Parking lot planning method

Abstract

The invention discloses a storage location planning method, and the method comprises the steps: obtaining the amount of goods in a warehouse on that day, predicting the predicted amount of goods in future time, determining the amount of goods for storage location planning according to the amount of goods in the warehouse on that day and the predicted amount of goods in future time, and determining the amount of goods for storage location planning when the future time is closer to that day. The goods quantity in the warehouse in the same day is the goods quantity for determining the storage location planning and the goods quantity in the future is the goods quantity for determining the storage location planning and the goods quantity in the future is the goods quantity for determining the storage location planning and the goods quantity for determining the storage location planning and the goods quantity for determining the storage location planning are more accurately determined. And then, an objective function with the minimum storage location remaining width in the goods storage locations is determined, and the storage location width capable of guaranteeing the minimum objective function is determined, so that the storage location utilization rate is increased, and the storage location accuracy is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehousing and logistics, and in particular relates to a storage location planning method. Background Art

[0002] Warehouses are equipped with several storage spaces for goods. Existing warehouse spaces are typically marked, and their dimensions remain largely unchanged. However, because the types and quantities of goods stored in a warehouse change over time, existing storage space dimensions may not be optimal. If a storage space is too large, it wastes warehouse space, reduces space utilization, and increases warehouse operating costs. Furthermore, excess space can accumulate dust and debris, making cleaning and maintenance more difficult. If a storage space is too small, large items cannot be stored smoothly, potentially causing them to be squeezed and damaged during storage, affecting their quality and value. Goods may also press against the lines or drift, reducing storage location accuracy.

[0003] The above information disclosed in this background technology is only used to increase the understanding of the background technology of this application. Therefore, it may contain information that does not constitute the prior art known to ordinary technicians in this field. Summary of the Invention

[0004] The present invention proposes a storage location planning method to solve the technical problem that the existing storage location size is fixed and cannot be adaptively adjusted according to the different types and quantities of goods stored in the warehouse at different times, resulting in waste of warehouse space or goods pressing the line, cargo location drift, etc., which leads to a decrease in storage location accuracy.

[0005] In order to achieve the above invention / design purpose, the present invention adopts the following technical solutions:

[0006] A storage location planning method, comprising:

[0007] Get the warehouse cargo inventory data of previous years, and predict the predicted cargo volume SkuP for the next t day based on the warehouse cargo inventory data of previous years t ;

[0008] Get the quantity of goods SkuS in the warehouse on that day 0 ;

[0009] Determine the quantity of goods for storage location planning SkuCal t ;

[0010] SkuCal t =α*(SkuS 0 )+β*SkuP t ;

[0011] α+β=1;

[0012]

[0013] a∈[5,30];

[0014] Get the number of storage location widths n and the width w of the jth storage location j , width z of the i-th cargo i ;The quantity of goods to be used for storage location planning SkuCal t Denoted as I;

[0015] The objective function for placing goods in a storage location with the minimum available width is: Among them, x i is the decision variable for the i-th product. If a certain storage location is selected, x i is 1, otherwise x i is 0; mod is the modulo operation;

[0016] The constraints are:

[0017] The length of the storage location is fixed;

[0018] The total width of the storage locations must not exceed the total width of the warehouse;

[0019] Storage location width w j Selection range constraint: w j ∈[w1, w2, …, w n ];

[0020] The number of storage locations required for a warehouse is a natural number;

[0021] The number of storage space width constraints required by the warehouse: ∑w j ≤b, b is the set quantity;

[0022] By obtaining all the cargo size and storage location width information, the storage location width that can ensure the minimum objective function is determined.

