Flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance

Abstract

The invention relates to a flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance, and belongs to the technical field of shop scheduling. Comprising the following steps: constructing a flexible job shop energy-saving batch flow scheduling mathematical model taking completion time, total energy consumption and total production cost as optimization targets and considering machine tool deterioration and preventive maintenance; a model solving method is put forward, a strategy is introduced based on population diversity of historical populations, and the coping capacity of the algorithm to environment changes is enhanced; developing a self-adaptive crossover mutation operator, and balancing the diversity and convergence of the algorithm; and proposing a neighborhood search strategy, and exploring a local optimal solution of the energy-saving batch flow scheduling problem. According to the method, the problem of flexible job shop energy-saving batch flow scheduling multi-objective optimization considering machine tool deterioration and preventive maintenance can be efficiently solved, under-maintenance or excessive maintenance of the machine tool is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with excellent completion time, total energy consumption and total production cost is obtained.

Description

Technical Field

[0001] The invention relates to an energy-saving batch flow scheduling optimization method for a flexible job shop taking into account machine tool deterioration and preventive maintenance, and belongs to the technical field of shop scheduling. Background Art

[0002] As the key basic equipment of the workshop, the state and performance of the machine tool directly affect the execution of the production scheduling plan. In the actual production of the workshop, the key components of the machine tool will be worn and fatigued during long-term operation due to factors such as temperature effects, vibration shock, load changes, and working environment, resulting in a decrease in the accuracy and performance of the machine tool, thereby causing changes in the processing time of the machine tool. With the accumulation of damage, the impact of this deterioration effect on the production scheduling of the workpiece will continue to intensify, not only resulting in an increase in completion time, energy consumption and cost, but also interfering with the normal production scheduling plan. Therefore, maintenance must be carried out in a timely manner. Although traditional periodic maintenance is simple and easy to implement, since not every maintenance is necessary, it leads to a lot of waste of resources such as cost and time. Compared with this, non-periodic preventive maintenance is more flexible and practical, which can effectively avoid under-maintenance or over-maintenance of machine tools, and reduce production interruptions and maintenance costs. Therefore, it is particularly important to study the optimization method of energy-saving batch flow scheduling in flexible workshops considering machine tool deterioration and preventive maintenance.

[0003] The deterioration effect of machine tools leads to uncertainty in the processing time of workpieces, which causes the Pareto optimal solution set in the decision space and the Pareto frontier in the target space of the energy-saving batch flow scheduling problem in flexible job shops to change as the scheduling proceeds, which in turn leads to the lack of robustness of traditional optimization methods; at the same time, when the deterioration accumulates to a certain extent, preventive maintenance is required. In this flexible maintenance environment, the convergence degree of the target value of the optimization scheduling scheme obtained based on different maintenance intervals is very different due to the constraints of various production resources in the workshop. Therefore, how to rationally characterize the deterioration effect of machine tools and, on this basis, construct a mathematical model for energy-saving batch flow scheduling in flexible job shops considering machine tool deterioration and preventive maintenance; considering the uncertainty of relevant parameters in the model and the characteristics of flexible maintenance, designing a targeted and efficient solution method is the key to achieving robust optimization of energy-saving batch flow scheduling in flexible job shops with machine tool deterioration and preventive maintenance.

[0004] At present, scholars have conducted relevant research on single machine / parallel machine deterioration scheduling, hybrid flow shop / flexible job shop deterioration scheduling and deterioration scheduling considering workpiece batching from the aspects of model construction and optimization algorithm design, and have achieved certain results. However, no relevant research has been retrieved that comprehensively considers machine tool deterioration, preventive maintenance and flexible production batch flow scheduling; for the energy-saving batch flow scheduling problem of flexible job shop, the increase in the problem scale of workpiece batching will lead to the more prominent impact of workpiece processing time uncertainty on shop scheduling, and the existing research on machine tool deterioration effect is mostly based on ideal linear (step) functions, which cannot accurately characterize the impact of machine tool deterioration on processing time in real production environment, resulting in the lack of practical guidance significance for the obtained optimization scheduling scheme. At the same time, under the competition of multiple manufacturing resources in the workshop, the choice of preventive maintenance interval also has an important impact on the performance of the scheduling scheme. Therefore, how to reasonably characterize the impact of machine tool deterioration on workpiece processing time, and on this basis, establish a mathematical model for energy-saving batch flow scheduling of flexible job shop considering machine tool deterioration and preventive maintenance and design an efficient and feasible optimization algorithm still needs further exploration. Summary of the invention

