Optimized scheduling method based on automatic production scheduling of furniture production and manufacturing

Abstract

The invention discloses an optimal scheduling method based on automatic production scheduling of furniture production and manufacturing, and belongs to the technical field of furniture production data processing, and the method comprises the steps: collecting order data and resource state information on a production line, generating an order priority sequence and availability data, and obtaining a task distribution scheme; on the basis of the task allocation scheme, an optimization algorithm is adopted to adjust order priority changes, and an optimized task sequence is generated; extracting the resource occupancy rate of each order from the optimized task sequence, and if the resource occupancy rate exceeds a threshold value, reallocating resources through a search algorithm to obtain a resource matching scheme; and according to the optimized task sequence and the resource matching scheme, generating a production scheduling scheme with the task sequence and the resource allocation condition of each task. According to the optimal scheduling method based on automatic production scheduling of furniture production and manufacturing, the problem that the production efficiency is difficult to improve due to the fact that a scheme considering time and cost cannot be quickly calculated in a dynamic environment in an existing method is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of furniture production data processing, and particularly relates to an optimization scheduling method based on automatic production scheduling of furniture production and manufacturing. BACKGROUND

[0002] Furniture manufacturing involves multi-variety and small-batch production, with a wide variety of orders and complex production resources. Scheduling optimization has become the core demand for industry development. However, existing methods have significant shortcomings in dealing with complex production environments, mainly in the inability to effectively balance order priority and dynamic resource allocation needs. Since furniture production involves multiple materials, equipment and processes, resource allocation needs to achieve optimal matching within a limited time, while existing methods often fail to quickly calculate a solution that takes into account time and cost in a dynamic environment, making it difficult to improve production efficiency. SUMMARY

[0003] In order to overcome the defects existing in the prior art, the present application provides an optimization scheduling method based on automatic production scheduling of furniture production and manufacturing to solve the above problems.

[0004] The technical scheme adopted by the present application to solve its technical problems is: an optimization scheduling method based on automatic production scheduling of furniture production and manufacturing, comprising the following steps: S1: Collecting order data and resource state information on the production line, generating order priority sequence and availability data, and obtaining task allocation scheme; S2: Based on the task allocation scheme, adjusting order priority changes using an optimization algorithm to generate an optimized task sequence; S3: Extracting the resource occupancy rate of each order from the optimized task sequence, and if the resource occupancy rate exceeds a threshold, reallocating resources through a search algorithm to obtain a resource matching scheme; S4: Generating a production scheduling scheme with task sequence and resource allocation for each task based on the optimized task sequence and resource matching scheme.

[0005] Preferably, in the step S1, the order data includes order quantity and delivery deadline, and the remaining delivery time in days is calculated based on the current date and the delivery deadline; The Pandas library is used to calculate the priority score of each order, where the priority score formula is P=Q / (D+1), P represents the priority score, Q represents the order quantity, and D represents the remaining delivery time in days, to obtain the order priority sequence.

[0006] Optionally, in the step S1, if there are orders with the same priority score in the order priority sequence, the sorted function of PBthon is used for secondary sorting in ascending order of delivery deadline to determine the order priority sequence.

[0007] Specifically, in the step S1, the resource status information includes the number and the occupancy rate of each day in the future within a nominal time window; Obtain all numbers whose occupancy rate is lower than a preset threshold and the dates when the occupancy rate is lower than the preset threshold, form corresponding availability data, and arrange the availability data in order of occupancy rate from low to high; according to the arrangement order of the availability data and the arrangement order of the first n orders in the priority sequence, the first n orders in the priority sequence are allocated to the corresponding availability data one by one, and a preliminary task allocation scheme is obtained.

[0008] Preferably, in the step S2, the order priority sequence is obtained from the task allocation scheme; The DEAP library is used to initialize the population of the order priority sequence, and a plurality of initial task sequences are obtained; The delivery deadline of each order in the initial task sequence is obtained, the minimum value of the delivery deadline of the initial task sequence is taken as the set threshold of the initial task sequence, and the completion date of the initial task sequence is obtained; The initial task sequence whose completion date is less than or equal to the set threshold is subjected to single-point crossover operation by the cAOnePoint function of the DEAP library, the task sequence is adjusted, and a new task sequence is generated; According to the new task sequence, the astar_path function of the NetworkA library is used to construct a priority graph, the orders are taken as nodes, and the priority difference is taken as edge weight, the shortest path from the starting order to the target order is searched, and an adjusted priority sequence is generated; The delivery time of the order is obtained from the adjusted priority sequence, and it is judged whether the delivery time meets the constraint condition, and an optimized task sequence is obtained.

