Flexible production line task scheduling method for shaft machining

Abstract

According to the flexible production line task scheduling method for shaft machining, the problem of complex constraint in actual production can be solved, the production efficiency is improved through a scheduling algorithm, and it is guaranteed that the production sequence conforms to the priority of orders and the actual operation condition of equipment. Meanwhile, the method can dynamically adapt to various sudden changes possibly occurring in the production process, such as equipment faults and order sudden changes, and continuity and flexibility of the whole production process are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a flexible production line task scheduling method for shaft processing. Background Art

[0002] The flexible manufacturing system is a complex hierarchical control system, and production scheduling is the core key technology of the entire flexible manufacturing system. After manufacturing has evolved from traditional large-scale assembly lines to flexible job shops, the number of options for resource allocation has increased with the increase in production resources, and the number of processing tasks included in the scheduling plan has increased with the increase in the number of workpieces, resulting in an increase in the calculation dimension of scheduling and a geometric increase in difficulty. Secondly, in the scheduling of fully automated flexible production lines, it is necessary to consider relevant constraints such as order delivery time and order priority, and the requirements for shortening product production cycles in production have increased. Finally, due to frequent demand disturbances in production, the flexible production method has increased the processing path of dynamic scheduling. Combined with the characteristics of frequent switching and heavy production tasks of flexible production of certain products, the scheduling difficulty has been further increased.

[0003] Among the many current production scheduling models, real-time scheduling of production lines is the scheduling model that best meets actual production needs, and it is also the most complex scheduling problem. In the process of real-time scheduling of production lines, it is necessary to consider constraints such as equipment quantity resources, equipment production capacity, state switching time, processing sequence, etc., as well as sudden events such as changes in user needs and sudden failures of processing equipment, making it difficult to establish a model for real-time scheduling of production systems. In manual or semi-manual job scheduling, schedulers can make reasonable scheduling arrangements for production lines based on experience based on information such as production task volume and production progress. In the process of production scheduling execution, schedulers pay attention to the progress of production at any time. When new orders with different needs appear in production, schedulers will adjust the operations in a timely manner. In a sense, this scheduling method is actually a real-time scheduling method. However, this model can often only rely on experienced schedulers of manufacturers to schedule production manually. Due to the non-flexible design of automated production line equipment and the difficulty in controlling the consistency of parts, there are problems such as low efficiency, high labor costs, and difficulty in controlling quality consistency, and it cannot meet the high requirements of product quality. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a flexible production line task scheduling method for shaft processing, which can solve the complex constraint problems in actual production, improve production efficiency through advanced algorithm design, and ensure that the production sequence meets the priority of the order and the actual operation status of the equipment. At the same time, the method can also dynamically adapt to various sudden changes that may occur in the production process, such as equipment failure, sudden change of order, etc., to ensure the continuity and flexibility of the entire production process.

[0005] In order to solve the above problems, the present invention provides a flexible production line task scheduling method for shaft processing, and the specific steps include:

[0006] Build a scheduling model: obtain relevant data from the production workshop, define constraints for obtaining data, and build a model objective function;

[0007] Design scheduling algorithm: Based on the scheduling algorithm library SimPy, the heuristic genetic algorithm is used to generate the initial solution based on the defined constraints; the global optimal solution is searched from the initial solution based on the search technology of the meta-heuristic genetic algorithm; the dynamic adjustment mechanism is used to monitor the production status in real time and adjust the production schedule;

[0008] Simulate the scheduling model and adjust the scheduling plan.

[0009] Among them, the optimal solution is the best scheduling solution among all solutions that meet the requirements.

[0010] Preferably, the objective function of the scheduling model is expressed as:

[0011] Total processing time: C max =max(C j ), where C j is the completion time of the jth job;

[0012] Total cost: Among them, C i is the cost of the ith operation, r i is the resource utilization rate;

[0013] Backorder: Among them, T dj The back-order time for the jth job.

[0014] Preferably, the target data in the objective function of the scheduling model are all minimum data.

[0015] Preferably, when simulating the scheduling model, a simulated scheduling scheme is used to evaluate the impact of different strategies on production efficiency and cost; based on the simulation result analysis, the parameters and strategies are adjusted, and the scheduling scheme is iteratively adjusted to generate the final scheduling algorithm.

[0016] Preferably, the specific steps of the heuristic genetic algorithm include:

[0017] Initialize the population;

[0018] Evaluate fitness: Calculate the fitness of each solution according to the objective function, and evaluate the efficiency of the corresponding scheduling solution based on the fitness of each solution; the algorithm formula of the fitness function is calculated as follows:

[0019]

[0020] Among them, α, β, γ are weight factors, C j is the completion time of the jth job, C i is the cost of the ith operation, r i is the resource utilization rate, T dj The delayed delivery time of the jth workpiece; C max The minimum total added time.

[0021] Preferably, the specific steps of searching the optimal solution by the meta-heuristic genetic algorithm are:

[0022] Selection: Select the best solution according to fitness for crossover and mutation operations;

[0023] Crossover and mutation operations: sequential crossover and transposition mutation are used;

[0024] Update population: New individuals generated by selection, crossover and mutation form a new population, repeat the update process, and terminate the update when the updated population meets the termination condition;

[0025] Decoding: Output the optimal scheduling solution.

