CN115034719A - Inventory sorting method, electronic device and computer-readable storage medium - Google Patents
Inventory sorting method, electronic device and computer-readable storage medium Download PDFInfo
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- G06Q10/00—Administration; Management
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- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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Abstract
The application discloses an inventory sorting method, an electronic device and a computer-readable storage medium. The method comprises the following steps: acquiring an ex-warehouse order; classifying and combining the ex-warehouse orders according to the types of the stored materials and the ex-warehouse data to obtain at least one sub-ex-warehouse order; sorting according to warehousing batches stored in the same type based on the sub ex-warehouse orders; in the sorting process, sorting is carried out according to a preset sorting rule or a sorting rule in an ex-warehouse order; and if the remaining quantity to be sorted in the current sub ex-warehouse order is greater than or equal to the inventory quantity of the batch to be sorted, all the inventory of the batch to be sorted is ex-warehouse, and if the remaining quantity to be sorted in the current sub ex-warehouse order is less than the inventory quantity of the batch to be sorted, the inventory sorting combination is determined by adopting a knapsack algorithm. By the mode, the sorting efficiency is improved, and the unfavorable inventory management problems of the inventory overstock phenomenon, unreasonable ex-warehouse order distribution and the like are avoided.
Description
Technical Field
The present application relates to the field of goods sorting technologies, and in particular, to an inventory sorting method, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of the warehouse logistics industry, more and more new problems can occur to the actual inventory sorting task. With the current sorting technology, the stored goods often have the condition that the standard of the minimum package is not uniform and the package has mantissa. After taking a delivery order, the optimal delivery combination of the delivery order number is hard to be calculated by manual calculation. This is mainly because manual calculations are more time consuming, thereby reducing sorting efficiency. If the combination of the package of the random taken-out library is adopted, the number of the taken-out packages is possibly large, so that the phenomenon of overstock of the inventory can be caused for enterprise customers, and zero inventory management is not facilitated; the larger sorting quantity of the previous batch may cause the shortage of the inventory quantity of the next batch, which is not favorable for the arrangement of the delivery plan of the customer.
Disclosure of Invention
The application provides an inventory sorting method, electronic equipment and a computer readable storage medium, which can improve sorting efficiency and avoid adverse inventory management problems such as inventory backlog phenomenon, unreasonable distribution of ex-warehouse orders and the like.
In a first aspect, the present application provides an inventory sorting method, including: acquiring an ex-warehouse order; classifying and combining the ex-warehouse orders according to the types of the stored materials and the ex-warehouse data to obtain at least one sub-ex-warehouse order; sorting according to warehousing batches stored in the same type based on the sub ex-warehouse orders; during the sorting process, sorting is carried out according to preset sorting rules or sorting rules in the delivery order; if the remaining quantity to be sorted in the current sub ex-warehouse order is larger than or equal to the inventory quantity of the batch to be sorted, all the inventories of the batch to be sorted are ex-warehouse, and if the sorting requirement meeting the condition exists, all the inventories meeting the sorting condition are sorted out of the warehouse; if the remaining quantity to be sorted in the current sub ex-warehouse order is smaller than the inventory quantity of the batch to be sorted, determining an inventory sorting combination by adopting a knapsack algorithm; the total capacity of the backpack is the number of the whole boxes required by the order for the child to leave the warehouse, the backpack capacity of the target material is the proportion of the volume of the target material to the total volume, and the backpack value of the target material is the proportion of the target material to the total number of the boxes filled with the same type of material.
Wherein, the letter sorting rule includes: the whole package is preferably taken out according to the stock; wherein, the whole package comprises a plurality of small packages; or, the whole package is preferentially taken out according to the inventory, and when the order requirement is not met, the target number of small packages are obtained from the whole package; or, the goods are delivered according to the stock according to the sorting sequence; or, generating sorting rules by using an exhaustion method; or, the whole package is firstly sorted out according to the stock, and when the residual quantity in the order is less than the quantity of the whole package, the whole package is directly sorted; or, the whole package is preferentially taken out according to the inventory, and when the residual quantity in the order is less than the quantity of the whole package, the goods are delivered according to the minimum package of the basic data; or, the whole package is produced according to the stock according to the basic data, and when the residual quantity in the order is less than the quantity of the whole package, the target quantity of small packages are obtained from the whole package; or, the whole package is preferentially taken out according to the stock, and when the residual quantity in the order is less than the quantity of the whole package, an error is reported.
When the knapsack algorithm is adopted to determine the inventory sorting combination, the particle swarm algorithm is utilized to determine the optimal sorting batch from a plurality of sorting batches.
Wherein determining an optimal sort batch from a plurality of sort batches using a particle swarm algorithm comprises: initializing a first speed and a first position for each target sort batch; iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch; calculating a fitness value of each target sorting batch at the second position; determining whether to update the historical optimal position of each target sorting batch and the historical optimal positions of all target sorting batch groups according to the fitness; and when the fitness meets a preset condition or the iteration times reach the maximum iteration times, determining the target sorting batch corresponding to the historical optimal position as the optimal sorting batch.
