CN111667138B - Customer service scheduling method and device based on multi-target discrete binary particle swarm algorithm - Google Patents
Customer service scheduling method and device based on multi-target discrete binary particle swarm algorithm Download PDFInfo
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Abstract
The invention discloses a customer service scheduling method and device based on a multi-target discrete binary particle swarm algorithm, and belongs to the technical field of service data processing. The method comprises the following steps: acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission parameter data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line; determining a preset strong constraint condition and a preset weak constraint condition according to service requirements, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition; calculating the customer service scheduling service data by using the multi-target discrete binary particle swarm algorithm to obtain feasibility solution data; and determining customer service scheduling scheme data according to the feasibility solution data. Compared with a single-target problem, the scheme provided by the invention has the advantages that the practical situation is better met, multiple choices are provided for decision makers to use under the condition of multiple solutions, the scheme is more flexible and more practical, the solving speed is high, and the excellent characteristic of searching the global optimal solution is achieved.
Description
Technical Field
The invention relates to the technical field of business data processing, in particular to a customer service scheduling method and device based on a multi-target discrete binary particle swarm algorithm.
Background
Aiming at the problem of data processing of customer service scheduling service in the traditional technology, simple data processing of manual scheduling is adopted, or the problem is realized by combining a plurality of computer algorithms with corresponding data processing operations.
With the expansion of the scale of companies, the problem of customer service shift scheduling service data processing is often related to many shift scheduling factors and dimensions, and it becomes increasingly difficult to obtain an efficient and appropriate shift scheduling scheme. The conventional method for processing the customer service scheduling problem of multiple targets adopts a weighted summation data processing method to convert a multi-target optimization problem into a single-target problem, and then adopts a proper computer algorithm to solve the problem, so that the single-target optimization is still realized fundamentally, and the determination of the weight of each target is still a big problem. Therefore, how to select the weight coefficients becomes a difficult point in research, and a decision maker may change preference degrees of different targets according to different actual conditions, and often it is difficult to determine the weight coefficients of different targets, so that a more effective scheduling scheme cannot be determined from multiple service demand dimensions or multiple service condition factors.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a customer service scheduling method and a customer service scheduling device based on a multi-target discrete binary particle swarm algorithm.
The technical scheme is as follows:
on the one hand, the customer service scheduling method based on the multi-target discrete binary particle swarm algorithm is provided, and the method
The method comprises the following steps:
acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission reference data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line;
determining a preset strong constraint condition and a preset weak constraint condition according to service requirements, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
calculating the customer service scheduling service data by using the multi-target discrete binary particle swarm algorithm to obtain feasibility solution data;
and determining customer service scheduling scheme data according to the feasibility solution data.
Further, calculating the customer service scheduling service data by using a multi-target discrete binary particle swarm algorithm to obtain feasible solution data, wherein the feasible solution data comprises the following steps:
initializing the customer service scheduling service data to obtain initialized data;
and calculating the initialization data to obtain feasibility solution data.
Further, the air conditioner is provided with a fan,
initializing the customer service scheduling service data to obtain initialized data, comprising:
coding customer service personnel, scheduling dates, rest days and/or traffic and initializing population operation to obtain initialization data; and/or the presence of a gas in the gas,
calculating the initialization data to obtain feasibility solution data, wherein the feasibility solution data comprises the following steps:
and performing individual optimal value updating, external file maintenance, global optimal value updating, updating and mutation operation and iterative computation on the initialization data to obtain a final non-inferior solution set.
Further, the air conditioner is provided with a fan,
the initializing population operation comprises the following steps:
randomly generating a plurality of approximate feasible solutions according to a preset strong constraint condition so as to correspond to the position of each particle;
and/or the presence of a gas in the atmosphere,
performing individual optimal value updating, external archive maintenance and global optimal value updating on the initialization data,
obtaining a final non-inferior solution set comprising:
updating an individual optimal value based on the uncertainty value judgment and the Pareto domination concept;
screening the optimal value of the individual based on fitness evaluation rules, and storing the optimal value in an external file;
selecting a non-inferior solution from the external archive as a global optimal value to guide the flight process of the particles;
and obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met.
