CN116882681A - Multi-target scheduling method, device and equipment for property work orders and storage medium - Google Patents
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Abstract
The invention discloses a fusion optimization algorithm-based multi-objective scheduling method, device, equipment and storage medium for a property bill, and relates to the technical field of artificial intelligence, wherein the method comprises the following steps: establishing a hypothesis condition of a property work order multi-target scheduling model; constructing a corresponding objective function and constraint conditions aiming at the hypothesis conditions to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function; and solving the multi-objective scheduling model of the property bill by using a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill. According to the method, the multi-target dispatching model of the property bill is constructed, the fusion optimization algorithm is utilized to solve the multi-target dispatching model of the property bill, the optimal dispatching scheme of the property bill is obtained, and the work bill tasks are distributed to proper staff, so that the total cost, the work bill matching degree and the staff satisfaction degree are optimal.
Description
Technical Field
The invention relates to the technical field of property management, in particular to a property work order multi-target scheduling method, device, equipment and storage medium based on a fusion optimization algorithm.
Background
With the continuous promotion of intelligent management technology, the formation of intelligent communities, the property management is gradually changed from offline to online, various online work management systems are widely applied, and for property enterprises, customer services including owner services of the most basic of owner payment, owner repair and the like play an important role in the aspects of recording, processing, tracking the completion condition of work and the like of the work management system. When a report is generated within a certain property service range, the report refers to the report in property management, which is also called report repair, property report, property barrier, property maintenance management, owner repair, property barrier and the like, and refers to the process of notifying maintenance or repair requirements of a certain object, equipment or facility to related responsible personnel by submitting a report or a request, and the work management system automatically generates a work order task, but a work order dispatching module in the work order management system is still realized based on certain specific rules, such as assignment to a specific organization according to the type of the work order request, the satisfied target is single, the real-time performance is low, and the optimal matching of the work order task and the property staff cannot be satisfied.
Disclosure of Invention
The embodiment of the invention provides a multi-objective dispatching method, device, equipment and storage medium for a property work order based on a fusion optimization algorithm, which aim to solve the problem that the existing work order management system cannot meet the optimal matching of work order tasks and property staff.
In a first aspect, an embodiment of the present invention provides a property bill multi-objective scheduling method based on a fusion optimization algorithm, including:
establishing a hypothesis condition of a property work order multi-target scheduling model;
constructing a corresponding objective function and constraint conditions aiming at the hypothesis conditions to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
solving the multi-objective scheduling model of the property bill by utilizing a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill;
the step of solving the property bill multi-target scheduling model by using a fusion optimization algorithm comprises the following steps:
coding the multi-objective scheduling problem of the property work order, setting the population size N and the maximum iteration number Max, and initializing the population P t Generating a property work order initial scheduling scheme;
genetic manipulation using the white whale optimization algorithm to generate progeny population Q t ；
Combining said progeny population Q t Combining the parent population to obtain a new population R t For the new population R t Performing rapid non-dominant ordering;
for the new population R t The individual in (2) performs dynamic crowding degree distance calculation, and combines the non-dominant ranking level and the dynamic crowding degree calculation result to obtain the new population R t Is selected to enter the next generation population P by a plurality of individuals with better preference t+1 ；
Updating the iteration times t=t+1, judging whether the maximum iteration times Max is reached, stopping iteration if the maximum iteration times Max is reached, outputting a property work order optimal scheduling scheme, and returning to continue to generate a new child population by using the beluga optimization algorithm if the maximum iteration times Max is not reached.
