Circulating packaging container transportation route scheduling plan generation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of logistics, in particular to a cyclic packaging container transportation route scheduling plan generation method and device, equipment and a storage medium, and the method comprises the steps: defining decision variables based on basic data parameters; establishing a target constraint condition and a scheduling optimization function based on the decision variable and the obtained transportation preset information; obtaining a target model file based on the transportation preset information, the target constraint condition and the scheduling optimization function; and obtaining a decision variable value based on a preset solver and the target model file, and determining a cyclic packaging container transportation route scheduling plan based on the decision variable value. According to the method, the minimum transportation cost is ensured while the minimum order stockout quantity is ensured, and the calculation efficiency of the scheduling plan is also improved.

Description

Technical Field

[0001] This application relates to the field of logistics technologies, and in particular, to a method, apparatus, device, and storage medium for generating a transportation route scheduling plan for reusable packaging containers. Background Art

[0002] In logistics, to facilitate the transportation of goods and reduce transportation costs, reusable packaging containers with industry standards are often used to package and transport goods. In this way, manufacturers, processing enterprises, and end-users of goods do not need to purchase packaging containers for packaging goods, and since reusable packaging containers can be recycled, the logistics packaging cost can be greatly reduced. For example, in the automotive parts supply chain, reusable packaging containers are widely used. Operating enterprises related to reusable packaging containers will build an operating network between different cities to facilitate the provision of corresponding reusable packaging containers to goods. The operating network includes operating sites set in different cities. The operating network can schedule reusable packaging containers between different operating sites according to the types, quantities, and logistics status within a preset period of reusable packaging containers in each operating site, so that different operating sites can all meet the needs of packaging and transporting goods as much as possible. It should be noted that the above-mentioned operating sites mainly include two forms: regional distribution centers and recycling points.

[0003] Currently, the method for generating a transportation route scheduling plan for reusable packaging containers is as follows: First, determine the initial inventory quantity of each type of reusable packaging container corresponding to the regional distribution center and the recycling point at the initial moment of the preset period; then, obtain the daily order volume of the regional distribution center and calculate the difference between the daily order volume and the initial inventory quantity; after that, if the difference is greater than 0, determine different recycling points or combinations of recycling points according to the initial inventory quantities of different recycling points to generate a transportation route scheduling plan for the currently stocked reusable packaging containers to the scheduling plan of the above-mentioned regional distribution center, so as to obtain multiple scheduling plans; then, calculate the transportation costs corresponding to different scheduling plans, select the scheduling plan with the lowest transportation cost, and synchronously adjust the inventory quantity of the recycling point corresponding to this scheduling plan. In addition, if the reusable packaging containers stocked at the recycling point cannot meet the requirements of the regional distribution center, use other regional distribution centers as new recycling points and formulate a new scheduling plan in combination with the existing scheduling plan; in the above manner, generate a corresponding scheduling plan for each regional distribution center.

[0004] However, the above method for scheduling reusable packaging containers mainly uses the greedy algorithm for manual calculation to obtain a scheduling plan. In actual situations, the number of regional distribution centers and recycling points involved in the operation network is more than one hundred. The calculation efficiency of generating a scheduling plan using the above method is very low. In addition, while trying to ensure that each regional distribution center is provided with a sufficient quantity of reusable packaging containers, it is difficult to guarantee the lowest transportation cost through the scheduling plan calculated by the above greedy algorithm. Summary of the Invention

[0005] To facilitate improving the efficiency of calculating the scheduling plan while ensuring the minimum order shortage quantity and the lowest transportation cost, the present application provides a method, device, equipment, and storage medium for generating a transportation route scheduling plan for reusable packaging containers.

[0006] In a first aspect, the present application provides a method for generating a transportation route scheduling plan for reusable packaging containers, including:

[0007] Defining decision variables based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0008] Establishing target constraint conditions and a scheduling optimization function based on the decision variables and the obtained transportation preset information; the transportation preset information includes: the transportation unit price of the packaging container, the transportation period of the packaging container, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, the demand quantity at the regional distribution center, and the shortage weight at the regional distribution center.

[0009] Obtaining a target model file based on the transportation preset information, the target constraint conditions, and the scheduling optimization function.

[0010] Obtaining the values of the decision variables based on a preset solver and the target model file, and determining the transportation route scheduling plan for the reusable packaging containers based on the values of the decision variables.

[0011] In a second aspect, the present application provides a device for generating a transportation route scheduling plan for reusable packaging containers, including:

[0012] A variable definition module, configured to define decision variables based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0013] A constraint and function generation module, configured to establish target constraint conditions and a scheduling optimization function based on the decision variables and the obtained preset transportation information; the preset transportation information includes: the transportation unit price of packaging containers, the transportation cycle of packaging containers, the planned arrival quantity of regional distribution centers, the planned supply quantity of recycling points, the demand quantity of regional distribution centers, and the shortage weight of regional distribution centers.

