CN115689084A - Port unmanned truck collection multi-vehicle dynamic scheduling method - Google Patents
Port unmanned truck collection multi-vehicle dynamic scheduling method Download PDFInfo
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Abstract
The invention belongs to the technical field of unmanned driving, and discloses a dynamic dispatching method for unmanned card-collecting multi-vehicle at a port, which comprises the following steps: randomly generating an initial population, and setting the maximum iteration number as m; selecting, crossing and mutating the initial population to generate a new next generation population; constructing a fitness function, and recording the individual with the maximum fitness function value in each generation of individuals; when the iteration frequency of the genetic algorithm is n, if the individual fitness function value is continuously unchanged, selecting n optimal initial solutions from the result of the genetic algorithm, updating the n optimal initial solutions by using a tabu search algorithm, if a second optimal individual fitness function value calculated by the tabu search algorithm is larger than a first optimal individual fitness function value before the tabu search algorithm is carried out, jumping to the step 2, otherwise, continuously updating the population until the iteration frequency reaches m, and outputting the result. The invention solves the problems of incomplete consideration, low working efficiency and high cost in unmanned truck collection scheduling.
Description
Technical Field
The invention belongs to the technical field of unmanned driving, and particularly relates to a dynamic dispatching method for a plurality of unmanned trucks at a port.
Background
At present, unmanned driving is rapidly developed, port unmanned truck collection takes place, and unmanned truck collection replaces manual drivers, so that how to improve the working efficiency of port unmanned truck collection and reduce the operation cost of port companies is very important. The scheduling arrangement of the unmanned port container truck on time in operation is very important, and the unmanned port container truck is not expected to waste too much parking free time in a parking area and the waiting time of container truck blockage or container loading and unloading in the operation process, which seriously influences the operation efficiency of the unmanned port container truck; meanwhile, due to the fact that the tasks of the loading and unloading boxes are likely to dynamically change at any time, the scheduling of the unmanned truck is required to be correspondingly changed, in order to research the scheduling problem of the unmanned truck path planning in the dynamic environment, the soft and hard time windows when the unmanned truck reaches each task point and the dynamic cost of fuel oil and carbon emission under the condition of load change in the operation process are further considered, a corresponding optimization model is built, and a corresponding genetic taboo hybrid algorithm is designed to solve the problem.
The invention researches the scheduling problem of the port unmanned container truck, considers the time penalty cost generated by the satisfaction degree of the unmanned container truck reaching the loading and unloading point on time under the constraint of a soft and hard time window and the dynamic cost of fuel and carbon emission under the condition of load change in the operation process of the unmanned container truck, constructs a non-adaptive mixed integer nonlinear programming model for solving the problem, and simultaneously provides a mixed algorithm based on the combination of a genetic algorithm and a taboo search algorithm. Finally, the improved hybrid algorithm obtained through the experimental result can effectively avoid premature and local optimal phenomena occurring in the calculation process, can better obtain an optimal solution, greatly reduces the operation cost of the unmanned truck collection compared with the existing mode, and improves the working efficiency of the unmanned truck collection.
Disclosure of Invention
Aiming at the technical problems, the invention provides a dynamic dispatching method for a plurality of unmanned port truck-collecting vehicles, aiming at reducing the operation cost of the unmanned port truck and improving the working efficiency of the unmanned port truck-collecting.
The invention provides a dynamic dispatching method for unmanned card-collecting multi-vehicle in a port, which comprises the following steps:
step 2, carrying out selection, crossing and mutation operations on the initial population to generate a next generation new population;
step 3, constructing a fitness function, and recording the individual with the maximum fitness function value in each generation of individuals;
and 4, when the iteration frequency of the genetic algorithm is n, if the recorded individual fitness function value is continuously unchanged, selecting k optimal initial solutions from the result of the genetic algorithm, then respectively updating the k optimal initial solutions by using a tabu search algorithm, if a second optimal individual fitness function value calculated by using the tabu search algorithm is larger than a first optimal individual fitness function value before the tabu search algorithm is carried out, jumping to the step 2, otherwise, continuously updating the population by using the tabu search algorithm until the update iteration frequency reaches m, jumping out of the program, and outputting the result.
