CN117196020A - Conflict resolution method based on improved genetic algorithm - Google Patents
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Abstract
The invention discloses a resource conflict resolution method based on an improved genetic algorithm, which is characterized by comprising the following steps of: step one, constructing a mathematical model based on time cost TT; establishing a mathematical model based on resource utilization rate BR; thirdly, constructing a target evaluation function P for resolving cloud manufacturing resource conflict; fourthly, configuring constraint conditions according to the quantity of supply and demand parties and task condition elements built by the target evaluation function P for resolving the cloud manufacturing resource conflict; and fifthly, solving an objective function for resolving the conflict of the cloud manufacturing resources to obtain an optimized result of resource matching. Compared with the prior art, the invention has the advantages that: the cloud manufacturing resource allocation optimization has the advantages of good effectiveness and practicability, high resource utilization rate, quick optimization process, reasonable resource allocation, high manufacturing resource allocation efficiency and more balanced area allocation.
Description
Technical Field
The invention relates to a resource conflict resolution method, in particular to a resource conflict resolution method based on an improved genetic algorithm.
Background
In the prior art, cloud manufacturing services often have various forms of resource conflicts in the process of calling and servicing. For example, various hardware production devices have selection resource conflicts (such as equipment resources of machine tools and the like) under multiple constraint conditions. They both lead to different degrees of conflict in the selection of resources by the system, considering the multiple constraints and randomly generated disturbances during use in the production plant.
In cloud manufacturing environments, service portfolios often generate manufacturing scenarios in a loosely coupled form, which are more complex and specialized in the cloud manufacturing field than traditional resource service portfolios. As the form of multiple resource demand parallel requests is the most common in cloud manufacturing mode, and as in the case of similar resource requests larger than available resources, resource conflicts resulting from the above relatively complex factors are techniques that each resource provider must resolve.
Cloud manufacturing is a new model of service-oriented networked manufacturing. As a new mode of calling manufacturing resources in a network to provide manufacturing services for users according to the manufacturing requirements of the users, cloud manufacturing fuses the existing intelligent technologies such as cloud computing, internet of things, high-performance storage and the like, realizes integrated management and unified allocation of the manufacturing resources, and ensures that various types of manufacturing activities can be completed efficiently and stably. The cloud manufacturing platform is used as a networked manufacturing service mode, provides a supply and demand chain of 'pay-as-you-go' for resource demanders and resource providers, optimizes a service mode and promotes the development of a three-party cooperation win-win concept including the cloud platform.
In a cloud manufacturing platform system, there are the following operational elements:
(1) Cloud manufacturing resources
Cloud manufacturing resources: refers to all relevant elements used to complete a manufacturing task or activity during the lifecycle of a full-manufacturing production. Cloud manufacturing resources are also classified in their form according to their own properties. Such as physical manufacturing resources, network service resources, human resources, etc.
(2) Resource provider
Resource provider: refers to a resource provider of manufacturing services throughout a cloud manufacturing system. The resource provider publishes the owned manufacturing resources to the shared network so that their resources can serve the relevant requesters. The resource in this form may be a physical resource such as standard part production, equipment manufacturing; network service resources such as simulation analysis, web services, or human resources such as technical experience, outsourcers. The release of resources may be not only business but also personal.
(3) Resource demander
Resource demander: refers to the resource demander of the manufacturing service in the whole cloud manufacturing system. They may be businesses or individuals. The method comprises the steps of searching or issuing the requirements of manufacturing services in cloud manufacturing, wherein resources required by users of the users are the resource types of resource providers. The personalized requirements of the manufacture of the self-product in the whole life cycle are met through the manufacturing service.
(4) Cloud manufacturing resource platform
Cloud manufacturing resource platform: refers to providing some of the following activities related to manufacturing production trade in a cloud manufacturing environment, including: sensing various manufacturing resource capacities; virtualized access of entity resources; discovery, matching and calling of resources; decomposing, combining and executing manufacturing tasks; transaction, settlement, evaluation, etc. of manufacturing services. The cloud manufacturing resource platform provides environment support for cloud manufacturing activities, provides resource integration and management, and is a tool set facing manufacturing services.
