CN106682769A - Method and apparatus for realizing product vehicle route - Google Patents
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Abstract
The invention discloses a method and apparatus for realizing a product vehicle route. The method includes the following steps: reading vortex and edge set information of city roads; based on a preset algorithm, calculating the vortex and edge information to obtain the shortest route information which includes the shortest routes of a delivery center to each client and the shortest route between respective client; using the preset encoding method to initialize population; based on the shortest route information and the initialized population, calculating the object function value corresponding to each solution; selecting the smallest object function value from the object function values corresponding to each solution and the object function value corresponding to the smallest object function value; and successively conduct selection operation, cross operation, variation operation, re-insertion and record updating on the object function value. According to the invention, the method greatly reduces the differences between actual transportation and theoretical transportation, and has practical significance for delivery of fresh agricultural products or transportation routes.
Description
Technical Field
The invention relates to the field of logistics distribution, in particular to a method and a device for realizing a product vehicle path.
Background
With the rapid development of economic globalization and network information technology, logistics distribution is attracting more and more attention as a new economic growth point, and with the increasing fierce market competition and the increasing requirements of customers, distribution will play a key role in future market competition. The adoption of an effective distribution strategy in the logistics distribution system can reduce waste, reduce cost and obviously improve economic benefits. Vehicle Routing distribution (VRP) issues originate from modern logistics systems, which are a typical combined optimization Problem as a key link in the logistics distribution optimization process and one of the leading-edge research hot problems of operational research and management disciplines. The Vehicle routing With Time window (VPRTW) is a constraint on customer service Time based on the VRP problem, and this constraint is closer to the reality of logistics distribution, and how well the problem is implemented will directly affect the cost, benefit, quality of service to customers, and the scientization of logistics distribution management, so the research on VRPTW is more and more focused on by people. This document focuses on the problem of vehicle routing for a variety of fresh produce with soft time window constraints.
Along with the development of science and technology and the improvement of living standard, the demand of consumers on fresh agricultural products is continuously increased, and meanwhile, the requirements of the consumers on the quality and the freshness of the fresh agricultural products are higher and higher. Therefore, on the premise of meeting the requirement of customers on freshness, an optimal distribution path is found, and the minimization of the distribution cost is an important problem to be solved in the field of logistics distribution. Because VRPTW is an NP problem, when the VRPTW is solved by adopting a general precise algorithm, the time complexity is higher, and the particle swarm algorithm is used for searching an optimal solution through the interaction among particles, lacks a variation mechanism and is easy to fall into local optimization, so that the mixed genetic algorithm is adopted for solving, and a good solving result is obtained. The following three problems have been solved in the conventional vehicle path problem.
(1) The actual condition of a road is rarely considered in the existing VRP, and the distance between each delivery client is calculated according to the straight-line distance, so that the actual road condition between the clients is separated.
(2) Most of the research in the prior VRP for fresh agricultural products is hard time window restriction, the vehicles are required to arrive in a specified time window, wait for the vehicles in the early period, reject the vehicles in the late period, and rarely consider the situation that the delivery vehicles arrive outside the time window, and accept a punishment in the early period or the late period.
(3) A fresh agricultural product is mostly considered in the distribution process in the prior VRP (virtual reality protocol) of the fresh agricultural products, and the condition that different types of fresh agricultural products with different freshness constraints are distributed at the same time is rarely considered.
Disclosure of Invention
Based on this, the invention aims to provide a product vehicle path realization method. The second purpose of the invention is to provide a device for realizing the product vehicle path.
To achieve the above object, the present invention employs the following techniques.
The invention discloses a method for realizing a product vehicle path, which is characterized by comprising the following steps:
reading point edge set information of the urban road;
calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path between the customers;
initializing the population by adopting a preset coding mode;
calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population;
selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value;
and sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
In the method of the present invention, the step of initializing the population by using a preset coding mode includes:
randomly scrambling a client sequence to obtain a new sequence 1;
randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles;
respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution;
and acquiring an initialized population according to the initial solution.
