CN109492800B - Vehicle path optimization method for automatic warehouse - Google Patents
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- CN109492800B CN109492800B CN201811270333.0A CN201811270333A CN109492800B CN 109492800 B CN109492800 B CN 109492800B CN 201811270333 A CN201811270333 A CN 201811270333A CN 109492800 B CN109492800 B CN 109492800B
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Abstract
The invention provides a vehicle path optimization method for an automated warehouse. According to the method, based on a mixed algorithm of simulated annealing and tabu search, constraint conditions are set for planning problems of determining the starting and ending positions of the warehouse-in and warehouse-out in a vehicle path, and the simulated annealing algorithm is adopted to obtain an initial solution, so that the tabu search algorithm can avoid selecting a randomly generated initial solution, and the initial solution of the simulated annealing algorithm is directly referenced for secondary optimization, so that a global solution with better effect is obtained. The method is applied to vehicle path optimization with determined start and end positions, and belongs to the field of warehouse operation scheduling. After optimization, the vehicle starts from the appointed warehouse-in position, traverses all warehouse positions in the warehouse, passes through each warehouse position once and only once, and finally reaches the appointed warehouse-out position. The optimization method is high in practicality and good in usability by combining with actual vehicle path scheduling conditions of the warehouse.
Description
Technical Field
The invention belongs to the field of automatic dispatching of warehouse logistics, and relates to a method for optimizing a vehicle path of an automatic warehouse.
Background
With the rapid development of the logistics storage industry, the past storage mode has hysteresis condition compared with the current demand of material production and circulation, so that the stereoscopic warehouse is paid more attention to and applied by more and more people. The operation efficiency of the stereoscopic warehouse depends on various factors including allocation strategies of warehouse goods positions, optimal selection of vehicle paths, combined design of software and hardware of the warehouse and the like. Therefore, research on problem models and algorithms for stereoscopic warehouse site optimization, vehicle path planning (Vehicle Routing Problem, VRP) has become an important research for automated scheduling in the warehouse industry.
The automated warehouse scheduling problem is an extension to the traveller (Traveling Salesman Problem, TSP) problem, which is one of the most difficult combinatorial optimization problems. Golden, magnanti and Nguyan, 1972, entitled "vehicle routing" studied vehicle path problems. Thereafter Golden and Stewart introduced the uncertainty probability theory into the VRP problem in 1978. By the 90 s of the 20 th century, the VRP problem was further studied thanks to the rapid development of personal computers. Similarly, various heuristic algorithms for solving the hybrid optimization problem also appear in this period, and these algorithms are also used for solving the VRP problem. Laporte and Gendreau et algorithm applications of VRPs such as genetic algorithm, ant colony algorithm, neural network algorithm, simulated annealing, tabu search, etc. were studied in 1998. And also proposes a trip probability problem PTSP, a vehicle path probability problem PVRP and a random vehicle path problem SVRP, and in addition, a time-dependent trip problem TD-TSP, a time-dependent vehicle path problem TD-VRP and a dynamic vehicle path problem.
Compared with abroad, the vehicle path problem is studied at home, the VRP is studied at the end of the 90 s, and then gradually paid attention to, so that more and more people begin to study deeply. Over the last decade has undergone several stages of development abroad. There is a long and extensive study both from the study of the algorithm itself and on the expansion of the vehicle path, and the number of studies on the problem of vehicle path per year is multiplied.
Disclosure of Invention
In view of the above-mentioned drawbacks of automated warehouse logistics scheduling in the prior art, the present invention provides a method for vehicle path optimization for automated warehouses. The problem to be solved by the invention is different from the typical TSP problem, and the research object is the vehicle path optimization problem of an automatic warehouse, and generally refers to vehicle path scheduling under the condition of determining the starting and ending positions of warehouse entry and warehouse exit. By adopting the vehicle path optimization method, all the warehouse positions in the warehouse are traversed from the appointed warehouse-in position in the blocking area of the warehouse, and the appointed warehouse-out position is finally reached after each warehouse position is only once.
