Material distribution route planning method, material distribution route planning device and electronic equipment

Abstract

The invention provides a material distribution route planning method, a material distribution route planning device and electronic equipment, and the method comprises the steps: obtaining a resource parameter set and distribution demand information of each distribution center, the resource parameter set comprising vehicle parameters of various vehicle types; a distribution route planning model is constructed according to a distribution route constraint set, and the distribution route constraint set comprises a vehicle use and cost association constraint condition, a cargo complete distribution constraint condition, a cargo loading and unloading and load relation constraint condition and a load capacity constraint condition; and based on the freight cost minimization function, inputting the resource parameter set and the delivery demand information to a delivery route planning model, and outputting a target delivery route and a target freight total cost corresponding to the target delivery route.

Description

Technical Field

[0001] The present disclosure relates to the field of logistics and distribution technology, and more specifically, to a material distribution route planning method, a material distribution route planning device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Material transportation between production plants is an essential process in a company's daily operations. Materials are delivered from supply plants to demand plants for further processing and production. The main steps in material transportation include palletizing, loading, transporting, and unloading. Because material transportation not only consumes a significant amount of time but also involves significant financial costs, developing an efficient material transportation solution is crucial for improving overall operational efficiency and reducing costs. Summary of the Invention

[0003] In view of this, the present disclosure provides a material delivery route planning method, a material delivery route planning device, an electronic device, a computer-readable storage medium, and a computer program product.

[0004] One aspect of the present disclosure provides a material delivery route planning method, comprising:

[0005] Obtaining resource parameter sets and distribution demand information for each distribution center, wherein the resource parameter sets include vehicle parameters for multiple vehicle types;

[0006] Constructing a delivery route planning model based on a set of delivery route constraints, wherein the set of delivery route constraints includes constraints related to vehicle use and cost, constraints related to complete delivery of goods, constraints related to the relationship between loading and unloading goods and load, and constraints related to load capacity;

[0007] Based on the freight cost minimization function, the resource parameter set and the distribution demand information are input into the distribution route planning model, and the target distribution route and the target total freight cost corresponding to the target distribution route are output.

[0008] According to an embodiment of the present disclosure, the resource parameter set includes: the number of each vehicle type, the unit usage cost, and the vehicle capacity;

[0009] The above-mentioned distribution demand information includes the number of distribution nodes, the time cost between different distribution nodes, cargo demand data, unit distribution cost, basic fixed cost and working time limit information.

[0010] According to an embodiment of the present disclosure, based on a freight cost minimization function, the resource parameter set and the delivery demand information are input into the delivery route planning model, and a target delivery route and a target total freight cost corresponding to the target delivery route are output, including:

[0011] Generate a planning model to be solved based on the resource parameter set, the distribution demand information, and the distribution route planning model;

[0012] Based on the freight cost minimization function, the optimization solver is used to solve the planning model to be solved to obtain the target delivery route and the target total freight cost.

[0013] According to an embodiment of the present disclosure, based on the freight cost minimization function, the optimization solver is used to solve the planning model to be solved to obtain the target delivery route and the target total freight cost, including:

[0014] Solve the above-mentioned planning model using an optimization solver to obtain multiple initial delivery routes;

[0015] Calculate the total initial freight cost for each of the above initial delivery routes based on the freight cost function;

[0016] Determining the minimum value of the plurality of initial freight total costs as the target freight total cost based on a minimization function;

[0017] The initial delivery route corresponding to the above target total freight cost is determined as the above target delivery route.

[0018] According to an embodiment of the present disclosure, when the vehicle type includes a first vehicle and a second vehicle, the freight cost minimization function is as shown in formula (1):

[0019] (1)

[0020] in, is the target total freight cost, M is the number of delivery nodes, is the number of the first vehicle, is the number of the second vehicle, and are the unit usage costs of the first vehicle and the second vehicle respectively, is the unit delivery cost, As basic fixed costs, They are all distribution nodes. Select the variable for the path, Using state variables for vehicles, is the freight transport volume variable.

[0021] According to the embodiment of the present disclosure, the above-mentioned vehicle use and cost association constraint is shown in formula (2), the cargo complete delivery constraint is shown in formula (3), the cargo loading and unloading and load relationship constraint is shown in formula (4), and the load capacity constraint is shown in formulas (5) and (6):

[0022] (2)

[0023] (3)

[0024] (4)

[0025] (5)

[0026] (6)

[0027] Where M is the number of distribution centers, is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

[0028] According to an embodiment of the present disclosure, the above-mentioned delivery route constraint set further includes a closed loop constraint, a delivery center round-trip constraint, and a time constraint.