[0005] In view of the above-mentioned deficiencies and improvement needs of the prior art, the present invention proposes an energy-saving batch flow scheduling optimization method for a flexible job shop taking into account machine tool deterioration and preventive maintenance. The purpose is to solve the problem of insufficient robustness of traditional optimization methods caused by machine tool deterioration and flexible maintenance, and to achieve multi-objective robust optimization scheduling of energy-saving batch flow in a flexible job shop taking into account machine tool deterioration and preventive maintenance.

[0006] In order to solve the above technical problems, the present invention proposes an energy-saving batch flow scheduling optimization method for a flexible job shop considering machine tool deterioration and preventive maintenance, comprising the following steps: S1, constructing a mathematical model for energy-saving batch flow scheduling for a flexible job shop considering machine tool deterioration and preventive maintenance with completion time, total energy consumption and total production cost as optimization objectives; S2, proposing a hybrid method of a knowledge-driven batch method and a dynamic adaptive multi-objective evolutionary algorithm to solve the model; S3, designing a problem knowledge-driven neighborhood strategy to further explore the local optimal solution to the problem.

[0007] According to the present invention, as a further preferred embodiment, in the step S1, the multi-objective optimization mathematical model for energy-saving batch flow scheduling of a flexible job shop considering machine tool deterioration and preventive maintenance has the optimization objectives of minimizing completion time, minimizing total energy consumption and total production cost, and the calculation formula is:

[0008] F=min(T e ,T m ,T c )

[0009] Among them, F is the objective function, Te 、T m 、T c They are the total energy consumption of the workshop, completion time and total production cost respectively.

[0010] T e =E c +E ct +E id +E m +E f

[0011]

[0012]

[0013]

[0014]

[0015] E f =E u ×T max

[0016] Among them, E c , E ct , E id , E m , E f and E u are respectively processing energy consumption, processing preparation energy consumption, idle energy consumption, transportation energy consumption, fixed energy consumption and common energy consumption generated per unit time in the workshop; i, j, k and r are the indexes of workpiece, process, machine tool and tooling respectively; n', N t ,q i , m are the total number of workpieces after batching, workpiece J i The batch size, number of tools, and workpiece J of the lth sub-batch i The number of processes and machine tools; and P m They are respectively machine tool M k Standby power, idle power and handling power of AGV; and Process Q i,j In machine tool M k The clamping time, tooling change time, tooling adjustment time and transfer process Q required for processing i,j' The time required to transport the machine to the new machine for processing; X i,j,k , Y i,j,k,r , Z i,j and MI k is a decision variable, when process Q i,j In machine tool M k Processing time Xi,j,k Takes 1, otherwise 0, if process Q i,j In machine tool M k Upper processing using tooling MT r Time i,j,k,r Takes 1, otherwise 0, if process Q i,j If it is processed on the same machine tool as the next process, then Z i,j Take 1, otherwise 0, if the machine tool M k If available within the scheduled time, MI k Takes 1, otherwise takes 0.

[0017]

[0018] in, For workpiece J i The qth i The first process is in the machine tool M k The end time on.

[0019] The total production cost of a workpiece consists of the processing cost of the workpiece and the maintenance cost of the machine tool, which can be expressed as:

[0020]

[0021] Among them, C i,j,k , M.C. k and NC k Process Q i,j In machine tool M k Unit time processing cost, machine tool M k Maintenance times and machine tool M k The average cost of maintenance.

[0022] In addition to satisfying the basic constraints of batch flow scheduling in a multi-resource flexible job shop, the machine tool deterioration constraint must also be satisfied, which can be expressed as:

[0023]

[0024] Among them, D k is the deterioration coefficient, IT k For machine tool M k The processing time before the maintenance is triggered, Q i,j In machine tool M k Initial processing time on.

