CN110909930B - Goods position distribution method of mobile goods shelf storage system for refrigeration house - Google Patents

Goods position distribution method of mobile goods shelf storage system for refrigeration house Download PDF

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CN110909930B
CN110909930B CN201911138483.0A CN201911138483A CN110909930B CN 110909930 B CN110909930 B CN 110909930B CN 201911138483 A CN201911138483 A CN 201911138483A CN 110909930 B CN110909930 B CN 110909930B
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詹燕
李豪
鲁建厦
李嘉丰
陈寿伍
王�琦
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Zhejiang University of Technology ZJUT
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Abstract

A goods position distribution method of a mobile goods shelf storage system facing a refrigeration house distributes items with strong correlation to the same sorting roadway, reduces the possibility of opening the sorting roadway for many times, takes the similarity coefficient of order items as the basis of the correlation size, comprehensively considers the item sorting frequency and the goods shelf gravity center, establishes a multi-target goods position distribution optimization model, then solves by adopting an improved invasive weed algorithm to obtain the optimal storage position of goods, generates part of initial populations by adopting a greedy algorithm, then sets a reasonable space diffusion operator, and finally introduces the evolution reversal operation of a genetic algorithm. The invention has stronger overall search and local search capability, obvious optimization effect and effectively improved warehouse picking efficiency and shelf stability.

Description

Goods position distribution method of mobile goods shelf storage system for refrigeration house
Technical Field
The invention belongs to the field of storage management, and particularly relates to a goods position distribution method of a mobile goods shelf storage system for a refrigeration house.
Background
In recent years, with the rapid development of the cold-chain logistics industry, the refrigeration storage is concerned by more and more logistics enterprises. The problems of warehouse energy consumption, investment cost and efficiency are always pain points in the refrigeration house, so that the selection of a storage system with compact storage and taking space and timely service time becomes a new direction for the development of the refrigeration house. As a new compact storage system, the movable shelf storage system only needs to leave a sorting roadway for the storage trolley to operate, the storage trolley is moved to distribute out of the roadway through the shelf, and then enters the roadway to complete the storage and discharge of goods through the storage trolley, and the storage system is simple in structure, high in space utilization rate and low in cost, and is widely applied to various large cold storages at home and abroad. The goods position distribution problem is the key problem of the mobile goods shelf storage system facing to the refrigeration house, and the problem that how to optimize the operation efficiency of the mobile goods shelf storage system through a reasonable goods position strategy and how to optimize the stability of goods shelves becomes urgent to solve is directly related to whether the warehouse can run efficiently and stably.
Commonly used cargo space allocation strategies include: positioning storage, random storage, nearby position storage, full turnover rate storage and classified storage. However, in the prior art, the distribution of the goods space of the mobile shelf warehouse system facing the cold storage is less researched, and the specific storage position of each item in the warehouse system is not researched temporarily. Aiming at the logistics characteristics of various storage varieties, high timeliness requirement, high cost, complex technical requirement and the like of the logistics of the refrigeration house, a reasonable goods location distribution strategy is adopted, the order response speed of the refrigeration house can be improved, the cost of the refrigeration house is reduced, and the stability of a goods shelf is improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a goods position distribution method of a mobile goods shelf storage system facing a cold storage, so as to optimize a storage mode of the storage and reduce the management cost of the storage.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a goods position distribution method of a mobile goods shelf storage system facing a cold storage comprises the following steps:
step 1, establishing a multi-target goods space allocation optimization model for the purpose of improving the picking efficiency, improving the shelf stability and improving the relevance of the items of the same picking roadway;
calculating an objective function f for improving the sorting efficiency1，
The movement of the storage trolley from the I/O position to the cargo space (x, y, z) is calculated, assuming that the pallet needs to be moved, the horizontal running time is:
Figure BDA0002280199190000021
wherein y represents the coordinate of the cargo space (x, y, z) along the y-direction, w represents the cargo space width, vyRepresenting the speed of the access trolley in the y direction; t is trIndicating rack movement to open picking laneRoad time, k denotes the goods to be picked at the picking position (x, y, z) in the picking lane k, d denotes the depth of the goods grid, l denotes the width of the picking lane, vxThe speed of the storage trolley in the x direction is shown, as can be seen from fig. 2, the goods on the first row of shelves need to be picked from the lane 1, the goods on the second row and the third row of shelves are both picked from the lane 2, … …, and so on;
and then calculating the running time of the access trolley moving from the I/O position to the cargo space (x, y, z) in the vertical direction as follows:
Figure BDA0002280199190000022
wherein z represents the coordinate of the cargo space (x, y, z) along the z direction, h is the cargo grid height, vzThe speed of the access trolley in the vertical direction is shown;
continuing to calculate the return of the access cart from the cargo space (x, y, z) to the I/O location, the return time on the horizontal plane is:
Figure BDA0002280199190000023
the run time in the vertical direction on the return of the recalculation is:
Figure BDA0002280199190000031
further, since the movement of the storage trolley in the horizontal plane and the movement in the vertical direction are simultaneous, the time taken to pick the goods is the maximum value of the running times in the two directions, and the time t taken to pick the goods at the goods location (x, y, z) is calculatedxyzComprises the following steps:
Figure BDA0002280199190000032
wherein, tpThe time for loading and unloading goods for the storage trolley;
the time taken to pick the item at the cargo space (x, y, z) is then:
txyz·pxyz (6)
wherein p isxyzIndicating the picking frequency of items deposited at the cargo space (x, y, z).
