CN105787595B - Emergency logistics transfer station site selection method based on improved ant colony algorithm - Google Patents
Emergency logistics transfer station site selection method based on improved ant colony algorithm Download PDFInfo
- Publication number
- CN105787595B CN105787595B CN201610112271.5A CN201610112271A CN105787595B CN 105787595 B CN105787595 B CN 105787595B CN 201610112271 A CN201610112271 A CN 201610112271A CN 105787595 B CN105787595 B CN 105787595B
- Authority
- CN
- China
- Prior art keywords
- disaster
- emergency
- transfer station
- ant colony
- colony algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Artificial Intelligence (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Computational Linguistics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
Abstract
The invention discloses an emergency logistics transfer station site selection method based on an improved ant colony algorithm, which is characterized in that a multi-objective optimization model is constructed by taking maximization of emergency demand coverage and minimization of emergency cost as targets, and the number of temporary transfer stations is determined by combining constraint conditions of the optimization model; solving a multi-objective optimization model by using a clustering ant colony algorithm and a greedy algorithm, sequentially carrying out clustering analysis on the disaster-affected point coverage conditions under each disaster level, determining the grouping of the disaster-affected points, and making a preliminary address selection scheme of the transfer station; evaluating the transfer stations which are not selected, replacing the initially selected transfer station with the largest evaluation function value, and relocating the transfer station until the site selection scheme is not changed any more; and outputting the final intermediate transfer station address selection scheme. The invention improves the ant colony algorithm on the aspects of state transition probability, shared tabu table and greedy algorithm idea application, improves the convergence speed and the solving quality, and provides a theoretical basis for the optimization of the address selection scheme of the emergency material transfer station.
Description
Technical Field
The invention relates to an emergency logistics transfer station site selection method based on an improved ant colony algorithm.
Background
In recent years, with the increasing level of technology, many natural disasters can be prevented and controlled gradually, but some disasters are still unavoidable and occur frequently, such as earthquake disasters. China is a country with multiple earthquake disasters, and several earthquake disasters in recent years cause great loss to personal safety, living facilities and the like of people in disaster areas, and increase unstable factors of society. In the sudden disasters, countries and governments give a large amount of financial subsidies and move a large number of professional rescue teams to carry out active rescue work for the first time. However, the damage caused by earthquake disasters is enormous and immeasurable. Every earthquake disaster happens urgently, support of emergency materials is needed, emergency logistics bear a vital role, rescue manpower, material resources and financial resources can be transported to a disaster area through an emergency logistics channel, and therefore life and property safety of the disaster area is guaranteed at the first time.
In spite of earthquake disasters in recent years, after the disasters occur, a plurality of emergency material demands are often put forward simultaneously, and higher requirements are put forward for the response capability and the material allocation capability of emergency logistics. In order to quickly and effectively respond to various emergency requirements of disaster areas, the problem of site selection of emergency material transfer stations is firstly solved.
The problem of site selection of a transfer station belongs to a branch of emergency logistics vehicle path problem research, and foreign experts and scholars begin earlier in the research and research of the field, so that quite abundant research results are obtained. At present, the exploration about the site selection problem of the transfer station mainly aims at maximizing profit, covering disaster-affected points to the maximum extent and fairness, and a relevant transfer station site selection model is constructed by integrating with a dynamic modeling idea. However, due to the complexity of practical problems, most of the existing researches aim at site selection of large-scale material storage warehouses, and the site selection problem in disaster areas is not considered; the selected intelligent algorithm has many problems in solving the address selection model of the emergency material transfer station, such as premature convergence, easy falling into local optimum and the like, and cannot meet the uncertainty requirements under various emergencies, and further deep discussion is needed.
Disclosure of Invention
The invention aims to solve the problems, provides an emergency logistics transfer station site selection method based on an improved ant colony algorithm, provides a site selection problem of a multi-supply-point multi-demand-point multi-material demand non-full-load emergency material transfer station with a soft time window, analyzes a static condition with known demand information and no change in the later period, constructs a temporary transfer station site selection model, and establishes a reasonable transfer station address for storing rescue materials collected from a material storage library and all social boundaries, thereby effectively shortening the allocation time of the emergency materials and realizing the maximization of disaster area requirements.
In order to achieve the purpose, the invention adopts the following technical scheme:
an emergency logistics transfer station site selection method based on an improved ant colony algorithm comprises the following steps:
(1) constructing a multi-objective optimization model by taking the maximization of the emergency demand coverage degree and the minimization of the emergency cost as targets, and determining the number of temporary transfer stations by combining the constraint conditions of time window limit, total investment amount, fairness, emergency scheduling efficiency and the like of the optimization model;
(2) solving a multi-objective optimization model by using a clustering ant colony algorithm and a greedy algorithm, sequentially carrying out clustering analysis on the disaster-affected point coverage conditions under each disaster level, determining the grouping of the disaster-affected points, and making a preliminary address selection scheme of the transfer station;
(3) establishing an evaluation function by combining the probability of different disaster grades of disaster-affected points in each disaster-affected group, the demand of different materials and whether the disaster-affected points are covered, evaluating the unselected transfer stations, replacing the initially selected transfer station with the largest evaluation function value, and relocating the transfer station until the site selection scheme is not changed any more;
(4) and outputting the final intermediate transfer station address selection scheme.