[0014] A model file generation module, configured to obtain a target model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function.

[0015] A scheduling plan generation module, configured to obtain decision variable values based on a preset solver and the target model file, and determine a transportation route scheduling plan for reusable packaging containers based on the decision variable values.

[0016] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the above method are implemented.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are implemented.

[0018] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0019] The above method, device, equipment and storage medium for generating a transportation route scheduling plan for reusable packaging containers define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers. Based on the decision variables and the obtained preset transportation information, target constraint conditions and a scheduling optimization function are established. The preset transportation information includes: the transportation unit price of the packaging container, the transportation period of the packaging container, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, the demand quantity at the regional distribution center, and the shortage weight at the regional distribution center. Based on the preset transportation information, the target constraint conditions, and the scheduling optimization function, a target model file is obtained. Based on a preset solver and the target model file, the values of the decision variables are obtained, and based on the values of the decision variables, a transportation route scheduling plan for the reusable packaging containers is determined. Through the above implementation, by constructing the decision variables and obtaining the preset transportation information, target constraint conditions and a scheduling optimization function can be generated, and then the preset solver can quickly solve the target constraint conditions and the scheduling optimization function to obtain a transportation route scheduling plan for the reusable packaging containers. The solver's processing of the target constraint conditions and the scheduling optimization function can not only greatly improve the solving speed, but also determine all possible transportation plans according to the target constraint conditions and the scheduling optimization function, and calculate the transportation costs of each transportation plan. In this way, it is convenient to ensure the minimum shortage quantity of orders while ensuring the lowest transportation cost, and also improve the calculation efficiency of the scheduling plan.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a flowchart of a method for generating a transportation route scheduling plan for reusable packaging containers provided in an embodiment of the present application;

[0023] Figure 2 It is a schematic structural diagram of a device for generating a transportation route scheduling plan for reusable packaging containers provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic structural diagram of a computer device provided in an embodiment of the present application;

[0025] Figure 4 It is an internal structure diagram of a computer-readable storage medium provided in an embodiment of the present application. Detailed implementation manners

[0026] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure, but not to limit the present disclosure.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or equipment.

[0028] In this article, the term "and / or" is only a relationship describing associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0029] Embodiment 1

[0030] Figure 1 It is a flowchart of a method for generating a transportation route scheduling plan for a reusable packaging container provided in Embodiment 1 of the present application. Refer to Figure 1 , this method can be executed by a device that executes this method. The device can be implemented in a software and / or hardware manner. The method includes:

[0031] S110. Define decision variables based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0032] Among them, in this embodiment, the packaging container is a shared rental recycling packaging box, which is applied to the shared rental recycling packaging box operation network. The shared rental recycling packaging box operation network includes a regional distribution center and a recycling point. The shared rental recycling packaging box circulates between the regional distribution center and the recycling point, and between the regional distribution centers for recycling use; the shared rental recycling packaging box can be used for the cross-provincial long-distance transportation of auto parts and vehicles, and is used for short-term rental. After the customer who rents the shared rental recycling packaging box completes the use of the shared rental recycling packaging box, the shared rental recycling packaging box will be further transported to a nearby recycling point; in other embodiments, the specific form of the packaging container is not specifically limited; the shared rental recycling packaging box operation network sets several regional distribution centers and recycling points according to the actual situation in a preset city. The regional distribution center is used to provide the packaging container of the packaging product to the user, and the recycling point is used to recycle the packaging container that has completed one cycle of packaging. In addition, the recycling point can provide the recycled packaging container to the regional distribution center. Moreover, the regional distribution centers can also provide packaging containers to each other.

[0033] Among them, the packaging container types include multiple types, such as type A, type B, type C..., and the packaging containers of different packaging container types are used to package products of different specifications; and the set of each packaging container type is denoted as the packaging container type set P, the set of each regional distribution center in the shared rental recycling packaging box operation network is denoted as the regional distribution center set CMC, and the set of each recycling point in the shared rental recycling packaging box operation network is denoted as CP. The time planned to complete the transportation demand of the user's product is a scheduling period. In this embodiment, a scheduling period is N days, and the set of each day in the scheduling period is the period set T, T = {0, 1, 2,..., N - 1}.

[0034] Among them, the above-mentioned packaging container type set, regional distribution center set, recycling point set, scheduling period, and period set are collectively referred to as basic data parameters. Through these basic data parameters, the decision variables can be further defined. The decision variables are multiple unknowns. In the subsequent part of this embodiment, the specific values of each unknown need to be solved, so as to determine the transportation plan with the lowest transportation cost according to the specific values of each unknown.