Further, the main objective of improving the picking efficiency is to minimize the total picking time of the goods, and the objective function expression is as follows:
Figure BDA0002280199190000033
wherein f is1Representing the total picking time of the goods;
calculating an objective function f for improving shelf stability2，
Figure BDA0002280199190000034
Wherein f is2Indicates the height of the center of gravity of the goods shelf as a whole, mxyzRepresenting the weight of an item stored at the cargo space (x, y, z);
calculating and improving item correlation objective function f of same picking roadway3，
The calculation formula of the similarity coefficient proposed by Russel and Rao is as follows:
Figure BDA0002280199190000041
wherein a represents the order quantity containing item i and item j simultaneously; b represents the order quantity containing only item i; c represents the order quantity containing only item j; d represents the order quantity that neither item i nor item j contains;
the method has the advantages that items with strong correlation are stored on the shelf of the same sorting roadway, so that the times of opening the sorting roadway can be effectively reduced, furthermore, the reciprocal of the sum of the similarity coefficients of the items of the same sorting roadway converted from an objective function is as small as possible, and the expression of the objective function is as follows:
Figure BDA0002280199190000042
wherein f is3Expressing the reciprocal of the sum of item similarity coefficients of the same picking lane, K is the number of the picking lane, i and j represent item numbers, g represents the number of the items, rikItem i is stored on the rack of the sorting lane k when the value 1 indicates that item i is not stored, otherwise rik＝0，rjkThe same process is carried out;
further constraints of the model are as follows:
Figure BDA0002280199190000043
wherein x is more than or equal to 1 and less than or equal to a represents the limitation of the number of rows of the goods shelves; y is more than or equal to 1 and less than or equal to b, which represents the limitation of the number of rows of the goods shelves; z is more than or equal to 1 and less than or equal to c represents the limitation of the number of the shelf layers;
Figure BDA0002280199190000044
indicating that each item can only occupy one cargo space;
and 2, constructing an evaluation function.
Processing the three objective functions by an ideal point method to construct an evaluation function, and firstly, finding each objective function fiOptimum value of fi *And taking it as an ideal point;
based on the distance between each objective function value and the ideal point, an evaluation function of each objective function is constructed:
Fi＝(fi-fi *)2 (12)
wherein, FiAn evaluation function value representing an ith objective function; f. ofiA function value representing an ith objective function; f. ofi *An optimal value representing the ith objective function;
further, on the basis of the above formula, a weight coefficient lambda is introducediThe sum of which is 1, the multiple target is optimizedTransformation of the chemo-function into the merit-function:
Figure BDA0002280199190000051
Figure BDA0002280199190000052
wherein f is a multi-objective optimization evaluation function.
Step 3, coding design: numbering the items and the goods positions simultaneously, adopting a natural number arrangement coding mode, wherein the coding length depends on the number of the items, when the number of the items is N, one code consists of N unrepeated natural numbers, and each natural number corresponds to a goods position number; FIG. 4 is a diagram of an example code for item 1 stored in slot 13, item 2 stored in slot 3, … …, and so on, until N items have been allocated slots.
Step 4, generating an initial population: initializing algorithm related parameters: initial number of weeds N0Maximum number of weed population NmaxMaximum number of iterations itermaxMaximum number of seeds S that can be produced per weedmaxAnd minimum value SminNonlinear modulation index n, initial value of standard deviation σ of weeds in spatial diffusioninitSum standard deviation final value σfinal；
Step 5, recording an objective function value of each weed, wherein the objective function value comprises f1，f2，f3And f, then recording the optimal weed individuals and the optimal solution.
Step 6, a weed propagation stage: the number of seeds produced by each weed was calculated from the value of the objective function per weed using the following formula.
