Goods arrival plan calculation method and device considering cost and goods receiving capacity fluctuation

Abstract

The invention discloses a goods arrival plan calculation method and device considering cost and goods receiving capacity fluctuation, electronic equipment and a storage medium, and relates to the technical field of logistics transportation. The goods arrival plan calculation method considering the cost and the goods receiving capacity fluctuation comprises the steps of establishing a warehouse order data dictionary to determine an input data type and an output data type of a supply network model; determining an objective function and constraint conditions of the supply network model according to the characteristics of the optimization objective; according to the supply network model, under the condition that constraint conditions are met, a corresponding objective function is matched for solving, and then an optimal solution or an optimal solution set of the model is determined according to an optimization objective; and comparing the optimal solution or the optimal solution set to determine a unique optimal supply scheme. The invention further discloses a goods arrival plan calculation device considering the cost and the goods receiving capacity fluctuation, electronic equipment and a storage medium so as to achieve the purposes that the time for formulating the goods arrival plan is short, the flexibility is high, the planning effect is good, and the feasibility is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of logistics and transportation technology, and in particular to a method, device, electronic device and storage medium for calculating an arrival plan that takes into account fluctuations in cost and receiving capacity. Background Art

[0002] The arrival plan is a detailed plan of the arrival time and quantity of products or materials made by the enterprise during the procurement and supply chain management process. The arrival plan is designed to ensure that the enterprise can obtain the required materials or products when needed to support production, sales or other business activities. A reasonable arrival plan can help enterprises reduce inventory costs, improve capital utilization efficiency, avoid production interruptions, and ensure the stability of the supply chain.

[0003] The arrival plan usually includes the arrangement of purchase orders, transportation arrangements, inventory management and supplier communication. There are many factors to consider in the arrival plan, such as the different delivery capabilities, reliability and response speed of different suppliers; the time and cost of different transportation methods and carriers in the transportation process are also different. Especially when it comes to international procurement, the complexity of the transportation link further increases the difficulty of making an arrival plan; in terms of warehouses, it is also necessary to find a balance between maintaining an appropriate inventory level and avoiding excessive inventory. Therefore, making a reasonable arrival plan requires an in-depth understanding and analysis of each link of the supply chain.

[0004] Currently, among many retail enterprises, many do not pay attention to or are unable to formulate a more reasonable arrival plan. The inability to reasonably arrange the supply quantity and supply rhythm in the future affects the entire production process, and ultimately makes the inventory often swing between too much and too little, resulting in inventory backlog costs or out-of-stock losses, which is one of the key points to improve enterprise efficiency. Summary of the invention

[0005] The embodiments of the present application provide a method, device, electronic device and storage medium for calculating an arrival plan that takes into account fluctuations in cost and receiving capacity, so as to solve the problems of the existing arrival plan that takes a long time to formulate, has low flexibility, has poor planning effects and is difficult to ensure feasibility.

[0006] On the one hand, an embodiment of the present application provides a method for calculating a delivery plan that takes into account fluctuations in cost and receiving capacity, including:

[0007] Establishing a warehouse order data dictionary to determine the input data type and output data type of the supply network model, the data dictionary at least includes: product inventory information, product in transit information, delivery information, and billing information;

[0008] According to the characteristics of the optimization target, the objective function and constraint conditions of the supply network model are determined, and the optimization target includes at least one of the following: minimizing the delay penalty of delayed orders, minimizing the early arrival days penalty of early arrival orders, minimizing the warehouse capacity fluctuation penalty and minimizing the cost;

[0009] According to the supply network model, while satisfying the constraint conditions, matching the corresponding objective function to solve, and then determining the optimal solution or optimal solution set of the model according to the optimization goal;

[0010] By comparing the optimal solution or optimal solution set, a unique optimal supply solution is determined.

[0011] In a possible embodiment, the objective function of the supply network model includes at least one of the following: an objective function for minimizing the penalty for delayed order arrival, an objective function for minimizing the penalty for warehouse capacity fluctuation, and an objective function for minimizing cost.

[0012] In a possible embodiment, the constraint condition of the supply network model includes at least one of the following:

[0013] The first constraint: an order can only choose one path;

[0014] Second constraint: The storage time of an order at each step in the path cannot exceed the storage time limit;

[0015] The third constraint: an order group can only select one order group path set;

[0016] Fourth constraint: The paths selected by all orders in an order group must be in an order group path set;

[0017] Fifth constraint: The time it takes for an order to arrive at the destination warehouse is the sum of the factory's response time, the route transportation time, and the route temporary storage time.