Further, the air conditioner is characterized in that,
based on fitness evaluation rule, screening the optimal value of the individual to obtain a non-inferior solution, and storing the non-inferior solution into an external file, wherein the screening comprises the following steps:
judging whether the quantity of the non-inferior solution sets reaches the capacity of the external file, if not, directly storing the screened non-inferior solutions into the external file; if yes, maintaining the external file based on a congestion distance sorting strategy; and/or the presence of a gas in the gas,
obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met, comprising the following steps:
judging whether an iteration termination condition is met, if so, ending the iteration to obtain a final non-inferior solution set; and if not, performing updating and mutation operation, and then entering the judging operation again until a final non-inferior solution set is obtained.
Further, the update and mutation operations include:
and updating the speed position of the particle, and repairing the particle by combining variation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition.
Further, updating the particle velocity position, and repairing the particle by combining the mutation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition, wherein the feasible solution comprises the following steps: and circularly calculating according to the following multi-objective function formula in sequence:
the velocity update formula:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )；
location update formula:
absolute probability formula for position change:
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 To learn the factor, ω is the inertia factor, usually c 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : global optimum value, s (v) id ) The probability that the particle will take the value of 1 next.
Further, determining a preset strong constraint condition and a preset weak constraint condition according to the requirements of the customer service shift scheduling service, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition, wherein the method comprises the following steps:
determining the preset strong constraint condition and the preset weak constraint condition comprising the number of the shift, the class of the shift, the number of the shift and the vacation condition according to the customer service scheduling service requirement;
and obtaining the multi-target function formula of the multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition.
Further, the preset strong constraint condition includes: the total of the scheduled people of all the shifts covering the system in each hour is more than the forecast, each person is scheduled for one shift at most every day, the minimum number of people allowed by the shift is 1, the continuous rest days of each person are less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the on-duty days of each person during two rest days are less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
On the other hand, a customer service scheduling device based on multi-target discrete binary particle swarm algorithm is provided, which comprises:
a data acquisition module to: acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission reference data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line;
a first determining module to: determining a preset strong constraint condition and a preset weak constraint condition according to service requirements, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
the calculation module is used for: calculating the customer service scheduling service data by using the multi-target discrete binary particle swarm algorithm to obtain feasible solution data;
and the second determining module is used for determining the customer service scheduling scheme data according to the feasibility solution data.
Further, the computing module is to: initializing the customer service scheduling service data to obtain initialized data; and calculating the initialization data to obtain feasibility solution data.
Further, initializing the customer service scheduling service data to obtain initialized data, including:
coding customer service personnel, scheduling dates, rest days and/or traffic and initializing population operation to obtain initialization data; and/or the presence of a gas in the gas,
calculating the initialization data to obtain feasibility solution data, wherein the feasibility solution data comprises the following steps:
and performing individual optimal value updating, external file maintenance, global optimal value updating, updating and mutation operation and iterative computation on the initialization data to obtain a final non-inferior solution set.
Further, the initializing population operation includes:
randomly generating a plurality of approximate feasible solutions according to a preset strong constraint condition so as to correspond to the position of each particle;
and/or the presence of a gas in the atmosphere,
performing individual optimal value updating, external archive maintenance and global optimal value updating on the initialization data,
obtaining a final non-inferior solution set comprising:
updating an individual optimal value based on the judgment of the infeasibility value and a Pareto domination concept;
screening the optimal value of the individual based on fitness evaluation rules, and storing the selected optimal value in an external file;
selecting a non-inferior solution from the external archive as a global optimal value to guide the flight process of the particles;
and obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met.
Further, the air conditioner is provided with a fan,
based on fitness evaluation rule, screening the optimal value of the individual to obtain a non-inferior solution, and storing the non-inferior solution into an external file, wherein the screening comprises the following steps:
judging whether the number of the non-inferior solution sets reaches the capacity of the external file, if not, directly storing the screened non-inferior solutions into the external file; if yes, maintaining the external file based on a congestion distance sorting strategy; and/or the presence of a gas in the gas,
obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met, wherein the judgment operation comprises the following steps:
judging whether an iteration termination condition is met, if so, ending the iteration to obtain a final non-inferior solution set; and if not, performing updating and mutation operation, and then entering the judging operation again until a final non-inferior solution set is obtained.