[0025] Machine tool available time window constraint. When preventive maintenance is triggered, if there are unfinished processing tasks on the machine tool, it is necessary to wait for them to be completed before maintenance can be performed, which is expressed as:

[0026]

[0027]

[0028] in, is a decision variable. If k Previous process Q ij Process Q gh The value is 1 if it is a subsequent process, otherwise it is 0; A i,j and A g,h They are the processable steps Q ij and Q gh Machine tool M k Collection; W k is a decision variable, if the machine tool M k If maintenance is triggered, it takes 1, otherwise it takes 0; For machine tool M k The starting time of the bth maintenance; LBD k and UPD k They are respectively machine tool M k The minimum and maximum deterioration for maintenance.

[0029] Machine tool maintenance constraints. Preventive maintenance may be triggered multiple times during a production cycle. k The end time of the bth maintenance It is expressed as:

[0030]

[0031] Among them, MT k For machine tool M k The time required for maintenance.

[0032] According to the present invention, as further preferred, step S2 includes the following sub-steps:

[0033] Design a population diversity maintenance strategy based on historical populations. Improve the diversity of the current population by integrating historical populations into the current population at a certain ratio in each search phase. The specific operations are as follows:

[0034] Step 1: Use the MFO operator to optimize the initialized population Population and obtain the offspring population Population2;

[0035] Step 2: Population and Population2 are merged in a 1:1 ratio, and Population3 is obtained by selecting the merged population based on the IGD-NS selection strategy;

[0036] Step 3: Based on the co-evolution operator, optimize the population Population3 to obtain the offspring population Population4;

[0037] Step 4: Randomly initialize the population to obtain Population5, merge Population3, Population4 and Population5 in a certain ratio, and select the population based on the IGD-NS selection strategy to obtain Population; the initial population ratio for small-scale problems is set to 0.1, the initial population ratio for large-scale problems is set to 0.3, and the ratio of Population3 and Population4 is 1:1;

[0038] Step 5: Repeat steps 1-4 above until the algorithm termination condition is reached.

[0039] On this basis, an adaptive crossover mutation operator based on the HV indicator is designed, which mainly includes:

[0040] The HV indicator is introduced to evaluate the population performance obtained by each iteration of the algorithm, and the difference ΔHV between the HV indicator of the current population and the previous iteration population is calculated. The crossover mutation probability value is adaptively adjusted according to its size to improve the convergence effect of the algorithm. Among them, the calculation formula of the HV indicator value is:

[0041]

[0042] Among them, the larger the HV value is, the stronger the comprehensive performance of the population is; c i is a non-dominated solution x and a reference point z ref A hypercube constructed as a diagonal.

[0043] Based on this, an adaptive crossover mutation operator based on the HV indicator is designed, which is expressed as:

[0044] ΔHV=HV u -HV u-1

[0045] p c =μ1×(cos(ΔHV×π))+τ1

[0046] p m =μ2×(sin(ΔHV×π))+τ2

[0047] Among them, the sine and cosine functions are used to ensure that the probability value is within the appropriate interval; HV1 = 0, u∈[1,2,…,maxcycle] is the iteration number index, μ1 and μ2 are random values ​​in the intervals [0.4,0.8] and [0.1,0.3] respectively; τ1, τ2 are random values ​​in the interval [0.05,0.1], here, they are 0.1 and 0.05 respectively.

[0048] According to the present invention, as further preferred, the problem knowledge-driven neighborhood strategy in step S3 includes the following sub-steps:

[0049] Step 1: Determine the maintenance time nodes of each machine tool under the existing scheduling scheme to form a machine tool maintenance information set A;

[0050] Step 2: Select the maintenance machine tool based on the maintenance trigger time in descending order;

[0051] Step 3: Based on the LBD formula k £D k ×IT k ±σ 2 GBPUSD k and Determine whether the machine tool should be adjusted. If so, move the subsequent affected processes on the machine tool to the left according to the greedy rule. Otherwise, remove the machine tool from set A.

[0052] Step 4: Repeat the above steps until set A is an empty set;

[0053] Step 5: Traverse the idle positions of the machine tools in descending order of processing time, and obtain the adjusted completion time based on the greedy rule;

[0054] Step 6: Adjust the tooling selection based on the post-tooling adjustment strategy of adjacent processes.

[0055] Where, σ is the diffusion coefficient, which follows a normal distribution [0.0005, 0.001]; and They are respectively machine tool M k The first process Q after the bth maintenance i,j The initial processing time, the start time of the first process and the end time of the last process before the bth maintenance.