Wherein initializing a first speed and a first position for each target sort batch comprises: acquiring an initialization range; the initialization range is a preset range or is between the maximum value and the minimum value of all sorting batches; the first speed and the first position of each target sort batch are initialized within an initialization range.
Wherein initializing a first speed and a first position for each target sort batch comprises: initializing a first speed and a first position of each target sorting batch, and setting a non-inferior solution set, wherein the non-inferior solution set is used for storing all non-inferior solutions of a global optimal solution found in the particle swarm optimization operation process; iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch, comprising: iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch, and generating a corresponding non-inferior solution set; determining whether to update the historical optimal position of each target sorting batch and the historical optimal positions of all target sorting batch groups according to the fitness, wherein the steps comprise: and comparing all the non-inferior solutions, and screening out the historical optimal position of each target sorting batch and a non-inferior solution set of the global optimal solution.
Wherein, iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch, comprises: the second velocity and the second position are obtained using the following equations:
wherein the content of the first and second substances,
the speed at the k-th iteration is indicated,
represents the velocity at the k +1 th iteration, k represents the number of iterations, w represents the inertial weight, c 1 Representing an individual learning factor, c 2 Represents a population learning factor, r 1 、r 2 In order to be able to make the coefficients of the perturbations,
representing the historical optimum position of each target sort batch after the kth iteration,
historical optimal positions of all target sorting batch groups after the kth iteration,
indicating the location of each target sort batch after the kth iteration.
If the number to be sorted of the backpack after sorting is still larger than 0, selecting a minimum package number from the remaining selectable inventory and putting the minimum package number into the sorting result.
In a second aspect, the present application provides an electronic device comprising a processor and a memory coupled to the processor; wherein the memory is adapted to store a computer program and the processor is adapted to execute the computer program to implement the method as provided in the first aspect above.
In a third aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the method as provided in the first aspect above.
The beneficial effect of this application is: different from the prior art, the inventory sorting method is provided, and the optimal ex-warehouse sorting combination is calculated according to the material packaging conditions of different warehouses in storage and the order number required by a customer. On the premise of ensuring that the quantity of the materials discharged from the warehouse is enough, the boxes are not dismounted as far as possible, namely the packages of the whole boxes are firstly discharged in first time; if the entire small package is produced as much as possible without the need to unpack it. The warehouse-out sorting scheme can facilitate sorting by warehouse pickers and picking machines according to the calculation result of the warehouse management system, greatly improve the sorting efficiency, and avoid the unfavorable inventory management problems of the overstock of the inventory, unreasonable distribution of the warehouse-out orders and the like. Meanwhile, the time-consuming problem of manual calculation can be reduced through the optimized warehouse management system, and the production and operation mode of the warehouse is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic flow chart diagram of a first embodiment of an inventory sorting method provided herein;
FIG. 2 is a schematic flow chart diagram of a second embodiment of an inventory sorting method provided herein;
FIG. 3 is a schematic flow chart diagram of a third embodiment of an inventory sorting method provided by the present application;
FIG. 4 is a diagram of an application scenario of the inventory sorting method provided by the present application;
FIG. 5 is a schematic structural diagram of an embodiment of an electronic device provided in the present application;
FIG. 6 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of an inventory sorting method provided by the present application. The method comprises the following steps:
step 11: and acquiring the ex-warehouse order.
Different inventories contain relevant item information such as total quantity, type, lot, packaging size and number of packages of the item. And the following quantitative relation equation is in a certain box of materials: the total amount of the materials in the box is equal to the specification of the package multiplied by the number of the packages.
In some embodiments, before the outbound order is obtained, the materials in the stock may be classified according to their types, and the stocks in the same type may be sorted according to the warehousing batches, so as to obtain the priority order of the stocks in the same type.
Then, different sorting rules are designed according to actual sorting requirements, the setting standard of the sorting rules needs to consider the conditions of the whole box quantity and the mantissa packaging, and the priority order of the material sorting rules is set.
Step 12: and classifying and combining the ex-warehouse orders according to the material types and the ex-warehouse data of the inventory to obtain at least one sub-ex-warehouse order.
Step 13: sorting according to warehousing batches stored in the same type based on the sub ex-warehouse orders; in the sorting process, sorting is carried out according to preset sorting rules or sorting rules in the delivery orders.
Step 14: if the remaining quantity to be sorted in the current sub ex-warehouse order is larger than or equal to the inventory quantity of the batch to be sorted, all the inventories of the batch to be sorted are ex-warehouse, and if the sorting requirement meeting the condition exists, all the inventories meeting the sorting condition are sorted out of the warehouse.
Step 15: if the remaining quantity to be sorted in the current sub ex-warehouse order is smaller than the inventory quantity of the batch to be sorted, determining an inventory sorting combination by adopting a knapsack algorithm; the total capacity of the backpack is the number of the whole boxes required by the order for the child to leave the warehouse, the backpack capacity of the target material is the proportion of the volume of the target material to the total volume, and the backpack value of the target material is the proportion of the target material to the total number of the boxes filled with the same type of material.
If the number to be sorted of the backpack after sorting is still larger than 0, a minimum number of packages is selected from the remaining selectable inventory and put into the sorting result.