[0018] The sixth constraint condition: the difference between the number of days of delayed arrival and the number of days of early arrival of an order is equal to the difference between the number of days the order arrives at the destination warehouse and the number of days the order arrives at the latest warehouse;

[0019] The seventh constraint: the total time it takes for an order to arrive at each terminal warehouse;

[0020] The eighth constraint condition: The terminal warehouse inventory is the sum of the current period inventory, the known incoming inventory and the decision incoming inventory, minus the known outgoing inventory;

[0021] Ninth constraint: The storage capacity of the terminal warehouse cannot exceed the limit;

[0022] Tenth constraint: The terminal warehouse capacity cannot exceed the limit;

[0023] The eleventh constraint: The terminal warehouse capacity utilization rate meets the maximum and minimum limits.

[0024] In a possible embodiment, the optimal solution or optimal solution set of the model is determined according to the optimization target, and the calculation method satisfies: total order cost = path transportation cost + path temporary storage cost + storage fee for early arrival.

[0025] In a possible embodiment, according to the supply network model, while satisfying the constraints, matching the corresponding objective function for solving, also includes: analyzing the data of the optimization target, and classifying the input data of the supply network model respectively to match the corresponding objective function and the constraints.

[0026] In a possible embodiment, determining a unique optimal supply solution by comparing the optimal solution or optimal solution set also includes: when a unique optimal supply solution cannot be obtained, minimizing cost is used as an optimization goal to determine the optimal supply solution.

[0027] In a possible embodiment, the priority of the constraints is: first constraint > second constraint > third constraint > fourth constraint > fifth constraint > sixth constraint > seventh constraint > eighth constraint > ninth constraint > tenth constraint > eleventh constraint.

[0028] On the one hand, an embodiment of the present application provides a device for calculating a delivery plan taking into account fluctuations in cost and receiving capacity, including:

[0029] A data receiving module is used to establish a warehouse order data dictionary to determine the input data type and output data type of the supply network model, wherein the data dictionary includes at least: product inventory information, product in-transit information, delivery information, and billing information;

[0030] An algorithm configuration module, for determining the objective function and constraint conditions of the supply network model according to the characteristics of the optimization target, wherein the optimization target includes at least one of the following: minimizing the delay penalty for delayed orders, minimizing the early arrival days penalty for early arrival orders, minimizing the warehouse capacity fluctuation penalty, and minimizing the cost;

[0031] A calculation module, used to match the corresponding objective function to solve the supply network model under the condition that the constraint conditions are met, and then determine the optimal solution or optimal solution set of the model according to the optimization goal;

[0032] The solution determination module is used to determine a unique optimal supply solution by comparing the optimal solution or the optimal solution set.

[0033] On the one hand, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any of the above-mentioned arrival plan calculation methods that take into account cost and receiving capacity fluctuations.

[0034] On the one hand, the present application provides a computer-readable storage medium, which includes program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any of the above-mentioned arrival plan calculation methods that take into account cost and receiving capacity fluctuations.

[0035] On the one hand, an embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; when a processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned arrival plan calculation methods that consider cost and receiving capacity fluctuations.

[0036] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least one of the following:

[0037] 1. Global optimization of planning eliminates the problem of local optimization and global non-optimality caused by isolated optimization. A feasible supply plan guided by cost and business preferences can be quickly obtained to reduce overall operating costs and improve operational efficiency.

[0038] 2. It can calculate in detail all kinds of cost data involved in the entire plan, so that business personnel can quickly evaluate the plan.

[0039] 3. The time range of the plan and the weight of each indicator can be freely adjusted, so that business personnel can quickly obtain planning results based on different considerations and facilitate comparison between multiple plans.

[0040] 4. It has high versatility and scalability. Enterprises do not need to redesign algorithms when making business adjustments and supply network adjustments, or only need to make minor adjustments.

[0041] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 A schematic diagram of an application scenario of a method for calculating a delivery plan taking into account fluctuations in cost and receiving capacity provided in an embodiment of the present application;

[0044] Figure 2 A flow chart of a method for calculating a delivery plan taking into account fluctuations in cost and receiving capacity provided in an embodiment of the present application;

[0045] Figure 3A An example of five-stage path selection for an order in a method for calculating a delivery plan that takes into account fluctuations in cost and receiving capacity provided in an embodiment of the present application;

[0046] Figure 3B An example of four-stage path selection for an order in a method for calculating a delivery plan that takes into account fluctuations in cost and receiving capacity provided in an embodiment of the present application;

[0047] Figure 4 A module diagram of a device for calculating a delivery plan that takes into account fluctuations in cost and receiving capacity provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0050] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0051] The following is a brief introduction to the design concept of the embodiment of the present application:

[0052] Currently, most supply plans are manually formulated based on past business experience. Due to the one-sidedness of human brain calculations, the supply plan will take into account a variety of complex logics such as factory response delivery date, transportation time and transportation costs of supply routes, warehouse capacity, warehouse storage capacity, etc., making it impossible to ensure that the existing resources are fully utilized. The efficiency of manual supply planning is low, and it is usually difficult to accurately verify the results, which ultimately affects the production and sales rhythm of the entire company. The workload of manual supply planning is very large, especially when the arrival plan involves multiple warehouses. The time cycle required for planning is long and lacks flexibility, and it is impossible to respond in time. When information such as order delivery date or supply network is adjusted due to emergencies, it is difficult for humans to switch quickly, and the arrival plan often affects the entire system. Every slight adjustment may cause a major change in the entire supply plan.

[0053] In view of this, the embodiments of the present application provide a method, device, electronic device and storage medium for calculating the arrival plan taking into account the fluctuations in cost and receiving capacity. Among them, the method for calculating the arrival plan taking into account the fluctuations in cost and receiving capacity includes: establishing a warehouse order data dictionary to determine the input data type and output data type of the supply network model, the data dictionary at least including: product inventory information, product in transit information, shipping information, billing information; according to the characteristics of the optimization target, determining the objective function and constraints of the supply network model, the optimization target at least including the following: minimizing the delay penalty for delayed orders, minimizing the early arrival days penalty for early arrival orders, minimizing the warehouse capacity fluctuation penalty and minimizing costs; according to the supply network model, matching the corresponding objective function to solve the problem while satisfying the constraints, and then determining the optimal solution or optimal solution set of the model according to the optimization target; by comparing the optimal solution or optimal solution set, determining the only optimal supply plan.

[0054] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0055] like Figure 1 As shown, it is a schematic diagram of an application scenario of the method for calculating the arrival plan considering the fluctuation of cost and receiving capacity provided by the embodiment of the present application. In the schematic diagram of the application scenario, a terminal device 101 and a server 102 are included. Among them, the terminal device 101 and the server 102 communicate with each other through a communication network.

[0056] The terminal device 101 is an electronic device used by the target object, and the electronic device may be a personal computer, a mobile phone, a tablet computer, a notebook, an e-book reader, a vehicle-mounted terminal, etc. In addition, a client related to the method for calculating the arrival plan considering the fluctuation of cost and receiving capacity may be installed on the terminal device 101, and the client may be software (for example, an APP, a browser, etc.), or a web page, a small program, etc. By implementing the above-mentioned method for calculating the arrival plan considering the fluctuation of cost and receiving capacity on the terminal device 101, the relevant operations of calculating the arrival plan considering the fluctuation of cost and receiving capacity are performed.

[0057] The server 102 may be an independent physical server or an edge device 102 in the field of cloud computing. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (English name: Content Delivery Network, abbreviated as CDN), as well as big data and artificial intelligence platforms.

[0058] There is no restriction on the number of the terminal devices 101 and / or servers 102 .

[0059] It should be noted that the arrival plan calculation method that takes into account the fluctuations in cost and receiving capacity in the embodiment of the present application can be executed by the terminal device 101 or the server 102 alone, or it can be executed jointly by the terminal device 101 and the server 102. For example, when executed jointly by the terminal device 101 and the server 102, the server 102 can provide the warehouse order data dictionary to the terminal device 101 to determine the input data type and output data type of the supply network model. The terminal device 101 can provide the objective function and constraints of the supply network model to the server 102. When the constraints are met, the server 102 matches the corresponding objective function for solution, and then determines the optimal solution or optimal solution set of the model according to the optimization goal. The terminal device 101 compares the optimal solution or optimal solution set to determine the only optimal supply plan.

[0060] The following describes the warehouse site selection method provided by the exemplary embodiment of the present application in combination with the above-mentioned application scenarios and with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation methods of the present application are not subject to any limitations in this regard.

[0061] like Figure 2 As shown, it is a schematic diagram of a method 200 for calculating a delivery plan considering fluctuations in cost and receiving capacity provided by an embodiment of the present application. In the schematic diagram of the method for calculating a delivery plan considering fluctuations in cost and receiving capacity, the following steps are shown:

[0062] Step S201, establishing a data dictionary, that is, establishing a warehouse order data dictionary, to determine the input data type and output data type of the supply network model, the data dictionary at least includes: product inventory information, product in transit information, delivery information, and billing information;

[0063] Step S202, determining the objective function and constraint conditions of the supply network model, that is, determining the objective function and constraint conditions of the supply network model according to the characteristics of the optimization target, wherein the optimization target includes at least one of the following: minimizing the delay penalty for delayed orders, minimizing the early arrival days penalty for early arrival orders, minimizing the warehouse capacity fluctuation penalty, and minimizing the cost;

[0064] Step S203, determining the optimal solution or optimal solution set of the model according to the optimization target, that is, according to the supply network model, matching the corresponding objective function to solve while satisfying the constraint conditions, and then determining the optimal solution or optimal solution set of the model according to the optimization target;

[0065] Step S204, determining a unique optimal supply solution, that is, determining a unique optimal supply solution by comparing the optimal solution or the optimal solution set.

