Goods collection and distribution management system for multiple suppliers in area

Abstract

The invention discloses a cargo collection and distribution management system for multiple suppliers in a region, and belongs to the technical field of e-commerce and logistics. In order to solve the defect that multiple logistics costs and dynamic changes cannot be systematically considered in an artificial planning mode in the prior art, the system firstly obtains supplier and cargo information and logistics cost data; then, algorithm hyper-parameters such as a configurable penalty coefficient and a time discount coefficient are set, and rigid limiting conditions including cargo-container matching, capacity and distribution uniqueness are constructed; further, on the basis of the parameters and constraints, a comprehensive optimization target capable of quantifying hidden costs such as collection and distribution, sea transportation, allocation and various delays and space waste is constructed; and finally, inputting all the elements into a cargo collection and distribution heuristic engine, automatically solving an optimal solution considering the cost, the time efficiency and the resource utilization rate by the engine under the constraint by taking a minimization optimization target as a guide, and outputting an accurate collection and distribution, boxing and allocation scheme.

Description

Technical Field

[0001] This application relates to the fields of e-commerce and logistics technology, and more specifically, to a cargo distribution management system for multiple suppliers within the region. Background Technology

[0002] Currently, benefiting from significant economies of scale, specific industries often form geographical clusters. For example, regions like East China and South China have developed mature industrial chains in the manufacturing of electronic products or small commodities. For cross-border e-commerce companies deeply involved in global trade, their business model often involves purchasing from multiple suppliers within the same region and then exporting the goods through a single core port within that region. This "multi-point procurement, single-point export" model presents unique challenges to logistics management. Since the volume of a single batch of goods from a single supplier is usually insufficient to fill a complete shipping container, consolidating multiple batches of goods from different suppliers into a single container (i.e., "consolidation") has become a common and necessary logistics operation. This operation not only involves the physical integration of goods but also addresses deeper complexities: on the one hand, shipping schedules have fixed cycles (e.g., once a week), while the delivery dates of goods from different suppliers change dynamically every day. This means that time coordination and waiting for goods with different delivery dates must be carried out. On the other hand, goods in the same container may need to be transported to different overseas destination warehouses, which will generate additional overseas warehouse transfer costs after the goods arrive at the destination port. Therefore, how to efficiently integrate the scattered goods from multiple suppliers within a region to form a globally optimal distribution and transportation solution has become the key to improving supply chain efficiency and reducing costs.

[0003] However, in existing technologies, when faced with the aforementioned complex decision-making scenarios, logistics dispatchers typically rely on personal experience, manual communication, and traditional tools such as spreadsheets for plan development. This manual planning model has significant drawbacks.

[0004] First, the decision-making process often fails to achieve global optimization. Operators may only focus on local optimization of a single link, such as prioritizing maximizing the loading rate of a single container, but they may not be able to simultaneously take into account the domestic transportation costs of gathering goods from various suppliers to a single distribution point, the delay costs caused to urgent goods due to waiting for consolidation, and the secondary transfer costs caused by different destinations after the goods arrive at the port of destination.

[0005] Secondly, the manual planning model lacks responsiveness and flexibility. When faced with dynamic changes in the supply chain, such as delayed supplier delivery dates, the recalculation and coordination of the entire plan usually involves multiple parties, making the process cumbersome and inefficient, and easily missing the optimal transportation opportunity.

[0006] In summary, existing distribution management methods fail to systematically and dynamically balance numerous variables such as factory distribution, cargo attributes, multi-dimensional costs (domestic distribution, sea freight, overseas allocation), and time windows. Consequently, the final solutions often struggle to find a true balance between cost, timeliness, and resource utilization, resulting in high overall logistics costs and low management efficiency, which cannot meet the needs of modern and sophisticated supply chain management. Summary of the Invention

[0007] To overcome the limitations of existing technologies, according to one aspect of this application, a cargo distribution management system for multiple suppliers within a region is provided, comprising:

[0008] The data preparation module is used to obtain supplier and goods information and logistics cost data;

[0009] The hyperparameter setting module is used to set the algorithm hyperparameters, which include the penalty coefficient, time discount coefficient, and estimated container quantity.

