CN114580678A - Product maintenance resource scheduling method and system - Google Patents
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Abstract
The application discloses a method and a system for scheduling product maintenance resources, wherein the method comprises the following steps: acquiring a product maintenance task, and determining personnel skill demand information and resource demand information of the product maintenance task; acquiring resource supply information and candidate service personnel information; according to the personnel skill demand information, the resource supply information and the candidate service personnel information, establishing a constraint condition and a maintenance scheduling model, and determining a target function of the maintenance scheduling model; and creating an optimization algorithm, obtaining the optimal solution of the objective function under the constraint condition by using the optimization algorithm, and determining a maintenance scheduling scheme according to the optimal solution of the objective function. The invention improves the accuracy of scheduling multi-skill human resources and material resources in maintenance service, improves the efficiency of scheduling resources, reduces the maintenance cost of products, saves the enterprise expenditure, improves the customer satisfaction degree, and has stronger practicability.
Description
Technical Field
The present invention relates to the field of maintenance resource scheduling technologies, and in particular, to a method, a system, an electronic device, and a computer-readable storage medium for scheduling product maintenance resources.
Background
With the development of science and technology, most products have the characteristics of complex structure, many parts, complex design and manufacturing technology, long product life cycle and the like, so that maintenance service faces a series of problems: complex structures and multiple parts can result in large resource and inventory occupancy; the discrete distribution of customers causes high technical service cost and difficult improvement of service efficiency; maintenance service personnel are required to have a certain skill level, a wide skill range and the like, and the difficulty of modeling and solving the scheduling problem is greatly increased by the characteristics. How to make scientific and correct decision on the scheduling of maintenance service resources is of great significance to the improvement of the maintenance efficiency of enterprises and the maintenance of the satisfaction degree of customers.
In recent years, many researchers have conducted extensive research in the field of resource scheduling, and have achieved many results. However, most of the existing scheduling models related to service personnel only consider multi-skill personnel scheduling, the scheduling object is single, and the scheduling of auxiliary tools and equipment parts is not considered; the dynamic property of the working efficiency of the staff is not considered, and in the scheduling of the multi-skill human resources, the staff executes the service tasks for a long time, and the accumulated working time generates the learning effect of the service tasks, so that the execution efficiency is improved. In addition, the collaboration execution links are numerous, a lot of uncertainties are generated, and the omission of the uncertainty factors may offset the advantages of the collaboration service mode. However, in the prior art, a proper scheduling model is not available, the various dynamic change factors and uncertain factors are considered, and the problems of low scheduling efficiency and inaccurate scheduling exist in scheduling decision making.
Therefore, it is necessary to design a maintenance scheduling method for multi-skill human resources and material resources under consideration of various variation factors, so as to provide a solution for the integrated maintenance resource scheduling of the multi-skill human resources and the material resources under various dynamic factors and uncertain factors, improve the accuracy of the maintenance scheduling, improve the efficiency of the maintenance scheduling, reduce the maintenance cost of products, and save the enterprise expenditure.
Disclosure of Invention
In view of the above, it is necessary to provide a method, a system, an electronic device and a computer storage medium for scheduling product maintenance resources, so as to solve the problems of inaccurate scheduling of maintenance resources and low scheduling efficiency in maintenance scheduling involving multi-skill human resources and material resources in the prior art.
In order to solve the above problem, the present invention provides a product maintenance resource scheduling method, including:
acquiring a product maintenance task, and determining personnel skill demand information and resource demand information of the product maintenance task;
acquiring candidate service staff information and resource supply information;
establishing a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the candidate service personnel information, and determining a constraint condition and an objective function of the maintenance scheduling model;
and calculating the optimal solution of the objective function under the constraint condition, and determining a maintenance scheduling scheme according to the optimal solution of the objective function.
Further, establishing a maintenance scheduling model, comprising: and determining the completion time of the maintenance task, the redundancy of the candidate service personnel and the resource performance cost ratio according to the personnel skill demand information, the resource supply information and the candidate service personnel information.
