Production resource allocation method for endless continuous casting and rolling of hot-rolled strip steel, and device and medium

Abstract

On the basis of a theoretical method for the ordered multi-knapsack problem, by using a greedy algorithm, i.e., loop statements, the present application solves the problem of combinatorial optimization among different steel grades and different thicknesses and widths in intelligent scheduling and production scheduling processes of endless continuous casting and rolling of hot-rolled strip steel; and while meeting machining constraint conditions, various aspects of requirements in production tasks are fulfilled by means of rational rolling planning, and a global optimal solution can be found more quickly so as to minimize a penalty value (i.e., to obtain a production sequence that minimizes the penalty value). The application is applied to a rolling production process of endless continuous casting and rolling of hot-rolled strip steel, and can realize the maximum utilization of production resources in endless continuous casting and rolling of hot-rolled strip steel (the ratio of total revenue to total cost is maximized), thereby ensuring that orders are completed within a delivery period, mitigating the problems of randomness and volatility caused by manual scheduling, improving production efficiency, and lowering production costs.

Description

Resource allocation methods, equipment and media for hot-rolled strip endless continuous casting and rolling production Technical Field

[0001] This application relates to the field of headless continuous casting and rolling technology, and more specifically, to a method, equipment and medium for allocating production resources in headless continuous casting and rolling of hot-rolled strip steel. Background Technology

[0002] Compared to hot rolling, continuous casting and rolling of hot-rolled strip steel offers advantages such as shorter processing time, higher yield, and lower energy consumption. It also enables stable mass production of ultra-thin strip steel. The range of steel types and applications it can produce is wide, and the rolled products are characterized by high dimensional accuracy and good uniformity of microstructure and properties. For enterprises, this can reduce costs and improve production efficiency. However, many companies lack a scientific and reasonable overall production plan, resulting in the untapped potential of continuous casting and rolling technology for hot-rolled strip steel. During the rolling process, companies may face numerous orders, and existing manual scheduling may lead to order delays and reduced resource utilization. When encountering urgent orders, adjustments cannot be made quickly, thus affecting subsequent order intake.

[0003] Furthermore, although there is some research on production scheduling in China, most of it has not been truly applied to the actual production of hot-rolled strip steel in the field of headless continuous casting and rolling technology. Moreover, the existing related technologies cannot maximize the utilization of production resources and cannot reduce the randomness and volatility caused by manual scheduling, making it difficult to further improve production efficiency and reduce production costs.

[0004] Summary of the Invention

[0005] In response to at least one defect or improvement requirement of the prior art, this application provides a method, equipment and medium for allocating production resources in the endless continuous casting and rolling of hot-rolled strip steel, for at least maximizing the utilization of production resources in the endless continuous casting and rolling of hot-rolled strip steel.

[0006] To achieve the above objectives, in a first aspect, this application provides a method for allocating production resources in the continuous casting and rolling of hot-rolled strip steel, comprising:

[0007] Based on the two-stage decision of prioritizing steel grades with shorter delivery times and larger required rolling volumes, a greedy algorithm is used to obtain the priority determination results for different steel grades under different production tasks.

[0008] Based on the ordered multi-knapsack problem model and following the thick-thin-thick rolling procedure, a greedy algorithm is used to obtain the minimum thickness jump penalty value to obtain the rolling order of different specifications of the same steel grade in the same production task.

[0009] Under the first constraint that the total number of castings required for production is not greater than the number of castings that can be performed within the maximum delivery period and the total number of castings for each steel grade per day is not greater than the number of castings that can be performed per day, obtain the number of castings for each steel grade in each production task per day.

[0010] Based on the number of castings per day for different steel grades in each production task, the production weight for each thickness is allocated according to the proportion of the total production for each thickness, and the production scheduling results for the corresponding steel grades in each production task are obtained respectively.

[0011] Based on one or more of the following factors: the priority determination results for production of different steel grades under different production tasks, the rolling order of different specifications of the same steel grade in the same production task, the number of castings per day for different steel grades in each production task, and the production scheduling results of the corresponding steel grades in each production task, the production resources for hot-rolled strip steel endless continuous casting and rolling are allocated.