Figure BDA0002280199190000061
Wherein f represents the objective function value of the current weed, fmaxAnd fminRepresenting the maximum and minimum values of the objective function representing the weeds in the current population respectively,
Figure BDA0002280199190000062
represents rounding down;
step 7, a spatial diffusion stage: weed seeds were spread around the parent weeds according to a normal distribution with a mean of 0 and a standard deviation of σ. As the number of iterations increases, σ will also vary from the initial value σinitDecrease to a final value σfinalSpecifically, the standard deviation is calculated as follows when a certain generation is reached:
Figure BDA0002280199190000063
wherein, σ represents a standard deviation value corresponding to the current algebra; itermaxRepresenting the maximum number of iterations; iter represents the current algebra; sigmainitRepresents an initial value of standard deviation; sigmafinalRepresents the final value of the standard deviation;
step 8, competition survival stage: after the parent individuals and the child individuals in the population are combined into a new population, in order to improve the local search capability of the algorithm, an evolution reversion operator of the genetic algorithm is introduced, two positions are randomly selected, the numbers of the two positions are interchanged, if the objective function value is reduced, the individual is accepted, and otherwise, the evolution reversion is invalid. Then, the new population is sorted according to the objective function value, excellent individuals with small objective function values are left, the vulnerable individuals with large objective function values are eliminated, and the number of individuals cannot exceed the maximum number N of the populationmax；
Step 9, completing the iteration once, and judging whether the maximum iteration number iter is reachedmaxAnd if so, outputting the optimal weed individuals, otherwise, returning to the step 6.
Further, the process of step 4 is as follows:
step 4.1, firstly, the time t required for picking the goods at the goods position (x, y, z) is calculatedxyzCreating a picking time matrix from which the time t required to pick a good at any location can be knownxyz；
4.2, using a greedy algorithm to generate 40% of initial weed populations, randomly generating the rest 60% of the weed populations, and using two greedy strategies in the initial weeds generated by the greedy algorithm, wherein the first method is to preferentially select a goods space with short picking time; the second is to preferentially select a cargo space with a small number of layers.
Still further, the process of step 4.2 is as follows:
step 4.2.1, preferentially selecting the goods location method with short picking time: taking out the serial number of each goods position and the corresponding picking time to create a matrix, wherein the first column is the goods position serial number, the second column is the corresponding picking time, then the goods position serial number and the picking time of each row are still corresponding, then the goods position serial number and the picking time are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one;
step 4.2.2, preferentially selecting the method for the goods location with the small layer number: taking out each goods position number and the number of layers where the goods position number is located to create a matrix, wherein the first column is the goods position number, the second column is the corresponding layer, then the goods position number and the number of layers of each row are still corresponding, then the goods position number and the number of layers are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one.
Further, the process of step 7 is as follows:
and 7.1, adopting a space diffusion operator based on sliding insertion, firstly randomly generating two random positions j and k, then taking the cargo space numbers from the position j to the position k as a queue, taking the number of the position j as the head of the queue, taking the number of the position k as the tail of the queue, then sequentially deleting the numbers from the tail of the queue, and inserting the numbers into the head of the queue until d times of insertion are performed. A schematic view of the sliding insertion is shown in fig. 5.
And 7.2, the dispersion process of the seeds follows normal distribution with the mean value of 0 and the standard deviation of sigma, and the size of sigma determines the search range of the seeds. Inspired by this, the number of execution times d of sliding insertion is made to obey N (0, σ)2) The absolute value of the random number alpha is taken and then is rounded up
Figure BDA0002280199190000071
As the number of execution times d of the slide insertion.
The invention has the following beneficial effects: aiming at the problem that the goods location distribution of a mobile goods shelf storage system facing a refrigeration house is less researched, particularly, the specific storage location of each item is not considered in the previous research, the invention considers the problem that the time for opening a picking roadway of a mobile heavy goods shelf is too long, proposes to distribute the items with strong correlation to the same picking roadway, reduces the possibility of opening the picking roadway for multiple times, takes the similarity coefficient of order items as the basis of the correlation size, comprehensively considers the picking frequency and the gravity center of the goods shelf, establishes a multi-target goods location distribution optimization model, then solves the optimal storage location of the goods by adopting an improved invasive weed algorithm, generates part of initial population by adopting a greedy algorithm, then sets a reasonable space diffusion operator, finally introduces the evolution operation of a genetic algorithm, has strong global search and local search capabilities, and has obvious optimization effect, effectively improve the warehouse picking efficiency and the shelf stability.
