CN113642808B - Dynamic scheduling method for cloud manufacturing resource change - Google Patents
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Abstract
The invention provides a dynamic scheduling method for cloud manufacturing resource change, and belongs to the technical field of advanced manufacturing. The method is characterized in that firstly, the method synthesizes the interference conditions of resource attribute change, new resource access, resource withdrawal and the like possibly occurring in the resource scheduling process, analyzes the change of resources in the cloud manufacturing environment, builds a dynamic scheduling model for cloud manufacturing resource change, and finally applies an improved particle swarm algorithm to solve the dynamic scheduling model of the resources so as to obtain a resource scheduling scheme and a real-time resource scheduling updating strategy in the cloud manufacturing environment which can meet the conditions. The method is widely applied to processing and manufacturing service enterprises under the cloud manufacturing platform, realizes the dispatching optimization of dynamic resources under the cloud manufacturing environment, and solves the problems of high manufacturing enterprise cost, unbalanced resource load and the like.
Description
Technical Field
The invention relates to a dynamic scheduling method for cloud manufacturing resource change, which comprises the steps of firstly integrating the resource attribute change, new resource access, resource withdrawal and interference condition of resource maintenance which possibly occur in the dynamic scheduling process of resources, analyzing the resource scheduling change under the cloud manufacturing environment, then constructing a dynamic scheduling model for cloud manufacturing resource change according to the attribute characteristics of manufacturing tasks and resources and the actual resource scheduling rule, and finally solving the dynamic scheduling model of the resources by applying an improved particle swarm algorithm to obtain a resource scheduling scheme and a real-time resource scheduling updating strategy under the cloud manufacturing environment which can meet the conditions. The invention belongs to the technical field of advanced manufacturing.
Background
The production mode of the manufacturing industry in the world is currently moving to intelligent manufacturing in a large scale, and along with the high-speed development of the emerging IT technologies such as cloud computing, grid manufacturing, internet and the like, the production and development of enterprises are also increasingly closely connected with networks. In this context, the concept of cloud manufacturing has grown. The cloud manufacturing is a novel service-oriented, high-energy and low-consumption networked manufacturing mode, integrates IT technologies such as cloud computing, internet of things, big data, semantic Web and the like, combines the Internet and manufacturing industry, and fully exerts the characteristics of high efficiency, information sharing and low loss of the Internet. Cloud manufacturing is used as a networked manufacturing mode, various scattered manufacturing resources and manufacturing capacity can be virtualized, a resource pool is formed after service, centralized management and operation are carried out through a network, and the production cost of enterprises can be reduced while the resource utilization rate is improved. In the context of cloud manufacturing, the system has a large number of highly dynamic manufacturing resources. How to efficiently and high-quality dynamically schedule resources is a current urgent problem, and when the conventional scheduling method is difficult to meet the requirements of cloud manufacturing resource dynamic scheduling, a new, more efficient and more stable scheduling method is very important.
Disclosure of Invention
The invention aims to provide a dynamic scheduling method capable of solving the problems and providing enough resources for resource demanders efficiently and stably in a cloud manufacturing environment. The method can effectively improve the flexibility of the cloud manufacturing service platform, ensure the minimum service cost, the minimum service completion time, the highest resource reliability and the optimal resource overall service efficiency on the premise of effectively completing the task.
The technical scheme of the invention is as follows:
the method comprises the steps of firstly integrating the requirements of a resource requiring party and a resource provider, which are uploaded to a cloud service platform together, combining the interference conditions of resource attribute change, new resource access, resource withdrawal and resource maintenance which possibly occur in the dynamic scheduling process of the resources, analyzing the change of resource scheduling in a cloud manufacturing environment, then constructing a dynamic scheduling model for the change of the cloud manufacturing resources according to the attribute characteristics of manufacturing tasks and the resources and the actual resource scheduling rules, and finally optimizing and solving the dynamic scheduling model of the resources by applying an improved particle swarm algorithm to obtain a resource scheduling scheme and a real-time resource scheduling updating strategy in the cloud manufacturing environment which can meet the conditions. The method comprises the following implementation steps:
(1) Service composition and dynamic analysis of cloud manufacturing resources
In a cloud manufacturing environment, various manufacturing resources with different functions are stored in a cloud platform in the form of data, and state attributes of the manufacturing resources dynamically change in real time along with the service process of the manufacturing resources. The cloud manufacturing system can classify cloud manufacturing resources according to functions or granularity through a resource classification technology, different subtasks in the cloud platform are different in resource requirements, and different types of resources are combined through a resource matching technology to jointly complete manufacturing tasks.