In the method of the present invention, the steps of performing a selection operation, a crossover operation, a mutation operation, a reinsertion, and a record update on the objective function value include:
selecting operation, namely selecting an even group of solutions according to the objective function value of each solution in the current population;
performing cross operation, namely pairwise pairing all solutions in the new population after the selection operation, and performing cross operation on the two paired solutions;
performing mutation operation, namely randomly generating a preset value for each solution, performing mutation operation on the current solution if the preset value is smaller than the mutation probability, and not performing mutation operation on the current solution if the preset value is not smaller than the mutation probability;
re-inserting, namely recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population;
updating the record, and if the optimal value of the new population is smaller than the optimal value of the recorded previous population, replacing the optimal value and the corresponding solution of the recorded previous population by the optimal value and the corresponding solution of the new population, and simultaneously keeping the number of times of solution unchanged to be 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
In the method of the present invention, the step of pairwise pairing all solutions in the new population after the selection operation, and performing a cross operation on the paired two solutions includes:
respectively randomly selecting a non-zero vector between two adjacent 0 s from the paired solution vector A and solution vector B, wherein the length of the non-zero vector is not more than N-K +1 and is marked as vector A1 and vector B1, otherwise, if the length of the randomly selected non-zero vector is more than N-K +1, the subsequent operation cannot be completed, namely, a certain vehicle is not distributed by any client;
sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector a that do not appear in vector B1 are formed into vector B2 in order;
respectively randomly selecting K-2 positions in gaps between the components of the vector A2 and the vector B2 to insert 0, and obtaining new vectors A3 and B3;
recombining the solution vectors according to the form of (0A 10A 30) to obtain a new solution vector C;
and recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
In the method of the present invention, the step of calculating the point edge set information according to a preset algorithm to obtain shortest path information, where the shortest path information includes a shortest path from a distribution center to each customer and a shortest path between each customer includes:
calculating the shortest path from one point a to the rest points in a vertex set V, wherein a represents a distribution center, and the vertex set V represents a point set formed by the distribution center and a customer;
dividing the vertex set V into two groups, wherein one group is the vertex set with the shortest path already obtained and is represented by R; adding the shortest path into the R when only one source point a is obtained initially until all the vertexes are added into the R, and finishing the algorithm;
the other group is the other set of the top points of undetermined shortest path and is represented by V \ R; and sequentially adding the shortest path lengths into the R according to the increasing order of the shortest path lengths, and keeping the shortest path length from the source point a to each vertex in the R less than or equal to the shortest path length from the source point a to any vertex in the V \ R in the adding process.
In the method of the present invention, the method further comprises:
acquiring road traffic information and basic parameters of VRP (variable resolution protocol) problems of fresh agricultural products with time windows;
wherein, the basic parameters mainly comprise the geographic position of a client, the demand of an order, the time window constraint of the client and the freshness constraint.
To achieve the second object, the present invention employs the following technique.
A product vehicle path realization apparatus, comprising:
the reading module is used for reading point edge set information of the urban road;
the first calculation module is used for calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path among the customers;
the initialization module is used for initializing the population by adopting a preset coding mode;
the second calculation module is used for calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population;
the selection module is used for selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value;
and the operation module is used for sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
In the device of the present invention, the initialization module is configured to randomly shuffle a client sequence to obtain a new sequence 1; randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles; respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution; and acquiring an initialized population according to the initial solution.
In the device method of the present invention, the operation module includes:
the selecting unit is used for selecting an even group of solutions according to the objective function value of each solution in the current population;
the crossing unit is used for pairwise pairing all solutions in the new population after the selection operation and carrying out crossing operation on the paired two solutions;
the variation unit is used for randomly generating a preset value for each solution, performing variation operation on the current solution if the preset value is smaller than the variation probability, and not performing variation operation on the current solution if the preset value is not smaller than the variation probability;
the reinsertion unit is used for recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population;
the updating unit is used for replacing the optimal value and the corresponding solution in the last recorded population by the optimal value and the corresponding solution of the new population if the optimal value of the new population is smaller than the optimal value of the last recorded population, and meanwhile, the number of times of solution invariance is 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
In the device method of the present invention, the crossing unit is configured to randomly select a non-zero vector between two adjacent 0 s from the paired solution vector a and solution vector B, and the length of the non-zero vector does not exceed N-K +1 and is recorded as vector a1 and vector B1, otherwise, if the length of the randomly selected non-zero vector exceeds N-K +1, the subsequent operation cannot be completed, that is, a certain vehicle is not distributed by any customer;
sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector a that do not appear in vector B1 are formed into vector B2 in order;
respectively randomly selecting K-2 positions in gaps between the components of the vector A2 and the vector B2 to insert 0, and obtaining new vectors A3 and B3;
recombining the solution vectors according to the form of (0A 10A 30) to obtain a new solution vector C;
and recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
The invention has the positive effects that:
reading point edge set information of the urban road; calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path between the customers; initializing the population by adopting a preset coding mode; calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population; selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value; and sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value. The invention greatly reduces the error between actual transportation and theoretical transportation, and has strong practical significance in both fresh agricultural product distribution and traffic routes.
Drawings
In order to more clearly illustrate the operation principle and the technical scheme of the invention, the following briefly describes the operation principle and the attached drawings needed in the technology. It is obvious that the drawings in the following description are only some working examples of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a flow chart of a product vehicle path implementation method of the present invention.
FIG. 2 is a graph of objective function optimization for a production vehicle path implementation of the present invention.
Fig. 3 is a driving route diagram of the vehicle 1 of the invention.
Fig. 4 is a driving route diagram of the vehicle 2 of the invention.
Fig. 5 is a driving route map of the vehicle 3 of the invention.
Fig. 6 is a driving route diagram of the vehicle 4 of the invention.
Fig. 7 is a driving route diagram of the vehicle 5 of the invention.
Fig. 8 is a driving route diagram of the vehicle 6 of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and the described embodiments are only some embodiments, not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a product vehicle path implementation method includes:
s1: reading point-edge set information of urban road
Note that, reading information: and automatically reading point edge set information of the urban road, namely G ═ V, E.
