CN112036640B - Division method and device for public facility service area - Google Patents
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Abstract
The invention provides a division method and a device for a public facility service area, wherein the division method comprises the following steps: step 1, managing all data of a set geographical area to form data files with different structure types; step 2, constructing a public facility service area division frame; the system comprises an algorithm unit, a structural unit and a mathematical model unit; the structure unit is used for forming a data structure unit with data in the data file; the algorithm unit comprises at least two algorithms, and the data in the data structure unit is called when the algorithms in the algorithm unit are used; step 3, importing the data file into a structural unit in the public facility service area division frame to form the data structural unit; and 4, processing the data in the data structure unit based on the algorithm unit and/or the mathematical model unit to obtain a partition scheme. The invention can improve the effect of dividing the service area of the public facilities and better meet the living demands.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for dividing a public facility service area.
Background
Public Facility Service area Partitioning (PFSD) is a Service area partitioning for Public facilities. For example, a school zone is divided for an obligation education school, a service zone is divided for a basic medical service center, and the like.
At present, researchers at home and abroad aiming at the PFSD problem have made some researches, especially in schools, hospitals, shelters, garbage recycling stations and the like. PFSD issues require service areas that are balanced in supply and demand, compact in shape, continuous in space, etc. The spatial continuous constraint enables the division of the public facility service area to better meet the practical requirements of division of academic districts and medical districts, but increases the solving difficulty of PFSD problems. Therefore, scholars often choose a certain type of public facilities for service area division, and design related solving algorithms by means of area location or division problem models. According to different algorithm ideas for solving the PFSD problem, the method can be divided into an accurate algorithm, a heuristic \ element heuristic algorithm and a hybrid algorithm. Typically, scholars use only one of these algorithms to perform service area division according to the particular needs and constraints of a particular facility. The prior scholars propose a general framework concept for the partition problem, but mainly focus on the equal partition problem, and the solution method cannot be directly applied to the PFSD problem.
(1) At present, a location model is mostly adopted for solving the PFSD problem, and space continuity constraint is lacked, so that a part of service areas are divided, and the actual management and living requirements are not met.
(2) At present, a single algorithm is mostly adopted for solving the PFSD problem, the scale of a solution case is limited, the solution quality is not high, and the solution time is long.
(3) At present, no general solution algorithm framework exists for the PFSD problem.
Therefore, there is a need to provide an improved solution to the above-mentioned deficiencies in the prior art.
Disclosure of Invention
The present invention provides a method and an apparatus for partitioning a service area of a public facility, so as to solve the above technical problems in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme:
a division method facing a public facility service area comprises the following steps:
step 1, managing all data of a set geographical area to form data files with different structure types;
acquiring data of a set geographical area, and converting the data of the set geographical area into data files of different structure types; the data in the data files with different structure types are in standard formats; the standard format refers to a data format supported by calculation;
step 2, constructing a public facility service area division frame; the public facility service area division frame comprises an algorithm unit, a structural unit and a mathematical model unit; the structure unit is used for forming a data structure unit with data in the data file; the algorithm unit comprises at least two algorithms, and the data in the data structure unit is called when the algorithms in the algorithm unit are used;
step 3, importing the data file into a structural unit in the public facility service area division frame to form the data structural unit;
and 4, processing the data in the data structure unit based on the algorithm unit and/or the mathematical model unit to obtain a partition scheme.
Further, in step 1, when all the data of the set geographic area are managed, the data of the set geographic area are classified by using a GIS tool and then converted into data files with different structure types and standard formats.
Further, the data file includes:
the geographic unit attribute file embodies the attribute information of each geographic unit;
a geographic unit adjacency relation file, which embodies all adjacent geographic unit information in a set geographic area;
an inter-geographic-unit distance file embodying a network distance between any two geographic units within a set geographic area.
Further, the structural unit includes:
the area unit is a unit for setting geographical area information;
the service area unit is a unit which is divided by public facilities and contains service partition information corresponding to each public facility;
a demand unit, which is a geographic unit with service demand;
the facility unit is a geographical unit where public facilities are located;
an adjacency unit that is a geographic unit adjacent to one geographic unit.
Further, the algorithm unit comprises an accurate algorithm, a meta heuristic algorithm, a mixed meta heuristic algorithm and a mathematic heuristic mixed algorithm.
Further, the meta-heuristic algorithm comprises a simulated annealing algorithm, a record updating algorithm, a dynamic threshold algorithm, a tabu algorithm, a variable neighborhood search and an iterative local search.