[0009] It is worth noting that in the step S3, the step of extracting the resource occupancy rate includes: obtaining the resource occupancy data of each order from the task sequence, calculating the ratio of the resource occupancy data of each order to the total resource capacity as the resource occupancy rate of each order, obtaining the orders whose resource occupancy rate exceeds a preset threshold, and obtaining a super-threshold order set, and the remaining orders are taken as a normal order set.

[0010] Specifically, in the step S3, the step of reallocating resources by a search algorithm to obtain an updated resource matching scheme includes: Based on the order set exceeding the threshold, the A* search algorithm is used to traverse the resource types. First, the open list and the closed list are initialized. Then, the f value of each node is calculated. The f value is the sum of the g value and the h value, where the g value is the path cost from the starting node to the current node and the h value is the estimated cost from the current node to the target node. Candidate resource allocation combinations are generated. From the candidate resource allocation combinations, obtain the task duration and resource capacity of each combination. Combine this with the determination of whether the allocation constraint that the resource capacity does not exceed the total capacity is met. Select the candidate resource allocation combination that meets the allocation constraint and has the shortest task duration. Combine the candidate resource allocation combination with the normal order set and use it as the resource matching scheme.

[0011] Preferably, in step S3, according to the resource matching scheme, the sorted function of PBthon is used to adjust the task priority. First, the priority key is defined as the task duration, and then a temporary task sequence is generated by sorting. From the temporary task sequence, obtain the resource utilization rate of each task to obtain the set of tasks to be optimized; For the set of tasks to be optimized, the resource allocation is adjusted using the linprog function of SciPB. First, the objective function c is defined as minimizing the total resource consumption, where c is the coefficient vector, A is the constraint matrix, and b is the constraint bound vector. Then, the updated resource matching scheme is generated by solving the problem.

[0012] The beneficial effects of this invention are as follows: In the optimized scheduling method for automatic production scheduling based on furniture manufacturing, an integrated solution is proposed to address the problems of difficulty in dynamically adjusting order priorities, uneven resource allocation, and exceeding occupancy limits in traditional scheduling. This solution generates an initial task allocation scheme by collecting order data and resource status information, and dynamically adjusts order priorities using an optimization algorithm to form an optimized task sequence, solving the inefficiency problem caused by static priority allocation. Simultaneously, by monitoring the resource occupancy rate in the task sequence, a search algorithm is used to reallocate resources for cases exceeding thresholds, ensuring balanced resource utilization and generating a resource matching scheme. Finally, this solution integrates the optimized task sequence and resource matching scheme to generate an efficient production scheduling scheme, maximizing production efficiency and balancing resource utilization. This solution significantly improves the flexibility and response speed of the production line, reduces resource waste, and enhances scheduling capabilities in complex production environments, thereby improving production efficiency. Attached Figure Description

[0013] Fig. 1 This is a flowchart of an optimized scheduling method for automatic production scheduling in furniture manufacturing, as described in one embodiment of the present invention. Fig. 2 This is a flowchart of step S1 in one embodiment of the present invention; Fig. 3This is a flowchart illustrating step S3 in one embodiment of the present invention. Detailed Implementation

[0014] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0015] like Figs. 1-3 As shown, an optimized scheduling method for automatic production scheduling in furniture manufacturing includes the following steps: S1: Collect order data and resource status information on the production line, generate order priority sequence and availability data, and obtain task allocation scheme; S2: Based on the task allocation scheme, an optimization algorithm is used to adjust the order priority changes and generate an optimized task sequence; S3: Extract the resource utilization rate of each order from the optimized task sequence. If the resource utilization rate exceeds the threshold, reallocate resources through a search algorithm to obtain a resource matching scheme. S4: Based on the optimized task sequence and resource matching scheme, generate a production scheduling scheme with the task sequence and resource allocation for each task.