Specifically, step 1 specifically includes:
step 11, encoding by adopting a natural number encoding mode, wherein if the number of task points in the unmanned truck path network is N, the chromosome is composed of an integer string from 1 to N;
generating an original population chromi = [1,2, 3., N ] by a random number generator, wherein i represents the ith individual in the population, and N represents N genes in each individual;
step 13, inserting k + 10 into the original population according to the maximum kernel load constraint of the unmanned truck and the time constraint of the expected arrival of the task point to generate an initial population:
(0,i 11 ,i 12 ,…,i 1s ,0；i 21 ,i 22 ,…,i 2t ,0；…；i k1 ,i k2 ,…,i kw ,0)
wherein i kw The w-th task point of the kth unmanned truck service is represented, 0 represents the starting unmanned truck parking lot, and the node between two 0 represents a task route.
Specifically, the selecting operation in step 2 specifically includes:
step 211, ordering according to the optimal fitness function value of the comprehensive objective function, and directly entering the first 10% of individuals with the highest fitness function value in the initial population into the next generation;
and step 212, selecting the remaining individuals by using a roulette selection mode, and enabling the selected individuals to enter the next step for cross operation or mutation operation.
Specifically, a greedy algorithm is applied to adaptively improve the cross mutation operator in step 2, the improved mutation rate is dynamic, and the cross probability is defined as follows:
wherein Δ = f a -f m ，f a Is the mean fitness function value, f m For the maximum fitness value, K1 is a constant coefficient.
Specifically, the interleaving operation in step 2 is:
generating a random number epsilon, epsilon belongs to [0,1];
if epsilon<P c Then, a partial matching crossover is adopted, and the specific operations are as follows:
selecting two given parent chromosomes A and B, then randomly selecting two cross points in the parent chromosome A and the parent chromosome B, transferring a gene sub-between the two cross points in the parent chromosome A to the front end of the parent chromosome B to form an offspring chromosome B1, directly copying a gene in the parent chromosome B to the back of the offspring chromosome B1 to form a child chromosome B2, and finally removing the same gene from front to back to form an offspring chromosome B3;
transferring the sub-gene between two cross points in the parent chromosome B to the front end of the parent chromosome A to form an offspring chromosome A1, directly copying the gene in the parent chromosome A to the back of the offspring chromosome A1 to form an offspring chromosome A2, and finally removing the same gene from front to back to form an offspring chromosome A3.
Specifically, the change operation in step 2 is:
if epsilon>P c Then, a given parent chromosome is selected by adopting an inversion mutation operation mode, two points are randomly selected on the parent chromosome, and the gene between the two points is subjected to inversion operation to generate a child chromosome A4.
Specifically, the tabu search algorithm in step 4 is designed as follows:
step 41, initializing;
step 42, selecting n preferred initial solutions from the results of the genetic algorithm, and respectively marking the solutions as x 1 ...x n When i =1, 2.. N, for x i Carrying out tabu search;
step 43, with the preferred initial solution x 1 ...x n And evaluating the n solutions generated by searching for the starting point by using a fitness function, taking the best solution to be the optimal solution, and outputting a result.
In particular, for x i The tabu search specifically includes:
a. put the current solution x new ＝x i Current optimal solution x best ＝x i ；
b. When the taboo search algorithm termination criterion is met, stopping operation and outputting a calculation result; otherwise, continuing c;
c. generation of neighborhood N (x) of current solution using 2-opt near ) Selecting a plurality of solutions to form a candidate solution set;
d. judging whether the candidate solution set existsThe candidate solution satisfies the scofflaw criterion, if the candidate solution exists, the best candidate solution x satisfying the scofflaw criterion is used * Substitution of x new Changing into a new current solution, replacing the current optimal solution, updating the tabu table at the same time, turning to the step a, and otherwise, continuing to the step e;
e. judging the tabu attribute of each candidate solution corresponding object in the candidate solution set, and selecting the best candidate solution x' in the non-tabu state in the candidate solution set to replace x new And (4) becoming a new current solution, replacing the current optimal solution, updating the tabu table and turning to the step a.