Further, the scheduling model comprises a personnel matching degree model, a resource performance model and a stochastic programming model;
the personnel matching degree model is used for determining personnel service time and candidate personnel redundancy according to the candidate service personnel information and the personnel skill demand information;
the resource performance model is used for determining a resource performance cost ratio and a resource in-place time according to the resource demand information and the resource supply information;
the stochastic programming model is used for obtaining maneuvering time according to the personnel skill demand information, the resource supply information and the candidate service personnel information;
the maintenance task completion time is the sum of the personnel service time, the resource in-place time and the maneuver time.
Further, the objective function of the scheduling model includes: minimizing repair task completion time, minimizing candidate servicer redundancy, and maximizing resource performance cost ratio.
Further, calculating an optimal solution of the objective function under the constraint condition includes:
generating an initial solution set of the scheduling model objective function according to the constraint condition;
optimizing the initial solution set by using a first preset algorithm to obtain a candidate solution set;
and obtaining an optimal solution meeting preset conditions according to the candidate solution set.
Further, optimizing the initial solution set by using a first preset algorithm to obtain a candidate solution set, including:
and calculating a characteristic value of the initial solution set by using a second preset algorithm, and generating the candidate solution set according to the characteristic value.
Further, obtaining an optimal solution meeting a preset condition according to the candidate solution set, including:
calculating the non-dominated ordering and crowdedness of the candidate solution set;
and obtaining the optimal solution which meets the preset conditions according to the non-dominated sorting and the crowding degree.
The invention also provides a product maintenance resource scheduling system, which comprises:
the task information acquisition module is used for acquiring a product maintenance task and determining personnel skill demand information and resource demand information of the product maintenance task;
the supply information acquisition module is used for acquiring resource supply information and candidate service personnel information;
the model establishing module is used for establishing a constraint condition and a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the candidate service personnel information and determining an objective function of the maintenance scheduling model;
and the optimization module is used for calculating the optimal solution of the objective function under the constraint condition and determining a maintenance scheduling scheme according to the optimal solution of the objective function.
The invention further provides an electronic device, which comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the product maintenance resource scheduling method according to any technical scheme is realized.
The invention also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements a product repair resource scheduling method according to any of the above technical solutions.
Compared with the prior art, the invention has the beneficial effects that: firstly, acquiring a product maintenance task, and determining personnel skill demand information and resource demand information of the product maintenance task; secondly, acquiring multi-skill human resources and material resource supply information; thirdly, establishing constraint conditions and a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the human resource information; and finally, calculating the optimal solution of the objective function of the maintenance scheduling model, and determining a maintenance scheduling scheme. The invention considers various dynamic factors and uncertain factors existing in the maintenance resource scheduling process, establishes a maintenance scheduling model which is more in line with the actual situation, and provides a basis for improving the scheduling accuracy; the optimal solution of the maintenance scheduling model is solved by establishing an optimization algorithm, and the method has the advantages of high calculation speed, high precision and good robustness; and a final resource scheduling scheme is determined according to the optimal solution, so that the maintenance service efficiency is improved, the scheduling cost is reduced, the customer satisfaction is improved, and the method has high practicability.
Drawings
FIG. 1 is a flowchart illustrating a product repair resource scheduling method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an embodiment of subtask execution logic according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of an algorithm for optimizing the initial solution set according to the present invention;
FIG. 4 is a diagram illustrating an embodiment of initial solution set encoding provided by the present invention;
FIG. 5 is a schematic diagram of an embodiment of the Pareto optimal solution set spatial distribution provided by the present invention
FIG. 6 is a schematic structural diagram of an embodiment of a product repair resource scheduling system according to the present invention;
fig. 7 is a block diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The invention provides a product maintenance resource scheduling method, a product maintenance resource scheduling system, electronic equipment and a computer-readable storage medium, which are respectively described in detail below.
The embodiment of the invention provides a product maintenance resource scheduling method, a flow schematic diagram of which is shown in fig. 1, and the method specifically comprises the following steps:
s101, acquiring a product maintenance task, and determining personnel skill demand information and resource demand information of the product maintenance task;
step S102, acquiring resource supply information and candidate service staff information;
step S103, establishing a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the candidate service personnel information, and determining a constraint condition and an objective function of the maintenance scheduling model;
step S104: and calculating the optimal solution of the objective function under the constraint condition, and determining a maintenance scheduling scheme according to the optimal solution of the objective function.