Further, the update and mutation operations include:
and updating the particle speed position, and repairing the particle by combining variation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition.
Further, updating the particle velocity position, and repairing the particle by combining the mutation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition, wherein the feasible solution comprises the following steps: and circularly calculating according to the following multi-objective function formula in sequence:
velocity update formula:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )；
location update formula:
absolute probability formula for position change:
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 To learn the factor, ω is the inertia factor, usually c 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : global optimum, s (v) id ) The probability that the particle will take the value of 1 next.
Further, the first determining module is configured to:
determining the preset strong constraint condition and the preset weak constraint condition comprising the number of the shift, the class of the shift, the number of the shift and the vacation condition according to the customer service scheduling service requirement;
and obtaining the multi-target function formula of the multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition.
Further, the preset strong constraint condition includes: the total of the scheduled people of all the shifts covering the system in each hour is more than the forecast, each person is scheduled for one shift at most every day, the minimum number of people allowed by the shift is 1, the continuous rest days of each person are less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the on-duty days of each person during two rest days are less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
The customer service scheduling method and device based on the multi-target discrete binary particle swarm algorithm provided by the embodiment of the invention have the following beneficial effects:
considering that the problem of solving 0-1 variable is greatly limited according to a standard particle swarm algorithm, the discrete binary particle swarm algorithm is proposed to solve the customer service scheduling problem, the multi-objective discrete binary particle swarm algorithm is used for solving the customer service scheduling optimization model, the fitness evaluation is carried out on a plurality of objectives in a comprehensive consideration mode according to the preference degree of a decision maker to the objectives, and particularly, the fitness evaluation is carried out on the plurality of objectives in a comprehensive consideration mode by adopting a concept based on Pareto domination, so that a feasible solution is obtained.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flow chart of a customer service scheduling method based on a multi-target discrete binary particle swarm algorithm according to an embodiment of the present invention;
FIG. 2 is a flow diagram of sub-steps of step 103 of FIG. 1;
fig. 3 is a schematic structural diagram of a customer service scheduling device based on a multi-objective discrete binary particle swarm algorithm according to an embodiment of the present invention;
FIG. 4 is a business flow diagram of a multi-objective discrete binary particle swarm algorithm in a preferred embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
According to the customer service scheduling method and device based on the multi-target discrete binary particle swarm algorithm, due to the fact that the standard particle swarm algorithm has great limitation on solving 0-1 variables, the discrete binary particle swarm algorithm is proposed to solve the problem of customer service scheduling, the optimization model of the customer service scheduling is solved through the multi-target discrete binary particle swarm algorithm, a decision maker is combined with the preference degree of targets, fitness evaluation is carried out on a plurality of targets in a comprehensive consideration mode, particularly, the multiple targets are comprehensively considered to be evaluated in the fitness evaluation mode through the concept based on Pareto domination, and therefore a feasible solution is obtained. Therefore, the customer service scheduling method and device based on the multi-target discrete binary particle swarm algorithm are suitable for various scenes relating to customer service scheduling service data processing.
The method and apparatus for scheduling a customer service based on a multi-target discrete binary particle swarm algorithm according to the embodiments of the present invention are described in detail below with reference to specific embodiments and accompanying drawings.
Fig. 1 is a flow chart of a customer service scheduling method based on a multi-target discrete binary particle swarm algorithm according to an embodiment of the present invention. Fig. 2 is a flow chart of sub-steps of step 103 in fig. 1. As shown in fig. 1, the customer service scheduling method based on the multi-target discrete binary particle swarm algorithm provided by the embodiment of the present invention includes the following steps:
101. and acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission parameter data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line.
Preferably, customer service scheduling service data are obtained and data cleaning is carried out. Here, the customer service scheduling service data includes the number of the customer service staff, the scheduling period, the number of shifts and/or the transmission parameter data including the service line.
It should be noted that, the process of step 101 may be implemented in other ways besides the way described in the above steps without departing from the spirit of the present invention, and the embodiment of the present invention does not limit the specific way.
102. And determining a preset strong constraint condition and a preset weak constraint condition according to the service requirement, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition.