[0066] In a possible embodiment, the objective function of the supply network model includes at least one of the following: an objective function that minimizes the penalty for orders not arriving on time, an objective function that minimizes the penalty for warehouse capacity fluctuations, and an objective function that minimizes costs. The selection and use of objective functions can assist in planning and achieve global optimization; it eliminates the problem of island-style optimization, such as only considering a single objective, which leads to local optimization but not global optimization. A feasible supply plan guided by cost and business preferences can be quickly obtained to reduce overall operating costs and improve operational efficiency. The objective function of this embodiment can selectively take into account order deadlines, warehouse capacity, and costs. Specifically,

[0067] The objective function to minimize the delay penalty for delayed orders is:

[0068]

[0069] Among them, DayDelay min represents the delay penalty indicator of delayed orders, d represents orders, D represents order sets, wht dl Represents the penalty weight for delayed arrival violation of the order, dp d represents the priority coefficient of order d, represents the order quantity of order d, Represents the delayed arrival time of order d and is a positive integer;

[0070] The objective function for minimizing the penalty for early arrival days for early arrival orders is:

[0071]

[0072] Among them, DayPre min represents the penalty index of the number of days of early arrival for the orders that arrive early, d represents the order, D represents the order set, wht ad Represents the penalty weight for early delivery date violation of the order, dp d represents the priority coefficient of order d, Represents the advance arrival time of order d and is a positive integer;

[0073] The objective function to minimize the penalty for warehouse capacity fluctuation is:

[0074]

[0075] Among them, Product min represents the penalty index for warehouse capacity fluctuation, e represents the capacity accounting cycle of the terminal warehouse, E represents the capacity accounting cycle set of the terminal warehouse, w represents the terminal warehouse, W represents the terminal warehouse set, wht ad Represents the capacity fluctuation weight of the terminal warehouse, Represents the maximum capacity percentage used by the terminal warehouse w in the capacity calculation period e, represents the minimum capacity percentage used by the terminal warehouse w in the capacity calculation period e;

[0076] The objective function to minimize the cost is:

[0077] Cost min =wht ct ×∑ d∈D k d (4)

[0078] Among them, Cost min represents the cost indicator, d represents the order, D represents the order set, wht ct represents the cost weight, k dRepresents the total cost of order d.

[0079] In a possible embodiment, multiple constraints can be designed to match the objective function of the supply network model so as to calculate in detail the various cost data involved in the entire plan, so as to facilitate business personnel to quickly evaluate the plan. The constraints of the supply network model may include at least one of the following:

[0080] The first constraint: an order can only choose one path, and the calculation method is:

[0081]

[0082] Among them, r represents the selected route, R d represents the set of available standard routes for order d, x d,r A Boolean variable representing whether order d chooses standard route r for transportation, with a value of 0 or 1;

[0083] Second constraint: The storage time of an order at each step in the path cannot exceed the storage time limit, which is calculated as follows:

[0084]

[0085] Where r represents the selected route, S represents the set of standard path stages, S = {0: factory, 1: factory-port of departure, 2: port of departure-port of destination, 3: port of destination-HUB, 4: HUB-destination warehouse}, d represents the order, represents the minimum storable time of path r in stage s, x d,r A Boolean variable representing whether order d chooses standard route r for transportation, with a value of 0 or 1. d,r,s represents an integer variable, the temporary storage time of order d in stage s of path r, represents the maximum storable time of path r in stage s;

[0086] The third constraint: an order group can only select one order group path set, and the calculation method is:

[0087]

[0088] Among them, n represents the order group route, N u represents the optional set of order group routes for order group u, v u,n A Boolean variable representing whether order group n selects order group path set u for transportation, with a value of 0 or 1;

[0089] Fourth constraint: The paths selected by all orders in an order group must be in an order group path set. The calculation method is:

[0090]

[0091] Among them, d represents the order, D u represents the order set contained in the order group set u, r represents the selected route, R d represents the set of available standard routes for order d, x d,r A Boolean variable representing whether order d chooses standard route r for transportation, with a value of 0 or 1. u represents the order set contained in the order group set u, v u,n A Boolean variable representing whether order group n selects order group path set u for transportation, with a value of 0 or 1;