[0010] The constraint generation module is used to construct constraints, which include cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints.

[0011] An optimization objective construction module is used to construct optimization objectives based on penalty coefficients, time discount coefficients, and constraints.

[0012] The heuristic optimization module is used to input supplier and cargo information, logistics cost data, algorithm hyperparameter constraints, and optimization objectives into the cargo distribution heuristic engine to obtain the distribution plan, packing plan, and allocation plan for each batch of cargo.

[0013] Compared with existing technologies, the main purpose of the regional multi-supplier cargo distribution management system provided in this application is to overcome the subjectivity and one-sidedness of traditional manual decision-making. It can automatically optimize a wide range of complex variables, including domestic distribution routes, sea freight consolidation, overseas warehouse allocation, and cargo timeliness, in order to generate a logistics execution plan that achieves the best overall performance in terms of cost, efficiency, and resource utilization.

[0014] Specifically, the core concept of this system lies in systematically acquiring the basic data required for global decision-making, including the geographical location of each supplier, the volume of goods to be shipped, delivery date, urgency, destination, and other cargo information, as well as logistics cost data for each transportation link (such as inter-factory transfers, factory-to-port transfers, sea freight, and overseas warehouse transfers). Based on this, the method does not employ fixed logic but introduces configurable algorithm hyperparameters, allowing users to set various penalty coefficients such as delay penalties and space waste penalties according to specific business strategies, and dynamically adjust the weights of different delivery dates using time discount coefficients, thereby integrating business considerations and operational strategies into the algorithm model. To ensure that the generated solution is fully feasible in the real world, the method further explicitly constructs rigid constraints including one cargo per container, volume limits, and unique distribution points, constituting the feasible region of the optimization problem. The core of this invention lies in constructing a unified, quantitative optimization objective function based on the aforementioned penalty coefficients and constraints. This function integrates all relevant factors, such as distribution costs, sea freight costs, transfer costs, and penalty costs incurred due to delays or space waste, into a comprehensive cost value to be minimized. Finally, all structured data, set parameters, constraints, and optimization objectives are input into a dedicated cargo distribution heuristic engine. This engine uses efficient algorithms to automatically explore and calculate in a vast potential combination space until it finds a solution that satisfies all constraints and minimizes the objective function value. Finally, it outputs precise distribution, packing, and allocation plans for each batch of goods in the form of clear instructions.

[0015] By adopting the above technical solution, the present invention can produce significant beneficial effects.

[0016] First, this method transforms the complex, multi-dimensional logistics planning problem into a clear mathematical optimization model and solves it through a heuristic engine, thereby discovering the global optimal solution or near-optimal solution that is difficult to achieve by humans. This significantly reduces the total logistics cost, including domestic distribution, international shipping, and overseas allocation. At the same time, by setting volume penalties, it effectively improves the container loading rate.

[0017] Secondly, this method transforms the originally time-consuming and labor-intensive scheduling work, which relies on human experience, into an automated and rapid calculation process. This greatly improves decision-making efficiency and gives the logistics system strong flexibility and dynamic response capabilities. When supplier production plans or goods delivery dates change, the system can quickly recalculate and adjust the plan to cope with uncertainties in the supply chain.

[0018] Ultimately, this invention provides a scientific, sophisticated, and repeatable decision-making framework that effectively avoids suboptimal decisions caused by incomplete information or subjective judgment in traditional models, fundamentally improving the sophistication of supply chain management and market competitiveness of cross-border e-commerce enterprises. Attached Figure Description

[0019] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a block diagram of a cargo distribution management system for multiple suppliers within a region, according to an embodiment of this application.

[0021] Figure 2 This is a flowchart of a cargo distribution management system for multiple suppliers within a region, according to an embodiment of this application.