In a second aspect, an embodiment of the present invention provides a property bill multi-objective scheduling apparatus based on a fusion optimization algorithm, including:
the condition establishing unit is used for establishing the assumption condition of the multi-target scheduling model of the property work order;
the model construction unit is used for constructing corresponding objective functions and constraint conditions aiming at the hypothesis conditions so as to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
the model solving unit is used for solving the multi-objective scheduling model of the property bill by utilizing a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill;
the step of solving the property bill multi-target scheduling model by using a fusion optimization algorithm comprises the following steps:
coding the multi-objective scheduling problem of the property work order, setting the population size N and the maximum iteration number Max, and initializing the population P t Generating a property work order initial scheduling scheme;
genetic manipulation using the white whale optimization algorithm to generate progeny population Q t ；
Combining said progeny population Q t Combining the parent population to obtain a new population R t For the new population R t Performing rapid non-dominant ordering;
for the new population R t The individual in (2) performs dynamic crowding degree distance calculation, and combines the non-dominant ranking level and the dynamic crowding degree calculation result to obtain the new population R t Is selected to enter the next generation population P by a plurality of individuals with better preference t+1 ；
Updating the iteration times t=t+1, judging whether the maximum iteration times Max is reached, stopping iteration if the maximum iteration times Max is reached, outputting a property work order optimal scheduling scheme, and returning to continue to generate a new child population by using the beluga optimization algorithm if the maximum iteration times Max is not reached.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the fusion optimization algorithm-based property order multi-objective scheduling method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform a property order multi-objective scheduling method based on a fusion optimization algorithm as described in the first aspect.
The embodiment of the invention provides a multi-objective dispatching method, device, equipment and storage medium for a property work order based on a fusion optimization algorithm, wherein the method comprises the following steps: establishing a hypothesis condition of a property work order multi-target scheduling model; constructing a corresponding objective function and constraint conditions aiming at the hypothesis conditions to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function; and solving the multi-objective dispatching model of the property bill by using a fusion optimization algorithm to obtain an optimal dispatching scheme of the property bill. According to the method, the multi-target dispatching model of the property bill is constructed, the fusion optimization algorithm is utilized to solve the multi-target dispatching model of the property bill, the optimal dispatching scheme of the property bill is obtained, and the work bill tasks are distributed to proper staff, so that the total cost, the work bill matching degree and the staff satisfaction degree are optimal.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a multi-objective scheduling method of a property bill based on a fusion optimization algorithm according to an embodiment of the present invention;
fig. 2 is a schematic sub-flowchart of a property work order multi-objective scheduling method based on a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 3 is another schematic flow chart of a multi-objective scheduling method for a property bill based on a fusion optimization algorithm according to an embodiment of the present invention;
fig. 4 is another schematic sub-flowchart of a property work order multi-objective scheduling method based on a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a property bill multi-objective scheduling device based on a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a subunit of a multi-objective scheduling device for a property bill based on a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of another subunit of a multi-objective scheduling device for a property bill based on a fusion optimization algorithm according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of another subunit of a multi-objective scheduling device for a property bill based on a fusion optimization algorithm according to an embodiment of the present invention;
fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a flow chart of a multi-objective scheduling method for a property bill based on a fusion optimization algorithm according to an embodiment of the present invention, and the method includes steps S101 to S103:
s101, establishing a hypothesis condition of a multi-target scheduling model of a property work order;
when a report is generated in a certain property service range, the work order management system can automatically generate a work order task, each work order corresponds to a material access node and a work node, all the starting positions, the material access nodes and the coordinates and the relative distances of the work nodes of the staff are assumed to be known, the staff has no maximum driving mileage constraint, and only one staff receives the work order. S102, constructing a corresponding objective function and constraint conditions aiming at the hypothesis conditions so as to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
in this embodiment, a multi-objective scheduling model of a property work order is built according to the objective of minimum total cost, highest work order matching degree and highest employee satisfaction, so that corresponding objective functions and constraint conditions are constructed for the above assumed conditions, where the objective functions are the target forms sought and expressed by design variables, and the objective functions are performance criteria of the system in engineering terms, and the constraint conditions are restrictions on decision schemes, and often appear in the form of inequality or equation.
In a specific embodiment, the minimum total cost function in the step S102 is expressed as follows: minF (minF) 1 ＝C 1 +C 2
wherein ,C1 Representing employee operating costs, i.e., fixed costs paid to employees by the property company; c (C) 2 The cost of the driving distance of the user is represented, namely the cost of the driving distance generated by staff in the way to the material point and the operation point; θ represents staff fixed cost, P represents staff set p= {1,2, …, k }, O represents staff position set o= {1,2, …, k' }, S represents staff position, material storage node and job node set, X ijk Representing decision variables, specifically representing that employee k is 1 when from node i to node j, otherwise 0, i e O, j e S, k e P, θ 1 Representing the transportation cost of staff per unit distance, w representing the maneuvering attributes of staff, w=0 being walking, w=1 being a motor vehicle, d ij Representing the distance between node i and node j, i, j e S.