Compared with the prior art, the product maintenance resource scheduling method provided by the embodiment includes the steps of firstly, obtaining a product maintenance task, and determining personnel skill demand information and resource demand information; secondly, acquiring multi-skill human resources and material resource supply information; thirdly, establishing constraint conditions and a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the human resource information; and finally, calculating the optimal solution of the objective function of the maintenance scheduling model by using an optimization algorithm, and determining a maintenance scheduling scheme. The invention considers various dynamic factors and uncertain factors existing in the maintenance resource scheduling process, establishes a maintenance scheduling model which is more in line with the actual situation, and provides a basis for improving the scheduling accuracy; the optimal solution of the maintenance scheduling model is solved by establishing an optimization algorithm, and the method has the advantages of high calculation speed, high precision and good robustness; and a final resource scheduling scheme is determined according to the optimal solution, so that the maintenance service efficiency is improved, the scheduling cost is reduced, the customer satisfaction is improved, and the method has high practicability.
As a specific example, step S101 includes: and acquiring the location of the product maintenance task, basic information of subtask division, required resource types and required personnel skill levels. In the subsequent scheduling process, according to the specific location of the maintenance task, the nearest resource provider and candidate service personnel are determined, the cost of the resource in-place time and the personnel in-place time is reduced, and the maintenance efficiency is improved.
As a specific example, in step S102, acquiring resource supply information and candidate service person information includes: and acquiring resource attribute information and candidate service personnel attribute information provided by the resource candidate supplier. Determining the location of a resource supplier and whether the quality condition of the resource meets the maintenance requirement and other information according to the resource attribute information; and determining information such as the skill level of the candidate service personnel, the accumulated working time, the location of the candidate service personnel and the like according to the attribute information of the candidate service personnel, and providing a data basis for subsequent scheduling.
As a preferred embodiment, in step S103, establishing a maintenance scheduling model includes: and determining the completion time of the maintenance task, the redundancy of the candidate service personnel and the resource performance cost ratio according to the personnel skill demand information, the resource supply information and the candidate service personnel information.
In order to ensure the maintenance quality and simultaneously minimize the maintenance cost and maximize the maintenance efficiency in the process of maintenance service scheduling, as a preferred embodiment, the objective function of the scheduling model includes:
minimizing repair task completion time, minimizing candidate servicer redundancy, and maximizing resource performance cost ratio.
As a preferred embodiment, the scheduling model includes a personnel matching degree model, a resource performance model and a stochastic programming model;
the personnel matching degree model is used for determining personnel service time and candidate personnel redundancy according to the candidate service personnel information and the personnel skill demand information;
the resource performance model is used for determining a resource performance cost ratio and resource in-place time according to the resource demand information and the resource supply information;
the stochastic programming model is used for obtaining maneuvering time according to the personnel skill demand information, the resource supply information and the candidate service personnel information;
the maintenance task completion time is the sum of the personnel service time, the resource in-place time and the maneuver time.
As a specific embodiment, the person matching degree model includes a learning efficiency matching degree model and a skill level matching degree model. By evaluating the learning efficiency and the actual skill level of the candidate service personnel, the calculation of the personnel matching degree is more accurate, and the practicability of the scheduling model is improved.
The learning efficiency matching degree model is used for analyzing the influence of a learning effect on the matching degree of the candidate service personnel and determining the learning efficiency matching degree model according to the ratio of the accumulated working time of the candidate service personnel to the accumulated working time of the rated personnel required by the maintenance task; and the skill level matching degree model is used for analyzing the influence of the skill level of the candidate service personnel on the matching degree of the candidate service personnel and determining the skill level matching degree model according to the ratio of the actual skill level value of the candidate service personnel to the skill demand value of the maintenance task.
And weighting the learning effect matching degree model and the skill level matching degree model to obtain a personnel matching degree model.
As a specific embodiment, the establishing of the learning efficiency matching degree model includes:
determining the rated accumulated working demand time of personnel required for completing each subtask;
acquiring the actual accumulated working time of each candidate service staff;
and taking the ratio of the actual accumulated working time of the candidate service staff to the rated accumulated working demand time of the staff as the matching degree of the learning efficiency.
As a specific embodiment, the creating of the skill level matching degree model includes:
determining the minimum requirement of the skill level of each subtask;
acquiring the actual skill level value of each candidate service person;
and obtaining a skill level matching degree model according to the actual skill level value of the candidate service personnel and the minimum required skill level of the personnel.