[0016] In the optimized scheduling method for automated production scheduling in furniture manufacturing, an integrated solution is proposed to address the difficulties in dynamically adjusting order priorities, uneven resource allocation, and exceeding occupancy limits in traditional scheduling. This solution generates an initial task allocation scheme by collecting order data and resource status information, and dynamically adjusts order priorities using an optimization algorithm to form an optimized task sequence, thus solving the inefficiency problem caused by static priority allocation. Simultaneously, by monitoring resource occupancy in the task sequence, a search algorithm is used to reallocate resources for cases exceeding thresholds, ensuring balanced resource utilization and generating a resource matching scheme. Finally, this solution integrates the optimized task sequence and resource matching scheme to generate an efficient production scheduling plan, maximizing production efficiency and balancing resource utilization. This solution significantly improves the flexibility and response speed of the production line, reduces resource waste, and enhances scheduling capabilities in complex production environments, thereby improving production efficiency.

[0017] It is worth noting that in step S1, the order data includes the order quantity and delivery deadline, and the remaining delivery time in days is calculated based on the current date and delivery deadline. The priority score for each order is calculated using the Pandas library. The priority score formula is P=Q / (D+1), where P represents the priority score, Q represents the number of orders, and D represents the remaining delivery time in days, thus obtaining the order priority sequence.

[0018] In this solution, order data, such as order quantity and delivery deadline, is extracted from the enterprise resource database using a database interface.

[0019] Suppose a production line receives three orders: Order A has 100 units with a remaining delivery time of 2 days; Order B has 50 units with a remaining delivery time of 1 day; and Order C has 80 units with a remaining delivery time of 4 days. Specifically, the priority score for each order is calculated using the Pandas library, with the formula P = Q / (D+1). For example, for Order A, P = 100 / (2+1) = 33.33; for Order B, P = 50 / (1+1) = 25; and for Order C, P = 80 / (4+1) = 16. The resulting priority sequence is: Order A (33.33), Order B (25), Order C (16).

[0020] Preferably, in step S1, if there are orders with the same priority score in the order priority sequence, the sorted function of PBthon is used to perform a secondary sorting based on the ascending order of delivery deadline to determine the order priority sequence.

[0021] If orders have the same priority score, they are re-sorted using PBthon's `sorted` function in ascending order of delivery time. For example, if orders B and C have the same priority score, order B will be ranked higher because its delivery time is 1 day, shorter than order C's 4 days. This sorting method ensures that urgent orders are processed first, improving on-time delivery rates.

[0022] Optionally, in step S1, the resource status information includes the number and the occupancy rate for each future day within the specified time window; Obtain all IDs with occupancy rates below a preset threshold and the dates on which the occupancy rate fell below the preset threshold, forming corresponding availability data. Sort the availability data in ascending order of occupancy rate. According to the priority sequence, obtain the same number of orders as the total number of availability data n, starting from the top of the priority sequence. Based on the order of availability data and the order of the first n orders in the priority sequence, assign the first n orders in the priority sequence to the corresponding availability data one by one to obtain a preliminary task allocation scheme.

[0023] Assume a production line has one machine and one worker. The machine is numbered M1, and the worker is numbered M2. The time window is 5 days from now. The daily occupancy rate within the specified time window records the occupancy status of each number on each day, expressed as a percentage representing the actual production time of the corresponding machine or worker within the specified daily working time. For example, M1 is 80% occupied on day 1 and 50% on day 2; M2 is 60% occupied on day 1 and 20% on day 2. For M1, its specified daily working time can be 24 hours. An 80% occupancy on the first day means that the actual production time on the first day was 24 hours. 80% = 19.2 hours. For M2, its rated daily working time can be 8 hours. The fact that 60% is occupied on the first day means that the actual production time on the first day is 8 hours. 60% = 4.8 hours. If the preset threshold is 30%, then the occupancy rate of M2 on the second day is lower than the threshold, and tasks can be assigned. The highest priority order A is assigned to M2 on the second day, generating a preliminary task allocation plan. If the occupancy rate of multiple numbers (M1, M2, M3) is lower than the threshold on a certain day, such as M2 being 20% ​​occupied on the second day, M2 being 15% occupied on the third day, and M3 being 10% occupied on the first day, then the availability data (M2,2) is formed by number M2 and the second day of the date when the occupancy rate is lower than the preset threshold. Similarly, the remaining availability data are (M2,3) and (M3,1), and the order after sorting according to the occupancy rate is (M3,1), (M2,3), and (M2,2). Then, the top 3 orders in the priority sequence are obtained, such as order A, order B, and order C. Order A is assigned to availability data (M3,1), indicating that order A is assigned to the first day of M3. Order B is assigned to availability data (M2,3), indicating that order A is assigned to the third day of M2. Order C is assigned to availability data (M2,2), indicating that order C is assigned to the second day of M2. By prioritizing and analyzing resource availability, the production line can quickly respond to high-priority orders while rationally allocating resources with low occupancy rates.

