Multi-cycle supply chain plan optimization method and device and computer equipment

Abstract

The invention relates to a multi-cycle supply chain plan optimization method and device and computer equipment. The method comprises the following steps: acquiring production information; generating constraint parameters and a multi-objective function according to the production information; establishing a multi-cycle optimization model according to the constraint parameters and the multi-objective function; and solving the multi-cycle optimization model through an operation planning optimization solving algorithm to obtain a supply chain optimization plan. By adopting the method, the multi-cycle optimization model can be established according to the production information and the inventory influence between the cycles, the multi-cycle optimization model is solved through an algorithm, a rolling inventory plan is provided, a product production main plan is quickly customized, and the production efficiency is improved. The problems of low efficiency and incomplete consideration of each factor in actual making of a multi-cycle monthly production plan are solved, and the production efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of supply chain planning, and in particular to a multi-period supply chain planning optimization method, device and computer equipment. Background Art

[0002] At present, the digitalization level of most chemical enterprises is not high, and the formulation of supply chain production plans is mainly carried out by arranging production plans based on manual experience and offline communication. For a large chemical enterprise, faced with a large variety of products, frequent changes in business processes, and the need to adjust production according to market changes, relying on manual experience to adjust production plans is not only a large workload and high requirements for employees who arrange production plans, but also difficult to obtain a global optimal solution that comprehensively considers different factors, resulting in negative effects such as insufficient raw materials, failure to meet market demand, and inventory backlogs. It is not a solution that maximizes benefits.

[0003] Traditional technology takes into account everything when formulating a single-cycle supply chain plan, but most companies use multiple cycles when formulating supply chain plans, ranging from 3 cycles at the shortest to 24 cycles at the longest. When dealing with multi-cycle supply chain plans, inventory will affect the formulation of the master production plan, and the inventory of each cycle affects each other. There is no provision for how to balance the relationship between inventory, production, and demand among multiple cycles when dealing with multi-cycle supply chain plans. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-cycle supply chain planning optimization method, device, computer equipment, computer-readable storage medium and computer program product that can balance the relationship between inventory, production and demand among multiple cycles in response to the above technical problems.

[0005] In a first aspect, the present application provides a multi-period supply chain planning optimization method. The method comprises:

[0006] Obtain production information;

[0007] generating constraint parameters and multi-objective functions according to the production information;

[0008] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0009] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0010] In one of the embodiments, the production data is parsed to obtain the production data.

[0011] In one of the embodiments, variable parameters are created, wherein the variable parameters include production variables, inventory variables, capacity variables, procurement variables, and demand variables;

[0012] generating production relationship constraints according to the production variables and the production data;

[0013] generating a multi-period inventory relationship constraint according to the inventory variable and the production data;

[0014] generating a capacity limit constraint according to the capacity variable and the production data;

[0015] generating a procurement restriction constraint according to the procurement variables and the production data;

[0016] generating a demand restriction constraint according to the demand variables and the production data;

[0017] generating an inventory limit constraint according to the inventory variable and the production data;

[0018] The constraint parameters are generated according to the generated production relationship constraint, the inventory relationship constraint, the production capacity restriction constraint, the procurement restriction constraint, the demand restriction constraint and the inventory restriction constraint.

[0019] In one of the embodiments, a profit maximization function is generated based on the production data and the demand variables;

[0020] Generate an inventory cost minimization function based on the production data and the inventory variables;

[0021] generating a movement cost minimization function based on the production data and the consumption variables;

[0022] Generating a function to minimize penalty factors according to the production data and the penalty variable;

[0023] A multi-objective function is generated according to the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0024] In one embodiment, the inventory data is parsed to extract the safety inventory and maximum inventory of the material in each period, and the initial inventory of the first period is obtained;

[0025] According to the beginning inventory and the inventory variable, obtain the ending inventory of the first period;

[0026] Obtaining the ending inventory of the current period according to the ending inventory of the previous period and the inventory variable;

[0027] Generate multi-period inventory relationship constraints based on the ending inventory of each period.