[0023] The above-mentioned warehouse location planning method predicts the predicted cargo volume SkuP on the tth day in the future based on the warehouse cargo inventory data of previous years. t The method is to predict through the time series model, the time series model is:

[0024] SkuP t =g(t)+s(t)+h(t)+ε (t) ;

[0025] Among them, the inventory goods curve

[0026] In the warehouse inventory data of previous years, S change points are inserted in chronological order; the change rate of the sth change point is δ s ,δ=(δ1,…,δs ,...,δ S ) T ;

[0027] a s (t) is the indicator function,

[0028] a(t)=(a1(t),...,a s (t)...,a S (t));

[0029] γ=(γ1,...,γ s ,...,γ S ) T ; q is a natural number;

[0030] C(t): Carrying capacity, which is a function that changes with time and limits the maximum growth that can be achieved;

[0031]

[0032] Where, m∈{-1,-2,...,-M}, M represents the data of the previous M years;

[0033] k: growth rate at t = 0;

[0034] Cycle Model

[0035] T: period;

[0036] N: the number of Fourier fitting series, determined by the period size;

[0037] a n 、b n Achieve a certain coefficient;

[0038] Holiday Effect Model

[0039]

[0040] k l : The coefficient of holiday l. The larger the holiday coefficient, the greater the impact of the holiday on inventory quantity;

[0041] Z(t) k : indicator function, if t is not a holiday, this item is 0, and the value of the entire h(t) is 0;

[0042] If t is a holiday, then Z(t) k If it is 1, h(t) returns the holiday coefficient k corresponding to holiday t. l .

[0043] The above-mentioned storage location planning method, when the current warehouse is empty, determines the storage location layout based on the objective function of maximizing the warehouse efficiency and the objective function of optimizing the space utilization.

[0044] According to the above-mentioned storage location planning method, the objective function for determining the storage location layout is:

[0045] w1 and w2 are weight coefficients;

[0046] Warehouse efficiency maximization location allocation objective function

[0047] Objective function for space utilization optimization The constraints are:

[0048] Among them, S ixy : Decision variable, which is 1 when the i-th item is stored in the (x, y) storage location, otherwise it is 0;

[0049] x: the index number of the storage location row, indicating the xth row in the storage location;

[0050] y: the index number of the storage location column, indicating the yth column in the storage location;

[0051] P i : The frequency of entry and exit of the i-th cargo in the unit cycle;

[0052] d xy : The distance from the storage location (x, y) to the nearest stocking area;

[0053] n x : The total number of rows of storage locations; the total number of rows of storage locations is determined by combining the optional storage location widths {w1,w2,...,w n} Make sure the total width of the selected storage locations does not exceed the total width of the warehouse ∑w j ≤w total and determined based on minimizing modular operations;

[0054] n y : The total number of columns of the storage location is obtained by dividing the total length of the warehouse by the length of a single storage location;

[0055] L x : The width of the xth row of storage locations;

[0056] V i : The volume of the i-th cargo;

[0057] C xy : The capacity of the storage location (x, y).

[0058] In the warehouse location planning method described above, for the objective function of warehouse location layout, the initial position of the goods is determined by the nearest neighbor algorithm; and the warehouse location layout of the goods is optimized by the simulated annealing algorithm.

[0059] In the above-mentioned storage location planning method, the method for determining the initial location of the goods through the nearest neighbor algorithm is as follows:

[0060] Sort all goods of the same category in descending order of demand;

[0061] Mark the space occupied by the category as free (0), and mark the space not belonging to the category as (-1);

[0062] Initialize the length and width variables of the space occupied by this category;

[0063] Traverse the goods and place them. For each item, find the nearest free position to the shipping area and place the item. Mark the best position found as occupied and place the current item. Determine the initial layout S of the goods. current .

[0064] The warehouse location planning method mentioned above, the method of optimizing the warehouse location layout through the simulated annealing algorithm is as follows:

[0065] Set initial parameters;

[0066] Get the initial layout S of the goods current ;

[0067] Calculate the total distance D of the current layout current ;

[0068] Recording start time t start ; Set a constraint time t constraint ;

[0069] Set the current layout S current As the optimal layout S best ; Set the current distance D current As the optimal distance D best ;

[0070] Start loop optimization:

[0071] Enter loop N iter Second-rate:

[0072] Generate a new SKU layout S new ;

[0073] Calculate the total distance D of the new layout new ;

[0074] Calculate the increment ΔD = D new -D current ;

[0075] if ΔD < 0, that is, the distance of the new layout is less than the distance of the current layout;

[0076] Then update the current layout S current = S new and the current distance D current = D new .

