Supply interruption-oriented two-stage warehouse-crossing vehicle scheduling algorithm

Abstract

The invention provides a supply interruption-oriented two-stage warehouse-crossing vehicle scheduling algorithm, and the method comprises the steps: obtaining product supply shortage data of warehouse-in vehicle supply interruption, constructing a demand decomposition algorithm through the product supply shortage data, an initial demand matrix of warehouse-out vehicles, and the unit stockout cost of each warehouse-out vehicle for different product types, calculating a demand reduction matrix; calculating an adjusted out-of-storage vehicle demand matrix based on the demand reduction matrix, and calculating out-of-stock cost; constructing a mathematical planning model based on the stockout cost, the transfer time cost and the out-of-storage vehicle scheduling cost; building a warehouse-crossing center transfer model under a single-time loading mode and a multi-time loading mode, and designing a mode conversion algorithm; designing a two-stage percentage threshold heuristic algorithm to solve a warehouse crossing center transfer model, and outputting an updated warehouse leaving vehicle demand matrix, a scheduling scheme and all cost information; through dynamic demand adjustment and a flexible operation mode, product time cost loss and stockout loss are minimized, and supply chain anti-interference capability is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of supply chain cross-docking scheduling, in particular to a two-stage cross-docking vehicle scheduling algorithm for supply interruption. BACKGROUND

[0002] Adopting the cross-docking scheduling mode can effectively reduce the transfer time and is a key means to improve product logistics efficiency. Traditional cross-docking scheduling researches are mostly concentrated in time optimization under supply-demand balance, and a single unloading / loading mode is adopted, that is, the vehicle must complete all unloading / loading tasks before leaving the platform. However, in the case of supply interruption leading to actual arrival quantity less than planned demand quantity, the rigid scheduling strategy of the traditional mode is relatively inefficient: on the one hand, it cannot intelligently allocate demand to the most urgent (highest shortage cost) areas; on the other hand, the outbound vehicle may occupy the platform for a long time due to waiting for a specific product that has not yet arrived, hindering the operation of other vehicles that can immediately load, and significantly increasing the time cost.

[0003] Therefore, there is an urgent need for a cross-docking vehicle flexible scheduling method for supply interruption, which should have two core functions: (1) in the case of supply interruption, the allocation quantity to each outbound vehicle (representing different areas) can be dynamically adjusted according to the urgency of product demand; (2) whether to adopt the multiple loading (ML) mode (allowing the outbound vehicle to enter and exit the platform multiple times) can be adaptively selected to achieve the optimal balance between increased scheduling cost and reduced time cost. Currently, there is no research that can effectively integrate these two aspects for joint optimization. SUMMARY

[0004] The purpose of the embodiments of the application is to provide a two-stage cross-docking vehicle scheduling algorithm for supply interruption, which can minimize the time cost loss and shortage loss of products and improve the anti-interference ability of the supply chain by constructing cross-docking center transfer models under single and multiple loading modes and realizing mode conversion through dynamic demand adjustment and flexible operation mode.

[0005] The embodiments of the application also provide a two-stage cross-docking vehicle scheduling algorithm for supply interruption, which comprises:

[0006] The product supply shortage data of the inbound vehicle supply interruption is obtained, a demand decomposition algorithm is constructed by using the product supply shortage data, the initial demand matrix of the outbound vehicle, and the unit shortage cost of each outbound vehicle for different product types, and a demand reduction matrix is calculated;

[0007] The adjusted outbound vehicle demand matrix is calculated based on the demand reduction matrix, and the shortage cost is calculated;

[0008] Based on the shortage cost, the transfer time cost and the outbound vehicle scheduling cost, a cross-docking center transfer model is constructed to minimize the total cost.

[0009] Establish a cross-docking center transfer model under single and multiple loading modes, and design a mode conversion algorithm;

[0010] Based on the objective function and constraint conditions, a three-layer coding scheme composed of vehicle serial number, product category, and loading quantity is constructed, a two-stage percentage threshold heuristic algorithm is designed to solve the cross-docking center transfer model, and the updated outbound vehicle demand matrix, scheduling scheme, and all cost information are output.

[0011] Optionally, in the two-stage cross-docking vehicle scheduling algorithm for supply interruption described in the embodiments of the present application, based on the inbound vehicle supply shortage data, an outbound vehicle demand decomposition algorithm is designed, which specifically includes:

[0012] Suppose that the shortage of the product of the first kind that cannot be delivered on time is , the product category set ; the initial demand of the outbound vehicle for the product of the first kind is ; ; ; ; ; ;

[0013] The demand decomposition algorithm at least needs to satisfy the following constraint conditions:

[0014] Constraint 1: The total amount of demand reduction matches the supply shortage: ;

[0015] Constraint 2: The demand reduction amount is not more than the initial demand and is non-negative: .

