Ultrahigh-speed optical module production line rescheduling method, device and equipment for dynamic order insertion and batch processing optimization and medium

Abstract

The invention discloses an ultrahigh-speed optical module production line rescheduling method, device and equipment for dynamic order insertion and batch processing optimization and a medium, and belongs to the technical field of hybrid flow shop rescheduling and intelligent optimization algorithms. Setting a scheduling strategy of the current batch processing order of which the new order processing priority is lower than the new order insertion moment so as to maximize the batch processing efficiency; and by taking minimization of the maximum completion time as an optimization target, solving the scheduling model through an ant colony optimization algorithm. The invention aims to provide an innovative solution by using the idea of intelligent manufacturing so as to improve the automation level, the intelligent degree and the optimization efficiency of production scheduling. Through the method, dynamic insertion of emergency orders can be effectively coped with, meanwhile, optimization of production efficiency and resource allocation is ensured, and the requirements of the modern manufacturing industry for flexible and efficient production processes are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of hybrid flow shop rescheduling and intelligent optimization algorithm, and more specifically, relates to a rescheduling method, device, equipment and medium for an ultra-high-speed optical module production line with insertion orders. Background Art

[0002] In modern manufacturing, production scheduling is a key link to ensure the efficient operation of the production process. Especially in technology-intensive industries such as ultra-high-speed optical modules, the scheduling optimization of production lines is particularly important. However, in the actual production process, urgent orders are often inserted, and these orders often need to be quickly adjusted in the existing production plan to adapt to market changes and customer needs. This dynamic insertion of orders poses a great challenge to the scheduling of production lines. Dynamic insertion of orders requires constant adjustment of the original production plan, which increases the complexity of scheduling; the insertion of urgent orders may interrupt the ongoing batch processing, affecting the operating efficiency of the machine and the rational allocation of resources; the insertion of new orders may cause the processing of the original orders to be delayed, increasing the residence time of work-in-progress, thereby extending the entire production cycle; batch processing can improve machine utilization efficiency and reduce unit product costs, but frequent order insertion will weaken this advantage, etc.

[0003] In today's context of intelligent manufacturing, the manufacturing industry is rapidly adopting advanced information technology and automation technology to improve production efficiency and flexibility. As an indispensable component in modern communication infrastructure, the optimization of the production process of ultra-high-speed optical modules has become particularly critical. With the development of intelligent manufacturing technology, the scheduling system of the production line increasingly needs to be able to handle highly complex and dynamic problems in order to adapt to the ever-changing market demand and order patterns.

[0004] Although existing production scheduling methods can provide solutions in some situations, they are often unable to handle large-scale dynamic insertion of orders. These methods may lack sufficient adaptability and intelligence to meet the needs of modern manufacturing for real-time optimization and resource allocation. Especially when urgent orders need to be quickly inserted into existing production plans, how to schedule efficiently while ensuring the continuity of the production process and the optimal use of resources is an important issue currently facing the manufacturing industry. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention proposes a method, device, equipment and medium for rescheduling an ultra-high-speed optical module production line for dynamic insertion and batch processing optimization, which improves the scheduling efficiency and production efficiency of the ultra-high-speed optical module production line through innovative scheduling strategies and intelligent optimization algorithms.

[0006] To achieve the above object, according to one aspect of the present invention, a method for rescheduling an ultra-high-speed optical module production line for dynamic order insertion and batch processing optimization is provided, comprising:

[0007] The rescheduling problem of the ultra-high-speed optical module production line is modeled to obtain a scheduling model, and a scheduling strategy is set for the current batch processing orders with a new order processing priority lower than the new order insertion time to maximize the batch processing efficiency;

[0008] Taking minimizing the maximum completion time as the optimization goal, the scheduling model is solved by using the ant colony optimization algorithm.

