CN110674997B - Mixed site selection system based on Hub storage - Google Patents
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Abstract
The invention discloses a Hub storage-based hybrid site selection system, which comprises the following steps: initializing related parameters of the project; randomly selecting n points from a facility point candidate set of the project to serve as initial solutions, and adding a solution set P; partitioning all facility point candidate sets by utilizing Kmeans++ algorithm clustering, wherein the designated impedance is taken as a distance calculation basis during clustering until a clustering center point is not changed; sequentially exchanging selected facility points and unselected facility points in the solution set P by using a TeitzBart algorithm, calculating a target value of the solution and comparing the target value; using the solution in the solution set P as an initial solution of a tabu algorithm and selecting the smallest target value in all new solutions; recording a current optimal solution and the occurrence times Nb est of the optimal solution; and outputting the current optimal solution and the target value, and finishing site selection. The method for selecting the address is combined with each other, and the preprocessing clustering of the data is added, so that the algorithm can be prevented from being limited to local optimum to a large extent, and the method has higher solving efficiency and solving quality.
Description
Technical Field
The invention belongs to the technical field of storage site selection, and particularly relates to a Hub storage-based hybrid site selection system.
Background
The problem of site selection is one of the classical problems in the fields of operations research and combinatorial optimization. The facility site selection problem has wide application in the aspects of production and life, logistics, even military, and the like, and the site selection result directly influences the service mode, service quality, service efficiency, cost, and the like, so the problem has great research significance. Weber in 1909 studied the problem of locating a warehouse on a plane to minimize the sum of distances between the warehouse and a plurality of customers (called Weber problem), formally pulling a preamble of facility site selection problem study; in 1964, the p-median problem and the p-center problem proposed by Hakimi greatly promote the theoretical research of facility site selection problems; in addition, a further type of facility site selection problem is that a plurality of facility points are selected to cover the demand points on the premise that all the demand points are known, and the problem is called a coverage problem. The coverage problem consists of a set coverage problem and a maximum coverage problem: the problem of covering is to solve the minimum number of facility points or construction cost under the condition of covering all the demand points; the maximum coverage problem is to find P facility points so that the facility can meet the maximum demand, given the number of facility points and the radius of service.
Therefore, heuristic algorithms are needed to solve the large-scale facility site selection problem. Heuristic algorithms refer to a class of intuitively or empirically constructed algorithms that can solve the feasible solution of the optimization problem in polynomial time. Common heuristic algorithms are: simulated annealing algorithms (Simulated Annealing Algorithm, SA), genetic algorithms (Genetic Algorithm, GA), ant colony algorithms (Ant Colony Algorithm, ACA), tabu Search algorithms (tab Search, TS), etc., but it is difficult for individual algorithms to accurately find solution efficiency and solution quality.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a Hub storage-based hybrid site selection system, which solves the problems that in the prior art, the solution efficiency and the solution quality are difficult to accurately calculate by a single algorithm.
The aim of the invention can be achieved by the following technical scheme:
a Hub storage-based hybrid site selection system comprises the following steps:
s1, initializing related parameters of items, and determining the maximum iteration number NC_max, the optimal solution occurrence number Nb_max, the facility number, the tabu table length n and the maximum service radius r of facility points;
s2, randomly selecting n points in a facility point candidate set of the project to serve as initial solutions, and adding a solution set P;
s3, partitioning all facility point candidate sets by utilizing Kmeans++ algorithm clustering, and repeatedly iterating until a clustering center point is not changed by taking designated impedance as a distance calculation basis during clustering;
s4, sequentially exchanging the selected facility points and the unselected facility points in the solution set P by utilizing a TeitzBart algorithm, calculating a target value of the solution, comparing the target value of the solution, updating the solution set P if the target value of the new solution is better than the target value in the solution set P, otherwise, keeping the solution set P unchanged, and repeating the algorithm operation until the solution set P is not updated;
s5, using the solutions in the solution set P as initial solutions of the tabu algorithm, adding facility points in the solution set P into a tabu table, exchanging each point in the tabu table with other points not in the tabu table, calculating target values of each new solution until all site selection points in the tabu table are traversed, and selecting the smallest target value in all new solutions;
s6, recording a current optimal solution and the occurrence frequency Nb est of the optimal solution, clearing a tabu table, updating iteration frequency NC=NC+1, and returning to the step S2 if NC is smaller than NC_max and Nb est is smaller than Nb est_max; if the maximum number of iterations is exceeded: NC > =nc_max, or the optimal solution iterates: nb > =Nb_max, then the determination of the optimal solution and the target value is completed;
and S7, outputting the current optimal solution and the target value, and finishing the address selection.