[0028] In one of the embodiments, the multi-period optimization model is converted into a mixed integer linear programming model by a linearization method;

[0029] The mixed integer linear programming model is solved by a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0030] In a second aspect, the present application also provides a multi-period supply chain planning optimization device. The device comprises:

[0031] Information acquisition module, used to obtain production information;

[0032] A parameter generation module, used to generate constraint parameters and multi-objective functions according to the production information;

[0033] A model building module, used for building a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0034] The model solving module is used to solve the multi-period optimization model through an operations research optimization solving algorithm to obtain a supply chain optimization plan.

[0035] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0036] Obtain production information;

[0037] generating constraint parameters and multi-objective functions according to the production information;

[0038] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0039] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Obtain production information;

[0042] generating constraint parameters and multi-objective functions according to the production information;

[0043] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0044] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0045] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0046] Obtain production information;

[0047] generating constraint parameters and multi-objective functions according to the production information;

[0048] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0049] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0050] The above-mentioned multi-period supply chain planning optimization method, device and computer equipment obtain production information; generate constraint parameters and multi-objective functions according to the production information; establish a multi-period optimization model according to the constraint parameters and the multi-objective function; solve the multi-period optimization model through an operations research optimization solution algorithm to obtain a supply chain optimization plan. The present invention associates the inventory impact between each cycle based on production information, builds a multi-period optimization model, solves the multi-period optimization model through an algorithm, provides a rolling inventory plan, and quickly customizes the product production master plan, which solves the problems of low efficiency and incomplete consideration of various factors in the actual formulation of multi-period monthly production plans, and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is an application environment diagram of a multi-period supply chain planning optimization method in one embodiment;

[0052] Figure 2 It is a flowchart of a multi-period supply chain planning optimization method in one embodiment;

[0053] Figure 3 A production flow chart of a multi-period supply chain planning optimization method in one embodiment;

[0054] Figure 4 It is a structural block diagram of a multi-period supply chain planning optimization device in one embodiment;

[0055] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The multi-period supply chain planning optimization method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains production information; according to the production information, it associates the inventory impact between each cycle, builds a multi-cycle optimization model, solves the multi-cycle optimization model through an algorithm, and obtains a supply chain optimization plan. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be instruments, sensor devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0058] In one embodiment, Figure 2 As shown in the figure, a multi-period supply chain planning optimization method is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0059] Step 202, obtaining production information.

[0060] Specifically, the production data is parsed to obtain the production data.

[0061] Among them, the production data includes material data, equipment data, BOM data, capacity submission data, inventory data, price data, demand submission data, and procurement data.

[0062] More specifically, the capacity submission data is parsed to extract the lower limit and upper limit of the device capacity of each period device;

[0063] Parse inventory data, extract the safety stock and maximum stock of materials in each period, and obtain the beginning stock of the first period;

[0064] Analyze price data, extract fixed costs, purchase prices, sales prices, inventory costs, and calculate unit production costs and unit profit margins of materials based on BOM data;

[0065] Analyze demand submission data and extract material demand, lower limit and upper limit of each period;

[0066] Parse the procurement data and extract the lower and upper limits of raw material procurement for each period.

[0067] Step 204: generating constraint parameters and multi-objective functions according to the production information.

[0068] The generation of constraint parameters and multi-objective functions according to the production information includes: creating variable parameters; generating production relationship constraints according to the production variables and the production data; generating multi-period inventory relationship constraints according to the inventory variables and the production data; generating capacity limit constraints according to the capacity variables and the production data; generating procurement limit constraints according to the procurement variables and the production data; generating demand limit constraints according to the demand variables and the production data; generating inventory limit constraints according to the inventory variables and the production data; generating the constraint parameters according to the generated production relationship constraints, the inventory relationship constraints, the capacity limit constraints, the procurement limit constraints, the demand limit constraints and the inventory limit constraints.

[0069] The variable parameters include production variables, inventory variables, capacity variables, procurement variables, demand variables, consumption variables and penalty variables. The details of each variable are as follows:

[0070] Production variables include: production plan quantity, by-product production plan quantity and sales variables;

[0071] Inventory variables include: production inventory, purchase inventory, sales inventory, consumption inventory, ending inventory, and inventory transfer;

[0072] Procurement variables include: raw material purchase volume;

[0073] Demand variables include: sales plan volume;

[0074] Consumption variables include: the amount of consumed materials and the amount of internal transfer consumption;

[0075] The penalty variables include: demand deviation penalty factor and safety stock deviation penalty factor.

