CN101118611A - Business process model resource configuring optimizing method based on inheritance algorithm - Google Patents
Business process model resource configuring optimizing method based on inheritance algorithm Download PDFInfo
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Abstract
An optimizing method of the business procedure model resource allocation based on the genetic algorithm in the field of the information technology and the enterprise engineering is essentially applied to solve the rationality problem that an enterprise allocates resources for the operating procedure and the problem of the rational utilization of activity execution under the condition of limited resources. The technical proposal is as follows: first, a total of all kinds of resources in the business procedure model, the lasting time of all the activities and the allocation situation of the needed resources are withdrawn as the parameters of the model resource allocation optimization. Second, two layers are divided to adopt the genetic algorithm. The population of the outer layer circulation R is generated based upon the total resource allocation proposal. Based on the allocation proposal of the needed resources in the execution of an individual alternative activity in R, the population of the inner layer circulation A is structured. Third, the calculation of the individual adaptation value is obtained by emulating the procedure model of the resource allocation proposal that each individual indicates. The present invention avoids analysis and calculation to an invalid combined proposal of resource allocation and increases the efficiency of the enterprise business model optimization.
Description
Technical Field
The invention relates to the field of information technology and enterprise engineering, in particular to a method for optimizing business process model resource allocation based on a genetic algorithm.
Background
The enterprise operation is performed around business activities, and the execution conditions of the activities are closely related to the resource allocation, so that the optimization of the resource allocation of the business process model becomes an important research content in the enterprise engineering field. The resource allocation of the enterprise business model mainly relates to two types of objects of activity and resource in the model, the two types of objects have different attributes except common basic attributes such as name, type, description and the like, wherein the selection of a resource allocation scheme (generally called a resource allocation scheme) required by the activity and a resource total allocation scheme directly influences the result of scheduling strategy optimization and is an important content in business model optimization.
The activity is the most important object in the business model of the enterprise, and the duration, the required resources and the quantity of the required resources are the most important attributes of the multiple attributes of the activity, which can affect the operation performance of the business process. Wherein the duration of the activity is directly related to the running time of the business process, and the configuration of the resources required for the execution of the activity not only restricts the duration of the activity, but also affects the running cost of the business process. During the modeling process, the administrator needs to set the duration and required resource allocation for the campaign based on empirical data or work plans. Where the activity duration may be a certain value, it may also follow a certain random probability distribution, even under different resource configurations. The optimization of the activity attribute parameters is to select one (or more) of the resource allocation schemes, so that the performance index obtained by simulating the process according to the allocation scheme meets the optimization target.
The resource attributes mainly comprise resource quantity, resource efficiency, resource cost and the like, and in the actual operation of the enterprise business process, the latter two generally reflect the actual condition of the resources, so that the condition of manual regulation is less. The amount of resources is often given a range by the manager during modeling, for example, a business may invest 60-70 machines, 30-40 engineers, etc. for a project, even though the amount of resources invested at different stages of the project's progress may vary. When the business process model is simulated, different model examples can be generated by selecting different quantity combinations, and the simulation result is naturally different. Therefore, the optimization aims to reasonably configure the total number of resources, and a balance point is sought between the waiting resources and the idle resources, so that the process performance indexes such as the resource utilization rate and the business process cost can meet the requirements of users.
Modern enterprises are complex social technical systems, the resource types are large, the quantity is large, the solution space needing to be optimized is extremely large, and the problem of optimizing the resource allocation of a business process model by using a traditional optimization algorithm is difficult to solve. The genetic algorithm derived from the theory of evolution of Darwin, the species selection theory of Weizmann and the genetic theory of Mendelian can process various complex problems which are difficult to solve by the traditional optimization algorithm, has wide adaptability and good robustness, and is more and more widely applied. However, because there is a constraint between the number of resources used for executing activities in the enterprise business process model and the total number of corresponding resources, changing any parameter (configuration scheme) may affect the selection of other parameters, which not only increases the complexity of processing problems, but also makes it difficult for the combination scheme to be expressed in a unified way.
Disclosure of Invention
The invention aims to provide a hierarchical optimization method for the resource allocation of a business process model based on a genetic algorithm, which aims at solving the problems of complexity, various parameters and the like of the current business model and the defects of the prior art in solving the problem of resource allocation optimization of the business process model, so that the problem of reasonability of resources allocated for the business process by an enterprise and the problem of reasonability utilization of resources actively executed under the condition of limited resources are solved, and the requirements of enterprise process optimization are met.