The highest worksheet matching degree function in step S102 is expressed as follows: minF (minF) 2 ＝2-U 1 -U 2
wherein ,U1 The matching degree of the active time of the staff k is expressed by the following formula:
U 2 the matching degree of the worksheet professional attribute and the employee professional portrait tag is expressed by the following formula:
wherein ,Bi Representing the planned start time of the work order i, i e N, N representing the work order set n= {1,2, …, i },represent the start time of employee k's active time, L i Representing the planned completion time of worksheet i, +.>Indicating the active time expiration time of employee k, X ijk Representing decision variables, in particular 1 when employee k goes from node i to node j, otherwise 0, i e O, j e S, k e P, S representing the set of employee locations, material storage nodes and job nodes, O representing the set of employee locations O= {1,2, …, k' }, P representing the set of employees P= {1,2, …, k }, at i Work order professional attribute representing work order i, i E N, at i E_At, at represents a worksheet professional property set, po k Professional portrait tag representing employee k, k.epsilon.P, po k E Po, po represents the employee specialty portrait tag set.
The highest employee satisfaction function in step S102 is expressed as follows: min F 3＝W
The work time saturation of employee k is expressed by the following formula:
the employee on-time saturation balance is expressed by the following formula:
employee satisfaction is primarily dependent on physiological level satisfaction contributing factors,employee satisfaction in this embodiment is represented by work time saturation ωt. Wherein ωt k Indicating the working time saturation of employee k, N k Representing the cumulative completion of employee k on the day, L i Representing the planned completion time of the work order i, B i Representing the planned start time of the work order i, i e N, N representing the work order set n= {1,2, …, i }, T k Represents the working time of staff k in the whole day, w represents the maneuvering property of staff, w=0 is walking, w=1 is a motor vehicle, and X ijk Representing decision variables, specifically 1 when employee k goes from node i to node j, otherwise 0, i e O, j e S, k e P, S representing the set of employee locations, material storage nodes, and job nodes, O representing the set of employee locations o= {1,2, …, k' }, P representing the set of employees p= {1,2, …, k }.
Therefore, in this embodiment, the constructed property bill multi-objective scheduling model is expressed as the following three formulas:
minF 1 ＝C 1 +C 1
minF 2 ＝2-U 1 -U 2
minF 3 ＝W
and comprehensively considering the related information of the work orders and the staff, and distributing the work order tasks to the proper staff so as to optimize the total cost, the work order matching degree and the staff satisfaction.
The constraint conditions in the step S102 include:
the above represents that each worksheet is taken on and only once by one employee, where P represents the employee set p= {1,2, …, k }, and C represents the job node set {1 } - ,2 - ,…,i - }，X rjk Representing decision variables, specifically representing that when staff k goes from a material storage point r to a node j, the staff k is 1, otherwise, the staff k is 0, r epsilon S, j epsilon C, k epsilon P, and S represents a set of staff positions, material storage nodes and operation nodes;
t rk <t i-k ,k∈P
the above represents the order of the material storage node and the work node, where P represents the employee set p= {1,2, …, k }, t i-k Indicating that employee k arrives at node i - Time of (job node), t rk Representing the time that employee k arrives at node r (the material storage node), k ε P;
the above represents the continuity of employee routes, where S represents the set of employee locations, material storage nodes, and job nodes, and P represents the set of employees, P= {1,2, …, k }, X ihk Representing a decision variable, specifically representing that employee k is 1 when going from node i to node h, otherwise 0, X hjk Representing a decision variable, in particular 1 when employee k goes from node h to node j, or 0 otherwise;
the above indicates that each employee starts from an initial position, and must return to the starting position after the complete work order works, wherein C indicates the working node set {1 } - ,2 - ,…,i - }，X ihk Representing a decision variable, specifically representing that employee k is 1 when going from node i to node h, otherwise 0, X gjk Representing a decision variable, specifically 1 when employee k goes from node g to node j, otherwise 0,O representing employee location set o= {1,2, …, k' }, P representing employee set p= {1,2, …, k };
the above formula indicates that all nodes i are visited once and only by one employee, where S indicates the set of employee locations, material storage nodes, and job nodes, and P indicates the set of employees p= {1,2, …, k }, X ijk Representing decision variables, particularly membersWork k is 1 from node i to node j, otherwise is 0, i, j e S, k e P.