[0076] The generating of multi-period inventory relationship constraints according to the inventory variables and the production data includes: parsing the inventory data to extract the safety inventory and maximum inventory of each period material, and obtaining the beginning inventory of the first period; obtaining the ending inventory of the first period according to the beginning inventory and the inventory variables; obtaining the ending inventory of the current period according to the ending inventory of the previous period and the inventory variables; and generating multi-period inventory relationship constraints according to the ending inventory of each period.

[0077] In one embodiment, a production relationship constraint is created:

[0078] The BOM data includes the output material ratio, the consumption material ratio and the output by-product ratio;

[0079] Production plan quantity*output material ratio=consumed material quantity*consumed material ratio;

[0080] Production plan quantity * output material ratio = by-product production plan quantity * output by-product ratio;

[0081] To create a multi-period inventory relationship constraint:

[0082] The inventory relationship for the first period is:

[0083] The ending inventory of the first cycle = the beginning inventory + the purchase inventory + the production inventory - the sales inventory - the consumption inventory;

[0084] The inventory relationship in the following cycle is:

[0085] Ending inventory = Ending inventory of the previous period + Purchase inventory + Production inventory - Sales outgoing inventory - Consumption outgoing inventory;

[0086] To create a capacity limit constraint:

[0087] The lower limit of the device capacity - α ≤ Σ the production variable of the producible material bill ≤ the upper limit of the device capacity + β;

[0088] Two capacity constraint slack variables α and β are introduced, which represent the allowable capacity constraint slack range respectively;

[0089] To create a purchasing restriction constraint:

[0090] The lower limit of raw material purchase quantity ≤ the raw material purchase quantity ≤ the upper limit of raw material purchase quantity;

[0091] To create a demand limit constraint:

[0092] The demand for each product is obtained through demand forecasting. Considering the inaccuracy of demand forecasting, an upper and lower limit is introduced for the demand of each product, allowing the sales plan to be within a range. At the same time, a new variable is added to evaluate the difference between the actual demand and the sales plan, which is recorded as:

[0093] Demand lower limit ≤ sales plan volume ≤ demand upper limit;

[0094] |Sales plan quantity - demand quantity|≤ε;

[0095] To create an inventory limit constraint:

[0096] Safety stock ≤ ending inventory ≤ maximum inventory;

[0097] |Ending inventory - safety stock|≤e.

[0098] The generation of constraint parameters and multi-objective functions according to the production information includes: generating a profit maximization function according to the production data and the demand variables; generating an inventory cost minimization function according to the production data and the inventory variables; generating a movement cost minimization function according to the production data and the consumption variables; generating a minimization penalty factor function according to the production data and the penalty variables; generating a multi-objective function according to the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0099] Specifically,

[0100] Step 3.1: Create a profit maximization function

[0101] Maximize∑Sales plan quantity×Sales price-∑Production plan quantity×Production cost

[0102] Step 3.2: Create an inventory cost minimization function

[0103] Minimize∑ Inventory Planned Quantity × Inventory Cost

[0104] Step 3.3: Create a function to minimize the movement cost

[0105] Minimize∑Internal transfer consumption×Internal transfer cost+∑Inventory transfer volume×Moving cost

[0106] Among them, the internal transfer cost and the moving cost are preset built-in parameters;

[0107] Step 3.4: Create a function to minimize the penalty factor

[0108] Minimize∑iw i ×α i +∑jw j ×β j +∑kw k ×ε k +∑lw l ×e l

[0109] Among them, α_i: capacity lower limit slack variable, w_i: penalty factor corresponding to capacity lower limit slack variable, β_j: capacity upper limit slack variable, w_j: penalty factor corresponding to capacity upper limit slack variable,

[0110] ε_k: the deviation between the sales plan and the demand, w_k: the penalty factor corresponding to the deviation between the sales plan and the demand, e_l: the deviation between the ending inventory and the safety stock, w_l: the penalty factor corresponding to the deviation between the ending inventory and the safety stock.