In the step (1), the emergency cost is the total cost of the transfer station, including the construction cost and the operation and maintenance cost of the transfer station.
In the step (1), the objective function with the aim of maximizing the emergency coverage requirement is as follows:
j is the number of the demand point of the disaster area, and j is 1 and 2 … m; s is disaster level classification, and s is 1, 2 … p; l is the serial number of the first kind of emergency resource, and l is 1, 2 … … k; x is the number ofjslRepresenting the demand of the demand point j on the resource l when the disaster level is s; when in use
In time, the demand point j is covered when the disaster level is s and the demand point is covered when the disaster level is s
It cannot be covered; p is a radical ofjsGenerating s stages for demand point jProbability of disaster.
In the step (1), an objective function with the emergency cost minimized as a target is as follows:
wherein, i is the serial number of the alternative emergency resource transfer station, i is 1, 2 … n; when v isiWhen the value is 1, the alternative point i is selected as the temporary transfer station, viWhen 0, i is not selected; h isiRepresents a fixed cost required to establish a temporary transfer station; r isiRepresenting the cost of operation and maintenance of a temporary transfer station.
In the step (1), the constraint conditions specifically include:
(1-1) if the disaster-affected point j is covered when the disaster level is s, at least one temporary transfer station is needed under the disaster level, and the response time of the temporary transfer station meets the lower limit of the time window of the first type of materials with the largest required time window under the disaster level;
(1-2) the sum of the site selection schemes of the transfer station is lower than the upper limit of the total fund planned to be invested;
(1-3) the quantity of various emergency resources transported to the demand points cannot exceed the proposed demand quantity;
(1-4) there is a limit to the number of transfer stations.
In the step (1), the constructed model belongs to a multi-target mathematical programming model, according to the characteristics of the model, the emergency requirement coverage maximization is taken as a main target, the cost of the emergency material transfer station is minimized on the premise of meeting the target, and the main target method is adopted for solving.
Further, in the step (1), the specific method for solving includes:
(1-a) solving a single-target mathematical programming model formed by an objective function and constraint conditions of which the emergency cost is minimized to obtain an optimal solution;
(1-b) combining the objective function with minimized emergency cost and the optimal solution obtained in the previous step and converting the objective function into a constraint condition;
and (1-c) solving a single-target mathematical programming model formed by an objective function with maximized emergency demand coverage and constraint conditions to obtain an optimal solution, wherein the obtained result is the optimal number and position of temporary transfer stations.
In the step (2), a decision variable is introduced on the basis of the state transition probability of the basic clustering ant colony algorithm, and the time of reaching the next node is compared with the lower limit of a time window with a larger time window required by the current node; when the decision quantity is 1, the disaster-affected point j can be covered when the disaster level is s; when the decision quantity is 0, it means that it cannot be covered.
Because the demand materials of the current disaster-stricken point have l types, the largest one of the demand time windows in the l types of demand materials is taken to be compared with the demand materials.
In the step (2), the independent tabu tables set for each ant in the basic clustering ant colony algorithm are shared, and all node information visited by the ants is stored.
In the step (2), a greedy algorithm is utilized, ants are initialized to search all transfer stations which are in accordance with the minimum time window lower limit according to the transfer probability at the initial address selection stage, and then transfer stations which are arranged in the front N positions in the selected number are selected as initial solutions, wherein N is the number of temporary transfer stations; in the grouping process, each uncovered demand point selects a transfer station nearby, and finally N groups are formed; and in the relocation process, replacing the transfer stations in sequence according to the evaluation function until the global optimization is achieved.
The step (2) specifically comprises:
(2-1) initializing cycle times, pheromone evaporation rate, pheromone increase rate, coordinates of a disaster-affected point, coordinates of alternative transfer stations, the number of temporary transfer stations and a time window;
(2-2) according to the transition probability, calculating an alternative transfer station to which the ants will arrive, and adding the coordinates of the disaster-affected points into a taboo table;
(2-3) judging whether all the node traversal is finished, if so, executing the step (2-4), and if not, executing the step (2-2);
(2-4) updating the pheromone concentration of the alternative transfer station, and adding 1 to the cycle number;
(2-5) repeating the steps (2-2) - (2-4) until the set number of cycles is met;
and (2-6) calculating according to the fitness function, arranging calculation results in a descending manner, and using the transfer stations arranged at the first N bits as initial solutions.