[0077] Otherwise, calculate the acceptance probability

[0078] Generate a random number r in the interval [0, 1];

[0079] if r < P, then update the current layout S current = S new and the current distance D current = D new ;

[0080] if D new < D best , then update the optimal layout S best = S new and the optimal distance D best = D new ;

[0081] Reduce the temperature T = T × α.

[0082] End the optimization:

[0083] When the iteration number N iter or the constraint time t is exceeded constraint , stop the loop.

[0084] Update the current layout S current and the current distance D current to the optimal layout S best and the optimal distance D best .

[0085] The above-mentioned storage location planning method, when the current warehouse is not an empty warehouse, takes the minimum handling cost of in-warehouse goods, the optimal inbound frequency of goods to be warehoused, and the maximum space utilization rate as the objective function, and determines the storage location layout through the genetic algorithm.

[0086] The above-mentioned storage location planning method, minimum handling cost

[0087] Minimum inbound frequency

[0088] Maximum space utilization rate

[0089] Constraint conditions:

[0090] The goods placed in the storage location cannot exceed the maximum capacity of the storage location

[0091] Each item can only be stored in one storage location

[0092] x ixy : Decision variable, whether the item i is stored in the storage location (x, y), 1 if it is in the storage location, otherwise 0;

[0093] C ixy : The transportation cost of item i from the current storage location to the target storage location;

[0094] S i : the quantity of goods i;

[0095] d i : The distance from item i to the preparation area;

[0096] k i : The incoming quantity of goods i;

[0097] h i : The height of cargo i;

[0098] w i : width of cargo i;

[0099] l i : Length of cargo i

[0100] n x : The total number of rows of storage locations;

[0101] n y : The total number of columns of storage locations;

[0102] Q i : The maximum capacity of the storage location.

[0103] 10. The storage location planning method according to claim 9, characterized in that the objective function f = w1·C norm +w2·H norm +w3·Y norm ;

[0104]

[0105] Among them, w1, w2, w3 are weight coefficients;

[0106] C is the current transportation cost; C min is the minimum transportation cost; C max is the maximum handling cost;

[0107] H is the current inventory frequency; H minis the minimum warehousing frequency; H max is the maximum inventory frequency;

[0108] Y is the current space utilization; Y max is the maximum space utilization; Y min is the minimum space utilization.

[0109] Compared with the prior art, the advantages and positive effects of the present invention are:

[0110] The storage location planning method of the present invention obtains the amount of goods in the warehouse on the current day, predicts the predicted amount of goods in the future, and determines the amount of goods used for storage location planning based on the amount of goods in the warehouse on the current day and the predicted amount of goods in the future. The closer the future time is to the current day, the greater the proportion of the goods in the warehouse on the current day that determines the amount of goods used for storage location planning. The further the future time is from the current day, the greater the proportion of the predicted amount of goods in the future that determines the amount of goods used for storage location planning, thereby more accurately determining the amount of goods used for storage location planning. Then, an objective function is established to minimize the remaining width of the storage location when the goods are placed in the storage location, and the storage location width that can ensure the minimum objective function is determined to improve storage location utilization and ensure storage location accuracy. DETAILED DESCRIPTION

[0111] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0112] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections. A person of ordinary skill in the art will understand the specific meanings of the above terms in the present invention in specific circumstances. In the description of the embodiments, specific features, structures, materials, or characteristics may be combined in any appropriate manner in any one or more embodiments or examples.

[0113] The terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the quantity of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0114] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0115] In modern large-scale warehousing and logistics, the existing paint-line model requires cumbersome and time-consuming adjustments to inventory lines, resulting in a long adjustment cycle, typically occurring every six months. However, factors such as e-commerce shopping festivals, explosive consumption during brand promotions, uneven regional distribution of cargo volume, and seasonal adjustments can lead to warehouse overflows, sudden demand surges, and product category changes during large-scale warehouse operations. Consequently, the initial inventory planning is no longer suitable for the existing inventory structure.