Further, the mathematical model unit comprises a PFSD model, a TP model and an SPP model; the PFSD model is used for the complete expression of PFSD problem; the TP model is used for acquiring an initial partitioning scheme of the set geographic area; the SPP model is used for obtaining a partitioning scheme of the public facility service area of the set geographic area.
Further, the mathematical heuristic hybrid algorithm adopts a multi-start mode, the TP model is used for generating an initial partitioning scheme, the meta-heuristic algorithm is used for updating the partitioning scheme, and the SPP model is used for improving the partitioning scheme.
Further, the mixed element heuristic algorithm adopts a multi-starting mode and uses the element heuristic algorithm to update the partition scheme.
In order to solve the above technical problems, the present invention further provides:
a utility service area-oriented partitioning apparatus comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above-described utility service area-oriented partitioning method when executing the computer program.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
(1) The PFSD model is constructed according to various factors and constraint conditions in the PFSD problem of the public facility service area, the PFSD problem is completely expressed, the requirements of supply and demand balance, compact shape, continuous space and the like are met, especially the continuous space constraint is realized, and the model is closer to the practical requirement.
(2) Comprehensively analyzing various solving algorithm characteristics, designing a PFSD (pulse frequency division multiple access) hybrid algorithm framework, comprising basic modules such as problem definition, modeling, initial solution algorithm, search operator, search strategy and the like, and supporting the design of an accurate algorithm, a meta heuristic algorithm and a hybrid algorithm.
(3) The algorithm framework can well process the PFSD problem of space continuous constraint, supports the quick realization of various algorithms, can efficiently solve the division problem of the public facility service areas of different scales, and particularly can obtain a high-quality service area planning scheme for medium-large scale case areas.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. Wherein:
FIG. 1 is a schematic block diagram of the present invention for utility service area partitioning;
FIG. 2 is a flow chart of algorithm selection during the utility service area-oriented partitioning of the present invention;
FIG. 3 is a distribution diagram of a set geographic region according to the present invention;
figure 4 is a utility service zone partitioning scheme effect of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. The various examples are provided by way of explanation of the invention, and not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present invention encompass such modifications and variations as fall within the scope of the appended claims and their equivalents.
The position, service capacity and service range of public facilities often attract close attention of the public, and the quality of life of people can be determined to a certain extent, particularly public facilities which guarantee the basic requirements of people, such as kindergartens, primary and secondary schools, community health service centers and aged sunshine centers. In a certain geographic area, the service range of the facilities is reasonably divided according to the position and the service capacity of the public facilities, and the basic requirements of the residents can be guaranteed. Therefore, the Service scope needs to be divided for the Public Facility by Public Facility Service Division (PFSD). When the PFSD is divided into public facility service areas, the service areas of education facilities, medical facilities, endowment facilities and the like of the same type and the same level are mainly divided, for example, the education facilities can be specifically limited to kindergarten, primary school or junior middle school; that is, the service area may be divided for any particular utility, which is determined based on actual demand.
When the service area of the public facility is divided, a PFSD hybrid algorithm is adopted, and a PFSD hybrid algorithm framework can support a plurality of different algorithms so as to solve the optimal service range of the public facility.
The main technical concept of dividing the service area of the public facility by adopting a PFSD hybrid algorithm is as follows: firstly, managing data such as geographic unit attributes, adjacency relations, network distances and the like of a research case area, and processing the data into a data format required by an algorithm framework; then converting the data into a corresponding data structure unit through data input operation; and then selecting and calling one of an accurate algorithm, a meta heuristic algorithm, a mixed meta heuristic algorithm and a mathematic heuristic hybrid algorithm according to the requirement to carry out space optimization solution, and finally outputting a PFSD (pulse frequency division multiple access) planning scheme meeting the requirement, wherein the schematic block diagram of the PFSD planning scheme is shown in figure 1.
The method comprises the following steps:
step 1, managing data of a set geographical area.
The set geographical area is a size of a geographical area to be studied, and it is necessary to divide a service area of a public facility into the set geographical area. Public facilities have different attribute categories, specifically including educational facilities, medical facilities, endowment facilities, and the like.
The geographical area is divided into a plurality of geographical units, and each geographical unit needs public facilities for service so as to meet the public life requirements. Correspondingly, the service object of a certain public facility has one geographic unit or several geographic units, namely, at least one geographic unit is arranged in each public facility service area. The set size of the geographical unit is set according to actual requirements,
in this embodiment, the set geographical area of the study is the jurisdiction of a certain city, and the set geographical area may also be referred to as a case area; then the geographic unit of the urban jurisdiction is a specific living community. The public facilities in the district A of a certain city are divided into services, and therefore, citizens in all living communities in the district A of the city are required to distribute and enjoy the resources of the public facilities.
