CN112183817A - Flexible workshop scheduling method - Google Patents
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- CN112183817A CN112183817A CN202010898908.4A CN202010898908A CN112183817A CN 112183817 A CN112183817 A CN 112183817A CN 202010898908 A CN202010898908 A CN 202010898908A CN 112183817 A CN112183817 A CN 112183817A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G06Q10/06316—Sequencing of tasks or work
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- G06Q50/04—Manufacturing
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a flexible workshop scheduling method, which comprises the following steps: firstly, establishing a mathematical model facing to a flexible job shop scheduling problem, and initializing all parameters; step two, the process-based and machine-based coding modes are adopted to complete particle initialization; step three, taking cross operation in the genetic algorithm as an updating strategy of the particles; a particle update strategy based on crossover operations in a genetic algorithm as part of a process; a particle update strategy as a machine part based on mutation operations; a particle update strategy based on mutation operations as globally optimal particles; and step four, simulating the snap-through of the annealing algorithm in the searching process based on the field searching strategy of taking the mutation operation in the genetic algorithm as a machine part, and finally outputting the globally optimal particles to realize the scheduling optimization of the production elements in the job shop.
Description
Technical Field
The invention belongs to the field of optimization of production systems, in particular relates to a scheduling method with multi-objective optimization capability, and specifically relates to a flexible job shop scheduling method based on a hybrid particle swarm algorithm.
Background
With the continuous upgrading of the production mode of the manufacturing industry, the characteristics of multiple varieties, small batch and mixed flow are presented, and the production control tends to be complex due to more and more types of orders, production links, types of equipment and technical states in the production environment. The traditional job shop scheduling method and manual management are difficult to realize efficient, accurate and optimized production management. An intelligent scheduling technology for a mixed line type flexible manufacturing workshop. The method solves the scheduling problem of the flexible job shop by using the hybrid particle swarm algorithm, not only effectively optimizes production targets such as completion time, load balance, bottleneck resources and the like, but also has the advantages of high scheduling result quality, high solution convergence speed, low calculation complexity and the like.
The flexible job shop scheduling problem is an NP-Hard problem, the problem relates to the distribution of production resources of various workpieces, processing machines and the like, each workpiece comprises the procedures of various processing technologies such as turning, milling, grinding and the like, the machine tool has the processing capacity of various technologies, the problem complexity is high, and meanwhile, optimization needs to be carried out aiming at various production targets.
Patent application No. CN2015102680235.5 is named as a multi-target method for flexible job shop scheduling. And optimizing the multi-target flexible job shop model by using an ant colony algorithm, evaluating a scheduling result by using an evaluation function, and updating a scheduling rule through the pheromone. The disadvantages of this scheduling method are: the method has higher computational complexity, is only suitable for solving small-scale problems, and the quality of scheduling results obtained by solving is not high.
Disclosure of Invention
The invention aims to provide a multi-objective optimization workshop scheduling method for a flexible job workshop. Compared with the traditional workshop scheduling method, when a large-scale scheduling problem is solved, the particle swarm algorithm has low calculation complexity, so that the method is high in convergence speed. In addition, the search range of the field is expanded based on the simulated annealing algorithm, the problem of premature convergence is solved, and the solving quality of the algorithm is improved. And acquiring the weight parameters of multi-objective optimization by using an analytic hierarchy process, and calculating the fitness value of the particles, so that the multi-objective optimization decision is more reasonable.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a flexible workshop scheduling method and provides a novel particle updating method. The workshop scheduling problem belongs to a discrete problem, and the traditional particle swarm algorithm is an optimization method facing to a continuous problem. Therefore, the update method of the particle is discretized. First, the algorithm encodes the machine tool and the workpiece. Secondly, by using the crossover and mutation operations of the genetic algorithm for reference, a brand new way is used for updating the particles, and excellent genes of the individual optimal particles and the global optimal particles can be inherited, so that the individual particles are updated towards the decision preference direction.