The person matching degree model is further described below with a specific embodiment.
The person matching degree model is expressed by the following formula:
PZij＝ω1*MHij+ω2*TPij
in the formula, PZijRepresenting candidate service person miTask pairjThe degree of personnel matching; omega1，ω2Is a weighted value; MHijRepresenting candidate service person miAnd subtask tasKjThe learning efficiency matching degree; TPijRepresenting candidate service person miAnd subtask taskjThe skill level matching degree of (1);
wherein, the learning efficiency matching degree is as follows:
in the formula (a)jFor candidate service personnel m in preset time periodiThe accumulated working time of (2); xt ofjIndicating the execution of a subtask taskjThe personnel rated cumulative work demand time required to reach its completion criteria is then reached.
The skill level matching degree is as follows:
in the formula, GijRepresentation taSkjAmong the required skills, by candidate service personnel miThe skills in charge; gijRepresents GijLength of (d);
representing candidate service person miAnd subtask taskjAs for the degree of matching of the skills k,
satisfies the following conditions:
wherein,
representing subtask taskjA requirement value for skill k;
representing candidate service person miActual level of skill k; x, Y are the lower and upper limits of skill level values.
As a preferred embodiment, the establishing the stochastic programming model includes:
determining uncertainty factors for the maintenance task;
quantifying the uncertainty factor;
and establishing a stochastic programming model by utilizing a preset confidence level and probability distribution according to the quantization result.
In step S201, the source of the uncertainty factor is first determined. In a product repair collaboration service chain, uncertainty arises not only within the participating collaborative individual units, but also may arise during the implementation of the collaborative relationships. For each cooperative unit, uncertainty mainly expresses in environment, organization and management, transportation capacity, resource and personnel states and the like; the uncertainty in the service process is mainly expressed in the description and quantification of service requirements, a service execution time window, task planning and the like.
In step S202, uncertainty in the service process is quantified, which is mainly expressed in uncertainty of time. Random variables are used to describe uncertain time variables, and the variables are approximately considered to obey a certain probability distribution. And taking a preset confidence level to establish a stochastic programming model.
As a specific example, the stochastic programming model is as follows:
Pr{gj(x,ξ)≤0,j＝1,2,...,p}≥α
in the formula, gj(x, xi) is an uncertain function, alpha and beta represent confidence levels, Pr represents the probability that the solved time is less than beta optimistic value, x and xi represent decision and random vector respectively,
representing the minimum value of the objective function T (χ, ξ) taken at a confidence level of at least β.
As a specific example, as shown in fig. 2, the constraint conditions include: the subtasks have sequential or parallel execution logic relation; the resource is used by at least one subtask; the subtasks require at least one service person to complete; when the subtasks are executed, all resources and personnel do not influence each other and conflict with each other; the skill level of the service personnel team cannot be lower than the skill level required for the service task; the subtasks can only be executed after the required resources and service personnel have arrived and the pre-order subtasks have been completed.
As a preferred embodiment, in step S104, calculating an optimal solution of the objective function under the constraint condition includes:
generating an initial solution set of the scheduling model objective function according to the constraint condition;
optimizing the initial solution set by using a first preset algorithm to obtain a candidate solution set;
and obtaining an optimal solution meeting preset conditions according to the candidate solution set.
As a specific example, the first preset algorithm is an optimization algorithm based on modified NSGA-II. The method comprises the following specific steps:
step S301: encoding the initial solution in a one-dimensional array mode, and randomly generating the initial solution according to requirements;
step S302: carrying out selection, crossing and mutation operations on the solution sets to obtain candidate solution sets;
step S303: judging whether the candidate solution set reaches a preset termination condition, and if the candidate solution set reaches the preset termination condition, ending the process; if not, the process proceeds to step S302.
As a preferred embodiment, optimizing the initial solution set by using a first preset algorithm to obtain a candidate solution set, includes:
and calculating a characteristic value of the initial solution set by using a second preset algorithm, and generating the candidate solution set according to the characteristic value.
As a specific embodiment, before step S302, a second preset algorithm is used to calculate a feature value of the initial solution set, where the second preset algorithm is a neural network fitting, and the feature value is a fitness of time.