(5) Cloud manufacturing tasks
Cloud manufacturing tasks: the cloud manufacturing resource platform generates corresponding manufacturing tasks for correspondingly matching manufacturing resources according to the specific content of the requirements. The cloud manufacturing task issued by the demand party can be decomposed into subtasks by the platform according to the complexity, and then the subtasks seek corresponding resource services.
The following is explained by taking the manufacturing and development of data acquisition software of a local link of a workshop production line as a background:
the resource demand side issues the application program requirement of the data acquisition software, the platform system searches the resource provider meeting the condition, and for the supply and demand relation capable of searching the result, a supply and demand matching task, namely a cloud manufacturing task, is formed.
In the prior art, the resource conflict resolution method comprises the following steps: the cloud manufacturing task is used as a matching task for searching, the actual production and manufacturing requirements cannot be met, the cloud manufacturing task is not decomposed into sub-tasks capable of performing actual production, the sub-tasks meet the development requirements of a resource provider as actual data acquisition software, for example, the sub-tasks can be a task built by a software framework, a development task for communication interaction between a software program and hardware, a business realization development task of the software program, a foreground UI development task of the software program and the like. The resource conflict resolution method in the prior art is low in resource utilization rate, slow in optimization process, unreasonable in resource allocation, poor in effectiveness and practicality of cloud manufacturing resource allocation optimization, low in allocation efficiency of the existing manufacturing resources and unbalanced in area allocation.
Disclosure of Invention
The invention provides a resource conflict resolution method based on an improved genetic algorithm, which aims to effectively solve the technical problems of low resource utilization rate, slow optimization process, unreasonable resource allocation, poor effectiveness and practicality of cloud manufacturing resource allocation optimization, low allocation efficiency of the existing manufacturing resources and unbalanced regional allocation in the prior art, and comprises the following steps:
step 101: based on the number of both resource providers and resource requirements, and the time category of the manufacturing service: manufacturing time and transportation time, constructing a mathematical model based on time cost TT:
wherein: cus indicates the number of resource demanders, and the range of numbers cus is: 1-n;
sup denotes the number of resource providers, the number range of sup is: 1-l;
tas represents the number of subtasks, and the number range of tas is: 1-m;
s represents a time class, and s has 2 classes: manufacturing time and transportation time;
TT represents the minimum manufacturing time to complete a manufacturing task;
mat (sup, cus, tas) represents the matching of the resource provider sup to the resource requester cus subtask tas;
t (sup, cus, tas, s) represents the time of the resource provider sup to the class s time category of the resource requester cus subtask tas;
t (sup, cus, tas, 1) =mt (sup, cus, tas); mt is thus expressed as sup versus the manufacturing time of cus subtask tas;
t (sup, cus, tas, 2) =tt (sup, cus, tas); tt is thus expressed as sup versus the transit time of cus subtask tas;
step 102: based on the decomposing condition of tasks and subtasks participating in conflict resolution and the variability of manufacturing capacity of resource providers, a mathematical model based on resource use efficiency BR is established:
wherein br (sup) represents the benefit conversion rate of the resource provider sup;
num (sup, cus) represents the number of tasks selected by the resource provider sup to the resource requester cus;
BR represents a resource utilization efficiency objective function, and improves the resource utilization efficiency through reasonable subtask allocation so as to maximize utilization of available resources;
step 103: constructing a target evaluation function P for cloud manufacturing resource conflict resolution according to the mathematical model based on the time cost TT established in the step 101 and the mathematical model based on the resource utilization efficiency BR established in the step 102,
where TM (cus) represents the maximum time of each task of the resource provider cus;
weight coefficient omega 1 、ω 2 Weights of time function and benefit function, respectively, and ω 1 +ω 2 ＝1；
Step 104: according to the quantity of supply and demand parties and task condition elements built by the target evaluation function P for resolving the cloud manufacturing resource conflict, configuring constraint conditions:
the constraint conditions of solving the objective function P are as follows: x and y;
the matching resource provider has unique properties:
wherein mat (sup, cus, tas) is equal to 1 or 0, i.e.: if mat (sup, cus, tas) =1 indicates that the resource provider sup matches the task tas of the resource requester cus, otherwise, mat (sup, cus, tas) =0, so there is a partial formula in the mathematical model of step 102:
y: the sum of the time of each subtask does not exceed the maximum time of the task:
step 105: solving an objective function for resolving cloud manufacturing resource conflict to obtain an optimized result of resource matching:
determining an fitness function fit (TT, BR) for cloud manufacturing resource conflict resolution;
encoding cloud manufacturing services of each subtask and resource provider participating in conflict resolution into chromosome genes;
