CN111260252A - Power communication network field operation and maintenance work order scheduling method - Google Patents
Power communication network field operation and maintenance work order scheduling method Download PDFInfo
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Abstract
The invention relates to the technical field of power communication networks, in particular to a power communication network field operation and maintenance work order scheduling method. A power communication network field operation and maintenance work order scheduling method comprises the following steps: step 1: confirming on-site operation and maintenance work order scheduling influence factors, carrying out demand analysis on the on-site operation and maintenance work order scheduling influence factors, summarizing the attributes of each influence factor, and establishing a constraint condition of a model on the basis; step 2: establishing a field operation and maintenance work order scheduling optimization model; and step 3: the solution was performed using a multi-population particle swarm algorithm in combination with the K-Means + + algorithm. The method is applied to solving the problem of low efficiency of work order scheduling in the power communication network field.
Description
Technical Field
The invention relates to the technical field of power communication networks, in particular to a power communication network field operation and maintenance work order scheduling method.
Background
With the continuous development of the power grid business and the proposal of the smart power grid in China, the importance of the communication technology in the whole power grid system is continuously improved. The devices of the power transmission network are continuously increased, the topology structure is increasingly complex, and the number and types of the carried services are increased, which puts higher requirements on the operation and maintenance work of the power transmission network. How to reasonably carry out on-site operation and maintenance work order scheduling, and making a high-efficiency and reasonable plan have important significance for practically meeting the operation and maintenance requirements and ensuring the normal operation of communication services, and are directly related to whether the power communication system is reliable and stable, so that the operation condition of the power system is influenced. Obviously, the method has important practical significance for the research work of scheduling the on-site operation and maintenance work order of the power transmission network.
The main objective of the work order scheduling of the on-site operation and maintenance operation of the power transmission network is to reasonably schedule the maintenance work orders submitted by each maintenance organization according to a formulated objective function on the premise of ensuring the normal operation of communication services (particularly production scheduling communication services) as much as possible. At present, the field operation and maintenance scheduling of the power communication network mainly has the problem of unsmooth field operation and maintenance data interaction, which causes that the operation and maintenance work order of the communication network can not be circulated in the first time, so that the high-efficiency implementation of field operation can be restricted, meanwhile, the field link data can not be returned in time, the implementation of the field operation standardization can also be influenced, and the problems of unreasonable field operation and maintenance task scheduling, low operation and maintenance personnel utilization rate and low operation and maintenance efficiency and the like are caused. In addition, in the power communication network field operation and maintenance work order scheduling process, various factors such as personnel skills, work difficulty, personnel positions, undertaken tasks, performance assessment, personnel utilization rate, service mutual exclusion, equipment difference and the like need to be comprehensively considered. According to the management requirements of the power communication network field operation and maintenance work sequence, the field operation and maintenance task scheduling characteristics of the power communication network are analyzed, the field operation and maintenance task scheduling technology is researched, the field operation and maintenance work order scheduling scheme of the power communication network is optimized, the operation and maintenance efficiency is improved, and the operation and maintenance work quality is guaranteed, so that the deep research of the power communication network field operation and maintenance work order scheduling optimization method is very important.
The prior art is as follows:
the technical scheme 1: a power communication network resource scheduling method based on marginal utility functions introduces marginal utility functions to research utility functions. And determining a utility function according to the relation between the utility value and a network service quality parameter (such as bandwidth) by taking the satisfaction degree of people on the obtained network service quality as a standard, then obtaining a scheduling model based on the utility according to the utility function, and finally solving the model. The method divides the network application of the power communication network into two types of elasticity and inelasticity, calculates utility functions through marginal utility functions and applies the utility functions to scheduling functions; when the model is solved, a solving algorithm is designed according to the necessary conditions of the optimal solution of the model.
The specific implementation process comprises the following steps:
(1) marginal utility and utility function for elastic, inelastic applications
Marginal utility versus utility function for elasticity applications:
marginal utility versus utility function for inelastic applications:
(2) determining utility function parameters
(3) The utility-based resource scheduling model is as follows,
wherein,
Uiis the utility function of the ith flow, C is the total bandwidth, N1，N2，N＝N1+N2Number of elastic, inelastic and network flows, respectively, biRepresenting the flow i (i ∈ [1, N)]) The allocated bandwidth.