S2: and calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from the distribution center to each customer and the shortest path between the customers.
S3: initializing population by adopting a preset coding mode
It should be noted that, in order to satisfy the structural specificity of the VRPSTWFAP solution and to perform the crossover operation and mutation operation more conveniently, the present invention initializes the population by using the following coding method (where one population corresponds to a set of solutions, each chromosome in the population corresponds to each solution, each component of the chromosome is called a gene, and the gene corresponds to a component of the solution), and the specific steps are as follows.
S4: calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population
Note that, the objective function value corresponding to each solution is calculated based on the shortest path from the distribution center to each customer and between the customers obtained in S2 and the initial population generated in S3. For each solution in the population, the total load capacity, the total travel distance, the time of arrival of the vehicle at the client, the freshness of various fresh agricultural products when the vehicle arrives at the client and the time of departure of the vehicle from the client of each vehicle in the solution are respectively calculated.
S5: the minimum objective function value and the solution corresponding to the minimum objective function value are selected from the objective function values corresponding to the respective solutions.
S6: and sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
S7: termination rule
In one embodiment, the step S3 includes:
s301: randomly scrambling the client sequence to obtain a new sequence 1.
S302: and randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles.
S303: respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution; then, the initialized seed can be obtained according to the initial solution.
In one embodiment, the calculating the objective function value of each solution in the population in step S4 includes:
s401: the initial population generated in step S2 is generated based on the shortest paths from the distribution center to and between the customers.
S402: and calculating the objective function value corresponding to each solution.
S403: for each solution in the population, the total load capacity, the total travel distance, the time of arrival of the vehicle at the client, the freshness of various fresh agricultural products when the vehicle arrives at the client and the time of departure of the vehicle from the client of each vehicle in the solution are respectively calculated.
In one embodiment, the step S5 includes:
s501: and selecting, according to the objective function value of each solution in the current population, an even group of solutions from the solutions by roulette so as to perform the following crossover operation and mutation operation.
S502: and performing cross operation, wherein a special coding mode is adopted for generating the initial solution, in order to ensure that the solution after cross is a feasible solution, the population adopting the special coding mode is subjected to special cross operation, in a new population after selection operation, pairwise pairing is performed on all solutions, and the two paired solutions are subjected to cross operation.
S503: and (3) mutation operation, wherein in order to avoid the situation that all solutions fall into local minimum values, for a group of solutions after the crossover operation, the mutation operation is carried out in the following mode: randomly generating a very small number for each solution, and if the number is smaller than the mutation probability, performing mutation operation on the current solution; otherwise, no mutation operation is performed on the current solution.
S504: and re-inserting, namely recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population so as to ensure that the number of the solutions in the population is consistent.
S505: updating records, namely calculating an optimal value of the new population and a solution corresponding to the optimal value in order to ensure the convergence of the algorithm, and if the optimal value of the new population is smaller than the optimal value of the previous population recorded in the step S4, replacing the optimal value of the previous population and the solution corresponding to the optimal value of the new population with the solution corresponding to the optimal value of the new population, and simultaneously keeping the number of times of solution unchanged to be 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
In one embodiment, step S2 includes:
s201: the shortest path from one point (set as a) to the rest points is calculated, wherein a represents a distribution center, V represents a point set formed by the distribution center and a client, and E represents an edge set.
S202: and dividing the vertex set V into two groups, wherein one group is the vertex set which is obtained by the shortest path and is represented by R, adding the shortest path into R when only one source point a is obtained initially until all the vertexes are added into R, and finishing the algorithm.
S203: and the other group is a set of other top points of which the shortest paths are not determined, represented by V \ R, and sequentially added into R according to the increasing order of the lengths of the shortest paths, and in the adding process, the length of the shortest path from the source point a to each top point is kept to be less than or equal to the length of the shortest path from the source point a to any top point in the V \ R.
In one embodiment, the interleaving operation in step S6 includes:
s601: and respectively randomly selecting a non-zero vector between two adjacent 0 s from the paired solution vector A and solution vector B, wherein the length of the non-zero vector is not more than N-K +1 and is marked as a vector A1 and a vector B1, otherwise, if the length of the randomly selected non-zero vector is more than N-K +1, the subsequent operation cannot be finished, namely, a certain vehicle is not distributed by any client.
S602: sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector A that do not appear in vector B1 are formed into vector B2 in order.
S603: k-2 positions are randomly chosen to insert 0 in the gaps between the components of vector A2 and vector B2, respectively, resulting in new vectors A3 and B3.
S604: the new solution vector C is obtained by recombination in the form of (0A 10A 30).
S605: and recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
In step S7, the rule is terminated, the number of times of invariance of the current optimal value and the number of algorithm cycles are calculated, and if one of the number of times of invariance of the current optimal solution or the number of algorithm cycles reaches an upper limit, the algorithm is terminated.
In another embodiment, the present invention is a method for product vehicle path realization comprising the steps of:
s1: and reading point edge set information of the urban road.