randomly generating an initial population meeting conflict resolution logic and rule constraints;
obtaining a next generation population by adopting a roulette selection method; the individuals of the initial population are crossed and mutated to obtain the optimal solution of the current population fitness function fit (TT, BR);
repeating the crossing and mutation operations on individuals of the initial population to obtain a next generation population until the maximum iteration times are reached;
comparing the optimal solutions of fitness functions fit (TT, BR) of each generation of population, and finding the chromosome with the largest fitness function fit (TT, BR) in the population, thereby obtaining the optimal solution for resolving the cloud manufacturing resource conflict.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: in the step 105:
the fitness function fit (TT, BR): according to the objective function P, the fitness function fit (TT, BR) is set to the inverse of the objective function P, i.e., fit (TT, BR) =1/P, and when TT is smaller and BR is larger, the fitness value fit is larger, indicating that the chromosome is excellent.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: in the step 105:
the coding mode of the chromosome genes is integer coding, and each chromosome represents all manufacturing sequences of tasks to be selected; the chromosome coding length is:is an integer string of (1) before chromosome coding +.>The subtasks corresponding to the individual gene positions are all obtained by the method>The total number of resource requesters is n, and the manufacturing subtasks of the resource requesters cus are divided into m.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: in the step 105: the step of randomly generating an initial population that satisfies conflict resolution logic and rule constraints includes:
determining a genetic population, and generating a certain amount of individuals in a random combination mode to serve as an initialization population; according to an optimization request for resolving resource conflict in a primary cloud manufacturing platform system;
acquiring the number of resource demander and assigning a digital number to the resource demander for identification processingThe number of resource demander is represented as cus according to the definition of the coding mode of chromosome gene 1 ,cus 2 ,…,cus m ；
Acquiring the task decomposition condition of a resource demander, and randomly setting the gene positions of the subtasks of the resource demander under the condition of ensuring that the subtasks are not lost according to the definition of the chromosome coding mode, so as to form the first half section of the chromosome of the gene positions [1, n ];
the number of the resource demander is obtained, and the number is assigned to the resource demander for identification processing, and the number is expressed as sup according to the definition of the chromosome coding mode 1 ,sup 2 ,…,sup k ；
Obtaining a matching result of the subtasks and the resource provider, randomly configuring the task ordering of the subtasks in the resource provider, thereby forming a corresponding relation between the subtasks and the resource provider, and realizing genes according to the definition of the chromosome coding modeAnd Gene->Corresponding relation of (3).
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: in the step 105, the roulette selection method obtains a next generation population, including the steps of:
step 501: calculating fitness value f (i) for each chromosome, where i = 1,2,3, …, n; n is equal to the total number of representing chromosomes;
step 502: calculating the probability p (i) =f (i)/sum (f) that each chromosome is inherited into the next generation population, wherein sum (f) represents the sum of fitness values of all chromosomes;
step 503: calculating cumulative probability for each chromosomeWhere i=1, 2, …, n;
step 504: randomly generating a pseudo random number r uniformly distributed in the interval of [0,1 ];
step 505: if r < q (1), selecting chromosome 1, otherwise selecting chromosome k such that q (k-1) < r.ltoreq.q (k) holds;
steps 504 and 505 are repeated n times in total to generate a next generation population.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: in the step 105, the parameters of the interleaving operation include: the crossover rate, the parameters of the mutation operation include: the variation rate is dynamically changed according to the population state, the variation range of the variation rate is 0.4-0.9, the variation range of the variation rate is 0.01-0.21, the update period formula of the variation rate and the variation rate is k, wherein k is an integer:
when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …), Q is an adjustment function; wherein:
U(gen)＝c×k
gen is the total number of iterations and,represents the maximum fitness of chromosome i of the t th generation,represents the maximum fitness of chromosome i of generation 1, and is used to update the Pc and Pm values when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …):
Pc(U(gen))＝Pc(U(gen)-k)+5Q
Pm(U(gen))＝Pm(U(gen)-k)+0.2Q
wherein Pc represents the crossover probability, pm represents the mutation probability;
after the new crossing rate and the new mutation rate are obtained through periodic updating, crossing and mutation operations are carried out;
the interleaving operation adopts an integer interleaving method: randomly selecting two chromosomes from the population, and taking out the individualsThe bit genes are randomly selected to be crossed;
the mutation operation adopts a switching mutation mode: two gene loci of a chromosome are randomly selected, and genes on the two gene loci are exchanged, thereby obtaining a new chromosome.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: parameters of the crossover operation: the variation range of the crossing rate is 0.5-0.8.