A domain search strategy is presented. The particle swarm algorithm has a fast convergence speed, but also has the problems of premature convergence and the like. The search space of the particle swarm optimization solving process is mainly determined by particles, individual optimal particles and global optimal particles. The domain search strategy is used for updating the individual optimal particles again, and the poor-quality solution is accepted with a certain probability by utilizing the snap-through characteristic of the simulated annealing algorithm, so that the search space of the scheduling solution is enlarged, the problems of easy falling into local optimization, premature convergence and the like are solved, and the solving quality is improved.
The method solves the scheduling problem of the flexible job shop through a hybrid particle swarm algorithm. The advantages are that:
1. the updating of the particles is realized by using the cross and variation operations, the convergence rate of the algorithm is effectively improved, and the method can be applied to solving the large-scale scheduling problem.
2. The jump characteristic of the simulated annealing algorithm is utilized to enlarge the search range of the scheduling solution, improve the solving quality of the algorithm and solve the problems of premature convergence and the like.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the present invention will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive labor.
FIG. 1 is a flow chart of the hybrid particle swarm algorithm implementation of the present invention;
FIG. 2 is a schematic diagram of the particle encoding mechanism of the present invention;
FIG. 3 is a schematic diagram of the particle update strategy based on the particle itself according to the present invention;
FIG. 4 is a schematic diagram of the particle update strategy of the present invention based on a process portion;
FIG. 5 is a schematic diagram of the machine portion-based particle update strategy of the present invention;
FIG. 6 is a schematic diagram of a particle update strategy based on globally optimal particles according to the present invention;
FIG. 7 is a machine part based domain search strategy of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example 1
The embodiment provides that a mathematical model for the flexible job shop scheduling problem is established first. The traditional production mode of job shops and flow shops is difficult to adapt to the requirements of personalized and green manufacturing of modern enterprises. Therefore, a production mode oriented to a mixed line type flexible job shop is researched, and a mathematical scheduling model is established aiming at the mixed line production mode.
As shown in fig. 1, the implementation steps of the hybrid particle swarm algorithm include: initializing all parameters, initializing population particles, calculating the fitness value of each particle, initializing individual optimal particles and global optimal particles and a field search strategy, and finally outputting the global optimal particles.
As shown in fig. 2, the initialization of the particles is accomplished using process-based and machine-based encoding. Each particle comprises two parts: a process vector, a machine vector, and the lengths of the two vectors are equal. All identical reference numbers in the process vector indicate that the reference number represents the work order number of the work piece, such as: the first occurrence "2" indicates the first pass of the workpiece 2. The index value at each location in the machine vector represents the index value of the machine tool selected by the process at that location. Such as: the first occurrence "5" indicates that the first process step of the workpiece 1 is arranged to be carried out on the machine tool 5.
As shown in fig. 3, the particle update strategy based on the particle itself uses the crossover operation in the genetic algorithm, selects two designated positions in the process vector and the machine vector of the particle, respectively, and exchanges the number values at the positions.
As shown in fig. 4, the particle update strategy based on process part mirrors the crossover operation in the genetic algorithm. First, two subsets of workpieces are randomly selected, such as: subset a: {1, 3} indicates that subset a selects workpiece 1 and workpiece 3, subset B: {2, 4} indicates that subset B selects workpiece 2 and workpiece 4. Then, the index values including workpiece 1 and workpiece 3 in the particle are copied to the new particle C1, and the index values including workpiece 2 and workpiece 4 in the individual optimal particle are sequentially copied to the remaining positions of the particle C1. Then, a new particle C2 was produced in the same operation. Finally, the fitness values of the particles C1, C2 are calculated, and the particle having the larger fitness value is selected as the updated particle.
As shown in fig. 5, the machine part based particle update strategy mirrors mutation operations in genetic algorithms. First, a guide vector R including only 0 s and 1 s is randomly generated, and the length of the guide vector R is equal to the length of the machine vector and the process vector in the particle. Then, the index values of the machine vectors of the particles and their individual optimum particles at the positions of the numerical value 1 in the index vector R are interchanged, and a new particle S1 is generated.
As shown in fig. 6, the particle update strategy based on the global best particle mirrors mutation operations in genetic algorithms. First, a steering vector R is randomly generated that contains only 0's and 1's, which is equal in length to the two vectors in the particle. The index value of the machine vector of the particle and its globally optimal particle at the position of the value 1 in the index vector R is then interchanged and a new particle H1 is generated.