[0111] Step 206: Establish a multi-period optimization model based on the constraint parameters and the multi-objective function.

[0112] Step 208, solving the multi-period optimization model by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0113] Specifically, the multi-period optimization model is converted into a mixed integer linear programming model through a linearization method; the mixed integer linear programming model is solved through a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0114] In the above multi-period supply chain planning optimization method, production information is obtained; constraint parameters and multi-objective functions are generated according to the production information; a multi-period optimization model is established according to the constraint parameters and the multi-objective function; the multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan. The present invention relates the inventory impact between each cycle according to the production information, builds a multi-period optimization model, solves the multi-period optimization model through an algorithm, provides a rolling inventory plan, and quickly customizes the product production master plan, which solves the problems of low efficiency and incomplete consideration of various factors in the actual formulation of multi-period monthly production plans, and improves production efficiency.

[0115] In a specific embodiment, the algorithm flow is explained below using the data of a chemical enterprise company as an example. Here, a three-cycle supply chain plan is formulated;

[0116] The material information table is as follows:

[0117] Material Code Material Group Material properties M01 1000 raw material M02 1000 raw material M03 1000 raw material M04 1001 raw material M05 1001 Intermediate M06 1001 product M07 1001 product

[0118] like Figure 3 As shown, Figure 3 This is a production flow chart of a multi-period supply chain planning optimization method in an embodiment, and the corresponding production information table is as follows:

[0119]

[0120]

[0121] The following are business constraint data:

[0122] The capacity limit table of the device is as follows:

[0123] Device Code Material Group List Production capacity limit Capacity limit Capacity value cycle E01

[1001] 0 100000 99000 1 E02

[1001] 0 50000 49000 1 E03

[1001] 0 50000 49000 1 E01

[1001] 0 110000 100000 2 E02

[1001] 0 55000 50000 2 E03

[1001] 0 50000 50000 2 E01

[1001] 0 90000 88000 3 E02

[1001] 0 45000 40000 3 E03

[1001] 0 45000 40000 3

[0124] The meaning of the capacity limit table is as follows:

[0125] The ideal capacity of device E01 is 99,000, but it is allowed not to be turned on, that is, the capacity is 0. It is also allowed to be turned on when exceeding a certain load, that is, the upper limit of the capacity is 100,000, and the restricted main product material group is

[1001] .

[0126] The demand restriction table is as follows:

[0127] Material Code Demand lower limit Demand Cap Demand value cycle M06 5000 55000 50000 1 M06 5000 55000 50000 2 M06 5000 55000 50000 3 M07 5000 5000 100000 1 M07 5000 5000 100000 2 M07 5000 5000 100000 3

[0128] The inventory data is as follows:

[0129]

[0130]

[0131] Below is the price data, only the data for the marginal profit calculation results are shown here;

[0132] Material Code Marginal Profit M06 80 M07 100 M05 -200

[0133] The plan optimization algorithm is executed below. In this embodiment, procurement restrictions are not considered.

[0134] First, combined with production information, we can know that in order to meet demand and maximize benefits, the results are as follows:

[0135] For M06, the production of M06 requires an intermediate product M05, and the marginal profit of M05 is -100. Therefore, in order to achieve the goal of maximizing benefits, M05 should be produced as little as possible while meeting the demand and inventory level restrictions of M06. In addition, considering the formulation of multiple cycles, the demand and production capacity restrictions of each cycle, the specific production plan is as follows:

[0136] The first cycle: 25,000 M01 and M02 are needed, 25,000 M05 are produced, and then 50,000 M06 are produced. The demand and safety stock have been met, and no more production is required;

[0137] The second cycle: 25,000 M01 and M02 are needed to produce 25,000 M05, and then 50,000 M06, which has met the demand and safety stock. However, considering that the production capacity limit of the third cycle is 45,000, the demand is 50,000, and the safety stock is 5,000, even full production cannot meet the demand; and the production capacity limit of this cycle is 55,000. If more is produced in this cycle and then put into inventory, the demand of the next cycle can be met. Therefore, the production plan is: 27,500 M01 and M02 are needed, 27,500 M05 are produced, and then 55,000 M06 are produced to meet the demand of 50,000, and the remaining 5,000 are put into inventory. The current inventory is 10,000.