Drawings
FIG. 1 is a schematic diagram of a mobile rack warehousing system, wherein 1 is a mobile rack, 2 is a mobile track, 3 is an I/O, and 4 is an access cart.
FIG. 2 is a top view of the mobile rack storage system.
Figure 3 is a flow chart of the IIWO algorithm.
Fig. 4 is a diagram of an example of encoding.
Fig. 5 is a drawing showing a slide insertion.
FIG. 6 is a graph of simulation results for improving picking efficiency.
FIG. 7 is a graph of simulation results for improving shelf stability.
Fig. 8 is a diagram of a simulation result of improving item relevance of the same picking lane.
FIG. 9 is a diagram of a simulation result of the multi-objective evaluation function.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 9, a goods location allocation method of a mobile goods shelf storage system facing a cold storage includes the following steps:
step 1, establishing a multi-target goods space allocation optimization model for the purpose of improving the picking efficiency, improving the shelf stability and improving the relevance of the items of the same picking roadway;
calculating an objective function f for improving the sorting efficiency1，
The movement of the storage trolley from the I/O position to the cargo space (x, y, z) is calculated, assuming that the pallet needs to be moved, the horizontal running time is:
Figure BDA0002280199190000081
wherein y represents the coordinate of the cargo space (x, y, z) along the y-direction, w represents the cargo space width, vyRepresenting the speed of the access trolley in the y direction; t is trIndicating the time of the shelf moving to open the picking roadway, k indicating the goods to be picked at the goods position (x, y, z) on the picking roadway k, d indicating the depth of the goods grid, l indicating the width of the picking roadway, vxThe speed of the storage trolley in the x direction is shown, as can be seen from fig. 2, the goods on the first row of shelves need to be picked from the lane 1, the goods on the second row and the third row of shelves are both picked from the lane 2, … …, and so on;
and then calculating the running time of the access trolley moving from the I/O position to the cargo space (x, y, z) in the vertical direction as follows:
Figure BDA0002280199190000091
wherein z represents the coordinate of the cargo space (x, y, z) along the z direction, h is the cargo grid height, vzThe speed of the access trolley in the vertical direction is shown;
continuing to calculate the return of the access cart from the cargo space (x, y, z) to the I/O location, the return time on the horizontal plane is:
Figure BDA0002280199190000092
the run time in the vertical direction on the return of the recalculation is:
Figure BDA0002280199190000093
further, since the movement of the storage trolley in the horizontal plane and the movement in the vertical direction are simultaneous, the time taken for picking the goods is the maximum value of the running time in the two directions, and the time t taken for picking the goods at the goods location (x, y, z) is calculatedxyzComprises the following steps:
Figure BDA0002280199190000094
wherein, tpThe time for loading and unloading goods for the storage trolley.
The time taken to pick the item at the cargo space (x, y, z) is then:
txyz·pxyz (6)
wherein p isxyzIndicating the picking frequency of items deposited at the cargo space (x, y, z).
Further, the main objective of improving the picking efficiency is to minimize the total picking time of the goods, and the objective function expression is as follows:
Figure BDA0002280199190000101
wherein f is1Representing the total pick time of the goods.
Calculating an objective function f for improving shelf stability2，
Figure BDA0002280199190000102
Wherein f is2Indicates the height of the center of gravity of the goods shelf as a whole, mxyzIndicating storage in goodsThe weight of the item at bit (x, y, z);
calculating and improving item correlation objective function f of same picking roadway3，
The calculation formula of the similarity coefficient proposed by Russel and Rao is as follows:
Figure BDA0002280199190000103
wherein a represents the order quantity containing item i and item j simultaneously; b represents the order quantity containing only item i; c represents the order quantity containing only item j; d represents the order quantity that neither item i nor item j contains.
The method has the advantages that items with strong correlation are stored on the shelf of the same sorting roadway, so that the times of opening the sorting roadway can be effectively reduced, furthermore, the reciprocal of the sum of the similarity coefficients of the items of the same sorting roadway converted from an objective function is as small as possible, and the expression of the objective function is as follows:
Figure BDA0002280199190000104
wherein f is3Expressing the reciprocal of the sum of item similarity coefficients of the same picking lane, K is the number of the picking lane, i and j represent item numbers, g represents the number of the items, rikItem i is stored on the rack of the sorting lane k when the value 1 indicates that item i is not stored, otherwise rik＝0，rjkThe same is true.