[0035] In this embodiment, the decision variables include: the inventory of the recycling point InvCP p,i,t , p ∈ P, i ∈ CP, t ∈ T, the inventory of the regional distribution center InvCMC p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the shortage quantity of the regional distribution center NegInvCMC p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the scheduling quantity between the recycling point and the regional distribution center Ship p,i,j,t, where \(p\in P\), \(i\in CP\), \(j\in CMC\), \(t\in T\), the scheduling volume \(X_{Ship}\) between regional distribution centers p,i,j,t , where \(p\in P\), \(i,j\in CMC\), \(t\in T\); among them, \(p\) represents the \(p\)-th type of packaging container in the set of packaging container types, \(i\) is the \(i\)-th recycling point in the set of recycling points, \(j\) represents the \(j\)-th regional distribution center in the set of regional distribution centers, and \(t\) represents the \(t\)-th day in the set of cycles \(T\); the inventory of the recycling point represents the inventory of the packaging containers of each type of packaging container at the recycling point every day during the scheduling period; the inventory of the regional distribution center represents the inventory of the packaging containers of each type of packaging container at the regional distribution center every day during the scheduling period; the shortage quantity of the regional distribution center represents the difference between the demand for the packaging containers of each type of packaging container at the corresponding regional distribution center every day during the scheduling period and the inventory of the corresponding regional distribution center; the scheduling volume between the recycling point and the regional distribution center represents the transportation volume of the packaging containers of each type of packaging container transported from each recycling point to each regional distribution center every day during the scheduling period; the scheduling volume between regional distribution centers represents the transportation volume of the packaging containers of each type of packaging container transported between each regional distribution center every day during the scheduling period.

[0036] S120. Establish target constraint conditions and a scheduling optimization function based on the decision variables and the obtained transportation preset information; the transportation preset information includes: the transportation unit price of the packaging container, the transportation cycle of the packaging container, the planned arrival quantity of the regional distribution center, the planned supply quantity of the recycling point, the demand of the regional distribution center, and the shortage weight of the regional distribution center.

[0037] Among them, in this embodiment, the transportation preset information is provided by the shared rental recycling packaging box operation network, and the transportation preset information includes: the transportation unit price \(U_{ShipCost}\) of the packaging container i,j , where \(i,j\in CP\cup CMC\), the transportation cycle \(LT\) of the packaging container i,j , where \(i,j\in CP\cup CMC\), the planned arrival quantity \(Receipt\) of the regional distribution center p,i,t , where \(p\in P\), \(i\in CMC\), \(t\in T\), the planned supply quantity \(Supply\) of the recycling point p,i,t , where \(p\in P\), \(i\in CP\), \(t\in T\), the demand \(Demand\) of the regional distribution center p,i,t, where \(p\in P\), \(i\in CMC\), \(t\in T\), and \(WNegInv\) is the shortage weight of the regional distribution center; among them, the unit price of packaging container transportation represents the transportation unit price of packaging containers between each recycling point and each regional distribution center, and between each regional distribution center; the transportation cycle of packaging containers represents the transportation cycle of packaging containers between each recycling point and each regional distribution center, and between each regional distribution center; the planned arrival quantity of the regional distribution center represents the planned arrival quantity of packaging containers of each packaging container type per day within the scheduling cycle; the planned supply quantity of the recycling point represents the planned supply quantity of packaging containers of each packaging container type per day within the scheduling cycle; the demand quantity of the regional distribution center represents the demand quantity of packaging containers of each packaging container type per day within the scheduling cycle.

[0038] It should be noted that this embodiment aims to achieve two goals. One is to minimize the transportation cost, and the other is to minimize the total shortage of packaging containers in the regional distribution center. The two goals correspond to two optimization objectives. However, in this embodiment, a solver is subsequently used to solve the transportation plan. Therefore, it is necessary to unify the two optimization objectives into one objective. By assigning different weights to the two optimization objectives respectively, it is convenient to unify the two optimization objectives into one objective; the above-mentioned shortage weight of the regional distribution center includes the first weight of the optimization objective of minimizing the transportation cost and the second weight of the optimization objective of minimizing the probability of shortage of packaging containers in the regional distribution center.

[0039] Among them, the objective constraint conditions and the scheduling optimization function are both established based on the decision variables and the obtained transportation preset information. When the subsequent solver solves the scheduling optimization function, the objective constraint conditions are used to constrain the balance of the inventory of packaging containers of each packaging container type per day at each recycling point and each regional distribution center within the scheduling cycle. The optimization objectives of the scheduling optimization function are: to minimize the transportation cost of the finally generated transportation plan and to minimize the probability of shortage of packaging containers in the regional distribution center.

[0040] S130. Obtain a target model file based on the transportation preset information, the objective constraint conditions, and the scheduling optimization function.

[0041] Among them, by performing corresponding processing on the transportation preset information, the objective constraint conditions, and the scheduling optimization function, the transportation preset information, the objective constraint conditions, and the scheduling optimization function can be unified into one file, and this file is denoted as the target model file.