As a preferred embodiment, obtaining an optimal solution meeting a preset condition according to the candidate solution set includes:
calculating the non-dominated ordering and crowdedness of the candidate solution set;
and obtaining an optimal solution meeting preset conditions according to the non-dominated sorting and the crowding degree.
The above solution is explained in more detail below with reference to a specific maintenance service task:
one complete maintenance service task needs h resources, and the resource demand vector is R ═ R1,r2,...,ri]，riDenotes the ith resource, resource riCan be provided by its corresponding supplier SRi＝[sri1,sri2,...,srij]Providing, srijAnd j, which represents the corresponding j supplier of the ith resource, and the supplier supplies the resource to the site of the maintenance service.
The maintenance task may consist of n subtasks [ task ]1,task2,...,taskn]Indicates, taskiThe i-th subtask is represented, and the subtask skill requirement vector is xi ═ xi1,xi2,...,xij]，xijRepresenting subtask taskjFor the requirement level of jth skill, the complete service task skill requirement vector S ═ S1,s2,...,sj]，sjMaximum value, s, representing the j skill level requirement for a full servicej＝max(xij) J is belonged to (1, s), and s is a skill number;
candidate attendant skill vector am＝[am1,am2,...,amj]，amjThe jth skill level for the mth candidate attendant. According to the vector S and the vector amOptional attendant vector d may be derivedi＝[di1,di2,...,dij]，dijAnd j optional service personnel corresponding to the ith skill required by the complete service. The service staff team vector P can be obtained from the optional service staff vector1,p2,...,pi]，piFor the ith skill in vector diThe corresponding service staff serial number in (1).
Different scheduling schemes may be generated by a combination of resource providers and teams of service personnel.
The constraints of the scheduling scheme include:
constraint 1: constraint of task completion confidence level:
equation (1) represents an optimistic value for finding a confidence level of service completion time not less than beta
Wherein Pr represents the probability of finding an optimistic time less than β;
an optimistic value representing a target value for a repair task completion time.
Constraint 2: each subtask has a sequential or parallel execution logic relationship, and each subtask can be executed only when required resources and service personnel arrive and the pre-subtask is completely completed, which can be expressed by the following formula:
T＝max(tei) (2)
equation (2) indicates that the repair service completion time is equal to the maximum value of the subtask completion times, teiRepresenting subtask taskiT denotes the repair service task completion time.
tei＝tsi+tri (3)
Equation (3) represents the sum of the start execution time and the execution time of the subtask, tsiRepresenting subtask taskiStart execution time of triRepresenting subtask taskiThe execution time of.
tsi＝max(max(Yij*ptj)，max(tz*oiz)，Mi) (4)
Equation (4) indicates that the start execution time of the subtask is the maximum value of the resource and person availability time and the preamble subtask completion time.
The formula (5) represents the execution time of the subtask, which is the ratio of the basic execution time and the matching degree; bt andirepresenting subtask taskiBasic execution time of, PZiIndicating the execution of a subtask taskiAverage skill matching of service personnel.
j∈(1，m),z∈(1,h),i∈(1,n) (6)
In the formula (6), j is the number of service personnel, z is the number of required resources, and n is the number of subtasks.
Mi＝max[te1Q1i,te2Q1i,...,tenQni] (7)
Equation (7) represents the maximum value of the sub-task completion time for the sub-task preamble. QijExpressed as a subtask logical relationship decision parameter, QijEqual to 1 or 0, respectively, indicates a subtask taskiTask being or notiThe preceding subtask of (1).
Constraint condition 3: the subtask has at least one preceding subtask:
constraint 4: the resource is used by at least one subtask:
constraint condition 5: the subtask requires at least one service person;
in the formula, YijThe parameters are determined dynamically for subtask demand engineers, with different groups of engineers, YijEqual to 1 or 0, respectively, indicates a subtask taskiWith or without the need for a jth attendant.
Constraint condition 6: the skill level of the service staff team cannot be lower than the skill level required for the service task:
spgi-si≥0 (11)
in the formula, spgiRepresenting the maximum value of the ith skill level in the team of service engineers.
The objective function of the scheduling model includes:
equation (12) represents target 1: the time for completion of the maintenance service is minimized,
an optimistic value representing a time target value.
Equation (13) represents target 2: the performance cost ratio of the maintenance service task is maximum; where R represents the total performance cost ratio of resources required for a maintenance service task, vijRepresents the performance cost ratio, ω, of resource i when scheduled from path jiRepresenting the weight value of resource i.