[0029] According to an embodiment of the present disclosure, the closed loop constraint is shown in formula (6), the distribution center round-trip constraint is shown in formula (7), and the time constraint is shown in formula (8):

[0030] (6)

[0031] (7)

[0032] (8)

[0033] Among them, M and N are the number of distribution centers and distribution nodes respectively. is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

[0034] Another aspect of the present disclosure provides a material delivery route planning device, comprising:

[0035] An acquisition module, configured to acquire a resource parameter set and distribution demand information of each distribution center, wherein the resource parameter set includes vehicle parameters of multiple vehicle types;

[0036] a construction module for constructing a delivery route planning model based on a delivery route constraint set, wherein the delivery route constraint set includes vehicle use and cost association constraints, cargo complete delivery constraints, cargo loading and unloading and load relationship constraints, and load capacity constraints;

[0037] The calculation module is used to input the above resource parameter set and the above distribution demand information into the above distribution route planning model based on the freight cost minimization function, and output the target distribution route and the target total freight cost corresponding to the above target distribution route.

[0038] Another aspect of the present disclosure provides an electronic device, comprising:

[0039] one or more processors;

[0040] a memory for storing one or more programs,

[0041] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0042] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0043] Another aspect of the present disclosure provides a computer program product comprising computer executable instructions, which are used to implement the method described above when the instructions are executed.

[0044] According to the embodiments of the present disclosure, a distribution route planning model is constructed by means of constraints on the association between vehicle use and costs, constraints on the complete delivery of goods, constraints on the relationship between loading and unloading goods and load, and constraints on load capacity. Based on the freight cost minimization function, the distribution route planning model is used to obtain the target distribution route and target total freight cost with the minimum freight cost by using the resource parameter set and distribution demand information, thereby improving the efficiency of logistics distribution and reducing transportation costs and calculation time. It is suitable for complex logistics systems with multiple vehicle models and multiple parking lots. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0046] Figure 1 The following schematically illustrates an exemplary system architecture to which the material distribution route planning method according to an embodiment of the present disclosure can be applied;

[0047] Figure 2 Schematically shows a flow chart of a material distribution route planning method according to an embodiment of the present disclosure;

[0048] Figure 3 The following schematically shows a pseudo code diagram of a material distribution route planning method according to an embodiment of the present disclosure;

[0049] Figure 4 Schematically shows a comparison diagram of no-load rates of different methods according to an embodiment of the present disclosure;

[0050] Figure 5 Schematically showing a comparison diagram of vehicle routes according to different methods of an embodiment of the present disclosure;

[0051] Figure 6 The following schematically shows a block diagram of a material distribution route planning device according to an embodiment of the present disclosure;

[0052] Figure 7 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0053] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0054] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0055] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0056] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0057] Currently, many companies still rely on traditional manual scheduling to develop material distribution plans. Specifically, after learning the material requirements of different factories for the next day, companies rely on staff to manually compare the requirements and match materials with vehicles. This method relies heavily on staff experience and judgment, making it difficult to ensure optimal distribution arrangements. Furthermore, when order volume increases significantly, manual scheduling often makes it difficult to complete delivery tasks in a timely manner, resulting in increased delivery costs and reduced vehicle utilization efficiency.

[0058] This traditional approach not only affects distribution efficiency and cost control, but also limits companies' flexibility in responding to market changes and demand fluctuations. Therefore, the industry urgently needs to develop an automated material distribution solution based on advanced algorithms. This technology aims to optimize distribution, improve efficiency, and reduce costs, thereby supporting the rapid development and market competitiveness of companies.

[0059] In view of this, the present disclosure provides a material distribution route planning method, a material distribution route planning device and an electronic device, the method comprising obtaining a resource parameter set and distribution demand information of each distribution center, wherein the resource parameter set includes vehicle parameters of multiple vehicle types; constructing a distribution route planning model based on a distribution route constraint set, wherein the distribution route constraint set includes vehicle use and cost association constraints, cargo complete distribution constraints, cargo loading and unloading and load relationship constraints and load capacity constraints; based on a freight cost minimization function, inputting the resource parameter set and distribution demand information into the distribution route planning model, and outputting a target distribution route and a target total freight cost corresponding to the target distribution route.