(2) Dynamic scheduling model based on cloud manufacturing resource change
Based on the characteristics of resource having distribution, diversity, dynamic property and sampling property in the cloud manufacturing environment, the related concept of dynamic scheduling constraint of the resource in the cloud manufacturing environment is provided by combining the result of dynamic attribute analysis of the resource in the cloud manufacturing environment, and a multi-objective optimization model taking total manufacturing service time, total manufacturing service cost, average reliability of manufacturing resources and average service efficiency of the manufacturing resources as optimization targets is constructed.
(3) Resource dynamic scheduling optimization solving method based on improved particle swarm algorithm
On the basis of the established dynamic scheduling model facing the cloud manufacturing resource variation, the variation conditions of various resources in scheduling are encoded, and formal description of the resource scheduling problem is completed. And calculating the service demand weight in the scheduling scheme by using an analytic hierarchy process, determining the preference of resource scheduling for different services, and finally solving the resource scheduling problem by using a particle swarm algorithm.
Preferably, the resources in the cloud manufacturing environment in the step 1 can be divided into generalized resources and narrow resources under the definition angle, the equipment which participates in the manufacturing process in the whole process is called narrow resources, and the sum of all related various resources in the whole life cycle from the initial design to the completion of the product is called generalized resources. The resource granularity can be divided into resources with single functions and resources with composite functions, the resources with the single functions have single and efficient capabilities, and the resources with the composite functions have multiple functions at the same time, so that the comprehensive service capability is high. All resources are stored in the cloud manufacturing service platform in the form of data.
Dynamic scheduling of resources in a cloud manufacturing environment may consider a manufacturing process as a dynamic process, where there may be a number of different perturbation factors throughout the period of resource scheduling, referred to as resource dynamics. The dynamics of resources in a cloud manufacturing environment are mainly reflected in the following angles:
(1) The attribute of the resource changes, and the attribute of the resource in the cloud manufacturing environment changes along with the evaluation of the user after the use. T at cloud manufacturing service manufacturing cycle 1 Time of day, initial resource set E in cloud manufacturing system 1 ＝{E 1 ,E 2 …E i …E l Comprehensive service capabilities because of the initial task set U 1 ＝{F 1 ,F 2 …F i …F n User's evaluation is changed, E 1 Updated to E 2 ，Service new manufacturing task set->
(2) New resource access, t in the whole life cycle of resource scheduling 2 Access E in cloud manufacturing service platform at moment l+1 ,E l+2 …E l+c Waiting for new resources, and resource setConstructing a new resource set-> Service t 2 New manufacturing task set formed by tasks not completed at the moment +.>
(3) Resource maintenance, at t of cloud manufacturing service period 3 Time of day, E in cloud manufacturing platform b ,E b+1 …E b+q When the resources fail, the cloud manufacturing service is withdrawn, and because the time required for maintenance is different, the time for re-accessing the maintenance resources into the platform is also different, and the resource set in the cloud manufacturing platform is formed by E 3 Updated to E 4 The cloud manufacturing service platform updates the resource set E according to the updated resource set E 4 For t 3 New manufacturing task set composed of tasks which are not completed at any timeThe service is continued.