[0016] Optionally, in the two-stage cross-docking vehicle scheduling algorithm for supply interruption described in the embodiments of the present application, based on the demand reduction matrix, the shortage cost is calculated, which specifically includes:

[0017] Suppose that the unit shortage cost of the outbound vehicle for the product of the first kind is ;

[0018] The demand reduction matrix is calculated based on the demand decomposition algorithm: ;

[0019] Based on the initial demand matrix of the outbound vehicle, the outbound vehicle demand matrix after demand adjustment is calculated, and the formula is as follows:

[0020] ;

[0021] ​​​Calculate the corresponding out-of-stock cost .

[0022] Optionally, in the two-stage cross-docking vehicle scheduling algorithm for supply disruption described in the embodiments of the present application, a cross-docking center transfer model is established to minimize the total cost The objective function total cost is expressed as:

[0023]

[0024] Wherein, represents the total completion time consumed by all products during the unloading / loading process at the cross-docking center, which is converted into cost by multiplying ; the second term is the total out-of-stock cost; represents the unit scheduling cost consumed by the outbound vehicle when entering and exiting the loading platform once, represents the total number of times the outbound vehicle enters and exits the loading platform;

[0025] The constraint conditions include out-of-stock supply and demand balance, inbound vehicle transfer balance, outbound vehicle demand balance, transfer correlation constraint, inbound / outbound vehicle unloading / loading time constraint, sequencing constraint, and unloading / loading mode constraint.

[0026] Optionally, in the two-stage cross-docking vehicle scheduling algorithm for supply disruption described in the embodiments of the present application, a cross-docking center transfer model is established under single and multiple loading modes, and a mode conversion algorithm is designed:

[0027] Based on the sequencing of vehicle numbers, a cross-docking center transfer model under the single and continuous loading scheduling mode is constructed;

[0028] Introducing the batch loading quantity parameter converts a single and uninterrupted continuous loading operation of an outbound vehicle into an interruptible multi-segment loading operation;

[0029] Suppose that after the supply disruption adjustment, the outbound vehicle has a demand for the first product, the number of loadings and the quantity of the first loading are calculated according to the following formula:

[0030]

[0031] Wherein, ;

[0032]

[0033] Wherein, is the floor operator, which originally represents the first The single and continuous loading process of the vehicle is converted into an interruptible segment loading operation, and the loading quantity of each operation is ;

[0034] The single and continuous loading process of the vehicle is converted into an interruptible segment loading operation, and the loading quantity of each operation is ;

[0035] Each segment loading operation is composed of three layers of information, i.e., vehicle number, product type and loading quantity, and the loading quantity of the first segment of the product is ;

[0036] Each segment loading operation is randomly sorted, and if the adjacent loading operations come from the same vehicle, the loading quantity is merged, and finally the merged vehicle demand matrix is obtained , wherein is the vehicle set after introducing the virtual vehicle.

[0037] Optionally, in the two-stage cross-dock vehicle scheduling algorithm for supply interruption in the embodiment of the application, the two-stage percentage threshold heuristic algorithm for solving the model includes a first stage and a second stage, and the first stage includes the following steps:

[0038] Initializing algorithm parameters, the algorithm parameters including the shortage quantity caused by supply interruption , unit shortage cost , population quantity , selection probability , maximum iteration number , batch loading quantity and threshold value ;

[0039] Generating a demand reduction matrix based on the demand decomposition algorithm , and calculating a vehicle demand matrix after demand adjustment ;

[0040] A single and continuous loading SL scheduling mode is adopted, an initial population of chromosomes is randomly generated based on a vehicle number-based coding mode;

[0041] According to a target function total cost, the fitness value of each chromosome is calculated, and the total cost includes time cost, shortage cost and scheduling cost, and the fitness value function is ;

[0042] A selection operation is performed, and a roulette strategy is adopted to select the first​ The probability of each chromosome , total selection One parent generation;

[0043] The crossover operation is performed using a partially mapped crossover (PMX) method, and the mutation operation is performed using a random swap mutation method.

[0044] Population update involves recording the chromosomes with the highest and lowest fitness in each generation, replacing the chromosome with the chromosome with the highest fitness, and iterating until the maximum number of iterations is reached. Output the total cost in SL mode. and the scheduling costs incurred. ;

[0045] judge With threshold The size, if The algorithm uses SL mode and outputs the results. The algorithm employs a multi-loading ML model to execute the second phase.

[0046] Optionally, in the two-stage cross-depot vehicle scheduling algorithm for supply disruptions described in this application embodiment, the second stage includes the following steps:

[0047] Based on batch loading quantity Each vehicle leaving the warehouse The single and continuous loading process is transformed into Section loading operations;

[0048] right The loading and unloading operations are randomly sorted, and a mode transformation algorithm is applied to generate a randomly sorted outbound vehicle demand matrix. ,in This belongs to the set of vehicles leaving the warehouse after the introduction of virtual vehicles;

[0049] Based on the new outbound vehicle demand matrix It adopts an encoding method based on virtual vehicle serial numbers to perform roulette wheel selection, crossover, and mutation operations;

[0050] Determine if the termination condition is met; if so, terminate the process and output the total cost under the multiple loading ML model. If the conditions are not met, the roulette wheel selection, crossover, and mutation operations will be executed again.