[0056] The beneficial effects of the present invention are: with the goal of minimizing the completion time, total energy consumption and total production cost, a mathematical model for energy-saving batch flow scheduling of flexible job shops considering machine tool deterioration and preventive maintenance is constructed; secondly, a dynamic adaptive multi-objective evolutionary algorithm based on the HV index and a knowledge-driven flexible job shop batch partitioning framework are designed according to the characteristics of the problem to solve the model. In each search stage, the historical population and the randomly initialized population are merged into the current population in a certain proportion to improve the population diversity of the algorithm; on this basis, an adaptive crossover mutation operator based on the HV index is designed to guide the evolution of the population and improve the convergence effect of the algorithm; a knowledge-driven neighborhood operator is proposed to fully explore the local optimal solution of the problem; the impact of machine tool deterioration and preventive maintenance on multi-variety variable batch production is effectively dealt with, the total energy consumption and production cost of the workshop are reduced, the completion time of the product is shortened, and the theoretical basis and practical support are provided for the actual production of multi-variety variable batches under dynamic uncertain environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] FIG. 1( a ) and FIG. 1( b ) are example diagrams of the energy-saving batch flow scheduling problem of a flexible job shop in the environment of machine tool deterioration and preventive maintenance according to the present invention, wherein FIG. 1( a ) is a scheduling scheme diagram before workpiece batching, and FIG. 1( b ) is a scheduling scheme diagram after workpiece batching optimization;

[0058] Figure 2 It is a model solving framework diagram of the hybrid method of the present invention based on the knowledge-driven batch method and the dynamic adaptive multi-objective evolutionary algorithm;

[0059] Figure 3 Pseudo code of the knowledge-driven neighborhood search strategy proposed in the present invention;

[0060] Figure 4a-4l It is a Turkey confidence interval diagram of the IGD, GD and MS indicators of each algorithm under different average maintenance times of the present invention with a confidence level of 95%;

[0061] Figure 5(1)-Figure 5(4) Box plots showing the comparison results of HV, IGD, GD and MS indicators for solving example problems using the knowledge-driven batch method and the dynamic adaptive multi-objective evolutionary algorithm and NSGA-II, MOPSO, ARMOEA, DNSGAII and SGEA in the embodiment of the present invention;

[0062] Figure 6 A Gantt chart of the optimal solution scheduling scheme for solving an example problem using a knowledge-driven batch method and a dynamic adaptive multi-objective evolutionary algorithm according to an embodiment of the present invention;

[0063] Figure 7 This is a Pareto front comparison chart of different algorithms for example problems in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. However, it should be understood that the embodiments are used to explain the present invention but are not used to limit the present invention.

[0065] To make the present invention more clear, the method of the present invention is described below with a specific embodiment, as shown in FIG. 1( a ) and FIG. 1( b ); the embodiment of the present invention provides an energy-saving batch flow scheduling optimization method for a flexible job shop in an environment of machine tool deterioration and preventive maintenance. The method steps are as follows: Figure 2 As shown, the following steps are included:

[0066] S1. Construct a mathematical model for energy-saving batch flow scheduling in a flexible job shop taking into account machine tool deterioration and preventive maintenance with completion time, total energy consumption and total production cost as optimization objectives.

[0067] S2. Model solving method based on knowledge-driven batch method and dynamic adaptive multi-objective evolutionary algorithm.

[0068] S3. Neighborhood search strategy driven by problem knowledge.

[0069] S4. Numerical experiments.

[0070] S5. Example verification.

[0071] Furthermore, the multi-objective optimization mathematical model for energy-saving batch flow scheduling of flexible job shops considering machine tool deterioration and preventive maintenance in step S1 is:

[0072] Objective function: F = min(T e ,T m ,T c )

[0073] Among them, F is the objective function, T e 、T m 、T c They are the total energy consumption of the workshop, completion time and total production cost respectively.