[0092] Fifth constraint: The time it takes for an order to arrive at the destination warehouse is the sum of the factory's response time, the route transportation time, and the route temporary storage time. The calculation method is:

[0093]

[0094] in, The factory that represents order d confirms that the delivery date is the available time for pickup. r1 represents the selected transportation route, r2 represents the temporary storage process of the selected transportation route, and R d represents the set of available standard routes for order d, s represents the standard path, S represents the set of standard path stages, S = {0: factory, 1: factory-port of departure, 2: port of departure-port of destination, 3: port of destination-HUB, 4: HUB-destination warehouse}, represents the transportation time of path r1, x d,r A Boolean variable representing whether order d chooses standard route r for transportation, with a value of 0 or 1. represents an integer variable, the temporary storage time of order d in stage s of path r2;

[0095] The sixth constraint condition: The difference between the number of days of delayed arrival and the number of days of early arrival of an order is equal to the difference between the number of days the order arrives at the destination warehouse and the number of days the order arrives at the latest warehouse. The calculation method is:

[0096]

[0097] in, Represents the advance arrival time of order d, represents the delayed arrival time of order d, y d Represents the time when order d arrives at the destination warehouse, Represents the latest arrival date of order d;

[0098] The seventh constraint condition is to calculate the total time it takes for an order to arrive at each terminal warehouse. The calculation method is:

[0099]

[0100] Among them, w represents the terminal warehouse, W represents the terminal warehouse set, t represents the time value, T represents the time set, α d,t,w A Boolean variable representing whether order d arrives at terminal warehouse w at time t, with a value of 0 or 1, y d Represents the time when order d arrives at the destination warehouse;

[0101] The eighth constraint condition: The terminal warehouse inventory is the sum of the current period inventory, the known incoming inventory, and the decision incoming inventory, minus the known outgoing inventory. The calculation method is:

[0102]

[0103] Among them, h w,t+1 represents the initial inventory of terminal warehouse w at time t+1, h w,t represents the initial inventory of terminal warehouse w at time t, represents the known inventory quantity of terminal warehouse w at time t, d represents the order, and D represents the order set. represents the order quantity of order d, α d,t,w A Boolean variable representing whether order d arrives at terminal warehouse w at time t, with a value of 0 or 1. represents the expected outbound quantity of terminal warehouse w at time t;

[0104] Ninth constraint: The terminal warehouse capacity cannot exceed the limit, and the calculation method is:

[0105]

[0106] Among them, h w,t represents the initial inventory of terminal warehouse w at time t, represents the upper limit of the storage capacity of the terminal warehouse w at time t;

[0107] Tenth constraint: The terminal warehouse capacity cannot exceed the limit, and the calculation method is:

[0108]

[0109] Among them, d represents the order, D represents the order set, represents the known inventory quantity of terminal warehouse w at time t, represents the order quantity of order d, α d,t,w Indicates whether order d arrives at terminal warehouse w at time t, represents the upper limit of the capacity of terminal warehouse w at time t;

[0110] Eleventh constraint: The terminal warehouse capacity utilization rate meets the maximum and minimum limits, and the calculation methods are:

[0111]

[0112] in, represents the upper limit of the capacity of the terminal warehouse w at time t, Represents the maximum capacity percentage used by the terminal warehouse w in the capacity calculation period e, represents the known inventory quantity of terminal warehouse w at time t, d represents the order, and D w Represents the set of orders that need to be delivered to the terminal warehouse w, represents the order quantity of order d, α d,t,w Indicates whether order d arrives at terminal warehouse w at time t, Represents the minimum capacity percentage used by terminal warehouse w during capacity calculation period e.

[0113] In the above embodiment, when the terminal warehouse w=1, it is a single warehouse situation. Therefore, the single warehouse can be regarded as a special case of multiple warehouses.

[0114] It should be noted that the second constraint, the fifth constraint, the sixth constraint, and the seventh constraint are all related to the order time range. By combining different constraints, business personnel can quickly obtain planning results based on different considerations, which facilitates comparison between multiple plans.