[0060] In the embodiments of this disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of all data involved (including, but not limited to, user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard the security of user personal information, network security, and national security.

[0061] Figure 1 The following schematically illustrates an exemplary system architecture 100 to which the material distribution route planning method according to an embodiment of the present disclosure can be applied. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0062] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0063] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0064] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0065] Server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal device.

[0066] It should be noted that the material distribution route planning method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the material distribution route planning device provided in the embodiment of the present disclosure can generally be set in the server 105. The material distribution route planning method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the material distribution route planning device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Alternatively, the material distribution route planning method provided in the embodiment of the present disclosure can also be executed by the terminal devices 101, 102, or 103, or can also be executed by other terminal devices different from the terminal devices 101, 102, or 103. Correspondingly, the material distribution route planning device provided in the embodiment of the present disclosure may also be set in the terminal device 101, 102, or 103, or in other terminal devices different from the terminal device 101, 102, or 103.

[0067] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0068] Figure 2 The flowchart of the material distribution route planning method according to an embodiment of the present disclosure is schematically shown.

[0069] like Figure 2 As shown, the material distribution route planning method includes operations S201 to S203.

[0070] In operation S201, a resource parameter set and distribution demand information of each distribution center are obtained, wherein the resource parameter set includes vehicle parameters of multiple vehicle types;

[0071] In operation S202, a delivery route planning model is constructed based on a delivery route constraint set, wherein the delivery route constraint set includes a vehicle use and cost association constraint, a cargo complete delivery constraint, a cargo loading and unloading and load relationship constraint, and a load capacity constraint;

[0072] In operation S203 , based on the freight cost minimization function, the resource parameter set and the delivery demand information are input into the delivery route planning model, and a target delivery route and a target total freight cost corresponding to the target delivery route are output.

[0073] According to an embodiment of the present disclosure, the resource parameter set includes: the number of each vehicle type, the unit usage cost, and the vehicle capacity;

[0074] Distribution demand information includes the number of distribution nodes, time cost between different distribution nodes, cargo demand data, unit distribution cost, basic fixed cost and working time limit information.

[0075] According to an embodiment of the present disclosure, the unit usage cost may be vehicle rental, the time cost may be expressed in the form of a time matrix, the cargo demand data may be expressed in the form of a cargo total quantity matrix, the unit delivery cost may be the toll per unit time, the basic fixed cost may be the cost of fixed expenditures, and the working time limit information may refer to the upper limit of the vehicle's working time.

[0076] According to an embodiment of the present disclosure, an electronic device can construct a delivery route planning model based on the aforementioned delivery route constraint set within a MATLAB environment. After inputting the aforementioned resource parameter set and delivery demand information into the electronic device, an optimization solver such as Gurobi can be directly invoked from MATLAB to perform optimization calculations on the constructed mathematical model (i.e., the delivery route planning model), thereby obtaining a target delivery route and target total freight cost. The Gurobi optimization solver can be integrated into the MATLAB environment and can directly utilize the model data constructed in MATLAB for processing. By setting the solution parameters, the efficiency and accuracy of the calculations can be guaranteed while meeting business requirements.

[0077] According to the embodiments of the present disclosure, a distribution route planning model is constructed by means of constraints on the association between vehicle use and costs, constraints on the complete delivery of goods, constraints on the relationship between loading and unloading goods and load, and constraints on load capacity. Based on the freight cost minimization function, the distribution route planning model is used to obtain the target distribution route and target total freight cost with the minimum freight cost by using the resource parameter set and distribution demand information, thereby improving the efficiency of logistics distribution and reducing transportation costs and calculation time. It is suitable for complex logistics systems with multiple vehicle models and multiple parking lots.

[0078] According to the embodiments of the present disclosure, the constraints associating vehicle use with costs can ensure that only used vehicles incur related costs, thereby optimizing resource utilization efficiency.

[0079] According to an embodiment of the present disclosure, the complete cargo delivery constraint requires that all cargo demands must be met to ensure the comprehensiveness of the service.

[0080] According to an embodiment of the present disclosure, the loading and unloading cargo and load relationship constraint condition means that at each delivery node, the quantity of loaded and unloaded cargo matches the load capacity of the vehicle to ensure the safety of the vehicle and the transportation efficiency.