[0116] Warehouse overflows are primarily impacted by e-commerce shopping festivals, with most occurring within the 2-7 days following them. Double 11, the biggest risk of warehouse overflows, currently lasts approximately a week. Continuous overcrowding can lead to improper placement of goods, increasing warehouse handling and sorting workloads and increasing storage costs. The direct cause of warehouse overflows is a failure to reassess the rationality of each warehouse's current storage locations and utilize data-driven forecasts to proactively adjust storage locations, leading to decreased warehouse utilization.

[0117] If the width of the storage space is not optimally designed, the number of available storage spaces in the warehouse will be reduced, reducing the overall storage capacity. This will result in inability to fully utilize the warehouse space to store more goods, resulting in wasted space and reduced utilization. Furthermore, goods may be squeezed or drifted, leading to reduced storage location accuracy.

[0118] Based on the dynamic changes in the storage of goods in the warehouse and the need to maximize the utilization of storage space, it is necessary to optimize the size and adjust the storage locations to improve the utilization of warehouse space and the accuracy of storage locations.

[0119] Analyze the time periods with large inventory fluctuations in previous years and obtain the adjustment time periods. When the adjustment time period is reached, it is determined that adjustment is needed. Dynamically match the existing inventory information with the forecast data to determine the amount of goods used for warehouse location planning, optimize the warehouse location size design, obtain the optimized warehouse location size, and prepare for the warehouse location layout adjustment.

[0120] A method of warehouse location planning:

[0121] Get the warehouse cargo inventory data of previous years, and predict the predicted cargo volume SkuP for the next t day based on the warehouse cargo inventory data of previous years t ;

[0122] Get the quantity of goods SkuS in the warehouse on that day 0 ;

[0123] Determine the quantity of goods for storage location planning SkuCal t ;

[0124] SkuCal t =α*(SkuS 0 )+β*SkuP t ;

[0125] α+β=1;

[0126]

[0127] a∈[5,30];

[0128] Get the number of storage location widths n and the width w of the jth storage location j , width z of the i-th cargo i ;The quantity of goods to be used for storage location planning SkuCal t Denoted as I;

[0129] The objective function for placing goods in a storage location with the minimum available width is:

[0130] Among them, x i is the decision variable for the i-th product. If a certain storage location is selected, x i is 1, otherwise x i is 0; mod is the modulo operation;

[0131] The constraints are:

[0132] The length of the storage location is fixed;

[0133] The total width of the storage locations must not exceed the total width of the warehouse;

[0134] Storage location width w j Selection range constraint: w j ∈[w1, w2, …, w n ];

[0135] The number of storage locations required for a warehouse is a natural number;

[0136] The number of storage space width constraints required by the warehouse: ∑w j ≤b, b is the set quantity;

[0137] By obtaining all the cargo size and storage location width information, the storage location width that can ensure the minimum objective function is determined.

[0138] The appropriate storage space size allows the warehouse to store more goods in the same space. When the storage space length is fixed, the actual size of the goods to be stored is analyzed and the warehouse space can be fully utilized by accurately planning the storage space width.

[0139] For the problem of optimizing storage space size, the enumeration idea is used to backfill the goods used for storage space planning into the storage space, and the storage space size is optimized with the goal of minimizing the remaining area of the storage space, thereby improving the storage space utilization rate and ultimately improving the overall operational efficiency of the warehouse.

[0140] Predict the predicted cargo volume SkuP for the next t day based on the warehouse cargo inventory data of previous years t The method is to predict through the time series model, the time series model is:

[0141] SkuP t =g(t)+s(t)+h(t)+ε (t) .

[0142] Among them, the inventory goods curve

[0143] In the warehouse inventory data of previous years, S change points are inserted in chronological order; the change rate of the sth change point is δ s ,δ=(δ1,…,δ s ,...,δ S ) T .

[0144] The inventory data of a certain product in the past year can be formed into a curve, and the change points are S points taken on average on this curve (the average means that the x-axis distance between two adjacent points is the same). The purpose is to obtain the slope of these change points, that is, the rate of change.