[0051] Optionally, in the two-stage cross-depot vehicle scheduling algorithm for supply disruptions described in this application embodiment, the partial mapping cross-PMX includes the following steps:

[0052] After grouping the parent population into pairs, select the two parent chromosomes that need to be crossovered and generate two distinct random integers.

[0053] The part between the two parent genes random integers is crossed; after crossing, the non-repeated numbers are reserved, the repeated numbers appearing outside the crossing part are eliminated by using the partial mapping method, and the corresponding relationship of the crossing part is used for mapping;

[0054] If the fitness value of the chromosome after crossing is higher than that before crossing, the crossing operation is accepted, and if the fitness value of the chromosome after crossing is lower than that before crossing, the original chromosome is reserved;

[0055] The two parent chromosomes left after the two-by-two grouping are repeatedly crossed.

[0056] Compared with the prior art, the application has the following advantages due to the above technical solutions:

[0057] Integrated innovation: for the first time, the demand dynamic adjustment under supply interruption and the adaptive selection of flexible operation mode (SL / ML) are combined into a unified decision framework for joint optimization.

[0058] Efficient algorithm: the two-stage percentage threshold heuristic algorithm can intelligently determine which mode is more efficient, avoiding blind calculation under the two modes and improving the solution efficiency. At the same time, by introducing the batch loading quantity parameter and using the three-layer coding scheme built by the vehicle serial number, product type and loading quantity, the modeling and solving problems under the flexible and complex ML mode are effectively solved.

[0059] Resilience enhancement: the method can significantly reduce the total cost under supply interruption and improve the ability of the cross-docking center to cope with uncertainty. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0061] Figure 1 is a flowchart of the two-stage cross-docking vehicle scheduling method provided by the embodiments of the present application;

[0062] Figure 2 is a flowchart of the two-stage percentage threshold heuristic algorithm provided by the embodiments of the present application;

[0063] Figure 3 is a flowchart of the demand decomposition algorithm for supply interruption provided by the embodiments of the present application;

[0064] Figure 4is a pseudo code diagram of a demand decomposition algorithm provided by an embodiment of the present application;

[0065] Figure 5 is a cross-docking scheduling flowchart in a single loading mode based on a demand decomposition algorithm provided by an embodiment of the present application;

[0066] Figure 6 is a loading operation diagram in a multiple loading mode provided by an embodiment of the present application;

[0067] Figure 7 is a diagram for converting vehicle sequencing in an SL mode into loading operation sequencing in an ML mode provided by an embodiment of the present application;

[0068] Figure 8 is an algorithm diagram for converting an ML mode into an SL mode by introducing a virtual vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0070] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0071] Please refer to Figure 1 , Figure 1 is a flowchart of a two-stage cross-docking vehicle scheduling algorithm for supply interruption in some embodiments of the present application. The two-stage cross-docking vehicle scheduling algorithm for supply interruption is used in a terminal device, and the two-stage cross-docking vehicle scheduling algorithm for supply interruption includes the following steps:

[0072] obtaining product supply shortage data of a warehouse-in vehicle supply interruption, constructing a demand decomposition algorithm by using the product supply shortage data, an initial demand matrix of a warehouse-out vehicle, and a unit shortage cost of each warehouse-out vehicle for different product types, and calculating a demand reduction matrix;

[0073] calculate an adjusted outbound vehicle demand matrix based on the demand reduction matrix, and calculate the stockout cost;

[0074] based on the stockout cost, the transfer time cost, and the outbound vehicle scheduling cost, construct a cross-docking center transfer model aiming to minimize the total cost;

[0075] establish a cross-docking center transfer model under single and multiple loading modes, and design a mode conversion algorithm;

[0076] based on the objective function and the constraint condition, construct a three-layer coding scheme composed of vehicle serial number, product type, and loading quantity, design a two-stage percentage threshold heuristic algorithm to solve the cross-docking center transfer model, and output the updated outbound vehicle demand matrix, the scheduling scheme, and all cost information.

[0077] According to the embodiment of the present application, based on the inbound vehicle supply shortage data, an outbound vehicle demand decomposition algorithm is designed, which specifically includes:

[0078] suppose that the stockout amount of the first product that cannot be delivered on time is , the product type set is ; the initial demand of the first product by the outbound vehicle is ; generate the demand reduction matrix of the outbound vehicle based on the demand decomposition algorithm ;

[0079] The demand decomposition algorithm needs to at least meet the following constraint conditions:

[0080] Constraint 1: the total amount of demand reduction matches the supply loss amount: ;

[0081] Constraint 2: the demand reduction amount is not more than the initial demand and is non-negative: .