In the step (3), the evaluation function is
i＝1，2…n；
Wherein G represents a disaster-affected point set in a disaster-affected group G, and N disaster-affected groups exist in the part; when in use
When the disaster situation level is s, the demand of the demand point j on the resource l can be met by the transfer station i, and when the disaster situation level is s, the demand point j can meet the demand of the resource l
And then, it cannot be satisfied.
The invention has the beneficial effects that:
(1) the invention provides the site selection problem of the emergency logistics transfer station with soft time windows and multiple supply points, multiple demand points and multiple material demands, and can provide a relevant theoretical basis for the research of the multi-target problem in the field;
(2) the invention constructs a mathematical model suitable for corresponding problems by applying an operational research theory, and can be used as a tool for drawing up a temporary transfer station site selection scheme in sudden disasters;
(3) the method combines the basic ant colony algorithm with multiple intelligent algorithm ideas, improves the aspects of state transition probability improvement, shared tabu table, greedy algorithm application and the like, designs the improved ant colony algorithm suitable for the corresponding model, and has certain reference function for the application of the ant colony algorithm in the field of emergency material transfer station site selection.
Drawings
FIG. 1 is a schematic diagram of an emergency logistics system of the present invention;
FIG. 2 is a schematic flow chart of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
In order to solve the defects in the site selection process of the current emergency material transfer station, the patent provides the site selection problem of the non-full-load emergency material transfer station with soft time windows and multiple supply points, multiple demand points and multiple material demands, analyzes the static condition that demand information is known and cannot change in the later period, constructs a temporary transfer station site selection model, and designs an improved ant colony algorithm for solving the model according to the characteristics of the model by combining multiple intelligent algorithm ideas, sharing a tabu table and other improved methods.
As shown in fig. 1, the supply point in the figure represents a large material distribution center in each area, and when a sudden disaster occurs, most of the materials need to be transported from the supply point to the disaster area, and the process of transporting the materials from the supply point to the transfer station is not considered for the moment; the material transfer station is the material center of establishing temporarily in the disaster area after earthquake disaster takes place, and most material all obtains from the supply point, and other not enough material can be scattered and collected. The optimized site selection scheme of the transfer station can effectively shorten the allocation time of emergency materials; the demand points represent disaster areas, namely emergency demand proposing points after an earthquake occurs, the disaster points are grouped according to the coverage maximization of the temporary transfer stations, and each group is provided with one temporary transfer station.
After an earthquake disaster occurs, different material requirements are simultaneously put forward in a plurality of disaster areas, so that the emergency rescue department is required to make optimal judgment at the first time, and different materials are conveyed to each disaster area. Because the types and the quantity of the stored materials of the material storage center are limited, in order to meet the requirements of the disaster area to the maximum extent, the invention considers the problem of site selection of the material transfer stations in the disaster area, namely, a plurality of temporary material transfer stations are established in the disaster area to store rescue materials collected from the material storage library and all social boundaries, and the material can be optimally configured in the transfer stations, so that the limited materials are reasonably distributed at the first time when a disaster occurs, and the requirements of the disaster area are met to the maximum extent.
The invention mainly researches the site selection problem of the emergency material transfer station with soft time windows, multiple supply points, multiple demand points and multiple material demands. Firstly, introducing an emergency demand maximization coverage idea to construct an emergency material transfer station site selection model; secondly, aiming at the constructed multi-target planning model, the ant colony algorithm is improved in the aspects of tabu table sharing, state transition probability and the like by combining various intelligent algorithm ideas, the convergence speed and the solving quality of the algorithm are improved, and the improved ant colony algorithm suitable for the corresponding model is designed.
(I) the model hypothesis of the location selection of the transit station
For better description and understanding of the post-disaster temporary transfer station site selection model, the model is constructed by the following assumptions:
(1) the demand information (the type and quantity of materials) of the emergency materials of each disaster point is known.
(2) Assume that the transfer station has no capacity restrictions.
(3) The emergency material demand of each disaster-affected point under different disaster grades is known, and the disaster grade is given by a disaster management or forecasting department or expert prediction and estimation.
(4) The priority of each disaster point to the rescue response time is higher than the requirement of the quantity of the corresponding emergency supplies.
(II) establishing model of site selection of transfer station
The temporary emergency material transfer station site selection model is a multi-objective optimization model, and related objectives mainly comprise two objectives of emergency demand coverage degree, transfer station construction and operation and maintenance cost.
1. Emergency demand coverage maximization objective
The objective function is:
in the above formula: j is the number of the demand point of the disaster area, and j is 1, 2 … m; s is disaster level classification, and s is 1, 2 … p; l is the number of the first emergency resource, and l is 1, 2. x is the number ofjslRepresenting the demand of the demand point j on the resource l when the disaster level is s; when in use
In time, the demand point j is covered when the disaster level is s and the demand point is covered when the disaster level is s
It cannot be covered; p is a radical ofjsThe probability of s-level disaster occurrence for the demand point j.