[0081] According to an embodiment of the present disclosure, the load capacity constraint condition is to ensure that the vehicle load at each delivery node does not exceed its maximum capacity, thereby preventing overloading and safety risks.

[0082] According to an embodiment of the present disclosure, based on a freight cost minimization function, a resource parameter set and delivery demand information are input into a delivery route planning model, and a target delivery route and a target total freight cost corresponding to the target delivery route are output, including:

[0083] Generate a planning model to be solved based on the resource parameter set, distribution demand information and distribution route planning model;

[0084] Based on the freight cost minimization function, the optimization solver is used to solve the planning model to obtain the target delivery route and the target total freight cost.

[0085] According to an embodiment of the present disclosure, based on the freight cost minimization function, an optimization solver is used to solve the planning model to be solved, and the target delivery route and the target total freight cost are obtained, including:

[0086] Use the optimization solver to solve the planning model to obtain multiple initial delivery routes;

[0087] Calculate the total initial freight cost for each initial delivery route based on the freight cost function;

[0088] determining a minimum value among a plurality of initial freight total costs as a target freight total cost based on a minimization function;

[0089] An initial delivery route corresponding to the target total freight cost is determined as a target delivery route.

[0090] According to an embodiment of the present disclosure, when the vehicle type includes a first vehicle and a second vehicle, the freight cost minimization function is shown as formula (1):

[0091] (1)

[0092] in, is the target total freight cost, M is the number of delivery nodes, is the number of the first vehicle, is the number of the second vehicle, and are the unit usage costs of the first vehicle and the second vehicle respectively, is the unit delivery cost, As basic fixed costs, They are all distribution nodes. Select the variable for the path, Using state variables for vehicles, is the freight transport volume variable.

[0093] It should be noted that the method disclosed herein is not only applicable to two types of vehicles, but is also applicable to three or more types of vehicles. The above is only provided as an example and does not limit the present disclosure to being applicable only to two types of vehicles.

[0094] According to the embodiment of the present disclosure, the constraints on the association between vehicle use and cost are shown in formula (2), the constraints on the complete delivery of goods are shown in formula (3), the constraints on the relationship between loading and unloading goods and load are shown in formula (4), and the constraints on load capacity are shown in formulas (5) and (6):

[0095] (2)

[0096] (3)

[0097] (4)

[0098] (5)

[0099] (6)

[0100] Where M is the number of distribution centers, is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

[0101] According to an embodiment of the present disclosure, the delivery route constraint set further includes a closed loop constraint, a delivery center round-trip constraint, and a time constraint.

[0102] According to the embodiments of the present disclosure, the closed loop constraint can ensure that the driving route of each vehicle is closed, avoid waste of resources and form an effective distribution cycle.

[0103] According to the embodiments of the present disclosure, the round-trip constraint of the distribution center can ensure that each vehicle departs from its distribution center and eventually returns to complete the integrity of the distribution task.

[0104] According to the embodiments of the present disclosure, the time constraint condition can ensure that all goods are delivered within the prescribed working hours, thereby improving the timeliness of the service.

[0105] According to an embodiment of the present disclosure, the total freight cost is minimized by a freight cost minimization function, including vehicle usage fees, road fees, fixed costs, etc. The design of this function takes into account various cost factors to ensure that costs are minimized while meeting all service requirements.

[0106] According to an embodiment of the present disclosure, the closed loop constraint is shown in formula (6), the distribution center round-trip constraint is shown in formula (7), and the time constraint is shown in formula (8):

[0107] (6)

[0108] (7)

[0109] (8)

[0110] Among them, M and N are the number of distribution centers and distribution nodes respectively. is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

[0111] According to an embodiment of the present disclosure, the vehicle usage status variable can be used to determine whether a specific vehicle is enabled for a delivery task through a binary variable. This helps control and optimize the vehicle's usage cost.

[0112] According to an embodiment of the present disclosure, vehicle travel paths, or route selection variables, are defined as binary variables that indicate whether a particular vehicle chooses a route from one node to another. These variables are central to optimizing routes and forming an efficient delivery network.

[0113] According to an embodiment of the present disclosure, the amount of cargo transported by a vehicle is a cargo transport volume variable, and a continuous variable is set to calculate the cargo transport volume on a selected path. Through these variables, the cargo loading of each vehicle can be accurately adjusted to improve the full load rate.