The method comprises the following steps:
the method comprises the following steps: setting initial parameters required by a genetic algorithm, wherein the initial parameters comprise maximum iteration times, population scale, tournament selection scale, cross probability, mutation probability and the like;
step two: extracting the total number of various resources in the business process model, the duration of each activity and the configuration condition of the required resources as parameters for optimizing the configuration of the model resources;
step three: generating a population R representing a resource total number configuration scheme according to the model information;
step four: cyclically select individuals R in R i Generating an initial population A according to the active resource configuration information;
step five: cyclically select individuals a in A i ；
Step six: calculating an individual a i An adaptation value of;
step seven: judging whether the condition of finishing the optimization of the resource allocation scheme required by the activity is met, if not, entering the step eight, and if so, entering the step nine;
step eight: selecting, crossing and mutating to generate a new population A;
step nine: the optimal individual a i The adaptive value of (b) is taken as the adaptive value of the corresponding individual in R;
step ten: judging whether the condition of finishing the optimization of the resource total number configuration scheme is met, if not, entering the step eleven, and if so, entering the step twelve;
step eleven: selecting, crossing and mutating to generate a new population R;
step twelve: and outputting the resource configuration scheme represented by the optimal individual as an optimization result.
The flow is shown in the attached figure.
The method adopts genetic algorithm in two layers, a natural number coding scheme is adopted in the algorithm, and one chromosome is coded intoA natural number sequence: (x) 1 ，x 2 ，…x n ). Wherein, the population R in the outer genetic algorithm is generated according to the total resource allocation scheme, and each chromosome represents one total resource allocation scheme. For individual R of R i Taking or rejecting the configuration scheme of the resources required by the activity execution if the required resource quantity exceeds r i Abandoning the scheme according to the quantity of the corresponding resources, thereby constructing a population A of the inner-layer genetic algorithm and avoidingAnd analyzing and calculating the invalid resource allocation combination scheme when the optimization is avoided.
Drawings
The attached drawing is a flow chart of the invention.
Detailed Description
The prototype system is developed based on the method of the invention and comprises an interface for providing an enterprise process model by a user, a resource configuration information extraction module, a resource total number configuration scheme optimization module, an active resource configuration scheme analysis processing module, an active resource configuration scheme optimization module, a process model simulation analysis module and an optimization result display module.
Specific implementations of the invention are further described below:
the method comprises the following steps: setting initial parameters required by genetic algorithm, generally setting the maximum iteration number between 200 generations and 1000 generations, setting the population scale between 20 and 100, setting the tournament selection scale between 2 and 10, setting the cross probability between 0.6 and 1.00 and setting the variation probability between 0.005 and 0.01. When the optimization is implemented, values are required to be taken according to the scale of the business process model and the difference of the optimization precision requirements;
step two: providing a process model needing optimization processing by a user through a human-computer interface, storing model information in an XML file, and extracting the total number of various resources, the duration of each activity and the configuration condition of required resources in a business process model by a resource configuration information extraction module to be used as parameters for optimizing the configuration of model resources;
an activity duration matrix C is used to represent the relationship between the duration of each activity and the configuration of the required resources. Activity duration matrix C = (a) 1 A 2 …A n ) T Where n represents the number of configuration schemes, the duration vector A i ＝(a 1 …a m a m+1 a m+2 a m+3 ) Where m = max (m) 1 ，m 2 ，…m n )，m i Indicating the number of resource types in the resource configuration scheme required for the execution of the activity, a 1 ～a m Indicating the amount of the respective resource required, a m+1 Indicates the type of probability distribution that the duration satisfies, a m+2 、a m+ 3 denotes a parameter corresponding to the distribution;
step three: analyzing the resource total configuration parameters through a resource total configuration scheme optimization module to generate a population R representing the resource total configuration scheme;
step four: cyclically select individuals R in R i For individual R in R i The configuration scheme of the resources required by the activity execution is chosen or rejected by the analysis processing module of the activity resource configuration scheme, if the quantity of the required resources exceeds r i Abandoning the scheme according to the quantity of the corresponding resources, thereby avoiding the analysis and calculation of the invalid resource allocation combination scheme during optimization and constructing a population A of the inner layer genetic algorithm;
step five: cyclically selecting individual a in A by the active resource allocation scheme optimization module i ；
Step six: calculating an individual a by a process model simulation analysis module i An adaptation value of;
the calculation of the individual adaptation values is obtained by simulating a process model using the resource allocation scheme represented by each individual. Because the optimization goal of the enterprise business model is to improve multiple performance indexes such as the operation cost, the product generation efficiency, the resource utilization rate, the length of the activity waiting queue, the waiting time and the like of the business process of the model, the multi-objective optimization problem is converted into the single-objective optimization problem by using a linear weighting method. The optimization objective function is determined as:
wherein Q (X) represents the utility value of the total target, X is an n-dimensional decision variable,resource allocation optimization parameters representing the model; omega i The weight coefficient representing each index is set by decision-making personnel of enterprises to meet
m is the number of indexes, and f is each index value obtained by simulation.