[0082] It should be noted that, according to the embodiment of the present application, based on the various product stockout data caused by supply interruption, a demand decomposition algorithm (as shown in Figure 3 ) is designed to calculate the dynamic demand adjustment data of the outbound vehicle, which specifically includes:

[0083] When the supply interruption occurs, the demand needs to be adjusted according to the "stockout cost priority" of the fresh food, and the core is to generate the demand reduction matrix ;

[0084] The demand decomposition algorithm is used to calculate the adjusted outbound vehicle demand matrix, to ensure that the total amount of demand reduction matches the supply interruption loss amount, and also includes constraint 3, as follows: ​​​​

[0085] The adjusted outbound vehicle demand matrix is calculated, and the subsequent vehicle scheduling process is based on the numerical values.

[0086] There are multiple outbound vehicle demand reduction matrices that meet the above constraints , and the specific choice needs to be considered comprehensively in combination with factors such as unit stockout cost. In the model solving part, the algorithm design that meets the above constraints is shown as Figure 4 .

[0087] According to an embodiment of the present application, based on the demand reduction matrix, the stockout cost is calculated, specifically including:

[0088] Suppose each outbound vehicle has a unit stockout cost for the first product;

[0089] The demand reduction matrix is calculated based on the demand decomposition algorithm ;

[0090] Based on the initial demand matrix of the outbound vehicle , the outbound vehicle demand matrix after demand adjustment is calculated , and the formula is as follows:

[0091] ;

[0092] The corresponding stockout cost is calculated.

[0093] According to an embodiment of the present application, a cross-docking center transfer model is established to minimize the total cost , and the objective function total cost is expressed as:

[0094]

[0095] wherein, total finished product time consumed by all products in the cross-docking center unloading / loading process, which is converted into cost by multiplying ; the second term is the total stockout cost; represents the unit scheduling cost consumed by the outbound vehicle entering and exiting the loading platform once, represents the total number of times the outbound vehicle enters and exits the loading platform;

[0096] The constraint conditions include stockout supply and demand balance, inbound vehicle transfer balance, outbound vehicle demand balance, transfer association constraint, inbound / outbound vehicle unloading / loading time constraint, sequencing constraint, and unloading / loading mode constraint.

[0097] It should be noted that based on the constraints of the in / out of warehouse truck transfer association, unloading / loading mode, etc., a single (Single loading, SL) loading mode is adopted, and a mixed integer cross-docking center transfer model is constructed to minimize the time cost, shortage cost and scheduling cost as the target:

[0098] Objective function:

[0099]

[0100] Constraint conditions:

[0101]

[0102] The formula parameters are shown in the following table:

[0103]

[0104] Constraint conditions (2-5) describe the quantity constraints that need to be met when supply interruption occurs. Constraint conditions (6) and (7) ensure that the transfer quantity is matched with both the loading quantity of the in-warehouse truck and the demand quantity of the out-warehouse truck. Constraint condition (8) specifies the relationship between variables and . Constraint conditions (9-11) are for in-warehouse trucks, which provide reasonable sequence rules for the in-warehouse truck's entry and exit platform time. Constraint condition (12) ensures that in the in-warehouse truck sequence, no in-warehouse truck will be placed before itself. Constraint conditions (13-16) are applicable to out-warehouse trucks in a similar manner (i.e., specifying the entry and exit time sequence of the out-warehouse truck and the self-reversing constraint). Constraint condition (17) associates the entry and exit times of the in-warehouse truck and the out-warehouse truck.

[0105] As shown in Figure 5 , the solving scheme of the mixed integer cross-docking center transfer model proposed by the embodiment of the present application under the single (SL mode) loading mode is shown by a specific example:

[0106] This embodiment contains 2 in-warehouse trucks, 3 out-warehouse trucks, and transfers 3 products. In-warehouse truck 1 has a supply interruption, causing the absence of products of categories 2 and 3 thereon. In addition, 1 unit of time is required for unloading / loading 1 unit of product, and the transfer time of the product from the in-warehouse platform to the out-warehouse platform is 2, as well as the transit time from the departure of the previous vehicle to the entry of the next vehicle to the platform.

[0107] Based on the above information, among the 3 out-warehouse trucks, it is decided which out-warehouse trucks should be adjusted in detail, so that the supply and demand are matched while the cost loss is minimized.

[0108] According to the out-warehouse truck loading information in Figure 5 , the initial demand of the 3 out-warehouse trucks is (The first row of the matrix indicates that the first and third products are loaded on the outbound vehicle 1, and so on).

[0109] Based on Figure 4 the proposed demand decomposition algorithm, two scheduling schemes can be generated as shown in Table 1, which are as follows: Figure 5

[0110] Scheme 1 selects to reduce the demand of the third product of the outbound vehicle 1 and the second product of the outbound vehicle 3, that is, At this time, the demands of the outbound vehicles 1 (OT1), OT2, and OT3 are [1 0 0; 0 1 0; 0 0 1], and the cross-docking scheduling needs to be completed by 3 vehicles, which costs 10 units of time and the scheduling cost of 3 outbound vehicles;

[0111] Scheme 2 selects to reduce the demand of the third product of the outbound vehicle 1 and the second product of the outbound vehicle 2, that is, At this time, only 2 outbound vehicles are needed to complete the cross-docking scheduling, which costs 8 units of time and the scheduling cost of 2 outbound vehicles. Obviously, the total cost of this scheme is less than that of scheme 1.