Equation (14) represents target 3: the candidate attendant has minimal redundancy. PE denotes personnel redundancy, q denotes number of people serving a team of personnel, s denotes number of resources required, spgiIndicating service personnelMaximum value of ith skill level in team, siRepresenting the maximum value of the ith skill level required for the service task.
As a specific example, the first preset algorithm is an optimization algorithm based on modified NSGA-II. From the above analysis, in a maintenance service task, the solution of multi-objective optimization is a set of equilibrium solutions, i.e. Pareto optimal solution set, according to the mutual competition and conflict relationship between the optimization targets. The NSGA-II adopts a rapid non-dominated sorting method, and simultaneously introduces a concept of crowdedness, so that a Pareto solution set has more uniformity and diversity, and the time complexity of an algorithm is reduced; meanwhile, due to the fact that the elite strategy is adopted, the optimal solution can be well stored in the iterative process, and the method is widely applied to multi-objective optimization. In view of this, the present embodiment utilizes an improved NSGA-II based optimization algorithm to perform multi-objective optimization on three objectives in the maintenance service scheduling problem.
As shown in fig. 3, optimizing the initial solution set by using a first preset algorithm to obtain an optimal solution meeting preset conditions, includes:
step S401: encoding the initial solution in a one-dimensional array mode, and randomly generating the initial solution according to requirements;
step S402: calculating the fitness of the initial solution, wherein the fitness of time is determined by fitting of a neural network, and the fitness of the performance cost ratio and the personnel redundancy is determined by formulas (13) and (14);
step S403: carrying out rapid non-dominated sorting and congestion degree calculation on the initial solution set; performing non-dominated sorting and congestion degree calculation on the initial solution;
step S404: selecting, crossing and mutating the solution set to obtain a new generation of population;
step S405: calculating the fitness of the new generation of population;
step S406: carrying out non-dominated sorting and congestion degree calculation operation on the new population, and selecting the new population according to the sequence value and congestion degree of the individuals;
step S407: judging whether a termination condition is reached, if not, entering a step S404; if the termination condition is reached, the operation is terminated.
In step S401, since the scheduling path of the resources required for the maintenance service and the composition of the service staff team are important factors affecting the three objects of the present invention, the scheduling path of each resource and the number of the service staff in the team are features that need to be highlighted when the chromosome is coded. This example uses integer coding, and the chromosome is composed of two parts [ L, P]As shown in fig. 4. The first part L represents the scheduling path vector of the resource, and the length is the required resource number h, LiIs equal to the number of suppliers of 1 to ith resources; the second part P is a team vector of service personnel, and the length is the number s, P of skill levels required by the servicejIs 1 to the jth skill, and thus the chromosome length is h + s. The first five bits represent the five resources required for decoding and are respectively allocated correspondingly. The last five bits represent the determination of the service person in combination with the optional person vector d with the corresponding number.
In step S402, the fitness of each initial solution is calculated, and the time target value is obtained by fitting through a BP neural network, where the training step of the neural network is:
step S501: randomly generating K non-repetitive feasible solutions;
step S502: selecting a feasible solution, and randomly generating N groups of random parameter samples according to actual probability distribution;
step S503: calculating the completion time of each sample, and sequencing from small to large;
step S504: according to a given confidence level alpha, taking the alpha N time as a time target value of the feasible solution;
step S505: repeating the steps S502-S504K times to obtain K groups of data;
step S506: the BP neural network is trained, and the BP neural network is trained,
step S507: and obtaining the trained BP neural network, and using the trained BP neural network to optimize the algorithm and reduce the algorithm complexity.
In step S403, the fast non-dominated sorting specifically includes the following steps:
the first step is as follows: setting i to be 1;
the second step is that: for all j ≠ 1.2.. n, and j ≠ i, the dominant versus non-dominant relationships between individuals x _ i and x _ j are compared, as defined above;
the third step: if no individual x _ j is better than x _ i, then x _ i is marked as a non-dominant individual;
the fourth step: let i ═ i +1, go to the second step until all non-dominant individuals are found.