[0009] In some optional implementation schemes, the modeling of the rescheduling problem of the ultra-high-speed optical module production line to obtain a scheduling model includes:

[0010] Assumptions are set: the number of workpieces processed by a machine in each batch is limited, the machine prioritizes processing complete batches of workpieces, the priority of inserted workpieces is lower than that of the workpieces in the current batch, and the original workpieces and inserted workpieces cannot be processed together;

[0011] Under the assumptions, a scheduling model is constructed with minimizing the maximum completion time as the optimization goal, in which the constraints are set: a batch processing machine can process a preset number of workpieces at most once, the process cannot be interrupted after it starts, any process is only processed once on the machine, the start time of any process is not greater than the completion time, the completion time of any process is not greater than the maximum completion time, each machine can only process one process at the same time, the completion time of the previous process of any workpiece is not greater than the start time of the next process, the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order, batch processing gives priority to processing components with a preset number of batches, and when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than the preset number, the two cannot be mixed into one batch for processing.

[0012] In some optional implementation schemes, solving the scheduling model by using an ant colony optimization algorithm includes:

[0013] Initialize the pheromone concentration, randomly select the starting point for each ant, select the next state according to the transition probability, and update the current ant's position to cyclically construct the scheduling plan until the end condition is met;

[0014] Evaluate each ant's scheduling plan and calculate its quality based on the fitness function;

[0015] Update the pheromone concentration and adjust the pheromone concentration according to the quality of the ant's path.

[0016] In some optional embodiments, the method further comprises:

[0017] Combined with the local search strategy, the solution constructed by ants is fine-tuned, the ant colony optimization algorithm is optimized and improved using parallel computing technology, and the pheromone update strategy is dynamically adjusted based on real-time feedback from the problem.

[0018] In some optional embodiments, the method further comprises:

[0019] Design scheduling problems of different scales and characteristics, test the effectiveness of the ant colony optimization algorithm, compare the production efficiency and maximum completion time under different conditions, and evaluate the stability and adaptability of the ant colony optimization algorithm in the face of different dynamic changes.

[0020] According to another aspect of the present invention, there is provided an ultra-high-speed optical module production line rescheduling device for dynamic order insertion and batch processing optimization, comprising:

[0021] A model building module is used to model the rescheduling problem of the ultra-high-speed optical module production line to obtain a scheduling model, and set a scheduling strategy for the current batch processing orders with a new order processing priority lower than the new order insertion time to maximize the batch processing efficiency;

[0022] The solution module is used to solve the scheduling model through the ant colony optimization algorithm with the optimization goal of minimizing the maximum completion time.

[0023] In some optional implementation schemes, the model building module is used to set assumptions: the number of workpieces processed in each batch by a machine is limited, the machine gives priority to processing complete batches of workpieces, the priority of inserted workpieces is lower than that of the current batch of workpieces, and the original workpieces and inserted workpieces cannot be mixed for processing; under the assumptions, a scheduling model is constructed with minimizing the maximum completion time as the optimization goal, wherein constraints are set: a batch processing machine processes a preset number of workpieces at most once, the process cannot be interrupted after it starts, any process is only processed once on the machine, the start time of any process is not greater than the completion time, the completion time of any process is not greater than the maximum completion time, each machine can only process one process at the same time, the completion time of the previous process of any workpiece is not greater than the start time of the next process, the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order, batch processing gives priority to processing components with a preset number of batches, and when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than the preset number, the two cannot be mixed into one batch for processing.

[0024] In some optional implementation schemes, the solution module is used to initialize the pheromone concentration, randomly select a starting point for each ant, select the next state according to the transition probability, and update the position of the current ant to cyclically construct a scheduling plan until the end condition is met; evaluate the scheduling plan of each ant and calculate the quality according to the fitness function; update the pheromone concentration and adjust the pheromone concentration according to the path quality of the ant.