[0042] S140. Obtain the decision variable values based on the preset solver and the target model file, and determine the transportation route scheduling plan for the reusable packaging containers based on the decision variable values.

[0043] Among them, a preset solver can solve the target model file to obtain the specific values of the above decision variables, and record the specific values of the decision variables as decision variable values; by performing corresponding processing on the decision variable values, a transportation plan that meets the lowest transportation cost and the lowest probability of the regional distribution center lacking packaging containers can be obtained, and this transportation plan is recorded as the recycling packaging container transportation route scheduling plan.

[0044] It should be noted that a method for generating a recycling packaging container transportation route scheduling plan provided in this embodiment is a logistics node transportation optimization solution for recycling carriers in the automotive industry. Customers, recycling points, and regional distribution centers are geographically separated. After the customer finishes using the recycling packaging container, the recycling packaging container will first be transported to the recycling point and then further transported to the regional distribution center. Then, the regional distribution center provides recycling packaging containers according to the customer's needs. Through the method for generating a recycling packaging container transportation route scheduling plan provided in this embodiment, a globally optimal scheduling optimization plan can be obtained. This globally optimal scheduling optimization plan can not only minimize the overall transportation cost but also minimize the total amount of packaging containers lacking in the regional distribution center to meet the customer's needs.

[0045] It should be noted that in this embodiment, decision variables are defined based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers; based on the decision variables and the obtained transportation preset information, target constraint conditions and a scheduling optimization function are established; the transportation preset information includes: the unit price of packaging container transportation, the transportation period of packaging containers, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, the demand quantity at the regional distribution center, and the shortage weight at the regional distribution center; based on the transportation preset information, the target constraint conditions, and the scheduling optimization function, a target model file is obtained; based on a preset solver and the target model file, decision variable values are obtained, and based on the decision variable values, a recycling packaging container transportation route scheduling plan is determined. Through the above implementation, by constructing decision variables and obtaining transportation preset information, target constraint conditions and a scheduling optimization function can be generated, and then a preset solver is used to quickly solve the target constraint conditions and the scheduling optimization function to obtain a recycling packaging container transportation route scheduling plan. The solver's processing of the target constraint conditions and the scheduling optimization function can not only greatly improve the solving speed but also determine all possible transportation plans based on the target constraint conditions and the scheduling optimization function and calculate the transportation costs of each transportation plan. In this way, it is convenient to ensure the lowest transportation cost while ensuring the smallest order shortage quantity and improve the calculation efficiency of the scheduling plan.

[0046] Embodiment 2

[0047] A method for generating a transportation route scheduling plan for a recyclable packaging container provided in Embodiment 2 of this application optimizes the step of "establishing target constraint conditions and a scheduling optimization function based on the decision variables and the obtained transportation preset information" in Embodiment 1. It should be noted that for parts not described in detail in this embodiment, reference can be made to the descriptions of other embodiments. The method includes:

[0048] S210. Define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory at the recycling point, the inventory at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0049] S221. Based on the inventory at the recycling point, the inventory at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers among the decision variables, and the packaging container transportation period, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, and the demand at the regional distribution center in the transportation preset information, establish target constraint conditions.

[0050] Among them, through the inventory at the recycling point InvCP p,i,t , p ∈ P, i ∈ CP, t ∈ T, the inventory at the regional distribution center InvCMC p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the shortage quantity at the regional distribution center NegInvCMC p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the scheduling quantity between the recycling point and the regional distribution center Ship p,i,j,t , p ∈ P, i ∈ CP, j ∈ CMC, t ∈ T, the scheduling quantity between regional distribution centers XShip p,i,j,t , p ∈ P, i, j ∈ CMC, t ∈ T, and the packaging container transportation period LT i,j , i, j ∈ CP ∪ CMC, the planned arrival quantity at the regional distribution center Receipt p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the planned supply quantity at the recycling point Supply p,i,t , p ∈ P, i ∈ CP, t ∈ T, and the demand at the regional distribution center Demand p,i,t , p ∈ P, i ∈ CMC, t ∈ T, a relational expression for balancing the inventory of each type of packaging container at each recycling point and each regional distribution center every day within the scheduling period can be constructed, thereby obtaining the target constraint conditions.

[0051] S222. Establish a scheduling optimization function based on the shortage quantity of the regional distribution center in the decision variables, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers, and the transportation unit price of the packaging container and the shortage weight of the regional distribution center in the transportation preset information.