[0012] Furthermore, the formulas characterizing the ordered multi-knapsack problem model include:

[0013] Among them, P i =P wi +P gi +P hi The width jump penalty between slab i and slab (i+1) of each steel grade is P. wi The thickness jump penalty value is P. gi The hardness jump penalty value is P. hi The penalty value generated between slab i and slab (i+1) is P. i The total penalty value is P; n is the number of thickness specifications for each type of steel.

[0014] The second constraint condition corresponding to the ordered multiple knapsack problem model includes:

[0015] Among them, L i L is the rolling weight of slab i within one day; W is the total rolling weight; L sum This refers to the total amount that the production line can roll in one day.

[0016] Furthermore, the secondary determination based on prioritizing steel grades with shorter delivery times and higher required rolling volumes includes:

[0017] The first determination is made based on the delivery time of different steel grades for each production task. If the delivery time is shorter, production will be given priority.

[0018] If the delivery dates are the same, a second determination will be made based on the total amount of steel required to be rolled for each production task, and the steel grade with the larger required rolling amount will be prioritized for production.

[0019] Furthermore, the method based on the ordered multi-knapsack problem model and following a thick-thin-thick rolling procedure, using a greedy algorithm to obtain the minimum thickness jump penalty value to acquire the rolling order among different specifications of the same steel grade in the same production task, includes:

[0020] The minimum value of the thickness specification under the same steel grade is selected as the minimum point. The values ​​on both sides of this point are selected from the other thickness specifications to complete the sorting of the thickness specifications. This point is the only minimum point in the sorting.

[0021] After sorting the thickness specifications once, the thickness jump penalty value is obtained; the model formula for the thickness jump penalty value includes: ΔH i =|H i -H i+1 |;

[0022] Constraints: α + β = 1, α·β ≥ 0;

[0023] Among them, H i ΔH represents the target rolling thickness of slab i; i H' represents the absolute jump in the target thickness of slab i; i P represents the relative jump in target thickness of slab i; α and β are the thickness jump penalty coefficients; g This is the thickness jump penalty value;

[0024] A greedy algorithm is used to iterate through the above-mentioned thickness specification sorting method. Each sorting is different. The thickness jump penalty value of all sorts is combined, and the thickness specification with the smallest thickness jump penalty value is retained.

[0025] Furthermore, under the first constraint that the total number of castings required for production is not greater than the number of castings that can be performed within the maximum delivery period and the total number of castings for each steel grade per day is not greater than the number of castings that can be performed per day, obtaining the number of castings per day for different steel grades in each production task includes:

[0026] After obtaining all possible daily casting order for each steel grade within the maximum delivery period, the casting order of different steel grades is combined through a loop statement to find the optimal solution that meets the first constraint condition, thereby obtaining the daily continuous casting number of different steel grades for each production task.

[0027] The first constraint includes:

[0028] Where D is the maximum delivery time in the production task; k is the number of castings that can be performed daily on the production line; r is the number of steel grades; N iThis represents the required number of castings based on the total rolling volume required for each type of steel in each production task; m is the number of steel types rolled in one day; p is the number of castings for each steel type rolled in one day; N xy This refers to the number of times different steel grades are poured daily for each production task within the delivery period.

[0029] Furthermore, the step of allocating the production weight for each thickness according to the proportion of the total production volume for different steel grades in each production task based on the daily casting frequency of each steel grade, and obtaining the production scheduling results for the corresponding steel grades in each production task, includes:

[0030] Based on the number of castings and the weight per casting for each steel grade in each production task, the total daily production capacity for each steel grade is obtained.

[0031] After determining the total daily production capacity, the daily production weight q for different thicknesses of the same steel grade is allocated according to the proportion of the total production capacity required for different thicknesses of the same steel grade in the production task to the total production capacity required for that steel grade in the production task. The specific formula includes:

[0032] Among them, E i This represents the total production volume required for the i-th thickness specification of a certain steel grade in a production task. Q represents the total amount of steel required to be produced in the production task; Q represents the total daily production capacity of each steel grade; q represents the daily production weight of different thickness specifications.

[0033] Secondly, this application provides an electronic device including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, enables the processing unit to perform the steps of the production resource allocation method described in any of the above claims.