The sorting rules described above include:
firstly, taking out the whole package according to the inventory priority; wherein, the whole package comprises a plurality of small packages.
The rule one is generated by using a knapsack algorithm, and the requirement of the sorting rule is to measure out the whole package without unpacking. Firstly, the best sorting queue is found out by utilizing a knapsack algorithm, then the sorting queue which meets the requirements best is found out according to the sorting quantity, and finally, if the quantity is remained, sorting is carried out according to the first-in first-out sequence.
And a second rule is that the whole package is preferentially taken out according to the inventory, and when the order requirement is not met, the small packages with the target quantity are obtained from the whole package.
In the case of actual sorting, there may be a case where unpacking and sorting are required due to the product reject ratio or transportation damage, and there may be a possibility that a fraction tray may occur. The specific sorting algorithm comprises the following steps: firstly, the number of boxes and the number of the boxes needing to be delivered are known, and then the boxes are searched one by one according to the minimum packet picking principle until the conditions are met. In the process of finding, if the quantity is large, the finding is continued, otherwise, a loop is skipped to find the next one. If the whole box can be taken, the whole box is taken as far as possible. If the box is to be unpacked, a complete minimum package is required. Otherwise, the mantissa disc is indicated, and the mantissa disc is larger than the required mantissa, and at this time, the rest mantissa discs need to be taken away, and an integral minimum packet is not taken.
Thirdly, according to the sorting sequence, delivering goods according to the inventory; the sorting is directly carried out according to the sequence of warehousing, and the sorting is carried out in a circulating way one by one until the position meeting the sorting condition is reached.
And fourthly, generating the sorting rule by using an exhaustion method.
And fifthly, the whole package is preferentially sorted according to the inventory, and when the residual quantity in the order is less than the quantity of the whole package, the whole package is directly sorted.
And a sixth rule that the whole package is preferentially taken out according to the inventory, and when the residual quantity in the order is less than the quantity of the whole package, the goods are delivered according to the minimum package of the basic data.
And a seventh rule, according to the inventory, producing the whole package according to the basic data, and when the residual quantity in the order is less than the quantity of the whole package, obtaining the target quantity of small packages from the whole package.
Sorting rule seven adds a processing method for the mantissas on the basis of sorting rule six, namely unpacking to complement the mantissas. This need for unpacking is a situation that takes into account the small space occupied by the cargo itself, but is of great value.
And eight rules, the whole package is preferentially taken out according to the inventory, and when the residual quantity in the order is less than the quantity of the whole package, an error is reported.
In some embodiments, the sorting rules for the same type of inventory lot may be mainly as follows according to the priority order: absolute priority sorting and batch-to-batch priority sorting, wherein the absolute priority sorting means that a certain stock is specified as an absolute priority sorting grade according to the ex-warehouse urgency degree of the stock, and the purpose of the absolute priority sorting is mainly to sort the stock preferentially; the same-batch sorting is mainly to solve the priority order of the same-batch ex-warehouse, and in the same batch, the same-batch sorting ignores other existing sorting rules of the system and preferentially ex-warehouse the stock of the batch.
The inventor has long studied and found that the establishment of the above scheme has a big premise: the total weight and the category of commodities are ignored, namely all commodities are defaulted to one category and can be put in the same backpack, but in the real situation, commodities which cannot be put in the same package obviously exist, in addition, the total weight of the backpack is required to be considered besides the commodity value and the volume, and various influence factors are comprehensively considered from multiple angles to obtain a better/optimal combination. This optimization problem, which requires consideration of multiple optimization objective functions, is also called a multi-objective knapsack problem. Multiple objectives are embodied herein as value and volume, where the optimization objectives increase volume relative to traditional backpack algorithms. Here, the optimization scheme based on the backpack algorithm requires not only the maximum total value of the combined articles, but also the minimum total volume, and the total mass of the articles inside the backpack should be limited by the quantity.
The multi-target backpack model can assume that N types of articles are provided, each type of article has M articles, and the price of each article is required to be selected from the N types of articles to be put into the backpack. Assuming that P is the value matrix of the item to be shipped, R is the volume matrix of the item to be shipped, C is the mass of the item to be shipped, and then X is the selection matrix, the multi-objective knapsack problem can be represented by the following formula:
s.t.C×X Z
here, P, R, X are all M × N matrices, and Z represents a limit on backpack mass size.
The above-mentioned state transition equation is a typical recursion formula in a dynamic programming algorithm. The knapsack algorithm can be solved by an exhaustive method with larger time complexity and a dynamic programming algorithm with smaller time complexity. From a programming perspective, the computation steps of the knapsack algorithm are all computed around the dp array, and the pseudo code for the dynamic programming solution of the knapsack algorithm is given below:
the initial value of the recurrence formula is initialized, if the backpack capacity C is 0, then dp [ i ] [0], no matter which articles are selected, the sum of the backpack value is determined to be 0. Also, dp [0] [ C ], is: i is 0, and when the item with the number 0 is stored, the maximum value of the backpack with each capacity can be stored.