[0025] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0028] The present invention utilizes the ant colony optimization algorithm in the intelligent optimization algorithm to effectively solve the production scheduling problem caused by the insertion of urgent orders in the production process. This method not only improves production efficiency and reduces the maximum completion time caused by production interruptions and delays, but also optimizes the configuration of machines and human resources and reduces resource waste. At the same time, the scheduling method shows strong adaptability and robustness, can quickly respond to dynamic changes in the production process, and ensures efficient and stable operation under different scales and complex environments. In addition, the implementation of the present invention also promotes the automation and intelligence of production scheduling, reduces human errors, improves the efficiency of production management, supports the lean production concept of sustainable development, helps to reduce production costs and reduce the impact on the environment, and has broad application prospects and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for rescheduling an ultra-high-speed optical module production line for dynamic order insertion and batch processing optimization provided by an embodiment of the present invention;

[0030] Figure 2 is a schematic diagram of an ant colony optimization algorithm provided by an embodiment of the present invention;

[0031] Figure 3 It is a modeling schematic diagram provided by an embodiment of the present invention;

[0032] Figure 4 is a schematic diagram of an ant colony optimization algorithm provided by an embodiment of the present invention;

[0033] Figure 5 It is a flow chart of an ultra-high-speed optical module production line rescheduling method for dynamic order insertion and batch processing optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention. In addition, 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.

[0035] Aiming at the problem that the insertion order in the rescheduling problem of the hybrid pipeline ultra-high-speed optical module production line causes the current batch processing order processing delay at the time of the new order insertion, affecting the normal production of the batch processing machine, the present invention proposes a scheduling strategy in which the new order processing priority is lower than the current batch processing order at the time of the new order insertion, so as to maximize the batch processing efficiency. Secondly, the above scheduling model is solved by the ant colony optimization algorithm, and the optimization goal is to minimize the maximum completion time. The algorithm is optimized and improved by combining the local search strategy and parallel computing technology. Then, the effectiveness and robustness of the proposed method on scheduling problems of different scales are verified by simulation experiments, and the scheduling model and the ant colony optimization algorithm are iteratively optimized according to the feedback from practical applications. The results show that the present invention can significantly reduce the maximum completion time and improve production efficiency. The advantage of the present invention is that it makes full use of the batch processing capacity of the machine, improves the scheduling efficiency through the optimization algorithm, and is suitable for various complex production environments. By maximizing the number of workpieces in each batch, the idle time and switching time of the machine are reduced, thereby achieving the optimal configuration of production resources and ensuring the efficient completion of production tasks.

[0036] The present invention provides an ultra-high-speed optical module production line rescheduling method for dynamic insertion and batch processing optimization, such as Figure 1 As shown, the details are as follows:

[0037] Step S1: Problem definition and modeling;

[0038] Step S2: Scheduling strategy formulation;

[0039] Step S3: using the ant colony optimization algorithm to solve the above scheduling model;

[0040] Step S4: Optimize and improve the algorithm;

[0041] Step S5: Verify the feasibility of the algorithm through simulation experiments;

[0042] Step S6: applying the optimized scheduling scheme to the actual ultra-high-speed optical module production line, and monitoring the implementation effect to ensure the feasibility and efficiency of the scheduling scheme;

[0043] Step S7: According to the feedback from actual applications, the scheduling model and the ant colony optimization algorithm are iteratively optimized to adapt to various changes that may occur in the production process.

[0044] In one embodiment, problem definition and modeling refers to: detailed modeling of the rescheduling problem of ultra-high-speed optical module production lines, including order characteristics (processing time, priority, deadline, etc.), machine characteristics (batch processing capacity, machine efficiency, etc.) and production constraints. Figure 3 shown.

[0045] The following assumptions are made based on the actual situation on site:

[0046] (1) A machine can process a limited number of workpieces per batch.

[0047] (2) The machine prioritizes processing complete batches of workpieces.

[0048] (3) The priority of the inserted single workpiece is lower than the current batch workpiece.

[0049] (4) The original workpiece and the inserted workpiece cannot be processed together.