[0112] According to the embodiment of the present application, on the basis of the cross-docking center transfer model in the traditional single loading (SL) mode, in order to speed up the transfer process and reduce the cost, a cross-docking center transfer model in the multiple loading (ML) mode is constructed (the product transfer process is shown in FIG. 2). Figure 6 ).

[0113] Figure 6 It is shown that in order to meet the demand of the outbound vehicle 1 (1 piece of each of the required product types 1 and 2) as soon as possible, after the loading of the product of type 1 is completed, the outbound vehicle 1 leaves the platform, spends the vehicle interruption time drives into the outbound vehicle 2, then loads 1 piece of each of the required products of types 1 and 2, and finally, spends the interruption time , the outbound vehicle 1 returns to the platform to load the product of type 2, and completes the scheduling. During this period, there is no delay in loading due to the failure of the products to arrive at the loading platform as in the SL loading mode. The ML mode realizes the continuity of the loading process, reduces the transfer time cost, but at the same time increases the scheduling cost.

[0114] In order to establish the cross-docking center transfer model in the ML mode, the present application proposes a batch loading quantity parameter , which means that at least pieces of products need to be loaded each time the outbound vehicle performs the loading operation, so as to realize the division of the loading process of the outbound vehicle into multiple segments, and each segment is called a loading operation, which is composed of three layers of information: the first outbound vehicle loads the first ​The number of products .

[0115] Specifically, if the number of products is unloaded based on the batch loading quantity , the total number of loading times and the number of products per loading are respectively:

[0116]

[0117] wherein, is a floor operator, is the serial number of the loading section. According to the formula (18-19), the loading process of the number of products is divided into sections, and the loading quantity of each section is .

[0118] According to one specific embodiment of the present application, the SL mode is converted into the ML mode, as shown in Figures 7-8 . This example includes 2 outbound vehicles and 2 products, and outbound vehicle 1 needs to load 25 of the first product and 5 of the second product; outbound vehicle 2 needs to load 15 of the second product, and the batch unloading quantity is set to .

[0119] According to the formula (18) and (19), the loading times corresponding to outbound vehicle 1 is 3, and 10 of the first product, 15 of the first product and 5 of the second product are loaded respectively; the loading times corresponding to outbound vehicle 2 is 1, and 15 of the second product is loaded. Therefore, the loading process needs to perform 4 loading operations, and each loading operation is composed of the outbound vehicle serial number, the product type and the loading quantity .

[0120] Different results will be obtained by performing different sequencing on the 4 loading operations, and different loading sequences are corresponded, as shown in Table 1 and Figure 8 . Table 1

[0121]

[0122] According to Table 1, the first possible loading sequence based on the outbound vehicle serial number is 1-2-1-1, and since the last two loading operations are for the same outbound vehicle, they can be combined (after the combination, it can be regarded as introducing a virtual vehicle 3). By summarizing the 4 loading operations, the outbound vehicle loading matrix under the introduction of the virtual vehicle is . Similarly, the loading matrix corresponding to the second possible loading sequence is , as shown in Figure 8 .

[0123] It can be seen that although the loading operation is divided into the same 4 segments, arranging the 4 segments into different unloading sequences will correspond to different loading matrices, and then different objective function values (such as time cost, scheduling cost, etc.) are obtained.

[0124] Based on this, it is known that for the ML mode, the key of the mathematical model and the solving algorithm is to sort the loading operation composed of three layers of information, and then convert it into the SL mode by introducing a virtual vehicle. The mode conversion algorithm is summarized as follows:

[0125] Based on the batch loading quantity The loading operation of the outbound vehicle demand matrix is divided into segments, and the decomposition method is shown in equations (18) and (19);

[0126] The segment loading operation is randomly arranged, as shown in Table 1;

[0127] Based on this sorting, if the adjacent two loading operations are from the same outbound vehicle, they are merged, and if not, a new virtual outbound vehicle is introduced, thereby converting the outbound vehicle demand matrix expressed in matrix form .

[0128] Wherein, The number of rows of the matrix is more than This is equivalent to changing the behavior of the outbound vehicle repeatedly entering and exiting the loading platform into increasing some virtual outbound vehicles to perform the loading operation.

[0129] Through the inbound vehicle demand matrix and the demand matrix of the introduced virtual outbound vehicle , the cost value of the product in the cross-docking scheduling process can be calculated, and the specific model is the same as equations (1)-(17), the difference is that is changed to . Thus, the cross-docking vehicle scheduling model under the ML mode is completely constructed.