[0034] Thirdly, this application provides a storage medium storing a computer program executable by an access authentication device, which, when run on the access authentication device, enables the access authentication device to perform the steps of the production resource allocation method described in any of the preceding claims.

[0035] In summary, compared with the prior art, the above-described technical solutions conceived in this application can achieve the following beneficial effects:

[0036] Based on the theoretical approach of the ordered multiple knapsack problem, this application employs a greedy algorithm (i.e., loop statements) to solve the intelligent scheduling problem and the combination optimization problem of different steel grades and thicknesses / widths in the production scheduling process of hot-rolled strip endless continuous casting and rolling. Under the premise of satisfying processing constraints, through reasonable rolling planning, it fulfills various requirements of the production task and can quickly find the global optimal solution, minimizing the penalty value (i.e., finding the production order that minimizes the penalty value). Applied to the rolling production process of hot-rolled strip endless continuous casting and rolling, this application can maximize the utilization of production resources (maximizing the ratio of total revenue to total cost), ensure that orders are completed within the delivery period, reduce the randomness and volatility problems caused by manual scheduling, improve production efficiency, and reduce production costs. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 is a core flowchart of a resource allocation method for hot-rolled strip steel endless continuous casting and rolling production provided in an embodiment of this application;

[0039] Figure 2 is a complete flowchart of a production resource allocation method for hot-rolled strip steel endless continuous casting and rolling provided in an embodiment of this application;

[0040] Figure 3 is a block diagram of an electronic device suitable for implementing the production resource allocation method described above, provided in an embodiment of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Furthermore, the technical features involved in the various embodiments described below can be combined with each other as long as they do not conflict with each other.

[0042] The terms "first," "second," or "nth," etc., used in the specification, claims, or accompanying drawings of this application are used to distinguish different objects and not to describe a particular order. Furthermore, the terms "comprising" or "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0043] During the rolling production process, enterprises may face numerous orders, and existing manual scheduling techniques may lead to order delivery delays and reduced resource utilization. When encountering urgent orders, adjustments cannot be made quickly, impacting subsequent order intake. Furthermore, while there is some research on production scheduling in China, most has not been truly applied to the actual production of hot-rolled strip steel in the field of continuous casting and rolling. Existing technologies cannot maximize the utilization of production resources or reduce the randomness and volatility caused by manual scheduling, hindering further improvements in production efficiency and reductions in production costs. In view of this technological status quo, this application provides a method, equipment, and medium for allocating production resources in hot-rolled strip steel continuous casting and rolling, aiming to at least maximize the utilization of production resources in this process.

[0044] Referring to Figures 1 and 2, one embodiment of this application provides a method for allocating production resources for hot-rolled strip endless continuous casting and rolling, which may include the following steps 1-5.

[0045] In some embodiments, some preparatory work is required before allocating production resources for hot-rolled strip endless continuous casting and rolling. The following description will be based on the production information of two steel grades, "Q215B" and "SPHC," in Contract 1, and one steel grade, "SPHC," in Contract 2, used for production scheduling (production resource allocation) of hot-rolled strip endless continuous casting and rolling.

[0046] First, the production requirements of different contracts (in this application, contract, order, and production task are all considered to mean the same thing; the creation of a contract or order for producing steel means the start of a steel production task. The same contract may include multiple orders, each of which is distinguished by a contract number) and the production requirements of different steel grades in the contracts are summarized into a table to unify the known necessary conditions in the production process and the assumed conditions of the production environment.

[0047] More specifically, the known conditions in Contract 1 and Contract 2 are summarized in a table according to "Contract Number", "Steel Type", production specifications of different steel types such as "Thickness" and "Width", and production information such as "Steel Coil Weight", "Delivery Date", and "Total Quantity" for each steel type and its different specifications. Note that different types of steel in the same contract and the same type of steel in different contracts should be distinguished. The classification method is shown in Table 1.

[0048] Table 1 Summary table of production information in the contract of this embodiment

[0049] Assuming there is one hot-rolled strip steel production line, consisting of a continuous casting unit and a rolling mill, the total weight of steel that can be produced per day is 4800-6000t, and the weight of one casting is 1200t.