S11, acquiring data of a set geographical area;
the data acquired from the set geographical area is generally data collected and stored in advance, and the data has a corresponding relationship with the set geographical area.
S12, converting the data of the set geographical area into data files with different structure types;
when the data of the set geographic area are arranged into data files of different structure types, the data are arranged into data files of three structure types by using a GIS tool.
The data files of the three structure types are respectively: a geographic unit attribute file, a geographic unit adjacency file, and an inter-geographic unit distance file. The geographic unit attribute file reflects attribute information of each geographic unit, the geographic unit adjacency relation file reflects information of all adjacent geographic units in a set geographic area, and the inter-geographic unit distance file reflects a network distance between any two geographic units in the set geographic area, wherein the network distance between the two geographic units can be an Euclidean distance, a Manhattan distance or an actual distance and the like.
When data are arranged into geographic unit attribute files by using a GIS tool, each geographic unit attribute file is arranged into a standard file according to a 7-field format, and the 7-field format is geographic unit ID and geographic unit service demandA i Geographic unit X i Coordinates, geographic unit Y i Coordinates, whether there are utilities in the geographic unit, the amount of excess permitted for the geographic unit, and utility capacity.
When the GIS tool is used for sorting data into the geographic unit adjacency relation file, the geographic unit adjacency relation file is sorted into the file according to a 4-field format, all geographic units with adjacent relations in a set geographic area are counted, and the 4-field format is an index and a unit ID i Cell ID j And are adjacent to each other.
When the data is arranged into a distance file between the geographic units by using a GIS tool, the distance file between the geographic units is arranged into a file for expressing the network distance between every two geographic units according to a 4-field format, and the 4-field format is an index, a unit IDi, a unit IDj and the network distance.
Step 13, uniformly arranging data formats in the data files with different structure types to form a standard format; the standard format refers to a data format supported by calculation; namely, the data format meeting the requirements of the subsequent algorithm;
when the data formats in the data files with different structure types are unified, the data formats of the data files with different structure types in the step 1 are unified into the data formats supported in the calculation process, and the data formats supported by the calculation are referred to as standard formats.
And 2, constructing a public facility service area division frame.
The division frame of the public facility service area comprises a structural unit, an algorithm unit, a mathematical model unit and a public operation unit; the structural unit specifies the organization and storage structure of the data in the framework, by which the utility service area Partitioning (PFSD) problem can be described and defined. The structure unit is used for storing data of a corresponding structure, and when the data of the corresponding structure is imported into the structure unit, a data structure unit is formed. That is, the data structure unit includes various structure units capable of describing and defining PFSD problems. The structural unit specifies the organization form and storage structure of the data in the framework.
The algorithm unit comprises different algorithms, and the data in the data structure unit is called when the algorithms in the algorithm unit are used for processing; when the public facility service area is planned, the data is processed through the algorithm, and the service area division result can be obtained quickly and efficiently.
The mathematical model unit comprises a PFSD model, a TP model and an SPP model. The service area division of the public facilities can be directly or indirectly carried out through different mathematical models, so that an accurate service area division result is obtained. The common operation unit comprises functional modules of data reading, algorithm selection, continuous judgment, constraint detection, target evaluation, partition updating and file output, and different functions of the partition process of the public facility service area are completed through the common operation unit.
Step 21, building a structural unit in the data structural unit;
when building a structural unit, the definition of the PFSD problem is divided according to the utility service area, thereby building a plurality of different structural units that can completely describe the problem.
The PFSD problem refers to assigning each geographic unit to a facility according to the location and service capability of the public facility in a set geographic area to form a service area; and requires the service area to satisfy the constraints of compact shape, balanced supply and demand, continuous space, and the like.
The structural units include corresponding data structures such as "area unit", "service area unit", "demand unit", "facility unit", "adjacent unit", and the like. Here, "area unit" refers to a unit for which geographical area information is set, a "service area unit" refers to a unit that includes service partition information corresponding to each public facility after division of the public facility, a "demand unit" refers to a geographical unit having a service demand, a "facility unit" mainly refers to a geographical unit in which the public facility is located, and an "adjacent unit" refers to a geographical unit in which one geographical unit is adjacent.