According to one aspect of the present invention, there is provided a vehicle path optimization method of an automated warehouse for realizing vehicle path scheduling when a warehouse-in and warehouse-out start and end position of the automated warehouse is determined, the method comprising the steps of:
step 1: for each region, the starting and ending positions of warehouse positions are not considered, the rest warehouse positions search the optimal initial solution by adopting a simulated annealing algorithm, and the serial numbers of the sequences of the better warehouse positions and the path lengths of vehicles are generated by adopting the simulated annealing algorithm, wherein random sequences are generated by using a random function in Matlab software as an initial warehouse position sequence S0, the path length D0 of the vehicles running according to the warehouse position sequences is calculated, initialization parameters are calculated, and initial values T are defined 0 Decrease the rate α, end value T f Setting a tabu table Tlist, setting a tabu table length tl, and selecting a solution number cl;
step 2: according to the decreasing formula T (n+1) =α×T (n), if T (n+1) +. f Return libraryThe bit Sequence and the corresponding vehicle path length d_best jump to step 6, otherwise the next step is executed;
step 3: given step length l=l t ，l t Let l=1, if l < l t Executing the step 4, otherwise returning to the step 2;
step 4: the Sequence of the new library bit and the vehicle path length D (n) corresponding to the Sequence of the new library bit are disturbed by using a fliplr function in Matlab software, the vehicle path length D (n+1) under the Sequence of the new library bit is calculated, if the vehicle path length of the Sequence D (n+1) of the new library bit is shortened by D (n) compared with the length before updating, namely D (n) > D (n+1), the Sequence of the new library bit and the vehicle path length D (n) corresponding to the Sequence of the new library bit are updated, the iteration times l=l+1 are caused, the step 3 is executed in a returning way, and otherwise, the step 5 is executed;
step 5: according to discriminant e [D(n+1)-D(n)]/T(n) Judging the sequence of the vehicle by more than rand, and if the judgment formula is met, updating the sequence of the vehicle library and calculating the path length of the vehicle under the new sequence of the vehicle library, wherein l=l+1; not satisfying the discriminant, not updating, also let l=l+1, and executing step 3, wherein the rand of the discriminant represents a random number of the interval (0, 1) randomly generated by Matlab software;
step 6: introducing the Sequence of the library bit Sequence generated in the step 2 and the corresponding vehicle path length d_best, clearing a tabu table Tlist, setting a tabu table length tl, and selecting a solution number cl;
step 7: judging whether the iteration times line=2000 of the termination condition is met, namely, looping 2000 times, if yes, ending and outputting an optimal library bit sequence_best and the shortest vehicle path length d_best2, otherwise, executing the step 8;
step 8: generating a neighborhood solution by utilizing a neighborhood structure of the Sequence of the current library bit Sequence, determining the start and end positions of the library, and determining cl candidate solutions in the Sequence;
step 9: judging whether the optimal Sequence of the library bit Sequence and the corresponding vehicle path length d_best are met or not for the candidate solution, if so, replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the Sequence with the optimal Sequence of the library bit Sequence and the vehicle path length D (n), replacing the 'best so far' state by the Sequence, and then jumping to the step 8; otherwise, go to step 10;
step 10: judging the tabu condition of each object corresponding to the cl candidate solutions, selecting the optimal state corresponding to the non-tabu object in the candidate solution set as a new current solution, replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new current solution, and then turning to the step 8.
In one embodiment, the initial value T defined in step 1 0 The selection of (a) is associated with the rank order of the bin sequence and the search speed and convergence result of searching for a preferred vehicle path length, and the end value Tf is associated with the number of iterations.
In one embodiment, the taboo table Tlist of step 1 is represented by the function zeros of Matlab software, initial taboo length
The method is used for limiting the iteration times of tabu objects, N is the number of parameters in the serial number, corresponds to the total number of library bits in an automatic warehouse, and is the number of candidate solutions +.>
Corresponding to the candidate bin ordering.
In one embodiment, the neighborhood structure in step 8 adopts two elements in the random exchange library bit sequence to determine the start and end positions of the warehouse entry and the warehouse exit, so that the start and end positions of the serial number are fixed, the return library bit sequence in step 2 does not contain the start and end points, and the start and end positions are supplemented after the random exchange in the step.
In one embodiment, the random exchange base bit sequence is a tabu object, the operation memory of the random exchange base bit sequence is carried out for each iteration according to the set tabu length, the operation mode is put in a tabu table in a specified iteration step number, and when the iteration number exceeds a limit range, the tabu table is removed by the tabu object.