[0050] The modeling approach was determined by recognizing that the main problem in production scheduling is the combination optimization of different contracts, steel grades, thicknesses, and widths. The model used is primarily an ordered multi-knapsack problem model. Under the condition of satisfying processing constraints, through reasonable rolling planning, the requirements of the contracts are fulfilled while minimizing the penalty value. More specifically:

[0051] During production scheduling, within the same unit rolling plan, there are certain range restrictions on the jumps in width, thickness, and hardness between two adjacent slabs. Once these ranges are exceeded, the solution is considered infeasible. In this example, it is necessary to calculate the penalty values ​​for the two steel grades "Q215B" and "SPHC" in Contract 1 and the steel grade "SPHC" in Contract 2, respectively. The width jump penalty value between slab i and slab i+1 for each steel grade is defined as P. wi The thickness jump penalty value is specified as P. gi The hardness jump penalty value is specified as P. hi Then P i =P wi +P gi +P hi Let P be the penalty value generated between slab i and slab i+1. According to the ordered multi-knapsack problem model theory, the penalty value generated under various constraints within the rolling unit plan is taken as the item cost (i.e., production cost, or total expenditure). Therefore, the model should satisfy the condition of minimizing the total penalty value P.

[0052] The total amount of hot-rolled strip steel that a production line can roll in a day (L) sum=4800~6000t as the backpack capacity. Since the jump value between the same thickness and width is 0, n in formula (1) is the number of thickness specifications for each type of steel. The slab i is the thickness specification for each type of steel. The number of thickness specifications for steel "Q215B" in Contract 1 is n=8, the number of thickness specifications for "SPHC" is n=10, and the number of thickness specifications for steel "SPHC" in Contract 2 is n=6. L i If the rolling weight of slab i in one day is specified, then the model should satisfy the condition that the total rolling weight W is maximum and does not exceed the knapsack capacity. The corresponding constraints of the ordered multiple knapsack problem model include equations (2) and (3).

[0053] In order to minimize the penalty value (i.e., minimize production cost or total expenditure) and minimize the influence of too many parameters on production scheduling during the combinatorial optimization process, this embodiment adopts a greedy algorithm, which uses loop statements to continuously narrow down the scope of the problem and finally obtains the optimal solution of the ordered multi-knapsack problem model.

[0054] The overall approach of this application is as follows: the production scheduling problem is determined to be an ordered multi-knapsack problem model, the optimization algorithm is a greedy algorithm, and a loop statement is used to continuously narrow down the scope of the problem, so as to finally obtain the optimal solution of the ordered multi-knapsack problem model, that is, to find the production order that minimizes the penalty value.

[0055] Step 1: Based on the secondary judgment of prioritizing steel grades with shorter delivery times and larger required rolling volumes, a greedy algorithm is used to obtain the priority judgment results for different steel grades under different production tasks.

[0056] In some embodiments, known and assumed conditions are used to determine the production priority of different steel grades under different contracts in the summarized table, as described more specifically below.

[0057] Input the production information table compiled in the aforementioned preparatory work. First, a judgment is made based on the delivery dates of different steel grades in each contract. If the delivery date is shorter, production is prioritized. If the delivery dates are the same, a second judgment is made based on the total amount of steel grades to be rolled in each contract. Steel grades with a larger required rolling volume are prioritized. After summarizing, it can be seen that the delivery dates for steel grade "Q215B" in contract number '101' and steel grade "SPHC" in contract number '201' are both 3 days. However, the total rolling volume required for steel grade "Q215B" is greater than that for steel grade "SPHC". The delivery date for steel grade "SPHC" in contract number '102' is 5 days, longer than the delivery dates of the first two types of steel grades. Therefore, the final rolling priority order is steel grade "Q215B" in contract number '101', steel grade "SPHC" in contract number '201', and steel grade "SPHC" in contract number '102'.

[0058] Step 2: Based on the ordered multi-knapsack problem model and following the thick-thin-thick rolling procedure, a greedy algorithm is used to obtain the minimum thickness jump penalty value to obtain the rolling order among different specifications of the same steel grade in the same production task. In some embodiments, Step 2 may specifically include:

[0059] Step 21: Since the jump value between the same thickness and width is 0, the width of steel of different specifications in the production information remains unchanged. Therefore, the width jump is not considered at this time, but the thickness jump is considered first. Slab i is the thickness specification of each type.