[0138] The third cycle: Production is carried out at full capacity, with a maximum of 45,000 M06s produced, while the demand is 50,000. The shortfall of 5,000 is made up through inventory to meet the demand. At this time, the remaining inventory is 5,000, which meets the safety stock level.

[0139] For M07, since there are no intermediate products involved and the marginal profit is positive, it is sufficient to produce at full capacity. The specific production plan is as follows:

[0140] First cycle: 25,000 M01 and M02, 50,000 M04 are needed to produce 50,000 M07, all to meet the demand;

[0141] Second cycle: 27,500 M01 and M02 and 55,000 M04 are needed to produce 55,000 M07. 50,000 are used to meet the demand, and the remaining 5,000 are put into inventory.

[0142] The third cycle: 22,500 units of M01 and M02 and 45,000 units of M04 are required to produce 45,000 units of M07. 5,000 units are moved from inventory to meet the demand of 50,000 units.

[0143] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0144] Based on the same inventive concept, the embodiment of the present application also provides a multi-period supply chain planning optimization device for implementing the multi-period supply chain planning optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more multi-period supply chain planning optimization device embodiments provided below can refer to the limitations of the multi-period supply chain planning optimization method above, and will not be repeated here.

[0145] In one embodiment, Figure 4 As shown, a multi-period supply chain planning optimization device is provided, including: an information acquisition module 410, a parameter generation module 420, a model building module 430 and a model solving module 440, wherein:

[0146] The information acquisition module 410 is used to acquire production information.

[0147] The parameter generation module 420 is used to generate constraint parameters and multi-objective functions according to the production information.

[0148] The model building module 430 is used to build a multi-period optimization model according to the constraint parameters and the multi-objective function.

[0149] The model solving module 440 is used to solve the multi-period optimization model through an operations research optimization solving algorithm to obtain a supply chain optimization plan.

[0150] The information acquisition module 410 is also used to parse the production data to obtain the production data.

[0151] The parameter generation module 420 is also used to create variable parameters, which include production variables, inventory variables, capacity variables, procurement variables and demand variables; generate production relationship constraints based on the production variables and the production data; generate multi-period inventory relationship constraints based on the inventory variables and the production data; generate capacity limit constraints based on the capacity variables and the production data; generate procurement limit constraints based on the procurement variables and the production data; generate demand limit constraints based on the demand variables and the production data; generate inventory limit constraints based on the inventory variables and the production data; generate the constraint parameters based on the generated production relationship constraints, the inventory relationship constraints, the capacity limit constraints, the procurement limit constraints, the demand limit constraints and the inventory limit constraints.

[0152] The parameter generation module 420 is also used to generate a profit maximization function based on the production data and the demand variables; to generate an inventory cost minimization function based on the production data and the inventory variables; to generate a movement cost minimization function based on the production data and the consumption variables; to generate a minimization penalty factor function based on the production data and the penalty variables; and to generate a multi-objective function based on the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0153] The parameter generation module 420 is also used to parse the inventory data to extract the safety stock and maximum inventory of each period material, and obtain the beginning inventory of the first period; obtain the ending inventory of the first period based on the beginning inventory and the inventory variable; obtain the ending inventory of the current period based on the ending inventory of the previous period and the inventory variable; generate multi-period inventory relationship constraints based on the ending inventory of each period.

[0154] The model solving module 440 is also used to convert the multi-period optimization model into a mixed integer linear programming model through a linearization method; and solve the mixed integer linear programming model through a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0155] Each module in the above multi-period supply chain planning optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0156] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store supply chain optimization data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-cycle supply chain planning optimization method is implemented.

[0157] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0158] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any one of the multi-period supply chain planning optimization methods in the above embodiments is implemented.