Further, in the step S2, the initial default value of the maximum service radius r of the facility point is infinity.
Further, in the step S2, the facility point candidate set is a service node set.
Further, in the step S2, a nearest point of each partition center of the facility point candidate set is selected to update the solution set P, and a target value of the solution is calculated.
Further, in the step S5, if the value is smaller than the target value of the original solution in the solution set P, the target value is the target value of the current optimal solution iterated by the tabu algorithm.
Further, in the step S5, the optimal solution is the current optimal solution of the iteration of the tabu algorithm.
Further, in the step S7, the optimal solution includes a facility point, service point information of each facility, and impedance information from each facility point to the service point.
The invention has the beneficial effects that:
1. the method for selecting the address is combined with each other, and the pre-processing clustering of the data is added, so that the guidance of the seed points in the adding processing can be avoided to a large extent, the algorithm is limited to local optimization, and the overall higher optimization effect is achieved in a shorter time.
2. The addressing system solves the selection of certain parameters such as the length of a tabu table and the size of a candidate set in a tabu algorithm and the acceptance criteria of the solution of the project, and performs a large number of repeated experiments so as to ensure stronger convergence and robustness of the addressing system in calculation.
3. According to the method, a KMeans++ method is combined to optimize a randomly generated initial solution, and a Teitz-Bart algorithm is utilized to optimize a KMeans++ updated solution, wherein the solution after two times of optimization is used as the initial solution of a tabu algorithm. The test result shows that compared with a single tabu algorithm, the mixed tabu algorithm has higher solving efficiency and solving quality.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.
FIG. 1 is an overall flow diagram of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
As shown in fig. 1, the present invention provides a Hub storage-based hybrid site selection system, which includes the following steps:
s1, initializing related parameters of items, and determining the maximum iteration number NC_max, the optimal solution occurrence number Nb_max, the facility number, the tabu table length n and the maximum service radius r of a facility point, wherein the initial default value of the maximum service radius r of the facility point is infinity.
S2, randomly selecting n points from a facility point candidate set (the facility point candidate set is a service network point set) of the project to serve as an initial solution, and adding the initial solution into a solution set P.
S3, partitioning all facility point candidate sets by utilizing Kmeans++ algorithm clustering, and repeatedly iterating until a clustering center point is not changed by taking designated impedance as a distance calculation basis during clustering; at the same time, the nearest point in the center of each partition of the facility point candidate set is selected to update the solution set P, and the target value of the solution is calculated.
S4, sequentially exchanging the selected facility points and the unselected facility points in the solution set P by utilizing a TeitzBart algorithm, calculating and comparing the target values of the solutions, updating the solution set P if the target values of the new solutions are better than the target values in the solution set P, otherwise, keeping the solution set P unchanged, and repeating the algorithm operation until the solution set P is not updated.
S5, using the solutions in the solution set P as initial solutions of the tabu algorithm, adding facility points in the solution set P into a tabu table, exchanging each point in the tabu table with other points not in the tabu table, calculating target values of each new solution until all site selection points in the tabu table are traversed, and selecting the smallest target value in all new solutions; if the value is smaller than the target value of the original solution in the solution set P, the target value is the target value of the current optimal solution of the iteration of the tabu algorithm, and the optimal solution is the current optimal solution of the iteration of the tabu algorithm.
S6, recording a current optimal solution and the occurrence frequency Nb est of the optimal solution, clearing a tabu table, updating iteration frequency NC=NC+1, and returning to the step S2 if NC is smaller than NC_max and Nb est is smaller than Nb est_max; if the maximum number of iterations is exceeded: NC > =nc_max, or the optimal solution iterates: nb > =Nb_max, then the determination of the optimal solution and the target value is completed;
and S7, outputting a current optimal solution and a target value, wherein the optimal solution comprises facility points, service point information of each facility and impedance information from each facility point to the service point, and finishing site selection.