[0024] Specifically, in step S2, the order priority sequence is obtained from the task allocation scheme; The DEAP library was used to initialize the population with the order priority sequence, resulting in multiple initial task sequences. The delivery deadline for each order in the initial task sequence is obtained. The minimum delivery deadline of the initial task sequence is used as a threshold for the initial task sequence, and the completion date of the initial task sequence is obtained. In one embodiment, when initializing the population using the DEAP library, the order priority sequence is used as a genome, with each gene representing the order of an order. For example, the initial population may contain multiple sets of sequences, such as Sequence 1: ABC, Sequence 2: BAC, and Sequence 3: BCA. Population initialization forms diverse sequences by randomly arranging orders, ensuring sufficient exploration space for subsequent optimization. In one embodiment, the delivery time deviation of the initial task sequence is calculated. Assuming the current date is September 15th, the delivery deadline for order A is September 18th, the delivery deadline for order B is September 19th, and the delivery deadline for order C is September 20th, then the threshold is set to September 18th. The predicted completion time for sequence 1 is September 17th; the predicted completion time for sequence 2 is September 19th; and the predicted completion time for sequence 3 is September 18th. Sequences 1 and 3 meet the requirements, while sequence 2 does not.

[0025] The `cAOnePoint` function in the DEAP library performs a single-point crossover operation on initial task sequences with completion dates less than or equal to a set threshold, adjusting the task sequences and generating new task sequences. For example, the `cAOnePoint` function in the DEAP library performs a single-point crossover on sequences that meet a deviation threshold. Sequence 1 (ABC) is crossed with another sequence 3 (BCA) that meets the threshold, and the crossover point is randomly selected, such as at the second order position, to generate new sequences ACB and BAC.

[0026] Based on the new task sequence, the priority graph is constructed using the astar_path function of the NetworkA library. Orders are used as nodes and priority differences are used as edge weights. The shortest path from the starting order to the target order is searched to generate the adjusted priority sequence. Obtain the order delivery time from the adjusted priority sequence, determine whether the delivery time meets the constraints, and obtain the optimized task sequence.

[0027] In one embodiment, a priority graph is constructed using the `astar_path` function of the NetworkA library based on a new sequence such as ACB. Orders A, B, and C are used as nodes, and the priority difference is used as the edge weight (e.g., the edge weight from A to B is the priority difference from A to B). The shortest path from A to C is searched to obtain the adjusted priority sequence, such as BAC.

[0028] The `astro_path` function uses heuristic search to quickly find paths that meet priority constraints, improving the rationality of the sequence. For example, consider verifying the delivery times of the adjusted sequence BAC. Assume B requires 1 day to complete, A requires 2 days, and C requires 2 days, for a total of 5 days. B's delivery deadline is September 17th, and its completion time is September 16th, meeting the requirements; A's delivery deadline is September 18th, and its completion time is September 18th, also meeting the requirements; C's delivery deadline is September 20th, and its completion time is September 20th, also meeting the requirements. The optimized sequence, through deviation control and path search, ensures all orders are delivered on time while balancing resource allocation.

[0029] It is worth noting that in step S3, the step of extracting resource occupancy rate includes: obtaining resource occupancy data for each order from the task sequence, calculating the ratio of resource occupancy data for each order to the total resource capacity as the resource occupancy rate for each order, obtaining orders whose resource occupancy rate exceeds a preset threshold, obtaining a set of orders exceeding the threshold, and taking the remaining orders as a set of normal orders.

[0030] Assume the production line has two types of resources: machine tools and workers. Order A requires 2 machine tools and 3 workers; Order B requires 1 machine tool and 2 workers; Order C requires 3 machine tools and 1 worker. The total resource capacity is 5 machine tools and 5 workers. Resource utilization is calculated by dividing the order resource requirement by the total capacity. For example, the machine tool utilization rate for order A is 2 / 5 = 40%, and the worker utilization rate is 3 / 5 = 60%. Setting a threshold of 50%, the worker utilization rate for order A and the machine tool utilization rate for order C (3 / 5 = 60%) exceed the threshold, forming an over-threshold order set {A, C}, resulting in the normal order set {B}.