Drawings
The various aspects of the present invention will become more apparent to the reader upon reading the detailed description of the invention with reference to the accompanying drawings. Wherein, the liquid crystal display device comprises a liquid crystal display device,
fig. 1 shows a flow chart of a method for optimizing a vehicle path in an automated warehouse under warehouse entry and exit start and end position determination conditions.
Fig. 2A and 2B are schematic sectional views of a nonferrous metal warehouse.
FIG. 3 is a primary optimization result obtained by the vehicle path optimization method of the present invention.
Detailed Description
For a more complete and thorough description of the present application, reference is made to the drawings, wherein like reference numerals represent the same or similar elements, and to various embodiments of the present invention. However, it will be understood by those of ordinary skill in the art that the examples provided below are not intended to limit the scope of the present invention. Furthermore, the drawings are for illustrative purposes only and are not drawn to their original dimensions.
Embodiments of various aspects of the invention are described in further detail below with reference to the drawings.
Based on a mixed algorithm of simulated annealing and tabu search, the method sets constraint conditions for planning problems of warehouse-in and warehouse-out starting and ending positions in a vehicle path, adopts a simulated annealing algorithm to obtain an initial solution, enables the tabu search algorithm to avoid selecting randomly generated initial solutions, directly refers to the initial solution of the simulated annealing algorithm to perform secondary optimization, and obtains a global solution with better effect. The method is applied to vehicle path optimization with determined start and end positions, and belongs to the field of warehouse operation scheduling. After optimization, the vehicle starts from the appointed warehouse-in position, traverses all warehouse positions in the warehouse, passes through each warehouse position once and only once, and finally reaches the appointed warehouse-out position.
Specific embodiments of the invention make the following assumptions:
(1) The actual process of the travelling crane is divided into three parts, an acceleration part is started, a uniform motion process after the specific speed is reached, and a deceleration process before the specific position is reached, wherein the average speed is taken as the travelling speed of the travelling crane.
(2) The goods can be successfully extracted when no fault exists in the running process of the crown block, namely the passing garage positions.
(3) The goods crane at each storage position can extract goods at one time, and the coordinates of the goods position can be represented by (X i ，Y i ，Z i ) Change to (X) i ，Y i ) I.e., two-dimensionally representing three-dimensional coordinates.
(4) In view of the warehouse-in and warehouse-out environment of an actual site, such as setting two safety channels, a warehouse gate is not provided with a warehouse site to ensure the site, and only one crown block is used for scheduling.
For a nonferrous metal warehouse, there are 11 rows of storage locations in each column, and there are 40 rows altogether, and the warehouse is divided into five areas (Area A, area B, area C, area D, area E) as shown, wherein the red solid points represent the in-out and out-in locations of the individual areas.
Referring to fig. 1 to 3, the specific implementation steps of the present invention are as follows:
step 1: for each region, the starting and ending positions of the warehouse positions are not considered, and the rest warehouse positions are searched for an optimal initial solution by adopting a simulated annealing algorithm. Taking Area A as an example, m library bits are total (without a starting point, namely m-2 library bits are selected), and a simulated annealing algorithm is used for generating a preferred library bit sequence serial number and a vehicle path length. Wherein, random sequence is generated by using random function in Matlab software as initial library bit sequence S0, and vehicle path length D0 corresponding to the library bit sequence is calculated. Initializing parameters and defining initial value T 0 =3000, drop ratio α=0.50, end value T f ＝0.01；
Step 2: given the decrease formula T (n+1) =α×T (n), if T (n+1). Ltoreq.T f Returning to the Sequence of the preferred library bit Sequence and the vehicle path length d_best, skipping to the step 6, otherwise, executing the next step; taking the vehicle path length of the step 1 as the condition of the special privilege rule of the tabu search algorithm, adding one to all the serial numbers of the library bit sequence generated by the step 1 (for example {5,3,2,4,1} is added by one to obtain {6,4,3,5,2 }), adding the serial numbers 1 and m at the first and last positions (the {6,4,3,5,2} is added at the first and last positions to obtain {1,6,4,3,5,2,7 }) to obtain a new library bit sequence string;