Preferably, according to the requirements of the customer service scheduling service, determining a preset strong constraint condition and a preset weak constraint condition comprising the number of the shift, the class of the shift, the number of the shift and the vacation condition; and obtaining a multi-target function formula of the multi-target discrete binary particle swarm algorithm according to a preset weak constraint condition. Illustratively, the preset strong constraints include: the total of the scheduled effect of all the shifts covering the schedule is larger than the forecast, each person can schedule one shift at most every day, the minimum number of people allowed by the shift is 1, the continuous rest days of each person is less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the working days of each person between two rest days is less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
It is noted that, the process of step 102 may be implemented in other ways besides the way described in the above steps without departing from the concept of the present invention, and the specific way is not limited by the embodiment of the present invention.
103. And calculating the customer service scheduling service data by using a multi-target discrete binary particle swarm algorithm to obtain feasibility solution data.
Preferably, as shown in fig. 2, the step 103 includes the following substeps:
1031. and initializing the customer service scheduling service data to obtain initialized data. Specifically, customer service personnel, shift schedule dates, rest days and/or traffic are coded and population initialization is carried out, and initialization data are obtained. Wherein, initializing the population comprises: randomly generating a plurality of approximate feasible solutions according to a preset strong constraint condition so as to correspond to the position of each particle;
1032. and calculating the initialization data to obtain feasibility solution data. Specifically, the initialization data is subjected to individual optimal value updating, external archive maintenance, global optimal value updating, updating and mutation operation and iterative computation to obtain a final non-inferior solution set. Specifically, it can be implemented as follows:
updating an individual optimal value based on the uncertainty value judgment and the Pareto domination concept;
screening the optimal value of the individual based on fitness evaluation rules, and storing the screened optimal value into an external file, preferably, judging whether the number of non-inferior solution sets reaches the capacity of the external file, and if the number of non-inferior solution sets does not exceed the capacity, directly storing the screened non-inferior solution into the external file; if yes, maintaining the external file based on a congestion distance sorting strategy;
selecting a non-inferior solution from an external archive as a global optimal value to guide the flight process of the particles;
obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met, preferably, judging whether the iteration termination condition is met, and if the iteration termination condition is met, ending the iteration to obtain the final non-inferior solution set; if not, updating and mutation operations are carried out, and then the judgment operation is carried out again until a final non-inferior solution set is obtained. Wherein, the updating and mutation operations comprise: and updating the speed position of the particle, and repairing the particle by combining variation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition. Preferably, the loop calculation is performed sequentially by the following multi-objective function formula:
velocity update formula:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )；
location update formula:
absolute probability formula for position change:
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 To learn the factor, ω is the inertia factor, usuallyc 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : global optimum value, s (v) id ) The probability that the particle will take the value of 1 next.
It is to be noted that, the process of step 103 may be implemented in other ways besides the way described in the above steps without departing from the concept of the present invention, and the embodiment of the present invention is not limited to the specific way.
104. And determining customer service scheduling scheme data according to the feasibility solution data. The corresponding customer service scheduling scheme data can be determined according to the specific service scene and the service requirement thereof, and the embodiment of the invention is not particularly limited.
In addition, preferably, the client scheduling method based on the multi-target discrete binary particle swarm algorithm further includes:
and determining a preset strong constraint condition and a preset weak constraint condition according to the service requirement, and obtaining the multi-objective function formula according to the preset weak constraint condition.
Fig. 3 is a schematic structural diagram of a customer service scheduling device based on a multi-target discrete binary particle swarm algorithm according to an embodiment of the present invention, and as shown in fig. 3, the customer service scheduling device based on the multi-target discrete binary particle swarm algorithm according to the embodiment of the present invention includes: a data acquisition module 21, a first determination module 22, a calculation module 23 and a second determination module 24.
The data acquisition module 21 is configured to acquire customer service scheduling service data, where the customer service scheduling service data at least includes a customer service staff number, a scheduling period, a number of shifts, and/or transmission parameter data of a service line.