[0031] Specifically, in step S3, the step of reallocating resources through a search algorithm to obtain an updated resource matching scheme includes: Based on the order set exceeding the threshold, the A* search algorithm is used to traverse the resource types. First, the open list and the closed list are initialized. Then, the f value of each node is calculated. The f value is the sum of the g value and the h value, where the g value is the path cost from the starting node to the current node and the h value is the estimated cost from the current node to the target node. Candidate resource allocation combinations are generated. From the candidate resource allocation combinations, obtain the task duration and resource capacity of each combination. Combine this with the determination of whether the allocation constraint that the resource capacity does not exceed the total capacity is met. Select the candidate resource allocation combination that meets the allocation constraint and has the shortest task duration. Combine the candidate resource allocation combination with the normal order set and use it as the resource matching scheme.

[0032] In one embodiment, the A-Star search algorithm is used to generate candidate resource allocation combinations based on a set of orders exceeding a threshold. The initial open list stores nodes to be evaluated, and the closed list records evaluated nodes. Each node represents a resource allocation state, where g is the cumulative cost of allocated resources and h is the estimated cost of allocating remaining resources. For example, prioritizing worker resources for order A, g is 3 workers, h estimates the machine tool requirement for order C to be 3 units, and the total f value is 6. The algorithm iterates through resource types, generating combinations such as {A worker - C machine tool} and {C machine tool - A worker}. This method uses heuristic search to quickly focus on efficient allocation schemes, reducing blind attempts. Specifically, when screening candidate resource allocation combinations, resource capacity constraints must be met. For example, the combination {A worker - C machine tool} requires a total of 3 workers and 3 machine tools, which does not exceed the total capacity, and the task duration is 5 hours, better than the 6 hours of another combination {A worker - B machine tool}. The combination with the shortest duration and that meets the constraints is selected as the candidate resource allocation combination.

[0033] Preferably, in step S3, according to the resource matching scheme, the sorted function of PBthon is used to adjust the task priority. First, the priority key is defined as the task duration, and then a temporary task sequence is generated by sorting. From the temporary task sequence, obtain the resource utilization rate of each task to obtain the set of tasks to be optimized; For the set of tasks to be optimized, the resource allocation is adjusted using the linprog function of SciPB. First, the objective function c is defined as minimizing the total resource consumption, where c is the coefficient vector, A is the constraint matrix, and b is the constraint bound vector. Then, the updated resource matching scheme is generated by solving the problem.

[0034] Based on a resource matching scheme, Python's `sorted` function is used to adjust task priorities. The priority key is defined as task duration; for example, orders A, B, and C have durations of 5 hours, 4 hours, and 6 hours, respectively. A temporary task sequence BAC is generated after sorting. The resource utilization rate of each task in the sequence is obtained. For example, order B has a worker utilization rate of 40% and a machine tool utilization rate of 20%; order A has a worker utilization rate of 40% and a machine tool utilization rate of 20%; order C has a worker utilization rate of 20% and a machine tool utilization rate of 60%, resulting in a set of tasks to be optimized. This sorting method, with duration as the core, simplifies the priority adjustment logic and improves scheduling flexibility. In one embodiment, SciPy's `linprog` function optimizes resource allocation for the set of tasks to be optimized. The objective function is defined as minimizing total resource utilization, where the coefficient vector `c` represents the resource demand weight of each order, the constraint matrix A limits the total resources to not exceed capacity, and the constraint bound vector `b` is the total capacity value. For example, after optimization, the number of workers in order A is reduced from 3 to 2, and the number of machines in order C is reduced from 3 to 2, resulting in a total utilization rate of 45%. This method uses mathematical optimization to finely adjust resource allocation and improve overall resource utilization efficiency.

[0035] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. An optimization scheduling method for automatic production scheduling based on furniture production, characterized in that, The method comprises the following steps: S1: Collecting order data and resource state information on the production line, generating order priority sequence and availability data, and obtaining task allocation scheme; S2: Based on the task allocation scheme, the order priority is adjusted by using the optimization algorithm, and the optimized task sequence is generated; S3: Extracting the resource occupancy rate of each order from the optimized task sequence, if the resource occupancy rate exceeds the threshold, reassigning resources through the search algorithm to obtain the resource matching scheme; S4: According to the optimized task sequence and the resource matching scheme, the production scheduling scheme with task sequence and resource allocation of each task is generated.