[0026] Preferably, the operation process of sequential crossover and transposition mutation is:

[0027] When performing sequential crossover, parts of chromosomes are randomly selected, selected genes from the two parent chromosomes are exchanged, and duplicated genes are repaired;

[0028] When performing transposition mutation, two chromosome positions are randomly selected and the genes at the corresponding positions are exchanged.

[0029] Beneficial effects:

[0030] The present invention constructs a multi-objective optimization production scheduling dynamic optimization model based on equipment quantity limitation, tool change time and order priority, and realizes efficient scheduling of the automated production line of shaft parts under resource constraints.

[0031] Through the designed scheduling method, the complex constraint problems in actual production can be effectively solved, the production efficiency can be improved, and the production sequence can be ensured to be consistent with the order priority and the actual operating status of the equipment.

[0032] The present invention can dynamically adapt to various sudden changes that may occur during the production process, such as equipment failure, sudden changes in orders, etc., to ensure the continuity and flexibility of the entire production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1A flowchart of constructing a scheduling model in a flexible production line task scheduling method for shaft processing provided by the present invention;

[0034] Figure 2 A flowchart of a scheduling algorithm in a flexible production line task scheduling method for shaft processing provided by the present invention;

[0035] Figure 3 A flowchart of a scheduling algorithm in a flexible production line task scheduling method for shaft processing provided by the present invention;

[0036] Figure 4 A system structure diagram corresponding to a flexible production line task scheduling method for shaft processing provided by the present invention. DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0038] like Figure 1 , Figure 2 As shown, the present invention provides a flexible production line task scheduling method for shaft processing, which includes:

[0039] Taking the shaft parts production line as an example, this paper studies the multi-objective flexible production scheduling algorithm under multi-constraint conditions based on artificial intelligence algorithm for the flexible production scheduling needs of workpiece production. The algorithm can not only cope with a variety of production constraints, but also strive to optimize the production efficiency and cost of the job shop to ensure the accuracy of the delivery date. The details are as follows.

[0040] The present invention provides a flexible production line task scheduling method for shaft processing, and the specific steps include:

[0041] Build a scheduling model: Obtain relevant data from the production workshop: Systematically record the resource situation, machine status, process requirements and material flow of the production workshop; define constraints, clarify key constraints such as machine capacity limitations, process sequence, state adjustment time and raw material supply; build an objective function, set a multi-objective optimization with the purpose of minimizing total processing time, minimizing production costs and minimizing delayed delivery; where minimization means obtaining the minimum value in the data;

[0042] Design scheduling algorithms: Based on the open source scheduling algorithm library SimPy, a rule-based heuristic genetic algorithm is used to quickly generate an initial feasible solution. A meta-heuristic search technique based on a genetic algorithm is used to search for a global optimal solution starting from the initial solution. A dynamic adjustment mechanism is used to monitor production status in real time, quickly respond to production anomalies and change requirements, and dynamically adjust production arrangements.

[0043] Simulate the scheduling model: Use simulated scheduling schemes to evaluate the impact of different strategies on production efficiency and cost; based on the simulation results, adjust parameters and strategies, iteratively optimize the scheduling scheme, and finally develop the scheduling algorithm.

[0044] The scheduling model is a model that can minimize the total processing time (Makespan), production cost, and ensure delivery time. The specific mathematical expression of the objective function is:

[0045] Minimize total processing time: C max =max(C j ), where C j is the completion time of the jth job.

[0046] Minimize total cost: Among them, C i is the cost of the ith operation, r i is the resource utilization rate.

[0047] Minimize backorders: Among them, T dj The back-order time for the jth job.

[0048] Assume that the constraints are: 1) Each machine can only process one task at a time; 2) Each task must be processed on a specified machine; 3) The processing order of tasks must follow a fixed roadmap. Based on the open source scheduling algorithm library SimPy, a rule-based heuristic genetic algorithm is used to quickly generate an initial feasible solution. The process of the genetic algorithm is as follows Figure 3 As shown. Genetic algorithm (GA) is a powerful tool for solving current scheduling problems. Genetic algorithm is a heuristic search algorithm that simulates the genetic mechanism in the natural selection process. The present invention uses SimPy library and DEAP (Distributed Evolutionary Algorithms in Python) library to implement a variety of heuristic algorithms including genetic algorithms. The specific steps of its genetic algorithm include:

[0049] 1) Initialize the population: Generate a certain number of populations randomly or according to some known heuristics to provide a good starting point. These initial populations consist of multiple chromosomes, each of which is a potential scheduling solution.

[0050] 2) Evaluate fitness: Calculate the fitness of each solution based on the objective function. The algorithm formula and calculation of the fitness function are as follows:

[0051]

[0052] Among them, α, β, and γ are weight factors, which are adjusted according to the actual situation. In the production scheduling problem, the fitness function can be based on multiple objectives such as minimizing the total processing time, minimizing the total cost, or maximizing the production efficiency; then the fitness of each chromosome is evaluated and calculated, that is, the efficiency of the scheduling scheme.