[0074] T e =E c +E ct +E id +E m +E f

[0075]

[0076]

[0077]

[0078]

[0079] E f =E u ×T max

[0080] Among them, E c , E ct , E id , E m , E f and E u are respectively processing energy consumption, processing preparation energy consumption, idle energy consumption, transportation energy consumption, fixed energy consumption and common energy consumption generated per unit time in the workshop; i, j, k and r are the indexes of workpiece, process, machine tool and tooling respectively; n', N t ,q i , m are the total number of workpieces after batching, workpiece J i The batch size, number of tools, and workpiece J of the lth sub-batch i The number of processes and machine tools; and P m They are respectively machine tool M k Standby power, idle power and handling power of AGV; and Process Q i,j In machine tool M k The clamping time, tooling change time, tooling adjustment time and transfer process Q required for processing i,j' The time required to transport the machine to the new machine for processing; X i,j,k , Y i,j,k,r , Z i,j and MI k is a decision variable, when process Q i,j In machine tool M k Processing time X i,j,k Takes 1, otherwise 0, if process Q i,j In machine tool M k Upper processing using tooling MT r Time i,j,k,r Takes 1, otherwise 0, if process Q i,j If it is processed on the same machine tool as the next process, then Z i,j Take 1, otherwise 0, if the machine tool M k If available within the scheduled time, MI k Takes 1, otherwise takes 0.

[0081]

[0082] in, For workpiece J i The qth i The first process is in the machine tool M k The end time on.

[0083] The total production cost of a workpiece consists of the processing cost of the workpiece and the maintenance cost of the machine tool, which can be expressed as:

[0084]

[0085] Among them, C i,j,k , M.C. k and NC k Process Q i,j In machine tool M k Unit time processing cost, machine tool M k Maintenance times and machine tool M k The average cost of maintenance.

[0086] In addition to satisfying the basic constraints of batch flow scheduling in a multi-resource flexible job shop, the machine tool deterioration constraint must also be satisfied, which can be expressed as:

[0087]

[0088] Among them, D k is the deterioration coefficient, IT k For machine tool M k The processing time before the maintenance is triggered, Q i,j In machine tool M k Initial processing time on.

[0089] Machine tool available time window constraint. When preventive maintenance is triggered, if there are unfinished processing tasks on the machine tool, it is necessary to wait for them to be completed before maintenance can be performed, which is expressed as:

[0090]

[0091]

[0092] in, is a decision variable. If k Previous process Q ij Process Q gh The value is 1 if it is a subsequent process, otherwise it is 0; A i,j and A g,h They are the processable steps Q ij and Q gh Machine tool M k Collection; W k is a decision variable, if the machine tool M k If maintenance is triggered, it takes 1, otherwise it takes 0; For machine tool M k The starting time of the bth maintenance; LBD k and UPD k They are respectively machine tool Mk The minimum and maximum deterioration for maintenance.

[0093] Machine tool maintenance constraints. Preventive maintenance may be triggered multiple times during a production cycle. k The end time of the bth maintenance It is expressed as:

[0094]

[0095] Among them, MT k For machine tool M k The time required for maintenance.

[0096] According to the present invention, as further preferred, step S2 includes the following sub-steps:

[0097] Based on the MFO operator, the global optimization is performed, and the search formula is: u = dis(x u ,f u )×e bt ×cos(2πt)+f u

[0098] Among them, dis(x u ,f u ) indicates moth x u With flames u The Euclidean distance between .

[0099] b is the defined spiral shape constant, which is taken as 0.01.

[0100] t is the path coefficient, and its value range is [-1,1].

[0101] Design a population diversity maintenance strategy based on historical populations. Improve the diversity of the current population by integrating historical populations into the current population at a certain ratio in each search phase. The specific operations are as follows:

[0102] Step 1: Use the MFO operator to optimize the initialized population Population and obtain the offspring population Population2;

[0103] Step 2: Population and Population2 are merged in a 1:1 ratio, and Population3 is obtained by selecting the merged population based on the IGD-NS selection strategy;

[0104] Step 3: Based on the co-evolution operator, optimize the population Population3 to obtain the offspring population Population4;

[0105] Step 4: Randomly initialize the population to obtain Population5, merge Population3, Population4 and Population5 in a certain ratio, and select the population based on the IGD-NS selection strategy to obtain Population; the initial population ratio for small-scale problems is set to 0.1, the initial population ratio for large-scale problems is set to 0.3, and the ratio of Population3 and Population4 is 1:1;

[0106] Step 5: Repeat steps 1-4 above until the algorithm termination condition is reached.