(4) Resource withdrawal, progress of execution of manufacturing task advances to t 4 Moment of time, E in cloud manufacturing platform p ,E p+1 ,E p+w Withdrawing the cloud manufacturing service when the resources are out of order, wherein the resource set in the cloud manufacturing service platform is formed by E 4 Updated to E 5 The revoked resources do not participate in the scheduling service later, and the cloud manufacturing service platform is used for updating the resources E according to the updated resources E 5 For the fourth stage task set U 4 At t 4 New manufacturing task set composed of tasks which are not completed at any timeThe service is continued.
In a cloud manufacturing environment, due to various disturbance factors, scheduling of resources can exhibit certain dynamics. In order to enable the dynamic scheduling process of cloud manufacturing resources to be smoothly carried out, the special relevant scheduling rules are as follows:
(1) Each manufacturing resource can independently complete one or more secondary manufacturing tasks, and the same secondary manufacturing task can only be processed by the same manufacturing resource at the same time;
(2) The first-level manufacturing task is decomposed to form a second-level manufacturing task, and the second-level manufacturing task is called a minimum unit to be serviced;
(3) Processing between different manufacturing resources for a secondary manufacturing task and a next secondary manufacturing task can result in certain logistic costs and logistic time;
(4) The service mode of the task orderly receives service according to the front-to-back sequence of the secondary manufacturing task, and the starting time of the latter secondary manufacturing task is required to be larger than the sum of the ending time of the former secondary manufacturing task and the logistics time between the resources selected by the two tasks;
(5) The total cost of the logistics among different resources is proportional to the logistics time and the logistics distance;
(6) Different types of first-level manufacturing tasks have the same execution priority;
(7) All cloud manufacturing resources can be used at time zero;
(8) The secondary tasks are uninterrupted once they begin processing until their manufacturing service is complete.
Preferably, in the step 2, a basic target of dynamic resource scheduling is determined, a concept of dynamic resource scheduling constraint in a cloud manufacturing environment is given, a multi-target optimized scheduling model is established, a resource scheduling process is formally represented, and the dynamic resource scheduling constraint in the cloud manufacturing environment mainly comprises the following contents:
(1) The total manufacturing service time constraint that the execution time of each task cannot exceed the maximum completion time specified by the user is: t=max (T ij_end )≤T max (i=1, 2 … n), T represents the total time of the manufacturing task, T max Representing the user-specified latest delivery date.
(2) Total manufacturing cost of service constraint not exceedingThe total budget of the user is:wherein C is max Representing the total budget cost of the user.
(3) The average reliability constraint of the resources is that,
(4) Manufacturing resources average service efficiency constraints,
(5) The processing time sequence constraint of the secondary manufacturing task, the service mode of the task receives service according to the sequence of the secondary manufacturing task, and the service start time of the post secondary manufacturing task cannot be smaller than the sum of the end time and the logistics time of the pre secondary manufacturing task, namely:
wherein,,representing a secondary manufacturing task F ij Completion time of->Representing a secondary manufacturing task F ij And F is equal to i(j+1) Logistics time between located resources, < >>Representing a secondary manufacturing task F i(j+1) Is a task start time of (1).
(6) The granularity constraint of subtasks, the secondary manufacturing task formed after the task is decomposed is the processing minimum unit granularity, and each manufacturing resource can independently complete one or more typesThe same secondary manufacturing task cannot be processed by different manufacturing resources at the same time, namely:
the resource scheduling method selects the total manufacturing service time, the total manufacturing service cost, the average service efficiency of manufacturing resources and the reliability of the manufacturing resources as the optimization targets of the resource dynamic scheduling model. The main contents are as follows:
(1) The total manufacturing service time refers to the total time from the start of the manufacturing service until the completion of the last secondary manufacturing task;
(2) The total manufacturing service costs include manufacturing tooling costs for manufacturing the secondary manufacturing tasks and logistic costs between different service resources for adjacent two secondary manufacturing tasks. The cloud manufacturing service platform should provide the manufacturing resources to the user that meet the goal of minimizing the total manufacturing service cost.