[0022] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] Therefore, in view of the technical deficiencies disclosed in the background art, this application proposes a cargo distribution management system for multiple suppliers in a region. Figure 1 This is a block diagram of a cargo distribution management system for multiple suppliers within a region, according to an embodiment of this application. Figure 1As shown in the embodiment of this application, a cargo distribution management system for multiple suppliers within a region includes: a data preparation module, a hyperparameter setting module, a constraint generation module, an optimization target construction module, and a heuristic optimization module. The data preparation module is used to acquire supplier and cargo information and logistics cost data. The hyperparameter setting module is used to set algorithm hyperparameters, including penalty coefficients, time discount coefficients, and estimated container quantities. The constraint generation module is used to construct constraints, including cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints. The optimization target construction module is used to construct optimization targets based on penalty coefficients, time discount coefficients, and constraints. The heuristic optimization module is used to input supplier and cargo information, logistics cost data, algorithm hyperparameter constraints, and optimization targets into a cargo distribution heuristic engine to obtain distribution schemes, packing schemes, and allocation schemes for each batch of cargo.

[0025] The following describes the architecture of the cargo distribution management system of multiple suppliers in the aforementioned region, using their data operation process as an example. Figure 2 As shown, the cargo distribution management system for multiple suppliers within the region provides a method for managing cargo distribution among multiple suppliers within a region, comprising the following steps: S110, acquiring supplier and cargo information and logistics cost data; S120, setting algorithm hyperparameters, including penalty coefficient, time discount coefficient, and estimated container quantity; S130, constructing constraints, including cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints; S140, constructing an optimization objective based on the penalty coefficient, time discount coefficient, and constraints; and S150, inputting supplier and cargo information, logistics cost data, algorithm hyperparameter constraints, and optimization objective into a cargo distribution heuristic engine to obtain a distribution plan, packing plan, and allocation plan for each batch of cargo.

[0026] In step S110, supplier and cargo information and logistics cost data are acquired. This step is accomplished by systematically collecting two core types of data. The first type is supplier and cargo information, which further includes factory information and a cargo list. Acquiring factory information aims to record the factory locations of all suppliers within the region to facilitate accurate calculation of transportation distances and costs. Acquiring the cargo list involves collecting detailed multi-dimensional attributes of each batch of goods to be shipped. These attributes specifically include: the actual volume occupied by the goods, the expected delivery date, the urgency level used to quantify delivery priority, and the final overseas delivery warehouse. In practice, this information can be automatically obtained from the enterprise's management software via an interface.

[0027] In this embodiment, after collecting entity and cargo information, it is also necessary to obtain the complete cost structure related to logistics activities, i.e., logistics cost data. This logistics cost data is subdivided into domestic transportation costs, ocean freight costs, and overseas transportation costs in this embodiment. Domestic transportation costs include the unit freight charges incurred between factories for consolidation, and the freight charges for escorting loaded containers from the distribution center factories to the port of departure. Ocean freight costs are standardized ocean freight quotes for a single container. Overseas transportation costs also cover two scenarios: first, the trunk line costs from the destination port to the designated overseas main warehouse; and second, the secondary transportation costs incurred when transferring goods between the main warehouse and other overseas warehouses.

[0028] To illustrate the data acquisition steps more specifically, consider the following application scenario: There are two suppliers in East China, Factory A and Factory B. Goods G1 and G2 from these two factories need to be consolidated and shipped overseas via Shanghai Port. In this scenario, the method first acquires relevant supplier and cargo information. Factory information includes the geographical locations of Factory A and Factory B. The cargo manifest specifies that cargo G1 has a volume of 20 cubic meters, is expected to be delivered in 3 days (high urgency), and is destined for Los Angeles. Cargo G2 has a volume of 25 cubic meters, is expected to be delivered in 5 days (moderate urgency), and is also destined for Los Angeles. Next, the method acquires detailed logistics cost data. This data includes the unit freight cost of transporting cargo G2 from Factory B to Factory A for consolidation, the transportation cost of transporting the container from Factory A or Factory B to Shanghai Port, and the ocean freight cost from Shanghai Port to Los Angeles Port. At this point, all the structured input information required to solve for the optimal consolidation and distribution plan has been collected.