[0052] Among them, the scheduling optimization function is based on the shortage quantity of the regional distribution center NegInvCMC in the decision variables p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the scheduling quantity Ship between the recycling point and the regional distribution center p,i,j,t , p ∈ P, i ∈ CP, j ∈ CMC, t ∈ T, the scheduling quantity XShip between regional distribution centers p,i,j,t , p ∈ P, i, j ∈ CMC, t ∈ T, and the transportation unit price UShipCost of the packaging container in the transportation preset information i,j , i, j ∈ CP ∪ CMC and the shortage weight WNegInv of the regional distribution center are established.

[0053] Exemplarily, the scheduling optimization function F = Min ∑ p,i,j,t UShipCost i,j *Ship p,i,j,t + ∑ p,i,j,t UShipCost i,j *XShip p,i,j,t + ∑ p,i,t WNegInv * NegInvCMC p,i,t .

[0054] S230. Obtain a target model file based on the transportation preset information, the target constraint conditions, and the scheduling optimization function.

[0055] S240. Obtain the decision variable values based on a preset solver and the target model file, and determine the transportation route scheduling plan for the reusable packaging containers based on the decision variable values.

[0056] Embodiment III

[0057] A method for generating a transportation route scheduling plan for reusable packaging containers provided in Embodiment III of this application optimizes the following in Embodiment II: "Based on the inventory quantity of the recycling point, the inventory quantity of the regional distribution center, the shortage quantity of the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers, and the transportation cycle of the packaging container, the planned arrival quantity of the regional distribution center, the planned supply quantity of the recycling point, and the demand quantity of the regional distribution center in the transportation preset information, establish target constraint conditions". It should be noted that for parts not described in detail in this embodiment, refer to the descriptions of other embodiments. The method includes:

[0058] S310. Define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory level at the recycling point, the inventory level at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0059] S321A. Based on the inventory level at the recycling point and the scheduling quantity between the recycling point and the regional distribution center among the decision variables, and the planned supply quantity of the recycling point in the preset transportation information, establish a first constraint condition.

[0060] Among them, the target constraint conditions include the first constraint condition. The first constraint condition is based on the inventory level at the recycling point InvCP p,i,t , p ∈ P, i ∈ CP, t ∈ T, the scheduling quantity between the recycling point and the regional distribution center Ship p,i,j,t , p ∈ P, i ∈ CP, j ∈ CMC, and the planned supply quantity of the recycling point Supply p,i,t , p ∈ P, i ∈ CP, t ∈ T is established.

[0061] Exemplarily, the first constraint condition is:

[0062] InvCP p,i,t-1 -Supply p,i,t -∑ j Ship p,i,j,t =InvCP p,i,t ; p ∈ P, i ∈ CP, j ∈ CMC, t ∈ T.

[0063] S321B. Based on the inventory level at the recycling point, the inventory level at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers among the decision variables, and the packaging container transportation period, the planned arrival quantity of the regional distribution center, and the demand quantity of the regional distribution center in the preset transportation information, establish a second constraint condition.

[0064] Among them, the target constraint conditions include the second constraint condition. The second constraint condition is based on the inventory level at the recycling point InvCP p,i,t , p ∈ P, i ∈ CP, t ∈ T, the inventory level at the regional distribution center InvCMC p,i,t , p ∈ P, i ∈ CMC, t ∈ T, the shortage quantity at the regional distribution center NegInvCMC p,i,t, where p ∈ P, i ∈ CMC, t ∈ T, the scheduling volume Ship between the recycling point and the regional distribution center p,i,j,t , where p ∈ P, i ∈ CP, j ∈ CMC, t ∈ T, the scheduling volume XShip between regional distribution centers p,i,j,t , where p ∈ P, i, j ∈ CMC, t ∈ T, and the transportation cycle LT of the packaging container in the transportation preset information i,j , where i, j ∈ CP ∪ CMC, the planned arrival volume Receipt of the regional distribution center p,i,t , where p ∈ P, i ∈ CMC, t ∈ T, the demand Demand of the regional distribution center p,i,t , where p ∈ P, i ∈ CMC, t ∈ T is established.

[0065] Exemplarily, the second constraint condition is:

[0066] p ∈ P, i ∈ CP, j ∈ CMC, t ∈ T.

[0067] S321C. The target constraint condition includes the first constraint condition and the second constraint condition.

[0068] S322. Based on the shortage volume of the regional distribution center, the scheduling volume between the recycling point and the regional distribution center, the scheduling volume between regional distribution centers in the decision variables, and the transportation unit price of the packaging container in the transportation preset information, establish a scheduling optimization function.

[0069] S330. Based on the transportation preset information, the target constraint condition, and the scheduling optimization function, obtain a target model file.

[0070] S340. Based on a preset solver and the target model file, obtain the values of the decision variables, and determine the transportation route scheduling plan of the reusable packaging container based on the values of the decision variables.