Further constraints of the model are as follows:
Figure BDA0002280199190000111
wherein x is more than or equal to 1 and less than or equal to a represents the limitation of the number of rows of the goods shelves; y is more than or equal to 1 and less than or equal to b, which represents the limitation of the number of rows of the goods shelves; z is more than or equal to 1 and less than or equal to c represents the limitation of the number of the shelf layers;
Figure BDA0002280199190000112
representing each itemOnly one cargo space is occupied.
And 2, constructing an evaluation function.
The invention selects an ideal point method to process the three objective functions and constructs an evaluation function. First, each objective function f is foundiOptimum value of fi *And takes it as an ideal point.
Further, based on the distance between each objective function value and the ideal point, an evaluation function of each objective function is constructed:
Fi＝(fi-fi *)2 (12)
wherein, FiAn evaluation function value representing an ith objective function; f. ofiA function value representing an ith objective function; f. ofi *Representing the optimal value of the ith objective function.
Further, on the basis of the above formula, a weight coefficient lambda is introducediAnd if the sum is 1, converting the multi-objective optimization function into an evaluation function:
Figure BDA0002280199190000113
Figure BDA0002280199190000114
wherein f is a multi-objective optimization evaluation function.
Step 3, coding design: the goods and the goods positions are numbered simultaneously, a natural number arrangement coding mode is adopted, the coding length depends on the number of the goods, when the number of the goods is N, one code consists of N unrepeated natural numbers, and each natural number corresponds to a goods position number. FIG. 4 is a diagram of an example code for item 1 stored in slot 13, item 2 stored in slot 3, … …, and so on, until N items have been allocated slots.
Step 4, generating an initial population: initializing algorithm related parameters: initial number of weeds N0Maximum number of weed population NmaxMaximum number of iterations itermaxMaximum number of seeds S that can be produced per weedmaxAnd minimum value SminNonlinear modulation index n, initial value of standard deviation σ of weeds in spatial diffusioninitSum standard deviation final value σfinal。
Step 4.1, firstly, the time t required for picking the goods at the goods position (x, y, z) is calculatedxyzCreating a picking time matrix from which the time t required to pick a good at any location can be knownxyz；
And 4.2, generating 40% of initial weed population by using a greedy algorithm, and randomly generating the remaining 60% of weed population. Two greedy strategies are adopted in initial weeds generated by a greedy algorithm, wherein the first method is to preferentially select a goods location with short picking time; the second is to preferentially select a cargo space with a small number of layers.
Step 4.2.1, preferentially selecting the goods location method with short picking time: taking out the serial number of each goods position and the corresponding picking time to create a matrix, wherein the first column is the goods position serial number, the second column is the corresponding picking time, then the goods position serial number and the picking time of each row are still corresponding, then the goods position serial number and the picking time are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one;
step 4.2.2, preferentially selecting the method for the goods location with the small layer number: taking out each goods position number and the number of layers where the goods position number is located to create a matrix, wherein the first column is the goods position number, the second column is the corresponding layer, then the goods position number and the number of layers of each row are still corresponding, then the goods position number and the number of layers are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one;
step 5, recording an objective function value of each weed, wherein the objective function value comprises f1，f2，f3And f, then recording the optimal weed individuals and the optimal solution.
Step 6, a weed propagation stage: the number of seeds produced by each weed was calculated from the value of the objective function per weed using the following formula.
Figure BDA0002280199190000131
Wherein f represents the objective function value of the current weed, fmaxAnd fminRepresenting the maximum and minimum values of the objective function representing the weeds in the current population respectively,
Figure BDA0002280199190000132
indicating a rounding down.
Step 7, a spatial diffusion stage: weed seeds were spread around the parent weeds according to a normal distribution with a mean of 0 and a standard deviation of σ. As the number of iterations increases, σ will also vary from the initial value σinitDecrease to the final value σfinalSpecifically, the standard deviation is calculated as follows when a certain generation is reached:
Figure BDA0002280199190000133
wherein, σ represents a standard deviation value corresponding to the current algebra; itermaxRepresenting the maximum number of iterations; iter represents the current algebra; sigmainitRepresents an initial value of standard deviation; sigmafinalThe final value of standard deviation is indicated.
And 7.1, adopting a space diffusion operator based on sliding insertion, firstly randomly generating two random positions j and k, then taking the cargo space numbers from the position j to the position k as a queue, taking the number of the position j as the head of the queue, taking the number of the position k as the tail of the queue, then sequentially deleting the numbers from the tail of the queue, and inserting the numbers into the head of the queue until d times of insertion are performed. A schematic view of the sliding insertion is shown in fig. 5.