Specifically, the 2-opt process is as follows:
after a plurality of optimal initial solutions are obtained from the result of the genetic algorithm, each optimal initial solution comprises k solution branches of the unmanned skar, and the solution branches are locally optimized by using a 2-opt method in the neighborhood of the solution branches.
Specifically, the basic principle of the 2-opt method is as follows:
replacing (i, i + 1), (j, j + 1) with (i, j), (i +1, j + 1), the path (i + 1.. J) in the line thus exchanged is reversed.
Compared with the prior art, the invention has the beneficial effects that at least:
1. the method improves the unmanned card collecting operation efficiency, and avoids wasting the idle time of parking in a parking area and the waiting time of card collecting blockage or boxing and unloading in the operation process;
2. according to the method, the optimal loading capacity of the unmanned container truck and the optimal time scheduling arrangement of cooperative work of a plurality of unmanned container trucks can be realized;
3. according to the method, when the scheduling task time and the task amount of the unmanned truck change suddenly, the unmanned truck scheduling scheme can be effectively re-planned, so that the unmanned truck operation cost of the port is minimized.
Drawings
FIG. 1 is a flow chart of a dynamic dispatching method for a plurality of unmanned port trucks of the invention;
FIG. 2 is a schematic diagram of the basic principle of the 2-opt method of the present invention;
fig. 3 is a flow chart of the improved genetic tabu mixing algorithm of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are intended to be a subset of the embodiments of the invention rather than a complete embodiment. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a flowchart illustrating an embodiment of a dynamic scheduling method for multiple unmanned port trucks, where the flowchart specifically includes the following steps:
Specifically, step 1 specifically includes:
and 11, coding by adopting a natural number coding mode, wherein if the number of the task points in the unmanned truck path network is N, the chromosome is composed of an integer string from 1 to N.
Compared with the traditional binary coding, the method has the advantages that the natural number coding is simpler and more visual, the natural number coding mode (0, 1,2,3, 8230; N) can more visually display the optimization result, and the program reading and modification are convenient.
Step 12, generating an original population, namely, chloromi = [1,2, 3., N ], by a random number generator, wherein i represents the ith individual in the population, and N represents N genes in each individual.
Step 13, inserting k + 10 into the original population according to the maximum kernel load constraint of the unmanned truck and the time constraint of the expected arrival of the task point to generate an initial population:
(0,i 11 ,i 12 ,…,i 1s ,0；i 21 ,i 22 ,…,i 2t ,0；…；i k1 ,i k2 ,…,i kw ,0)
wherein, the first and the second end of the pipe are connected with each other,i kw the w-th task point of the kth unmanned truck service is represented, 0 represents the starting unmanned truck parking lot, and the node between two 0 represents a task route.
The chromosomes are divided into k segments, i.e., there are a total of k paths, and the chromosomes are analyzed as follows: the first vehicle starts from the starting point truck-collecting parking lot and traverses to the task point i 11 ,i 12 ,…,i 1s Directly returning to the truck parking lot 0 after boxing or unloading; the second vehicle starts from the starting point truck-collecting parking lot and traverses to the task point i 21 ,i 22 ,…,i 2t The container is directly returned to the container truck parking lot 0 after being boxed or unpacked; and ending the decoding until all task points are completed. Taking chromosome 02406310578 as an example, 3 trucks are shown to load and unload the tasks of 8 task points, and the decoded truck path is:
sub-path 1: truck collection parking lot 0-task point 2-task point 4-truck collection parking lot 0,
sub-path 2: truck collection parking lot 0-task point 6-task point 3-task point 1-truck collection parking lot 0,
sub-path 3: truck collection parking area 0-task point 5-task point 7-task point 8-truck collection parking area 0.
The time constraint of the unmanned truck to reach each task point is that the truck needs to meet the expected arrival time of each task point to the maximum extent on the path, the satisfaction of the truck to reach each task point is reduced if the time constraint is avoided, and the soft time window expected to reach at the task point i is set as [ a ] i ，b i ]If the card is stuck to a i Before or b i And then, the satisfaction degree of the task completion is reduced, so that a certain time penalty cost is generated for the port.