And the objective function (4.1) is a main function and indicates that the selected temporary transfer station can provide the maximum material demand for the disaster-stricken point under the condition that the disaster level is uncertain.
2. Minimization of emergency costs
The objective function is:
in the above formula, i is the serial number of the alternative emergency resource transfer station, i is 1, 2 … n; when v isiWhen the value is 1, the alternative point i is selected as the temporary transfer station, viWhen 0, i is not selected; h isiRepresents a fixed cost required to establish a temporary transfer station; r isiRepresenting the cost of operation and maintenance of a temporary transfer station.
The objective function (4.2) aims to minimize the total cost of the transfer station, including the construction cost and the operation and maintenance cost of the transfer station.
The constraint conditions which the temporary emergency resource transfer station model constructed in the part should meet are as follows:
(1) if the disaster-affected point j is covered when the disaster level is s, at least one temporary transfer station is needed under the disaster level, and the response time of the temporary transfer station meets the lower limit of the time window of the first type material with a larger required time window under the disaster level. By tijIndicating the time of delivery of rescue material from i to j, using LTjsAnd the lower limit of the time window of the material with a larger required time window at the disaster point j when the disaster grade is s is shown.
Wherein, if
It means that the disaster-affected point j can be covered by the transfer station i when the disaster level is s, i.e. tij≤LTjs(ii) a If it is
It means that it cannot be covered by the relay station i.
(2) The total of the site selection schemes of the transfer station is lower than the upper limit of the total planned investment, and F represents the upper limit of the total planned investment, namely:
(3) in order to avoid waste and simultaneously guarantee fair allocation of emergency resources as much as possible, the various emergency resource amounts transported to demand points cannot exceed the demand amounts proposed by the demand points, namely:
wherein y isijlRepresenting the amount of the class I goods and materials transported from the transfer station i to the disaster-affected point j; x is the number ofjlAnd representing the total demand of the demand point j on the l-th type resource.
(4) In order to ensure the efficiency of emergency resource scheduling, the number of transfer stations should be limited, and the selected transfer stations belong to the set of alternative transfer stations. M represents the maximum number of transfer stations, and N represents the set of alternative transfer stations, as follows:
(5) both decision variables are 0-1 variables, constrained as follows:
vi∈{0,1}i＝1，2…n (4.7)
the model constructed by the invention belongs to a multi-target mathematical programming model, according to the characteristics of the model, the emergency requirement coverage maximization is taken as a main target, the cost of an emergency material transfer station is minimized on the premise of meeting the target, the main target method is adopted for solving, and the specific steps are as follows:
step 1: removing the target (4.1), solving a single-target mathematical programming model consisting of the target (4.2) and the constraints (4.3) - (4.8), and obtaining an optimal solution f2*。
Step 2: the target (4.2) was converted to constraints as follows:
step 3: and (3) removing the target (4.2), solving a single-target mathematical programming model consisting of the target (4.1) and the constraints (4.3) - (4.9), and obtaining a result which is the optimal solution of the model.
The invention solves the problem of address selection of the material transfer station by combining the idea of an improved clustering ant colony algorithm with a greedy algorithm.
(III) Algorithm improvement and design
1. Ant colony algorithm improvement
Aiming at the defects that the problem solving speed of the model and the basic ant colony algorithm is too slow and the problem is easy to fall into local optimum, the invention improves and innovates the algorithm.
The site selection model of the transfer station constructed by the invention mainly applies the thought of maximum coverage, and most of the existing research on the site selection of the maximum coverage adopts a Lagrange relaxation algorithm to solve the problem. In terms of actual research results, the intelligent algorithm is more effective for solving the combinatorial optimization problem. In order to make up for the defects of the basic ant colony algorithm, the invention determines to adopt the idea of combining the improved clustering ant colony algorithm with the greedy algorithm to solve the problem of initial coverage.
The clustering ant colony algorithm is also called as clustering-based ant colony algorithm, researchers gather ants by observing how workers carry the corpses of the ants, the scale of the ant pile directly determines the selection probability of the ants, and the selection probability is common with the determination of pheromone concentration in the basic ant colony algorithm. However, due to this feature, the clustering ant colony algorithm also inherits the disadvantages of the basic ant colony algorithm: firstly, stagnation happens occasionally, and a global optimal solution is not easy to find; ② the convergence speed is too slow.