When the service area division is carried out on the public facilities in the set geographic area, the data in the corresponding data structure can be quickly called through the data structure unit, and the purpose of accurate and precise division is achieved.
Step 22, constructing a mathematical model unit for dividing service areas of public facilities;
the mathematical model unit comprises a PFSD model, a TP model and an SPP model. The PFSD model completely expresses PFSD problems and meets the requirements of balanced supply and demand, compact shape, continuous space and the like. Acquiring an initial partitioning scheme of a set geographical area through a TP model; and solving through the SPP model to obtain a partitioning scheme for setting the service area of the public facility in the geographic area.
Step 23, establishing an algorithm unit;
the algorithm unit comprises an accurate algorithm, a meta heuristic algorithm, a mixed meta heuristic algorithm and a mathematic heuristic mixed algorithm. The precise algorithm is mainly used for precisely dividing the public facility service area of the set geographic area by solving the PFSD model. Meta-heuristic algorithms include simulated annealing algorithm (SA), record update algorithm (RRT), dynamic threshold algorithm (OBA), tabu Algorithm (TA), variable Neighborhood Search (VNS), iterative Local Search (ILS), and the like. These algorithmic processes are prior art and will not be described in detail here.
The mixed meta-heuristic algorithm is a combination of multi-start (M) and meta-heuristic algorithms, for example, a combination of multi-start (M) and Simulated Annealing (SA) to form an M-SA mixed meta-heuristic algorithm, and a combination of multi-start (M) and Iterative Local Search (ILS) to form an M-ILS mixed meta-heuristic algorithm.
The mathematical heuristic hybrid algorithm adopts a multi-start mode, uses the TP model to generate an initial partition scheme, uses a meta-heuristic algorithm to update the partition scheme, namely uses the meta-heuristic algorithm (such as SA, ILS and the like) as a basic structure, adopts a multi-start (M) mode, and uses a Transport Problem (TP) model to generate the initial partition scheme; and updating the partition scheme in the searching process by using an SA algorithm, wherein the SA-SPP mathematical heuristic hybrid algorithm and the ILS-SPP mathematical heuristic hybrid algorithm are included. And 3, importing the data file into a data structure of the public facility service area division frame to form a data structure unit.
When the data structure unit is formed by importing the data file into the data structure of the utility service area division frame, specifically, the data file with three structure types in the standard format in step 1 is imported into the utility service area division frame through the data reading function in the common operation unit, and is converted into corresponding structure units such as "area unit", "service area unit", "demand unit", "facility unit", "adjacent unit", and the like, so as to form the data structure unit, and the data storage is realized through the structure units, and the data structure unit is used for being called and processed when the utility service area division is performed, that is, the standard data file is imported into the frame, and the data is stored according to different data structures.
And 4, processing the data in each data structure unit based on the algorithm unit and/or the mathematical model unit as shown in the figure 2, and then obtaining a partition scheme.
And when the data in the data structure unit is processed based on the algorithm unit and/or the mathematical model unit, the geographic unit data in the set geographic area is processed based on the algorithm unit and/or the mathematical model unit. The service area partition scheme can be obtained by selecting and calling only a certain algorithm in the algorithm unit to perform space optimization solution, or by using a certain algorithm in the algorithm unit and a certain model in the mathematical model unit to perform solution in a combined manner.
Examples of the optimization solution by different algorithms are given below, respectively.
The optimization process of the precise algorithm is based on constructing a Mixed Integer Linear Programming (MILP) mathematical model of the PFSD, and the PFSD model is calculated through CPLEX to obtain a corresponding optimized partition scheme.
The optimization process of the precise algorithm comprises the following steps:
step S411, constructing a Mixed Integer Linear Programming (MILP) mathematical model of the PFSD;
in constructing a hybrid integer Linear programming (MILP) mathematical model of PFSD, the PFSD problem is represented in the form of a mathematical set, i.e., one geographic area U containing n geographic units and m facilities, the set U = {1,2, \8230; n }. Each geographic cell i has an attribute A i And B i 。
Wherein A is i Represents the service demand of the space unit i; b is i Representing the installation capacity in a geographic cell i, B i =0 indicates that there is no facility in the geographic cell i.
Variable D ij Representing the distance between geographic cell i and geographic cell j.
Variable C ij Representing whether geographic cell i is contiguous with geographic cell j.
The set of contiguous cells of geographic cell i is denoted N i ＝{j|C ij ＝1}。
The variable K represents the number of facility service areas to be divided.
Facility service area aggregation
Representing K facility service areas.