[0114] According to an embodiment of the present disclosure, vehicle sequence and load variables use indicative quantities and load variables to track the movement sequence of vehicles and cargo loads at each node, thereby ensuring the logical consistency and efficiency of the delivery task.

[0115] According to embodiments of the present disclosure, the delivery route planning model can be constructed and solved in the MATLAB environment. Using MATLAB, the model can be concretely implemented in a programming format, allowing each parameter and variable to be precisely defined and processed. MATLAB provides powerful data processing capabilities, enabling efficient management and manipulation of large datasets, and preparing the correct format and data structure for Gurobi solver input.

[0116] Figure 3 The pseudo code diagram of the material distribution route planning method according to an embodiment of the present disclosure is schematically shown. Figure 4 A schematic diagram showing a comparison of idle rates of different methods according to an embodiment of the present disclosure is shown schematically. Figure 5 A schematic diagram of vehicle route comparison according to different methods of an embodiment of the present disclosure is schematically shown.

[0117] In a specific embodiment, it is assumed that there are 3 distribution centers and 5 distribution nodes, and the material distribution task is completed by using large trucks and small trucks.

[0118] Parameter settings:

[0119] Distribution Center and Vehicles: There are 3 distribution centers ( ), distributed in different geographical locations. Each distribution center is equipped with large trucks and small trucks, of which the number of large trucks is 4 ( = 4), the number of carts is 3 ( = 3).

[0120] Vehicle capacity: The capacity of large vehicles is set to 2600 units, and the capacity of small vehicles is set to 1380 units.

[0121] Cost parameters: The base price for a large vehicle for one day is 100 units ( = 100), the base price of the car is 50 units ( = 50). The fare per unit time is set to 0.05 units ( = 0.05), the fixed cost is 0 units ( = 0).

[0122] Time and distance: The travel time from node i to node j is represented by a 5x5 matrix The unit is minutes. For example, the travel time from node 1 to node 2 is 90 minutes.

[0123] Goods demand: The total amount of goods from node i to node j is represented by the matrix The unit is the length of the cargo. For example, it takes 5216 units of cargo to be transported from node 1 to node 2.

[0124] Example of a decision variable:

[0125] Path selection variables :If the first truck of the first distribution center chooses the path from node 1 to node 2, then = 1 if the value is set, otherwise 0.

[0126] Vehicle usage status variables :If the first truck of the first distribution center is used, then = 1 if the value is set, otherwise 0.

[0127] Freight volume variables :If the first truck of the first distribution center transports 1000 units of goods on the path from node 1 to node 2, then = 1000.

[0128] Objective function:

[0129] Based on the above parameters, the total freight cost is calculated, including vehicle usage fees, road fees, and fixed fees. For example, if the first truck of the first distribution center is used and travels the route from node 1 to node 2, the cost of this part is calculated as: .

[0130] Reference Figure 3 The pseudocode diagram shown above constructs a delivery route planning model based on the aforementioned delivery route constraint set. Using the Gurobi solver, the lowest-cost delivery solution is found within these parameters and constraints. An iterative solution process determines the optimal route, usage status, and cargo load for each vehicle. By precisely controlling Gurobi parameters, particularly MIPGap, this optimization implementation ensures an optimal balance between cost-effectiveness and computational efficiency. Ultimately, the solver successfully finds a cost-effective transportation solution within a 3.5% optimality margin. See Tables 1 and 2 for a comparison of the solutions.

[0131] Table 1: Original plan

[0132]

[0133] Table 2: Disclosure

[0134]

[0135] According to the embodiments of the present disclosure, see Figure 4 The no-load ratio comparison shown and Figure 5Comparing the vehicle routes shown, the disclosed method makes transportation planning more accurate, reduces calculation time, and improves transportation efficiency. For example, through optimization, the average empty load ratio of large and small vehicles in the original plan decreased by 0.16, from the original average empty load ratio of 0.5 to the optimized 0.34, significantly improving vehicle utilization efficiency. Furthermore, the number of vehicles used was reduced from 10 to 6, a 40% reduction in vehicle utilization, significantly reducing transportation costs.

[0136] Figure 6 The block diagram of the material delivery route planning device according to an embodiment of the present disclosure is schematically shown.