The non-dominant individual set obtained through the above steps is the first level non-dominant layer of the population, and then, ignoring the marked non-dominant individuals (i.e. the individuals do not perform the next round of comparison), and following steps S601-S604, the second level non-dominant layer is obtained; and so on until the entire population is stratified.
In step S403, the specific steps of the congestion degree calculation are as follows:
the congestion degree i _ d of each point is 0;
for each target, sorting the population in a non-dominated manner, and enabling the crowdedness of two individuals on the boundary to be infinite, namely i _ d ∞;
and (3) calculating the crowdedness of other individuals:
wherein: i _ d represents the congestion degree of the point i, f _ j ^ (i +1) represents the jth objective function of the point i +1, and f _ j ^ (i-1) represents the jth objective function value of the point i-1.
The selection process moves the optimization towards pareto optimal solutions. The operator is selected to avoid the loss of effective genes and improve the survival probability of high-performance individuals. Through the non-dominance sorting and the crowding degree calculation, each individual obtains two attributes of a non-dominance sorting level and a crowding degree.
In step S405, individuals are selected by using a binary tournament, and selection, crossover, and mutation operations are performed on the solution set to obtain a new generation population. The method comprises the following specific steps:
randomly selecting an individual i and an individual j from a population;
firstly, comparing the non-dominant ranking grades of the two individuals, and if the non-dominant ranking grades are not equal, selecting a smaller value; if the two values are equal, the two congestion degrees are compared, giving priority to the less congested individuals.
The crossover and mutation operations cooperate to complete global and local searches of the space, occur with a certain probability, and create new individuals to improve the searching ability of the algorithm.
The present embodiment employs two-point crossing and one-point bit variation.
The technical effects achieved by the present invention are further explained below by combining the results of the simulation experiment. Setting the population size to be 200, the iteration times to be 200 and the cross probability to be 0.9; the variation probability is 0.1; each operation parameter is shown in table 1, and the result spatial distribution of the Pareto optimal solution set obtained after 200 iterations is shown in fig. 5 (the target two takes a negative value for easy observation), where f (1), f (2), and f (3) are the fitness of the target one, two, and three, respectively.
As can be seen from the figure, the optimization method provided by the invention can effectively solve the problems, provides good theoretical guidance for engineering practice, and has strong practicability.
TABLE 1
The present embodiment further provides a product maintenance resource scheduling system, a structural block diagram of which is shown in fig. 6, where the product maintenance resource scheduling system 600 includes:
a task information obtaining module 601, configured to obtain a product maintenance task, and determine staff skill requirement information and resource requirement information of the product maintenance task;
a supply information obtaining module 602, configured to obtain resource supply information and candidate service staff information;
the model establishing module 603 is configured to establish a constraint condition and a maintenance scheduling model according to the task demand information, the resource supply information, and the candidate service staff information, and determine an objective function of the maintenance scheduling model;
and an optimizing module 604, configured to calculate an optimal solution of the objective function under the constraint condition, and determine a maintenance scheduling scheme according to the optimal solution of the objective function.
As shown in fig. 7, in the product maintenance resource scheduling method, the present invention further provides an electronic device 700, which may be a mobile terminal, a desktop computer, a notebook, a palmtop computer, a server, or other computing devices. The electronic device includes a processor 701, a memory 702, and a display 703.
The storage 702 may be, in some embodiments, an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 702 may also be, in other embodiments, an external storage device to the computer device, such as a plug-in hard drive provided on the computer device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, memory 702 may also include both internal and external storage units of the computer device. The memory 702 is used for storing application software installed on the computer device and various data, such as program codes for installing the computer device. The memory 702 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a product repair resource scheduling method program 704 is stored in the memory 702, and the product repair resource scheduling method program 704 is executable by the processor 701, so as to implement a product repair resource scheduling method according to embodiments of the present invention.
The display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 703 is used to display information at the computer device and to display a visual user interface. The components 701 and 703 of the computer device communicate with each other via a system bus.
The embodiment also provides a computer readable storage medium, on which a program of a product maintenance resource scheduling method is stored, and when the processor executes the program, the product maintenance resource scheduling method is implemented as described above.
According to the computer-readable storage medium and the computing device provided by the above embodiments of the present invention, the content specifically described for implementing the product maintenance resource scheduling method according to the present invention can be referred to, and the method has similar beneficial effects to the product maintenance resource scheduling method described above, and is not described herein again.