The first determination module 22 is configured to: and determining a preset strong constraint condition and a preset weak constraint condition according to the service requirement, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition. Preferably, the first determining module 22 is configured to: determining a preset strong constraint condition and a preset weak constraint condition comprising the number of the shifts, the class of the shifts, the number of the shifts and the vacation situation according to the customer service scheduling service requirement; and obtaining a multi-target function formula of the multi-target discrete binary particle swarm algorithm according to a preset weak constraint condition. Illustratively, the preset strong constraints include: the total of the scheduled effect of all the shifts covering the schedule is larger than the forecast, each person can schedule one shift at most every day, the minimum number of people allowed by the shift is 1, the continuous rest days of each person is less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the working days of each person between two rest days is less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
And the calculating module 23 is configured to calculate the customer service scheduling service data by using a multi-target discrete binary particle swarm algorithm, and obtain feasibility solution data. Preferably, the calculation module 23 is configured to: initializing the customer service scheduling service data to obtain initialized data; and calculating the initialization data to obtain feasibility solution data. Further preferably, the initializing operation of the customer service scheduling service data to obtain the initialization data includes: the method comprises the steps that customer service personnel, shift scheduling dates, rest days and/or service volumes are coded and subjected to population initialization operation, and initialization data are obtained; and/or calculating the initialization data to obtain feasibility solution data, wherein the feasibility solution data comprises the following steps: and performing individual optimal value updating, external file maintenance, global optimal value updating, updating and mutation operation and iterative computation on the initialization data to obtain a final non-inferior solution set.
Wherein, initializing the population comprises: randomly generating a plurality of approximate feasible solutions according to a preset strong constraint condition so as to correspond to the position of each particle; and/or performing individual optimal value updating, external archive maintenance and global optimal value updating on the initialization data to obtain a final non-inferior solution set, wherein the steps comprise: updating an individual optimal value based on the judgment of the infeasibility value and a Pareto domination concept; screening the optimal value of the individual based on fitness evaluation rules, and storing the optimal value in an external file; selecting a non-inferior solution from an external archive as a global optimal value to guide the flight process of the particles; and obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met. Based on fitness evaluation rule, screening the optimal value of the individual to obtain a non-inferior solution, and storing the non-inferior solution into an external file, wherein the screening comprises the following steps: judging whether the number of the non-inferior solution sets reaches the capacity of the external file, if not, directly storing the screened non-inferior solutions into the external file; if yes, maintaining an external file based on a congestion distance sorting strategy; and/or obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met, wherein the judgment operation comprises the following steps: judging whether an iteration termination condition is met, if so, ending the iteration to obtain a final non-inferior solution set; if not, updating and mutation operations are carried out, and then the judgment operation is carried out again until a final non-inferior solution set is obtained.
Preferably, the update and mutation operations include: and updating the particle speed position, and repairing the particle by combining variation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition.
Further preferably, the updating of the particle velocity position and the repairing of the particle by combining the mutation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition comprises: the loop calculation is performed in sequence by the following multi-objective function formula:
velocity update formula:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )；
location update formula:
absolute probability formula for position change:
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 To learn the factor, ω is the inertia factor, usually c 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : global optimum, s (v) id ) The probability that the particle will take the value of 1 next.
And a second determining module 24, configured to determine the customer service scheduling plan data according to the feasible solution data.
The customer service scheduling scheme based on the multi-target discrete binary particle swarm algorithm provided by the embodiment of the invention is further explained in combination with a preferred embodiment.
Fig. 4 is a service flow chart of the multi-target discrete binary particle swarm algorithm in the preferred embodiment, and as shown in fig. 4, the whole algorithm service flow includes the following steps:
step 1: coding;
the encoding scheme is shown in table 1 below:
TABLE 1
Step 2: initializing a population, and randomly generating a plurality of approximate feasible solutions corresponding to the position of each particle under the condition of meeting a strong constraint condition when initializing the population;
and 3, step 3: and updating the individual optimal value. After the population is initialized, the individual optimal value of each particle is the initial position of the initialized particle; after the particle velocity and position update is performed, the individual optimum values are updated based on the judgment of the infeasibility value and the Pareto domination concept.
And 4, step 4: and (5) maintaining an external file. Firstly, non-inferior solution screening is carried out on the individual optimal value based on the evaluation rule of fitness, and the individual optimal value is stored in an external file. Judging whether the quantity of the non-inferior solution sets reaches the capacity of the external file, if not, directly storing the screened non-inferior solutions into the external file; and if so, maintaining the external file based on the congestion distance sorting strategy.