[0159] Obtain production information;

[0160] generating constraint parameters and multi-objective functions according to the production information;

[0161] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0162] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: parsing the production data to obtain production data.

[0164] In one embodiment, when the processor executes the computer program, the following steps are also implemented: creating variable parameters, the variable parameters including production variables, inventory variables, capacity variables, procurement variables and demand variables; generating production relationship constraints based on the production variables and the production data; generating multi-period inventory relationship constraints based on the inventory variables and the production data; generating capacity limit constraints based on the capacity variables and the production data; generating procurement limit constraints based on the procurement variables and the production data; generating demand limit constraints based on the demand variables and the production data; generating inventory limit constraints based on the inventory variables and the production data; generating the constraint parameters based on the generated production relationship constraints, the inventory relationship constraints, the capacity limit constraints, the procurement limit constraints, the demand limit constraints and the inventory limit constraints.

[0165] In one embodiment, when the processor executes the computer program, the following steps are also implemented: generating a profit maximization function based on the production data and the demand variables; generating an inventory cost minimization function based on the production data and the inventory variables; generating a movement cost minimization function based on the production data and the consumption variables; generating a minimization penalty factor function based on the production data and the penalty variables; generating a multi-objective function based on the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0166] In one embodiment, when the processor executes the computer program, the following steps are also implemented: parsing the inventory data to extract the safety stock and maximum stock of each period material, and obtaining the beginning inventory of the first period; obtaining the ending inventory of the first period based on the beginning inventory and the inventory variable; obtaining the ending inventory of the current period based on the ending inventory of the previous period and the inventory variable; generating multi-period inventory relationship constraints based on the ending inventory of each period.

[0167] In one embodiment, when the processor executes the computer program, the following steps are also implemented: converting the multi-period optimization model into a mixed integer linear programming model through a linearization method; solving the mixed integer linear programming model through a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0168] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the multi-period supply chain planning optimization methods in the above embodiments is implemented.

[0169] Obtain production information;

[0170] generating constraint parameters and multi-objective functions according to the production information;

[0171] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0172] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0173] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: parsing the production data to obtain production data.

[0174] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: creating variable parameters, the variable parameters including production variables, inventory variables, capacity variables, procurement variables and demand variables; generating production relationship constraints based on the production variables and the production data; generating multi-period inventory relationship constraints based on the inventory variables and the production data; generating capacity limit constraints based on the capacity variables and the production data; generating procurement limit constraints based on the procurement variables and the production data; generating demand limit constraints based on the demand variables and the production data; generating inventory limit constraints based on the inventory variables and the production data; generating the constraint parameters based on the generated production relationship constraints, the inventory relationship constraints, the capacity limit constraints, the procurement limit constraints, the demand limit constraints and the inventory limit constraints.

[0175] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: generating a profit maximization function based on the production data and the demand variables; generating an inventory cost minimization function based on the production data and the inventory variables; generating a movement cost minimization function based on the production data and the consumption variables; generating a minimization penalty factor function based on the production data and the penalty variables; generating a multi-objective function based on the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0176] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: parsing the inventory data to extract the safety stock and maximum inventory of each period material, and obtaining the beginning inventory of the first period; obtaining the ending inventory of the first period based on the beginning inventory and the inventory variable; obtaining the ending inventory of the current period based on the ending inventory of the previous period and the inventory variable; generating multi-period inventory relationship constraints based on the ending inventory of each period.

[0177] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: converting the multi-period optimization model into a mixed integer linear programming model through a linearization method; solving the mixed integer linear programming model through a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0178] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0179] Obtain production information;

[0180] generating constraint parameters and multi-objective functions according to the production information;

[0181] Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function;

[0182] The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: parsing the production data to obtain production data.

[0184] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: creating variable parameters, the variable parameters including production variables, inventory variables, capacity variables, procurement variables and demand variables; generating production relationship constraints based on the production variables and the production data; generating multi-period inventory relationship constraints based on the inventory variables and the production data; generating capacity limit constraints based on the capacity variables and the production data; generating procurement limit constraints based on the procurement variables and the production data; generating demand limit constraints based on the demand variables and the production data; generating inventory limit constraints based on the inventory variables and the production data; generating the constraint parameters based on the generated production relationship constraints, the inventory relationship constraints, the capacity limit constraints, the procurement limit constraints, the demand limit constraints and the inventory limit constraints.