S103, solving the multi-objective scheduling model of the property bill by using a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill;
the embodiment is based on a white whale optimization algorithm (BWO), improves the characteristics of three-stage optimization models in the white whale optimization algorithm, fuses the white whale optimization algorithm with a non-dominant ordered genetic algorithm (NSGA-II), and designs a multi-objective optimization algorithm suitable for solving a property engineering single multi-objective scheduling model, namely a fusion optimization algorithm in the embodiment. The white whale optimization algorithm is a group intelligent optimization algorithm imitating the behavior of the white whale population, and solves the problem that local exploration and global optimization are difficult to balance by imitating the natural behavior of white whale companion, predation and whale to create three stages of exploration, development and whale in a model. In the embodiment, a fusion optimization algorithm is utilized to solve a multi-objective scheduling model of a property work order.
In this embodiment, as shown in fig. 2, the step S103 includes steps S201 to S205:
s201, coding a multi-objective scheduling problem of a property work order, setting a population size N and a maximum iteration number Max, and initializing a population P t Generating a property work order initial scheduling scheme;
in the embodiment, the multi-objective scheduling problem of the property work order is encoded, initial parameters are set, namely, the size N of the group is set, the maximum iteration number Max is set, and each individual in the group refers to a work order proposal in a work order system; initializing population P t Refers to initializing a viable population to meet optimization requirements prior to iterative optimization using an algorithm.
The encoding of the multi-objective scheduling problem of the property bill, as shown in fig. 3, includes steps S301 to S303:
s301, for the known n tasks, adopting a natural number coding mode to code, wherein the natural number coding length is n, and the coding expression is as follows:
P i ＝[i 1 ,i 2 ,i 3 ,…i n], wherein ,in Representing an nth property order to be allocated;
s302, aiming at work order i n Screening all staff P meeting the constraint conditions, and then calculating the time Z required by all staff P meeting the constraint conditions to reach the work order work position k Select Z k Minimum employee k, worksheet i n Assigned to employee k;
s303, continuing to screen and distribute the next work order until all work orders are distributed to corresponding staff.
In this embodiment, the decoding process includes: first for the first work order i 1 Find employee k meeting constraint and being the smallest 1 Will i 1 Assigned to k 1 And let k 1 Delete from the employee set (P= {1,2, …, k }; then, for the second work order i 2 Find employee k satisfying constraint and smallest in the remaining employee set 2 Will i 2 Assigned to k 2 The method comprises the steps of carrying out a first treatment on the surface of the Finally, the above process is repeated until all i in the individual are assigned to the corresponding employee k.
Z in the step S302 k The calculation formula of (2) is as follows:
wherein ,Zk Representing the time required for employee k to reach the worksheet job site, k' representing the initial/final position of employee k, r representing the material storage point, i - Representing the job position of worksheet i, i e N, N representing worksheet set N= {1,2, …, i }, d k′r Represents the distance, d, between nodes k', r ri -representing the distance, v, between node r and node i k Representing the moving speed of employee k, rnd represents the disturbance factor rnd= (0.9,1.1), the diversity of the initial solution can be guaranteed, so that the initial solution is randomly distributed in the solution space, so as not to fall into a locally optimal solution in the search.
S202, performing genetic operation by using a white whale optimization algorithm,generation of progeny population Q t ；
S203, the offspring population Q t Combining the parent population to obtain a new population R t For the new population R t Performing rapid non-dominant ordering;
s204, for the new population R t The individual in (2) performs dynamic crowding degree distance calculation, and combines the non-dominant ranking level and the dynamic crowding degree calculation result to obtain the new population R t Is selected to enter the next generation population P by a plurality of individuals with better preference t+1 ；
In this embodiment, with the iteration of the population in the multi-objective scheduling problem of the property bill (the iteration of the population refers to the process of repeating the steps S202-S204 to find a better solution), the non-dominant level in the population will become smaller, and the calculation of the crowdedness becomes a key step in the environmental selection. However, the conventional method for calculating the congestion degree of the individual results in that the individual in a more congested area is eliminated, but when one individual is eliminated, the congestion degree value of the other individuals is changed, so that the diversity of the individual maintained by using the method for calculating the congestion degree is not optimal. In order to solve the above-described problem of the congestion degree calculation method, in this embodiment, an individual selected based on the dynamic congestion degree ranking method is used.