[0145] a s (t) is the indicator function,

[0146] a(t)=(a1(t),...,a s (t)...,a S (t)) T ;

[0147] To predict the cargo volume on day t, the growth rate k needs to be added with the slopes of all the change points before day t.

[0148] γ=(γ1,...,γ s ,...,γ S ) T ; q is a natural number;

[0149] C(t): Carrying capacity, which is a function that changes with time and limits the maximum growth that can be achieved;

[0150]

[0151] Where, m∈{-1,-2,...,-M}, M represents the data of the previous M years;

[0152] The cargo volume for the next t days, for the current day m years ago.

[0153] k: growth rate at t=0.

[0154] The value range of C(t) is:

[0155] Max part: predict the value of day t, and its maximum value C(t) is the maximum value of day t in previous years (m represents the mth year, because it is a previous year, so m is a negative number);

[0156] The second term is equivalent to That is to say, add up the changes on day t between year m and year m-1 and take the average (there are M-1 items in total, so divide by M-1).

[0157] Cycle Model

[0158] T: period;

[0159] N: the number of Fourier fitting series, determined by the period size;

[0160] a n 、b n Achieve a certain coefficient.

[0161] Holiday Effect Model

[0162]

[0163] k l : The coefficient of holiday l. The larger the holiday coefficient, the greater the impact of the holiday on inventory quantity;

[0164] Z(t) k : indicator function, if t is not a holiday, this item is 0, and the value of the entire h(t) is 0;

[0165] If t is a holiday, then Z(t) k If it is 1, h(t) returns the holiday coefficient k corresponding to holiday t. l .

[0166] The above time series model greatly improves the accuracy of cargo volume prediction.

[0167] The following describes the method of determining the storage location layout using empty and non-empty warehouses:

[0168] When the warehouse is currently empty, the location layout is determined based on the objective functions of maximizing warehouse efficiency and optimizing space utilization. By maximizing the balance between outbound efficiency and space utilization, location planning is optimized, reducing outbound delivery time and increasing picking speed. This also improves warehouse space utilization, reduces unused space, and ultimately enhances overall warehouse operational efficiency.

[0169] The objective function for determining the storage location layout is:

[0170] w1 and w2 are weight coefficients;

[0171] Warehouse efficiency maximization location allocation objective function

[0172] Objective function for space utilization optimization The constraints are:

[0173] Among them, S ixy : Decision variable, which is 1 when the i-th item is stored in the (x, y) storage location, otherwise it is 0;

[0174] x: the index number of the storage location row, indicating the xth row in the storage location;

[0175] y: the index number of the storage location column, indicating the yth column in the storage location;

[0176] P i : The frequency of entry and exit of the i-th cargo in the unit cycle;

[0177] d xy : The distance from the storage location (x, y) to the nearest stocking area;

[0178] n x : The total number of rows of storage locations; the total number of rows of storage locations is determined by combining the optional storage location widths {w1,w2,...,w n} Make sure the total width of the selected storage locations does not exceed the total width of the warehouse ∑w j ≤w total and determined based on minimizing modular operations;

[0179] n y : The total number of columns of the storage location is obtained by dividing the total length of the warehouse by the length of a single storage location;

[0180] L x : The width of the xth row of storage locations;

[0181] V i : The volume of the i-th cargo;

[0182] C xy : The capacity of the storage location (x, y).

[0183] For the objective function of warehouse layout, the nearest neighbor algorithm is used to determine the initial location of the goods, and the simulated annealing algorithm is used to optimize the warehouse layout. The simulated annealing algorithm can explore new possible layouts in the global search space and, while satisfying the constraints, use the probabilistic acceptance criterion to jump out of the local optimal solution, thereby approaching the global optimal solution.

[0184] The method for determining the initial location of the goods using the nearest neighbor algorithm is:

[0185] Sort all goods of the same category in descending order of demand;

[0186] Mark the space occupied by the category as free (0), and mark the space not belonging to the category as (-1);

[0187] Initialize the length and width variables of the space occupied by this category;

[0188] Traverse the goods and place them. For each item, find the nearest free position to the shipping area and place the item. Mark the best position found as occupied and place the current item. Determine the initial layout S of the goods. current .