[0050] Under the above assumptions, the optimization goal is to minimize the maximum completion time and divide the original workpieces into batches {G1, G2, ..., G u}, group the inserted workpieces into batches {H1, H2, ..., H v}, after inserting the new workpiece order, all workpieces are divided into batches into sets {J1, J2, ..., J n}.

[0051] Objective function:

[0052] min makespan=max(makespan i )(1)

[0053] Constraints:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] P m >P im (11)

[0064] (B O +B i <16)→(B O =0∪B i =0)(12)

[0065] Makespan is the maximum completion time, i is the completion time of job i; B k is the number of workpieces in the kth batch; S ij For process o i j's start time, C ij For process o i The completion time of j, T ijz For process o i Processing time of j on machine z; PT k refers to the total processing time of the kth batch, GT is the processing time of a single workpiece; X ijz For process o i Whether j is processed on machine z is a 0-1 variable, 1 is processed on z, n is the number of machines; M is a large positive real number, G ijhk For process o i j and process o hk The order of priority is 0-1 variable, ij is 1 in front; PIN indicates the priority of inserting workpieces, POR j represents the priority of the original workpiece, j is the batch at the time of insertion; P m is the priority of batch processing components with 16 batches, P im The priority of batch processing components with less than 16 batches; B o Indicates the number of workpieces in a batch after the original workpieces are batched, B i Indicates the number of workpieces in a batch after inserting workpiece batches.

[0066] Among the above constraints, (2) means that a batch processing machine can process at most L(16) workpieces at a time; (3) means that the process cannot be interrupted after it starts; (5) means that any process is only processed once on the machine; (6) means that the start time of any process is not greater than the completion time; (7) means that the completion time of any process is not greater than the maximum completion time; (8) means that each machine can only process one process at the same time; (9) means that the completion time of the previous process of any workpiece is not greater than the start time of the next process; (10) means that the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order; (11) means that when batch processing is performed, components with 16 batches are processed first; (12) means that when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than 16, the two cannot be mixed into one batch for processing, such as Figure 5 shown.

[0067] In one embodiment, the scheduling strategy formulation refers to: formulating a scheduling strategy to ensure that the processing priority of new orders is lower than that of current batch orders to reduce the impact of batch processing interruptions.

[0068] In one embodiment, using the ant colony optimization algorithm to solve the above scheduling model means: first, the pheromone concentration is initialized in the problem solution space, then each ant constructs a scheduling plan based on the pheromone concentration, and then guides the ant search by combining problem-specific heuristic information, such as order urgency, machine idle time, etc., and finally, according to the quality of the solution constructed by the ant, the pheromone concentration is updated to strengthen the search area of ​​the high-quality solution. Figure 2 shown.

[0069] In one embodiment, an ant colony optimization algorithm is used to solve the above scheduling model, specifically including:

[0070] The pheromone concentration represents the quality of each solution in the solution space, and τ ij represents the pheromone concentration from state i to state j.

[0071] Heuristic information is problem-specific information used to guide ants to choose a better path. In the present invention, the heuristic information can be the urgency of the order or the idle time of the machine, etc., and is expressed as n ij express.

[0072] The probability of an ant moving from state i to state j is determined by the product of pheromone concentration and heuristic information, as follows: Among them, α and β are algorithm parameters that control the relative importance of pheromone concentration and heuristic information, and N i is the set of states that can be transferred from state i.

[0073] After the ant completes a path search, it updates the pheromone concentration according to the quality of the path. The pheromone update formula is as follows: ij =(1-ρ)·τ ij +Δτ ij , where ρ is the pheromone volatilization rate, Δτ ij It is the amount of pheromone deposited by ants, which is usually inversely proportional to the quality of the path and can be determined based on the quality of the path. The formula is as follows: Where L is the length of the path constructed by the ants, that is, the total processing time or the maximum completion time.