Furthermore, when solving the multi-objective scheduling problem of the property work sheet, it is important to improve population diversity and convergence, so that the algorithm can be helped to explore the space better, avoid sinking into a local optimal solution, and improve the searching efficiency. The dynamic crowding degree distance is just a method for improving the diversity of the population, and the improvement of the diversity of the population is helpful for searching for a better solution, namely a better scheduling scheme is obtained. The congestion degree corresponds to the distance between the populations in the algorithm iteration process in this embodiment, and can be understood as the difference between the scheduling schemes.
In a specific embodiment, as shown in FIG. 5, in the step S204, the new population R t The dynamic crowding degree distance calculation is carried out on the individuals in the (a), and the method comprises the following steps:
s401, calculating the crowding degree of all individuals according to the following formula:
wherein ,dk For the crowding degree of individual k, T is the target number.
S402, deleting the smallest individual x according to the obtained crowding degree 1 ；
S403, re-pairing (x 1+1) and (x1 -1) recalculating the congestion level of the individual corresponding to the congestion level, and continuing to delete the individual x having the smallest congestion level 2 Repeating the steps until the number of the residual individuals is equal to the population size;
and S205, updating iteration times t=t+1, judging whether the maximum iteration times Max are reached, stopping iteration if the maximum iteration times Max are reached, outputting a property work order optimal scheduling scheme, and returning to continuously utilize a beluga optimization algorithm to generate a new child population if the maximum iteration times Max are not reached.
In this embodiment, as shown in fig. 5, a multi-objective optimization algorithm suitable for solving a multi-objective scheduling model of a property bill includes: initializing population P t Randomly generating N individuals, coding the multi-objective scheduling problem of the property bill by using a natural number coding mode, and then carrying out rapid non-dominant sorting and dynamic crowding distance calculation, wherein the dynamic crowding distance calculation mode is described in S401, and selecting the next generation father individual P t+1 Obtaining offspring individuals Q by using a white whale optimization algorithm t Combining parent and offspring populations to form a population R of size 2N t And (3) performing environment selection by using the rapid non-dominant sorting and the dynamic crowding degree distance, judging whether the maximum iteration number Max is reached, if so, outputting a property work order optimal scheduling scheme, and if not, returning to continuously utilize a beluga optimization algorithm to generate a new child population.
As shown in fig. 6, the embodiment of the present invention further provides a property bill multi-objective scheduling apparatus 500 based on a fusion optimization algorithm, including:
a condition establishing unit 501, configured to establish a hypothesis condition of a multi-objective scheduling model of a property bill;
the model construction unit 502 is configured to construct a corresponding objective function and constraint condition for the hypothesis condition, so as to construct a property bill multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
and the model solving unit 503 is configured to solve the property bill multi-objective scheduling model by using a fusion optimization algorithm, so as to obtain a property bill optimal scheduling scheme.
In one embodiment, as shown in fig. 7, the model solving unit 503 includes:
the coding unit 601 is configured to code a multi-objective scheduling problem of a property work order, set a population size N and a maximum iteration number Max, and initialize a population P t Generating a property work order initial scheduling scheme;
genetic element 602 for genetic manipulation using a beluga optimization algorithm to generate a progeny population Q t ；
A merging unit 603 for merging the child populations Q t Combining the new population Rt with the parent generation population to obtain a new population Rt, and carrying out rapid non-dominant sorting on the new population Rt;
a first calculating unit 604, configured to perform dynamic crowding distance calculation on individuals in the new population Rt, select a plurality of better individuals from the new population Rt to enter the next generation population P by combining the non-dominant ranking level and the dynamic crowding calculation result t+1 ；
The judging unit 605 is configured to update the iteration times t=t+1, judge whether the maximum iteration times Max is reached, terminate the iteration if the maximum iteration times Max is reached, output a property work order optimal scheduling scheme, and return to continue to generate a new child population by using the white whale optimization algorithm if the maximum iteration times Max is not reached.