[0060] Step 22: For the production and rolling of the same steel grade, the rolling thickness variation follows the thick-thin-thick rolling procedure requirement. Therefore, the minimum thickness specification of this steel grade is selected as the minimum value point. The minimum thicknesses of steel grade "Q215B" with contract number '101', steel grade "SPHC" with contract number '201', and steel grade "SPHC" with contract number '102' are 1.15cm, 1.3cm, and 1.05cm, respectively. Referring to Table 1, the values ​​on both sides of this minimum value point are selected from the other thickness specifications to complete one sorting of thickness specifications. The minimum value point must be the only minimum value point in this sorting. After completing one sorting, the penalty value is calculated according to the model formula of the thickness jump penalty value (including formulas (4) to (7)): ΔH i =|H i -H i+1 | (4)

[0061] The corresponding constraints are: α + β = 1, α·β ≥ 0 (7)

[0062] Among them, H i ΔH represents the target rolling thickness of slab i; i H' represents the absolute jump in the target thickness of slab i; i Let be the target thickness relative jump amount for slab i; n be the number of thickness specifications for each type of steel; α and β are thickness jump penalty coefficients, both of which can be taken as 0.5.

[0063] Step 23: Using a greedy algorithm, iterate through the thickness specification sorting method in Step 22, with each sort being different. Combine the final thickness jump penalty values ​​of all sorts and retain the thickness specification sort with the smallest thickness jump penalty value. The production thickness sort corresponding to the minimum thickness jump penalty value for steel grade "Q215B" in contract number '101' is: [2.0, 1.8, 1.6, 1.5, 1.15, 1.2, 1.3, 1.4]; the production thickness sort corresponding to the minimum thickness jump penalty value for steel grade "SPHC" in contract number '201' is: [4.0, 3.5, 3.0, 1.3, 1.4, 2.75]; the production thickness sort corresponding to the minimum thickness jump penalty value for steel grade "SPHC" in contract number '102' is: [4.0, 3.5, 3.0, 2.75, 1.4, 1.05, 1.1, 1.15, 1.2, 1.3].

[0064] Step 3: Under the constraints that the total number of castings required for production does not exceed the number of castings that can be performed within the maximum delivery period, and the total number of castings for each steel grade per day does not exceed the number of castings that can be performed per day, obtain the daily continuous casting number for different steel grades in each production task. In some embodiments, step 3 may specifically include:

[0065] Step 31: Assuming the hot-rolled strip endless continuous casting and rolling production line can produce a total of 6000t per day and the weight of each casting is 1200t, the number of castings that can be performed per day can be calculated. (symbol (Indicates rounding up); based on the total rolling quantity M required for each type of steel in each contract. i Calculate the required number of pours. The number of castings for steel grade "Q215B" with contract number '101', steel grade "SPHC" with contract number '201', and steel grade "SPHC" with contract number '102' are 5, 2, and 6, respectively.

[0066] Step 32: To ensure that production of each contract steel grade can be completed within the delivery period, allocate the number of castings N per day for each contract steel grade within the delivery period according to the following constraints. xy The constraint is:

[0067] Where D is the maximum delivery period in the contract, r is the number of steel grades, m is the number of steel grades rolled in one day, and p is the number of times each steel grade is cast in one day. Constraint equation (8) ensures that the total number of castings required for production is not greater than the number of castings that can be performed within the maximum delivery period; constraint equation (9) ensures that the total number of castings for each steel grade per day is not greater than the number of castings that can be performed per day.

[0068] In some embodiments, step 32 may further include the following sub-steps:

[0069] Step 321: For a certain steel grade I, if its delivery date d is greater than or equal to the number of castings N calculated in step 31... i Then write N i A sequence of 1s [1,1,…,1,1] represents the number of N steel grades that will be delivered within the delivery period. i The watering frequency is once per day. For the remaining (dN) of this steel grade within the delivery period... i If the number of waterings per day is represented by 0, then the final sequence is [1,1,…,1,0,…,0,0], where the element 1 has N values. i There are , element 0 has (dN) i There are 1, 1, 0 elements in this sequence. The elements in this sequence can be arranged in different ways to represent the possible order of daily casting times for steel grade I within its delivery period d. For steel grade "SPHC" with contract number '201', the delivery period of 3 days is greater than its casting times of 2, so the final sequence is [1,1,0]. Randomly sorting the elements in this sequence is the possible order of daily casting times for this steel grade within 3 days.