[0074] The fitness function is used to evaluate the quality of the scheduling plan constructed by the ants, which is usually opposite to the optimization goal, such as minimizing the maximum completion time. The formula can be expressed as: F(σ) = max k∈J (C k), where σ is a scheduling scheme, J is the set of all orders, and C k is the completion time of order k.

[0075] like Figure 4 As shown, the algorithm flow is as follows:

[0076] Step 1: Initialize pheromone concentration τ ij ;

[0077] Step 2: For each ant, first randomly select the starting point, and then according to the transition probability P ij Select the next state and update the current ant's position to build a scheduling plan in a loop until the end condition is met;

[0078] Step 3: Evaluate each ant’s scheduling plan and calculate its quality based on the fitness function;

[0079] Step 4: Update the pheromone concentration and adjust τ according to the quality of the ant's path ij ;

[0080] Step 5: Repeat steps 2-4 until the stopping condition is met.

[0081] In one embodiment, the optimization and improvement of the algorithm includes: first, combining the local search strategy to fine-tune the solution constructed by the ants to further improve the quality of the solution; second, using parallel computing technology to improve the search efficiency of the algorithm; finally, dynamically adjusting the pheromone update strategy based on real-time feedback from the problem.

[0082] In one embodiment, the feasibility of the algorithm is verified through simulation experiments, including: designing scheduling problems of different scales and characteristics to test the effectiveness of the algorithm; comparing indicators such as production efficiency and maximum completion time under different conditions; and evaluating the stability and adaptability of the algorithm in the face of different dynamic changes.

[0083] The present invention also provides an ultra-high-speed optical module production line rescheduling device for dynamic order insertion and batch processing optimization, comprising:

[0084] A model building module is used to model the rescheduling problem of the ultra-high-speed optical module production line to obtain a scheduling model, and set a scheduling strategy for the current batch processing orders with a new order processing priority lower than the new order insertion time to maximize the batch processing efficiency;

[0085] The solution module is used to solve the scheduling model through the ant colony optimization algorithm with the optimization goal of minimizing the maximum completion time.

[0086] In one embodiment, the above-mentioned model construction module is used to set the following assumptions: the number of workpieces processed in each batch by a machine is limited, the machine gives priority to processing a complete batch of workpieces, the priority of the inserted workpiece is lower than the current batch of workpieces, and the original workpiece and the inserted workpiece cannot be mixed for processing; under the assumptions, a scheduling model is constructed with minimizing the maximum completion time as the optimization goal, wherein the following constraints are set: a batch processing machine processes a preset number of workpieces at most once, the process cannot be interrupted after it starts, any process is only processed once on the machine, the start time of any process is not greater than the completion time, the completion time of any process is not greater than the maximum completion time, each machine can only process one process at the same time, the completion time of the previous process of any workpiece is not greater than the start time of the next process, the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order, batch processing gives priority to processing components with a preset number of batches, and when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than the preset number, the two cannot be mixed into one batch for processing.

[0087] In one embodiment, the above-mentioned solution module is used to initialize the pheromone concentration, randomly select a starting point for each ant, select the next state according to the transition probability, and update the position of the current ant to cyclically construct a scheduling plan until the end condition is met; evaluate the scheduling plan of each ant and calculate the quality according to the fitness function; update the pheromone concentration and adjust the pheromone concentration according to the path quality of the ant.

[0088] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0089] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0090] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0091] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for rescheduling ultra-high-speed optical module production lines for dynamic order insertion and batch processing optimization, characterized in that: include: The rescheduling problem of the ultra-high-speed optical module production line is modeled to obtain a scheduling model, and a scheduling strategy is set to set the current batch processing order with a new order processing priority lower than the new order insertion time to maximize the batch processing efficiency; Taking minimizing the maximum completion time as the optimization goal, the scheduling model is solved by using the ant colony optimization algorithm.