And 7.2, the dispersion process of the seeds follows normal distribution with the mean value of 0 and the standard deviation of sigma, and the size of sigma determines the search range of the seeds. Inspired by this, the number of execution times d of sliding insertion is made to obey N (0, σ)2) The absolute value of the random number alpha is taken and then is rounded up
Figure BDA0002280199190000134
As a slideNumber of times d of execution of live insertion.
Step 8, competition survival stage: after the parent individuals and the child individuals in the population are combined into a new population, in order to improve the local search capability of the algorithm, an evolution reversal operator of the genetic algorithm is introduced. Randomly selecting two positions, interchanging the numbers of the two positions, accepting the individual if the objective function value is reduced, or else, carrying out evolution reversion and invalidation. Then, the new population is sorted according to the objective function value, excellent individuals with small objective function values are left, the vulnerable individuals with large objective function values are eliminated, and the number of individuals cannot exceed the maximum number N of the populationmax。
Step 9, completing the iteration once, and judging whether the maximum iteration number iter is reachedmaxAnd if so, outputting the optimal weed individuals, otherwise, returning to the step 6.
The actual refrigeration house movable shelf warehousing system of a certain enterprise is used as a research object, and is programmed and simulated in matlab. The basic simulation parameters of the mobile shelf warehousing system researched by the invention are shown in table 1.
Table 1 shows the simulation basic parameters of the mobile shelf warehousing system
Simulation parameters Value taking
Width w of the grid 1.3m
Height h of the grid 1.4m
Depth d of the grid 1.1m
Total number of rowsa 6
Total number of columns b 10
Number of layers c 4
X-direction speed v of storage trolley on horizontal planex 2m/s
Y-direction speed v of storage trolley on horizontal planey 2m/s
Speed v of storage trolley in vertical directionz 1m/s
The time for loading and unloading goods of the access trolley is tp 5s
Width l of picking roadway 4.3m
Speed v of movement of the goods shelfr 4m/min
The time for opening the picking roadway by moving the goods shelf is tr 64.5s
TABLE 1
In the optimization task of the distribution of certain goods space in the warehouse, 200 goods items are required to be stored in the warehouse, the warehousing system has 240 goods spaces, each goods space can only store one goods item, the sorting frequency and the weight of each warehousing goods item are known, and the similarity coefficient between every two goods items is calculated by the formula (9) based on the historical orders. In order to enable the warehouse to operate efficiently and stably, the improved invasive weed algorithm is adopted to carry out simulation analysis on the optimization of the allocation of the goods space of the warehouse.
IIWO algorithm parameter settings are shown in table 2. Table 2 shows IIWO algorithm parameters.
Figure BDA0002280199190000141
Figure BDA0002280199190000151
TABLE 2
The sorting efficiency simulation result is improved:
fig. 6 is an iterative curve of the modified invasive weed algorithm, with the ideal point of objective function 1 being 582.43, which is 0.7% better than 586.5 at the beginning.
Improving the simulation result of the center of gravity of the goods shelf:
fig. 7 is an iterative curve of the improved invasive weed algorithm, the ideal point of the objective function 2 is 2.59, and the optimization effect is 14.5% compared with 3.03 at the beginning.
And (3) improving the correlation simulation result of the same picking roadway item:
fig. 8 is an iterative curve of the modified invasive weed algorithm, the ideal point of the objective function 3 is 0.11, and the optimization effect is 17.3% compared with the initial 0.133.
And (3) multi-target evaluation function simulation results:
the enterprise focuses on improving shelf stability and item relevance of the same picking roadway by comprehensively considering three objective functions, and the importance of improving the picking efficiency is the lowest because the shelf moving and opening the picking roadway is long, and the improvement of the picking efficiency depends on the item relevance of the same picking roadway to a great extent, namely (lambda)1，λ2，λ3) And (5) taking values of (0.2, 0.4 and 0.4), and substituting the values into a multi-objective optimization evaluation function to carry out simulation solution.
FIG. 9 is an iterative curve of the improved invasive weed algorithm, the optimal value of the multi-objective optimization evaluation function is 0.24, and the optimization effect is 87.4% compared with the initial 1.91.
The simulation results of the four objective functions can be verified, and the method provided by the invention can better solve the problem of goods allocation optimization.