And 2, selecting, crossing and mutating the initial population to generate a next generation new population.
Specifically, the selecting operation in step 2 specifically includes:
and step 211, sequencing according to the optimal fitness function value of the comprehensive objective function, and directly entering the first 10% of individuals with the highest fitness function value in the initial population into the next generation.
And step 212, selecting the remaining individuals by using a roulette selection mode, and enabling the selected individuals to enter the next step for cross operation or mutation operation.
The method adopts a selection strategy combining an essence model and proportion selection, firstly, the selection strategy is sequenced according to the optimal fitness function value of a comprehensive objective function, the first 10% of individuals with the highest population fitness function value directly enter the next generation to prevent excellent chromosomes from being eliminated, then, the selection operation is carried out on the rest individuals by using a roulette selection mode, and the individuals with high fitness function values in the population are more likely to be selected to enter the next step.
Individuals who have a high fitness function value in the population are more likely to be selected using roulette to select the desired individuals for further crossover and variation.
Specifically, a greedy algorithm is applied to adaptively improve the cross mutation operator in step 2, the improved mutation rate is dynamic, and the cross probability is defined as follows:
wherein, Δ = f a -f m ，f a Is the average fitness function value, f m K1 is a constant coefficient for the maximum fitness value.
Specifically, the interleaving operation in step 2 is:
generating a random number epsilon, epsilon belongs to [0,1];
if epsilon<P c Then, the partial matching intersection is adopted, and the specific operation is as follows:
selecting two given parent chromosomes A and B, then randomly selecting two cross points in the parent chromosome A and the parent chromosome B, transferring a sub-gene between the two cross points in the parent chromosome A to the front end of the parent chromosome B to form an offspring chromosome B1, directly copying a gene in the parent chromosome B to the back of the offspring chromosome B1 to form an offspring chromosome B2, and finally removing the same gene from front to back to form an offspring chromosome B3;
transferring the sub-gene between two cross points in the parent chromosome B to the front end of the parent chromosome A to form an offspring chromosome A1, directly copying the gene in the parent chromosome A to the back of the offspring chromosome A1 to form an offspring chromosome A2, and finally removing the same gene from front to back to form an offspring chromosome A3.
Specifically, the change operation in step 2 is:
if epsilon>P c Then, a given parent chromosome is selected by adopting an inversion mutation operation mode, two points are randomly selected on the parent chromosome, and the gene between the two points is subjected to inversion operation to generate a child chromosome A4.
And 3, constructing a fitness function, and recording the individual with the maximum fitness function value in each generation of individuals.
And 4, when the iteration frequency of the genetic algorithm is n, if the recorded individual fitness function value is continuously unchanged, selecting k optimal initial solutions from the result of the genetic algorithm, then respectively updating the k optimal initial solutions by using a tabu search algorithm, if a second optimal individual fitness function value calculated by using the tabu search algorithm is larger than a first optimal individual fitness function value before the tabu search algorithm is carried out, jumping to the step 2, otherwise, continuously updating the population by using the tabu search algorithm until the update iteration frequency reaches m, jumping out of the program, and outputting the result.
Specifically, the tabu search algorithm in step 4 is designed as follows:
step 41, initializing;
step 42, selecting n preferred initial solutions from the results of the genetic algorithm, and respectively marking the solutions as x 1 ...x n When i =1, 2.. N, for x i Carrying out tabu search;
step 43, with the preferred initial solution x 1 ...x n And evaluating the n solutions generated by searching as a starting point by using a fitness function, taking the best solution to be the optimal solution, and outputting a result.
Specifically, for x i The tabu search specifically includes:
a. put the current solution x new ＝x i Current optimal solution x best ＝x i ；
b. When the taboo search algorithm termination criterion is met, stopping operation and outputting a calculation result; otherwise, continuing c;
c. generation of neighborhood N (x) of current solution using 2-opt near ) Selecting a plurality of solutions to form a candidate solution set;
d. judging whether the candidate solution set has candidate solutions satisfying the scofflaw criterion, if so, using the best candidate solution x satisfying the scofflaw criterion * Substitution of x new Changing into a new current solution, replacing the current optimal solution, updating the tabu table at the same time, and turning to the step a if not, continuing to the step e;
e. judging the tabu attribute of the object corresponding to each candidate solution in the candidate solution set, and selecting the best candidate solution x' in a non-tabu state in the candidate solution set to replace x new And (4) becoming a new current solution, replacing the current optimal solution, updating the tabu table and turning to the step a.