(3) The comprehensive strength, equipment service capability and computing capability of a resource provider in a cloud manufacturing environment all affect the service efficiency of resources, and meanwhile, the efficiency of different resources serving the same task is different.
(4) The reliability of the resource in the cloud manufacturing environment is taken as the self attribute of the resource, depends on the comprehensive evaluation of the reliable service quality and the reliable service time of the resource by the user, and is dynamically updated along with the continuous participation of the resource in manufacturing.
Preferably, the multi-objective function of dynamic scheduling based on resource variation in cloud manufacturing environment is:
wherein f 1 (x) Representing a total manufacturing service time objective function, f 2 (x) Representing a total manufacturing service cost objective function, f 3 (x) Representing an average reliability objective function of manufacturing resources, f 4 (x) Mean garment for representing manufacturing resourcesAnd (5) a business efficiency objective function.
Preferably, in the step 3, the change condition of each type of resource in the scheduling is encoded, so as to complete formal description of the resource scheduling problem. And calculating the service demand weight in the scheduling scheme by using an analytic hierarchy process, determining the preference of resource scheduling for different services, and finally solving the resource scheduling problem by improving a particle swarm algorithm.
Preferably, the resource scheduling in the cloud manufacturing environment comprises a resource layer, a primary manufacturing task layer and a secondary manufacturing task layer, wherein the resource layer is an optional resource set of the secondary manufacturing task layer. In addition, interference events such as resource attribute change, new resource access, resource withdrawal and resource maintenance can also occur in the use process of the cloud manufacturing resource to influence the resource scheduling process. The method solves the scheduling problem of dynamic resources by utilizing an improved particle swarm algorithm, and mainly comprises the following steps:
(1) According to the method, a group of particles are initialized within a feasible solution range according to interference events possibly occurring in the resource scheduling problem and the specific solutions of the corresponding interference events, and each particle represents a potential optimal solution of the resource dynamic scheduling problem.
(2) The method adopts a linear weighting mode to convert a multi-objective optimization problem into a single objective function for solving, and when constructing the fitness function, the minimum value of manufacturing time and manufacturing service cost is required to be met, and the maximum value of average manufacturing service efficiency and average resource reliability is required to be met.
(3) The iterative operation mainly comprises the steps of calculating the fitness value of each particle in the population, updating the individual optimal particles and the global optimal particles, and updating the speed and the position of the particles according to a speed and position updating formula.
Compared with the prior art, the method has the advantages that: the method aims at dynamic change of resources in cloud manufacturing environment, analyzes problems such as resource attribute change, resource damage and maintenance and resource withdrawal which may occur in the scheduling process of cloud manufacturing resources, and gives a scheduling strategy of a service system after a disturbance event occurs to the cloud manufacturing resources so as to ensure that cloud manufacturing tasks are completed smoothly. The multi-objective optimization model is established by combining major influencing factors in the scheduling problem, such as manufacturing service time, manufacturing service cost, average reliability of resources, average service efficiency of resources and the like, and the problem is optimized and solved by adopting an improved particle swarm optimization algorithm, so that the flexibility of the cloud manufacturing service platform is effectively improved. The method can ensure the minimum service cost, the minimum service completion time, the highest average reliability of resources and the optimal overall service efficiency of the resources on the premise of effectively completing the tasks.
Drawings
FIG. 1 is a diagram showing the relationship between resource changes in the present invention
FIG. 2 is a full-phase flow chart of resource scheduling for improved particle swarm algorithm based on resource variation in the present invention
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Example 1: as shown in fig. 1, the basic content of the resource dynamic scheduling flow includes:
(1) And (3) service combination and dynamic analysis of cloud manufacturing resources, wherein the state attribute of the cloud manufacturing resources dynamically changes along with the service progress of the cloud manufacturing resources in real time under the cloud manufacturing environment. The cloud platform needs to adjust the scheduling policy in real time according to the change of the resource state attribute.