In one embodiment, as shown in fig. 8, the encoding unit 601 includes:
the coding unit 701 is configured to perform coding on known n tasks by using a natural number coding mode, where the natural number coding length is n, and the coding expression is as follows:
P i ＝[i 1 ,i 2 ,i 3 ,…i n] wherein ,in Representing an nth property order to be allocated;
screening unit 702, for work order i n Screening all staff P meeting the constraint conditions, and then calculating the time Z required by all staff P meeting the constraint conditions to reach the work order work position k Select Z k Minimum employee k, worksheet i n Assigned to employee k;
the allocation unit 703 is configured to continue to screen and allocate for the next work order until all work orders are allocated to corresponding employees.
In one embodiment, as shown in fig. 9, the first computing unit 604 includes:
a second calculation unit 801 for calculating the crowdedness of all individuals according to the following formula:
wherein ,dk The crowding degree of the individual k is determined, and T is the target number;
a deleting unit 802 for deleting the smallest individual x according to the obtained congestion degree 1 ；
A circulation unit 803 for re-pairing (x 1+1) and (x1 -1) recalculating the congestion level of the individual corresponding to the congestion level, and continuing to delete the individual x having the smallest congestion level 2 And continuing to repeat the steps until the number of the residual individuals is equal to the population size.
According to the property bill multi-target scheduling device based on the fusion optimization algorithm, the property bill multi-target scheduling model is constructed, the fusion optimization algorithm is utilized to solve the property bill multi-target scheduling model, the optimal property bill scheduling scheme is obtained, and the work bill tasks are distributed to proper staff, so that the total cost, the work bill matching degree and the staff satisfaction degree are optimal.
The above-described property bill multi-objective scheduling apparatus based on the fusion optimization algorithm may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 900 is a server, and the server may be a stand-alone server or a server cluster formed by a plurality of servers.
With reference to fig. 10, the computer device 900 includes a processor 902, a memory, and a network interface 905, which are connected by a system bus 901, wherein the memory may include a non-volatile storage medium 903 and an internal memory 904.
The non-volatile storage medium 903 may store an operating system 9031 and a computer program 9032. The computer program 9032, when executed, may cause the processor 902 to perform a fusion optimization algorithm-based property bill multi-objective scheduling method.
The processor 902 is operative to provide computing and control capabilities supporting the operation of the entire computer device 900.
The internal memory 904 provides an environment for the execution of a computer program 9032 in the non-volatile storage medium 903, which computer program 9032, when executed by the processor 902, may cause the processor 902 to perform a fusion optimization algorithm-based property order multi-objective scheduling method.
The network interface 905 is used for network communication such as providing transmission of data information, etc. It will be appreciated by those skilled in the art that the architecture shown in fig. 10 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 900 to which the present inventive arrangements may be implemented, and that a particular computer device 900 may include more or fewer components than shown, or may combine some components, or have a different arrangement of components.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 10 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 10, and will not be described again.
It should be appreciated that in an embodiment of the invention, the processor 902 may be a central processing unit (Central Processing Unit, CPU), the processor 902 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units may be stored in a storage medium if implemented in the form of software functional units and sold or used as stand-alone products. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A property bill multi-target scheduling method based on a fusion optimization algorithm is characterized by comprising the following steps:
establishing a hypothesis condition of a property work order multi-target scheduling model;
constructing a corresponding objective function and constraint conditions aiming at the hypothesis conditions to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
solving the multi-objective scheduling model of the property bill by utilizing a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill;
the step of solving the property bill multi-target scheduling model by using a fusion optimization algorithm comprises the following steps:
coding the multi-objective scheduling problem of the property work order, setting the population size N and the maximum iteration number Max, and initializing the population P t Generating a property work order initial scheduling scheme;
genetic manipulation using the white whale optimization algorithm to generate progeny population Q t ；
Combining said progeny population Q t Combining the parent population to obtain a new population R t For the new population R t Performing rapid non-dominant ordering;
for the new population R t The individual in (2) performs dynamic crowding degree distance calculation, and combines the non-dominant ranking level and the dynamic crowding degree calculation result to obtain the new population R t Is selected to enter the next generation population P by a plurality of individuals with better preference t+1 ；
Updating the iteration times t=t+1, judging whether the maximum iteration times Max is reached, stopping iteration if the maximum iteration times Max is reached, outputting a property work order optimal scheduling scheme, and returning to continue to generate a new child population by using the beluga optimization algorithm if the maximum iteration times Max is not reached.