Initializing the pseudo code:
it can be seen from the recursive formula dp [ i ] [ j ] ═ max (dp [ i-1] [ j ], dp [ i-1] [ j-weight [ i ] + value [ i ]) that dp [ i ] [ j ] is derived from dp [ i-1] a ] and dp [ i-1] [ j-weight [ i ] ]. The traversal sequence adopted by the invention is that the type of the article is traversed first, and the backpack capacity corresponding to the article is traversed. The corresponding pseudo-code is as follows:
the dynamic programming is used for solving the steps of the knapsack algorithm, and the solution idea of the dynamic programming is to abstract a digital state firstly, and then determine an initialization state and write a state transition equation. Typically the transition calculation for the latter state will depend on the previous state(s) that have been calculated. In order to achieve global optimization, the previous state needs to achieve local optimization, and the calculation of the next state depends on the calculation of the previous state, so that the final global optimization can be achieved, that is, the problem has an optimal substructure. And each local optimum state is not changed by the following calculation, which is called no-aftereffect. Briefly, the optimal state of dynamic programming depends on multiple locally optimal results, which is why dynamic programming requires form filling.
Taking the backpack capacity of 20 as an example below, except that the rows with item number 0 are all 0 (parameter initialization), the other rows are derived step by the recursive formula summarized above by us. Assuming ten items, which are arranged in the order of a, B, C, D, E, F, G, H, I, J, respectively, the backpack capacity (volume) of the items is: [2, 2, 6, 5, 4, 3, 2, 5, 4, 6] the value of the article is: [6, 1, 5, 4, 10, 8, 2, 9, 3, 7] the capacity of the backpack is limited to be less than or equal to 20; then we can use the order of the items as the stages of the system, i.e. the first item (a) as the first stage, the second item (B) as the second stage, the third item (C) as the third stage, and so on, and the whole system is divided into 10 stages in total. Specifically, in the table filling, the number of the selectable articles is sequentially increased, and the subproblems are iterated gradually. Thus, the state c (k) of the system is the current capacity of the backpack, and the state transition equation is: c (k +1) ═ c (k) + x (k), stage indices: v (c (k), x (k)) c (k). The total objective function f (c (k)) is the maximum total value of the articles loaded by the backpack from the state c (k) of the k-th section to the process end, namely: f (c (k)), (v (c (k)), x (k)) + f (c (k + 1))).
Wherein c (k): the state of the kth stage is the backpack capacity at the kth stage; x (k): in the decision of the kth stage, taking 1 as a decision means that the kth item is put into the backpack, and taking 0 as an opposite decision; v (c (k), x (k)): the stage index of the kth stage, namely the value of the kth item which can be obtained by decision; f (c (k)): the overall objective function, i.e., the maximum value of the backpack capacity to be able to carry the item.
The following table is a detailed dynamic programming algorithm form filling process:
the table above is an example given for the purpose of demonstrating a dynamic programming algorithm, where a0 is used to initialize the iteration parameters, a1 to a10 represent ten items, and 0 to 20 represent the growing process for the backpack size. For example, article number a6, when the backpack capacity is 6, the backpack value is max { dp [ a5] [6-3] +8, dp [ a5] [6] } ═ max {14, 16} ═ 16, so that when the backpack capacity is 6, the maximum value of the first two articles is 16. For another example, when the article number is a9 and the backpack capacity is 14, the backpack value is dp [ a9] [14] ═ max { dp [ a8] [14-4] +3, dp [ a8] [14] } ═ max {27, 33} ═ 33.
Optimizing the space complexity: if we only want to get the final maximum value without backtracking, we can calculate dp [ i ] [ C ] only depending on dp [ i-1] [ C ] and dp [ i-1] [ C-w [ i ] ], i.e. only depending on values equal to or less than C when the index C is updated. Therefore, the state can be represented by a one-dimensional array, the updating process is changed into a reverse order process, and the condition that the values of the parts less than or equal to the part C are not changed when the part C is updated is ensured. The overall spatial complexity then changes from being a two-dimensional matrix to a one-dimensional array.
The knapsack algorithm is used in the inventory sorting so that the optimal combination of whole packages can be found, and the sorting quantity coming out by the knapsack algorithm is closest to the order quantity coming out of the warehouse. However, the number of sortings and the actual number of orders obtained by the backpacking algorithm are still in-and-out, so this error may take into account the selection of a minimum number of whole packages in the selectable inventory to be placed in the sort results.
In the inventory sorting, the invention also uses an exhaustive solution of a knapsack algorithm, namely, all possible conditions meeting the sorting condition are listed, and then an optimal solution is selected from the conditions. The difference between the exhaustion method and the dynamic programming method is that the dynamic programming has a recurrence formula, the final result depends on the derivation result of the previous step, and the exhaustion method lists the possible situations and then selects the optimal combination.
In the present embodiment, there are five types of articles, each type of article includes four specific articles, and it is now required to select one of the five types of articles to be put into the backpack, so that the total value of the articles in the backpack is maximized, the total volume is minimized, and the total mass of the backpack does not exceed 85 kg. The model of the multi-objective knapsack problem is then:
s.t.C×X≤(85 85 85 85)
wherein, P x Representing the value of the articles in the backpack; r x To representThe volume of the articles in the backpack; c represents the quality of the article; and X is the selected article. P is the value of each item and R is the volume of each item. The values of the P, R and C matrices for this example are as follows:
in some embodiments, in order to solve the multi-objective knapsack problem, a particle swarm algorithm needs to be introduced.