Claims (4)

1. A goods position distribution method of a mobile goods shelf warehousing system facing a refrigeration house is characterized by comprising the following steps:
step 1, establishing a multi-target goods space allocation optimization model for the purpose of improving the picking efficiency, improving the shelf stability and improving the relevance of the items of the same picking roadway;
calculating an objective function f for improving the sorting efficiency1，
The movement of the storage trolley from the I/O position to the cargo space (x, y, z) is calculated, assuming that the pallet needs to be moved, the horizontal running time is:
Figure FDA0003512559600000011
wherein y represents the coordinate of the cargo space (x, y, z) in the y-direction, w represents the cargo compartment width, vyRepresenting the speed of the access trolley in the y direction; t is trIndicating the time of the shelf moving to open the picking roadway, k indicating the goods to be picked at the goods position (x, y, z) on the picking roadway k, d indicating the depth of the goods grid, l indicating the width of the picking roadway, vxRepresenting the speed of the access trolley in the x direction;
and then calculating the running time of the access trolley moving from the I/O position to the cargo space (x, y, z) in the vertical direction as follows:
Figure FDA0003512559600000012
wherein z represents the coordinate of the cargo space (x, y, z) along the z direction, h is the cargo grid height, vzThe speed of the access trolley in the vertical direction is shown;
continuing to calculate the return of the access cart from the cargo space (x, y, z) to the I/O location, the return time on the horizontal plane is:
Figure FDA0003512559600000013
the run time in the vertical direction on the return of the recalculation is:
Figure FDA0003512559600000014
further, since the movement of the storage trolley in the horizontal plane and the movement in the vertical direction are simultaneous, the time taken to pick the goods is the maximum value of the running times in the two directions, and the time t taken to pick the goods at the goods location (x, y, z) is calculatedxyzComprises the following steps:
Figure FDA0003512559600000015
wherein, tpThe time for loading and unloading goods for the storage trolley;
the time taken to pick the item at the cargo space (x, y, z) is then:
txyz·pxyz (6)
wherein p isxyzRepresenting a picking frequency of items deposited at the cargo space (x, y, z);
further, the goal of improving the picking efficiency is to minimize the total picking time of the goods, and the expression of the objective function is as follows:
Figure FDA0003512559600000021
wherein f is1Representing the total picking time of the goods;
calculating an objective function f for improving shelf stability2，
Figure FDA0003512559600000022
Wherein f is2Indicates the height of the center of gravity of the goods shelf as a whole, mxyzRepresenting the weight of an item stored at the cargo space (x, y, z);
calculating and improving item correlation objective function f of same picking roadway3，
The calculation formula of the similarity coefficient proposed by Russel and Rao is as follows:
Figure FDA0003512559600000023
wherein A represents the order quantity simultaneously containing item i and item j; b represents the order quantity containing only item i; c represents the order quantity containing only item j; d represents the order quantity that neither item i nor item j contains;
the method has the advantages that items with strong correlation are stored on the shelf of the same sorting roadway, so that the times of opening the sorting roadway can be effectively reduced, furthermore, the reciprocal of the sum of the similarity coefficients of the items of the same sorting roadway converted from an objective function is as small as possible, and the expression of the objective function is as follows:
Figure FDA0003512559600000024
wherein f is3Expressing the reciprocal of the sum of item similarity coefficients of the same picking lane, K is the number of the picking lane, i and j represent item numbers, g represents the number of the items, rikItem i is stored on the rack of the sorting lane k when the value 1 indicates that item i is not stored, otherwise rik＝0；rjkDepositing item j as 1On the shelf of lane k, otherwise rjk＝0；
Further constraints of the model are as follows:
Figure FDA0003512559600000025
wherein x is more than or equal to 1 and less than or equal to a represents the limitation of the number of rows of the goods shelves; y is more than or equal to 1 and less than or equal to b, which represents the limitation of the number of rows of the goods shelves; z is more than or equal to 1 and less than or equal to c represents the limitation of the number of the shelf layers;
Figure FDA0003512559600000026
indicating that each item can only occupy one cargo space;
step 2, establishing an evaluation function
Processing the three objective functions by an ideal point method to construct an evaluation function, and firstly, finding each objective function fiOptimum value of fi *And taking it as an ideal point;
based on the distance between each objective function value and the ideal point, an evaluation function of each objective function is constructed:
Fi＝(fi-fi *)2 (12)
wherein, FiAn evaluation function value representing an ith objective function; f. ofiA function value representing an ith objective function; f. ofi *An optimal value representing the ith objective function;
further, on the basis of the above formula, a weight coefficient lambda is introducediAnd if the sum is 1, converting the multi-objective optimization function into an evaluation function:
Figure FDA0003512559600000031
Figure FDA0003512559600000032
wherein f is a multi-objective optimization evaluation function;
step 3, coding design: numbering the items and the goods positions simultaneously, adopting a natural number arrangement coding mode, wherein the coding length depends on the number of the items, when the number of the items is N, one code consists of N unrepeated natural numbers, and each natural number corresponds to a goods position number;