(2) Based on a dynamic scheduling model of cloud manufacturing resource variation, a related concept of dynamic scheduling constraint of resources in a cloud manufacturing environment is given by combining research results of dynamic properties of the resources in the cloud manufacturing environment, and a multi-target optimized scheduling model is constructed.
(3) And (3) based on the improved particle swarm optimization, calculating service demand weights in a scheduling scheme by using a hierarchical analysis method on the basis of the dynamic scheduling model, determining the preference of resource scheduling on different services, and finally solving the resource scheduling problem by the particle swarm optimization.
As shown in fig. 2, for a resource scheduling full-stage flow based on an improved particle swarm algorithm, the specific optimization scheduling solving steps are as follows:
(1) Initializing a particle population, and setting initial positions, initial speeds and iteration times of population particles.
(2) Respectively calculating minimum values T of total manufacturing service time when separately considering manufacturing time min Minimum value C of manufacturing service costs when considering total manufacturing service costs alone min Maximum Rel of average reliability when considering average reliability of manufacturing resources alone max Maximum value E of resource service efficiency when considering average service efficiency of resources alone max And then, carrying out normalization processing on each independent target to obtain a normalized fitness function, and finally, calculating the fitness value of each particle in the population.
(3) Updating the individual optimal particles and the global optimal particles.
(4) And updating the position and the speed of the particles according to the position formula and the speed formula.
(5) Judging whether the maximum iteration number is reached, if so, ending and returning to the task completed at the moment and the corresponding resource number. And if the maximum iteration number is not reached, returning to the step 2.
(6) And decoding the optimal individual and converting the optimal individual into a scheduling result.
(7) At t=t in save step 6 1 A set of manufacturing tasks that have not yet started at the moment.
(8)E 1 ，E 2 ···E i And updating the change of the resource attribute.
(9) And (3) carrying out data processing on the new manufacturing task set and the updated resources, and then carrying out operation according to the steps 1,2, 3, 4 and 5.
(10) Decoding the optimal individual and converting the optimal individual into a scheduling result;
(11) At t=t in the save step 10 2 A set of manufacturing tasks that have not yet started at the moment.
(12)E 1+1 ，E 1+2 ···E 1+c And waiting for new resources to access the cloud platform.
(13) And (3) carrying out data processing on the new manufacturing task set and the resources stored in the previous step, and then carrying out operation according to the steps 1,2, 3, 4 and 5.
(14) Decoding the optimal individual and converting the optimal individual into a scheduling result;
(15) At t=t in the save step 14 3 A set of manufacturing tasks that have not yet started at the moment.
(16)E b ，E b+1 ···E b+q And (5) maintaining the resources.
(17) And (3) carrying out data processing on the new manufacturing task set and the resources stored in the previous step, and then carrying out operation according to the steps 1,2, 3, 4 and 5.
(18) Decoding the optimal individual and converting the optimal individual into a scheduling result;
(19) The manufacturing task set that has not yet started at time t=4 in step 18 is saved.
(20)E p ，E p+1 ···E p+w And withdrawing the resources from the cloud platform.
(21) And (3) carrying out data processing on the new manufacturing task set and the resources stored in the previous step, and then carrying out operation according to the steps 1,2, 3, 4 and 5.
(22) Decoding the optimal individual and converting the optimal individual into a scheduling result;
(23) And storing the completed manufacturing task and the corresponding resource number thereof, and outputting a global scheduling scheme.
Taking a cloud manufacturing resource scheduling scene as an example, solving the service demand weight in the scheduling scheme based on a hierarchical analysis method:
(1) Constructing a hierarchical model, and constructing a ladder hierarchical model which takes an optimal resource scheduling scheme as a target layer, and takes total manufacturing service time, total manufacturing service cost, average manufacturing service efficiency and average reliability of resources as index factor layers by combining a research object of the method based on the principle of carrying out weight solving on a hierarchical analysis method.