Aiming at the analysis, the invention makes the following improvements on the clustering ant colony algorithm:
(1) improvement of state transition probability. The part introduces a decision variable on the basis of the state transition probability of the basic ant colony algorithm
The time to reach the next node is compared with the lower limit of the time window with the larger required time window of the current node. When in use
In time, it means that the disaster point j can be covered when the disaster level is s, i.e. tij≤LTjs(ii) a When in use
And if so, indicating that the data cannot be covered. The improved state transition probabilities are as follows:
the introduction of this decision variable can effectively guide ants how to select the next node. When the distance from the next candidate transfer station to the current disaster-affected point is within the time window range, the transfer station can be considered; otherwise, skipping directly. The improvement shortens the convergence speed of the clustering ant colony algorithm to the maximum extent.
(2) A tabu table is shared. In the basic ant colony algorithm, a separate tabu table is set for each ant, and nodes passed by the ants after one circulation are sequentially collected into the tabu tables. This prevents ants from repeatedly traversing previous nodes in a single cycle, thereby allowing all nodes to complete the traversal.
In the invention, in order to enable all traversed ants to better exchange information, the tabu table is correspondingly improved, namely all ants share the same tabu table, and node information visited by the ants is stored inside. The method has the advantages that any ant can only access nodes which are not accessed in the shared tabu table, so that all ants work and cooperate, all disaster-affected points are traversed to the maximum extent, iteration times of the algorithm are reduced, and the operation efficiency is improved.
(3) And (4) application of a greedy algorithm idea. The basic idea of the greedy algorithm is to find the optimal solution of each part from local in the process of seeking the optimal solution, and further integrate the optimal solutions into an overall optimal solution. The invention applies this idea to solve the problem of overlay addressing. At the initial stage of address selection, ants are initialized to search all transfer stations which are in accordance with the lower limit of the shortest time window according to the transfer probability, and then the transfer stations which are arranged at the top N positions in a selected number of times are selected as initial solutions; in the grouping process, each uncovered demand point selects a transfer station nearby, and finally N groups are formed; and in the relocation process, replacing the transfer stations in sequence according to the evaluation function until the global optimization is achieved. The process embodies the idea of the greedy algorithm, and the defects that the clustering ant colony algorithm is early and easy to fall into local optimum can be effectively overcome by applying the process at this stage.
2. Improved algorithm design for site selection of transit station
The goal of the relay station site selection model provided by the invention is to consider how to select the number and the positions of temporary relay stations under different disaster levels, so that the demand and the time window can be met to the maximum extent. Based on consideration of the optimization combination problem, the method decides to adopt an improved clustering ant colony algorithm and a greedy algorithm to solve the initial coverage problem, and further utilizes a multi-stage heuristic algorithm to group the disaster-affected points and relocate the transfer station.
Based on the characteristic that the disaster level is uncertain, the multi-stage heuristic algorithm of the part solves from two stages, firstly, considering that the coverage degree of disaster-affected points is different due to the difference of the disaster level, and how to divide the grouping of the disaster-affected points is a problem needing to be mainly solved by the invention; second, the selected temporary transfer stations may cover different disaster-stricken groups, so that the relocation problem of the transfer stations also needs to be considered.
Combining the characteristics of the model, the algorithm designed by the part comprises the following steps:
(1) determining the number N of the temporary transfer stations which can be built according to the target and the constraint condition;
(2) and preliminarily selecting a temporary transfer station by utilizing a clustering ant colony algorithm and a greedy strategy. Clustering by taking a larger value of a time window required by a disaster point to the goods and materials as a standard when the disaster level is p, establishing a fitness function f, and selecting the first N transfer stations with descending values as initial transfer stations;
(3) considering the situation that the disaster-affected points are covered when the disaster level is p-1 and p-2.. 1, and determining the grouping of the disaster-affected points;
(4) carrying out relocation of the transfer stations, establishing an evaluation function f (value) for the demand of different materials and whether the demand is covered or not by combining the probability of different disaster grades of the disaster-stricken points in each disaster-stricken group, evaluating the unselected transfer stations, and replacing the selected transfer stations by the transfer stations with the maximum evaluation function values;
(5) and (4) repeating the steps 2, 3 and 4 until the address selection scheme of the transfer station is not changed.
The formula (4.11) is an evaluation function established in the site selection and relocation phase of the transfer station, wherein G represents a disaster-affected point set in a disaster-affected group G, and the part has N disaster-affected groups; when in use
When the disaster situation level is s, the demand of the demand point j on the resource l can be met by the transfer station i, and when the disaster situation level is s, the demand point j can meet the demand of the resource l
And then, it cannot be satisfied.
In the above steps, the initial solution of the site selection of the transfer station in step (2) is the key point of the present invention, so the present invention analyzes this in detail:
first, the number of transfer stations is calculated. And (3) removing the target (4.1) by using a main target method, solving a single-target mathematical programming model consisting of the target (4.2) and the constraints (4.3) - (4.8), and obtaining an optimal solution, namely the number N of the temporary transfer stations required by the method.