The above conflict resolution method based on improved genetic algorithm further comprises the following steps: parameters of the crossover operation: the variation rate is 0.1-0.15.
Compared with the prior art, the invention has the advantages that:
1. the conflict resolution method based on the improved genetic algorithm solves the problem of manufacturing resource conflict in the cloud manufacturing environment. Based on the innovation taking the transit time cost and the resource utilization efficiency as optimization targets, corresponding model constraint conditions are given, and the establishment of a conflict resolution model is completed. And the improved genetic algorithm is applied to optimize the model, and a multi-layer integer coding mode is selected in the algorithm, so that the problem of more elements to be compiled is well solved.
2. The mode of cross variation of selected points is designed to increase the diversity of the population so as to adhere to the situation of local convergence as much as possible. The crossover and mutation adopt a self-adaptive learning mode, and as the operation of the genetic algorithm is improved, the crossover rate and mutation rate can be dynamically changed. Crossover and mutation operations play an important role in the performance of genetic algorithms, with the most important crossover and mutation operating parameters being Pc and Pm. When Pc, particularly Pm, is too large, healthier individuals in the population are easily destroyed, which is unfavorable for the convergence of the solution and the generation of the optimal solution; whereas if Pc and Pm are too small, it is difficult to create a new individual. In conventional genetic algorithms, pc and Pm are often determined by extensive experimentation or experience, which is a cumbersome and cost-effective method. According to the invention, the probability of Pc and Pm is dynamically determined according to a certain preset mode rule according to the periodic performance of the current population state (fitness value) and the fitness value in the iteration process according to the iteration times, the crossover and mutation adopt a self-adaptive learning mode, and the crossover rate and the mutation rate can dynamically change along with the improvement of the operation of a genetic algorithm, so that flexible matching of manufacturing resources can be realized, and the resource conflict resolution level of the cloud manufacturing platform system is improved.
Drawings
FIG. 1 is a diagram of task matching and service relationships between the resource supply and demand parties of a conflict resolution method based on an improved genetic algorithm.
FIG. 2 is a flow chart of a method of conflict resolution based on an improved genetic algorithm of the present invention.
FIG. 3 is an algorithm flow chart of a conflict resolution method based on an improved genetic algorithm of the present invention.
FIG. 4 is a schematic cross-operation diagram of a method for resolving conflicts based on improved genetic algorithms according to the present invention.
FIG. 5 is a schematic diagram of the mutation operation of a method for resolving conflicts based on an improved genetic algorithm according to the present invention.
FIG. 6 is a schematic diagram of cloud manufacturing resource service conflict resolution optimization results for one specific case of a conflict resolution method based on an improved genetic algorithm of the present invention.