And 5: and updating the global optimal value. And randomly selecting a non-inferior solution from an external archive as a global optimal value to guide the flight process of the particles. Then, step 7 is executed to see whether the condition is satisfied, and if not, step 6 is executed.
Step 6: update and mutation operations.
Step 601: updating the particle velocity position, and performing cycle calculation in sequence through the following 3 multi-objective function formulas;
speed update equation 1:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 To learn the factor, ω is the inertia factor, usually c 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : a global optimum value;
location update equation 2: and in a probability mapping mode, a sigmoid function is adopted to map the speed to a [0,1] interval as a probability, and the probability is the probability that the next value of the particle is 1, namely:
absolute probability of position change equation 3: the position is changed from 0 to 1, and the current position is changed from 1 to 0, which are called absolute changes; the probability is expressed as:
step 602: and (5) performing mutation operation. In the process of updating the particles, an infeasible solution which does not meet the hard constraint condition may be generated, and the particles are repaired by combining the mutation operation in the genetic algorithm to obtain a feasible solution which meets the hard constraint condition. This step is performed and steps 2 to 5 are repeated.
And 7: it is determined whether a termination condition is met, where the termination condition generally includes a predetermined maximum number of iterations or a boundary that gives a final fitness value. The termination condition is given by the number of iterations. If not, returning to the step 6, and starting the next round of iterative computation; and if so, ending the iteration to obtain a final non-inferior solution. And (4) collecting.
Compared with the prior art, the customer service scheduling method and the customer service scheduling device based on the multi-target discrete binary particle swarm algorithm have the following beneficial effects:
considering that the problem of solving 0-1 variable is greatly limited according to a standard particle swarm algorithm, the discrete binary particle swarm algorithm is proposed to solve the customer service scheduling problem, the multi-objective discrete binary particle swarm algorithm is used for solving the customer service scheduling optimization model, the fitness evaluation is carried out on a plurality of objectives in a comprehensive consideration mode according to the preference degree of a decision maker to the objectives, and particularly, the fitness evaluation is carried out on the plurality of objectives in a comprehensive consideration mode by adopting a concept based on Pareto domination, so that a feasible solution is obtained.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the true scope of the embodiments of the present application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (8)
1. A customer service scheduling method based on a multi-target discrete binary particle swarm algorithm is characterized by comprising the following steps:
acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission parameter data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line;
determining a preset strong constraint condition and a preset weak constraint condition according to service requirements, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
calculating the customer service scheduling service data by using the multi-target discrete binary particle swarm algorithm to obtain feasibility solution data;
determining customer service scheduling scheme data according to the feasibility solution data;
the method for determining the multi-target discrete binary particle swarm algorithm according to the customer service shift scheduling service requirement comprises the following steps of:
determining the preset strong constraint condition and the preset weak constraint condition comprising the number of the shift, the class of the shift, the number of the shift and the vacation condition according to the customer service scheduling service requirement;
obtaining the multi-target function formula of the multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
the preset strong constraint conditions comprise: the total of the scheduled people of all covered classes in each hour is more than the forecast, each person is scheduled for one class at most every day, the minimum number of people allowed by the class is 1, the continuous rest days of each person is less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the on-duty days of each person in the two rest days are less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
2. The customer service scheduling method according to claim 1, wherein the customer service scheduling service data is calculated by using a multi-objective discrete binary particle swarm algorithm to obtain feasibility solution data, and the feasibility solution data comprises:
initializing the customer service scheduling service data to obtain initialized data;
and calculating the initialization data to obtain feasibility solution data.
3. The customer service shift arrangement method according to claim 2,
initializing the customer service scheduling service data to obtain initialized data, comprising:
the method comprises the steps that customer service personnel, shift scheduling dates, rest days and/or service volumes are coded and subjected to population initialization operation, and initialization data are obtained; and/or the presence of a gas in the gas,
calculating the initialization data to obtain feasibility solution data, wherein the feasibility solution data comprises the following steps:
and performing individual optimal value updating, external file maintenance, global optimal value updating, updating and mutation operation and/or iterative computation on the initialization data to obtain a final non-inferior solution set.