[0070] Step 322: For a certain steel grade I, if its delivery period d is less than the number of castings N calculated in step 31... i Then N i Decompose into d sets of positive integers [s1, s2, ..., s d The elements in this sequence can be arranged in different ways to represent the possible daily casting times of steel grade I within its delivery period d. For steel grade "SPHC" with contract number '102', the delivery period of 5 days is less than the number of castings of 6, so the final sequence is [2, 1, 1, 1, 1]. Randomly sorting the elements in this sequence gives the possible daily casting times of this steel grade within 5 days. Similarly, for steel grade "Q215B" with contract number '101', the sequence is [2, 2, 1]. Randomly sorting the elements in this sequence gives the possible daily casting times of this steel grade within a delivery period of 3 days.

[0071] Step 323: After obtaining the possible daily casting times for each type of steel in the contract under the two cases through steps 321 and 322, if the delivery period d of the steel type is less than the maximum delivery period D in the contract, add (Dd) zero elements to the end of the sorted sequence to represent all possible daily casting times for each type of steel within the maximum delivery period. Since the steel type can only be produced within its own delivery period, the position of the zero element added to the end of the sequence remains fixed. This zero element indicates that the steel type is not produced. In this embodiment, the maximum delivery period for all steel grades is D=5. For steel grade "SPHC" with contract number '201' and a delivery period of 3 days, 5-3=2 zero elements are added to all possible sorting sequences obtained in step 321. For example, adding 2 zero elements to one of the possibilities [1,0,1] results in [1,0,1,0,0], which is the sorting of the possible daily casting times within the maximum delivery period of 5 days. The positions of the last two zero elements in this sequence remain fixed. Similarly, for steel grade "SPHC" with contract number '102' and steel grade "Q215B" with contract number '101', 5-5=0 zero elements and 5-3=2 zero elements are added to all possible sorting sequences obtained in step 322, respectively.

[0072] Step 324: After obtaining the possible daily casting order for each steel grade within the maximum delivery period, the casting order of different steel grades is combined using a loop statement to find the optimal solution that satisfies the constraints (8) and (9) in Step 32, thereby obtaining the daily casting order for each type of steel grade. Finally, the optimal casting order combination for the three types of steel grades in the contract is obtained, as shown in Table 2.

[0073] Table 2 Optimal Casting Combination for Three Types of Steel in the Contract

[0074] The three types of steel can be produced within their respective delivery periods, with a total of 5+2+6=13 castings < 5*5=25 castings, and the sum of the castings per day for the three types of steel is less than 5.

[0075] Step 4: Based on the daily casting frequency of different steel grades for each production task, allocate the production weight for each thickness according to the proportion of the total production volume for different thicknesses, and obtain the production scheduling results for the corresponding steel grades in each production task. In some embodiments, step 4 may further include:

[0076] The daily production volume Q for each steel grade is determined based on the allocation of daily casting times within the delivery period of different steel grades under each contract and the weight per casting.

[0077] After determining the total daily production volume Q, the daily production weight q for different thicknesses of the same steel grade is allocated according to the proportion of the total production volume required for different thicknesses of the same steel grade in the order to the total production volume required for that steel grade in the contract.

[0078] Where E i This represents the total required production volume for the i-th thickness specification of a certain steel grade in the production information summary table of the contract; This indicates the total production volume required for this steel grade in the contract. See Table 3 for the production schedule of the three steel grades within the maximum delivery period.

[0079] Table 3. Production Scheduling Results for Three Types of Steel in Contracts with the Maximum Delivery Period

[0080] Step 5: Based on one or more of the following: the priority determination results for production of different steel grades under different production tasks obtained in Step 1; the rolling order of different specifications of the same steel grade in the same production task obtained in Step 2; the number of castings per day for different steel grades in each production task obtained in Step 3; and the production scheduling results of the corresponding steel grades in each production task obtained in Step 4, allocate production resources for hot-rolled strip steel continuous casting and rolling.