S2: and calculating the point edge set information according to a Dijkstra algorithm to obtain the shortest path from the distribution center to each customer and among the customers.
S3: and initializing the population by adopting a preset coding mode.
S4: and calculating an objective function value of each solution in the population, recording the optimal value of the current population and the solution corresponding to the optimal value, comparing the objective function values corresponding to each solution, and selecting the minimum objective function value and the solution corresponding to the minimum objective function value from the objective function values.
S5: and carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
S6: the rule is terminated. The invention aims to solve the problem of the path of fresh agricultural product vehicles with soft time window constraint by adopting a method of combining a genetic algorithm and a Dijkstra algorithm.
The invention greatly reduces the error between actual transportation and theoretical transportation, and has strong practical significance in both fresh agricultural product distribution and traffic routes.
Firstly, a vehicle path optimization problem model of various fresh agricultural products with soft time window constraint is established as follows:
suppose a logistics company has a distribution center, N customers and K vehicles of the same type, which are represented by (0, 1.., N), wherein 0 represents the distribution center; w kinds of fresh agricultural products; the fresh agricultural products required by the customers have the freshness ofi，wWherein 0.3 is less than or equal toi，wLess than or equal to 1; the time window is [ E ]i，Si](ii) a Demand qi，w(ii) a Time t of arrival i of delivery vehicle ki，θk，i，w(ii) a Average service time per customer is T0(ii) a The time from client i to client i is Tk，ij(ii) a Wherein the number of possible passing intersections is lk，ij(ii) a Distribution distance dk，ij(ii) a Number of customers m serviced by vehicle kk(ii) a The fixed cost of each vehicle is c; a unit travel distance distribution fee of c1(unit delivery cost and cold fibrillation cost); the early unit time penalty cost is p1(ii) a Penalty cost p late arrival per unit time2(ii) a The unit punishment cost of the fresh agricultural products w is gamma when the freshness of the fresh agricultural products w is lower than the freshness required by customers in the guarantee periodwExceeding the shelf life will result in a large penalty of gamma (gamma > gamma)w) (ii) a The vehicle starts from the distribution center, and returns to the distribution center after completing the distribution task, the demand of each customer is less than the maximum load capacity of the vehicle, and the distance between the customer and the distribution center is less than the maximum driving distance. The nature of the various fresh agricultural products distributed is greatly related.
Establishing a mathematical model:
fixed cost of delivery vehicle:
delivery of vehicle travel cost:
time window penalty cost:
freshness of fresh agricultural product w:
wherein, thetaw0Is the initial freshness of the product w, which is related to the nature of the fresh produce.
Freshness penalty cost:
total cost mathematical model:
constraint conditions are as follows:
1≤mk≤N (4)。
tk，i+Tk，ij+To＝tkj(6)。
0＜θk，i，w≤1 (8)。
wherein,
equation (2) represents the payload constraint for each vehicle.
Equation (3) represents the travel distance constraint for each vehicle.
Equation (4) represents the per-vehicle service customer constraint.
Equation (5) indicates that the number of customers serviced by all vehicles is equal to the total number of orders.
Equations (6) and (7) represent time constraints.
Equation (8) represents the freshness constraint.
Equation (9) represents the number of customers serviced by vehicle k.
Equation (10) indicates that each customer has only one vehicle to service.
Equation (11) indicates that each vehicle departs from the distribution center and returns to the distribution center.
Equations (12) and (13) represent decision variables 0 and 1.
The mathematical model solving steps are as follows:
and solving the mathematical model by adopting a hybrid genetic algorithm, wherein N represents the number of orders, and K represents the number of 0, which is equal to the number of vehicles plus 1.
Step1 reads the information: and automatically reading point edge set information of the urban road, namely G (V, E), and calculating the shortest path from the distribution center to each customer and among the customers by adopting a Dijkstra algorithm.
The specific Dijkstra algorithm is as follows:
step1.1 makes R ═ { a }, l (a) ═ 0, and for an arbitrary point x ∈ V \ R, makes l (x) ═ d (a, x), where if (a, x) ∈ E, d (a, x) is the length of the edge (a, x), otherwise, d (a, x) ∞.
When R ≠ V, find a point x ∈ V \ R, so that l (x) ═ min { l (x) | x ∈ V \ R }, and let R ═ R ≠ u { x }.
Step1.3 for an arbitrary point y ∈ V \ R, and (x, y) ∈ E, then l (y) ═ min { l (u), l (x + c (x, y)) }.
Step1.4 the above procedure was repeated, knowing that all spots were added to R.
Step2 population initialization: in order to satisfy the structural specificity of VRPSTWFAP solutions and to perform crossover and mutation operations more conveniently, the present invention initializes population by using the following coding scheme (where one population corresponds to a group of solutions, each chromosome in the population corresponds to each solution, each component of chromosome is called gene, and the gene corresponds to a component of the solution), and the following steps are specifically performed.
Step2.1 randomly shuffles the client sequence to give a new sequence 1.
Step2.2 randomly selects K-1 to insert 0 in N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles.
And (3) respectively supplementing 0 to both ends of the sequence 2 by Step2.3 to obtain a sequence 3, wherein the sequence 3 is an initial solution.