[0071] Embodiment 4

[0072] A method for generating a transportation route scheduling plan of a reusable packaging container provided in Embodiment 4 of the present application optimizes the "obtaining a target model file based on the transportation preset information, the target constraint condition, and the scheduling optimization function" in Embodiment 1; it should be noted that for parts not detailed in this embodiment, reference can be made to the descriptions of other embodiments. The method includes:

[0073] S410. Define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers.

[0074] S420. Establish target constraint conditions and a scheduling optimization function based on the decision variables and the obtained preset transportation information. The preset transportation information includes: the transportation unit price of the packaging container, the transportation period of the packaging container, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, the demand quantity at the regional distribution center, and the shortage weight at the regional distribution center.

[0075] S431. Obtain a primary model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function.

[0076] Among them, the primary model file includes: the preset transportation information, the target constraint conditions, and the scheduling optimization function.

[0077] S432. Process the primary model file based on a preset programming language to obtain a target model file.

[0078] Among them, the preset programming language is preferably java in this embodiment, and is not specifically limited in other embodiments. By processing the primary model file with the preset programming language, the preset transportation information, the target constraint conditions, and the scheduling optimization function can be expressed in the form of this programming language, so as to obtain a target model file, and the target model file is convenient for solving through a preset solver.

[0079] S440. Obtain decision variable values based on a preset solver and the target model file, and determine a scheduling plan for the transportation route of the reusable packaging container based on the decision variable values.

[0080] Embodiment Five

[0081] A method for generating a scheduling plan for the transportation route of a reusable packaging container provided in Embodiment Five of the present application optimizes the "obtaining decision variable values based on a preset solver and the target model file" in Embodiment One. It should be noted that for parts not detailed in this embodiment, reference can be made to the descriptions of other embodiments. The method includes:

[0082] S510. Define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, and the scheduling quantity between regional distribution centers.

[0083] S520. Establish target constraint conditions and a scheduling optimization function based on the decision variables and the obtained preset transportation information. The transportation preset information includes: the transportation unit price of packaging containers, the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, the planned supply quantity at recycling points, the demand quantity at regional distribution centers, and the shortage weight at regional distribution centers.

[0084] S530. Obtain a target model file based on the transportation preset information, the target constraint conditions, and the scheduling optimization function.

[0085] S541. Process the target model file based on the preset solver to obtain decision variable values. The decision variable values include: the inventory value at recycling points, the inventory value at regional distribution centers, the shortage value at regional distribution centers, the scheduling value between recycling points and regional distribution centers, and the scheduling value between regional distribution centers.

[0086] Among them, by inputting the target model file into the preset solver for processing, decision variable values corresponding to each decision variable can be obtained. It should be noted that decision variables are unknowns, while decision variable values have specific numerical values. In addition, the solver includes but is not limited to open-source solvers and non-open-source commercial solvers, and specific details are not limited.

[0087] S542. Determine the scheduling plan for the transportation route of reusable packaging containers based on the decision variable values.

[0088] Embodiment Six

[0089] A method for generating a scheduling plan for the transportation route of reusable packaging containers provided in Embodiment Six of the present application optimizes the "determining the scheduling plan for the transportation route of reusable packaging containers based on the decision variable values" in Embodiment One. It should be noted that for parts not detailed in this embodiment, the descriptions in other embodiments can be referred to. The method includes:

[0090] S610. Define decision variables based on basic data parameters. The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods. The decision variables include: the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, and the scheduling quantity between regional distribution centers.

[0091] S620. Establish a target constraint condition and a scheduling optimization function based on the decision variable and the obtained preset transportation information; the preset transportation information includes: the transportation unit price of the packaging container, the transportation cycle of the packaging container, the planned arrival quantity of the regional distribution center, the planned supply quantity of the recycling point, the demand quantity of the regional distribution center, and the shortage weight of the regional distribution center.

[0092] S630. Obtain a target model file based on the preset transportation information, the target constraint condition, and the scheduling optimization function.

[0093] S641. Obtain the decision variable value based on a preset solver and the target model file.

[0094] S642. Perform tabular processing on the decision variable value to obtain a cyclic packaging container transportation route scheduling plan.

[0095] Among them, tabular processing is to process the decision variable value into a corresponding table, which can display the quantity of each type of packaging container transported between the recycling point and the regional distribution center and between the regional distribution centers every day during the scheduling cycle; and this table is recorded as the cyclic packaging container transportation route scheduling plan.

[0096] It should be noted that displaying the cyclic packaging container transportation route scheduling plan in the form of a table is convenient for users to intuitively understand the cyclic packaging container transportation route scheduling plan.

[0097] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0098] Embodiment Seven

[0099] Based on the same inventive concept, this embodiment also provides a device for generating a transportation route scheduling plan for reusable packaging containers, which is used to implement the method for generating a transportation route scheduling plan for reusable packaging containers involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for generating a transportation route scheduling plan for reusable packaging containers provided below can refer to the limitations on the method for generating a transportation route scheduling plan for reusable packaging containers in the above text, and will not be repeated here.