[0107] On this basis, an adaptive crossover mutation operator based on the HV indicator is designed, which mainly includes:

[0108] The HV indicator is introduced to evaluate the population performance obtained by each iteration of the algorithm, and the difference ΔHV between the HV indicator of the current population and the previous iteration population is calculated. The crossover mutation probability value is adaptively adjusted according to its size to improve the convergence effect of the algorithm. Among them, the calculation formula of the HV indicator value is:

[0109]

[0110] Among them, the larger the HV value is, the stronger the comprehensive performance of the population is; c i is a non-dominated solution x and a reference point z ref A hypercube constructed as a diagonal.

[0111] Based on this, an adaptive crossover mutation operator based on the HV indicator is designed, which is expressed as:

[0112] ΔHV=HV u -HV u-1

[0113] p c =μ1×(cos(ΔHV×π))+τ1

[0114] p m =μ2×(sin(ΔHV×π))+τ2

[0115] Among them, the sine and cosine functions are used to ensure that the probability value is within the appropriate interval; HV1 = 0, u∈[1,2,…,maxcycle] is the iteration number index, μ1 and μ2 are random values ​​in the intervals [0.4,0.8] and [0.1,0.3] respectively; τ1, τ2 are random values ​​in the interval [0.05,0.1], here, they are 0.1 and 0.05 respectively.

[0116] Furthermore, the pseudo code of a problem knowledge-driven neighborhood strategy in step S3 is as follows: Figure 3As shown, the specific implementation steps are:

[0117] Step 1: Determine the maintenance time nodes of each machine tool under the existing scheduling scheme to form a machine tool maintenance information set A;

[0118] Step 2: Select the maintenance machine tool based on the maintenance trigger time in descending order;

[0119] Step 3: Based on the LBD formula k £D k ×IT k ±σ 2 GBPUSD k and Determine whether the machine tool should be adjusted. If so, move the subsequent affected processes on the machine tool to the left according to the greedy rule. Otherwise, remove the machine tool from set A.

[0120] Step 4: Repeat the above steps until set A is an empty set;

[0121] Step 5: Traverse the idle positions of the machine tools in descending order of processing time, and obtain the adjusted completion time based on the greedy rule;

[0122] Step 6: Adjust the tooling selection based on the post-tooling adjustment strategy of adjacent processes.

[0123] Where, σ is the diffusion coefficient, which follows a normal distribution [0.0005, 0.001]; and They are respectively machine tool M k The first process Q after the bth maintenance i,j The initial processing time, the start time of the first process and the end time of the last process before the bth maintenance.

[0124] Furthermore, the step S4 includes the following sub-steps:

[0125] In order to verify the effectiveness of the energy-saving batch flow scheduling optimization method for flexible job shops proposed in this invention, 90 groups of test cases were randomly generated and solved using the proposed knowledge-driven batch method and the dynamic adaptive multi-objective evolutionary algorithm hybrid method, NSGA-II, MOPSO, ARMOEA, DNSGAII and SGEA; these examples include 10 to 20 workpieces, 10 to 15 machine tools, the average number of processes for each workpiece is 6 to 15, an average of 3 machine tools can be selected for processing in each process, the average batch of workpieces is 10 to 30, the unit maintenance cost of the machine tool follows the normal distribution [3,6], and the average maintenance time of the machine tool is 10 to 50. The statistical results of 10 solutions of each algorithm are shown in Table 1. As can be seen from Table 1, for the average values ​​of each indicator, the proposed knowledge-driven batch method and the dynamic adaptive multi-objective evolutionary algorithm hybrid method are significantly better than the comparison algorithm. The Turkey confidence intervals of the IGD, GD, and MS indicators of each algorithm at different average maintenance times with a confidence level of 95% are shown in the figure below. Figure 4a-4l shown.