[0115] In actual application scenarios, the first, third, and fourth constraints are all related to the path selection of the order. The reason is that there may often be multiple factories, multiple route points, and multiple destination warehouses. There may be multiple routes from the factory, and different routes may pass through ports, hubs, or directly send to terminal warehouses. In a multi-warehouse scenario, if a batch of goods from the same factory has different destination warehouses, the route passing through the hub can be selected for distribution. A single path refers to a complete path from the factory to the warehouse. Since there will be temporary storage points in the path (such as sea transportation passing through the port of departure and the port of arrival, etc.), and different temporary storage points may have different temporary storage times and temporary storage fees, in order to incorporate the temporary storage information into the path and standardize the path form, the single path specified in the algorithm will include 5 stages, namely, the factory stage, the factory to the port of departure stage, the port of departure to the port of arrival stage, the port of arrival to the hub stage, and the hub to the terminal warehouse stage. As shown in the figure below, the five stages will each record their transportation time, transportation costs, temporary storage time, and rate information. For some routes, they may not pass through ports, etc. The algorithm will add virtual path stages for them in the pre-processing stage, and set the temporary storage time of the virtual path segment to 0, and the related cost to 0, so that each route will contain five stages. The following table 1 shows that a single path will contain 5 stages:

[0116] Table 1

[0117]

[0118] like Figure 3A An example of five-stage path selection for an order is shown for the calculation method of the arrival plan considering the fluctuation of cost and receiving capacity. For example, the order is shipped from factory 1, factory 2, factory 3, and factory 4, where the possible paths for the order to arrive at terminal warehouse 1 from factory 1 include: Path 1: factory 1-terminal warehouse 1; Path 2: factory 1-port of departure 1-port of arrival 1-terminal warehouse 1; Path 3: factory 1-port of departure 1-port of arrival 2-HUB-terminal warehouse 1. This is because retail companies usually set up multiple warehouses according to sales areas, and the factory end usually uses one vehicle to transport multiple orders shipped at the same time to save freight. Therefore, when optimizing the arrival plan of multiple warehouses in a unified manner, it is necessary to consider the problem of merging multiple orders. If there are orders to multiple different warehouses in a batch of shipped orders, the batch of orders must pass through the HUB and complete the distribution operation in the HUB, that is, the orders to different warehouses are separated and handed over to different vehicles for transportation. In this case, multiple orders shipped in a batch need to go through the same route before arriving at the HUB, and the temporary storage time at each point in the route also needs to be consistent. After arriving at the HUB, different transportation times can be planned based on the receiving capacity and inventory status of the destination warehouse.

[0119] Figure 3B An example of four-stage path selection for an order is shown for the calculation method of the arrival plan that takes into account fluctuations in cost and receiving capacity. For example: shipping from Factory 1, Factory 2, and Factory 3, where the possible paths for an order to reach the terminal warehouse from Factory 1 include: Path 1: Factory 1-Terminal warehouse; Path 2: Factory 1-Port of Departure 1-Port of Arrival 1-Terminal warehouse; Path 3: Factory 1-Port of Departure 1-Port of Arrival 2-Terminal warehouse. In this way, a single path will contain 4 stages, namely, the factory stage, the factory to the port of departure stage, the port of departure to the port of arrival stage, and the port of arrival to the terminal warehouse stage. As shown in the figure below, the four stages will each record their transportation time, transportation costs, and temporary storage time and rate information. Table 2 below shows that a single path will contain 4 stages:

[0120] Table 2

[0121]

[0122]

[0123] Figure 3B The four stages shown can also be viewed as Figure 3A The special case of the five stages shown is that the HUB path is NULL, the temporary storage time is 0, and the cost is also 0.

[0124] In a possible embodiment, the objective function and constraints are used to make the supply network model highly versatile and extensible. When adjusting business and supply networks, enterprises do not need to redesign algorithms, or only need to make minor adjustments. Using the supply network model, enterprises can determine the optimal solution or optimal solution set of the model according to the optimization goal, compare them, and determine the only optimal supply plan. The calculation method of the optimal solution or optimal solution set can satisfy: total order cost = path transportation cost + path temporary storage cost + storage cost of early arrival, including the use of the following calculation method to obtain the total order cost:

[0125]

[0126] Among them, k d represents the total cost of order d, represents the shipping cost by volume for order d, Represents the temporary storage cost of order d, wct d The unit price for storing the goods in the order destination warehouse on a daily basis. represents the order quantity of order d, represents the advance arrival time of order d, r represents the selected route, R d represents the set of available standard routes for order d, represents the transportation cost of order d choosing route r, x d,r A Boolean variable representing whether the order d chooses the standard route r for transportation, with a value of 0 or 1. s represents the standard route, and S represents the set of standard route stages. S = {0: factory, 1: factory-port of departure, 2: port of departure-port of destination, 3: port of destination-HUB, 4: HUB-destination warehouse}, represents the daily storage fee for order d choosing path r in stage s, z d,r,s Represents the storage time of order d in stage s of path r.