[0100] In this embodiment, as Figure 2 shown, a device for generating a transportation route scheduling plan for reusable packaging containers is provided, including:

[0101] A variable definition module, configured to define decision variables based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, and the scheduling quantity between regional distribution centers;

[0102] A constraint and function generation module, configured to establish target constraint conditions and a scheduling optimization function based on the decision variables and the obtained preset transportation information; the preset transportation information includes: the transportation unit price of packaging containers, the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, the planned supply quantity at recycling points, the demand quantity at regional distribution centers, and the shortage weight at regional distribution centers;

[0103] A model file generation module, configured to obtain a target model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function;

[0104] A scheduling plan generation module, configured to obtain the values of decision variables based on a preset solver and the target model file, and determine a transportation route scheduling plan for reusable packaging containers based on the values of the decision variables.

[0105] Each module in the above device for generating a transportation route scheduling plan for reusable packaging containers can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0106] It should be noted that in this embodiment, decision variables are defined based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers; based on the decision variables and the obtained transportation preset information, target constraint conditions and a scheduling optimization function are established; the transportation preset information includes: the transportation unit price of the packaging container, the transportation period of the packaging container, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, the demand quantity at the regional distribution center, and the shortage weight at the regional distribution center; based on the transportation preset information, the target constraint conditions, and the scheduling optimization function, a target model file is obtained; based on a preset solver and the target model file, decision variable values are obtained, and based on the decision variable values, a scheduling plan for the transportation route of the reusable packaging container is determined. Through the above implementation, the target constraint conditions and the scheduling optimization function can be generated through the constructed decision variables and the obtained transportation preset information, and then the preset solver can quickly solve the target constraint conditions and the scheduling optimization function to obtain the scheduling plan for the transportation route of the reusable packaging container. The solver's processing of the target constraint conditions and the scheduling optimization function can not only greatly improve the solving speed, but also determine all possible transportation plans according to the target constraint conditions and the scheduling optimization function, and calculate the transportation costs of each transportation plan. In this way, it is convenient to ensure the minimum order shortage quantity while ensuring the lowest transportation cost, and also improve the calculation efficiency of the scheduling plan.

[0107] In one embodiment, in terms of establishing target constraint conditions and a scheduling optimization function based on the decision variables and the obtained transportation preset information, the constraint and function generation module is specifically configured to: based on the inventory quantity at the recycling point, the inventory quantity at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers among the decision variables, and the transportation period of the packaging container, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, and the demand quantity at the regional distribution center among the transportation preset information, establish target constraint conditions;

[0108] Based on the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers among the decision variables, and the transportation unit price of the packaging container and the shortage weight at the regional distribution center among the transportation preset information, establish a scheduling optimization function.

[0109] In one embodiment, in establishing the objective constraint conditions based on the inventory at the recycling point, the inventory at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers among the decision variables, and the packaging container transportation cycle, the planned arrival quantity at the regional distribution center, the planned supply quantity at the recycling point, and the demand quantity at the regional distribution center in the preset transportation information, the constraint and function generation module is specifically configured to: establish a first constraint condition based on the inventory at the recycling point, the scheduling quantity between the recycling point and the regional distribution center among the decision variables, and the planned supply quantity at the recycling point in the preset transportation information;

[0110] Establish a second constraint condition based on the inventory at the recycling point, the inventory at the regional distribution center, the shortage quantity at the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, the scheduling quantity between regional distribution centers among the decision variables, and the packaging container transportation cycle, the planned arrival quantity at the regional distribution center, and the demand quantity at the regional distribution center in the preset transportation information;

[0111] The objective constraint conditions include the first constraint condition and the second constraint condition.

[0112] In one embodiment, in obtaining the target model file based on the preset transportation information, the objective constraint conditions, and the scheduling optimization function, the model file generation module is specifically configured to: obtain a primary model file based on the preset transportation information, the objective constraint conditions, and the scheduling optimization function;

[0113] Process the primary model file based on a preset programming language to obtain the target model file.

[0114] In one embodiment, in obtaining the decision variable values based on a preset solver and the target model file, the scheduling plan generation module is specifically configured to: process the target model file based on the preset solver to obtain the decision variable values, and the decision variable values include: the inventory value at the recycling point, the inventory value at the regional distribution center, the shortage value at the regional distribution center, the scheduling value between the recycling point and the regional distribution center, and the scheduling value between regional distribution centers.

[0115] In one embodiment, in determining the cyclic packaging container transportation route scheduling plan based on the decision variable values, the scheduling plan generation module is specifically configured to:

[0116] Perform tabular processing on the decision variable values to obtain the cyclic packaging container transportation route scheduling plan.