(4) Model solution
The technical scheme 2 is as follows: a power communication network real-time task scheduling algorithm drpa (dynamic reactive scheduling) based on a task dynamic priority allocation policy dpa (dynamic priority allocation). The DPA is a dynamic priority assignment strategy which comprehensively considers the task value, deadline and free time 3 characteristic parameters of the power communication network field operation and maintenance work order scheduling and provides comprehensive task dynamic value density and execution urgency. The DRTP is a novel real-time task scheduling algorithm of the power communication network, which is provided on the basis of a DPA strategy, analyzes the possible situations in task scheduling in detail and discusses the conditions of task preemption and non-preemption scheduling. The specific implementation process comprises the following steps:
(1) the validation system avoids bumpy conditions.
(2) The dynamic priority of the task is calculated.
(3) Comparing the task with highest priority in the active task and the waiting task with the priority of executing the task.
(4) An appropriate scheduling policy is determined.
Technical scheme 3: a genetic algorithm for obtaining near-optimal performance models a power communication network work monotonicity problem under multi-project construction, and an objective function is regarded as minimization of resource use difference.
The specific implementation process comprises the following steps:
(1) defining a fitness function f (x) on a search space U, giving a population size N and a cross rate PcAnd the rate of variation PmAlgebraic T;
(2)randomly generating N individuals s in U1,s2,…snForming an initial population S ═ S1,s2,…snThe algebraic counter t is set to be 1;
(3) calculating the fitness of each individual in the S;
(4) and if the termination condition is met, taking the individual with the maximum fitness in the S as a result, and finishing the algorithm.
(5) According to the selection probability P (x)i) Randomly selecting 1 individual from S and copying the chromosome for N times, and then combining the copied N chromosomes into a population S1;
(6) according to the crossing rate PcRandomly determining c chromosomes from S1 according to the determined number c of chromosomes participating in crossing, performing crossing operation by pairing, and replacing the original chromosomes with the generated new chromosomes to obtain a population S2;
(7) according to the rate of variation PmRandomly determining m chromosomes from S2 for the determined mutation times m, performing mutation operation, and replacing the original chromosomes with the new chromosomes to obtain population S3;
(8) taking the population S3 as a new generation population, namely replacing S with S3, and jumping to the step (3) when t is t + 1;
the defects of the prior art are as follows:
the technical scheme 1 is that the method for scheduling the network resources of the electric power communication network based on the marginal utility function is adopted, network applications are divided into elastic applications and inelastic applications according to the characteristics of the marginal utility function, and the utility function of each application is solved through the marginal utility function.
The technical scheme 2 adopts a real-time task scheduling algorithm based on DPA, the algorithm analyzes various possible conditions of task preemption scheduling and bumpy scheduling which may occur in a system, and provides a condition of avoiding bumping, the algorithm can improve the value gain of the system, reduce the failure rate of a task in a deadline, and greatly reduce the times of task preemption, but the algorithm does not consider the human factor constraint condition of an electric power communication network, neglects the difference of human resource skills and capacity, and reduces the probability of obtaining a more optimal target value solution in a certain sense.
In the technical scheme 3, a genetic algorithm is adopted to model the problem under the construction of multiple projects, and the objective function is regarded as the minimization of resource use difference, so that the algorithm effectively solves the problem of parallel scheduling, but does not consider the problem of scheduling of multi-task human resources in a power grid system. This severely limits the application of algorithm models and does not reasonably and effectively utilize the existing resources of the grid system, resulting in low job scheduling efficiency.
Disclosure of Invention
The invention aims to solve the technical problems at least to a certain extent, and provides a power communication network field operation and maintenance work order scheduling method based on multi-swarm particle swarm algorithm, which aims to ensure the power communication network field operation and maintenance work quality and improve the operation and maintenance work efficiency.
The technical scheme of the invention is as follows: a power communication network field operation and maintenance work order scheduling method comprises the following steps:
step 1: confirming on-site operation and maintenance work order scheduling influence factors, carrying out demand analysis on the on-site operation and maintenance work order scheduling influence factors, summarizing the attributes of each influence factor, and establishing a constraint condition of a model on the basis;
step 2: establishing a field operation and maintenance work order scheduling optimization model;
and step 3: the solution was performed using a multi-population particle swarm algorithm in combination with the K-Means + + algorithm.
The step 1 specifically comprises:
(1-1) confirming influence factors to be considered in the whole scheduling process of the operation and maintenance work of the power communication site;
and (1-2) establishing constraint conditions of the model.