[0185] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: generating a profit maximization function based on the production data and the demand variables; generating an inventory cost minimization function based on the production data and the inventory variables; generating a movement cost minimization function based on the production data and the consumption variables; generating a minimization penalty factor function based on the production data and the penalty variables; generating a multi-objective function based on the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

[0186] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: parsing the inventory data to extract the safety stock and maximum inventory of each period material, and obtaining the beginning inventory of the first period; obtaining the ending inventory of the first period based on the beginning inventory and the inventory variable; obtaining the ending inventory of the current period based on the ending inventory of the previous period and the inventory variable; generating multi-period inventory relationship constraints based on the ending inventory of each period.

[0187] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: converting the multi-period optimization model into a mixed integer linear programming model through a linearization method; solving the mixed integer linear programming model through a branch and cut plane algorithm to obtain a supply chain optimization plan.

[0188] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0189] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0190] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-period supply chain planning optimization method, characterized in that: The method comprises: Obtain production information; generating constraint parameters and multi-objective functions according to the production information; Establishing a multi-period optimization model according to the constraint parameters and the multi-objective function; The multi-period optimization model is solved by an operations research optimization solution algorithm to obtain a supply chain optimization plan.

2. The method according to claim 1, characterized in that The obtaining of production data comprises: The production data is parsed to obtain production data.

3. The method according to claim 2, characterized in that The generating of constraint parameters and multi-objective functions according to the production information comprises: Creating variable parameters, wherein the variable parameters include production variables, inventory variables, capacity variables, procurement variables, and demand variables; generating production relationship constraints according to the production variables and the production data; generating a multi-period inventory relationship constraint according to the inventory variable and the production data; generating a capacity limit constraint according to the capacity variable and the production data; generating a procurement restriction constraint according to the procurement variables and the production data; generating a demand restriction constraint according to the demand variables and the production data; generating an inventory limit constraint according to the inventory variable and the production data; The constraint parameters are generated according to the generated production relationship constraint, the inventory relationship constraint, the production capacity restriction constraint, the procurement restriction constraint, the demand restriction constraint and the inventory restriction constraint.

4. The method according to claim 3, characterized in that According to the production information, constraint parameters and multi-objective functions are generated. include: Wherein, the variable parameters also include consumption variables and penalty variables; generating a profit maximization function according to the production data and the demand variables; Generate an inventory cost minimization function based on the production data and the inventory variables; generating a movement cost minimization function based on the production data and the consumption variables; Generating a function to minimize penalty factors according to the production data and the penalty variable; A multi-objective function is generated according to the profit maximization function, the inventory cost minimization function, the movement cost minimization function and the minimization penalty factor function.

5. The method according to claim 3, characterized in that: generating a multi-period inventory relationship constraint according to the inventory variable and the production data include: Wherein, the production data includes inventory data; Parse the inventory data to extract the safety stock and maximum stock of materials in each period, and obtain the beginning stock of the first period; According to the beginning inventory and the inventory variable, obtain the ending inventory of the first period; Obtaining the ending inventory of the current period according to the ending inventory of the previous period and the inventory variable; Generate multi-period inventory relationship constraints based on the ending inventory of each period.

6. The method according to claim 1, characterized in that Solving the multi-period optimization model by an operations research optimization algorithm to obtain a supply chain optimization plan includes: Converting the multi-period optimization model into a mixed integer linear programming model through a linearization method; The mixed integer linear programming model is solved by a branch and cut plane algorithm to obtain a supply chain optimization plan.

7. A multi-period supply chain planning optimization device, characterized in that: The device comprises: Information acquisition module, used to obtain production information; A parameter generation module, used to generate constraint parameters and multi-objective functions according to the production information; A model building module, used for building a multi-period optimization model according to the constraint parameters and the multi-objective function; The model solving module is used to solve the multi-period optimization model through an operations research optimization solving algorithm to obtain a supply chain optimization plan.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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