(2) According to the basic principle and solving step of the analytic hierarchy process, the method adopts the analytic hierarchy process to solve the weight of the influencing factors in the adjusting model. In the present exampleAssuming that the total manufacturing service cost is as important as the total manufacturing service time, the total manufacturing service time is slightly important as compared with the average manufacturing service efficiency, the total manufacturing service time is slightly important as compared with the average reliability of the resources, the average manufacturing service efficiency is as important as the average resource service efficiency, and an initial judgment matrix A= (a) is constructed ij ) n×n The following is provided.
(3) Calculating weights
Normalizing the initial judgment matrix A to obtain a standard matrixAs will be described below,
for a pair ofAdding the elements in the matrix according to the rows and carrying out normalization processing to obtain a final weight vector +.>
W＝[0.333,0.333,0.167,0.167] T
Calculating the maximum eigenvalue lambda of the judgment matrix A max ，
λ max ＝4
A consistency check is performed on the weights that are sought,
as can be seen from the following table, at n=4, the average random uniformity index ri=0.90,
CR＝0.0237≤0.1
to sum up, the weights required for the example are the following weights according to the consistency test:
W＝[0.333,0.333,0.167,0.167] T
the resource dynamic scheduling based on the improved particle swarm algorithm comprises the following specific operation steps:
(1) Encoding
The invention adopts real number coding and integer coding for particles, adopts a linear weighting mode for the fitness function, calculates each index weight value, adopts a analytic hierarchy process for solving, and enables the task and the resource to be served to form an initial scheme solution space for solving the scheduling problem. For example, the task type of the manufacturing task set U and the secondary manufacturing task type list are:
i.e. there are n primary manufacturing tasks, each primary manufacturing task F i Can be decomposed into k i Secondary manufacturing task F ij The first layer code of particles represents the second task fabrication layer, and is assigned by an integer between 1 and n, and can be represented as [ 11 11 1 22 22 2 ]]The second layer of the code of particles represents a second order layer of task execution, initially by assigning [0,1 ]]The completion of the random real number in between can be expressed as [0.3 0.4 0.5 0.2 0.6 0.7 0.42 0.53 0.62 0.27 ]]The third layer code represents the processing resource number layer correspondingly allocated to each secondary manufacturing task and can be represented as [ 13 5 2 4 3 1 2 4 ]3]The method comprises the steps of carrying out a first treatment on the surface of the The final particle encoding is expressed as:
(2) Construction of fitness function
According to the definition of the objective function, namely, when the minimum manufacturing time and the minimum manufacturing service cost are met, the average manufacturing service efficiency and the average resource reliability are maximized, and for the improved particle swarm algorithm, a single objective function is adopted as a fitness function to evaluate and select particles, so that the multi-objective optimization problem is required to be converted into a form of the single objective function to be solved.
Wherein W is 1 Indicating total manufacturing service time, W 2 Representing the total manufacturing service cost, W 3 Representing average service efficiency of manufacturing resources, W 4 A weight value representing an average reliability of the manufacturing resource; t (T) min Representing the minimum value of manufacturing service time when only the manufacturing service time is considered, C min Representing the minimum value of the service cost when considering only the manufacturing service cost, E max Representing the maximum value of manufacturing service efficiency when only the average manufacturing service efficiency is considered, rel max Representing the maximum value of the average resource reliability when only the average resource reliability is considered.
(3) Particle position and velocity update
And updating the particle speed and the position by recording the particle individual optimal value and the population optimal value, updating the task execution sequence layer and the resource layer, generating a new task execution sequence by updating the task execution sequence layer and the resource layer, and calculating an fitness function value corresponding to the new task execution sequence. The update operation of the particle velocity and position is performed as follows, where ω represents the inertial weight.
(4) Improvements in particle swarm algorithms
The invention adopts linear weight to replace inertial weight, so as to eliminate the problem that the next particle is updated to directly pass through the optimal solution position caused by overlarge particle speed due to larger inertial weight value and the calculation failure caused by the problem that the particle is trapped in the local search space caused by overlarge particle speed due to smaller inertial weight value. The inertial weight ω varies linearly according to the formula.