Based on a hybrid integer linear programming (MILP) mathematical model, the PFSD model for the utility service area partition is determined as follows:
V＝∑ i∈U ∑ k∈S A i D ij y ik (1)
wherein V represents the total distance of the demand unit to the facility unit; y is ik Whether the geographic unit i is distributed to the facility unit k is shown, and the value is only 0 or 1;
in order to achieve optimal zoning for utility service partitioning, the total distance V from demand unit to facility unit is the smallest for the zone objective for the present invention. And (3) adding constraint conditions to obtain the minimum value of the total distance V, and performing solution optimization on the step (1).
And step S412, calculating a PFSD model through CPLEX so as to obtain a corresponding optimized partition scheme.
The purpose of optimizing the total distance V is achieved by adding constraints in the present application. I.e. the total distance V from the demand unit to the facility unit in all the partitions is minimized under the constraint.
In the present embodiment, the constraint conditions are as follows
Constraint (2) requires that each geographic unit i must be and is uniquely assigned to a facility service area.
Constraint (3) is a facility capacity limit that is required to ensure that the demand of each facility service area cannot exceed the facility capacity.
The constraints (4-7) are constructed based on the network flow concept, ensuring spatial continuity of the facility service area k.
The constraint conditions (8-9) are limitations on the values of the decision variables.
Wherein f is ijk Non-negative integers representing from geographic cell i to geographic cell j within facility service area kAnd (4) flow rate.
And (4) optimizing the calculation of the total distance V from the demand unit to the facility unit by using the constraint conditions (2-9) so as to minimize the total distance V.
The meta-heuristic algorithm specifically comprises a simulated annealing algorithm (SA), a record updating algorithm (RRT), a dynamic threshold algorithm (OBA), a Tabu Algorithm (TA), a Variable Neighborhood Search (VNS), an Iterative Local Search (ILS) and the like. These algorithms are prior art and the details of the algorithms will not be described in detail here. The optimization process of the meta heuristic algorithm is to solve the division of the public facility service area in the set geographic area by using one algorithm in the meta heuristic algorithm.
In this embodiment, taking the simulated annealing algorithm (SA) as an example to describe the optimization process using meta-heuristic algorithm, the method includes the following steps:
(1) Initializing parameters: initial temperature InitT, loop times Loop, global best partitioning scheme S;
loop is a fixed parameter, and generally has a corresponding default value, which may be set by a user.
Substituting the initial partitioning scheme S into the objective function, wherein the smaller the obtained total distance V is, the better the initial partitioning scheme S is. Taking the initial partitioning scheme S corresponding to the minimum total distance V as the best partitioning scheme, wherein the actual solution of the scheme is the optimal solution of the algorithm, and the solution of the partitioning problem is the partitioning scheme;
(2) Calculating the Cooling coefficient Cooling;
(3) Obtaining an initial partition scheme S by calling a region growing method;
(4) And (5) circularly executing Loop sub-steps (5) to (7):
(5) Calculating the current temperature T;
(6) Calling a Metropolis method to obtain a current best partition scheme S';
(7) Obtaining a global best scheme S by comparing the objective function values substituted into S' and S;
if f (S ') < f (S'), then S 'partition scheme is better, assigning S' to S;
otherwise, the S division scheme does not need to be updated;
(8) And returning to S after Loop execution is finished.
The mixed element heuristic algorithm supports various element heuristic algorithms at the same time, specifically adopts a multi-start mode to update a partition scheme by using the element heuristic algorithm, and then adopts a multi-start (M) mode to solve the partition of the public facility service area in the set geographic area by using the element heuristic algorithm in the optimization process. For example, combining multiple start (M) and SA algorithms to form an M-SA hybrid heuristic algorithm; and combining the multi-start (M) algorithm and the ILS algorithm to form the M-ILS mixed element heuristic algorithm. The advantages of different element heuristic algorithms can be exerted through the mixed element heuristic algorithm, and the solving quality of the element heuristic algorithm is improved.