2. The optimization scheduling method for automatic production scheduling based on furniture production according to claim 1, characterized in that: In the step S1, the order data includes order quantity and delivery period, and the remaining delivery time is calculated according to the current date and delivery period; The priority score of each order is calculated using Pandas library, and the priority score formula is P=Q / (D+1), P represents the priority score, Q represents the order quantity, and D represents the remaining delivery time, to obtain the order priority sequence.

3. The optimization scheduling method for automatic production scheduling based on furniture production according to claim 2, characterized in that: In the step S1, if there are orders with the same priority score in the order priority sequence, the sorted function of PBthon is used for secondary sorting according to the ascending order of delivery period to determine the order priority sequence.

4. The optimization scheduling method for automatic production scheduling based on furniture production according to claim 2, characterized in that: In the step S1, the resource state information includes number and occupancy rate of each day in the future within the rated time window; Get all numbers with occupancy rate below the preset threshold and the date when the occupancy rate is below the preset threshold, form the corresponding availability data, and arrange the availability data in the order of occupancy rate from low to high; According to the arrangement order of the priority sequence, from the front to the back of the order, get the same number of orders as the total number n of availability data; According to the arrangement order of the availability data and the arrangement order of the first n orders in the priority sequence, the first n orders in the priority sequence are allocated to the corresponding availability data one by one to obtain the preliminary task allocation scheme.

5. The optimization scheduling method for automatic production scheduling based on furniture production according to claim 1, characterized in that: In the step S2, the order priority sequence is obtained from the task allocation scheme; Initialize the population of order priority sequence using DEAP library to obtain multiple initial task sequences; Get the delivery period of each order in the initial task sequence, and take the minimum value of the delivery period of the initial task sequence as the set threshold value of the initial task sequence, and get the completion date of the initial task sequence; Perform single-point crossover operation on the initial task sequence with completion date less than or equal to the set threshold value by using the cAOnePoint function of DEAP library to adjust the task sequence and generate new task sequence; According to the new task sequence, the astar_path function of NetworkA library is used to construct priority graph, taking order as node and priority difference as edge weight, searching the shortest path from the starting order to the target order, and generating the adjusted priority sequence; Get the order delivery time from the adjusted priority sequence, and judge whether the delivery time meets the constraint condition to obtain the optimized task sequence.

6. The optimization scheduling method for automatic production scheduling based on furniture production according to claim 1, characterized in that: In the step S3, the step of extracting the resource occupancy rate comprises: obtaining resource occupancy data of each order from the task sequence, calculating a ratio of the resource occupancy data of each order to the total resource capacity as the resource occupancy rate of each order, obtaining orders with resource occupancy rates exceeding a preset threshold, obtaining a set of threshold-exceeding orders, and obtaining the remaining orders as a set of normal orders.

7. The optimization scheduling method for automatic production scheduling of furniture production according to claim 6, characterized in that: In the step S3, the step of reallocating resources by a search algorithm to obtain an updated resource matching scheme comprises: According to the set of threshold-exceeding orders, the A-star search algorithm is used to traverse resource types, first, an open list and a closed list are initialized, then the f value of each node is calculated, the f value is the sum of the g value and the h value, the g value is the path cost from the starting node to the current node, the h value is the estimated cost from the current node to the target node, and a candidate resource allocation combination is generated; From the candidate resource allocation combination, the task duration and resource capacity of each combination are obtained, and whether the allocation constraint condition that the resource capacity does not exceed the total capacity is met is combined to screen out the candidate resource allocation combination that meets the allocation constraint condition and has the shortest task duration, and the candidate resource allocation combination is combined with the set of normal orders to obtain a resource matching scheme.

8. The optimization scheduling method for automatic production scheduling of furniture production according to claim 7, characterized in that: In the step S3, according to the resource matching scheme, the sorted function of PBthon is used to adjust the task priority, first, the priority key is defined as the task duration, then the temporary task sequence is sorted; From the temporary task sequence, the resource occupancy rate of each task is obtained to obtain a set of tasks to be optimized. For the set of tasks to be optimized, the SciPB linprog function is used to adjust resource allocation, first, the objective function c is defined as minimizing the total resource occupancy, wherein c is the coefficient vector, A is the constraint matrix, and b is the constraint limit vector, then the solution is obtained to generate an updated resource matching scheme.

Images