[0127] In a possible embodiment, according to the supply network model, when the constraints are met, the corresponding objective function is matched for solving, and it also includes: analyzing the data of the optimization target, and classifying the input data of the supply network model respectively to match the corresponding objective function and the constraints. For example, the input data related to the order time can be selected to match the second constraint, the fifth constraint, the sixth constraint or the seventh constraint, and the corresponding objective function is set, so that the business personnel can quickly obtain the planning results based on different considerations, which is convenient for the scheme design.

[0128] In a possible embodiment, by comparing the optimal solution or optimal solution set, a unique optimal supply solution is determined, and the method further includes: when a unique optimal supply solution cannot be obtained, minimizing cost is used as an optimization goal to determine the optimal supply solution. This embodiment is intended to provide a cost-first option when the optimal supply solution cannot be uniquely determined in actual application scenarios; similarly, an option of completing the order on time can also be provided.

[0129] In a possible embodiment, the priority of the constraints is: first constraint>second constraint>third constraint>fourth constraint>fifth constraint>sixth constraint>seventh constraint>eighth constraint>ninth constraint>tenth constraint>eleventh constraint. This embodiment provides a priority selection of constraints to guide the selection of appropriate constraints to match the corresponding objective function.

[0130] refer to Figure 4 , which is a module diagram of a delivery plan calculation device 400 that takes into account cost and receiving capacity fluctuations provided in an embodiment of the present application, with reference to Figure 4 The device includes: a data receiving module 402, an algorithm configuration module 404, a calculation module 406, and a solution determination module 408. Among them:

[0131] The data receiving module 402 is used to establish a warehouse order data dictionary to determine the input data type and output data type of the supply network model, and the data dictionary at least includes: product inventory information, product in-transit information, delivery information, and billing information;

[0132] The algorithm configuration module 404 is used to determine the objective function and constraint conditions of the supply network model according to the characteristics of the optimization target, wherein the optimization target includes at least one of the following: minimizing the delay penalty of delayed orders, minimizing the early arrival days penalty of early arrival orders, minimizing the warehouse capacity fluctuation penalty and minimizing the cost;

[0133] A calculation module 406 is used to match the corresponding objective function to solve the supply network model under the condition that the constraint conditions are met, and then determine the optimal solution or optimal solution set of the model according to the optimization target;

[0134] The solution determination module 408 is used to determine a unique optimal supply solution by comparing the optimal solution or the optimal solution set.

[0135] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application, and the electronic device can realize the function of the aforementioned arrival plan calculation device considering the fluctuation of cost and receiving capacity, referring to Figure 5 , the electronic device comprises:

[0136] At least one processor 501, and a memory 502 connected to the at least one processor 501. The specific connection medium between the processor 501 and the memory 502 is not limited in the embodiment of the present application. Figure 5 In the example, the processor 501 and the memory 502 are connected via a bus 500. The bus 500 is Figure 5 The connections between other components are shown in bold lines, and are not intended to be limiting. The bus 500 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 501 can also be called a controller, and there is no limitation on the name.

[0137] In the embodiment of the present application, the memory 502 stores instructions that can be executed by at least one processor 501. By executing the instructions stored in the memory 502, the at least one processor 501 can execute the arrival plan calculation method considering the fluctuation of cost and receiving capacity discussed above. The processor 501 can implement Figure 4 The functions of each module in the device shown.

[0138] Among them, the processor 501 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 502 and calling data stored in the memory 502, the various functions of the device and processing data, the device can be monitored as a whole.

[0139] In one possible design, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 501. In some embodiments, the processor 501 and the memory 502 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0140] Processor 501 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the arrival plan calculation method considering the fluctuation of cost and receiving capacity disclosed in the embodiments of the present application can be directly embodied as a hardware processor execution, or a combination of hardware and software modules in the processor.

[0141] The memory 502 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 502 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 502 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 502 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0142] By programming the processor 501, the code corresponding to the method for calculating the arrival plan taking into account the fluctuation of the cost and receiving capacity introduced in the above embodiment can be fixed into the chip, so that the chip can execute the calculation method when running. Figure 1 The steps of the method for calculating the arrival plan considering the fluctuation of cost and receiving capacity in the embodiment shown are as follows: How to design and program the processor 501 is a technology well known to those skilled in the art and will not be described in detail here.

[0143] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the arrival plan calculation method discussed above that takes into account cost and receiving capacity fluctuations.