In the step (1-1), the following is specifically performed:
the scheduling problem of the operation and maintenance work of the power communication field is described as follows: operation and maintenance personnel, operation and maintenance resources and operation and maintenance work orders 3 are required to be considered in the whole operation scheduling process; carrying out demand analysis on the 3 types of operation and maintenance influence factors, and summarizing the attributes of the influence factors;
for the operation and maintenance personnel, various types of skills, proficiency and authority states are included;
for the operation and maintenance work order, each operation and maintenance project has different work sequences, and each process has required work skills, work resources and corresponding completion time;
and for the operation and maintenance resources, the operation and maintenance types and the operation and maintenance quantity are included.
In the step (1-2), the concrete steps are as follows:
on the basis of analyzing the influence factors of the field operation and maintenance work order scheduling, according to the actual requirements of the field operation and maintenance of the power communication network, the constraint conditions of the summary model are as follows:
the task work sequences among different projects are not constrained, and the same project has constraints;
if the candidate item set for the current working group is empty, some time is needed, and the deployment can be carried out after partial resources are released;
after starting, the task work sequence can not be interrupted;
each task work sequence can be completed by 1-3 persons;
time to pre-allocate and complete the project.
The step 2 specifically comprises the following steps:
(2-1) establishing a skill maximization work order scheduling optimization model MaxF, wherein MaxF is a maximization function of the sum of average skill factors, and F is the sum of the average skill factors;
(2-2) establishing a time-optimal work order scheduling optimization model MinZ, wherein MinZ is a time sum minimization function, and Z is a time sum;
and (2-3) constructing a constraint formula according to the scheduling constraint conditions of the on-site operation and maintenance work order.
The step 3 specifically comprises:
(3-1) dividing subgroups based on a K-Means + + clustering algorithm;
(3-2) solving using a multi-population particle swarm algorithm.
The step (3-1) is specifically as follows:
randomly selecting one particle from the particle group as a first central particle C1；
Firstly, calculating the shortest Euclidean distance D (x) between each particle and the current existing central particle, then calculating the probability P (x) of each particle being selected as the next central particle by a formula, and then selecting the next central particle by a roulette method;
repeating the steps until K central particles are selected;
calculating Euclidean distances between the particle and all central points for the rest particles, and attributing the particle to the central point closest to the particle;
for each subgroup, calculating the position of the center point thereof by calculating the average value of the particles in the subgroup in all dimensions;
and if the central point is not changed, finishing the clustering process.
The step (3-2) is specifically as follows:
initializing a particle swarm, and dividing the particle swarm into K subgroups according to a K-Means + + algorithm;
calculating the fitness of each particle according to an objective function;
updating the historical optimal position of each particle, the optimal position of each subgroup and the optimal position of the whole subgroup;
let the position of the particle i be Xi＝(x1,x2,…,xα) At a velocity of Vi＝(v1,v2,…,vα) The position and speed variation formula of the particle is as follows:
wherein W is the inertial weight, k represents the current iteration number, Pi kFor the historical optimal position of the particle i,
is the global optimum position of the subgroup to which the particle i belongs, C1And C2Is a learning factor which represents the search of solution space by the factor of the particular particle itself and the search of solution space by the social factor, a1And a2Is a random number that follows a linear distribution;
updating the speed and position of each particle according to a formula;
performing K-Means + + clustering division on the whole particle swarm again every M iterations;
checking whether an exit condition is met; if so, ending the algorithm, otherwise, jumping to the step: the fitness of each particle is calculated according to the objective function.
Compared with the prior art, the beneficial effects are: the K-Means + + algorithm used by the invention is an improved algorithm of the K-Means algorithm, and is optimized on the point of selecting the initial clustering center, so that the clustering centers are distributed relatively uniformly, and the subgroup dividing effect is good.
The invention uses multi-population particle swarm algorithm, divides the particle swarm into a plurality of subgroups according to a certain rule, carries out optimization operation in each subgroup individually, and exchanges information among the populations by sharing global optimal solution or dynamic population recombination and other modes. Therefore, the population diversity of the whole particle swarm is effectively increased, the algorithm is prevented from falling into the local optimal solution, and the finally obtained solution is more optimal.