Wherein Max-ITER represents the maximum number of iterations, ω max ，ω min Respectively representing the maximum value and the minimum value of the inertia weight omega, and f represents the current iteration number.
Through iterative operation, a scheduling scheme (node selection) of the manufacturing task set U can be finally obtained.
Claims (3)
1. A dynamic scheduling method for cloud manufacturing resource change is characterized in that: the method comprises the following steps:
step 1, comprehensively analyzing resource scheduling demand conditions in a cloud manufacturing environment by a cloud manufacturing platform according to manufacturing service demands uploaded by a resource demand party and a resource provider;
step 2, constructing a dynamic scheduling model oriented to cloud manufacturing resource change according to the attribute characteristics of manufacturing resources and by combining with an actual resource scheduling rule;
step 3, optimizing and solving a dynamic scheduling process of the resources by applying an improved particle swarm algorithm to obtain a resource scheduling scheme and a real-time scheduling update strategy under the cloud manufacturing environment meeting the conditions;
the application of the improved particle swarm algorithm in the step 3 to dynamically schedule and optimize resources in a cloud manufacturing environment comprises the following steps:
(1) Determining a coding mode of a resource scheduling problem, and realizing formal description of multi-level tasks and resources in resource scheduling;
(2) Calculating the fitness value of each particle in the population according to the particle swarm algorithm flow, and updating the individual optimal particles and the global optimal particles;
(3) Updating the speed and the position of the particles according to a speed and position updating formula;
(4) Executing a loop condition, judging whether the algorithm reaches preset iteration times, if so, ending the algorithm and outputting the global optimal particles and fitness values thereof at the moment, and if not, continuing to execute the loop;
the particle swarm algorithm comprises the following steps:
(1) Setting various parameters in an algorithm, and randomly initializing the initial position and the speed of the particles;
(2) Calculating the fitness value of each particle;
(3) Comparing the fitness value of the current position of the particle with the fitness value of the optimal position of the particle in the searching process, reserving the optimal fitness value as the historical optimal fitness value of the individual particle, and simultaneously updating the historical optimal position by using the current position of the particle;
(4) Comparing the size of the historical optimal fitness value of the individual particles with the fitness value of the particle population at the optimal position, and reserving the larger fitness value as the current global optimal fitness value, wherein the current position is used as the optimal position;
(5) Updating the speed and the position of the particles according to a speed and position updating formula;
(6) And (3) recalculating the particle fitness value after the position and speed updating, and if the particle fitness value fails to reach the termination condition, continuing the operation of the step (2).
2. The cloud manufacturing resource variation oriented dynamic scheduling method as described in claim 1, wherein: the method comprises the following scheduling rules:
(1) Each manufacturing resource can independently complete one or more secondary manufacturing tasks, and the same secondary manufacturing task can only be processed by the same manufacturing resource at the same time;
(2) The first-level manufacturing task is decomposed to form a second-level manufacturing task, and the second-level manufacturing task is called a minimum unit to be serviced;
(3) Processing between different manufacturing resources for a secondary manufacturing task and a next secondary manufacturing task can create logistic costs and logistic time;
(4) The service mode of the task orderly receives service according to the front-to-back sequence of the secondary manufacturing task, and the starting time of the latter secondary manufacturing task is required to be larger than the sum of the ending time of the former secondary manufacturing task and the logistics time between the resources selected by the two tasks;
(5) The total cost of the logistics among different resources is proportional to the logistics time and the logistics distance;
(6) Different types of first-level manufacturing tasks have the same execution priority;
(7) All cloud manufacturing resources can be used at time zero;
(8) The secondary tasks are uninterrupted once they begin processing until their manufacturing service is complete.
3. The cloud manufacturing resource variation oriented dynamic scheduling method as described in claim 1, wherein: the constraint requirements described mainly include total manufacturing service time constraints, total manufacturing service cost constraints, resource average reliability constraints, manufacturing resource average service efficiency constraints, secondary manufacturing task processing timing constraints, and subtask granularity constraints.
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