[0126] Table 1 Statistical results of different algorithms on test cases (mean)

[0127]

[0127] Further, the example verification in step S5 comes from a production workshop of a machine tool manufacturing enterprise. A workshop of the enterprise has 16 processing equipment such as gantry machining center, horizontal CNC machine tool, medium-sized CNC machine tool, small CNC machine tool, vertical machining center, boring and milling machining center, etc. The processing batches of a batch order are 10, 15 and 20 bed, column and slide parts respectively. The number of processes of each part is 22, 15 and 18 respectively, and there are 9 available toolings. The deterioration coefficient of the machine tool is set to obey uniform distribution [0.005, 0.15] according to the production experience of the workers and historical data information, the diffusion coefficient obeys uniform distribution [0.0005, 0.015], the average maintenance time of the machine tool is 30min, and the maintenance cost of the machine tool is 10, 15, 20, 10, 15, 20, 10, 15, 20, 10, 15, 20, 10, 15, 20, 10, 15, 20 and 15 respectively. The proposed knowledge-driven batch method and the dynamic adaptive multi-objective evolutionary algorithm hybrid method, NSGA-II, MOPSO, ARMOEA, DNSGAII and SGEA were used to solve the problem. The comparison results of each indicator after 10 solutions are shown in the following figure. Figures 5(1)-5(4) As shown. Figures 5(1)-5(4)It can be seen that the proposed knowledge-driven batch method and improved multi-objective evolutionary algorithm hybrid method are significantly better than the comparison algorithms in all indicators. The proposed method has improved the HV, IGD, GD and MS indicators by an average of 1.2%, 56.7%, 53.1% and 39.4%, which can effectively reduce the total energy consumption and cost of the workshop and shorten the completion time of the product. In addition, DAMOEA has significant differences with NSGA-II, MOPSO, ARMOEA, DNSGAII and SGEA in all evaluation indicators. This result once again verifies the effectiveness of the optimization method proposed in this paper. The optimal scheduling solution is obtained as shown in 2344 Figure 6 As shown in Figure 2. At this time, the number of sub-batches of each workpiece is 2, 3, and 5 respectively; the sub-batches are 4, 6; 4, 4, 7; 4, 4, 4, 4, 4 respectively. The completion time is 3960.29 min, the total energy consumption of the workshop is 3066.12 kW·min, and the total processing cost is 76100.9 CNY. The Pareto frontiers of different algorithms in actual cases are shown in Figure 2. Figure 7 As shown, it can be seen that the number of Pareto solutions obtained based on DAMOEA is larger and the distribution is better, which is conducive to decision makers to select a satisfactory scheduling plan based on actual production.

[0128] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable technicians in this field to understand the content of the present invention and implement it. It cannot be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the essence of the present invention belong to the protection scope of the present invention.

Claims

1. A flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance, characterized in that: The following steps are involved: S1. Multi-objective optimization mathematical model for energy-saving batch flow scheduling in a flexible job shop considering machine tool deterioration and preventive maintenance. S2. Model solving method based on knowledge-driven batch method and dynamic adaptive multi-objective evolutionary algorithm. S3. A neighborhood search strategy for the energy-saving batch flow scheduling problem in a flexible job shop considering machine tool deterioration and preventive maintenance driven by problem knowledge.