step 4, generating an initial population: initializing algorithm related parameters: initial number of weeds N0Maximum number of weed population NmaxMaximum number of iterations itermaxMaximum number of seeds S that can be produced per weedmaxAnd minimum value SminNonlinear modulation index n, initial value of standard deviation σ of weeds in spatial diffusioninitSum standard deviation final value σfinal；
Step 5, recording an objective function value of each weed, wherein the objective function value comprises f1，f2，f3And f, then recording the optimal weed individuals and the optimal solution;
step 6, a weed propagation stage: calculating the number of seeds generated by each weed according to the objective function value of each weed by using the following formula;
Figure FDA0003512559600000033
wherein f represents the objective function value of the current weed, fmaxAnd fminRepresenting the maximum and minimum values of the objective function representing the weeds in the current population respectively,
Figure FDA0003512559600000034
represents rounding down;
step 7, a spatial diffusion stage: weed seeds are scattered around the parent weeds according to normal distribution with the mean value of 0 and the standard deviation of sigma, and the sigma can also be distributed from the initial value sigma as the iteration number is increasedinitDecrease to the final value σfinalSpecifically, the standard deviation is calculated as follows when a certain generation is reached:
Figure FDA0003512559600000035
wherein, σ represents a standard deviation value corresponding to the current algebra; itermaxRepresenting the maximum number of iterations; iter represents the current algebra; sigmainitRepresents an initial value of standard deviation; sigmafinalRepresents the final value of the standard deviation;
step 8, competition survival stage: after the parent individuals and the offspring individuals in the population are combined into a new population, in order to improve the local search capability of the algorithm, an evolution reversal operator of the genetic algorithm is introduced, two positions are randomly selected, the numbers of the two positions are interchanged, if the objective function value is reduced, the individuals are accepted, otherwise, the evolution reversal is invalid, then the new population is sorted according to the objective function value, excellent individuals with small objective function values are left, the disadvantaged individuals with large objective function values are eliminated, and the number of the individuals cannot exceed the maximum number N of the populationmax；
Step 9, completing the iteration once, and judging whether the maximum iteration number iter is reachedmaxAnd if so, outputting the optimal weed individuals, otherwise, returning to the step 6.
2. The method for allocating the cargo space of the mobile goods shelf warehouse system facing the cold storage according to claim 1, wherein the process of the step 4 is as follows:
step 4.1, firstly, the time t required for picking the goods at the goods position (x, y, z) is calculatedxyzCreating a picking time matrix from which the time t required to pick a good at any location can be knownxyz；
4.2, using a greedy algorithm to generate 40% of initial weed populations, randomly generating the rest 60% of the weed populations, and using two greedy strategies in the initial weeds generated by the greedy algorithm, wherein the first method is to preferentially select a goods space with short picking time; the second is to preferentially select a cargo space with a small number of layers.
3. The method for allocating the cargo space of the mobile goods shelf warehouse system facing the cold storage according to claim 2, wherein the process of the step 4.2 is as follows:
step 4.2.1, preferentially selecting the goods location method with short picking time: taking out the serial number of each goods position and the corresponding picking time to create a matrix, wherein the first column is the goods position serial number, the second column is the corresponding picking time, then the goods position serial number and the picking time of each row are still corresponding, then the goods position serial number and the picking time are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one;
step 4.2.2, preferentially selecting the method for the goods location with the small layer number: taking out each goods position number and the number of layers where the goods position number is located to create a matrix, wherein the first column is the goods position number, the second column is the corresponding layer, then the goods position number and the number of layers of each row are still corresponding, then the goods position number and the number of layers are arranged according to the ascending order of the second column, and at the moment, the generated partial population codes correspond to the first column one by one.
4. The goods location distribution method of the mobile goods shelf storage system facing the cold storage according to any one of claims 1 to 3, wherein the process of the step 7 is as follows:
step 7.1, adopting a space diffusion operator based on sliding insertion, firstly randomly generating two random positions j and k, then taking the goods space numbers from the position j to the position k as a queue, taking the number of the position j as the head of the queue, taking the number of the position k as the tail of the queue, then sequentially deleting the numbers from the tail of the queue, and inserting the numbers into the head of the queue until d times of insertion are executed;
step 7.2, the dispersion process of the seeds obeys normal distribution with the mean value of 0 and the standard deviation of sigma, the size of sigma determines the search range of the seeds, and the execution times d of the sliding insertion obeys N (0, sigma)2) The absolute value of the random number alpha is taken and then is rounded up
Figure FDA0003512559600000041
As the number of execution times d of the slide insertion.
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