number of factories, each column in the array represents the parts with the corresponding number and is assigned to the corresponding factories for trial production, solution X 2 consists of a test execution sequence TS and a personnel list PN, the length of the sequence or list is k, TS and PN respectively consist of test tasks and technician numbers, and each column in the array represents the test tasks with the corresponding number and are executed by the corresponding technicians.
3. The high-end equipment trial and experiment co-scheduling method considering process uncertainty as claimed in claim 1, wherein the selecting operation is performed by adopting a roulette manner based on fitness values of individuals in the population, and the method comprises:
S2301, decoding to obtain total span time of trial production and test process of each initial solution, marking the value as f (X), namely making f (X) =C max, and calculating probability of each individual xi in initial population X 0 inheriting to next generation population
S2302, calculating cumulative probability of each individual in X 0
S2303, defining a variable Q, generating a random number in a (0, 1) interval, assigning the random number to the variable Q, judging the interval to which the variable Q belongs, and if Qx i-1<Q<Qxi, selecting an individual X i in an initial population X 0;
S2304, repeatedly executing the step S2303 until PopSize population individuals are selected.
4. The method for collaborative scheduling of high-end equipment trial production and experiments considering process uncertainty as claimed in claim 1, wherein the method for collaborative scheduling is characterized in that the neighborhood structure NS K is used for carrying out local search on a solution pi' to obtain a local optimal solution pi ", comparing the local optimal solution pi" with a variable neighborhood search algorithm optimal solution pi, if pi "is better than pi, letting pi=pi", recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure, and updating the selection probability of the neighborhood structure NS K, and comprises the following steps:
S2601, a neighborhood structure NS K(K＝1,2,…,Kmax of a variable neighborhood search algorithm), and setting initial selection probability SP K of each neighborhood structure to be the same in the stage of setting algorithm parameters, wherein
S2602, in the local search process of the variable neighborhood search algorithm, recording the times S K of successful search of the local optimal solution and the times L K of failure of each neighborhood structure, and updating the selection probability of each neighborhood according to the following formula: Wherein SUC K represents the local search success rate of the neighborhood structure NS K, and the calculation formula is Delta is a sufficiently small positive number;
S2603, defining a variable Q ', generating a random number in the (0, 1) interval, assigning the random number to the variable Q ', judging the interval to which the variable Q ' belongs, and if SUC K-1<Q'<SUCK, selecting a neighborhood structure NS K as the type of neighborhood search.
5. The utility model provides a high-end equipment trial production and experimental collaborative scheduling system of taking into account process uncertainty which characterized in that includes:
The input parameter acquisition module is used for setting input parameters of a hybrid genetic algorithm based on neighborhood search according to part data in a high-end equipment trial production process, factory data, task data in a test process and technician data in the test process, wherein the input parameters comprise a planned completion time interval e t of the test, a manufacturing process C= { C 1,c2, the..once, cs } of each part, and a first part machining period of the part A subsequent processing period d i;
The solving module is used for acquiring a global optimal solution according to the input parameters and a mixed genetic algorithm based on neighborhood search, acquiring a part set distributed to each factory in a trial production stage according to the global optimal solution, the processing sequence of the part in the factory and the execution sequence of subsequent test tasks, and assigning technicians to each test task;
The obtaining a global optimal solution according to the input parameters and a hybrid genetic algorithm based on neighborhood search comprises the following steps:
S21, setting execution parameters of a hybrid genetic algorithm based on neighborhood search;
s22, generating an initial population, wherein individuals in the initial population are represented by two-stage codes PS-PN and TS-PN, decoding the two-stage codes to obtain an individual fitness value, and obtaining and recording a current optimal solution;
S23, executing selection operation by adopting a roulette mode based on the fitness value of each individual in the population;
S24, performing cross operation on the population individuals selected in the S23 based on the cross probability to obtain a new population with the same scale as the initial population;