Secondly, the obtained number of the transfer stations is used as a constraint condition (4.9), the target (4.2) is removed, a single-target planning model formed by the target (4.1) and the constraint conditions (4.3) - (4.9) is solved, and an improved clustering ant colony algorithm is used for calculating an initial solution of the address selection problem of the transfer stations in the process.
(1) Initialization data, NC (number of cycles), ρ (pheromone evaporation rate), τ (pheromone increase rate), J (disaster-affected point coordinate), I (candidate transfer station coordinate), N (number of temporary transfer stations), LT (time window).
(2) According to transition probability
And (5) calculating an alternative transfer station i to be reached by the ants, and adding j into a taboo table.
(3) Whether all node traversals are completed. If yes, executing step 4, otherwise executing step 2.
(4) Updating the pheromone concentration tau of the i-pointiNumber of cycles + 1.
(5) And (5) repeating the steps 2, 3 and 4 until the circulation meets NC times.
(6) According to fitness function
Carry out a calculation ofThe results are arranged in descending order with the first N-bit transfer stations as the initial solution.
In the generation process of the optimal addressing result, the invention draws up a fitness function
Wherein p isjsProbability of occurrence of disaster level s for disaster-affected point j, τiIndicating the pheromone concentration accumulated by the alternative transfer station i after NC cycles. The fitness function represents that when the disaster grade is s, the product of the sum of the probabilities of s-grade disasters occurring in the disaster-affected point of a certain temporary transfer station and the pheromone concentration of the transfer station is selected, and the results are arranged in a descending manner. In the invention, the transfer station with the first N bits is selected as an initial solution, and the judgment criterion of the initial solution is that the temporary transfer station covers the disaster-affected point with the higher probability of s-level disaster to the maximum extent. And an initial stage s ═ p of site selection of the alternative transfer station.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An emergency logistics transfer station site selection method based on an improved ant colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
(1) constructing a multi-objective optimization model by taking the maximization of the emergency demand coverage degree and the minimization of the emergency cost as targets, and determining the number of temporary transfer stations by combining the constraint conditions of time window limit, total investment amount, fairness and emergency scheduling efficiency of the optimization model;
(2) solving a multi-objective optimization model by using a clustering ant colony algorithm and a greedy algorithm, sequentially carrying out clustering analysis on the condition that the disaster-affected points under each disaster level are covered, determining the grouping of the disaster-affected points, and making a preliminary address selection scheme of the transfer station;
(3) establishing an evaluation function by combining the probability of different disaster grades of disaster-affected points in each disaster-affected group, the demand of different materials and whether the disaster-affected points are covered, evaluating the unselected transfer stations, replacing the initially selected transfer station with the largest evaluation function value, and relocating the transfer station until the site selection scheme is not changed any more;
(4) and outputting the final intermediate transfer station address selection scheme.
2. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (1), the emergency cost is the total cost of the transfer station, including the construction cost and the operation and maintenance cost of the transfer station.
3. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (1), the constraint conditions specifically include:
(1-1) if the disaster-affected point j is covered when the disaster level is s, at least one temporary transfer station is needed under the disaster level, and the response time of the temporary transfer station meets the lower limit of the time window of the first type of materials with the largest required time window under the disaster level;
(1-2) the sum of the site selection schemes of the transfer station is lower than the upper limit of the total fund planned to be invested;
(1-3) the quantity of various emergency resources transported to the demand points cannot exceed the proposed demand quantity;
(1-4) there is a limit to the number of transfer stations.
4. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (1), the constructed model belongs to a multi-target mathematical programming model, according to the characteristics of the model, the emergency requirement coverage maximization is taken as a main target, the cost of the emergency material transfer station is minimized on the premise of meeting the target, and the main target method is adopted for solving.
5. The method for locating the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 4, wherein the method comprises the following steps: in the step (1), the specific method for solving includes:
(1-a) solving a single-target mathematical programming model formed by an objective function and constraint conditions of which the emergency cost is minimized to obtain an optimal solution;
(1-b) converting the objective function with minimized emergency cost and the optimal solution obtained in the step (1-a) into constraint conditions in a combined manner;
and (1-c) solving a single-target mathematical programming model formed by an objective function with maximized emergency demand coverage and constraint conditions to obtain an optimal solution, wherein the obtained result is the optimal number and position of temporary transfer stations.
6. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (2), a decision variable is introduced on the basis of the state transition probability of the basic clustering ant colony algorithm, and the time of reaching the next node is compared with the lower limit of a time window with a larger time window required by the current node; when the decision quantity is 1, the disaster-affected point j can be covered when the disaster level is s; when the decision quantity is 0, it means that it cannot be covered.
7. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (2), the independent tabu tables set for each ant in the basic clustering ant colony algorithm are shared, and all node information visited by the ants is stored.
8. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (2), a greedy algorithm is utilized, ants are initialized to search all transfer stations which are in accordance with the minimum time window lower limit according to the transfer probability at the initial address selection stage, and then transfer stations which are arranged in the front N positions in the selected number are selected as initial solutions, wherein N is the number of temporary transfer stations; in the grouping process, each uncovered demand point selects a transfer station nearby, and finally N groups are formed; and in the relocation process, replacing the transfer stations in sequence according to the evaluation function until the global optimization is achieved.
9. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: the step (2) specifically comprises:
(2-1) initializing cycle times, pheromone evaporation rate, pheromone increase rate, coordinates of a disaster-affected point, coordinates of alternative transfer stations, the number of temporary transfer stations and a time window;
(2-2) according to the transition probability, calculating an alternative transfer station to which the ants will arrive, and adding the coordinates of the disaster-affected points into a taboo table;
(2-3) judging whether all the node traversal is finished, if so, executing the step (2-4), and if not, executing the step (2-2);
(2-4) updating the pheromone concentration of the alternative transfer station, and adding 1 to the cycle number;
(2-5) repeating the steps (2-2) - (2-4) until the set number of cycles is met;
and (2-6) calculating according to the fitness function, arranging calculation results in a descending manner, and using the transfer stations arranged at the first N bits as initial solutions.
10. The method for site selection of the emergency logistics transit station based on the improved ant colony algorithm as claimed in claim 1, wherein the method comprises the following steps: in the step (3), the evaluation function is
Wherein G represents a disaster-affected point set in a disaster-affected group G, and N disaster-affected groups exist in the part; when in use
When the disaster situation level is s, the demand of the demand point j on the resource l can be met by the transfer station i, and when the disaster situation level is s, the demand point j can meet the demand of the resource l
And then, it cannot be satisfied.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610112271.5A CN105787595B (en) | 2016-02-29 | 2016-02-29 | Emergency logistics transfer station site selection method based on improved ant colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610112271.5A CN105787595B (en) | 2016-02-29 | 2016-02-29 | Emergency logistics transfer station site selection method based on improved ant colony algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105787595A CN105787595A (en) | 2016-07-20 |
| CN105787595B true CN105787595B (en) | 2021-04-16 |
Family
ID=56386523
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610112271.5A Active CN105787595B (en) | 2016-02-29 | 2016-02-29 | Emergency logistics transfer station site selection method based on improved ant colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105787595B (en) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341628B (en) * | 2016-12-30 | 2021-04-27 | 闽江学院 | Hub and spoke type logistics network hub station site selection and distribution method based on probability tabu algorithm |
| CN107341610B (en) * | 2017-07-05 | 2023-08-08 | 中国人民解放军第二军医大学 | Configuration method and system for guaranteeing action medicinal materials of earthquake emergency medical rescue |
| CN107633358B (en) * | 2017-09-14 | 2022-04-12 | 北京京东尚科信息技术有限公司 | Facility site selection and distribution method and device |
| CN108108857B (en) * | 2018-01-12 | 2021-11-09 | 福建师范大学 | Emergency resource scheduling optimization method for river sudden pollution event |
| CN108537384B (en) * | 2018-04-10 | 2020-12-08 | 湘潭大学 | Ship building material site layout optimization method and system |
| CN109754121A (en) * | 2019-01-09 | 2019-05-14 | 天津工业大学 | Dual robot cooperates with polling path optimization method |
| CN111461470A (en) * | 2019-01-21 | 2020-07-28 | 顺丰科技有限公司 | Method and device for selecting addresses in transit field and computer readable storage medium |
| CN110245818A (en) * | 2019-01-21 | 2019-09-17 | 北京航空航天大学 | Hub location method and apparatus |
| CN109977066B (en) * | 2019-03-08 | 2020-12-15 | 北京交通大学 | Emergency delivery all-in-one machine of territory wisdom |
| CN111695844A (en) * | 2019-03-14 | 2020-09-22 | 顺丰科技有限公司 | Method and device for addressing distributed points and connection points, server and storage medium |
| CN110046760B (en) * | 2019-04-10 | 2022-06-14 | 武汉理工大学 | Medical rescue point addressing method considering urgency of disaster-affected point demand |
| CN110852587B (en) * | 2019-10-31 | 2022-04-12 | 华中科技大学 | Emergency shelter site selection and resource distribution method and system |
| CN110808911B (en) * | 2019-11-13 | 2022-05-17 | 天津津航计算技术研究所 | Networking communication routing method based on ant colony pheromone |
| CN111242544B (en) * | 2020-01-10 | 2023-10-20 | 东南大学 | Site selection method for rescue material storage point of dangerous goods transportation accident |