When determining the inventory sorting combination based on the knapsack algorithm in the above embodiment, the particle swarm algorithm may be utilized to determine the optimal sorting batch from the plurality of sorting batches.
Specifically, referring to fig. 2, the determining the optimal sorting batch from the plurality of sorting batches using the particle swarm algorithm may be as follows:
step 21: a first speed and a first position for each target sort batch are initialized.
Acquiring an initialization range; the initialization range is a preset range or is between the maximum value and the minimum value of all sorting batches.
The first speed and the first position of each target sort batch are initialized within an initialization range.
Step 22: and iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch.
The second velocity and the second position are obtained using the following equations:
wherein, the first and the second end of the pipe are connected with each other,
the speed at the k-th iteration is indicated,
represents the velocity at the k +1 th iteration, k represents the number of iterations, w represents the inertial weight, c 1 Represents an individual learning factor, c 2 Represents a population learning factor, r 1 、r 2 In order to be able to make the coefficients of the perturbations,
representing the historical optimal position of each target sort batch after the kth iteration,
historical optimal positions of all target sorting batch groups after the kth iteration,
indicating the location of each target sort batch after the kth iteration.
Step 23: and calculating the fitness value of each target sorting batch at the second position.
Step 24: and determining whether to update the historical optimal position of each target sorting batch and the historical optimal positions of all target sorting batch groups according to the fitness.
Step 25: and when the fitness meets a preset condition or the iteration times reach the maximum iteration times, determining the target sorting batch corresponding to the historical optimal position as the optimal sorting batch.
The particle swarm algorithm comprises the following basic steps:
1. velocity and position of the initial particle, the velocity representing the next step of the particle stackThe direction and distance of the surrogate movement, the location, is one solution to the problem being solved. Assuming that there are N particles in the D-dimensional search space, each particle representing a solution, then: initial position X of particle i i ＝(x i1 ，x i2 ，…，x iD ) Initial velocity V of particle i i ＝(v i1 ，v i2 ，…，v iD ) Assume a set P i ＝(p i1 ，p i2 ，…，p iD ) Represents the optimal position (individual optimal solution) searched by the particle i, and the set P g ＝(p g1 ，p g2 ，...，p gD ) Indicating the best position (population-best solution) searched by the population.
2. According to the parameters, the speed and the position of all the particles are updated iteratively, and the formula is as follows:
velocity update formula:
location update formula:
the velocity updating formula comprises three terms, wherein the first term is an inertia part, is composed of inertia weight and the self velocity of the particle and represents the trust of the particle on the previous self motion state; the second term is a cognitive part, which represents the thought of the particle, namely the experience part of the particle, and can be understood as the distance and the direction between the current position of the particle and the historical optimal position of the particle; the third term is a social part, which represents information sharing and cooperation between particles, i.e. from experience of other excellent particles in the population, which can be understood as the distance and direction between the current position of the particle and the historical optimal position of the population.
Some parameters of the above formula explain: d is an element of [1, 2]D represents particle dimension, D represents particle dimension serial number, k represents iteration times, w represents inertia weight, represents influence of the speed of the previous generation particle on the speed of the current generation particle, or the trust degree of the particle on the current self motion state, and the particle depends on the self motion stateThe inertial weight keeps the particles moving inertia and the tendency to search the extended space. Generally, the larger the w value is, the stronger the ability to explore a new area is, the stronger the global optimization ability is, but the weaker the local optimization ability is. On the contrary, the weaker the global optimizing ability is, the stronger the local optimizing ability is. The larger w is beneficial to global search, and the local extreme value is jumped out, so that the local optimum is not trapped; and a smaller w is beneficial to local search, so that the algorithm can quickly converge to an optimal solution. When the problem space is large, in order to achieve a balance between the search speed and the search accuracy, it is common practice to make the algorithm have a high global search capability in the early stage to obtain a proper seed, and have a high local search capability in the later stage to improve the convergence accuracy, so w is not suitable to be a fixed constant. c. C 1 The acceleration weight which represents the next action of the particle and is from the weight occupied by the experience part of the particle to push the particle to the optimal position of the individual is also called individual learning factor, c 2 The acceleration weight which represents that the next action of the particle comes from the weight occupied by the experience part of other particles and pushes the particle to the optimal position of the population, is also called the population learning factor and is generally taken as c 1 ＝c 2 ；r 1 、r 2 For disturbance coefficients, the interval [0, 1] is generally taken]The random number in the search table is used for increasing the randomness of the search.