In this embodiment, taking the M-SA algorithm as an example, a process of performing optimization by using the hybrid heuristic algorithm is briefly described:
(1) Initializing parameters: multiple starting times M, initial temperature InitT, cycle times Loop and a global best partition scheme S;
(2) A multi-start mechanism, which executes the steps (3) - (8) for M times in a circulating way
(3) Obtaining an initial partition scheme S by calling a region growing method;
(4) Calculating the Cooling coefficient Cooling;
(5) And (5) circularly executing Loop sub-steps (6) to (8):
(6) Calculating the current temperature T;
(7) Calling a Metropolis method to obtain a current best partition scheme S';
(8) Obtaining a global best scheme S by comparing the objective function values substituted into S 'and S';
if S ' < S, then the S ' partition scheme is better, and S ' is assigned to S;
otherwise, the S division scheme does not need to be updated;
(9) After M times of circulation execution, returning to S;
an example of the M-SA algorithm is as follows:
the mathematical heuristic mixing algorithm is an algorithm formed by mixing a meta heuristic algorithm and a mathematical model and is called as a mathematical heuristic algorithm. Mathematical heuristic mixing algorithms have been used to solve complex facility siting problems.
In this embodiment, the SPP model and the SA algorithm are combined to form an SA-SPP mathematical heuristic hybrid algorithm; and combining the SPP model with the ILS algorithm to form an ILS-SPP mathematical heuristic hybrid algorithm. The convergence speed and the solving quality of the meta heuristic algorithm can be further improved by the mathematic heuristic hybrid algorithm.
The mathematical heuristic hybrid algorithm takes a meta heuristic algorithm (such as SA, ILS and the like) as a basic structure, adopts a multi-start (M) mode, and uses a Transport Problem (TP) mathematical model to generate an initial partitioning scheme; updating a partition scheme in the searching process by using an SA algorithm, and simultaneously recording all appeared service partitions to form a service area set; and finally, calling a Set Partitioning Problem (SPP) mathematical model to solve so as to further improve the service area partitioning scheme and obtain a solution with higher quality.
The TP model for obtaining the initial partition scheme is as follows:
V＝∑ i∈U ∑ k∈S (1+ε ik )D ik y ik (10)
the TP model is used for solving the minimum value of the total distance V in the objective function (10) under the constraint conditions (11-13). In order to obtain different initial schemes, a random coefficient epsilon is introduced into an objective function of a TP model ik (|ε ik |<0.02). TP model studentThe solution of (a) is likely to divide a demand unit into several blocks. To preserve geographic cell integrity, the assignment is made according to the partition in which the larger block resides. Solutions generated by the TP model may not meet the spatial continuity constraint and need to be detected, determined, and repaired.
Wherein, the Set-Partitioning Problem (SPP) model: when the SPP model is solved, a large number of partitions need to be constructed, and then the optimal partition combination is selected through the model. The solution results depend on the number and quality of the constructed partitions. During the neighborhood search of meta-heuristic algorithms, a large number of service partitions are generated. All the service partitions that appear are recorded to form a set omega. Each service partition i has a target attribute O i And unit set U i 。
And (3) constructing an SPP model, selecting a subset from the service area set omega, realizing the minimum V value in the objective function (14), and fully covering the geographic unit set U. Using decision variables x i Indicating whether candidate serving partition i is selected.
The SPP model is described as follows:
V＝∑ i∈Ω O i x i (14)
in this embodiment, the SA-SPP algorithm is taken as an example to describe the optimization process using the mathematical heuristic hybrid algorithm:
(1) Initializing parameters: the method comprises the steps of a service partition set sPool, multiple starting times M, an initial temperature initT, a cycle time Loop and a global best partition scheme S;
(2) Multiple starting mechanism, executing M times steps (3) - (9) circularly
(3) Acquiring an initial partitioning scheme S by calling a TP mathematical model;
(4) Calculating the Cooling coefficient Cooling;
(5) And (5) circularly executing Loop sub-steps (6) to (9):
(6) Calculating the current temperature T;
(7) Calling a Metropolis method to obtain a current best partition scheme S';
(8) Obtaining a global best scheme S by comparing the objective function values substituted into S' and S;
if f (S ') < f (S), then the S ' partition scheme is better, assigning S ' to S;
otherwise, the S-partition scheme does not need to be updated;
(9) Adding the partition scheme S' to a service partition set sPool;
(10) After M times of circulation execution, calling an SPP mathematical model to obtain a partitioning scheme S ";
(11) Obtaining a global best scheme S by comparing the objective function values substituted into S 'and S';
if f (S ") < f (S), then S" is a better partitioning scheme, assigning S "to S";
otherwise, the S division scheme does not need to be updated;
(12) Returning to S;
an example of the SA-SPP algorithm is as follows:
and S is used for recording the global best partition scheme, and the step (3-11) is used for completing M times of SA algorithm solution. And (4) firstly calling a TP model to obtain an initial partitioning scheme S. And the step (5-9) is the core part of the SA algorithm. And (5) calculating the Cooling coefficient Cooling of the SA algorithm, wherein the Cooling coefficient Cooling is related to the Loop times Loop. And (6-9) iteratively executing until a termination condition is met. T is the current temperature calculated based on the cooling coefficient, operators are a neighborhood search operator set, and the current best scheme S' is obtained through a Metropolis method, so that S is updated. And (10) recording the partition schemes appearing in the iterative process to form a service partition set. And (11-12) constructing an SPP model based on the service partition set, calling the model to solve the partition scheme S', and finally obtaining the partition scheme S with the best global situation.