S25, decoding individuals in the current population to obtain fitness values C max of the individuals, marking the best individuals as pi, selecting a neighborhood structure NS k based on a roulette mode, generating a neighborhood solution pi' of pi according to the neighborhood structure NS k, and designing the following 6 neighborhood structures in order to enable solutions generated by each transformation to be feasible in consideration of the requirements of complexity and constraint relation of high-end equipment trial production and test processes:
neighborhood structure 1 defining variables a, b, in interval The two integers are randomly generated in the interval [1, k ], and assigned to the variables a, b, so that a < b, the encoding of the sequence PN between the positions a, b are processed in reverse order;
Neighborhood structure 2 defining variables a, b, in interval The two integers are randomly generated in the interval [1, k ], and assigned to the variables a, b, so that a < b exchanges the codes on the left side of the position a and the right side of the position b in the sequence PN;
defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning coding positions to the variables a and b, so that a < b, exchanging the codes at the position a and the position b in the sequence FS, randomly generating two integers in the interval [1, k ], assigning the two integers to the a and the b, wherein a < b, and exchanging the codes at the position a and the position b in PN;
defining variables a and b, arbitrarily selecting two part codes with the same manufacturing process from PS, assigning coding positions to the variables a and b, so that a < b, exchanging the codes at the position a and the position b in the sequence PS, randomly generating two integers in the interval [1, k ], assigning the two integers to the a and the b, wherein a < b, and exchanging the codes at the position a and the position b in PN;
Neighborhood structure 5 defining variables a, b, c, in interval Three different integer values are randomly generated in the interval [1, k ], assigned to a, b, c, wherein a < b < c, the codes between the left side of the position a and a, b in the sequence PN and the codes between the right side of the position c and the codes between the position b and c are interchanged, and the codes between the left side of the position a and the codes between the position b and the codes between the position c are interchanged;
neighborhood structure 6 defining variables a, b, c, in interval Three different integer values are randomly generated in the sequence PS and FS, and assigned to variables a, b and c, so that a < b < c, codes between positions a, b and c in the sequence PS and FS are exchanged, and codes on the left side of a and the right side of c are respectively processed in reverse order; S26, carrying out local search on a solution pi ' by using a neighborhood structure NS K to obtain a local optimal solution pi ', comparing the local optimal solution pi ' with a variable neighborhood search algorithm optimal solution pi, if pi ' is superior to pi, letting pi = pi ', recording the success times of searching the local optimal solution by the neighborhood structure, otherwise, increasing the failure times of searching the local optimal solution by the neighborhood structure NS K, updating the selection probability of the neighborhood structure NS 3864, judging whether the gen 2≤Gen2 is established, if yes, assigning gen 2 +1 to the gen 2, and returning to the step S25, otherwise, executing the step S27;
s27, comparing the optimal solution pi of the variable neighborhood search algorithm with worst individuals in the initial population X 0, replacing the solution with pi if pi is better than the worst individuals in the worst initial population, comparing pi with a global optimal solution pi best, and letting pi best =pi if pi is better than pi best;
S28, judging whether the gen 1≤Gen1 is met, if so, assigning gen 1 +1 to gen 1, taking the updated population as input of the next iteration, returning to the step S23, otherwise, outputting a global optimal solution pi best;
the generation of the initial population, the calculation of the fitness value of each individual, the acquisition and the recording of the current optimal solution, comprises the following steps:
Based on a genetic and variable neighborhood search hybrid algorithm, parts and factory data in the high-end equipment trial process and task and technician data in the trial process are encoded to obtain an initial population X 0,X0 formed by PopSize initial solutions, each solution is formed by two sequence combinations of PS-FS and TS-PN, wherein the PS-FS encoding represents the corresponding relation and sequence of the parts and the factory in the trial stage, the TS-PN represents the assignment sequence of the trial task and the technician in the trial stage, each initial solution in the initial population is decoded to obtain an adaptation value C max of population individuals, the value represents the total time span of the trial and trial process, the best individual is recorded as a current global optimal solution pi best, the initial iteration number of the hybrid optimization algorithm is set to be g 1 =1, and the initial iteration number of the variable neighborhood search is set to be 2 =1.
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