step 3: given step length l=l t ，l t Let l=1. If l < l t Executing the step 4, otherwise returning to the step 2;
step 4: and (4) disturbing the sequence of the sequence numbers of the library bits by using a fliplr function in Matlab software, and calculating the vehicle path length D (n+1) under the sequence number of the new library bits. If the vehicle path length of the new library bit Sequence number D (n+1) is shortened by D (n) compared with the length before updating, namely D (n) > D (n+1), updating the vehicle path length D (n) corresponding to the library bit Sequence number Sequence and the new library bit Sequence number, and returning l=l+1 to execute the step 3, otherwise executing the step 5; and (3) adopting a tabu search algorithm, and initially solving the tabu search algorithm into the new library bit sequence string generated in the step (2). Setting the length of the tabu table as sixty percent of the library bits, and setting the tabu object as the exchange operation of the library bit sequence. The neighborhood structure uses a mode of randomly exchanging two library bits to generate a certain number of domain solutions, and calculates the spending distance of a new vehicle path;
step 5: according to discriminant e [D(n+1)-D(n)]/T(n) Judging the sequence of the library and calculating the vehicle path length of the new sequence if the sequence of the library meets the judgment formula, wherein l=l+1; if the discriminant is not satisfied, updating is not performed, and let l=l+1, and step 3 is executed;
step 6: introducing the return value Sequence and the vehicle path length d_best generated in the step 2, clearing a tabu table Tlist, setting a tabu table length tl, and selecting a solution number cl;
step 7: it is determined whether the termination condition line=2000, i.e., 2000 cycles, is satisfied. If yes, ending and outputting an optimal library bit sequence_best and a vehicle path d_best2 of the library bit Sequence; otherwise, executing the step 8;
step 8: generating a neighborhood solution by utilizing a neighborhood structure of a Sequence of a current library bit Sequence, determining the start and end positions of the library, and determining cl candidate solutions in the Sequence;
step 9: and judging whether the candidate solution meets the optimal Sequence of the library bit Sequence and the vehicle path d_best of the library bit Sequence. If yes, replacing the tabu object entering the tabu table earliest by using the optimal sequences Sequence and D (n), replacing the tabu object entering the tabu table earliest by using the tabu object corresponding to the Sequence, replacing the 'best so far' state by using the Sequence, and then jumping to the step 8; otherwise, go to step 10; judging whether the optimal solution is met or not (in the method, the optimal solution Sequence and the vehicle path length d_best calculated by the optimal solution Sequence are accepted better than the simulated annealing algorithm), if yes, selecting the current solution as the optimal library bit Sequence and the vehicle path, otherwise, selecting the optimal state corresponding to the non-tabu object in the candidate solution set as a new current solution, and listing the exchange condition as a tabu object and updating a tabu table.
Step 10: judging the tabu condition of each object corresponding to the cl candidate solutions, selecting the optimal state corresponding to the non-tabu object in the candidate solution set as a new current solution, replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new current solution, and then turning to the step 8.
With this method, the vehicle path length is 448.724 when the number of iterations is 2000. Compared with the conventional tabu search algorithm, the effect is obviously improved by adopting the optimal solution calculated by simulated annealing to replace a random generation method. Due to the influence of the determination of the start and end positions, the optimization method belongs to a suboptimal solution, and under the condition of determining the start and end positions with a standard tabu search algorithm, the optimization effect is obvious through the TSPLIB standard library eil provided by the university of Heidelberg as an example.
The calculation results are shown in the following table:
| determining the start and end points | Maximum value | Minimum of | Average of | Offset rate | Standard deviation of | Number of iteration steps |
| TS | 535.7403 | 492.0961 | 516.2882 | 20.91％ | 16.03 | 2000 |
| The method of the invention | 489.1728 | 448.724 | 469.2680 | 9.8％ | 12.23 | 2000 |
Hereinabove, the specific embodiments of the present invention are described with reference to the accompanying drawings. However, those of ordinary skill in the art will appreciate that various modifications and substitutions can be made to the specific embodiments of the invention without departing from the spirit and scope thereof. Such modifications and substitutions are intended to be within the scope of the following claims.