[0029] In step S120, algorithm hyperparameters are set, including penalty coefficients, time discount coefficients, and estimated container quantity. After collecting basic data, the process proceeds to setting the algorithm's operating strategy, i.e., setting the algorithm hyperparameters. This step aims to transform the company's business strategy and operational preferences into quantifiable instructions that the algorithm can follow, guiding the search direction of the subsequent optimization engine. In this embodiment, the hyperparameters to be set include penalty coefficients, time discount coefficients, and estimated container quantity.

[0030] Specifically, operators can define a series of key hyperparameter values ​​based on specific business objectives and operational priorities. In this embodiment, the penalty coefficient is further refined into three categories: first, the urgency penalty coefficient, used to quantify the penalty for delays caused by high-urgency goods; the higher the value, the more the algorithm prioritizes ensuring the timeliness of such goods; second, the delayed transportation penalty coefficient, used to convert the time cost of goods waiting for consolidation into a cost, balancing timeliness and transportation economy; and third, the volume penalty coefficient, used to impose a virtual cost on the idle space within the container, thereby incentivizing the algorithm to find loading solutions with higher space utilization.

[0031] In addition to the penalty coefficient, this step also sets another key parameter: the time discount coefficient. This coefficient is used to dynamically adjust the delay penalty weights for different delivery dates. Its core mechanism is to apply a higher delay weight to goods with closer delivery dates and a lower weight to goods with farther delivery dates. This mechanism allows the algorithm to finely and dynamically adjust the processing priority between goods with different delivery dates. Finally, this step also includes setting the estimated number of containers. The estimated number of containers is a reasonable upper limit estimated based on the total volume of the goods to be shipped and the standard volume of a single container. Its purpose is to define an effective boundary for the algorithm's search space, thereby improving computational efficiency while ensuring solution quality.

[0032] In a specific application scenario of the setup phase, suppose the logistics manager's core objective is to prioritize the timely dispatch of urgent order cargo G1 while minimizing ocean freight costs. Therefore, the following settings can be made in this step: First, set the urgency penalty coefficient to a high value, such as 1000, to clearly convey the instruction to prioritize this urgent cargo. Second, to achieve cost savings, set the volume penalty coefficient to a high level, such as 50, to encourage the algorithm to generate more saturated consolidation solutions. Then, set the time discount coefficient and delayed transportation penalty coefficient to standard values. Finally, based on the total volume of the cargo to be dispatched being 190 cubic meters, and the standard 40-foot high cube container volume being approximately 76 cubic meters, set the estimated number of containers to 4. After completing these settings, a set of quantitative instructions containing clear business logic is formed and ready to be input into the subsequent optimization engine.

[0033] To facilitate understanding, the above parameters and variables are systematically organized and presented as follows:

[0034] There are N regional factories, namely F i Let i = 1, 2, ..., N, and there be W overseas destination warehouses, each with a value of R. i Let i = 1, 2, ..., W, and K containers be shipped at a unit price of S.

[0035] Factory Fi M every week i A batch of goods needs to be shipped, code is express The number of days from today's delivery date. express volume, express The degree of urgency, express Overseas destination warehouses express Should it be placed in container number k?

[0036] Given the freight unit price between N factories and the freight unit price from N factories to the port, where C i→j F represents i and F j The unit price of shipping between C i→j→port =C i→j +C j→port Indicates from F i via F j Transshipment to the port; Indicates from overseas warehouse R i To R j The unit price of transportation, Indicates from the dock via R i Go to R j .

[0037] Indicates whether container number k is destined for overseas warehouse number r, Y k ∈{0,1} indicates whether container k is in use. This indicates that container number k is collected and distributed at factory number i.

[0038] α, β, γ represent the penalty coefficients for urgency, delayed transportation, and container volume, respectively, and λ ~ (0, 1) is the time discount coefficient. The penalty coefficient is smaller for longer-term variables, and vice versa.

[0039] This indicates the latest delivery date of the goods in container k, in days from today.

[0040] This indicates the earliest delivery date of the goods in container k, in days from today.

[0041] This represents the volume-weighted average delivery date for the goods in container k.

[0042] The number of periods representing the average delivery date delay of goods in container k, rounded down (e.g., one week, τ = 7), is used as a power of λ to increase the weight of near-term goods and decrease the weight of far-term goods. This indicates that the penalty is only applied when the result is positive.