[0081] After completing the allocation and scheduling of production resources, a production scheduling table and a rolling sequence diagram can be output to simulate production.

[0082] Based on the theoretical approach of the ordered multiple knapsack problem, this application employs a greedy algorithm (i.e., loop statements) to solve the intelligent scheduling problem and the combination optimization problem of different steel grades and thicknesses / widths in the production scheduling process of hot-rolled strip endless continuous casting and rolling. Under the premise of satisfying processing constraints, through reasonable rolling planning, it fulfills various requirements of the production task and can quickly find the global optimal solution, minimizing the penalty value (i.e., finding the production order that minimizes the penalty value). Applied to the rolling production process of hot-rolled strip endless continuous casting and rolling, this application can maximize the utilization of production resources (maximizing the ratio of total revenue to total cost), ensure that orders are completed within the delivery period, reduce the randomness and volatility problems caused by manual scheduling, improve production efficiency, and reduce production costs.

[0083] Figure 3 schematically illustrates a block diagram of an electronic device suitable for implementing the production resource allocation method described above, according to an embodiment of this application. The electronic device shown in Figure 3 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0084] As shown in FIG3, the electronic device 1000 described in this embodiment includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage portion 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the production resource allocation method flow according to embodiments of this application.

[0085] RAM 1003 stores various programs and data required for the operation of system 1000. Processor 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Processor 1001 executes various operations of the production resource allocation method flow according to embodiments of this application by executing programs in ROM 1002 and / or RAM 1003. It should be noted that the programs may also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 may also execute various operations of the production resource allocation method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0086] According to embodiments of this application, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to a bus 1004. The system 1000 may also include one or more of the following components connected to the I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1010 as needed so that computer programs read from it can be installed into the storage section 1008 as needed.

[0087] The production resource allocation method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for executing the production resource allocation method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by processor 1001, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, and / or units described above can be implemented using computer program modules.

[0088] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or it may exist independently without being assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, can implement the steps of the production resource allocation method according to embodiments of this application.

[0089] According to embodiments of this application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include one or more memories other than the ROM 1002 and / or RAM 1003 described above.

[0090] It should be noted that the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product.

[0091] The flowcharts and / or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowcharts and / or block diagrams may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. Furthermore, it should be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0092] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the technical features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, and all such combinations and / or combinations fall within the scope of this application.

[0093] Although this application has been shown and described with reference to specific exemplary embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made to this application without departing from the spirit and scope of the application as defined by the appended claims and their equivalents. Therefore, the scope of this application should not be limited to the above embodiments, but should be determined not only by the appended claims, but also by their equivalents.

Claims

1. A method for allocating production resources in the continuous casting and rolling of hot-rolled strip steel, characterized in that, include: Based on the two-stage decision of prioritizing steel grades with shorter delivery times and larger required rolling volumes, a greedy algorithm is used to obtain the priority determination results for different steel grades under different production tasks. Based on the ordered multi-knapsack problem model and following the thick-thin-thick rolling procedure, a greedy algorithm is used to obtain the minimum thickness jump penalty value to obtain the rolling order of different specifications of the same steel grade in the same production task. Under the first constraint that the total number of castings required for production is not greater than the number of castings that can be performed within the maximum delivery period and the total number of castings for each steel grade per day is not greater than the number of castings that can be performed per day, obtain the number of castings for each steel grade in each production task per day. Based on the number of castings per day for different steel grades in each production task, the production weight for each thickness is allocated according to the proportion of the total production for each thickness, and the production scheduling results for the corresponding steel grades in each production task are obtained respectively. Based on one or more of the following factors: the priority determination results for production of different steel grades under different production tasks, the rolling order of different specifications of the same steel grade in the same production task, the number of castings per day for different steel grades in each production task, and the production scheduling results of the corresponding steel grades in each production task, the production resources for hot-rolled strip steel endless continuous casting and rolling are allocated.