[0130] According to the embodiment of the application, the cross-docking center transfer model under the single and multiple loading modes is established, and the mode conversion algorithm is designed:

[0131] The cross-docking center transfer model under the single and continuous loading scheduling mode is constructed based on the sorting of the vehicle serial number;

[0132] The batch loading quantity parameter is introduced, and the single and uninterrupted continuous loading operation of an outbound vehicle is converted into a multiple segment loading operation which can be interrupted;

[0133] Suppose that the outbound vehicle is adjusted due to supply interruption Demand for this product The number of times it was loaded Passing the exam Quantity of each shipment Calculate using the following formula:

[0134]

[0135] in, ;

[0136]

[0137] in, For the floor operation, the original floor is rounded down. The process of loading goods onto a single, continuous vehicle is transformed into an interruptible process. Section loading operation, the quantity loaded in each operation is ;

[0138] Section loading operation introduction Each virtual vehicle corresponds to one loading quantity. ;

[0139] Each loading operation consists of three layers of information: vehicle serial number, product type, and loading quantity. This means the process begins from the vehicle leaving the warehouse. Loading the first The quantity of the products is ;

[0140] Each loading operation is randomly ordered. If adjacent loading operations come from the same outbound vehicle, the loading quantities are combined to obtain the combined outbound vehicle demand matrix. ,in This belongs to the set of vehicles leaving the warehouse after the introduction of virtual vehicles.

[0141] Based on this, the sorting problem of loading operations in the complex ML model is transformed into the sorting problem of the outbound vehicle sequence number, that is, it is transformed into a single loading (SL) model after introducing virtual vehicles.

[0142] This paper proposes a cross-warehouse transfer model using a flexible multiple loading (ML) mode. It also proposes a model transformation algorithm to convert the model into a more easily solvable single loading (SL) mode cross-warehouse transfer model, thus achieving both feasibility and computational efficiency.

[0143] like Figure 2 As shown in the embodiment of the present invention, the two-stage percentage threshold heuristic algorithm solution model includes a first stage and a second stage. The first stage includes the following steps:

[0144] initializing algorithm parameters, the algorithm parameters including the shortage quantity caused by supply interruption , unit shortage cost , population quantity , selection probability , maximum iteration number , batch loading quantity and threshold value ;

[0145] generating a demand reduction matrix based on a demand decomposition algorithm , calculating a demand-adjusted outbound vehicle demand matrix ;

[0146] adopting a single and continuous loading SL scheduling mode, generating an initial population of a number of chromosomes randomly based on a vehicle serial number coding method ;

[0147] calculating the fitness value of each chromosome according to the target function total cost, the total cost including time cost, shortage cost and scheduling cost, and the fitness value function is ;

[0148] performing a selection operation, adopting a roulette strategy to select the probability of the first chromosome , and selecting parent chromosomes in total;

[0149] adopting a partial mapping crossover PMX method to perform a crossover operation, and adopting a random exchange mutation method to perform a mutation operation;

[0150] population updating, recording the highest and lowest fitness chromosomes in each generation population, replacing the lowest fitness chromosome with the highest fitness chromosome, and iterating until the maximum iteration number is reached , outputting the total cost under the SL mode and the consumed scheduling cost ;

[0151] judging the size of and the threshold value , if , the algorithm adopts the SL mode and outputs the result, if , the algorithm adopts a multiple loading ML mode to perform a second stage.

[0152] It should be noted that the first stage includes steps S1-S8, which are as follows:

[0153] S1, obtaining various product shortage data caused by supply interruption, such as Figure 1The supply of the product category 2 is interrupted;

[0154] S2, correspondingly, the demand of all outbound vehicles also needs to be adjusted, based on the unit shortage cost (combined with the difference in cargo location, urgency, and freshness difference) of each outbound vehicle for different product categories, the demand decomposition algorithm is proposed to calculate the dynamic demand adjustment data of the outbound vehicle;

[0155] S3, based on the demand reduction matrix, the adjusted outbound vehicle demand matrix is calculated to ensure that the total demand reduction matches the missing amount of the supply interruption, and the corresponding cumulative shortage cost is calculated;

[0156] S4, the adjustment of the outbound vehicle demand significantly affects the supply and demand matching and cross-docking process of the inbound / outbound vehicle, combined with the constraints such as the transfer correlation of the inbound / outbound vehicle and the unloading / loading mode, a mixed integer cross-docking center transfer model is constructed to minimize the time cost, shortage cost and scheduling cost;

[0157] S5, further, based on the cross-docking center transfer model in the traditional single (Single loading, SL) loading mode, to speed up the transfer process and reduce the cost, a cross-docking center transfer model in multiple loading (Multiple loading, ML) mode is constructed;

[0158] S6, in order to improve the operation efficiency of the model, a batch loading quantity parameter is introduced , and a mode conversion algorithm is designed to convert the ML mode into the SL mode which is easier to solve;

[0159] S7, a two-stage percentage threshold heuristic algorithm is proposed to intelligently determine which mode (SL, ML) has high solving efficiency and low cost, avoiding blind calculation in both modes. At the same time, the three-layer architecture of vehicle serial number, product category and loading quantity is adopted in the coding scheme, which effectively solves the modeling and solving problems in the ML mode.

[0160] S8, based on the optimal scheduling scheme output by the algorithm, the optimal outbound vehicle demand adjustment matrix, each inbound / outbound unloading / loading scheduling scheme, and each cost information are obtained.