Take a certain case zone ZY as an example. Case zone ZY includes 324 geographical units, 15 geographical units with utilities located, total service demand 3873, total capacity of all utilities 4470. Case zone distribution is shown in fig. 3, where the grey geographical cells increase in service demand as the color becomes darker, the black dots represent facilities, and the size of the dots represents facility capacity. After the solution algorithm provided by the algorithm framework, the system is divided into 15 partitions according to the number of the public facilities, wherein a partition scheme of the public facility service area solved by the PFSD model is shown in fig. 4.
The embodiment of the device comprises:
the dividing means comprises a processor, a memory and a computer program stored in said memory and executable on said processor, said processor realizing the steps of the above method when executing said computer program. The contents of the method steps have been introduced in the method embodiments, and are not described in detail herein.
In summary, the invention designs a PFSD hybrid algorithm framework based on the comprehensive analysis of the PFSD problem and its solution algorithm. The method comprises the following steps of PFSD problem definition, basic data structure expression, service area balance detection, service area space continuity constraint and other public operations, building a PFSP Mixed Integer Linear Programming (MILP) model, generating an initial service area division scheme based on a mathematical model and a heuristic algorithm, various neighborhood search operators, a search strategy, a starting strategy, a disturbance strategy and an acceptance criterion, a meta-heuristic algorithm library, a mathematical model set and other basic modules. The solution of the method follows the principles of effectiveness, simplicity and flexibility of algorithm implementation, and has the characteristics of reusability, expansibility and the like. Based on an algorithm framework, various solving algorithms such as a mathematical model, meta heuristic, mixed meta heuristic and mathematical heuristic mixing can be flexibly and rapidly realized, and the practical application of PFSD problems of different scales is met.
The invention can realize the following beneficial technical effects:
(1) The PFSD mathematical model is constructed according to various factors and constraint conditions in the PFSD problem of the public facility service area, the PFSD problem is completely expressed, the requirements of supply and demand balance, compact shape, continuous space and the like are met, especially the space is continuously constrained, and the model is closer to the practical requirement.
(2) Comprehensively analyzing various solving algorithm characteristics, designing a PFSD (particle swarm optimization) mixed algorithm framework, comprising basic modules such as problem definition, modeling, initial solution algorithm, search operator, search strategy and the like, and supporting the design of an accurate algorithm, a meta heuristic algorithm and a mixed algorithm.
(3) The algorithm framework can well process the PFSD problem of space continuous constraint, supports the quick realization of various algorithms, can efficiently solve the division problem of the public facility service areas of different scales, and particularly can obtain a high-quality service area planning scheme for medium-large scale case areas.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A division method facing a public facility service area is characterized by comprising the following steps:
step 1, managing all data of a set geographical area to form data files with different structure types;
acquiring data of a set geographical area, and converting the data of the set geographical area into data files of different structure types; the data in the data files with different structure types are in standard formats; the standard format refers to a data format supported by calculation;
step 2, constructing a public facility service area division frame; the public facility service area division frame comprises an algorithm unit, a structural unit and a mathematical model unit; the structure unit is used for forming a data structure unit with data in the data file; the algorithm unit comprises at least two algorithms, and the data in the data structure unit is called when the algorithms in the algorithm unit are used;
step 3, importing the data file into a structural unit in the public facility service area division frame to form the data structural unit;
step 4, processing the data in the data structure unit based on the algorithm unit and/or the mathematical model unit to obtain a partition scheme; the method specifically comprises the following steps:
based on a mixed integer linear programming mathematical model for constructing the PFSD, calculating the PFSD model through CPLEX to obtain a corresponding optimized partition scheme;
the method comprises the following steps:
step S411, constructing a PFSD mixed integer linear programming mathematical model;
in constructing a mixed integer linear programming mathematical model of PFSD, the PFSD problem is represented in the form of a mathematical set, i.e. a geographical areaUIncludednA geographic unit andma facility, a setU={1, 2, … n}; each geographic cell i has an attributeA i AndB i ；