Step seven: and judging whether the condition of finishing the optimization of the resource allocation scheme required by the activity is met, if not, entering the step eight, and if so, entering the step nine. The end conditions include: reaching the maximum genetic algebra, meeting the optimization target of the current optimal individual adaptive value and the like;
step eight: selecting, crossing and mutating to generate a new population A;
tournament selection is used as a selection mode. Suppose population P = { a 1 ，a 2 ，…，a n And satisfy the descending order of individual fitness values f (a) 1 )≥f(a 2 )≥…≥f(a n ) In the selection process of size q, the probability that individuals not more than j in the order of magnitude are selected isThe probability that individuals with a ranking of no more than j-1 are selected is
That individual a j The selection probability of (2) is:
The crossover operation follows the following method:
1) Sequentially traversing each individual in the population, and calculating the cross probability p of each individual according to the formula (1) c Generating a random number ra if ra < p c Then the individual is placed in a mating pool.
In the formula p c0 Denotes the crossover probability of the initial setting, f max Representing the maximum fitness value in the contemporary population, f representing the fitness value of the individual,
representing the average fitness value of the contemporary population;
2) And (4) randomly pairing the individuals in the mating pool, and uniformly crossing each pair of individuals with bias probability according to the formula (2).
In the formula a 1i And a 2i Respectively represent ith genes, a 'in selected two parent chromosomes' 1i And a' 2i Respectively representing the ith gene in two newly generated offspring chromosomes, f 1 And f 2 Respectively representing the adaptive values of two parent individuals, ra being the random number generated when each gene bit is crossed, p cx Is an offset probability, a' i It represents a legal value randomly selected from the range of values for the gene locus.
The method can firstly dynamically adjust the cross probability, the individuals with the adaptive value larger than the population average adaptive value correspond to lower cross rate, the individuals can be protected to enter the next generation, the individuals with the adaptive value lower than the population average adaptive value correspond to higher cross rate, and the individuals are easy to be eliminated. Secondly, when gene crossing is carried out, the corresponding gene of the individual with high adaptive value can be reserved, and the synthetic gene can be regenerated to replace the corresponding gene of the individual with low adaptive value. Therefore, the diversity of the population and the convergence of the algorithm are maintained, the legality of the crossed individuals is ensured, and the optimization capability of the problem to be solved is effectively improved.
The mutation operation follows the following method: :
1) Making a judgment on the probability of occurrence of mutation at an individual level, i.e. using the original p m Calculating individual a in population based on i Probability of occurrence of mutation: p' m (a i )＝1-(1-p m ) L (wherein L is the length of the individual bit string), if the random number x is not more than p' m (a i ) Then the individual is mutated;
2) Performing mutation operation on gene level, i.e. traversing the gene position of the selected individual, and calculating the mutation probability p' of each gene according to the formula (3) m If the random number x is less than or equal to p ″) m The gene is mutated.
p″ m ＝p m /p″ m (3)
And carrying out mutation by randomly selecting a legal value from the value range of the gene position.
Step nine: the optimal individual a i The adaptive value of (a) is taken as the adaptive value of the corresponding individual in R;
step ten: and judging whether the condition of finishing the optimization of the resource total number configuration scheme is met, if not, entering the step eleven, and if so, entering the step twelve. The end conditions include: reaching the maximum genetic algebra, meeting the optimization target of the current optimal individual adaptive value and the like;
step eleven: and selecting, crossing and mutating to generate a new population R. Wherein each genetic manipulation follows the pattern described in step eight;
step twelve: and outputting the resource configuration scheme represented by the optimal individual as an optimization result by an optimization result display module.
The method has feasibility and high efficiency, and can effectively solve the problem of reasonability of resources distributed to the operation process by an enterprise and the problem of reasonability utilization of the resources actively executed under the condition of limited resources, thereby better assisting decision makers of the enterprise to carry out effective business modeling management and analysis. Particularly, the hierarchical genetic optimization method uses the decomposition-coordination idea of large-system optimization for reference, effectively avoids the analysis and calculation of an invalid resource allocation combination scheme during optimization, reduces the complexity of optimization, effectively improves the efficiency of enterprise business model optimization, and provides a new idea for further global and multi-type parameter optimization.