[0117] Embodiment VIII

[0118] In this embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 3 . The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for generating a cyclic packaging container transportation route scheduling plan.

[0119] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0120] Embodiment Nine

[0121] In this embodiment, a computer-readable storage medium is provided, as shown in Figure 4 . A computer program is stored thereon, and when the computer program is executed by a processor, it implements the steps in the above method embodiments.

[0122] Embodiment Ten

[0123] In this embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, it implements the steps in the above method embodiments.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present disclosure are all information and data authorized by the user or fully authorized by all parties.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided by the present disclosure can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided by the present disclosure can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0126] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0127] The above-described embodiments merely represent several implementation manners of the present disclosure. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patents of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present disclosure, several modifications and improvements can still be made, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the appended claims.

Claims

1. A method for generating a transportation route scheduling plan for a recyclable packaging container, characterized in that, Including: Defining decision variables based on basic data parameters; The basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, and the scheduling quantity between regional distribution centers; Establishing target constraint conditions and a scheduling optimization function based on the decision variables and the obtained transportation preset information; the transportation preset information includes: the transportation unit price of packaging containers, the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, the planned supply quantity at recycling points, the demand quantity at regional distribution centers, and the shortage weight at regional distribution centers; Obtaining a target model file based on the transportation preset information, the target constraint conditions, and the scheduling optimization function; Obtaining the values of decision variables based on a preset solver and the target model file, and determining the scheduling plan for the transportation route of reusable packaging containers based on the values of the decision variables.

2. The method according to claim 1, wherein The establishing of the target constraint conditions and the scheduling optimization function based on the decision variables and the obtained transportation preset information includes: Establishing target constraint conditions based on the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, the scheduling quantity between regional distribution centers among the decision variables, and the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, the planned supply quantity at recycling points, and the demand quantity at regional distribution centers among the transportation preset information; Establishing a scheduling optimization function based on the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, the scheduling quantity between regional distribution centers among the decision variables, and the transportation unit price of packaging containers and the shortage weight at regional distribution centers among the transportation preset information.

3. The method according to claim 2, wherein The establishing of the target constraint conditions based on the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, the scheduling quantity between regional distribution centers among the decision variables, and the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, the planned supply quantity at recycling points, and the demand quantity at regional distribution centers among the transportation preset information includes: Establishing a first constraint condition based on the inventory at recycling points, the scheduling quantity between recycling points and regional distribution centers among the decision variables, and the planned supply quantity at recycling points among the transportation preset information; Establishing a second constraint condition based on the inventory at recycling points, the inventory at regional distribution centers, the shortage quantity at regional distribution centers, the scheduling quantity between recycling points and regional distribution centers, the scheduling quantity between regional distribution centers among the decision variables, and the transportation period of packaging containers, the planned arrival quantity at regional distribution centers, and the demand quantity at regional distribution centers among the transportation preset information; The target constraint conditions include the first constraint condition and the second constraint condition.

4. The method according to claim 1, wherein Obtaining a target model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function includes: Obtaining a primary model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function; Processing the primary model file based on a preset programming language to obtain the target model file.

5. The method according to claim 1, wherein Obtaining decision variable values based on the preset solver and the target model file includes: Processing the target model file based on the preset solver to obtain decision variable values, where the decision variable values include: the inventory value of the recycling point, the inventory value of the regional distribution center, the shortage value of the regional distribution center, the scheduling value between the recycling point and the regional distribution center, and the scheduling value between regional distribution centers.

6. The method according to claim 1, wherein Determining a transportation route scheduling plan for the reusable packaging containers based on the decision variable values includes: Performing tabular processing on the decision variable values to obtain a transportation route scheduling plan for the reusable packaging containers.

7. A device for generating a transportation route scheduling plan for a reusable packaging container, characterized in that, The device includes: A variable definition module for defining decision variables based on basic data parameters; the basic data parameters include: a set of packaging container types, a set of regional distribution centers, a set of recycling points, a scheduling period, and a set of periods; the decision variables include: the inventory quantity of the recycling point, the inventory quantity of the regional distribution center, the shortage quantity of the regional distribution center, the scheduling quantity between the recycling point and the regional distribution center, and the scheduling quantity between regional distribution centers; A constraint and function generation module for establishing target constraint conditions and a scheduling optimization function based on the decision variables and the obtained preset transportation information; the preset transportation information includes: the transportation unit price of the packaging container, the transportation period of the packaging container, the planned arrival quantity of the regional distribution center, the planned supply quantity of the recycling point, the demand quantity of the regional distribution center, and the shortage weight of the regional distribution center; A model file generation module for obtaining a target model file based on the preset transportation information, the target constraint conditions, and the scheduling optimization function; A scheduling plan generation module for obtaining decision variable values based on a preset solver and the target model file, and determining a transportation route scheduling plan for the reusable packaging containers based on the decision variable values.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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