| CN111428851B (en) * | 2020-03-30 | 2022-10-28 | 南方科技大学 | Rescue plan determination method, rescue plan determination device, server and storage medium |
| CN111444300B (en) * | 2020-04-07 | 2022-09-02 | 南方科技大学 | Layout method, device, server and storage medium for emergency rescue station |
| CN113762864A (en) * | 2021-01-06 | 2021-12-07 | 北京京东振世信息技术有限公司 | Logistics site location method and device |
| CN113420904B (en) * | 2021-02-01 | 2023-05-26 | 中国人民解放军空军工程大学 | Distribution method of general equipment under unbalanced supply and demand condition |
| CN112949096B (en) * | 2021-04-14 | 2023-04-07 | 杭州电力设备制造有限公司 | Method and system for layout planning of tool cabinet |
| CN113240285A (en) * | 2021-05-17 | 2021-08-10 | 上海中通吉网络技术有限公司 | Logistics tail end differential distribution method and equipment |
| CN113554323A (en) * | 2021-07-28 | 2021-10-26 | 河北工业大学 | Emergency guarantee optimization design method for key nodes of road network |
| CN115907238B (en) * | 2023-02-27 | 2023-07-28 | 南京邮电大学 | Multi-target emergency material center location method based on improved hunter optimization algorithm |
| CN115965171A (en) * | 2023-03-10 | 2023-04-14 | 吉奥时空信息技术股份有限公司 | Micro-park site selection method based on ant colony optimization algorithm |
| CN116402217B (en) * | 2023-04-04 | 2023-11-21 | 煤炭科学研究总院有限公司 | Fire station site selection method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2927711B1 (en) * | 2008-02-19 | 2010-10-08 | B2Boost Com | DEVICE FOR EXCHANGING DOCUMENTS BETWEEN TWO PARTS THROUGH A NETWORK |
| CN102289712A (en) * | 2011-08-10 | 2011-12-21 | 天津商业大学 | Method for optimizing minimum emergency logistic path based on fish-ant colony algorithm |
| CN103473612B (en) * | 2013-09-06 | 2016-09-28 | 周伟华 | Addressing and transport optimizing method towards ultra-large logistics distribution |
-
2016
- 2016-02-29 CN CN201610112271.5A patent/CN105787595B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN105787595A (en) | 2016-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105787595B (en) | Emergency logistics transfer station site selection method based on improved ant colony algorithm | |
| CN107480922B (en) | Method for establishing goods position distribution scheduling model under two-end type same-rail double-vehicle running mode | |
| CN105469201A (en) | Method for logistics dispensing center work task processing and scheduling | |
| CN106527381B (en) | A kind of fast evaluation method towards parallel batch processing machine dynamic dispatching | |
| CN110334837A (en) | A kind of more AGV dispatching methods decomposed based on petri net | |
| CN107527119A (en) | Water resources optimal operation method based on improved multi-target quantum genetic algorithm | |
| CN107730056B (en) | Emergency material modular scheduling method based on improved NSGA-II algorithm | |
| CN107341628B (en) | Hub and spoke type logistics network hub station site selection and distribution method based on probability tabu algorithm | |
| CN112230675B (en) | Unmanned aerial vehicle task allocation method considering operation environment and performance in collaborative search and rescue | |
| CN104331749A (en) | AGV optimization scheduling method based on simulated annealing particle swarm | |
| CN106228184A (en) | A kind of based on the blast furnace fault detection system and the method that optimize extreme learning machine | |
| CN104077634B (en) | active-reactive type dynamic project scheduling method based on multi-objective optimization | |
| CN102262702A (en) | Decision-making method for maintaining middle and small span concrete bridges | |
| CN108717581A (en) | A kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation | |
| CN113313285A (en) | Multi-constraint vehicle path optimization method, system, storage medium and equipment | |
| CN107506937B (en) | Hydropower station power generation capacity evaluation method based on association rule | |
| CN103942609A (en) | Product service supply chain optimization designing method | |
| CN116227847A (en) | Method for establishing emergency resource scheduling model in chemical industry park and scheduling method | |
| CN110598946B (en) | Flood prevention material rescue distribution method based on non-dominated artificial bee colony | |
| CN106845696B (en) | Intelligent optimization water resource configuration method | |
| CN108241911A (en) | A kind of Site Selection Method of Distribution Center for optimization algorithm of being looked for food based on bacterium | |
| CN106651017A (en) | Method for site selection of land consolidation project based on ant colony optimization algorithm | |
| CN101909078A (en) | New service set construction-based dynamic service selection method | |
| CN114723360A (en) | Logistics vehicle dispatching management model based on improved particle swarm algorithm | |
| Li et al. | An Improved Firefly Algorithm used to Location Selection of Distribution Center for Logistics UA V |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wei Xiao Inventor after: Kong Lingkun Inventor after: Zhang Tongyi Inventor after: Zhou Yongli Inventor after: Ma Shujie Inventor before: Wei Xiao Inventor before: Zhang Tongyi Inventor before: Zhou Yongli Inventor before: Ma Shujie |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |