CN117408604A - Large-scale stereoscopic warehouse cargo space distribution method based on intelligent calculation - Google Patents

Large-scale stereoscopic warehouse cargo space distribution method based on intelligent calculation Download PDF

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CN117408604A
CN117408604A CN202311232966.3A CN202311232966A CN117408604A CN 117408604 A CN117408604 A CN 117408604A CN 202311232966 A CN202311232966 A CN 202311232966A CN 117408604 A CN117408604 A CN 117408604A
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cargo
goods
cargo space
warehouse
target value
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刘景森
李浩然
胡萍
范文凯
陈洋
付翊雯
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Henan University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment

Abstract

The invention provides a large-scale stereoscopic warehouse goods position distribution method based on intelligent calculation. The method is characterized in that from the aspects of efficiency and safety, a large-scale cargo space distribution model is established by taking three principles of efficiency priority, shelf stability and load balancing based on turnover rate as targets; and adds the situation that part of the cargo space is occupied so as to better construct and apply to the actual cargo space distribution scene.

Description

Large-scale stereoscopic warehouse cargo space distribution method based on intelligent calculation
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a large-scale stereoscopic warehouse goods position distribution method based on intelligent calculation.
Background
Along with the continuous progress and development of technology, intelligent storage is used as a transfer station in a logistics supply chain, integrates storage, transportation and distribution, and is a key link in a modern logistics system. The automatic stereoscopic warehouse realizes automation of goods access by means of a series of intelligent logistics facilities, becomes an important component in intelligent warehouse, and performs goods space allocation optimization on goods to be warehoused also becomes an important way for reducing warehouse cost and improving whole warehouse efficiency. The goods space distribution means that a batch of goods with different attributes are reasonably distributed on the goods space, so that the purposes of reducing the storage operation cost, improving the utilization rate of the goods shelf, ensuring the stability and safety of the goods shelf and the like are achieved. Therefore, the automatic stereoscopic warehouse goods space allocation optimization method has important theoretical value and practical significance.
At present, one of the most effective methods for solving the cargo space allocation optimization problem is a group intelligent optimization algorithm, the algorithms do not depend on mathematical characteristics of the problem in the solving process, and can obtain an approximate optimal solution within acceptable time, so that a good thought and scheme are provided for optimizing and solving the problem.
However, as the scale of the stereoscopic warehouse is continuously improved, the quantity of warehoused goods is gradually increased, and factors and constraint conditions which need to be considered such as shelf stability, stacker load balancing and the like are also increased, so that the performance of most intelligent algorithms is reduced when the problem of large-scale goods allocation is optimized, and even an effective scheme cannot be optimized.
Disclosure of Invention
Aiming at the problem of goods space distribution of a large-scale stereoscopic warehouse, the invention provides a large-scale stereoscopic warehouse goods space distribution method based on intelligent calculation, which aims at further improving the distribution efficiency and the goods shelf utilization rate on the basis of ensuring the stability and the safety of goods shelves.
The invention provides a large-scale stereoscopic warehouse goods allocation method based on intelligent calculation, which comprises the following steps:
step 1: selecting a swarm intelligence algorithm, and initializing related parameters of the swarm intelligence algorithm, including: the number N of individuals in the population, the maximum evolution algebra and the individual dimension D; the individual dimension D corresponds to a cargo space number allocated to the cargo warehouse, and the position of each individual corresponds to an allocation scheme which is used for indicating the corresponding relation between the cargo and the cargo space;
step 2: acquiring warehouse information and cargo information, and randomly initializing the positions of all individuals of a population; the warehouse information comprises the total number of goods places and occupied goods place information, wherein the goods information comprises goods information to be put in a warehouse and goods information already on the goods places;
step 3: simulation of the stacker transport process to construct a turnover-based efficiency first objective function f 1 Construction of the shelf stability objective function f 2 And constructing a stacker load balancing objective function f 3 Constructing a composite objective function F based on the three objective functions;
step 4: calculating a turnover-based efficiency priority target value f according to a current allocation scheme for each individual in the population 1 Target value f of shelf stability 2 And stacker load balancing target value f 3 And according to the three target values f 1 、f 2 And f 3 Calculating a composite target value F corresponding to the allocation scheme;
step 5: finding an individual corresponding to the minimum composite target value F as an optimal individual, and recording the position of the optimal individual;
step 6: updating the positions of all individuals in the population;
step 7: comparing the composite target values corresponding to the optimal individuals before and after updating, and if the composite target values after updating are smaller, replacing the positions of the optimal individuals before updating by the positions of the optimal individuals after updating; otherwise, the position of the optimal individual before updating is reserved;
step 8: and comparing whether the current iteration number reaches the maximum iteration number, if not, returning to the step 4 to continue the next iteration, if so, stopping iteration, and outputting the optimal composite target value F and the allocation scheme at the moment.
Further, in step 4, an efficiency priority target value f based on the turnover rate is calculated according to the current allocation scheme 1 The method specifically comprises the following steps:
calculating the actual time of arrival of the stacker at the cargo space
Wherein i represents the i-th warehouse entry cargo, i=1, 2,..m, M is the number of cargo to be warehouse entry; y is i A shelf column index number representing the good i; z i A shelf layer index number representing the good i; l is the length, width and height of the shelf; v (V) y The horizontal movement speed of the stacker is; v (V) z Is the vertical movement speed of the stacker;
calculating fork extending and receiving time of stacker during each warehouse entry
Wherein x is i A shelf row index number representing the good i; v (V) x The fork extending speed or the fork collecting speed of the stacker;
calculating according to formula (3) to obtain an efficiency priority target value f based on turnover rate 1 ：
Wherein E is i The turnover rate of the cargo i is indicated.
Further, in step 4, the shelf stability target value f is calculated according to the current allocation scheme 2 The method specifically comprises the following steps:
calculating according to formula (4) to obtain the target value f of shelf stability 2 ：
Wherein w is i K is the number of cargoes on the cargo space before distribution for the weight corresponding to the cargoes i already put in storage; w (w) j The weight of the j-th warehouse-in goods after being distributed is M, and the quantity of the goods to be warehouse-in; l is the length, width and height of the shelf.
Further, in step 4, a stacker load balancing target value f is calculated according to the current allocation scheme 3 The method specifically comprises the following steps:
calculating according to a formula (5) to obtain a stacker load balancing target value f 3 ：
Wherein P is the number of lanes; p is p i K is the number of cargoes on the cargo space before distribution for the roadway where the cargoes i are already put in storage; p is p j M is the quantity of goods to be put in storage for the tunnel where the j-th goods are located after being distributed;the values assigned to each lane are equally distributed for all cargo.
Further, in step 4, according to the three target values f 1 、f 2 And f 3 The calculating of the composite target value F corresponding to the allocation scheme specifically comprises the following steps:
minF＝μ 1 •f 1 +μ 2 •α•f 2 +μ 3 •β•f 3 (6)
s.t.
μ 1 +μ 2 +μ 3 ＝1 (7)
wherein mu 1 、μ 2 Sum mu 3 Are all weights; a. b and c are the total row number, total column number and total layer number of the goods shelves respectively; alpha and beta are amplification factors; k is the number of cargoes on the cargo space before distribution, M is the number of cargoes to be put in storage, x i Shelf row index number, y, representing cargo i i A shelf column index number representing the good i; z i The shelf tier index number representing the good i.
Further, a penalty strategy is adopted to solve the feasible solution of the composite objective function F, wherein the penalty strategy refers to that when the generated solution does not meet the constraint condition, a larger penalty coefficient is added to the composite target value corresponding to the solution.
Further, a repair strategy is adopted to solve the feasible solution of the composite function target F, wherein when the generated solution does not meet the constraint condition, an empty cargo space number is searched out in the neighborhood of the cargo space number generating the conflict, and the empty cargo space number is adopted as the cargo space of the target cargo.
The invention has the beneficial effects that:
from the aspects of efficiency and safety, the invention aims at three principles of efficiency priority, shelf stability and load balancing based on turnover rate, and establishes a large-scale cargo space distribution model; and the condition that part of goods space is occupied is added to better construct and apply to the actual goods space distribution scene, and the intelligent swarm algorithm optimizes a superior and feasible distribution scheme when solving the problem of large-scale stereoscopic warehouse goods space distribution by adopting the coding and decoding mode and the constraint condition processing strategy provided by the method.
Drawings
Fig. 1 is a flow diagram of a large-scale stereoscopic warehouse cargo space distribution method based on intelligent computation according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an optimal allocation scheme obtained by combining the cargo space allocation method of the present invention with a repair strategy in example 1 according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an optimal allocation scheme obtained by combining the cargo space allocation method with the repair strategy in example 2 according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the embodiment of the invention provides a large-scale stereoscopic warehouse goods allocation method based on intelligent calculation, which specifically comprises the following steps:
s101: selecting a swarm intelligence algorithm, and initializing related parameters of the swarm intelligence algorithm, including: the number N of individuals in the population, the maximum evolution algebra Max_iter and the individual dimension D; the individual dimension D corresponds to a cargo space number allocated to the cargo warehouse, and the position of each individual corresponds to an allocation scheme which is used for indicating the corresponding relation between the cargo and the cargo space;
s102: acquiring warehouse information and cargo information, and randomly initializing the positions of all individuals of a population; the warehouse information comprises the total number of goods places and occupied goods place information, wherein the goods information comprises goods information to be put in a warehouse and goods information already on the goods places;
s103: simulation of the stacker transport process to buildEfficiency priority objective function f based on turnover rate 1 Construction of the shelf stability objective function f 2 And constructing a stacker load balancing objective function f 3 Constructing a composite objective function F based on the three objective functions;
specifically, each cargo is transported to a designated cargo space by a stacker at the time of cargo warehousing. In order to save working time and improve the working efficiency of the whole warehouse, goods are generally distributed to a place which is close to the entrance of the goods shelf as much as possible during distribution. On this basis, the influence of the cargo turnover rate on the warehouse efficiency is also considered. The cargo turnover rate reflects the frequency of cargo in and out of the warehouse in a period of time, and compared with other cargoes in and out of the warehouse in the same period of time, cargoes with higher turnover rate have higher frequency and are more placed in places closer to the entrance of the goods shelf, so that the cargoes can turn over more quickly. Thus, the present embodiment will simulate the stacker transporting process and establish an objective function based on the turnover rate.
For the stereoscopic warehouse with multiple roadways, if the distributed goods are in a few or even the same roadway, the stackers of the roadways can run under high load, and the stackers of other roadways are in an idle state, so that the automatic stereoscopic warehouse cannot exert the maximum working efficiency. Therefore, the goods are dispersed on the goods shelves at the two sides corresponding to different roadways as much as possible when the goods are distributed to the goods space, so that the operation efficiency of the warehouse is improved.
S104: calculating a turnover-based efficiency priority target value f according to a current allocation scheme for each individual in the population 1 Target value f of shelf stability 2 And stacker load balancing target value f 3 And according to the three target values f 1 、f 2 And f 3 Calculating a composite target value F corresponding to the allocation scheme;
s105: finding an individual corresponding to the minimum composite target value F as an optimal individual, and recording the position of the optimal individual;
s106: updating the positions of all individuals in the population;
s107: comparing the composite target values corresponding to the optimal individuals before and after updating, and if the composite target values after updating are smaller, replacing the positions of the optimal individuals before updating by the positions of the optimal individuals after updating; otherwise, the position of the optimal individual before updating is reserved;
s108: comparing whether the current iteration number reaches the maximum iteration number, if not, returning to the step S104 to continue the next iteration, if so, stopping iteration, and outputting the optimal composite target value F and the allocation scheme at the moment.
According to the large-scale stereoscopic warehouse goods allocation method based on intelligent calculation, on the basis of the establishment of a goods allocation model, the three principles of efficiency priority based on turnover rate, shelf stability and stacker load balance are taken as targets, a large-scale goods allocation mathematical model is established, and the situation that partial goods are occupied is added, so that the actual goods allocation scene can be better constructed and applied, and the group intelligent algorithm can optimize a superior and feasible allocation scheme when the problem of large-scale stereoscopic warehouse goods allocation is solved by the coding and decoding mode provided by the method.
Example 2
On the basis of the embodiment, the embodiment of the invention provides a large-scale stereoscopic warehouse goods space distribution method, which specifically comprises the following steps:
s201: setting algorithm initial parameters: the number N of individuals (allocation schemes) in the population, the maximum evolution algebra Max_iter and the individual dimension D, and randomly generating the initial position x of each individual in the search space i (i＝1,2,...,N)；
S202: setting the number of warehouse goods places, warehouse-in goods information and goods information on the goods places;
s203: optimizing cargo space allocation scheme x according to constraint condition processing strategy i (i=1, 2,., N), the allocation scheme optimized is x i ′(i＝1,2,...,N)；
S204: calculating efficiency priority target f based on turnover rate according to allocation scheme 1 。
Specifically, when calculating the actual time for the stacker to reach the target cargo space, the running time of the stacker in two directions should be calculated first, and then the maximum time of the two should be taken as the actual working time. The actual time for the stacker to reach the cargo space is:
wherein i represents the i-th warehouse entry shipment (i=1, 2,., M); m is the total warehouse entry goods quantity; y is i Shelf column index number indicating that the assigned ith item is at the y-th item i A column shelf; z i Shelf layer index number indicating that the assigned ith item is at z-th i A layer shelf; l is the specification of a shelf, and the length, width and height are set to be equal to L (m); v (V) y Horizontal movement speed (m/s) of the stacker; v (V) z Vertical movement speed (m/s) of the stacker.
The fork extending and receiving time of the stacker in each warehouse entry is as follows:
wherein x is i Shelf row index number indicating that the assigned ith item is at the xth i A discharging rack; v (V) x The speed (m/s) of the fork extending (receiving) of the stacker.
As can be seen from the efficiency priority objective, in addition to calculating the time for each item to be put in storage, it is also considered to place the item with high turnover rate on the item location closer to the entrance of the shelf, so as to optimize the warehouse efficiency. The method changes the turnover rate E of the ith goods i As a frequency coefficient, and multiplied by the stacker operating time, the objective function is changed to a minimum product of the warehouse-in time and the turnover rate. The first objective function thus established is as follows:
the target value f corresponding to the current allocation scheme can be calculated according to the formulas (1) to (3) 1 。
S205: calculating shelf stability target f according to allocation scheme 2 。
Specifically, in order to prevent the overall goods shelf center of gravity from being too high due to unreasonable goods distribution, heavy goods are placed on the lower layer during distribution, and light goods are placed on the upper layer, so that the overall goods shelf center of gravity is lower, overturning is avoided, and the stability of the goods shelf is enhanced. Thus, in this embodiment, a second objective function targeting the minimum height of the center of gravity of the shelf from the ground is constructed as follows:
wherein w is i The weight (kg) corresponding to the stored goods i; k is the number of cargoes already on the cargo space; w (w) j The weight (kg) of the jth warehouse entry cargo;
s206: calculating a stacker load balancing target f according to a distribution scheme 3 。
Specifically, in this embodiment, the standard deviation of the number of occupied cargo space in the racks at both sides of the roadway is used to reflect the equilibrium degree of cargo space allocation, and the third objective function with the minimum standard deviation as the objective is constructed as follows:
wherein P is the number of lanes; p is p i The position of the tunnel where the ith cargo is located on the cargo space; p is p j The position of a roadway where the j-th goods are located after being distributed;the values assigned to each lane are equally distributed for all cargo.
S207: a composite target value F is calculated.
Specifically, by constructing and analyzing the three objective functions, an optimized objective function of the cargo space allocation problem in the automated stereoscopic warehouse can be established as follows:
minF＝μ 1 •f 1 +μ 2 •α·f 2 +μ 3 ·β·f 3 (6)
s.t.
μ 1 +μ 2 +μ 3 ＝1 (7)
wherein f 1 ，f 2 ，f 3 The three objective functions are respectively; mu (mu) 1 ，μ 2 ，μ 3 Linear weights of three targets respectively, and satisfying constraint conditions (7); a, b and c are rows, columns and layers of the shelves respectively; alpha and beta are the amplification coefficients of the second objective function and the third objective function respectively, and are used for carrying out normalization processing on the objective function values which are not in the same order of magnitude; constraints (11), (12) indicate that the allocated cargo space cannot conflict.
S208: counting the compound target value F of all individuals in the population, and finding out the optimal compound target value F min And record its position x' best 。
S209: each individual in the population is location updated.
S210: step S203-S207 is used for obtaining a composite target value of the new individual, comparing the new individual with the old individual, and if the new individual is better, replacing the previous generation individual position with the new individual; otherwise, the original individual position is kept.
S211: comparing whether the current iteration number iter reaches the maximum iteration number, if the iter is smaller than or equal to Max_iter, returning to S208, and continuing the optimization process of iterative evolution.
S212: outputting the optimal composite target value F and the distribution scheme x' best 。
Further, the problem of allocation of cargo space in an automated stereoscopic warehouse is a constraint optimization problem, and when solving such problems, the conversion of constraint optimization into unconstrained optimization is a common processing mode, and a punishment strategy is one of the main methods. The idea of the penalty strategy is to add a larger penalty coefficient to the fitness value of the objective function corresponding to the solution when the solution generated by the algorithm does not meet the constraint condition, so as to reduce the priority of the infeasible solution.
However, as the size and complexity of the problem increases, the feasible solution decreases dramatically under constraints, and if the penalty strategy is continued to be used, the algorithm will be inefficient and sometimes cannot find the feasible solution. Therefore, the method aims at constraint condition processing in the large-scale cargo space distribution problem, and a new repair strategy is provided, so that the group intelligent algorithm can solve the large-scale cargo space distribution problem more effectively.
Specifically, as can be seen from the constraint conditions (11) and (12), since there is no conflict between the goods locations in the allocation scheme and the allocated goods locations must be empty, the repair strategy will repair the goods location number with conflict, and the specific operation is to search for an empty goods number in the neighborhood of the goods location number with conflict, so that the allocation scheme which is not feasible originally is repaired into a feasible scheme.
The repair strategy provided by the embodiment of the invention ensures that each set of allocation schemes generated by the swarm intelligence algorithm in evolution solving are feasible, and effectively avoids the problem that the algorithm cannot find a feasible solution in solving the cargo space allocation problem.
In order to solve the problem that the constraint condition cannot be effectively distributed due to the fact that the constraint condition is difficult to process under a large-scale condition, a constraint condition processing strategy is provided, distribution difficulty is effectively reduced, storage efficiency is improved, and storage safety is improved. The method is simple to implement, has strong stability and applicability, and fully meets the design and actual requirements.
Example 3
In order to verify the effectiveness of the cargo space allocation method and the repair strategy provided by the invention, the invention also provides the following test.