And then calculating the fitness value (value of an objective function) of each particle at a new position, wherein the fitness value is used for evaluating the position of the particle, determining whether to update the historical optimal position of the individual particle and the historical optimal position of the population, and ensuring that the particle is searched towards the direction of the optimal solution. After the (k +1) th iteration is noted,
as the position of the particle i, the fitness value SP k+1 After k iterations, the optimum value of a single particle is
Corresponding fitness value is SP k If SP k+1 ＝SP k Then, then
Otherwise
Also from
The particle with the largest fitness value is selected and then the corresponding particle is updated
And finally, an optimization stopping criterion is generally used for two conditions, wherein the first condition is that the iteration times reach the maximum iteration steps, and the second condition is that an acceptable satisfactory solution appears, namely, the optimization is stopped after the difference between the adaptive value of the optimal solution after the last iteration and the adaptive value of the optimal solution after the current iteration is less than a certain value.
It should be noted that the initialization of algorithm parameters can shorten the optimized convergence time if the initialization range of the particles is well selected, and we need to analyze the parameters according to specific problems. If the optimal solution is judged to be in a certain range according to the experience of people, initializing the particles in the range; and if the determination cannot be carried out, taking the value boundary of the particle as an initialization range.
Compared with a single-target particle swarm optimization algorithm, the multi-target particle swarm optimization algorithm has the advantages that target functions are increased, only one target function is needed, and therefore the problem of accepting or rejecting among multi-target function optimization is solved. The contradictory multi-objective functions bring two problems to the algorithm, one is how to select the individual optimal solution; another is how to select a globally optimal solution. In the multi-target particle swarm algorithm, because some objective function values become better and some objective function values become worse after position updating, the selection of the individual optimal solution is chosen in the single-target particle swarm algorithm only by comparing the sizes of the fitness values corresponding to the target positions of each iteration.
To solve the above problems, a non-inferior solution is introduced. If there is one solution in the feasible domain of the multi-objective optimization problem and there is no other feasible solution, so that the objective function values in one solution are all inferior to the solution, the solution is called non-inferior solution of the multi-objective optimization problem. The set of all non-inferior solutions is called a non-inferior solution set.
In some embodiments, referring to fig. 3, there may be the following flow:
step 31: initializing a first speed and a first position of each target sorting batch, and setting a non-inferior solution set, wherein the non-inferior solution set is used for storing all non-inferior solutions of a global optimal solution found in the particle swarm optimization operation process.
Step 32: and iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch, and generating a corresponding non-inferior solution set.
Step 33: and calculating the fitness value of each target sorting batch at the second position.
Step 34: and comparing all the non-inferior solutions, and screening out the historical optimal position of each target sorting batch and a non-inferior solution set of the global optimal solution.
To solve the above problem, a non-inferior solution is introduced. If there is one solution in the feasible domain of the multi-objective optimization problem and there is no other feasible solution, so that the objective function values in one solution are all inferior to the solution, the solution is called non-inferior solution of the multi-objective optimization problem. The set of all non-inferior solutions is called the non-inferior solution set.
The following steps of the multi-objective particle swarm optimization algorithm are as follows:
1. initializing a population, and then setting a non-inferior solution set for storing all non-inferior solutions of the globally optimal solution found in the algorithm operation process.
2. Iterate and update to produce the position of the next generation particle and the corresponding read non-inferior solution set.
3. And comparing all the non-inferior solutions, and screening out the non-inferior solution set of the historical optimal solution and the global optimal solution of each particle.
4. And repeating the step 2 and the step 3 until the optimization stopping criterion is met.
5. And finally, finding a globally optimal non-inferior solution set.
The multi-target search algorithm based on the particle swarm optimization can be the following process, the population initialization module randomly initializes the position x and the speed v of the particles, the fitness value calculation module calculates the individual fitness value according to a fitness value calculation formula, and the particle optimal updating module updates the individual optimal particles according to the new particle positions. And the non-inferior solution set updating module screens non-inferior solutions according to the new particle domination relationship. The particle velocity and location update module updates the particle velocity and location according to the individual optimal particle location and the global particle location.
And (3) fitness calculation: the particle fitness value refers to the formula of the multi-target knapsack model, the fitness value of each individual is two, namely, the value and the volume, and meanwhile, the individual needs to meet the quality constraint.
Screening a non-inferior solution set: the screening non-inferior solution set is mainly divided into an initial screening non-inferior solution set and an updating non-inferior solution set. The initial screening of the non-inferior solution set refers to when one particle is not dominated by other particles (i.e. P without other particles existing) after the particle initialization x ，R x All are better than the particle), the particle is put in the non-inferior solution set, and one particle is randomly selected from the non-inferior solution set as the population local-superior particle before particle update. Updating the non-inferior solution set means that when the new particle is not supported by other particles and the particles in the current non-inferior solution set, the new particle is put into the non-inferior solution set, and one particle is randomly selected from the non-inferior solution set as the group optimal particle before the particle is updated each time.
Velocity update formula:
location update formula:
the particle is optimized as follows: the particle optimization comprises an individual optimal particle and a population optimal particle, wherein the updating mode of the individual optimal particle is to select a dominant particle from the current new particle and the individual optimal particle, and when neither particle is the dominant particle, one particle is randomly selected from the current new particle and the individual optimal particle to serve as the individual optimal particle. The population-optimal particle is one randomly selected from the non-inferior solution set.