4. The customer service scheduling method according to claim 3,
the initializing population operation comprises the following steps:
randomly generating a plurality of approximate feasible solutions according to a preset strong constraint condition so as to correspond to the position of each particle;
and/or the presence of a gas in the atmosphere,
performing individual optimal value updating, external archive maintenance and global optimal value updating on the initialization data,
obtaining a final non-inferior solution set comprising:
updating an individual optimal value based on the judgment of the infeasibility value and a Pareto domination concept;
screening the optimal value of the individual based on fitness evaluation rules, and storing the optimal value in an external file;
selecting a non-inferior solution from the external archive as a global optimal value to guide the flight process of the particles;
and obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met.
5. The customer service shift scheduling method according to claim 4,
based on fitness evaluation rule, screening the optimal value of the individual to obtain a non-inferior solution, and storing the non-inferior solution into an external file, wherein the screening comprises the following steps:
judging whether the number of the non-inferior solution sets reaches the capacity of the external file, if not, directly storing the screened non-inferior solutions into the external file; if yes, maintaining the external file based on a congestion distance sorting strategy; and/or the presence of a gas in the gas,
obtaining a final non-inferior solution set according to the judgment operation of whether the iteration termination condition is met, comprising the following steps:
judging whether an iteration termination condition is met, if so, ending the iteration to obtain a final non-inferior solution set; and if not, performing updating and mutation operation, and then entering the judging operation again until a final non-inferior solution set is obtained.
6. The customer service shift scheduling method of claim 5, wherein the updating and mutation operations comprise:
and updating the particle speed position, and repairing the particle by combining variation operation in the genetic algorithm to obtain a feasible solution meeting a preset strong constraint condition.
7. The customer service shift arrangement method according to claim 6, wherein the updating of the particle velocity position and the repairing of the particle by combining the mutation operation in the genetic algorithm are performed to obtain a feasible solution meeting a preset strong constraint condition, and comprises: the loop calculation is performed in sequence by the following multi-objective function formula:
velocity update formula:
v i+1 ＝ω×v i +c 1 ×rand×(pbest i -x i )+c 2 ×rand×(gbest i -x i )；
location update formula:
absolute probability formula for position change:
wherein v is i For particle velocity, rand is a random number between (0, 1), x i As the current position of the particle, c 1 、c 2 For the learning factor, ω is the inertia factor, c 1 ＝c 2 ＝2，pbest i : individual optimum, gbest i : global optimum, s (v) id ) The probability that the particle will take the value of 1 next.
8. A customer service scheduling device based on multi-target discrete binary particle swarm algorithm is characterized by comprising:
a data acquisition module to: acquiring customer service scheduling service data, wherein the customer service scheduling service data at least comprises the transmission reference data including the work number of a customer service worker, the scheduling period, the number of shifts and/or a service line;
a first determination module to: determining a preset strong constraint condition and a preset weak constraint condition according to service requirements, and determining a multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
the calculation module is used for: calculating the customer service scheduling service data by using the multi-target discrete binary particle swarm algorithm to obtain feasible solution data;
the second determining module is used for determining customer service scheduling scheme data according to the feasibility solution data;
the method for determining the multi-target discrete binary particle swarm algorithm according to the customer service shift scheduling service requirement comprises the following steps of:
determining the preset strong constraint condition and the preset weak constraint condition comprising the number of the shift, the class of the shift, the number of the shift and the vacation condition according to the customer service scheduling service requirement;
obtaining the multi-target function formula of the multi-target discrete binary particle swarm algorithm according to the preset weak constraint condition;
the preset strong constraint conditions comprise: the total of the scheduled people of all covered classes in each hour is more than the forecast, each person is scheduled for one class at most every day, the minimum number of people allowed by the class is 1, the continuous rest days of each person is less than or equal to the set maximum continuous rest number, the person who is maintained for vacation does not participate in scheduling in the date, and the on-duty days of each person in the two rest days are less than or equal to the set rest interval; and/or, the weak constraints include: the total shift number is minimum, the night shift is less on the premise of meeting the traffic, the better, and the working time of each person is shorter on the premise of meeting the traffic.
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