Detailed Description
PREFERRED EMBODIMENTS FOR CARRYING OUT THE INVENTION
The resource conflict resolution method based on the improved genetic algorithm is characterized by comprising the following steps of:
step 101: based on the number of both resource providers and resource requirements, and the time category of the manufacturing service: manufacturing time and transportation time, constructing a mathematical model based on time cost TT:
wherein: cus indicates the number of resource demanders, and the range of numbers cus is: 1-n;
sup denotes the number of resource providers, the number range of sup is: 1-l;
tas represents the number of subtasks, and the number range of tas is: 1-m;
s represents a time class, and s has 2 classes: manufacturing time and transportation time;
TT represents the minimum manufacturing time to complete a manufacturing task;
mat (sup, cus, tas) represents the matching of the resource provider sup to the resource requester cus subtask tas;
t (sup, cus, tas, s) represents the time of the resource provider sup to the class s time category of the resource requester cus subtask tas;
t (sup, cus, tas, 1) =mt (sup, cus, tas); mt is thus expressed as sup versus the manufacturing time of cus subtask tas;
t (sup, cus, tas, 2) =tt (sup, cus, tas); tt is thus expressed as sup versus the transit time of cus subtask tas;
step 102: based on the decomposing condition of tasks and subtasks participating in conflict resolution and the variability of manufacturing capacity of resource providers, a mathematical model based on resource use efficiency BR is established:
wherein br (sup) represents the benefit conversion rate of the resource provider sup;
num (sup, cus) represents the number of tasks selected by the resource provider sup to the resource requester cus;
BR represents a resource utilization efficiency objective function, and improves the resource utilization efficiency through reasonable subtask allocation so as to maximize utilization of available resources;
step 103: constructing a target evaluation function P for cloud manufacturing resource conflict resolution according to the mathematical model based on the time cost TT established in the step 101 and the mathematical model based on the resource utilization efficiency BR established in the step 102,
where TM (cus) represents the maximum time of each task of the resource provider cus;
weight coefficient omega 1 、ω 2 Weights of time function and benefit function, respectively, and ω 1 +ω 2 ＝1；
Step 104: according to the quantity of supply and demand parties and task condition elements built by the target evaluation function P for resolving the cloud manufacturing resource conflict, configuring constraint conditions:
the constraint conditions of solving the objective function P are as follows: x and y;
the matching resource provider has unique properties:
wherein mat (sup, cus, tas) is equal to 1 or 0, i.e.: if mat (sup, cus, tas) =1 indicates that the resource provider sup matches the task tas of the resource requester cus, otherwise, mat (sup, cus, tas) =0, so there is a partial formula in the mathematical model of step 102:
y: the sum of the time of each subtask does not exceed the maximum time of the task:
step 105: solving an objective function for resolving cloud manufacturing resource conflict to obtain an optimized result of resource matching:
determining an fitness function fit (TT, BR) for cloud manufacturing resource conflict resolution;
encoding cloud manufacturing services of each subtask and resource provider participating in conflict resolution into chromosome genes;
randomly generating an initial population meeting conflict resolution logic and rule constraints;
obtaining a next generation population by adopting a roulette selection method; the individuals of the initial population are crossed and mutated to obtain the optimal solution of the current population fitness function fit (TT, BR);
repeating the crossing and mutation operations on individuals of the initial population to obtain a next generation population until the maximum iteration times are reached;
comparing the optimal solutions of fitness functions fit (TT, BR) of each generation of population, and finding the chromosome with the largest fitness function fit (TT, BR) in the population, thereby obtaining the optimal solution for resolving the cloud manufacturing resource conflict.
In the step 105: the fitness function fit (TT, BR): according to the objective function P, the fitness function fit (TT, BR) is set to the inverse of the objective function P, i.e., fit (TT, BR) =1/P, and the larger the fitness value fit, the more excellent the chromosome.
In the step 105: the coding mode of the chromosome genes is integer coding, and each chromosome represents all manufacturing sequences of tasks to be selected; the chromosome coding length is:is an integer string of (1) before chromosome coding +.>The subtasks corresponding to the individual gene positions are all obtained by the method>The total number of resource requesters is n, and the manufacturing subtasks of the resource requesters cus are divided into m.
In the step 105: the step of randomly generating an initial population that satisfies conflict resolution logic and rule constraints includes:
determining a genetic population, and generating a certain amount of individuals in a random combination mode to serve as an initialization population; according to an optimization request for resolving resource conflict in a primary cloud manufacturing platform system;
acquiring the number of the resource demander, assigning a numerical number to the resource demander for identification processing, and expressing the number of the resource demander as cus according to the definition of the chromosome gene coding mode 1 ，cus 2 ，…，cus m ；
Acquiring the task decomposition condition of a resource demander, and randomly setting the gene positions of the subtasks of the resource demander under the condition of ensuring that the subtasks are not lost according to the definition of the chromosome coding mode, so as to form the first half section of the chromosome of the gene positions [1, n ];
the number of the resource demander is obtained, and the number is assigned to the resource demander for identification processing, and the number is expressed as sup according to the definition of the chromosome coding mode 1 ,sup 2 ,…,sup k ；
Obtaining a matching result of the subtasks and the resource provider, randomly configuring the task ordering of the subtasks in the resource provider, thereby forming a corresponding relation between the subtasks and the resource provider, and realizing genes according to the definition of the chromosome coding modeAnd Gene->Corresponding relation of (3).