[0043] In step S130, constraints are constructed, including cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints. After setting the macro-level guidance for the algorithm, this method enters the crucial step of defining its computational boundary, namely, constructing constraints. This step aims to accurately transform the physical and business rules that logistics operations must follow into hard constraints that the algorithm model can identify and strictly enforce, thereby ensuring that the final output solution is feasible.

[0044] This step is defined through a series of mathematical or logical expressions. In this embodiment, the constructed constraints cover cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints.

[0045] Cargo-container matching constraints:

[0046] Uniqueness constraint of distribution:

[0047] Capacity constraints:

[0048] Container usage constraints:

[0049] Destination Uniqueness:

[0050] The cargo-container matching constraint ensures that each individual shipment is assigned to only one container. The uniqueness constraint ensures that each active container is assigned a unique collection and distribution point, consistent with the actual operational process of goods being collected before being packed into containers. The capacity constraint guarantees that the total volume of all goods loaded into any container does not exceed the container's rated capacity, preventing physical overloading. The container usage constraint establishes a logical link between cargo allocation and container activation status: the system only allows cargo to be placed into a container when it is marked as active. The destination uniqueness constraint aims to ensure that each container has at most one primary port of destination or warehouse of destination, providing a benchmark for subsequent transportation and allocation cost calculations.

[0051] To clarify the specific function of the aforementioned constraints, let's continue with the previous application context: Suppose the system is evaluating an alternative solution involving a container K1. At this stage, the system performs rigorous checks: it confirms that each batch of goods appears on only one packing list, thus satisfying the cargo-container-box matching constraint; next, it verifies whether K1 is assigned to only one distribution center, thus satisfying the distribution uniqueness constraint; subsequently, it sums up the volumes of all goods allocated to K1, ensuring the total volume does not exceed the capacity constraint; simultaneously, once K1 contains goods, its activation status variable must be 1, thus satisfying the container usage constraint; finally, the system also verifies whether K1 has only one primary destination warehouse, thus satisfying the destination uniqueness constraint. Only when the solution passes all these checks is it deemed feasible and proceeds to the subsequent evaluation stage.

[0052] In step S140, an optimization objective is constructed based on the penalty coefficient, time discount coefficient, and constraints. After preparing all data and parameters, the core step in this embodiment is to construct a unified and quantifiable optimization objective function. The goal of this function is to integrate all explicit expenditures and implicit opportunity costs related to the decision into a single, computer-solvable minimization objective.

[0053] The process of constructing the optimization objective involves breaking down the total cost into multiple sub-items, then using preset hyperparameters and decision variables to mathematically express each sub-item, and finally summing all the sub-items with weights. In this embodiment, the final optimization objective J is a function reflecting the total cost, which mainly includes the actual distribution and transportation costs, overseas allocation costs, and components such as packing penalties, emergency penalties, and delay penalties that characterize operational performance.

[0054] The mathematical expression for the optimization objective is as follows:

[0055]

[0056] Indicates distribution cost

[0057] Indicates allocation cost

[0058] Indicates emergency punishment

[0059] Indicates packing penalty

[0060] Indicates delayed penalty

[0061] It's worth noting that the value of K is unknown in the optimization objective, so we need to use some empirical value to make an assumption, namely, how many containers can be used at most for the current goods. We can preset a value based on experience:

[0062] Continuing with the aforementioned application context, assume the optimization engine is evaluating a specific plan to combine urgent cargo G1 and general cargo G2 into container K1. The optimization objective construction and calculation steps involve the following processes: First, calculating the total distribution and transportation cost under this plan, including the cost of transporting G2 from factory B to factory A, the transportation cost of K1 from factory A to the port, and its ocean freight. Second, calculating the overseas allocation cost; since both shipments have the same destination warehouse in this scenario, this cost is zero. Third, calculating the packing penalty, which is the remaining space after subtracting the sum of the volumes of G1 and G2 from the total volume of K1, multiplied by a volume penalty coefficient. Fourth, calculating the time penalty, which is the expected delay in G1 due to waiting for G2, which has a later delivery date; the algorithm calculates a corresponding, larger penalty based on the higher urgency level of G1. Finally, the sum of the results of the above four parts is the comprehensive cost value J of this plan, which will be used to compare its advantages and disadvantages with other alternative plans.