Randomly shuffle the 12-bit customer sequence to a new sequence 1:
| 1 | 6 | 3 | 4 | 2 | 7 | 8 | 5 | 0 | 1 | 9 | 2 |
randomly selecting 2 positions from 10 gaps of the sequence 1 to insert 0, namely inserting 0 into the third and fifth gap positions to obtain a sequence 2:
| 1 | 6 | 3 | 0 | 4 | 2 | 0 | 7 | 8 | 5 | 0 | 1 | 9 | 2 |
and (3) respectively supplementing 0 at two ends of the sequence 2 to obtain an initial solution:
| 0 | 1 | 6 | 3 | 0 | 4 | 2 | 0 | 7 | 8 | 5 | 0 | 1 | 9 | 2 | 0 |
step3 calculates the objective function value of each solution in the population: and calculating an objective function value corresponding to each solution according to the shortest paths from the distribution center to the customers and among the customers obtained at Step1 and the initial population generated at Step2. For each solution in the population, the total load capacity, the total travel distance, the time of arrival of the vehicle at the client, the freshness of various fresh agricultural products when the vehicle arrives at the client and the time of departure of the vehicle from the client of each vehicle in the solution are respectively calculated.
Step4 records the optimal value of the current population and the corresponding solution: and comparing the objective function values corresponding to each solution, and selecting the minimum objective function value and the solution corresponding to the minimum objective function value.
Step5 selection operation: and selecting an even group of solutions from the current population by roulette according to the objective function value of each solution in the current population so as to perform the following crossover operation and mutation operation.
Step6 crossover operation: because the generation of the initial solution in Step2 adopts a special encoding mode, in order to ensure that the solution after the crossing is a feasible solution, the population after the special encoding mode is subjected to special crossing operation. In the new population after the selection operation, pairwise pairing is performed on all solutions, and the two solutions paired together are subjected to a cross operation:
step6.1 randomly selects a non-zero vector between two adjacent 0 from the paired solution vector A and solution vector B respectively, and the length of the non-zero vector is not more than N-K +1 and is marked as vector A1 and vector B1. On the contrary, if the length of the randomly selected non-zero vector exceeds N-K +1, the subsequent operation cannot be completed, that is, a certain vehicle is not scheduled to be delivered by any customer.
Step6.2 makes all non-zero components in solution vector B that do not appear in vector A1 into vector A2 in order; similarly, all non-zero components in solution vector A that do not appear in vector B1 are formed into vector B2 in order.
Step6.3 randomly selects K-2 positions to insert 0 in the gap between the components of vector A2 and vector B2, respectively, to obtain new vectors A3 and B3. Since the length of both vector A1 and vector B1 has been constrained to be equal to or less than N-K +1 in Step6.1, the gap between the A2 and B2 components is equal to or greater than K-2, ensuring that each 0 has a positional insertion.
Step6.4 recombines to get a new solution vector C according to the form of (0A 10A 30); similarly, the new solution vector D is obtained by recombination in the form of (0B 10B 30). At this time, the solution vector C and the solution vector D are new pairwise solutions obtained by performing a cross operation on the solution vector a and the solution vector B. The structure of solution vector C and solution vector D is the same as the structure of the initial solution, thus ensuring that the crossover operation changes one pair of feasible solutions into a new pair of feasible solutions.
Example, at VRPSTWFAP, a crossover operation of 12 orders is delivered by 3 vehicles:
solution 1:
| 0 | 1 | 3 | 9 | 1 | 0 | 8 | 4 | 2 | 7 | 0 | 6 | 0 | 5 | 2 | 0 |
solution 2:
| 0 | 0 | 2 | 6 | 1 | 0 | 7 | 8 | 4 | 3 | 5 | 2 | 0 | 1 | 9 | 0 |
if two intersections are chosen to be solution 1: 11 and 13, solution 2: 1 and 6, after Step6.1, vector A1 and vector B1 are obtained:
A1:
| 6 |
B1
| 0 | 2 | 6 | 1 |
after Step6.2, vectors A2 and B2 can be obtained
A2
| 0 | 2 | 1 | 7 | 8 | 4 | 3 | 5 | 2 | 1 | 9 |
B2
| 1 | 3 | 9 | 8 | 4 | 2 | 7 | 5 |
After Step6.3, vector A3 and vector B3 can be obtained:
A3
| 0 | 2 | 1 | 0 | 7 | 8 | 4 | 3 | 5 | 2 | 1 | 9 |
B3
| 1 | 3 | 9 | 0 | 8 | 4 | 2 | 7 | 5 |
after Step6.4, a new solution vector C and a new solution vector D after cross operation can be obtained
C
| 0 | 6 | 0 | 0 | 2 | 1 | 0 | 7 | 8 | 4 | 3 | 5 | 2 | 1 | 9 | 0 |
D
| 0 | 0 | 2 | 6 | 1 | 0 | 1 | 3 | 9 | 0 | 8 | 4 | 2 | 7 | 5 | 0 |
Step6.5 repeating the above process for the initial population to obtain the new population after the crossover operation.