The method fully analyzes the field scheduling service characteristics of the power communication network field work order, combines the characteristics of operation and maintenance personnel, operation resources and the like, constructs a wearable operation and maintenance work order scheduling mathematical model of the power communication network field under multi-resource constraint, combines a K-Means + + algorithm to solve based on a multi-swarm particle swarm algorithm, solves the problems of low work task request efficiency, incapability of self-adjustment of work tasks and the like in operation and maintenance field work scheduling, improves the service quality and the utilization rate of resources, and realizes reasonable dispatching of the operation and maintenance work order under multi-resource constraint.
Drawings
FIG. 1 is a flow chart of the K-Means + + clustering algorithm of the present invention.
FIG. 2 is a flow chart of a multi-population swarm algorithm of the present invention.
FIG. 3 is a graph comparing average latency of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
A power communication network field operation and maintenance work order scheduling method comprises the following steps:
step 1: confirming on-site operation and maintenance work order scheduling influence factors, carrying out demand analysis on the on-site operation and maintenance work order scheduling influence factors, summarizing the attributes of each influence factor, and establishing a constraint condition of a model on the basis;
step 2: establishing a field operation and maintenance work order scheduling optimization model;
and step 3: the solution was performed using a multi-population particle swarm algorithm in combination with the K-Means + + algorithm.
Specifically, step 1: confirming the on-site operation and maintenance work order scheduling influence factors, carrying out demand analysis on the on-site operation and maintenance work order scheduling influence factors, summarizing the attributes of each influence factor, and establishing the constraint conditions of the model on the basis.
(1-1) confirming influence factors to be considered in the whole scheduling process of the operation and maintenance work of the power communication site:
the scheduling problem of the operation and maintenance work of the power communication field is described as follows: operation and maintenance personnel, operation and maintenance resources and operation and maintenance work orders 3 are required to be considered in the whole operation scheduling process. And carrying out demand analysis on the 3 types of operation and maintenance influence factors, and summarizing the attributes of the influence factors.
A. For the operation and maintenance personnel, various types of skills, proficiency and authority states are included;
B. for the operation and maintenance work order, each operation and maintenance project has different work sequences, and each process has required work skills, work resources and corresponding completion time;
C. and for the operation and maintenance resources, the operation and maintenance types and the operation and maintenance quantity are included.
(1-2) establishing constraint conditions of the model:
on the basis of analyzing the influence factors of the field operation and maintenance work order scheduling, according to the actual requirements of the field operation and maintenance of the power communication network, the constraint conditions of the summary model are as follows:
A. the task work sequence between different projects has no constraints, and there are constraints in the same project.
B. If the candidate set for the current working group is empty, it takes some time and waits for partial resource release before deployment.
C. After startup, the task work sequence cannot be interrupted.
D. Each task work sequence can be completed by 1-3 persons.
E. Time to pre-allocate and complete the project.
Step 2: and establishing a field operation and maintenance work order scheduling optimization model.
(2-1) establishing a skill maximization work order scheduling optimization model MaxF, wherein MaxF is a maximization function of the sum of average skill factors, and F is the sum of the average skill factors;
(2-2) establishing a time-optimal work order scheduling optimization model MinZ, wherein MinZ is a time sum minimization function, and Z is a time sum;
and (2-3) constructing a constraint formula according to the scheduling constraint conditions of the on-site operation and maintenance work order.
And step 3: the solution was performed using a multi-population particle swarm algorithm in combination with the K-Means + + algorithm.
(3-1) dividing the subgroups based on the K-Means + + clustering algorithm.
A. Randomly selecting one particle from the particle group as a first central particle C1。
B. Firstly, the shortest Euclidean distance D (x) between each particle and the current existing central particle is calculated, then the probability P (x) that each particle is selected as the next central particle is calculated according to a formula, and then the next central particle is selected by a roulette method.
C. Repeat step B until K center particles are selected.
D. For the remaining particles, the Euclidean distances between the particle and all the center points are calculated, and the particle is assigned to the center point closest to the particle, as shown in the following table.
Table 1: european style distance meter
E. For each subgroup, the position of its center point is calculated by calculating the average of the particles in the subgroup in all dimensions.
F. And if the central point is not changed, finishing the clustering process.
(3-2) solving Using Multi-population particle swarm Algorithm
A. Initializing a particle swarm, and dividing the particle swarm into K subgroups according to a K-Means + + algorithm.
B. The fitness of each particle is calculated according to the objective function.
C. And updating the historical optimal position of each particle, the optimal position of each subgroup and the optimal position of the whole group.