[0063] In step S150, supplier and cargo information, logistics cost data, algorithm hyperparameter constraints, and optimization objective are input into the cargo distribution heuristic engine to obtain the distribution plan, packing plan, and allocation plan for each batch of cargo. After the entire mathematical model is constructed, this method enters the automated solution stage: all supplier and cargo information, logistics cost data, set algorithm hyperparameters, constructed constraints, and the final optimization objective function are treated as a single input set and processed by a dedicated cargo distribution heuristic engine. This engine solves the aforementioned mathematical optimization problem through automated and efficient computation, satisfying all constraints, thereby finding a feasible solution that minimizes the optimization objective function value.

[0064] In this embodiment, the internal workflow of the cargo distribution heuristic engine follows an iterative improvement framework. The process begins with generating an initial solution; the engine quickly derives a feasible initial solution as the starting point for subsequent optimizations. Subsequently, the engine enters a continuous optimization loop, which includes the following core steps: First, the current solution is fine-tuned or perturbed to obtain an alternative solution, such as swapping goods in two containers or changing the distribution point. Next, the current solution and the newly generated alternative solution are substituted into the optimization objective function to calculate their combined cost. Then, the two cost values ​​are compared; if the alternative solution has a lower cost, it replaces the current solution, serving as the starting point for the next iteration.

[0065] The iterative process described above will not continue indefinitely. This embodiment sets explicit termination conditions for the engine, which may include: the number of iterations reaching a preset threshold; the optimization target value no longer showing significant improvement in consecutive iterations; or the total computation time reaching a predetermined upper limit. When any of these conditions is met, the iterative process stops. After the iteration terminates, the engine will output the solution with the lowest overall cost value, which is the optimal or near-optimal solution found throughout the entire search process. This solution is presented in the form of a clear and structured instruction set, which is the final operational result delivered by this invention. Specifically, it includes: a distribution plan that clarifies the cargo collection points, a packing plan that lists the loading list, and an allocation plan that specifies the overseas secondary transportation routes.

[0066] Accordingly, it should be understood that in the embodiments of this application, the optimization target construction module is further used to: construct distribution cost, allocation cost, emergency penalty, packing penalty and delay penalty based on penalty coefficient, time discount coefficient and constraint conditions; and construct the optimization target based on distribution cost, allocation cost, emergency penalty, packing penalty and delay penalty.

[0067] Furthermore, the heuristic optimization module is also configured to: S1: generate an initial solution; S2: fine-tune or perturb the initial solution to obtain alternative solutions; S3: input the initial solution and alternative solutions into the optimization objective to obtain the optimization objective value of the initial solution and the optimization objective value of the alternative solutions; S4: in response to the optimization objective value of the initial solution being greater than the optimization objective value of the alternative solutions, set the alternative solutions as the initial solutions and repeatedly execute steps S2 to S4 until a preset condition is met; and S5: when the preset condition is met, output the distribution plan, packing plan and allocation plan for each batch of goods.

[0068] Specifically, the preset condition is one of the following: the number of iterations exceeds a preset threshold; the target value has not changed after a preset number of iterations; or the preset computation time limit has been reached.

[0069] Here, those skilled in the art will understand that the specific functions and operations of each module in the aforementioned cargo distribution management system for multiple suppliers within a region have been referenced above. Figure 2 The description of the cargo distribution management methods of multiple suppliers within the region is detailed here, and therefore, its repeated description will be omitted.

[0070] As described above, the cargo distribution management system for multiple suppliers within a region according to embodiments of this application can be implemented in various terminal devices, such as servers for managing cargo distribution among multiple suppliers within a region. In one example, the cargo distribution management system for multiple suppliers within a region according to embodiments of this application can be integrated into a terminal device as a software module and / or a hardware module. For example, the cargo distribution management system for multiple suppliers within a region can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the cargo distribution management system for multiple suppliers within a region can also be one of many hardware modules of the terminal device.