[0189] The method of optimizing storage location layout through simulated annealing algorithm is as follows:

[0190] Set the initial parameters:

[0191] Set the initial temperature T0 = 1000;

[0192] Set the cooling rate α = 0.95;

[0193] Set the maximum number of iterations N iter =1000.

[0194] Get the initial layout S of the goods current ;

[0195] Calculate the total distance D of the current layout current .

[0196] Recording start time t start ; Set a constraint time t constraint , ensuring that the algorithm is completed within the specified time.

[0197] Set the current layout S current As the optimal layout S best ; Set the current distance D current As the optimal distance D best .

[0198] Start loop optimization:

[0199] Enter loop N iter Second-rate:

[0200] Generate a new SKU layout S new ;

[0201] Calculate the total distance D of the new layout new ;

[0202] Calculate the incremental ΔD = D new - D current ;

[0203] If ΔD < 0, that is, the distance of the new layout is less than the distance of the current layout;

[0204] Then update the current layout S current = S new and the current distance D current = D new .

[0205] Otherwise, calculate the acceptance probability

[0206] Generate a random number r in the interval [0, 1];

[0207] If r < P, then update the current layout S current = S new and the current distance D current = D new ;

[0208] If D new < D best , then update the optimal layout S best = S new and the optimal distance D best = D new ;

[0209] Reduce the temperature T = T × α.

[0210] End the optimization:

[0211] When the iteration number N iter or the constraint time t is exceeded constraint , stop the loop.

[0212] Update the current layout S current and the current distance D current to the optimal layout S best and the optimal distance D best .

[0213] When the current warehouse is not an empty warehouse, with the minimum handling cost of the in-warehouse goods, the optimal warehousing frequency of the goods to be warehoused, and the maximum space utilization rate as the objective function, determine the warehouse location layout through the genetic algorithm.

[0214] Minimum handling cost

[0215] Minimum warehousing frequency

[0216] Maximum space utilization rate

[0217] Constraints:

[0218] The goods placed in the storage location cannot exceed the maximum capacity of the storage location

[0219] Each item can only be stored in one storage location

[0220] x ixy : Decision variable, whether the item i is stored in the storage location (x, y), 1 if it is in the storage location, otherwise 0;

[0221] C ixy : The transportation cost of item i from the current storage location to the target storage location;

[0222] S i : the quantity of goods i;

[0223] d i : The distance from item i to the preparation area;

[0224] k i : The incoming quantity of goods i;

[0225] h i : The height of cargo i;

[0226] w i : width of cargo i;

[0227] l i : Length of cargo i

[0228] n x : The total number of rows of storage locations;

[0229] n y : The total number of columns of storage locations;

[0230] Q i : The maximum capacity of the storage location.

[0231] To simplify the solution of the model, we normalize the objective function and use the weight coefficient change method to linearize multiple optimization objectives. This method assigns different weights w to different optimization objectives. i , which is transformed into a single-objective optimization problem.

[0232] Objective function f = w1·C norm +w2·H norm +w3·Y norm ;

[0233]

[0234] Among them, w1, w2, and w3 are weight coefficients used to adjust the importance of different optimization objectives.

[0235] C is the current transportation cost; C min is the minimum transportation cost; C max is the maximum handling cost;

[0236] H is the current inventory frequency; H min is the minimum warehousing frequency; H max is the maximum inventory frequency;

[0237] Y is the current space utilization; Y max is the maximum space utilization; Y min is the minimum space utilization.

[0238] The warehouse location planning method analyzes historical data to identify time periods with large inventory fluctuations in previous years and obtains adjustment time periods; secondly, the Prophet forecasting model is used to predict the types of goods and inventory levels in this time period; through forecasting, the cargo volume is dynamically monitored and data-driven advance deployment is carried out; finally, based on relevant data, genetic algorithms combined with ant colony algorithms are used to dynamically adjust warehouse locations to adapt to fluctuations caused by e-commerce shopping festivals, seasonal demand, and sudden demand, thereby saving space to the greatest extent, conducting lean warehouse location management, and formulating more efficient, economical, and adaptable warehouse location layout plans to improve warehouse utilization and operational efficiency.