The improved vortex search algorithm is selected to be combined with a common punishment strategy and a restoration strategy provided by the method respectively to test two calculation examples with different complexity degrees. The improved vortex search algorithm is an intelligent calculation algorithm, the calculation example 1 is a large-scale environment with an empty initial cargo space, the calculation example 2 is a large-scale environment with a non-empty initial cargo space, statistical data of 30 times of optimizing results are shown in table 1, and a schematic diagram of an optimal allocation scheme optimized by using a repairing strategy is shown in fig. 2 and 3. In table 1, if the solution solved by the algorithm does not satisfy the constraint condition, it makes no sense to express the corresponding objective function value by NA.
Table 1 improved vortex search algorithm combined with the solution of the present method to two examples
As can be seen from table 1, under the large-scale cargo space distribution environment (i.e. calculation example 1) where the initial cargo space is empty, the two strategies can both obtain a feasible scheme, but the overall objective function values corresponding to the two strategies are compared, the repair strategy is obviously better, and the constraint condition that the repair strategy provided by the method can be used for more effectively solving the problem of large-scale stereoscopic warehouse cargo space distribution is illustrated.
For example 2 of a large-scale complex environment, the algorithm does not find a feasible cargo space allocation scheme for 30 times by using a punishment strategy for optimization, which also shows that when constraint conditions of problems are increased, the punishment strategy is difficult to effectively process the constraint conditions, so that the algorithm can not solve the feasible scheme all the time. The repairing strategy provided by the method can not only avoid the problem that a feasible solution cannot be found, but also keep good optimizing effect and stability.
The white, gray and red boxes in fig. 2 and 3 represent the empty cargo space, the occupied cargo space before distribution and the cargo space after distribution by the present method, respectively. The cargo space distribution effect diagrams of fig. 2 and 3 can be clearly seen, and the optimal distribution scheme solved by combining the method enables cargoes to be concentrated on cargo spaces close to the entrances and exits, the whole gravity center of the goods shelf is lower, and the distribution of the cargoes is reasonable. The efficiency target, the shelf stability target and the stacker load balancing target of the large-scale goods space distribution problem can be well met.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. The intelligent calculation-based large-scale stereoscopic warehouse cargo space distribution method is characterized by comprising the following steps of:
step 1: selecting a swarm intelligence algorithm, and initializing related parameters of the swarm intelligence algorithm, including: the number N of individuals in the population, the maximum evolution algebra and the individual dimension D; the individual dimension D corresponds to a cargo space number allocated to the cargo warehouse, and the position of each individual corresponds to an allocation scheme which is used for indicating the corresponding relation between the cargo and the cargo space;
step 2: acquiring warehouse information and cargo information, and randomly initializing the positions of all individuals of a population; the warehouse information comprises the total number of goods places and occupied goods place information, wherein the goods information comprises goods information to be put in a warehouse and goods information already on the goods places;
step 3: simulation of the stacker transport process to construct a turnover-based efficiency first objective function f 1 Construction of the shelf stability objective function f 2 And constructing a stacker load balancing objective function f 3 Constructing a composite objective function F based on the three objective functions;
step 4: calculating a turnover-based efficiency priority target value f according to a current allocation scheme for each individual in the population 1 Target value f of shelf stability 2 And stacker load balancing target value f 3 And according to the three target values f 1 、f 2 And f 3 Calculating a composite target value F corresponding to the allocation scheme;
step 5: finding an individual corresponding to the minimum composite target value F as an optimal individual, and recording the position of the optimal individual;
step 6: updating the positions of all individuals in the population;
step 7: comparing the composite target values corresponding to the optimal individuals before and after updating, and if the composite target values after updating are smaller, replacing the positions of the optimal individuals before updating by the positions of the optimal individuals after updating; otherwise, the position of the optimal individual before updating is reserved;
step 8: and comparing whether the current iteration number reaches the maximum iteration number, if not, returning to the step 4 to continue the next iteration, if so, stopping iteration, and outputting the optimal composite target value F and the allocation scheme at the moment.