Step7 mutation operation: in order to avoid the situation that all solutions fall into local minimum values, for a group of solutions after the crossover operation, mutation operation is carried out in the following mode: randomly generating a very small number for each solution, and if the number is smaller than the mutation probability, performing mutation operation on the current solution; otherwise, no mutation operation is performed on the current solution.
Step8 reinsertion: and recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population so as to ensure that the number of solutions in the population is consistent.
Step9 updates the record: in order to ensure the convergence of the algorithm, calculating an optimal value of the new population and a solution corresponding to the optimal value, and if the optimal value of the new population is smaller than the optimal value of the previous population recorded in Step4, replacing the optimal value of the previous population and the solution corresponding to the previous population recorded by the optimal value of the new population and the solution corresponding to the previous population, and simultaneously keeping the number of times of solution unchanged as 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
Step10 terminates the rule: and (4) calculating the invariant times of the current optimal value and the algorithm cycle times, if one of the invariant times or the algorithm cycle times of the current optimal solution reaches an upper limit, terminating the algorithm, otherwise, returning to Step 5.
The vehicle path problem is an NP-hard that can solve the exact solution only when there are few nodes. Although many documents and patents show that the problem of vehicle paths with time windows can be solved, no encoding method for feasible solutions is disclosed, and it is not guaranteed that feasible solutions can be obtained every time. Such as the conservation algorithms proposed by Clarke and Wright, the Tabu search algorithm studied by Pureza and Franca, and so on. Although the algorithms are effective when used for solving the vehicle path problem, the algorithms have some defects, such as the saving algorithm constructs a path from large to small according to the saved amount, has the advantage of high operation speed, but has the problems of disordered uncombined points and difficult combination of edge points; the Tabu search searches are too greedy to search both a local region and a neighborhood, resulting in a leaf barrier. In order to avoid the defects, the invention adopts an improved genetic algorithm, not only adopts a group search strategy and an information exchange strategy among individuals, but also introduces a Dijkstra algorithm and actual main road data, improves the cross operation in algorithm coding on the basis, ensures the feasibility of the solution after the cross operation, and finally optimizes the number of delivery vehicles through a circulation strategy, so that the expenses of delivery manpower, material resources and the like are reduced as much as possible. Most importantly, many documents or patents show that the problem of vehicle paths with time windows can be solved, but a feasible solution coding method is not disclosed, and the feasible solution can not be obtained every time. The improved hybrid genetic algorithm not only discloses a coding method, but also ensures that feasible solution can be realized each time by combining road data.
The improved hybrid genetic algorithm solves the problem of optimizing the vehicle paths of various fresh agricultural products, and reduces the distribution cost to the minimum under the condition of meeting the requirements of customers. Is different from the prior path optimization problem aiming at the shortest time.
The present invention also provides a product vehicle path fulfillment system, the system comprising: the system comprises a basic parameter setting module, an actual road condition calculation module, a genetic algorithm optimization module and a result display module.
In one embodiment, the basic parameter setting module is used for inputting basic parameter data required by a fresh agricultural product path optimization problem and realizing the entry function of data such as population quantity, maximum iteration times, vehicle load, penalty cost and the like.
The actual road condition calculation module is used for calculating the shortest actual distance from the distribution center to the client and among the clients, so that the calculation result is closer to the actual operation result, and the calculation of the genetic algorithm optimization module is accelerated.
The genetic algorithm optimization module is used for optimizing the distribution path and the distribution vehicle number of the fresh agricultural products, so that the distribution cost is reduced to the minimum.
And the result display module is used for displaying the objective function optimization curve and the final delivery path of the fresh agricultural products.
The optimization system comprises a basic parameter setting module, an actual road condition calculation module, a genetic algorithm optimization module and a result display module.
The basic parameter setting module is used for inputting basic parameter data required by a path optimization problem of fresh agricultural products, and can realize the function of recording data such as population quantity, maximum iteration times, vehicle load, penalty cost and the like.
The actual road condition calculation module is used for calculating the shortest actual distance from the distribution center to the client and among the clients, so that the calculation result is closer to the actual operation result, and the calculation of the genetic algorithm optimization module is accelerated.
The genetic algorithm optimization module is used for optimizing the distribution path and the distribution vehicle number of the fresh agricultural products, so that the distribution cost is reduced to the minimum.
And the result display module is used for displaying the objective function optimization curve and the final delivery path of the fresh agricultural products.
In order to verify the utility and advantages of the present invention, the following is a detailed description by way of example.
The model established by the invention only considers 1 distribution center, the model of the distribution vehicle is single, the load capacity, the maximum driving distance and the driving speed (constant speed) are known, and the distribution center is taken as a starting point. The fresh agricultural products can be mixed and delivered, the required amount of each client does not exceed the maximum loading capacity of the delivery vehicle, the position and the required amount of the client are known, and each client has one vehicle and only one vehicle for completing the delivery at one time. And in the distribution process of each vehicle, only unloading and no loading are carried out, and the vehicle returns to the distribution center after the distribution task is completed. In this example, 3 fresh agricultural products, 10 dispensing vehicles, 222 location points are set, 20 of which are customer points, and the point with the location number of 102 is used as a dispensing center. For example, refer to fig. 3-8, which are driving route maps of the vehicles 1-6.