2. The method according to claim 1, characterized in that The scheduling model is obtained by modeling the rescheduling problem of the ultra-high-speed optical module production line, including: Assumptions are set: the number of workpieces processed by a machine in each batch is limited, the machine prioritizes processing complete batches of workpieces, the priority of inserted workpieces is lower than that of the workpieces in the current batch, and the original workpieces and inserted workpieces cannot be processed together; Under the assumptions, a scheduling model is constructed with minimizing the maximum completion time as the optimization goal, in which the constraints are set: a batch processing machine can process a preset number of workpieces at most once, the process cannot be interrupted after it starts, any process is only processed once on the machine, the start time of any process is not greater than the completion time, the completion time of any process is not greater than the maximum completion time, each machine can only process one process at the same time, the completion time of the previous process of any workpiece is not greater than the start time of the next process, the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order, batch processing gives priority to processing components with a preset number of batches, and when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than the preset number, the two cannot be mixed into one batch for processing.

3. The method according to claim 1 or 2, characterized in that: Solving the scheduling model by using the ant colony optimization algorithm includes: Initialize the pheromone concentration, randomly select the starting point for each ant, select the next state according to the transition probability, and update the current ant's position to cyclically construct the scheduling plan until the end condition is met; Evaluate each ant's scheduling plan and calculate its quality based on the fitness function; Update the pheromone concentration and adjust the pheromone concentration according to the quality of the ant's path.

4. The method according to claim 3, characterized in that: The method further comprises: Combined with the local search strategy, the solution constructed by ants is fine-tuned, the ant colony optimization algorithm is optimized and improved using parallel computing technology, and the pheromone update strategy is dynamically adjusted based on real-time feedback from the problem.

5. The method according to claim 4, characterized in that The method further comprises: Design scheduling problems of different scales and characteristics, test the effectiveness of the ant colony optimization algorithm, compare the production efficiency and maximum completion time under different conditions, and evaluate the stability and adaptability of the ant colony optimization algorithm in the face of different dynamic changes.

6. An ultra-high-speed optical module production line rescheduling device for dynamic order insertion and batch processing optimization, characterized in that: include: A model building module is used to model the rescheduling problem of the ultra-high-speed optical module production line to obtain a scheduling model, and set a scheduling strategy for the current batch processing orders with a new order processing priority lower than the new order insertion time to maximize the batch processing efficiency; The solution module is used to solve the scheduling model through the ant colony optimization algorithm with the optimization goal of minimizing the maximum completion time.

7. The device according to claim 6, characterized in that The model building module is used to set assumptions: the number of workpieces processed by a machine is limited, the machine gives priority to processing a complete batch of workpieces, the priority of inserted workpieces is lower than that of the current batch of workpieces, and the original workpieces and inserted workpieces cannot be processed together; under the assumptions, a scheduling model is built with minimizing the maximum completion time as the optimization goal, wherein constraints are set: a batch processing machine processes a preset number of workpieces at most once, the process cannot be interrupted after starting, any process is only processed once on the machine, the start time of any process is not greater than the completion time, the completion time of any process is not greater than the maximum completion time, each machine can only process one process at the same time, the completion time of the previous process of any workpiece is not greater than the start time of the next process, the priority of inserting a new order is lower than the current batch processing order at the time of inserting the new order, batch processing gives priority to processing components with a preset number of batches, and when the sum of the number of original workpieces in a batch and the number of inserted workpieces in a batch is less than the preset number, the two cannot be mixed into one batch for processing.

8. The device according to claim 6 or 7, characterized in that The solution module is used to initialize the pheromone concentration, randomly select a starting point for each ant, select the next state according to the transition probability, and update the position of the current ant to cyclically construct a scheduling plan until the end condition is met; evaluate the scheduling plan of each ant and calculate the quality according to the fitness function; update the pheromone concentration and adjust the pheromone concentration according to the path quality of the ant.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method according to any one of claims 1 to 5 when executed by a processor.

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