The Hub storage-based hybrid site selection system solves the problem of site selection of facilities with known open facility quantity and minimum impedance (time or distance or cost), in the facility site selection network, the meaning of impedance (connection line) between the client network point and the service network point can represent different meanings of time, distance or cost, and the influence of different factors such as time, distance and cost on the site selection is considered, so that the aim of reducing cost and improving efficiency can be achieved.
Meanwhile, a great amount of repeated tests are carried out on the selection of certain parameters such as the length of a tabu table, the size of a candidate set and the like in the tabu algorithm and the acceptance criteria of the solution, so that the stronger convergence and the robustness of the algorithm are ensured.
In commercial application, the large-scale facility addressing problem (hundreds of nodes) generally has higher requirements on the speed and precision of algorithm solving, and the improvement of the selection and search mechanism of the initial nodes can greatly improve the solving efficiency and the solving quality of the algorithm. The mixed tabu algorithm of the site selection method firstly combines a KMeans++ method to optimize a randomly generated initial solution, secondly utilizes a Teitz-Bart algorithm to optimize a KMeans++ updated solution, and the solution after two times of optimization is used as the initial solution of the tabu algorithm. The test result shows that compared with a single tabu algorithm, the mixed tabu algorithm has higher solving efficiency and solving quality.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (7)
1. A Hub storage-based hybrid site selection system is characterized in that,
solving the facility site selection problem with the known number of open facilities and minimum impedance as a target, wherein the impedance is as follows: time or distance or cost;
the method comprises the following steps:
s1, initializing related parameters of items, and determining the maximum iteration number NC_max, the optimal solution occurrence number Nb_max, the facility number, the tabu table length n and the maximum service radius r of facility points;
s2, randomly selecting n points in a facility point candidate set of the project to serve as initial solutions, and adding a solution set P;
s3, partitioning all facility point candidate sets by utilizing Kmeans++ algorithm clustering, and repeatedly iterating until a clustering center point is not changed by taking designated impedance as a calculation basis during clustering;
s4, sequentially exchanging the selected facility points and the unselected facility points in the solution set P by utilizing a TeitzBart algorithm, calculating a target value of the solution, comparing the target value of the solution, updating the solution set P if the target value of the new solution is better than the target value in the solution set P, otherwise, keeping the solution set P unchanged, and repeating the algorithm operation until the solution set P is not updated;
s5, using the solutions in the solution set P as initial solutions of the tabu algorithm, adding facility points in the solution set P into a tabu table, exchanging each point in the tabu table with other points not in the tabu table, calculating target values of each new solution until all site selection points in the tabu table are traversed, and selecting the smallest target value in all new solutions;
s6, recording a current optimal solution and the occurrence frequency Nb est of the optimal solution, clearing a tabu table, updating iteration frequency NC=NC+1, and returning to the step S2 if NC is smaller than NC_max and Nb est is smaller than Nb est_max; if the maximum number of iterations is exceeded: NC > =nc_max, or the optimal solution iterates: nb > =Nb_max, then the determination of the optimal solution and the target value is completed;
and S7, outputting the current optimal solution and the target value, and finishing the address selection.
2. The Hub storage based hybrid siting system according to claim 1, wherein in said step S1, the initial default value of the maximum service radius r of the facility point is infinity.
3. The Hub storage based hybrid siting system according to claim 1, wherein in step S2, said facility point candidate set is a service point set.
4. The Hub storage based hybrid siting system according to claim 1, characterized in that in said step S2, a nearest point of each partition center of the facility point candidate set is selected to update the solution set P and a target value of the solution is calculated.
5. The Hub storage based hybrid site selection system of claim 1, wherein in step S5, if the value is smaller than the target value of the original solution in the solution set P, the target value is the target value of the current optimal solution in this iteration of the tabu algorithm.
6. The Hub storage based hybrid siting system according to claim 5, wherein in step S5, the optimal solution is the current optimal solution for this iteration of the tabu algorithm.
7. The Hub storage based hybrid siting system according to claim 1, wherein in said step S7, the optimal solution comprises a point of service information of each facility and a point of service information of each facility.
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| CN107341628A (en) * | 2016-12-30 | 2017-11-10 | 闽江学院 | A kind of axis-spoke logistics network Hub Location and distribution method based on probability Tabu search algorithm |
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| CN107341628A (en) * | 2016-12-30 | 2017-11-10 | 闽江学院 | A kind of axis-spoke logistics network Hub Location and distribution method based on probability Tabu search algorithm |
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