Specifically, the 2-opt process is as follows:
after a plurality of optimal initial solutions are obtained from the result of the genetic algorithm, each optimal initial solution comprises k solution branches of the unmanned skar, and the solution branches are locally optimized by using a 2-opt method in the neighborhood of the solution branches.
Specifically, the basic principle of the 2-opt method is as follows:
the paths (i + 1.. J) in the line thus exchanged are reversed by replacing (i, i + 1), (j + 1), (i +1, j + 1).
After the exchange, a relatively rich domain solution can be obtained, and the feasible solution is further improved.
The basic principle of the 2-opt method is shown in FIG. 2.
Fig. 3 is a flow chart of the improved genetic tabu mixing algorithm of the present invention.
The invention provides a hybrid algorithm based on the combination of a genetic algorithm and a tabu search algorithm, which refines a search space and improves convergence performance by dynamically adjusting crossover and variation probabilities, and then uses a plurality of better solutions obtained by the genetic algorithm as a plurality of initial solutions for tabu search to find a global optimal solution. Finally, the improved hybrid algorithm obtained through the experimental result can effectively avoid premature and local optimal phenomena occurring in the calculation process, can better obtain an optimal solution, greatly reduces the operation cost of the unmanned truck collection compared with the existing mode, and improves the working efficiency of the unmanned truck collection.
The invention combines the genetic algorithm with the tabu search, on one hand, a better initial solution is found for the tabu search, the frequency of calling the tabu search algorithm is reduced, on the other hand, the defect of poor climbing capability of the genetic algorithm is overcome, and the quality of the solution of the whole algorithm is improved by combining the genetic algorithm and the tabu search.
The above-mentioned embodiments only express the preferable mode of the invention, and the description is more specific and detailed, but not to be understood as the limitation of the patent scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A dynamic dispatching method for a plurality of unmanned truck collection vehicles in a port is characterized by comprising the following steps:
step 1, initializing, randomly generating an initial population, and setting the maximum iteration number as m;
step 2, carrying out selection, crossing and mutation operations on the initial population to generate a next generation new population;
step 3, constructing a fitness function, and recording the individual with the maximum fitness function value in each generation of individuals;
and 4, when the iteration frequency of the genetic algorithm is n, if the recorded individual fitness function value is continuously unchanged, selecting k optimal initial solutions from the result of the genetic algorithm, then respectively updating the k optimal initial solutions by using a tabu search algorithm, if a second optimal individual fitness function value calculated by the tabu search algorithm is larger than a first optimal individual fitness function value before the tabu search algorithm is carried out, jumping to the step 2, otherwise, continuously updating the population by using the tabu search algorithm until the update iteration frequency reaches m, jumping out of the program, and outputting the result.
2. The dynamic dispatching method for the unmanned port truck-mounted vehicles according to claim 1, wherein the step 1 specifically comprises:
step 11, encoding by adopting a natural number encoding mode, wherein if the number of task points in the unmanned truck path network is N, the chromosome is composed of an integer string from 1 to N;
step 12, generating original population chrom by random number generator i ＝[1,2,3,...,N]Wherein i represents the ith individual in the population, and N represents N genes in each individual;
step 13, inserting k + 10 into the original population according to the maximum kernel load constraint of the unmanned truck and the time constraint of the expected arrival of the task point to generate the initial population:
(0,i 11 ,i 12 ,…,i 1s ,0；i 21 ,i 22 ,…,i 2t ,0；…；i k1 ,i k2 ,…,i kw ,0)
wherein i kw Represents the w-th task point of the kth unmanned truck service, 0 represents the starting unmanned truck parking lot, and the node between two 0 represents a task route.