[0071] Alternatively, in another example, the cargo distribution management system of multiple suppliers in the region and the terminal device can also be separate devices, and the cargo distribution management system of multiple suppliers in the region can be connected to the terminal device via wired and / or wireless networks, and transmit interactive information in accordance with the agreed data format.

[0072] Below, for reference Figure 3 This describes an electronic device according to embodiments of the present application.

[0073] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0074] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0075] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0076] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the regional cargo distribution management method of the various embodiments of this application described above, and / or other desired functions. Various content such as supplier and cargo information and logistics cost data may also be stored in the computer-readable storage medium.

[0077] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0078] The input device 13 may include, for example, a keyboard, a mouse, etc.

[0079] The output device 14 can output various information to the outside, including the distribution plan, packing plan, and allocation plan for each batch of goods. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0080] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0081] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the regional goods distribution management method for multiple suppliers according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0082] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0083] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps of the regional goods distribution management method for multiple suppliers according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0084] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications made by those skilled in the art to the technical solution of the present invention by means of equivalent substitution or equivalent transformation without departing from all the technical content described in the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A cargo distribution management system for multiple suppliers within a region, characterized in that, include: The data preparation module is used to obtain supplier and goods information and logistics cost data; The hyperparameter setting module is used to set the algorithm hyperparameters, which include the penalty coefficient, time discount coefficient, and estimated container quantity. The constraint generation module is used to construct constraints, which include cargo-container matching constraints, distribution uniqueness constraints, capacity constraints, container usage constraints, and destination uniqueness constraints. An optimization objective construction module is used to construct optimization objectives based on penalty coefficients, time discount coefficients, and constraints. The heuristic optimization module is used to input supplier and cargo information, logistics cost data, algorithm hyperparameter constraints, and optimization objectives into the cargo distribution heuristic engine to obtain the distribution plan, packing plan, and allocation plan for each batch of cargo.

2. The cargo distribution management system for multiple suppliers within a region according to claim 1, characterized in that, The supplier and cargo information includes factory information and cargo list. The factory information includes the location information of all factories in the region, and the cargo list includes volume, expected delivery date, urgency level, and overseas destination warehouse.

3. The cargo distribution management system for multiple suppliers within a region according to claim 2, characterized in that, The logistics cost data includes domestic transportation costs, ocean freight costs, and overseas transportation costs. The domestic transportation costs include the unit freight charges for transportation between factories and the freight charges for transporting containers from each factory to the port of departure. The overseas transportation costs include the freight charges from the port of destination to the designated overseas master warehouse and the unit transportation price for transfers between multiple overseas warehouses.

4. The cargo distribution management system for multiple suppliers within a region according to claim 1, characterized in that, The penalty coefficients include the urgency penalty coefficient, the delayed transportation penalty coefficient, and the volume penalty coefficient.

5. The cargo distribution management system for multiple suppliers within a region according to claim 4, characterized in that, The optimization target construction module is further used for: Based on penalty coefficients, time discount coefficients, and constraints, we construct distribution costs, allocation costs, emergency penalties, packing penalties, and delay penalties. The optimization objective is constructed based on distribution costs, allocation costs, emergency penalties, packing penalties, and delay penalties.

6. The cargo distribution management system for multiple suppliers within a region according to claim 1, characterized in that, The heuristic optimization module is also used for: S1: Generate the initial solution; S2: Fine-tune or perturb the initial solution to obtain alternative solutions; S3: Input the initial solution and the candidate solution into the optimization objective to obtain the optimization objective value of the initial solution and the optimization objective value of the candidate solution; S4: In response to the fact that the optimization objective value of the initial solution is greater than the optimization objective value of the candidate solution, the candidate solution is set as the initial solution and steps S2 to S4 are executed repeatedly until the preset condition is met. S5: When the preset conditions are met, output the collection and distribution plan, packing plan and transfer plan for each batch of goods.

7. The cargo distribution management system for multiple suppliers within a region according to claim 6, characterized in that, The preset condition is one of the following conditions: The number of iterations exceeds a preset threshold; The loop iterated for a preset number of times, but the target value remained unchanged. The preset computation time limit has been reached.

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