[0161] According to the embodiment of the application, the second stage includes the following steps:

[0162] Based on the batch loading quantity , the single and continuous loading process of each outbound vehicle is converted into segment loading operation;

[0163] The segment loading operation is randomly sorted, and the mode conversion algorithm is applied to generate the outbound vehicle demand matrix after random sorting , wherein The set of outbound vehicles after the virtual vehicle is introduced;

[0164] Based on the new outbound vehicle demand matrix The roulette wheel selection, crossover and mutation operations are performed by using the coding mode based on the virtual vehicle serial number.

[0165] It is judged whether the termination condition is met, if yes, the process is ended, and the total cost in the multi-loading (ML) mode is outputted. If not, the roulette wheel selection, crossover and mutation operations are performed again.

[0166] According to the embodiment of the present application, the partial mapping crossover (PMX) comprises the following steps:

[0167] The two parent chromosomes which need to be crossed are selected after the parent population is grouped in pairs, and two different random integers are generated.

[0168] The parts between the two parent gene random integers are crossed; after the crossing, the non-repeated numbers are retained, the repeated numbers appearing outside the crossing part are eliminated by using the partial mapping method, and the corresponding relationship of the crossing part is used for mapping.

[0169] If the fitness value of the chromosome after the crossing is higher than that before the crossing, the crossing operation is accepted, if not, the original chromosome is retained.

[0170] The two parent chromosomes which are left after the grouping in pairs are crossed repeatedly.

[0171] The second aspect of the present application provides a cross-docking scheduling system, which comprises:

[0172] A data acquisition module is configured to acquire supply interruption information, product type parameters, cross-docking scheduling environment, etc.

[0173] An optimization module is configured to execute a two-stage percentage threshold heuristic algorithm and output optimal unloading / loading sequence, outbound vehicle demand matrix, total cost, etc.

[0174] A control module is configured to apply the obtained scheduling scheme to the job scheduling system of the cross-docking center.

[0175] The system can avoid the situation that the arrived goods do not match the current outbound vehicle demand and need to wait for a long time, thereby reducing the transfer time, but the multiple entry and exit of the outbound vehicles also increase the scheduling cost, and a balance needs to be sought between the transfer time cost and the scheduling cost.

[0176] In summary, the present application has the following advantages:

[0177] Integrated innovation: The dynamic demand adjustment under supply disruption and the adaptive selection of flexible operation modes (SL / ML) are integrated into a unified decision framework for joint optimization.

[0178] Algorithm efficiency: The proposed two-stage percentage threshold heuristic algorithm can intelligently determine which mode is more efficient, avoiding blind calculation in both modes and improving solution efficiency. Meanwhile, by introducing the batch loading quantity parameter and adopting a three-layer coding scheme based on vehicle serial number, product type, and loading quantity, the modeling and solving problems in the flexible and complex ML mode are effectively solved.

[0179] Resilience enhancement: The method can significantly reduce the total cost under supply disruption, improving the ability of cross-docking centers to cope with uncertainty.

[0180] The above is only the preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features, as long as the modifications, equivalent replacements, improvements, etc. are within the spirit and principles of the present application, they should be included in the protection scope of the present application.

Claims

1. A two-stage cross-depot vehicle scheduling algorithm for supply disruptions, characterized in that, include: Obtain product supply shortage data due to supply disruptions in inbound vehicles, construct a demand decomposition algorithm by combining the product supply shortage data with the initial demand matrix of outbound vehicles and the unit shortage cost of each outbound vehicle for different product types, and calculate the demand reduction matrix. Calculate the adjusted outbound vehicle demand matrix based on the demand reduction matrix, and calculate the stockout cost; Based on stockout costs, transit time costs, and outbound vehicle scheduling costs, a cross-warehouse center transit model is constructed with the goal of minimizing total costs. Establish cross-warehouse transfer models under single and multiple loading modes, and design a mode conversion algorithm; Based on the objective function and constraints, a three-layer coding scheme consisting of vehicle serial number, product type, and loading quantity is constructed. A two-stage percentage threshold heuristic algorithm is designed to solve the cross-warehouse transfer model, and the updated outbound vehicle demand matrix, scheduling scheme, and all cost information are output.

2. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions as described in claim 1, characterized in that, Based on data on the shortage of inbound delivery vehicles, an algorithm for decomposing inbound vehicle demand was designed, which includes: Let the total number of cases be the first. Out-of-stock quantity of products that cannot be delivered on time Product category collection Outbound vehicle For the Initial demand for this product , Generate outbound vehicles based on demand decomposition algorithm Demand Reduction Matrix ; The requirement decomposition algorithm must at least satisfy the following constraints: Constraint 1: The total decrease in demand must match the amount of supply shortage. ; Constraint 2: The reduction in demand shall not exceed the initial demand and shall be non-negative. .

3. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions as described in claim 2, characterized in that, Based on the demand reduction matrix, the cost of stockouts is calculated, specifically including: Set up each outbound vehicle For the Unit stockout cost of this product ; Calculate the demand reduction matrix based on the demand decomposition algorithm. ; Initial demand matrix based on outbound vehicles Calculate the adjusted outbound vehicle demand matrix. The formula is as follows: ； Calculate the corresponding out-of-stock cost .

4. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions as described in claim 3, characterized in that, Establish to minimize total cost For the cross-warehouse central transshipment model with the objective of [objective], the total cost of the objective function is expressed as: ； in, This represents the total completion time consumed during the unloading / loading process of all products at the cross-docking center, multiplied by... Convert time into cost; the second item is the total cost of stockouts. This represents the unit dispatch cost incurred by a vehicle entering and leaving the loading platform once. This indicates the total number of times a vehicle enters or exits the loading platform. The constraints include supply and demand balance for stockouts, balance of inbound vehicle transfers, balance of outbound vehicle demand, transfer-related constraints, unloading / loading time constraints for inbound / outbound vehicles, sorting constraints, and unloading / loading mode constraints.

5. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions according to claim 4, characterized in that, Establish cross-warehouse transfer models for single and multiple loading modes, and design a mode transformation algorithm: A cross-warehouse transfer model is constructed based on the sorting of vehicle serial numbers under a single and continuous loading scheduling mode. Introducing batch loading quantity parameters This transforms a single, uninterrupted loading operation of a single outbound vehicle into a multi-stage loading operation that can be interrupted. Assume that the outbound vehicles are adjusted due to supply disruption. For the Demand for this product The number of times it was loaded Passing the exam Quantity of each shipment Calculate using the following formula: ； in, ; ； in, For the floor operation, the original floor is rounded down. The process of loading goods onto a single, continuous vehicle is transformed into an interruptible process. Section loading operation, the quantity loaded in each operation is ; Section loading operation introduction Each virtual vehicle corresponds to one loading quantity. ; Each loading operation consists of three layers of information: vehicle serial number, product type, and loading quantity. This means the process begins from the vehicle leaving the warehouse. Loading the first The quantity of the products is ; Each loading operation is randomly ordered. If adjacent loading operations come from the same outbound vehicle, the loading quantities are combined to obtain the combined outbound vehicle demand matrix. ,in This belongs to the set of vehicles leaving the warehouse after the introduction of virtual vehicles.

6. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions according to claim 5, characterized in that, The two-stage percentage threshold heuristic algorithm solution model includes a first stage and a second stage. The first stage includes the following steps: Initialize algorithm parameters, including the amount of stockouts due to supply disruptions. Unit out-of-stock cost Population size Probability of choice Maximum number of iterations Batch loading quantity With threshold ; A demand reduction matrix is ​​generated based on a demand decomposition algorithm. Calculate the adjusted outbound vehicle demand matrix. ; The scheduling mode adopts a single and continuous loading (SL) method, and the vehicle sequence number is randomly generated based on the coding method. The initial population of chromosomes; Calculate the fitness value for each chromosome based on the total cost of the objective function. Including time cost, stockout cost, and scheduling cost, the fitness value function is: ; Perform the selection operation, using a roulette wheel strategy to select the first... The probability of each chromosome , total selection One parent generation; The crossover operation is performed using a partially mapped crossover (PMX) method, and the mutation operation is performed using a random swap mutation method. Population update involves recording the chromosomes with the highest and lowest fitness in each generation, replacing the chromosome with the chromosome with the highest fitness, and iterating until the maximum number of iterations is reached. Output the total cost in SL mode. and the scheduling costs incurred. ; judge With threshold The size, if The algorithm uses SL mode and outputs the results. The algorithm employs a multi-loading ML model to execute the second phase.

7. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions as described in claim 6, characterized in that, The second stage includes the following steps: Based on batch loading quantity Each vehicle leaving the warehouse The single and continuous loading process is transformed into Section loading operations; right The loading and unloading operations are randomly sorted, and a mode transformation algorithm is applied to generate a randomly sorted outbound vehicle demand matrix. ,in This belongs to the set of vehicles leaving the warehouse after the introduction of virtual vehicles; Based on the new outbound vehicle demand matrix It adopts an encoding method based on virtual vehicle serial numbers to perform roulette wheel selection, crossover, and mutation operations; Determine if the termination condition is met; if so, terminate the process and output the total cost under the multiple loading ML model. If the conditions are not met, the roulette wheel selection, crossover, and mutation operations will be executed again.

8. The two-stage cross-depot vehicle scheduling algorithm for supply disruptions according to claim 5, characterized in that, Partial mapping cross PMX includes the following steps: After grouping the parent population into pairs, select the two parent chromosomes that need to be crossovered and generate two distinct random integers. Cross over the random integer portion between the two parent genes; after the crossover, non-repeating numbers are retained, and duplicate numbers appearing outside the crossover portion are eliminated by partial mapping, using the correspondence of the crossover portion for mapping; If the fitness value of the chromosome after crossover is higher than that before crossover, the crossover operation is accepted; if it is lower than that before crossover, the original chromosome is retained. The remaining two parent chromosomes, after being grouped into pairs, are subjected to repeated crossover processing.

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