Establishing a mathematical model of the distribution cost of fresh agricultural products:
(1) the method for acquiring road traffic information (referring to main road data of Shanghai city) and basic parameters of VRP (virtual router redundancy protocol) problems of fresh agricultural products with time windows mainly comprises the following steps: customer geographic location, order demand, customer time window constraints, freshness constraints, etc.
(2) The coordinates of the distribution central points are (121.4149, 31.22065), the maximum load capacity of the distribution vehicles is 30t, the vehicle speed is 10km/h, the maximum travel distance of each vehicle is 300km, the early penalty cost is 5 yuan, the late penalty cost is 10 yuan, the unit travel distance cost is 1 yuan, the fresh agricultural products are in the quality guarantee period, the unit penalty costs when the freshness exceeds the freshness required by a customer are respectively 20 yuan, 15 yuan, 10 yuan, and the unit penalty costs when the freshness exceeds the quality guarantee period are all 100 yuan.
The order points are distributed by using 10 iso-vehicles, and an objective optimization function curve graph and an optimization route graph for 6 vehicles to distribute the customers can be obtained through the hybrid genetic algorithm designed by the invention.
The present invention also provides a product vehicle path implementing apparatus, including: the device comprises a reading module, a first calculation module, an initialization module, a second calculation module, a selection module and an operation module.
The reading module is used for reading point edge set information of the urban road.
The first calculation module is used for calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path between the customers.
The initialization module is used for initializing the population by adopting a preset coding mode. The initialization module is specifically used for randomly scrambling a customer sequence to obtain a new sequence 1; randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles; respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution; and acquiring an initialized population according to the initial solution.
The second calculation module is used for calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population.
The selection module is used for selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value.
The operation module is used for sequentially carrying out selection operation, crossover operation, mutation operation, reinsertion and record updating on the objective function value. The operation module includes:
and the selecting unit is used for selecting the even group of solutions according to the objective function value of each solution in the current population.
And the crossing unit is used for pairwise matching all solutions in the new population after the selection operation and crossing the two matched solutions.
And the mutation unit is used for randomly generating a preset value for each solution, performing mutation operation on the current solution if the preset value is smaller than the mutation probability, and not performing mutation operation on the current solution if the preset value is not smaller than the mutation probability.
And the reinsertion unit is used for recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population.
The updating unit is used for replacing the optimal value and the corresponding solution in the last recorded population by the optimal value and the corresponding solution of the new population if the optimal value of the new population is smaller than the optimal value of the last recorded population, and meanwhile, the number of times of solution invariance is 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
The crossing unit is specifically configured to randomly select a non-zero vector between two adjacent 0 s from the paired solution vector a and solution vector B, where the length of the non-zero vector does not exceed N-K +1 and is denoted as vector a1 and vector B1, and otherwise, if the length of the randomly selected non-zero vector exceeds N-K +1, the subsequent operation cannot be completed, that is, a certain vehicle is not scheduled to be delivered by any customer.
Sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector A that do not appear in vector B1 are formed into vector B2 in order.
K-2 positions are randomly chosen to insert 0 in the gaps between the components of vector A2 and vector B2, respectively, resulting in new vectors A3 and B3.
The new solution vector C is obtained by recombination in the form of (0A 10A 30).
And recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
The operation principle of the present invention is described in detail above, and the description of the operation principle is only used to help understand the method and the core technical idea of the present invention; for those skilled in the art, the technical idea of the present invention may be changed in the specific embodiments and applications. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A product vehicle path realization method, characterized in that the method comprises:
reading point edge set information of the urban road;
calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path between the customers;
initializing the population by adopting a preset coding mode;
calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population;
selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value;
and sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
2. The method of claim 1, wherein the step of initializing the population with a predetermined code pattern comprises:
randomly scrambling a client sequence to obtain a new sequence 1;
randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles;
respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution;
and acquiring an initialized population according to the initial solution.