In the step 105, the roulette selection method obtains a next generation population, including the steps of:
step 501: calculating fitness value f (i) for each chromosome, where i = 1,2,3, …, n; n is equal to the total number of representing chromosomes;
step 502: calculating the probability p (i) =that each chromosome is inherited into the next generation population
f (i)/sum (f), wherein sum (f) represents the sum of fitness values of all chromosomes;
step 503: calculating cumulative probability for each chromosomeWhere i=1, 2, …, n;
step 504: randomly generating a pseudo random number r uniformly distributed in the interval of [0,1 ];
step 505: if r < q (1), selecting chromosome 1, otherwise selecting chromosome k such that q (k-1) < r.ltoreq.q (k) holds;
steps 504 and 505 are repeated n times in total to generate a next generation population.
In the step 105, the parameters of the interleaving operation include: the crossover rate, the parameters of the mutation operation include: variation rate; the crossover rate adopts a mode of dynamic change according to population state, the change range of the crossover rate is 0.4-0.9, the change range of the mutation rate is 0.01-0.21, the update period formula of the crossover rate and the mutation rate is k, wherein k is an integer:
when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …), Q is an adjustment function; wherein:
U(gen)＝c×k
gen is the total number of iterations and,represents the maximum fitness of chromosome i of the t th generation,represents the maximum fitness of chromosome i of generation 1, and is used to update the Pc and Pm values when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …):
Pc(U(gen))＝Pc(U(gen)-k)+5Q
Pm(U(gen))＝Pm(U(gen)-k)+0.2Q
wherein Pc represents the crossover probability, pm represents the mutation probability;
after the new crossing rate and the new mutation rate are obtained through periodic updating, crossing and mutation operations are carried out;
the interleaving operation adopts an integer interleaving method: randomly selecting two chromosomes from the population, and taking out the individualsThe bit genes are randomly selected to be crossed;
the mutation operation adopts a switching mutation mode: two gene loci of a chromosome are randomly selected, and genes on the two gene loci are exchanged, thereby obtaining a new chromosome.
Parameters of the crossover operation: the variation range of the crossing rate is 0.6-0.7.
Parameters of the crossover operation: the variation rate is 0.11-0.14.
The practical application of the present invention will be further explained by taking a cloud manufacturing resource service as an example of a specific case.
TABLE 1 task alternatives
Simulating the possible operating conditions of a manufacturing task at a certain time period: 10 resource providers can provide production and manufacturing resources, 6 resource requesters submit respective demand applications and generate corresponding manufacturing tasks, each manufacturing task can be decomposed into 11 subtasks at most, matching relations among the subtasks and time costs are shown in the following two tables, and the production resources are resolved through a genetic algorithm according to the above assumed solution constraint mode. In Table 1, sub-task 1, resource requester number 1, may provide manufacturing services by resource provider number 5; subtask 4 of resource demander 3, manufacturing services can be provided by resource provider 4 or 7, and the other is similarly represented.
In the time cost of the time alternative, if there is no corresponding task, the time is zero. In table 2, the time cost of subtask 1 of resource demander No. 1 is 3 minutes in the case of providing the manufacturing service by resource provider No. 5; the corresponding time cost is 3 minutes or 6 minutes in the case that the resource provider number 4 or 7 provides the manufacturing service for the subtask 4 of the resource provider number 3, and the other similar expressions.
TABLE 2 time alternatives
Based on the resource conflict resolution of the genetic algorithm, the objective function is effectively verified by using the problem model with the data in the table 1 and the table 2 as input.
With the manufacturing time category and the shipping time category known, the total time of the tasks matched by the digested resource provider is calculated. The results are shown in Table 3:
TABLE 3 calculation of parameters and results for time function
Taking the resource usage efficiency br (sup=i, i=1, …, 10) of each resource provider as: 96%,95%,93%,92.5%,96%,98%,95%,96.5%,97%,94% are substituted into the mathematical model of step 102 to obtain the resource utilization benefit values shown in table 4.
TABLE 4 resource usage benefit value
| Resource provider (sup) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Efficiency value (BR) | 1.95 | 4.8 | 3.79 | 3.83 | 3.89 | 2.95 | 3.8 | 4.89 | 3.88 | 2.85 |
To obtain the result of the conflict resolution optimization, the solution optimization is performed by a genetic algorithm according to the optimization objective function in step 103 to obtain one of the manufacturing resource conflict resolution schemes, as shown in fig. 6. Resource provider number 1: providing manufacturing service for sub-task 3 of resource demander No. 4 from day 13 for a period of 5 days; the manufacturing service will be provided to sub-task 9 of resource requester 6 starting on day 50 for a period of 10 days. The same applies to the task allocation of other resource providers. The optimization result provides a resource task allocation scheme. From this example, it can be seen that the manufacturing service of the resource provider will be completed within 80-90 days, and the service is guaranteed to be provided, and at the same time, the purpose of meeting the time cost is achieved, and the use efficiency of idle resources of the resource provider is also improved.
Claims (8)
1. The resource conflict resolution method based on the improved genetic algorithm is characterized by comprising the following steps of:
step 101: based on the number of both resource providers and resource requirements, and the time category of the manufacturing service: manufacturing time and transportation time, constructing a mathematical model based on time cost TT:
wherein: cus indicates the number of resource demanders, and the range of numbers cus is: 1-n;
sup denotes the number of resource providers, the number range of sup is: 1-l;
tas represents the number of subtasks, and the number range of tas is: 1-m;
s represents a time class, and s has 2 classes: manufacturing time and transportation time;
TT represents the minimum manufacturing time to complete a manufacturing task;
mat (sup, cus, tas) represents the matching of the resource provider sup to the resource requester cus subtask tas;
t (sup, cus, tas, s) represents the time of the resource provider sup to the class s time category of the resource requester cus subtask tas;
t (sup, cus, tas, 1) =mt (sup, cus, tas); mt is thus expressed as sup versus the manufacturing time of cus subtask tas;
t (sup, cus, tas, 2) =tt (sup, cus, tas); tt is thus expressed as sup versus the transit time of cus subtask tas;
step 102: based on the decomposing condition of tasks and subtasks participating in conflict resolution and the variability of manufacturing capacity of resource providers, a mathematical model based on resource use efficiency BR is established:
wherein br (sup) represents the benefit conversion rate of the resource provider sup;
num (sup, cus) represents the number of tasks selected by the resource provider sup to the resource requester cus;
BR represents a resource utilization efficiency objective function, and improves the resource utilization efficiency through reasonable subtask allocation so as to maximize utilization of available resources;
step 103: constructing a target evaluation function P for cloud manufacturing resource conflict resolution according to the mathematical model based on the time cost TT established in the step 101 and the mathematical model based on the resource utilization efficiency BR established in the step 102,
where TM (cus) represents the maximum time of each task of the resource provider cus;
weight coefficient omega 1 、ω 2 Weights of time function and benefit function, respectively, and ω 1 +ω 2 ＝1；
Step 104: according to the quantity of supply and demand parties and task condition elements built by the target evaluation function P for resolving the cloud manufacturing resource conflict, configuring constraint conditions:
the constraint conditions are as follows: x and y;
the matching resource provider has unique properties:
wherein mat (sup, cus, tas) is equal to 1 or 0, i.e.: if mat (sup, cus, tas) =1 indicates that the resource provider sup matches the task tas of the resource requester cus, otherwise, mat (sup, cus, tas) =0, so there is a partial formula in the mathematical model of step 102:
y: the sum of the time of each subtask does not exceed the maximum time of the task:
step 105: solving an objective function for resolving cloud manufacturing resource conflict to obtain an optimized result of resource matching:
determining an fitness function fit (TT, BR) for cloud manufacturing resource conflict resolution;
encoding cloud manufacturing services of each subtask and resource provider participating in conflict resolution into chromosome genes;
randomly generating an initial population meeting conflict resolution logic and rule constraints;
obtaining a next generation population by adopting a roulette selection method; performing cross operation and mutation operation on individuals of the initial population to obtain an optimal solution of a current population fitness function fit (TT, BR);
repeating the cross operation and the mutation operation on the individuals of the initial population to obtain the next generation population until the maximum iteration times are reached;
comparing the optimal solutions of fitness functions fit (TT, BR) of each generation of population, and finding the chromosome with the largest fitness function fit (TT, BR) in the population, thereby obtaining the optimal solution for resolving the cloud manufacturing resource conflict.
2. The improved genetic algorithm-based conflict resolution method of claim 1, wherein: in the step 105:
the fitness function fit (TT, BR): according to the objective function P, the fitness function fit (TT, BR) is set to the inverse of the objective function P, i.e., fit (TT, BR) =1/P.