Claims (6)
1. A method for optimizing the resource allocation of a business process model based on a genetic algorithm is characterized by comprising the following steps: (01) Setting initial parameters required by a genetic algorithm, wherein the initial parameters comprise maximum iteration times, population scale, tournament selection scale, cross probability, mutation probability and the like; (02) Extracting the total number of various resources, the duration of each activity and the configuration condition of the required resources in the business process model as parameters for optimizing the configuration of the model resources; (03) Generating a population R representing a resource total number configuration scheme according to the model information; (04) Cyclically selecting individuals R in R i Generating an initial population A according to the active resource configuration information; (05) Cyclically select individuals a in A i (ii) a (06) Calculating an individual a i An adaptation value of; (07) Judging whether the condition of finishing the optimization of the resource allocation scheme required by the activity is met, if not, entering the step (08), and if so, entering the step (09); (08) selecting, crossing and mutating to generate a new population A; (09) The optimal individual a i The adaptive value of (a) is taken as the adaptive value of the corresponding individual in R; (10) Judging whether a condition for finishing the optimization of the resource total number configuration scheme is met, if not, entering the step (11), and if so, entering the step (12); (11) selecting, crossing and mutating to generate a new population R; (12) And outputting the resource configuration scheme represented by the optimal individual as an optimization result.
2. The genetic algorithm-based business process model resource allocation optimization method of claim 1, wherein the activity duration matrix C is used in step (01) to represent the allocation of the duration of each activity to the required resourcesThe relationship between the cases. Activity duration matrix C = (a) 1 A 2 …A n ) T Where n represents the number of configuration schemes, duration vector A i ＝(a 1 …a m a m+1 a m+2 a m+3 ) Where m = max (m) 1 ，m 2 ，…m n )，m i Representing the number of resource types in the resource configuration scheme required for the execution of the activity, a 1 ～a m Indicating the amount of the respective resource required, a m+1 Indicates the type of probability distribution that the duration satisfies, a m+2 、a m+3 A parameter indicating a correspondence of the distribution;
3. the genetic algorithm-based business process model resource allocation optimization method of claim 1, wherein the genetic algorithm is adopted in two layers in steps (04) to (11), a natural number coding scheme is adopted in the algorithm, and a chromosome is coded into a natural number sequence: (x) 1 ，x 2 ，…x n ). Wherein, the population R in the outer layer genetic algorithm is generated according to the total resource allocation scheme, and each chromosome represents one total resource allocation scheme. For individual R in R i A configuration scheme for accepting or rejecting resources required by the execution of the activity, wherein the required resource quantity exceeds r i Abandoning the scheme according to the quantity of the corresponding resources, thereby constructing a population A of the inner-layer genetic algorithm, and avoiding the analysis and calculation of the invalid resource configuration combination scheme during optimization;
4. the genetic algorithm-based business process model resource allocation optimization method of claim 1, wherein the calculation of the individual adaptation values in step (06) is obtained by simulating a process model using a resource allocation scheme represented by each individual;
5. the genetic algorithm-based business process model resource allocation optimization method according to claim 1, characterized in that the following method is followed when performing the crossover operation in steps (08), (11):
1) Sequentially traversing each individual in the population, and calculating the cross probability p of each individual according to the formula (1) c Generating a random number r a If r is a ＜p c ， The individual is placed in a mating pool.
In the formula p c0 Denotes the crossover probability of the initial setting, f max Representing the maximum fitness value in the contemporary population, f representing the fitness value of the individual,
representing the average fitness value of the contemporary population;
2) And (4) randomly pairing the individuals in the mating pool, and uniformly crossing each pair of individuals with bias probability according to the formula (2).
In the formula a 1i And a 2i Respectively represent ith genes, a 'in selected two parent chromosomes' 1i And a' 2i Respectively representing the ith gene in two newly generated offspring chromosomes, f 1 And f 2 Respectively representing the adaptive values of two parent individuals, ra being the random number generated when each gene bit is crossed, p cx Is a 'bias probability' i It represents a legal value randomly chosen from the range of values for that locus.
6. The method for optimizing resource allocation of a business process model based on genetic algorithm as claimed in claim 1, wherein the mutation operation is performed in steps (08), (11) as follows:
1) Making a judgment on the probability of occurrence of mutation at an individual level, i.e. using the original p m Calculating individual a in population on the basis i Probability of occurrence of variation: p' m (a i )＝1-(1-p m ) L (wherein L is the length of the individual bit string), if the random number x is not more than p' m (a i ) Mutating the individual;
2) Performing variation operation on gene level, i.e. traversing the gene position of the selected individual, and calculating the variation probability p' of each gene according to the formula (3) m If the random number x is less than or equal to p ″) m The gene is mutated.
p″ m ＝p m /p″ m (3)
And carrying out mutation by randomly selecting a legal value from the value range of the gene position.
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