3. The method of claim 1, wherein the step of performing a select operation, a cross operation, a mutation operation, a reinsertion, and an update of the objective function value comprises:
selecting operation, namely selecting an even group of solutions according to the objective function value of each solution in the current population;
performing cross operation, namely pairwise pairing all solutions in the new population after the selection operation, and performing cross operation on the two paired solutions;
performing mutation operation, namely randomly generating a preset value for each solution, performing mutation operation on the current solution if the preset value is smaller than the mutation probability, and not performing mutation operation on the current solution if the preset value is not smaller than the mutation probability;
re-inserting, namely recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population;
updating the record, and if the optimal value of the new population is smaller than the optimal value of the recorded previous population, replacing the optimal value and the corresponding solution of the recorded previous population by the optimal value and the corresponding solution of the new population, and simultaneously keeping the number of times of solution unchanged to be 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
4. The method of claim 3, wherein all solutions in the new population after the selecting operation are pairwise paired, and the step of cross-matching the paired solutions comprises:
respectively randomly selecting a non-zero vector between two adjacent 0 s from the paired solution vector A and solution vector B, wherein the length of the non-zero vector is not more than N-K +1 and is marked as vector A1 and vector B1, otherwise, if the length of the randomly selected non-zero vector is more than N-K +1, the subsequent operation cannot be completed, namely, a certain vehicle is not distributed by any client;
sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector a that do not appear in vector B1 are formed into vector B2 in order;
respectively randomly selecting K-2 positions in gaps between the components of the vector A2 and the vector B2 to insert 0, and obtaining new vectors A3 and B3;
recombining the solution vectors according to the form of (0A 10A 30) to obtain a new solution vector C;
and recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
5. The method of claim 1, wherein the step of calculating the point edge set information according to a preset algorithm to obtain shortest path information, the shortest path information including shortest paths from a distribution center to each customer and shortest paths between each customer comprises:
calculating the shortest path from one point a to the rest points in a vertex set V, wherein a represents a distribution center, and the vertex set V represents a point set formed by the distribution center and a customer;
dividing the vertex set V into two groups, wherein one group is the vertex set with the shortest path already obtained and is represented by R; adding the shortest path into the R when only one source point a is obtained initially until all the vertexes are added into the R, and finishing the algorithm;
the other group is the other set of the top points of undetermined shortest path and is represented by V \ R; and sequentially adding the shortest path lengths into the R according to the increasing order of the shortest path lengths, and keeping the shortest path length from the source point a to each vertex in the R less than or equal to the shortest path length from the source point a to any vertex in the V \ R in the adding process.
6. The method of claim 1, further comprising:
acquiring road traffic information and basic parameters of VRP (variable resolution protocol) problems of fresh agricultural products with time windows;
wherein, the basic parameters mainly comprise the geographic position of a client, the demand of an order, the time window constraint of the client and the freshness constraint.
7. A product vehicle path realization apparatus, comprising:
the reading module is used for reading point edge set information of the urban road;
the first calculation module is used for calculating the point edge set information according to a preset algorithm to obtain shortest path information, wherein the shortest path information comprises the shortest path from a distribution center to each customer and the shortest path among the customers;
the initialization module is used for initializing the population by adopting a preset coding mode;
the second calculation module is used for calculating an objective function value corresponding to each solution according to the shortest path information and the initialization population;
the selection module is used for selecting the minimum objective function value from the objective function values corresponding to the solutions and the solution corresponding to the minimum objective function value;
and the operation module is used for sequentially carrying out selection operation, cross operation, mutation operation, reinsertion and record updating on the objective function value.
8. The product vehicle path realization device of claim 7, wherein the initialization module is configured to randomly shuffle the customer sequence into a new sequence 1; randomly selecting K-1 insertion 0 from N-1 gaps of the sequence 1 to obtain a sequence 2, wherein N represents the total number of customers, and K represents the total number of delivered vehicles; respectively supplementing 0 to both ends of the sequence 2 to obtain a sequence 3, wherein the sequence 3 is an initial solution; and acquiring an initialized population according to the initial solution.
9. The product vehicle path realization apparatus of claim 7, wherein the operation module comprises:
the selecting unit is used for selecting an even group of solutions according to the objective function value of each solution in the current population;
the crossing unit is used for pairwise pairing all solutions in the new population after the selection operation and carrying out crossing operation on the paired two solutions;
the variation unit is used for randomly generating a preset value for each solution, performing variation operation on the current solution if the preset value is smaller than the variation probability, and not performing variation operation on the current solution if the preset value is not smaller than the variation probability;
the reinsertion unit is used for recombining a group of solutions after the mutation operation and part of solutions in the original population to obtain a new population;
the updating unit is used for replacing the optimal value and the corresponding solution in the last recorded population by the optimal value and the corresponding solution of the new population if the optimal value of the new population is smaller than the optimal value of the last recorded population, and meanwhile, the number of times of solution invariance is 0; otherwise, the optimal value in the record and the corresponding solution are not updated, and simultaneously the number of times of solution invariance is increased by 1.
10. The apparatus as claimed in claim 9, wherein the crossing unit is configured to randomly select a non-zero vector between two adjacent 0 s from the paired solution vector a and solution vector B, and the length of the non-zero vector does not exceed N-K +1, and is denoted as vector a1 and vector B1, otherwise, if the length of the randomly selected non-zero vector exceeds N-K +1, the subsequent operation cannot be completed, that is, a certain vehicle is not distributed by any customer;
sequentially composing all non-zero components of solution vector B that do not occur in vector A1 into vector A2; similarly, all non-zero components in solution vector a that do not appear in vector B1 are formed into vector B2 in order;
respectively randomly selecting K-2 positions in gaps between the components of the vector A2 and the vector B2 to insert 0, and obtaining new vectors A3 and B3;
recombining the solution vectors according to the form of (0A 10A 30) to obtain a new solution vector C;
and recombining the solution vectors into a new solution vector D in the form of (0B 10B 30).
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