The invention discloses a product maintenance resource scheduling method, a system, electronic equipment and a computer readable storage medium, firstly, obtaining a product maintenance task and determining task demand information; secondly, acquiring multi-skill human resources and material resource supply information; thirdly, establishing constraint conditions and a maintenance scheduling model according to the task demand information, the resource supply information and the human resource information; and finally, calculating the optimal solution of the objective function of the maintenance scheduling model by using an optimization algorithm, and determining a maintenance scheduling scheme.
The invention considers various dynamic factors and uncertain factors existing in the maintenance resource scheduling process, establishes a maintenance scheduling model which is more in line with the actual situation, and provides a basis for improving the scheduling accuracy; the optimal solution of the maintenance scheduling model is solved by establishing an optimization algorithm, and the method has the advantages of high calculation speed, high precision and good robustness; and determining a final resource scheduling scheme according to the optimal solution, so that the maintenance service efficiency is improved, the scheduling cost is reduced, and the customer satisfaction is improved. Provides good theoretical guidance for engineering practice and has strong practicability.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A method for scheduling product maintenance resources is characterized by comprising the following steps:
acquiring a product maintenance task, and determining personnel skill demand information and resource demand information of the product maintenance task;
acquiring candidate service staff information and resource supply information;
establishing a maintenance scheduling model according to the personnel skill demand information, the resource supply information and the candidate service personnel information, and determining a constraint condition and an objective function of the maintenance scheduling model;
and calculating the optimal solution of the objective function under the constraint condition, and determining a maintenance scheduling scheme according to the optimal solution of the objective function.
2. The product repair resource scheduling method of claim 1, wherein establishing a repair scheduling model comprises: and determining the completion time of the maintenance task, the redundancy of the candidate service personnel and the resource performance cost ratio according to the personnel skill demand information, the resource supply information and the candidate service personnel information.
3. The product repair resource scheduling method of claim 2, wherein the scheduling model comprises a personnel matching degree model, a resource performance model, and a stochastic programming model;
the personnel matching degree model is used for determining personnel service time and candidate personnel redundancy according to the candidate service personnel information and the personnel skill demand information;
the resource performance model is used for determining a resource performance cost ratio and resource in-place time according to the resource demand information and the resource supply information;
the stochastic programming model is used for obtaining maneuvering time according to the personnel skill demand information, the resource supply information and the candidate service personnel information;
the repair task completion time is the sum of the personnel service time, the resource in-place time and the maneuver time.
4. The product repair resource scheduling method of claim 2, wherein the objective function of the scheduling model comprises:
minimizing repair task completion time, minimizing candidate servicer redundancy, and maximizing resource performance cost ratio.
5. The method according to claim 1, wherein calculating an optimal solution of the objective function under the constraint condition comprises:
generating an initial solution set of the scheduling model objective function according to the constraint condition;
optimizing the initial solution set by using a first preset algorithm to obtain a candidate solution set;
and obtaining an optimal solution meeting preset conditions according to the candidate solution set.
6. The method of claim 5, wherein optimizing the initial solution set using a first predetermined algorithm to obtain a candidate solution set comprises:
and calculating a characteristic value of the initial solution set by using a second preset algorithm, and generating the candidate solution set according to the characteristic value.
7. The method for scheduling product maintenance resources according to claim 5, wherein obtaining an optimal solution meeting a preset condition according to the candidate solution set comprises:
calculating the non-dominated ordering and crowdedness of the candidate solution set;
and obtaining the optimal solution which meets the preset conditions according to the non-dominated sorting and the crowding degree.
8. A product repair resource scheduling system, comprising:
the task information acquisition module is used for acquiring a product maintenance task and determining personnel skill demand information and resource demand information of the product maintenance task;
the supply information acquisition module is used for acquiring resource supply information and candidate service staff information;
the model establishing module is used for establishing a constraint condition and a maintenance scheduling model according to the task demand information, the resource supply information and the candidate service personnel information and determining a target function of the maintenance scheduling model;
and the optimization module is used for calculating the optimal solution of the objective function under the constraint condition and determining a maintenance scheduling scheme according to the optimal solution of the objective function.
9. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program that, when executed by the processor, implements a product repair resources scheduling method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a method of scheduling product repair resources according to any one of claims 1 to 7.
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