2. The fusion optimization algorithm-based property order multi-objective scheduling method according to claim 1, wherein the minimum total cost function is expressed as follows: minF (minF) 1 ＝C 1 +C 2
wherein ,C1 Representing employee operating costs, C 2 Representing the user travel distance cost, θ represents the employee fixed cost, P represents the employee set p= {1,2, …, k }, O represents the employee position set o= {1,2, …, k' }, S represents the employee position, the material storage node, and the job node set, X ijk Representing employee k as 1 from node i to node j, otherwise 0, i ε O, j ε S, k ε P, θ 1 Representing the transportation cost of staff per unit distance, w representing the maneuvering attributes of staff, w=0 being walking, w=1 being a motor vehicle, d ij Representing the distance between node i and node j, i e O, j e S.
3. The fusion optimization algorithm-based property order multi-objective scheduling method according to claim 1, wherein the highest order matching degree function is expressed as follows: minF (minF) 2 ＝2-U 1 -U 2
wherein ,Bi Representing the planned start time of the work order i, i e N, N representing the work order set n= {1,2, …, i },represent the start time of employee k's active time, L i Representing the planned completion time of worksheet i, +.>Indicating the active time expiration time of employee k, X ijk Represents 1 when employee k goes from node i to node j, otherwise 0, i e O, j e S, k e P, S representing the set of employee locations, material storage nodes, and job nodes, P representing employee set P= {1,2, …, k }, O representing employee location set O= {1,2, …, k' }, at i Representing the professional properties of the worksheet i, i.e. N, at i E_At, at represents a worksheet professional property set, po k Professional portrait tag representing employee k, k.epsilon.P, po k E, po represents a staff specialty portrait tag set;
the highest employee satisfaction function is expressed as follows: minF (minF) 3 ＝W
wherein ,representing the working time saturation mean of all employees, W represents the maneuver property of the employee, w=0 is walking, w=1 is a vehicle, ωt k Indicating the working time saturation of employee k, N k Representing the cumulative completion of employee k on the day, L i Representing the planned completion time of the work order i, B i Representing the planned start time of the work order i, i e N, N representing the work order set n= {1,2, …, i }, T k Representing the working time of staff k in the whole day, X ijk Represents 1 when employee k goes from node i to node j, otherwise 0, i e O, j e S, k e P, O represents the employee location set o= {1,2, …, k' }, S represents the set of employee locations, material storage nodes, and job nodes, P represents the employee set p= {1,2, …, k }.
4. The fusion optimization algorithm-based property order multi-objective scheduling method according to claim 1, wherein the constraint condition comprises:
the above represents that each worksheet is taken on and only once by one employee, where P represents the employee set p= {1,2, …, k }, and C represents the job node set {1 } - ,2 - ,…,i - }，X rjk Representing that when staff k goes from a material storage point r to a node j, the staff k is 1, otherwise, the staff k is 0, r epsilon S, j epsilon C, k epsilon P, and S represents a set of staff positions, material storage nodes and operation nodes;
t rk <t i-k ,k∈P
the above represents the order of the material storage node and the work node, where P represents the employee set p= {1,2, …, k }, t i-k Indicating that employee k arrives at node i - Time of (job node), t rk Representing the time of arrival of employee k at node r (material storage node), k ε P；
The above represents the continuity of employee routes, where S represents the set of employee locations, material storage nodes, and job nodes, and P represents the set of employees, P= {1,2, …, k }, X ihk Representing employee k as 1 from node i to node h, otherwise 0, X hjk Representing employee k as 1 when from node h to node j, and 0 otherwise;
the above indicates that each employee starts from an initial position, and must return to the starting position after the complete work order works, wherein C indicates the working node set {1 } - ,2 - ,…,i - }，X ihk Representing employee k as 1 from node i to node h, otherwise 0, X gjk Representing 1 when employee k goes from node g to node j, otherwise 0,O represents the employee location set o= {1,2, …, k' }, P represents the employee set p= {1,2, …, k };
the above formula indicates that all nodes i are visited once and only by one employee, where S indicates the set of employee locations, material storage nodes, and job nodes, and P indicates the set of employees p= {1,2, …, k }, X ijk Representing employee k as 1 from node i to node j, and 0, i, j e S, k e P.