3. The improved genetic algorithm-based conflict resolution method of claim 1, wherein: in the step 105:
the coding mode of the chromosome genes is integer coding, and each chromosome represents all manufacturing sequences of tasks to be selected; the chromosome coding length is:is an integer string of (1) before chromosome coding +.>The subtasks corresponding to the individual gene positions are all obtained by the method>The total number of resource requesters is n, and the manufacturing subtasks of the resource requesters cus are divided into m.
4. The improved genetic algorithm-based conflict resolution method of claim 1, wherein: in the step 105: the step of randomly generating an initial population that satisfies conflict resolution logic and rule constraints includes:
determining a genetic population, and generating a certain amount of individuals in a random combination mode to serve as an initialization population; according to an optimization request for resolving resource conflict in a primary cloud manufacturing platform system;
the number of resource demander is obtained, and the number is assigned to the resource demander for identification processing, and according to the coding mode of chromosome geneIs defined by (a) to represent the number of resource requesters as cus 1 ,cus 2 ,…,cus m ；
Acquiring the task decomposition condition of a resource demander, and randomly setting the gene positions of the subtasks of the resource demander under the condition of ensuring that the subtasks are not lost according to the definition of the chromosome coding mode, so as to form the first half section of the chromosome of the gene positions [1, n ];
the number of the resource demander is obtained, and the number is assigned to the resource demander for identification processing, and the number is expressed as sup according to the definition of the chromosome coding mode 1 ,sup 2 ,…,sup k ；
Obtaining a matching result of the subtasks and the resource provider, randomly configuring the task ordering of the subtasks in the resource provider, thereby forming a corresponding relation between the subtasks and the resource provider, and realizing genes according to the definition of the chromosome coding modeAnd Gene->Corresponding relation of (3).
5. The improved genetic algorithm-based conflict resolution method of claim 1, wherein: in the step 105, the roulette selection method obtains a next generation population, including the steps of:
step 501: calculating fitness value f (i) for each chromosome, where i = 1,2,3, …, n; n is equal to the total number of representing chromosomes;
step 502: calculating the probability p (i) =f (i)/sum (f) that each chromosome is inherited into the next generation population, wherein sum (f) represents the sum of fitness values of all chromosomes;
step 503: calculating cumulative probability for each chromosomeWhere i=1, 2, …, n;
step 504: randomly generating a pseudo random number r uniformly distributed in the interval of [0,1 ];
step 505: if r < q (1), selecting chromosome 1, otherwise selecting chromosome k such that q (k-1) < r.ltoreq.q (k) holds;
steps 504 and 505 are repeated n times in total to generate a next generation population.
6. The improved genetic algorithm-based conflict resolution method of claim 1, wherein: in the step 105, the parameters of the interleaving operation include: the crossover rate, the parameters of the mutation operation include: the variation rate is 0.4-0.9, the variation rate is 0.01-0.21, the update period formula of the variation rate and the crossover rate is k, wherein k is an integer:
when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …), Q is an adjustment function; wherein:
U(gen)＝c×k
gen is the total number of iterations and,represents the maximum value of fitness of chromosome i of the t th generation,/->Represents the maximum fitness of chromosome i of generation 1, and is used to update the Pc and Pm values when the number of iterations satisfies the U (gen) condition (c=1, 2,3 …):
Pc(U(gen))＝Pc(U(gen)-k)+5Q
Pm(U(gen))＝Pm(U(gen)-k)+0.2Q
wherein Pc represents the crossover probability, pm represents the mutation probability;
after the new crossing rate and the new mutation rate are obtained through periodic updating, crossing and mutation operations are carried out;
the interleaving operation adopts an integer interleaving method: randomly selecting two chromosomes from the population, and taking out the individualsThe bit genes are randomly selected to be crossed;
the mutation operation adopts a switching mutation mode: two gene loci of a chromosome are randomly selected, and genes on the two gene loci are exchanged, thereby obtaining a new chromosome.
7. The improved genetic algorithm-based conflict resolution method of claim 6, wherein: parameters of the crossover operation: the variation range of the crossing rate is 0.5-0.8.
8. The improved genetic algorithm-based conflict resolution method of claim 6, wherein: parameters of the crossover operation: the variation rate is 0.1-0.15.
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