2. The production resource allocation method as described in claim 1, characterized in that, The formulas characterizing the ordered multi-knapsack problem model include: Among them, P i =P wi +P gi +P hi The width jump penalty between slab i and slab (i+1) of each steel grade is P. wi The thickness jump penalty value is P. gi The hardness jump penalty value is P. hi The penalty value generated between slab i and slab (i+1) is P. i The total penalty value is P; n is the number of thickness specifications for each type of steel. The second constraint condition corresponding to the ordered multiple knapsack problem model includes: Among them, L i L is the rolling weight of slab i within one day; W is the total rolling weight; L sum This refers to the total amount that the production line can roll in one day.

3. The production resource allocation method as described in claim 1, characterized in that, The secondary determination based on prioritizing steel grades with shorter delivery times and higher required rolling volumes includes: The first determination is made based on the delivery time of different steel grades for each production task. If the delivery time is shorter, production will be given priority. If the delivery dates are the same, a second determination will be made based on the total amount of steel required to be rolled for each production task, and the steel grade with the larger required rolling amount will be prioritized for production.

4. The production resource allocation method as described in claim 2, characterized in that, The method based on the ordered multi-knapsack problem model and following the thick-thin-thick rolling procedure, using a greedy algorithm to obtain the minimum thickness jump penalty value to obtain the rolling order among different specifications of the same steel grade in the same production task, includes: The minimum value of the thickness specification under the same steel grade is selected as the minimum point. The values ​​on both sides of this point are selected from the other thickness specifications to complete the sorting of the thickness specifications. This point is the only minimum point in the sorting. After completing one sorting of thickness specifications, the thickness jump penalty value is obtained; the model formula for the thickness jump penalty value includes: ΔH i =|H i -H i+1 |; Constraints: α + β = 1, α·β ≥ 0; Among them, H i ΔH represents the target rolling thickness of slab i; i H' represents the absolute jump in the target thickness of slab i; i P represents the relative jump in target thickness of slab i; α and β are the thickness jump penalty coefficients; g This is the thickness jump penalty value; A greedy algorithm is used to iterate through the above-mentioned thickness specification sorting method. Each sorting is different. The thickness jump penalty value of all sorts is combined, and the thickness specification with the smallest thickness jump penalty value is retained.

5. The production resource allocation method as described in claim 1, characterized in that, Under the first constraint that the total number of castings required for production is not greater than the number of castings that can be performed within the maximum delivery period and the total number of castings for each steel grade per day is not greater than the number of castings that can be performed per day, the number of castings per day for different steel grades in each production task is obtained as follows: After obtaining all possible daily casting order for each steel grade within the maximum delivery period, the casting order of different steel grades is combined through a loop statement to find the optimal solution that meets the first constraint condition, thereby obtaining the daily continuous casting number of different steel grades for each production task. The first constraint includes: Where D is the maximum delivery time in the production task; k is the number of castings that can be performed daily on the production line; r is the number of steel grades; N i This represents the required number of castings based on the total rolling volume required for each type of steel in each production task; m is the number of steel types rolled in one day; p is the number of castings for each steel type rolled in one day; N xy This refers to the number of times different steel grades are poured daily for each production task within the delivery period.

6. The production resource allocation method as described in claim 5, characterized in that, The process of allocating the production weight for each thickness according to the proportion of the total production volume for different steel grades in each production task based on the daily casting frequency of each steel grade, and obtaining the production scheduling results for the corresponding steel grades in each production task, includes: Based on the number of castings and the weight per casting for each steel grade in each production task, the total daily production capacity for each steel grade is obtained. After determining the total daily production capacity, the daily production weight q for different thicknesses of the same steel grade is allocated according to the proportion of the total production capacity required for different thicknesses of the same steel grade in the production task to the total production capacity required for that steel grade in the production task. The specific formula includes: Among them, E i This represents the total production volume required for the i-th thickness specification of a certain steel grade in a production task. Q represents the total amount of steel required to be produced in the production task; Q represents the total daily production capacity of each steel grade; q represents the daily production weight of different thickness specifications.

7. An electronic device, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, enables the processing unit to perform the steps of the production resource allocation method according to any one of claims 1-6.

8. A storage medium, characterized in that, It stores a computer program executable by an access authentication device, which, when run on the access authentication device, enables the access authentication device to perform the steps of the production resource allocation method according to any one of claims 1-6.

Images