5. The fusion optimization algorithm-based property bill multi-objective scheduling method according to claim 1, wherein the new population R t The dynamic crowding degree distance calculation is carried out on the individuals in the (a), and the method comprises the following steps:
the crowdedness of all individuals was calculated as follows:
deleting the smallest individual X according to the obtained crowding degree 1 ；
Re-pair (x) 1+1) and (x1 -1) recalculating the congestion level of the individual corresponding to the congestion level, and continuing to delete the individual x having the smallest congestion level 2 Repeating the steps until the number of the residual individuals is equal to the population size;
wherein ,dk For the crowding degree of individual k, T is the target number.
6. The fusion optimization algorithm-based property bill multi-objective scheduling method according to claim 1, wherein the encoding of the property bill multi-objective scheduling problem comprises:
for the known n tasks, a natural number coding mode is adopted for coding, wherein the natural number coding length is n, and the coding expression is as follows:
P i ＝[i 1 ,i 2 ,i 3 ,…i n] wherein ,in Representing an nth property order to be allocated;
for work order i n Screening all staff P meeting the constraint conditions, and then calculating the time T required by all staff P meeting the constraint conditions to reach the work order work position k Select T k Minimum employee k, worksheet i n Assigned to employee k;
screening and allocation are continued for the next work order until all work orders are allocated to the corresponding employees.
7. The fusion optimization algorithm-based property bill multi-objective scheduling method according to claim 6, wherein the Z k The calculation formula of (2) is as follows:
wherein ,Zk Representing the time required for employee k to reach the worksheet job site, k' representing the initial/final position of employee k, r representing the material storage point, i - Representing the job position of worksheet i, i e N, N representing worksheet set N= {1,2, …, i }, d k′r Representing the distance, d, between node k' and material storage point r ri - Representing material storage point r and node i - Distance between v k Indicating the movement speed of employee k, rnd indicating the disturbance factor.
8. The utility model provides a property work order multi-target scheduling device based on fusion optimization algorithm which characterized in that includes:
the condition establishing unit is used for establishing the assumption condition of the multi-target scheduling model of the property work order;
the model construction unit is used for constructing corresponding objective functions and constraint conditions aiming at the hypothesis conditions so as to construct a property work order multi-objective scheduling model; the objective function comprises a minimum total cost function, a highest work order matching degree function and a highest employee satisfaction degree function;
the model solving unit is used for solving the multi-objective scheduling model of the property bill by utilizing a fusion optimization algorithm to obtain an optimal scheduling scheme of the property bill;
the step of solving the property bill multi-target scheduling model by using a fusion optimization algorithm comprises the following steps:
coding the multi-objective scheduling problem of the property work order, setting the population size N and the maximum iteration number Max, and initializing the population P t Generating a property work order initial scheduling scheme;
genetic manipulation using the white whale optimization algorithm to generate progeny population Q t ；
Combining said progeny population Q t Combining the parent population to obtain a new population R t For the new population R t Performing rapid non-operationDominant ordering;
for the new population R t The individual in (2) performs dynamic crowding degree distance calculation, and combines the non-dominant ranking level and the dynamic crowding degree calculation result to obtain the new population R t Is selected to enter the next generation population P by a plurality of individuals with better preference t+1 ；
Updating the iteration times t=t+1, judging whether the maximum iteration times Max is reached, stopping iteration if the maximum iteration times Max is reached, outputting a property work order optimal scheduling scheme, and returning to continue to generate a new child population by using the beluga optimization algorithm if the maximum iteration times Max is not reached.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the fusion optimization algorithm-based property order multi-objective scheduling method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which when executed by a processor causes the processor to perform the fusion optimization algorithm-based property order multi-objective scheduling method according to any one of claims 1 to 7.
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