[0137] like Figure 6 As shown, the material distribution route planning device 600 includes an acquisition module 610 , a construction module 620 , and a calculation module 630 .

[0138] An acquisition module 610 is configured to acquire a resource parameter set and distribution demand information for each distribution center, wherein the resource parameter set includes vehicle parameters of multiple vehicle types;

[0139] A construction module 620 is configured to construct a delivery route planning model based on a delivery route constraint set, wherein the delivery route constraint set includes a vehicle usage and cost association constraint, a cargo complete delivery constraint, a cargo loading and unloading and load relationship constraint, and a load capacity constraint;

[0140] The calculation module 630 is used to input the resource parameter set and the distribution demand information into the distribution route planning model based on the freight cost minimization function, and output the target distribution route and the target total freight cost corresponding to the target distribution route.

[0141] According to the embodiments of the present disclosure, a distribution route planning model is constructed by means of constraints on the association between vehicle use and costs, constraints on the complete delivery of goods, constraints on the relationship between loading and unloading goods and load, and constraints on load capacity. Based on the freight cost minimization function, the distribution route planning model is used to obtain the target distribution route and target total freight cost with the minimum freight cost by using the resource parameter set and distribution demand information, thereby improving the efficiency of logistics distribution and reducing transportation costs and calculation time. It is suitable for complex logistics systems with multiple vehicle models and multiple parking lots.

[0142] According to an embodiment of the present disclosure, the resource parameter set includes: the number of each vehicle type, the unit usage cost, and the vehicle capacity;

[0143] Distribution demand information includes the number of distribution nodes, time cost between different distribution nodes, cargo demand data, unit distribution cost, basic fixed cost and working time limit information.

[0144] According to an embodiment of the present disclosure, the calculation module 630 includes:

[0145] A generation unit, used to generate a planning model to be solved based on a resource parameter set, distribution demand information, and a distribution route planning model;

[0146] The solving unit is used to solve the planning model to be solved by using an optimization solver based on the freight cost minimization function to obtain the target delivery route and the target total freight cost.

[0147] According to an embodiment of the present disclosure, the solving unit includes:

[0148] A solving subunit is used to solve the planning model to be solved by using an optimization solver to obtain multiple initial delivery routes;

[0149] a calculation subunit, configured to calculate an initial total freight cost of each initial delivery route based on a freight cost function;

[0150] a first determining subunit, configured to determine a minimum value among a plurality of initial freight total costs as a target freight total cost based on a minimization function;

[0151] The second determining subunit is configured to determine the initial delivery route corresponding to the target total freight cost as the target delivery route.

[0152] According to an embodiment of the present disclosure, the delivery route constraint set further includes a closed loop constraint, a delivery center round-trip constraint, and a time constraint.

[0153] According to the embodiments of the present invention, any number of modules, sub-modules, units, and sub-units, or at least part of the functions of any number of them, can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a computer program module, which can perform the corresponding functions when the computer program module is executed.

[0154] For example, any number of the acquisition module 610, the construction module 620, and the calculation module 630 can be combined into a single module / unit / sub-unit, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functionality of one or more of these modules / units / sub-units can be combined with at least part of the functionality of other modules / units / sub-units and implemented in a single module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the acquisition module 610, the construction module 620, and the calculation module 630 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 610 , the construction module 620 , and the calculation module 630 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0155] It should be noted that the material distribution route planning device part in the embodiment of the present disclosure corresponds to the material distribution route planning method part in the embodiment of the present disclosure. The description of the material distribution route planning device part specifically refers to the material distribution route planning method part, which will not be repeated here.

[0156] Figure 7 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0157] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0158] Various programs and data required for the operation of the electronic device 700 are stored in the RAM 703. The processor 701, ROM 702, and RAM 703 are connected to each other via a bus 704. The processor 701 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0159] According to an embodiment of the present disclosure, electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to bus 704. System 700 may also include one or more of the following components connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 708 including a hard disk; and a communication section 709 including a network interface card such as a LAN card or modem. Communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. Removable media 711, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 710 as needed, so that computer programs read from the removable media can be installed into storage section 708 as needed.

[0160] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0161] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0162] According to embodiments of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0163] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703 .

[0164] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method provided by the embodiment of the present disclosure.