Claims (5)
1. A vehicle path optimization method for an automated warehouse, configured to implement vehicle path scheduling when determining a start and end position of a warehouse entry and exit of the automated warehouse, the vehicle path optimization method comprising the steps of:
step 1: for each region, the starting and ending positions of warehouse positions are not considered, and the rest warehouse positions adopt simulated withdrawalSearching an optimal initial solution by using a fire algorithm, generating a serial number of a better library bit sequence and a vehicle path length by using the simulated annealing algorithm, wherein a random sequence is generated by using a random function in Matlab software and is used as an initial library bit sequence S0, calculating the vehicle path length D0 running according to the library bit sequence, initializing parameters and defining an initial value T 0 Decreasing the ratio alpha, ending the value Tf, setting the tabu table Tlist, setting the tabu table length tl, and selecting the solution number cl, wherein;
step 2: returning to the Sequence of the library bit Sequence and the corresponding vehicle path length d_best according to the descent formula T (n+1) =α×t (n), if T (n+1) +.tf, and jumping to step 6, otherwise executing the next step;
step 3: given step length l=l t ，l t Let l=1, if l < l t Executing the step 4, otherwise returning to the step 2;
step 4: the Sequence of the new library bit and the vehicle path length D (n) corresponding to the Sequence of the new library bit are disturbed by using a fliplr function in Matlab software, the vehicle path length D (n+1) under the Sequence of the new library bit is calculated, if the vehicle path length of the Sequence D (n+1) of the new library bit is shortened by D (n) compared with the length before updating, namely D (n) > D (n+1), the Sequence of the new library bit and the vehicle path length D (n) corresponding to the Sequence of the new library bit are updated, the iteration times l=l+1 are caused, the step 3 is executed in a returning way, and otherwise, the step 5 is executed;
step 5: according to discriminant e [D(n+1)-D(n)]/T(n) Judging the sequence of the vehicle by more than rand, and if the judgment formula is met, updating the sequence of the vehicle library and calculating the path length of the vehicle under the new sequence of the vehicle library, wherein l=l+1; not satisfying the discriminant, not updating, also let l=l+1, and executing step 3, wherein rand in the discriminant represents a random number of the interval (0, 1) randomly generated by Matlab software;
step 6: introducing the Sequence of the library bit Sequence generated in the step 2 and the corresponding vehicle path length d_best, clearing a tabu table Tlist, setting a tabu table length tl, and selecting a solution number cl;
step 7: judging whether the iteration times line=2000 of the termination condition is met, namely, looping 2000 times, if yes, ending and outputting an optimal library bit sequence_best and the shortest vehicle path length d_best2, otherwise, executing the step 8;
step 8: generating a neighborhood solution by utilizing a neighborhood structure of the Sequence of the current library bit Sequence, determining the start and end positions of the library, and determining cl candidate solutions in the Sequence;
step 9: judging whether the optimal Sequence of the library bit Sequence and the corresponding vehicle path length d_best are met or not for the candidate solution, if so, replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the Sequence with the optimal Sequence of the library bit Sequence and the vehicle path length D (n), replacing the 'best so far' state by the Sequence, and then jumping to the step 8; otherwise, go to step 10;
step 10: judging the tabu condition of each object corresponding to the cl candidate solutions, selecting the optimal state corresponding to the non-tabu object in the candidate solution set as a new current solution, replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new current solution, and then turning to the step 8.
2. The method for optimizing a vehicle path in an automated warehouse according to claim 1, wherein the initial value T defined in step 1 0 Is associated with the selection of the bin sequence ordering and the search speed and convergence result of the search for the preferred vehicle path length, the end value T f Associated with the number of iterations.
3. The vehicle path optimization method of an automated warehouse according to claim 1, wherein the tabu table Tlist of step 1 is represented by a function zeros of Matlab software, initial tabu length
For limiting the iteration times of the tabu objects, wherein N is the number of parameters in the serial number, corresponds to the total number of library bits in the automatic warehouse, and is the number of candidate solutions
Corresponding to the candidate bin ordering.
4. The method for optimizing the vehicle path of an automated warehouse according to claim 1, wherein the neighborhood structure of step 8 uses two elements in a random exchange library bit sequence to determine the start and end positions of the warehouse entry and exit, so that the start and end positions of the serial number are fixed, the return library bit sequence in step 2 does not contain the start and end points, and the start and end positions are supplemented after the random exchange in the step.
5. The method for optimizing a vehicle path in an automated warehouse according to claim 1, wherein the random swap library bit sequence is a tabu object, the operation of the random swap library bit sequence is memorized for each iteration according to a set tabu length, the operation mode is placed in a tabu table in a specified iteration step number, and when the iteration number exceeds a limited range, the tabu table is removed by the tabu object.
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