2. The intelligent calculation-based large-scale stereoscopic warehouse cargo space allocation method according to claim 1, wherein in step 4, the efficiency priority target value f based on turnover rate is calculated according to the current allocation scheme 1 The method specifically comprises the following steps:
calculating the actual time of arrival of the stacker at the cargo space
Wherein i represents the i-th warehouse entry cargo, i=1, 2,..m, M is the number of cargo to be warehouse entry; y is i A shelf column index number representing the good i; z i A shelf layer index number representing the good i; l is the length, width and height of the shelf; v (V) y The horizontal movement speed of the stacker is; v (V) z Is the vertical movement speed of the stacker;
calculating fork extending and receiving time of stacker during each warehouse entry
Wherein x is i A shelf row index number representing the good i; v (V) x The fork extending speed or the fork collecting speed of the stacker;
calculating according to formula (3) to obtain an efficiency priority target value f based on turnover rate 1 ：
Wherein E is i The turnover rate of the cargo i is indicated.
3. The intelligent computing-based large-scale stereoscopic warehouse cargo space allocation method according to claim 1, wherein in step 4, the shelf stability target value f is computed according to the current allocation scheme 2 The method specifically comprises the following steps:
calculating according to formula (4) to obtain the target value f of shelf stability 2 ：
Wherein w is i K is the number of cargoes on the cargo space before distribution for the weight corresponding to the cargoes i already put in storage; w (w) j The weight of the j-th warehouse-in goods after being distributed is M, and the quantity of the goods to be warehouse-in; l is the length, width and height of the shelf.
4. The intelligent calculation-based large-scale stereoscopic warehouse cargo space distribution method according to claim 1, wherein in step 4, a stacker load balancing target value f is calculated according to a current distribution scheme 3 The method specifically comprises the following steps:
calculating according to a formula (5) to obtain a stacker load balancing target value f 3 ：
Wherein P is the number of lanes; p is p i K is the number of cargoes on the cargo space before distribution for the roadway where the cargoes i are already put in storage; p is p j M is the quantity of goods to be put in storage for the tunnel where the j-th goods are located after being distributed;the values assigned to each lane are equally distributed for all cargo.
5. The intelligent calculation-based large-scale stereoscopic warehouse cargo allocation method according to claim 1, wherein in step 4, the three target values f are used 1 、f 2 And f 3 The calculating of the composite target value F corresponding to the allocation scheme specifically comprises the following steps:
min F＝μ 1 •f 1 +μ 2 •α·f 2 +μ 3 ·β·f 3 (6)
s.t.
μ 1 +μ 2 +μ 3 ＝1 (7)
wherein mu 1 、μ 2 Sum mu 3 Are all weights; a. b and c are the total row number, total column number and total layer number of the goods shelves respectively; alpha and beta are amplification factors; k is the number of cargoes on the cargo space before distribution, M is the number of cargoes to be put in storage, x i Shelf row index number, y, representing cargo i i A shelf column index number representing the good i; z i The shelf tier index number representing the good i.
6. The intelligent computation-based large-scale stereoscopic warehouse cargo allocation method according to claim 5, wherein a penalty strategy is adopted to solve a feasible solution of the composite objective function F, and the penalty strategy is that when the generated solution does not meet the constraint condition, a larger penalty coefficient is added to the composite target value corresponding to the solution.
7. The intelligent computation-based large-scale stereoscopic warehouse cargo space allocation method according to claim 5, wherein a repair strategy is adopted to solve a feasible solution of a composite function target F, wherein when the generated solution does not meet the constraint condition, an empty cargo space number is searched in the neighborhood of the cargo space number generating conflict, and the empty cargo space number is adopted as the cargo space of the target cargo.
CN202311232966.3A 2023-09-22 2023-09-22 Large-scale stereoscopic warehouse cargo space distribution method based on intelligent calculation Pending CN117408604A (en)

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