[0239] This application can flexibly respond to changes in warehouse utilization, improve operational efficiency, reduce the error rate of inventory adjustment, and reduce the workload of managers and unnecessary staff; calculate the optimal storage location for each category and SKU, can quickly adjust production plans, can respond more quickly to changes in market demand, accurately plan and dynamically adjust inventory locations, ensure the timely warehousing, storage and delivery of goods, promote the coordinated operation of various links in the supply chain, improve warehouse management efficiency and accuracy to stabilize production line operations, and reduce the risk of production interruptions.

[0240] By digitally transforming existing traditional warehouses, there is no need to build large-scale new storage facilities. Instead, existing resources can be fully utilized, greatly saving social logistics costs.

[0241] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A storage location planning method, characterized in that: The method is: Get the warehouse cargo inventory data of previous years, and predict the predicted cargo volume SkuP for the next t day based on the warehouse cargo inventory data of previous years t ; Get the quantity of goods SkuS in the warehouse on that day 0 ; Determine the quantity of goods for storage location planning SkuCal t ; SkuCal t =α*(SkuS 0 )+β*SkuP t ; α+β＝1； a∈[5，30]； Get the number of storage location widths n and the width w of the jth storage location j , width z of the i-th cargo i ;The quantity of goods to be used for storage location planning SkuCal t Denoted as I; The objective function for placing goods in a storage location with the minimum available width is: Among them, x i is the decision variable for the i-th product. If a certain storage location is selected, x i is 1, otherwise x i is 0; mod is the modulo operation; The constraints are: The length of the storage location is fixed; The total width of the storage locations must not exceed the total width of the warehouse; Storage location width w j Selection range constraint: w j ∈[w1, w2, …, w n ]; The number of storage locations required for a warehouse is a natural number; The number of storage space width constraints required by the warehouse: ∑w j ≤b, b is the set quantity; By obtaining all the cargo size and storage location width information, the storage location width that can ensure the minimum objective function is determined.

2. The storage location planning method according to claim 1, characterized in that: Predict the predicted cargo volume SkuP for the next t day based on the warehouse cargo inventory data of previous years t The method is to predict through the time series model, the time series model is: SkuP t =g(t)+s(t)+h(t)+ε (t) ; Among them, the inventory goods curve In the warehouse inventory data of previous years, S change points are inserted in chronological order; the change rate of the sth change point is δ s ,δ=(δ1,…,δ s ,...,δ S ) T ; a s (t) is the indicator function, a(t)=(a1(t),...,a s (t)...,a S (t)); γ=(γ1,...,γ s ,...,c S ) T ; q is natural number; C(t): Carrying capacity, which is a function that changes with time and limits the maximum growth that can be achieved; Where, m∈{-1,-2,...,-M}, M represents the data of the previous M years; k: growth rate at t = 0; Cycle Model T: period; N: the number of Fourier fitting series, determined by the period size; a n 、b n Achieve a certain coefficient; Holiday Effect Model k l =(k1,...,k L ) T ; k l : The coefficient of holiday l. The larger the holiday coefficient, the greater the impact of the holiday on inventory quantity; Z(t) k : indicator function, if t is not a holiday, this item is 0, and the value of the entire h(t) is 0; If t is a holiday, then Z(t) k If it is 1, h(t) returns the holiday coefficient k corresponding to holiday t. l .

3. The storage location planning method according to claim 1, characterized in that: When the current warehouse is empty, the warehouse location layout is determined based on the warehouse efficiency maximization location allocation objective function and the space utilization optimization objective function.