3. The method as claimed in claim 2, wherein the selecting operation in step 2 specifically comprises:
step 211, ordering according to the optimal fitness function value of the comprehensive objective function, and directly entering the first 10% of individuals with the highest fitness function value in the initial population into the next generation;
and step 212, selecting the remaining individuals by using a roulette selection mode, and enabling the selected individuals to enter the next step for cross operation or mutation operation.
4. The dynamic scheduling method for the unmanned port truck-concentration multiple vehicles according to claim 3, wherein a greedy algorithm is applied in the step 2 to adaptively improve the cross mutation operator, the improved mutation rate is dynamic, and the cross probability is defined as follows:
wherein, Δ = f a -f m ，f a Is the mean fitness function value, f m K1 is a constant coefficient for the maximum fitness value.
5. The method for dynamically scheduling the unmanned port truck with multiple vehicles according to claim 4, wherein the cross operation in the step 2 is as follows:
generating a random number epsilon, epsilon belongs to [0,1];
if epsilon<P c Then, the partial matching intersection is adopted, and the specific operation is as follows:
selecting two given parent chromosomes A and B, then randomly selecting two cross points in the parent chromosome A and the parent chromosome B, transferring a sub-gene between the two cross points in the parent chromosome A to the front end of the parent chromosome B to form an offspring chromosome B1, directly copying a gene in the parent chromosome B to the back of the offspring chromosome B1 to form an offspring chromosome B2, and finally removing the same gene from front to back to form an offspring chromosome B3;
transferring the sub gene between two cross points in the parent chromosome B to the front end of the parent chromosome A to form an offspring chromosome A1, directly copying the gene in the parent chromosome A to the back of the offspring chromosome A1 to form an offspring chromosome A2, and finally removing the same gene from front to back to finally form an offspring chromosome A3.
6. The dynamic dispatching method for the unmanned port truck with multiple vehicles according to claim 5, wherein the change operation in the step 2 is as follows:
if epsilon>P c Then, a given parent chromosome is selected by adopting an inversion mutation operation mode, two points are randomly selected on the parent chromosome, and the gene between the two points is subjected to inversion operation to generate a child chromosome A4.
7. The dynamic dispatching method for the unmanned port truck-mounted vehicles according to claim 1, wherein the tabu search algorithm in the step 4 is designed as follows:
step 41, initializing;
step 42, selecting n preferred initial solutions from the results of the genetic algorithm, and respectively marking as x 1 ...x n When i =1, 2.. N, for x i Carrying out tabu search;
step 43, with said preferred initial solution x 1 ...x n And evaluating the n solutions generated by searching as a starting point by using a fitness function, taking the best solution to be the optimal solution, and outputting a result.
8. The method as claimed in claim 7, wherein x is dynamically scheduled for the unmanned port truck i The tabu search specifically includes:
a. put the current solution x new ＝x i Current optimal solution x best ＝x i ；
b. When the taboo search algorithm termination criterion is met, stopping operation and outputting a calculation result; otherwise, continuing c;
c. generation of neighborhood N (x) of current solution using 2-opt near ) Selecting a plurality of solutions to form a candidate solution set;
d. judging whether the candidate solution set has candidate solutions meeting the scofflaw criterion, if so, using the best candidate solution x meeting the scofflaw criterion * Substitution of x new Changing into a new current solution, replacing the current optimal solution, updating the tabu table at the same time, and turning to the a, otherwise, continuing to the e;
e. judging the tabu attribute of each candidate solution corresponding object in the candidate solution set, selecting the best candidate solution x in the candidate solution set in a non-tabu state to replace x new And (4) becoming a new current solution, replacing the current optimal solution, updating the tabu table at the same time, and then turning to the step a.
9. The method as claimed in claim 8, wherein the 2-opt process is as follows:
after obtaining a plurality of optimal initial solutions from the result of the genetic algorithm, each optimal initial solution comprises k solution branches of the unmanned skar, and the solution branches are locally optimized by using a 2-opt method in the neighborhood of the solution branches.
10. The dynamic dispatching method for the unmanned port truck-concentrated multiple vehicles according to claim 9, characterized in that the basic principle of the 2-opt method is as follows:
the paths (i + 1.. J) in the line thus exchanged are reversed by replacing (i, i + 1), (j + 1), (i +1, j + 1).
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