D. Let the position of the particle i be Xi＝(x1,x2,…,xα) At a velocity of Vi＝(v1,v2,…,vα) The position and speed variation formula of the particle is as follows:
wherein W is the inertial weight, k represents the current iteration number, Pi kFor the historical optimal position of the particle i,
is the global optimum position of the subgroup to which the particle i belongs, C1And C2Is a learning factor which represents the search of solution space by the factor of the particular particle itself and the search of solution space by the social factor, a1And a2Are random numbers that follow a linear distribution.
The velocity and position of each particle is updated according to the formula, as shown in the table below.
Table 2: particle data sheet
E. And performing K-Means + + clustering division on the whole particle swarm again every M iterations.
F. It is checked whether an exit condition is satisfied. If yes, the algorithm is ended, otherwise, the step B is skipped.
The following description is made in conjunction with a simulation example:
assuming that the processing environment is a power communication network environment, the acquired data is derived from the power communication network site work order. The specific treatment process is as follows:
(1) power communication network on-site operation and maintenance workerThe monotonicity problem is described as: one item group has m1,m2,…mmMembers, each member having a different type of skill and proficiency. Project team now needs to complete p1,p2,…pnDifferent items, each item containing j1,j2,…jkAnd (4) task work sequence. The flow task work sequence is given in advance, and the time to complete each task work sequence is also given: t is t1,t2,…tk. Each task work sequence is represented by a required work skill type and work time.
Each task work sequence for a different project may require a staff with different skills. Staff wi(i-1, 2, …, m) has a skill s1,s2,…sfThe corresponding skill factor is y1,y2,…yf(f is the number of skill classes, skill factor yiIs equal to [0,2]0 means no skill; 1 denotes the average skill, which means that the worker has mastered the basic skill; 2 indicates that the skill is skilled and that the skill factor is given by the team leader (expert assessment methods may be used). Since project team members have various skills with skill factors greater than 0, when they are empty, the members will do a many-to-many relational mapping with the task work sequence: one employee may be selected to deploy multiple tasks, and one task may be selected to be completed by multiple employees. After deployment is successful, tasks may be assigned to the current task before it is completed.
(2) Establishing on-site operation and maintenance work order optimization model
① establishing skill maximization work order scheduling optimization model MaxF
MaxF is the maximization function of the sum of the average skill factors, and F is the sum of the average skill factors and is expressed as follows:
wherein, yijA skill factor of a skill type required by the ith task sequence possessed by the jth operation and maintenance personnel; number ofiTo complete the ithThe number of operation and maintenance personnel of the task sequence; n is the number of items to be completed; k is the number of task sequences per project to be completed.
② time-optimal work order scheduling optimization model MinZ
MinZ is a time-sum minimization function, and Z is a time-sum, expressed as follows:
wherein, cijThe time required for the jth operation and maintenance personnel to complete the ith task sequence; x is the number ofijThe completion degree of the jth operation and maintenance personnel for the ith task sequence is calculated; and m is the number of operation and maintenance personnel.
③ constructing a constraint formula
According to the scheduling constraint condition of the on-site operation and maintenance work order, a constraint formula is constructed as follows:
wherein, tiIs the end time of a task work sequence. To ensure a high quality of the task, the staff deployment should ensure that a number of free staff with the highest capacity value are allocated to the current task.
(3) Initialization of particle swarm
① divide each dimension in the search space equally into n, where n is the number of particles, i.e. the number m of members of the power communications network project group.
② initializing each particle in the particle group, when initializing the value of the ith dimension, randomly selecting a section from the unselected sections of the dimension, randomly generating a value in the section, and finally marking the section as selected.
③ loop through initializing the remaining particles in step ②.
(4) And (4) dividing the particle swarm into K subgroups according to the K-Means + + clustering algorithm in the step 3-1, namely dividing all members of the project group into K subgroups after the clustering process is finished.
(5) And 3-2, calculating the adaptive value of each particle, and updating the self optimal position and worst position of the particle and the global optimal position and global worst position of the subgroup.
(6) Dynamic adjustment of inertial weights and learning factors based on cloud model
Parameters related to the particle swarm optimization include inertia weight and learning factors, and the parameters have important influences on the convergence speed, the quality of solution, the stability and other performances of the particle swarm optimization.
① inertial weight adjustment strategy
The inertial weight determines the degree to which the particle inherits the historical velocity, affecting the degree to which the particle balances between the global search and the local search. The value of W is generally between [0,1], and if W is only defined as a constant, the performance of the algorithm is not facilitated. A classic value-taking algorithm of W is a linear decreasing mode, and the formula is as follows:
wherein, Wi+1Is the value of the inertial weight in the (i + 1) th iteration, i-count is the maximum iteration number specified by the algorithm, WmaxAnd WminAre two constants that represent the maximum and minimum values of the inertial weight specified in the algorithm.
The cloud model algorithm is adopted to dynamically adjust the inertia weight W, so that the phenomenon of 'oscillation' caused by unreasonable inertia weight is avoided. The formula is as follows:
where μ is the corresponding degree of membership, favgIs the population mean fitness value.
② learning factor
The learning factor is divided into two parts: individual cognition C1 and population cognition C2. One common dynamic adjustment strategy is:
the learning factor is adjusted by using a cloud model, taking C1 as an example, and the formula is as follows:
C1＝C1 max-μ*(C1 max-C1 min)
wherein, C1 maxAnd C1 minRespectively, the maximum and minimum values set for C1, and μ is the corresponding degree of membership.
(7) And when the iteration times reach multiples of M, dividing the particle swarm by using a K-Means + + algorithm again.
(8) Checking whether the iteration times reach the maximum value, and if so, finishing the algorithm; otherwise, jumping to the step (5).
Through the steps, the optimal solution of the site operation and maintenance work order scheduling under the multi-resource constraint can be finally obtained.
The algorithm proposed by the method is simulated by combining the existing network data of a provincial power communication network part in China, and the overhaul plan of the region in 2017 and 4 months is taken as a research object. The list of overhaul tasks to be scheduled in the overhaul period and the overhaul plans to be scheduled manually are shown in tables 3 and 4, respectively:
table 3: experimental data overhaul task in 4-month overhaul period
Table 4: maintenance plan made by manual scheduling
The overhaul tasks are respectively scheduled by using a multi-population particle swarm algorithm, the algorithm is solved for 5 times in order to reduce errors as much as possible, and the final result is averaged. The number of particles in the particle swarm is 80, the number of the divided subgroups is 5, the maximum value and the minimum value of the inertia weight are 0.6 and 1 respectively, and a learning factor C1And C2The maximum and minimum values of (2) and (1.2) respectively, and the maximum number of iterations of the algorithm is 100.
Table 5: maintenance plan scheduled by using multi-population particle swarm optimization
As shown in fig. 3, the left side represents the average waiting time of manual scheduling, and the right side represents the average waiting time of multi-population-swarm-algorithm scheduling.
The key points of the protection of the invention are as follows: and establishing a work order scheduling optimization model with maximized skills and optimal time and a constraint formula according to the on-site operation and maintenance work order scheduling influence factors and the scheduling strategy. And dividing the particle swarm based on a K-Means + + clustering algorithm. And (5) solving by using a multi-population particle swarm algorithm to obtain an optimal solution. A work order dispatching model in a power communication network on-site operation and maintenance work order dispatching method patent is composed of a work order completion quality sum maximization function, a work order completion time sum minimization function, an operation and maintenance personnel utilization rate maximization function, a work order waiting time sum minimization function and constraint conditions, and provides a work order dispatching model solved based on a cuckoo algorithm and an extreme value dynamics optimization algorithm. A work order scheduling model in a power communication network field operation and maintenance work order scheduling method patent is composed of an average skill factor sum maximization function, a task completion time sum minimization function and constraint conditions, and a virus genetic algorithm solving work order scheduling model is provided.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A power communication network field operation and maintenance work order scheduling method is characterized by comprising the following steps:
step 1: confirming on-site operation and maintenance work order scheduling influence factors, carrying out demand analysis on the on-site operation and maintenance work order scheduling influence factors, summarizing the attributes of each influence factor, and establishing a constraint condition of a model on the basis;
step 2: establishing a field operation and maintenance work order scheduling optimization model;
and step 3: the solution was performed using a multi-population particle swarm algorithm in combination with the K-Means + + algorithm.
2. The method for scheduling the on-site operation and maintenance work order of the power communication network according to claim 1, wherein the method comprises the following steps: the step 1 specifically comprises:
(1-1) confirming influence factors to be considered in the whole scheduling process of the operation and maintenance work of the power communication site;
and (1-2) establishing constraint conditions of the model.