4. The storage location planning method according to claim 3, characterized in that: The objective function for determining the storage location layout is: w1 and w2 are weight coefficients; Warehouse efficiency maximization location allocation objective function Objective function for space utilization optimization The constraints are: S ixy ∈{0, 1}; Among them, S ixy : Decision variable, which is 1 when the i-th item is stored in the (x, y) storage location, otherwise it is 0; x: the index number of the storage location row, indicating the xth row in the storage location; y: the index number of the storage location column, indicating the yth column in the storage location; P i : The frequency of entry and exit of the i-th cargo in the unit cycle; d xy : The distance from the storage location (x, y) to the nearest stocking area; n x : The total number of rows of storage locations; the total number of rows of storage locations is determined by combining the optional storage location widths {w1,w2,...,w n } Make sure the total width of the selected storage locations does not exceed the total width of the warehouse ∑w j ≤w total and determined based on minimizing modular operations; n y : The total number of columns of the storage location is obtained by dividing the total length of the warehouse by the length of a single storage location; L x : The width of the xth row of storage locations; V i : The volume of the i-th cargo; C xy : The capacity of the storage location (x, y).

5. The storage location planning method according to claim 4, characterized in that: For the objective function of warehouse location layout, the initial location of the goods is determined by the nearest neighbor algorithm; and the warehouse location layout of the goods is optimized by the simulated annealing algorithm.

6. The storage location planning method according to claim 5, characterized in that: The method for determining the initial location of the goods using the nearest neighbor algorithm is: Sort all goods of the same category in descending order of demand; Mark the space occupied by the category as free (0), and mark the space not belonging to the category as (-1); Initialize the length and width variables of the space occupied by this category; Traverse the goods and place them. For each item, find the nearest free position to the shipping area and place the item. Mark the best position found as occupied and place the current item. Determine the initial layout S of the goods. current .

7. The storage location planning method according to claim 6, characterized in that: The method of optimizing storage location layout through simulated annealing algorithm is as follows: Set initial parameters; Get the initial layout S of the goods current ; Calculate the total distance D of the current layout current ; Recording start time t start ; Set a constraint time t constraint ; Set the current layout S current As the optimal layout S best ; Set the current distance D current As the optimal distance D best ; Start loop optimization: Enter loop N iter Second-rate: Generate a new SKU layout S new ; Calculate the total distance D of the new layout new ; Calculate the increment ΔD = D new -D current ; if ΔD<0, the distance of the new layout is smaller than the distance of the current layout; Update the current layout current =S new and the current distance D current =D new . Otherwise, calculate the acceptance probability Generate a random number r in the interval [0,1]; If r < P, update the current layout S current = S new and the current distance D current = D new ; if D new <D best , then update the optimal layout S best =S new and optimal distance D best =D new ; Lower the temperature T = T × α. End optimization: When the number of iterations N is reached iter Or the constraint time t is exceeded constraint , stop the loop. Set the current layout S current and the current distance D current Update to the optimal layout S best and optimal distance D best .

8. The storage location planning method according to claim 1, characterized in that: When the current warehouse is not empty, the warehouse layout is determined by genetic algorithm with the minimum handling cost of goods in the warehouse, the optimal warehousing frequency of goods to be warehoused and the maximum space utilization as the objective function.

9. The storage location planning method according to claim 8, characterized in that: Minimum handling cost Minimum warehousing frequency Maximum space utilization Constraints: The goods placed in the storage location cannot exceed the maximum capacity of the storage location Each item can only be stored in one storage location x ixy : Decision variable, whether the item i is stored in the storage location (x, y), 1 if it is in the storage location, otherwise 0; C ixy : The transportation cost of item i from the current storage location to the target storage location; S i : the quantity of goods i; d i : The distance from item i to the preparation area; k i : The incoming quantity of goods i; h i : The height of cargo i; w i : width of cargo i; l i : Length of cargo i n x : The total number of rows of storage locations; n y : The total number of columns of storage locations; Q i : The maximum capacity of the storage location.

10. The storage location planning method according to claim 9, characterized in that: Objective function f = w1·C norm +w2·H norm +w3·Y norm ; Among them, w1, w2, w3 are weight coefficients; C is the current transportation cost; C min is the minimum transportation cost; C max is the maximum handling cost; H is the current inventory frequency; H min is the minimum warehousing frequency; H max is the maximum inventory frequency; Y is the current space utilization; Y max is the maximum space utilization; Y min is the minimum space utilization.

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