[0053] 3) Selection: Select the better solution based on fitness for subsequent crossover and mutation operations.

[0054] 4) Crossover and mutation: Order Crossover (OX) and Swap Mutation (SM) are used. The former randomly selects a section of chromosome, exchanges the genes of the two parent chromosomes, and then repairs the duplicated genes. The latter randomly selects two positions and exchanges the genes on them.

[0055] 5) Update the population: New individuals generated through selection, crossover and mutation form a new population. This process is repeated until the termination condition is met, such as reaching the maximum number of iterations or the quality of the solution no longer improves.

[0056] 6) Decoding: that is, outputting the results. The algorithm will output the chromosome with the highest fitness value, that is, the optimal production scheduling solution. Through the above-mentioned complex scheduling algorithm combining the heuristic and meta-heuristic methods of genetic algorithms, the multi-objective flexible job shop scheduling problem under multiple constraints is effectively solved, achieving dual optimization of production efficiency and cost.

[0057] Based on the best production scheduling plan obtained, through the dynamic adjustment mechanism, the production status is monitored in real time, the production anomalies and change requirements are quickly responded to, and the production arrangements are dynamically adjusted, so as to achieve rapid scheduling of production resources and data and improve production efficiency.

[0058] The system used in the present invention takes into account various factors in the actual production process of the flexible production line, such as the type of production line, the equipment of the production line, the production efficiency of the production line, etc. It also takes into account the functional requirements of the production scheduling system, such as the formulation of production plans, the adjustment of production plans, the execution of production plans, and the monitoring of production plans. Based on the needs of shaft parts manufacturers and combined with the scheduling process of manufacturing workshops, the system structure provided by the present invention is as follows: Figure 4 shown.

[0059] For equipment management, its functions include: (1) collecting and counting equipment output, energy consumption, online time, utilization rate, and OEE; (2) providing real-time monitoring data and charts for each device; (3) providing basic daily and weekly reports for equipment; and (4) accurately monitoring the operation of all equipment.

[0060] For production management, multi-process product production management is adopted, which can decompose a "full process order" into multiple "process orders", and then assign the "process orders" to single / multiple devices to generate equipment "production tasks".

[0061] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

Claims

1. A flexible production line task scheduling method for shaft processing, characterized in that the specific steps include: Build a scheduling model: obtain relevant data from the production workshop and define constraints for obtaining data; Construct model objective function; Design scheduling algorithm: Based on the scheduling algorithm library SimPy, a heuristic genetic algorithm is used to generate the initial solution based on the defined constraints; Based on the search technology of meta-heuristic genetic algorithm, the global optimal solution is searched from the initial solution; the dynamic adjustment mechanism is adopted to monitor the production status in real time and adjust the production schedule; Simulate the scheduling model and adjust the scheduling plan.

2. The flexible production line task scheduling method for shaft processing according to claim 1 is characterized in that: The expression of the objective function of the scheduling model is: Total processing time: C max =max(C j ), where C j is the completion time of the jth job; Total cost: Among them, C i is the cost of the ith operation, r i is the resource utilization rate; Backorder: Among them, T dj The back-order time for the jth job.

3. The flexible production line task scheduling method for shaft processing according to claim 2 is characterized in that: The target data in the objective function of the scheduling model are all minimum data.

4. The flexible production line task scheduling method for shaft processing according to claim 3 is characterized in that: When simulating the scheduling model, a simulated scheduling scheme is used to evaluate the impact of different strategies on production efficiency and cost; based on the simulation results, the parameters and strategies are adjusted, and the scheduling scheme is iteratively adjusted to generate the final scheduling algorithm.

5. The flexible production line task scheduling method for shaft processing according to claim 1 is characterized in that: The specific steps of the heuristic genetic algorithm include: Initialize the population; Evaluate fitness: Calculate the fitness of each solution according to the objective function, and evaluate the efficiency of the corresponding scheduling solution based on the fitness of each solution; the algorithm formula of the fitness function is calculated as follows: Among them, α, β, γ are weight factors, C j is the completion time of the jth job, C j is the cost of the ith operation, r i is the resource utilization rate, T dj The delayed delivery time of the jth workpiece; C max The minimum total added time.

6. The flexible production line task scheduling method for shaft processing according to claim 1 is characterized in that: The specific steps of the meta-heuristic genetic algorithm to search for the optimal solution are: Selection: Select the best solution according to fitness for crossover and mutation operations; Crossover and mutation operations: sequential crossover and transposition mutation are used; Update population: New individuals generated by selection, crossover and mutation form a new population, repeat the update process, and terminate the update when the updated population meets the termination condition; Decoding: Output the optimal scheduling solution.

7. The flexible production line task scheduling method for shaft processing according to claim 6 is characterized in that: The operation process of the sequential crossover and transposition mutation is as follows: When performing sequential crossover, parts of chromosomes are randomly selected, selected genes from the two parent chromosomes are exchanged, and duplicated genes are repaired; When performing transposition mutation, two chromosome positions are randomly selected and the genes at the corresponding positions are exchanged.

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