As shown in fig. 7, the domain search strategy based on the machine part takes advantage of the mutation operation in the genetic algorithm and the snapback of the simulated annealing algorithm. First, two positions are randomly selected in the machine vector of the individual best particle, such as: a first step of the workpiece 2 and a second step of the workpiece 4. Then, two machine tools are randomly selected for replacement on the selectable machine sets of the two processes, and new particles R1 are generated. Finally, the fitness value of the particle R1 is calculated, and a poor solution is received with a certain probability by adopting the Metropolis criterion, wherein the probability value is determined by the temperature value in the specific simulated annealing formula, and the value can change along with the temperature value in the current formula.
The global optimal particles obtained through the calculation of the algorithm are the optimal scheduling scheme of the multi-objective optimization workshop scheduling method of the flexible job workshop, the scheduling scheme is the current optimal scheduling scheme of the production workshop, all the procedures of the workpieces to be processed at present are distributed to the most appropriate machine for processing, and therefore the workshop only needs to organize production according to the scheme.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A flexible workshop scheduling method is characterized by comprising the following steps:
firstly, establishing a mathematical model facing to a flexible job shop scheduling problem, and initializing all parameters;
step two, the process-based and machine-based coding modes are adopted to complete particle initialization;
step three, taking cross operation in the genetic algorithm as an updating strategy of the particles; a particle update strategy based on crossover operations in a genetic algorithm as part of a process; a particle update strategy as a machine part based on mutation operations; a particle update strategy based on mutation operations as globally optimal particles;
and step four, simulating the snap-through of the annealing algorithm in the searching process based on the field searching strategy of taking the mutation operation in the genetic algorithm as a machine part, and finally outputting the globally optimal particles to realize the scheduling optimization of the production elements in the job shop.
2. The flexible workshop scheduling method according to claim 1, wherein in the second step, each particle comprises two parts: a process vector and a machine vector, wherein the lengths of the two vectors are equal; the same reference number value is used in the process vector to represent the work order number of the workpiece, and the reference number value is used in the machine vector to represent the reference number value of the machine tool selected by the process at the position.
3. The flexible workshop scheduling method according to claim 1, wherein in the third step, two designated positions are selected from the process vector and the machine vector of the particle, respectively, and the number values at the positions are interchanged.
4. The flexible workshop scheduling method according to claim 3, wherein in the third step, the particle update strategy of the process part is specifically as follows:
firstly, randomly selecting two workpiece subsets A, B, copying the label value containing the subset A in the particles to a new particle C1, and copying the label value containing the subset B in the individual optimal particles to the rest positions of the particles C1 in sequence;
then, a new particle C2 was generated in the same operation;
finally, the fitness values of the particles C1 and C2 are calculated, and the particle with the larger fitness value is selected as the updated particle.
5. The flexible workshop scheduling method according to claim 4, wherein in the third step, the particle update strategy of the machine part is specifically as follows:
firstly, randomly generating a guide vector R only containing 0 and 1, wherein the length of the guide vector R is equal to that of a machine vector and a process vector in the particle;
then, the index values of the machine vectors of the particles and their individual optimal particles at the positions of the value 1 in the index vector R are interchanged, and a new particle S1 is generated.
6. The flexible workshop scheduling method according to claim 5, wherein in the third step, the particle update strategy of the globally optimal particle is specifically as follows:
firstly, randomly generating a guide vector T only containing 0 and 1, wherein the length of the guide vector T is equal to that of a machine vector and a process vector in a particle;
then, the index value of the machine vector of the particle and its global optimum particle at the position of the value 1 in the index vector T is interchanged, and a new particle H1 is generated.
7. The flexible workshop scheduling method according to claim 6, wherein the fourth step is specifically:
firstly, randomly selecting two positions in a machine vector of an individual optimal particle, then randomly selecting two machine tools for replacement on an optional machine set of two procedures, and generating a new particle R1; finally, the fitness value of particle R1 is calculated and a poor solution is received using the Metropolis criterion.
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