wherein the content of the first and second substances,A i representing spatial cellsiThe service demand of (a);B i representing a geographic unitiThe volume of the inner installation is that,B i =0 for geographical unitiNo facility is arranged inside;
variables ofD ij Representing a geographic unitiAnd geographic unitjThe distance between them;
variables ofC ij Representing a geographic unitiAnd geographic unitjWhether or not to abut;
geographic unitiIs represented as a set of contiguous cellsN i ={ j | C ij =1}；
Variables ofKIndicating the number of facility service areas to be divided;
facility service area aggregationS={s 1 , s 2 … s K } (s k ⊂U) To representKA facility service area;
based on the mixed integer linear programming mathematical model, the determined PFSD model of the utility service area partition is as follows:
wherein the content of the first and second substances,Vrepresenting the total distance of the demand unit to the facility unit;y ik representing a geographic unitiWhether or not to be distributed to facility unitskThe value is only 0 or 1;
to make the total distanceVObtaining a minimum value, increasing constraint conditions, and carrying out solution optimization on the (1);
step S412, calculating a PFSD model through CPLEX so as to obtain a corresponding optimized partition scheme;
the constraint conditions are as follows
Constraint (2) requires that each geographic unit beiMust be and is uniquely assigned to one facility service area;
the constraint (3) is a facility capacity limit, which is required to ensure that the demand of each facility service area cannot exceed the facility capacity;
the constraints (4) - (7) are constructed based on the network flow concept to ensure the service area of the facilitykThe spatial continuity of (c);
the constraint conditions (8) - (9) are limits on the values of the decision variables;
wherein the content of the first and second substances,f ijk non-negative integer representing service area of facilitykInternal slave geographic unitiTo a geographic unitjThe flow rate of (c);
by means of the constraints (2) to (9), the total distance of the demand unit to the installation unit can be determinedVThe calculation of (2) realizes the optimization calculation to make the total distanceVAnd minimum.
2. The utility-oriented service area division method according to claim 1, wherein in step 1, when all the data of the set geographical area are managed, the data of the set geographical area are classified by using a GIS tool and then converted into data files of different structure types with standard formats.
3. The utility-service-area-oriented partitioning method according to claim 1 or 2, wherein the data file includes:
the geographic unit attribute file embodies the attribute information of each geographic unit;
a geographic unit adjacency relation file, which embodies all adjacent geographic unit information in a set geographic area;
an inter-geographic-unit distance file embodying a network distance between any two geographic units within a set geographic area.
4. The utility-oriented service area partitioning method according to claim 1, wherein the structural unit comprises:
the area unit is a unit for setting geographical area information;
the service area unit is a unit which is divided by public facilities and contains service partition information corresponding to each public facility;
the demand unit is a geographic unit with service demand;
the facility unit is a geographical unit where public facilities are located;
an adjacency unit that is a geographic unit adjacent to one geographic unit.
5. The utility-oriented service area partitioning method as claimed in claim 1, wherein the algorithm unit comprises a precision algorithm, a meta heuristic algorithm, a mixed meta heuristic algorithm, a mathematical heuristic hybrid algorithm.
6. The utility-oriented service area partitioning method of claim 5, wherein the meta-heuristic algorithm comprises simulated annealing algorithm, record-update algorithm, dynamic threshold algorithm, tabu algorithm, variable neighborhood search, iterative local search.
7. The utility-oriented service area partitioning method according to claim 6, wherein the mathematical model unit comprises a PFSD model, a TP model and an SPP model; the PFSD model is used for the complete expression of PFSD problem; the TP model is used for acquiring an initial partitioning scheme of the set geographic area; the SPP model is used for obtaining a partitioning scheme of the public facility service area of the set geographic area.
8. The utility-service-area-oriented partitioning method according to claim 7, wherein the mathematical heuristic hybrid algorithm employs a multi-boot approach, uses the TP model to generate an initial partitioning scheme, uses a meta-heuristic algorithm to update a partitioning scheme, and then uses the SPP model to improve the partitioning scheme.
9. The utility-service-area-oriented partitioning method according to claim 5, wherein the hybrid meta-heuristic algorithm updates the partitioning scheme using a multi-start approach and a meta-heuristic algorithm.
10. A utility service area-oriented partitioning apparatus, comprising a processor, a memory, and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the utility service area-oriented partitioning method of any one of claims 1 to 9 when executing the computer program.
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