3. The method for scheduling the on-site operation and maintenance work order of the power communication network according to claim 2, wherein the method comprises the following steps: in the step (1-1), the following is specifically performed:
the scheduling problem of the operation and maintenance work of the power communication field is described as follows: operation and maintenance personnel, operation and maintenance resources and operation and maintenance work orders 3 are required to be considered in the whole operation scheduling process; carrying out demand analysis on the 3 types of operation and maintenance influence factors, and summarizing the attributes of the influence factors;
for the operation and maintenance personnel, various types of skills, proficiency and authority states are included;
for the operation and maintenance work order, each operation and maintenance project has different work sequences, and each process has required work skills, work resources and corresponding completion time;
and for the operation and maintenance resources, the operation and maintenance types and the operation and maintenance quantity are included.
4. The on-site operation and maintenance work order scheduling method of the power communication network according to claim 3, wherein the method comprises the following steps: in the step (1-2), the concrete steps are as follows:
on the basis of analyzing the influence factors of the field operation and maintenance work order scheduling, according to the actual requirements of the field operation and maintenance of the power communication network, the constraint conditions of the summary model are as follows:
the task work sequences among different projects are not constrained, and the same project has constraints;
if the candidate item set for the current working group is empty, some time is needed, and the deployment can be carried out after partial resources are released;
after starting, the task work sequence can not be interrupted;
each task work sequence can be completed by 1-3 persons;
time to pre-allocate and complete the project.
5. The on-site operation and maintenance work order scheduling method of the power communication network according to claim 4, wherein the method comprises the following steps: the step 2 specifically comprises the following steps:
(2-1) establishing a skill maximization work order scheduling optimization model MaxF, wherein MaxF is a maximization function of the sum of average skill factors, and F is the sum of the average skill factors;
(2-2) establishing a time-optimal work order scheduling optimization model MinZ, wherein MinZ is a time sum minimization function, and Z is a time sum;
and (2-3) constructing a constraint formula according to the scheduling constraint conditions of the on-site operation and maintenance work order.
6. The method for scheduling the on-site operation and maintenance work order of the power communication network according to claim 5, wherein the method comprises the following steps: the step 3 specifically comprises:
(3-1) dividing subgroups based on a K-Means + + clustering algorithm;
(3-2) solving using a multi-population particle swarm algorithm.
7. The method for scheduling the on-site operation and maintenance work order of the power communication network according to claim 6, wherein the method comprises the following steps: the step (3-1) is specifically as follows:
randomly selecting one particle from the particle group as a first central particle C1；
Firstly, calculating the shortest Euclidean distance D (x) between each particle and the current existing central particle, then calculating the probability P (x) of each particle being selected as the next central particle by a formula, and then selecting the next central particle by a roulette method;
repeating the steps until K central particles are selected;
calculating Euclidean distances between the particle and all central points for the rest particles, and attributing the particle to the central point closest to the particle;
for each subgroup, calculating the position of the center point thereof by calculating the average value of the particles in the subgroup in all dimensions;
and if the central point is not changed, finishing the clustering process.
8. The method for scheduling the on-site operation and maintenance work order of the power communication network according to claim 7, wherein the method comprises the following steps: the step (3-2) is specifically as follows:
initializing a particle swarm, and dividing the particle swarm into K subgroups according to a K-Means + + algorithm;
calculating the fitness of each particle according to an objective function;
updating the historical optimal position of each particle, the optimal position of each subgroup and the optimal position of the whole subgroup;
let the position of the particle i be Xi＝(x1,x2,…,xα) At a velocity of Vi＝(v1,v2,…,vα) The position and speed variation formula of the particle is as follows:
wherein W is the inertial weight, k represents the current iteration number, Pi kFor the historical optimal position of the particle i,
is the global optimum position of the subgroup to which the particle i belongs, C1And C2Is a learning factor which represents the search of solution space by the factor of the particular particle itself and the search of solution space by the social factor, a1And a2Is a random number that follows a linear distribution;
updating the speed and position of each particle according to a formula;
performing K-Means + + clustering division on the whole particle swarm again every M iterations;
checking whether an exit condition is met; if so, ending the algorithm, otherwise, jumping to the step: the fitness of each particle is calculated according to the objective function.
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