[0144] In some possible implementations, various aspects of the method for calculating an arrival plan that takes into account fluctuations in cost and receiving capacity provided in the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the method for calculating an arrival plan that takes into account fluctuations in cost and receiving capacity according to various exemplary implementations of the present application described above in this specification.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for calculating an arrival plan taking into account fluctuations in cost and receiving capacity, characterized in that: include: Establishing a warehouse order data dictionary to determine the input data type and output data type of the supply network model, the data dictionary at least includes: product inventory information, product in transit information, delivery information, and billing information; According to the characteristics of the optimization target, the objective function and constraint conditions of the supply network model are determined, and the optimization target includes at least one of the following: minimizing the delay penalty of delayed orders, minimizing the early arrival days penalty of early arrival orders, minimizing the warehouse capacity fluctuation penalty and minimizing the cost; According to the supply network model, while satisfying the constraint conditions, matching the corresponding objective function to solve, and then determining the optimal solution or optimal solution set of the model according to the optimization goal; By comparing the optimal solution or optimal solution set, a unique optimal supply solution is determined.

2. The method for calculating the arrival plan considering fluctuations in cost and receiving capacity as claimed in claim 1, characterized in that: The objective function of the supply network model includes at least one of the following: an objective function that minimizes penalties for orders that fail to arrive on time, an objective function that minimizes penalties for warehouse capacity fluctuations, and an objective function that minimizes costs.

3. The method for calculating the arrival plan considering fluctuations in cost and receiving capacity as claimed in claim 1, characterized in that: The constraints of the supply network model include at least one of the following: The first constraint: an order can only choose one path; Second constraint: The storage time of an order at each step in the path cannot exceed the storage time limit; The third constraint: an order group can only select one order group path set; Fourth constraint: The paths selected by all orders in an order group must be in an order group path set; Fifth constraint: The time it takes for an order to arrive at the destination warehouse is the sum of the factory's response time, the route transportation time, and the route temporary storage time. The sixth constraint condition: the difference between the number of days of delayed arrival and the number of days of early arrival of an order is equal to the difference between the number of days the order arrives at the destination warehouse and the number of days the order arrives at the latest warehouse; The seventh constraint: the total time it takes for an order to arrive at each terminal warehouse; The eighth constraint condition: The terminal warehouse inventory is the sum of the current period inventory, the known incoming inventory and the decision incoming inventory, minus the known outgoing inventory; Ninth constraint: The storage capacity of the terminal warehouse cannot exceed the limit; Tenth constraint: The terminal warehouse capacity cannot exceed the limit; The eleventh constraint: The terminal warehouse capacity utilization rate meets the maximum and minimum limits.

4. The method for calculating the arrival plan considering the fluctuation of cost and receiving capacity as claimed in claim 1, characterized in that: The optimal solution or optimal solution set of the model is determined according to the optimization target, and the calculation method satisfies: total order cost = path transportation cost + path temporary storage cost + storage cost of early arrival.

5. The method for calculating the arrival plan considering fluctuations in cost and receiving capacity as claimed in claim 1, characterized in that: According to the supply network model, while satisfying the constraint conditions, matching the corresponding objective function to solve, further comprising: The data of the optimization target is analyzed, and the input data of the supply network model is classified respectively to match the corresponding objective function and the constraint condition.

6. The method for calculating the arrival plan considering fluctuations in cost and receiving capacity as claimed in claim 1, characterized in that: By comparing the optimal solution or optimal solution set, a unique optimal supply solution is determined, which also includes: When it is impossible to obtain a unique optimal supply solution, minimizing cost is used as the optimization goal to determine the optimal supply solution.

7. The method for calculating the arrival plan considering the fluctuation of cost and receiving capacity as claimed in claim 3, characterized in that: The priorities of the constraints are: First constraint > second constraint > third constraint > fourth constraint > fifth constraint > sixth constraint > seventh constraint > eighth constraint > ninth constraint > tenth constraint > eleventh constraint.

8. A device for calculating arrival plans taking into account fluctuations in costs and receiving capacity, characterized in that: include: A data receiving module is used to establish a warehouse order data dictionary to determine the input data type and output data type of the supply network model, wherein the data dictionary includes at least: product inventory information, product in-transit information, delivery information, and billing information; An algorithm configuration module, for determining the objective function and constraint conditions of the supply network model according to the characteristics of the optimization target, wherein the optimization target includes at least one of the following: minimizing the delay penalty for delayed orders, minimizing the early arrival days penalty for early arrival orders, minimizing the warehouse capacity fluctuation penalty, and minimizing the cost; A calculation module, used to match the corresponding objective function to solve the supply network model under the condition that the constraint conditions are met, and then determine the optimal solution or optimal solution set of the model according to the optimization goal; The solution determination module is used to determine a unique optimal supply solution by comparing the optimal solution or the optimal solution set.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the arrival plan calculation method considering cost and receiving capacity fluctuations as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It includes program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute the arrival plan calculation method considering cost and receiving capacity fluctuations as described in any one of claims 1 to 7.

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