In this example, the value, volume and mass of each type of article are as follows:
| first kind | Second class | Class III | Class IV | Fifth class | |
| Value/hundred yuan | 10.5 11.8 10.8 12.4 | 14.5 15.7 18.6 17.9 | 22.7 22 20.9 25.5 | 27 27.5 28.9 32 | 5.3 6.2 6.8 6.4 |
| volume/L | 0.2 0.25 0.3 0.3 | 0.3 0.35 0.37 0.32 | 0.4 0.38 0.5 0.45 | 0.6 0.45 0.5 0.6 | 0.1 0.15 0.2 0.2 |
| Mass/kg | 4.5 5.8 7.7 9.6 | 19.4 17 16.5 15.3 | 9.1 7.7 13.5 11.2 | 13.5 11.2 8.5 9.9 | 1.9 2.5 1.4 1.8 |
Selecting one item from each type of item to be placed in the backpack, so that the total value of the backpack is maximum, the volume is minimum, and the total mass of the backpack is less than 85 kg. The particle swarm algorithm parameters are as follows: the number of particles is 500, the number of iterations is 500, and the distribution of the finally obtained non-inferior solution in the target space is shown in fig. 4.
As can be seen from fig. 4, the non-inferior solution searched by the multi-objective algorithm of the present embodiment forms a pareto surface, and the search achieves a good effect. And (3) calculating a non-inferior solution set:
the factors of the value of the goods, the occupied volume and the weight of the goods are comprehensively considered, the total value of 3/the total volume of 3/the total weight of 3 is a better combination, and the combination selects a fourth item from the first class of articles, selects a first item from the second class of articles, selects a third item from the third class of articles, selects a second item from the fourth class of articles and selects a first item from the fifth class of articles. In summary, in the logistics planning, the method can be used for overall planning of the goods to be transported so as to achieve the best economic and social benefits.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of an electronic device 50 provided in the present application, where the electronic device includes a processor 51 and a memory 52 coupled to the processor 51; wherein the memory 52 is used for storing computer programs and the processor 51 is used for executing the computer programs to realize the following methods:
acquiring an ex-warehouse order; classifying and combining the ex-warehouse orders according to the types of the stored materials and the ex-warehouse data to obtain at least one sub-ex-warehouse order; sorting according to warehousing batches stored in the same type based on the sub ex-warehouse orders; during the sorting process, sorting is carried out according to preset sorting rules or sorting rules in the delivery order; if the remaining quantity to be sorted in the current sub ex-warehouse order is larger than or equal to the inventory quantity of the batch to be sorted, all the inventories of the batch to be sorted are ex-warehouse, and if the sorting requirement meeting the condition exists, all the inventories meeting the sorting condition are sorted out of the warehouse; if the remaining quantity to be sorted in the current sub ex-warehouse order is smaller than the inventory quantity of the batch to be sorted, determining an inventory sorting combination by adopting a knapsack algorithm; the total capacity of the backpack is the number of the whole boxes required by the order for the child to leave the warehouse, the backpack capacity of the target material is the proportion of the volume of the target material to the total volume, and the backpack value of the target material is the proportion of the target material to the total number of the boxes filled with the same type of material.
It will be appreciated that the processor 51 is also arranged to execute a computer program enabling the implementation of the method of any of the embodiments described above.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application. The computer-readable storage medium 60 stores a computer program 61, the computer program 61 realizing the following method when executed by a processor:
acquiring an ex-warehouse order; classifying and combining the ex-warehouse orders according to the types of the stored materials and the ex-warehouse data to obtain at least one sub-ex-warehouse order; sorting according to warehousing batches stored in the same type based on the sub ex-warehouse orders; in the sorting process, sorting is carried out according to a preset sorting rule or a sorting rule in an ex-warehouse order; if the remaining quantity to be sorted in the current sub ex-warehouse order is larger than or equal to the inventory quantity of the batch to be sorted, all the inventories of the batch to be sorted are ex-warehouse, and if the sorting requirement meeting the condition exists, all the inventories meeting the sorting condition are sorted out of the warehouse; if the remaining quantity to be sorted in the current sub ex-warehouse order is smaller than the inventory quantity of the batch to be sorted, determining an inventory sorting combination by adopting a knapsack algorithm; the total capacity of the backpack is the number of the whole boxes required by the order for the child to leave the warehouse, the backpack capacity of the target material is the proportion of the volume of the target material to the total volume, and the backpack value of the target material is the proportion of the target material to the total number of the boxes filled with the same type of material.
It will be appreciated that the computer program 61, when executed by a processor, is also capable of implementing the method of any of the embodiments described above.