[0165] When the computer program is executed by the processor 701, the above functions defined in the system / device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0166] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 709, and / or installed from a removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0167] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0168] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or coupled in various ways, and all such combinations and / or couplings fall within the scope of the present disclosure.

[0169] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A material distribution route planning method, characterized in that: include: Obtaining a resource parameter set and distribution demand information for each distribution center, wherein the resource parameter set includes vehicle parameters of multiple vehicle types; Constructing a delivery route planning model based on a delivery route constraint set, wherein the delivery route constraint set includes vehicle use and cost association constraints, cargo complete delivery constraints, cargo loading and unloading and load relationship constraints, and load capacity constraints; Based on a freight cost minimization function, the resource parameter set and the delivery demand information are input into the delivery route planning model, and a target delivery route and a target total freight cost corresponding to the target delivery route are output.

2. The method according to claim 1, characterized in that The resource parameter set includes: the number of each vehicle type, unit usage cost and vehicle capacity; The distribution demand information includes the number of distribution nodes, the time cost between different distribution nodes, cargo demand data, unit distribution cost, basic fixed cost and working time limit information.

3. The method according to claim 1, characterized in that Based on the freight cost minimization function, the resource parameter set and the delivery demand information are input into the delivery route planning model, and a target delivery route and a target total freight cost corresponding to the target delivery route are output, including: Generate a planning model to be solved according to the resource parameter set, the distribution demand information and the distribution route planning model; Based on the freight cost minimization function, the planning model to be solved is solved using an optimization solver to obtain the target delivery route and the target total freight cost.

4. The method according to claim 3, characterized in that Based on the freight cost minimization function, the planning model to be solved is solved using an optimization solver to obtain the target delivery route and the target total freight cost, including: Solving the to-be-solved planning model using an optimization solver to obtain a plurality of initial delivery routes; Calculating the total initial freight cost of each of the initial delivery routes based on the freight cost function; determining a minimum value among the plurality of initial freight total costs as the target freight total cost based on a minimization function; An initial delivery route corresponding to the target total freight cost is determined as the target delivery route.

5. The method according to claim 1, wherein In the case where the vehicle type includes a first vehicle and a second vehicle, the freight cost minimization function is shown in formula (1): （1） in, is the target total freight cost, M is the number of delivery nodes, is the number of the first vehicle, is the number of the second vehicle, and are the unit usage costs of the first vehicle and the second vehicle respectively, is the unit delivery cost, As basic fixed costs, They are all distribution nodes. Select the variable for the path, Using state variables for vehicles, is the freight transport volume variable.

6. The method according to claim 1, characterized in that The constraints on vehicle use and cost are shown in formula (2), the constraints on complete cargo delivery are shown in formula (3), the constraints on the relationship between cargo loading and unloading and load are shown in formula (4), and the constraints on load capacity are shown in formulas (5) and (6): （2） （3） （4） （5） （6） Where M is the number of distribution centers, is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

7. The method according to claim 1, characterized in that The distribution route constraint set also includes closed loop constraints, distribution center round-trip constraints, and time constraints.

8. The method according to claim 7, characterized in that The closed loop constraint is shown in formula (6), the distribution center round-trip constraint is shown in formula (7), and the time constraint is shown in formula (8): （6） （7） （8） Among them, M and N are the number of distribution centers and distribution nodes respectively. is the number of the first vehicle, is the number of the second vehicle, They are all distribution nodes. is the vehicle driving path, that is, the vehicle starts from the distribution center m and passes through After three delivery nodes, it returns to the delivery center m. Using state variables for vehicles, is the amount of cargo transported by the vehicle, is the load of the vehicle when it leaves the delivery node j, L is the length of the vehicle, and l is the length of the stacked goods.

9. A material distribution route planning device, characterized in that: include: an acquisition module, configured to acquire a resource parameter set and distribution demand information of each distribution center, wherein the resource parameter set includes vehicle parameters of multiple vehicle types; a construction module for constructing a delivery route planning model based on a delivery route constraint set, wherein the delivery route constraint set includes a vehicle use and cost association constraint, a cargo complete delivery constraint, a cargo loading and unloading and load relationship constraint, and a load capacity constraint; The calculation module is used to input the resource parameter set and the distribution demand information into the distribution route planning model based on the freight cost minimization function, and output a target distribution route and a target total freight cost corresponding to the target distribution route.

10. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 8.

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