In summary, the inventory sorting method provided by the application calculates the optimal ex-warehouse sorting combination according to the material packaging conditions of different warehouses in storage and the order number required by customers. On the premise of ensuring that the quantity of the materials discharged from the warehouse is enough, the boxes are not dismounted as much as possible, namely the packaging of the whole box is firstly carried out; if the entire small package is produced as much as possible without the need to unpack it. The warehouse-out sorting scheme can facilitate sorting by warehouse pickers and picking machines according to the calculation result of the warehouse management system, greatly improve the sorting efficiency, and avoid the unfavorable inventory management problems of the overstock of the inventory, unreasonable distribution of the warehouse-out orders and the like. Meanwhile, the time-consuming problem of manual calculation can be reduced through the optimized warehouse management system, and the production and operation mode of the warehouse is greatly improved.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated units in the other embodiments described above may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. An inventory sorting method, the method comprising:
acquiring an ex-warehouse order;
classifying and combining the ex-warehouse orders according to the types of the materials in the warehouse and the ex-warehouse data to obtain at least one sub-ex-warehouse order;
sorting according to warehousing batches of the same type of stock based on the sub ex-warehouse orders; during the sorting process, sorting is carried out according to a preset sorting rule or according to a sorting rule in the ex-warehouse order;
if the remaining quantity to be sorted in the current sub ex-warehouse order is larger than or equal to the inventory quantity of the batch to be sorted, all the inventories of the batch to be sorted are ex-warehouse, and if the sorting requirement meeting the condition exists, all the inventories meeting the sorting condition are sorted out of the warehouse;
if the remaining quantity to be sorted in the current sub ex-warehouse order is smaller than the inventory quantity of the batch to be sorted, determining an inventory sorting combination by adopting a knapsack algorithm; the total backpack capacity is the number of whole boxes required by the sub-warehouse-out order, the backpack capacity of the target material is the proportion of the volume of the target material to the total volume, and the backpack value of the target material is the proportion of the target material to the total number of the boxes filled with the same type of material.
2. The method of claim 1, wherein the sorting rules comprise:
the whole package is preferably taken out according to the stock; wherein, the whole package comprises a plurality of small packages;
or, the whole package is preferentially taken out according to the stock, and when the order requirement is not met, the small packages with the target quantity are obtained from the whole package;
or, the goods are delivered according to the stock according to the sorting sequence;
or, generating sorting rules using an exhaustive method;
or, the whole package is firstly sorted out according to the stock, and when the residual quantity in the order is less than the quantity of the whole package, the whole package is directly sorted;
or, the whole package is preferentially taken out according to the inventory, and when the residual quantity in the order is less than the quantity of the whole package, the goods are delivered according to the minimum package of the basic data;
or, the whole package is produced according to the stock according to the basic data, and when the residual quantity in the order is less than the quantity of the whole package, the target quantity of small packages are obtained from the whole package;
or, the whole package is preferentially taken out according to the stock, and when the residual quantity in the order is less than the quantity of the whole package, an error is reported.
3. The method of claim 1, wherein determining the inventory sorting combination using a backpacking algorithm determines an optimal sorting batch from the plurality of sorting batches using a particle swarm algorithm.
4. The method of claim 3, wherein said determining an optimal sort batch from a plurality of sort batches using a particle swarm algorithm comprises:
initializing a first speed and a first position for each target sort batch;
iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch;
calculating a fitness value of each target sorting batch at the second location;
determining whether to update the historical optimal position of each target sorting batch and the historical optimal positions of all target sorting batch groups according to the fitness;
and when the fitness meets a preset condition or the iteration times reach the maximum iteration times, determining the target sorting batch corresponding to the historical optimal position as the optimal sorting batch.
5. The method of claim 4, wherein initializing the first speed and the first position for each target sort batch comprises:
acquiring an initialization range; the initialization range is a preset range or is between the maximum value and the minimum value of all sorting batches;
initializing a first speed and a first position for each target sort batch within the initialization range.
6. The method of claim 4, wherein initializing the first speed and the first position for each target sort batch comprises:
initializing a first speed and a first position of each target sorting batch, and setting a non-inferior solution set, wherein the non-inferior solution set is used for storing all non-inferior solutions of a global optimal solution found in the particle swarm optimization operation process;
the iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch includes:
iteratively updating the first speed and the first position of each target sorting batch to obtain a second speed and a second position corresponding to each target sorting batch, and generating a corresponding non-inferior solution set;
the determining whether to update the historical optimal position of each target sorting batch and the historical optimal positions of all target sorting batch groups according to the fitness comprises the following steps:
and comparing all the non-inferior solutions, and screening out the historical optimal position of each target sorting batch and a non-inferior solution set of the global optimal solution.
7. The method of claim 4, wherein iteratively updating the first speed and the first position of each target sorting batch to obtain the second speed and the second position corresponding to each target sorting batch comprises:
the second speed and the second position are obtained using the following equations:
wherein the content of the first and second substances,
the speed at the k-th iteration is indicated,
represents the velocity at the k +1 th iteration, k represents the number of iterations, w represents the inertial weight, c 1 Representing an individual learning factor, c 2 Represents a population learning factor, r 1 、r 2 In order to be able to make the coefficients of the perturbations,
representing the historical optimal position of each target sort batch after the kth iteration,
historical optimal positions of all target sorting batch groups after the kth iteration,
indicating the location of each target sort batch after the kth iteration.
8. Method according to claim 1, characterized in that if the number to be sorted after sorting of the backpacks is still greater than 0, a minimum number of packages is selected from the remaining selectable inventory to be placed in the sorting result.
9. An electronic device, comprising a processor and a memory coupled to the processor;
wherein the memory is adapted to store a computer program and the processor is adapted to execute the computer program to implement the method according to any of claims 1-8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
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