2. The energy-saving batch flow scheduling optimization method for a flexible job shop considering machine tool deterioration and preventive maintenance as described in claim 1 is characterized in that: A multi-objective optimization mathematical model for energy-saving batch flow scheduling in a flexible job shop considering machine tool deterioration and preventive maintenance includes: The optimization goal of the model is to minimize the completion time, total energy consumption and total processing cost. The calculation formula is: F=min(TEC,T max ,TC) Among them, F is the objective function, TEC, T max , TC are the total energy consumption, completion time and total production cost of the workshop respectively. TEC=E c +E ct +E id +E m +E f AND f =And u ×T max Among them, E c , E ct , E id , E m , E f and E u are respectively processing energy consumption, processing preparation energy consumption, idle energy consumption, transportation energy consumption, fixed energy consumption and common energy consumption generated per unit time in the workshop; i, j, k and r are the indexes of workpiece, process, machine tool and tooling respectively; n', N t ,q i , m are the total number of workpieces after batching, workpiece J i The batch size, number of tools, and workpiece J of the lth sub-batch i The number of processes and machine tools; and P m They are respectively machine tool M k Standby power, idle power and handling power of AGV; and Process Q i,j In machine tool M k The clamping time, tooling change time, tooling adjustment time and transfer process Q required for processing i,j' The time required to transport the machine to the new machine for processing; X i,j,k , Y i,j,k,r , Z i,j and MI k is a decision variable, when process Q i,j In machine tool M k Processing time X i,j,k Takes 1, otherwise 0, if process Q i,j In machine tool M k Upper processing using tooling MT r Time i,j,k,r Takes 1, otherwise 0, if process Q i,j If it is processed on the same machine tool as the next process, then Z i,j Take 1, otherwise 0, if the machine tool M k If available within the scheduled time, MI k Takes 1, otherwise takes 0. in, For workpiece J i The qth i The first process is in the machine tool M k The end time on. Among them, C i,j,k , M.C. k and NC k Process Q i,j In machine tool M k Unit time processing cost, machine tool M k Maintenance times and machine tool M k The average cost of maintenance. In addition to satisfying the basic constraints of batch flow scheduling in a multi-resource flexible job shop, the machine tool deterioration constraint must also be satisfied, which can be expressed as: Among them, D k is the deterioration coefficient, IT k For machine tool M k The processing time before the maintenance is triggered, Q i,j In machine tool M k Initial processing time on. Machine tool available time window constraint. When preventive maintenance is triggered, if there are unfinished processing tasks on the machine tool, it is necessary to wait for them to be completed before maintenance can be performed, which is expressed as: i,g∈[1,n'],j∈[1,q i ],h∈[1,q g ],k∈A i,j ∩A g,h ,r∈[1,N t ] W k ∈{0,1},k∈[1,m], if LBD k ≤D k ×ET i,j,k ≤UPD k ,W k Randomly generated within an optional set; If D k ×ET i,j,k ≥UPD k ,W k =1; otherwise, W k =0 in, is a decision variable. If k Previous process Q ij Process Q gh The value is 1 if it is a subsequent process, otherwise it is 0; A i,j and A g,h They are the processable steps Q ij and Q gh Machine tool M k Collection; W k is a decision variable, if the machine tool M k If maintenance is triggered, it takes 1, otherwise it takes 0; For machine tool M k The starting time of the bth maintenance; LBD k and UPD k They are respectively machine tool M k The minimum and maximum deterioration for maintenance. Machine tool maintenance constraints. Preventive maintenance may be triggered multiple times during a production cycle. k The end time of the bth maintenance It is expressed as: Among them, MT k For machine tool M k The time required for maintenance.

3. The energy-saving batch flow scheduling optimization method for a flexible job shop considering machine tool deterioration and preventive maintenance as described in claim 1 or 2, characterized in that: Population diversity maintenance strategies based on historical populations include: By integrating the historical population into the current population at a certain ratio in each search phase, the diversity of the current population can be improved. The specific operations are as follows: Step 1: Use the MFO operator to optimize the initialized population Population and obtain the offspring population Population2; Step 2: Population and Population2 are merged in a 1:1 ratio, and Population3 is obtained by selecting the merged population based on the IGD-NS selection strategy; Step 3: Based on the co-evolution operator, optimize the population Population3 to obtain the offspring population Population4; Step 4: Randomly initialize the population to obtain Population5, merge Population3, Population4 and Population5 in a certain ratio, and select the population based on the IGD-NS selection strategy to obtain Population; the initial population ratio for small-scale problems is set to 0.1, the initial population ratio for large-scale problems is set to 0.3, and the ratio of Population3 and Population4 is 1:1; Step 5: Repeat steps 1-4 above until the algorithm termination condition is reached.

4. The energy-saving batch flow scheduling optimization method for a flexible job shop considering machine tool deterioration and preventive maintenance as described in claim 3 is characterized in that: Adaptive crossover mutation operator based on HV indicator, including: The HV indicator is introduced to evaluate the population performance obtained by each iteration of the algorithm, and the difference ΔHV between the HV indicator of the current population and the previous iteration population is calculated. The crossover mutation probability value is adaptively adjusted according to its size to improve the convergence effect of the algorithm. Among them, the calculation formula of the HV indicator value is: Among them, the larger the HV value is, the stronger the comprehensive performance of the population is; c i is a non-dominated solution x and a reference point z ref A hypercube constructed as a diagonal. Based on this, an adaptive crossover mutation operator based on the HV indicator is designed, which is expressed as: ΔHV=HV u -HV u-1 p c =μ1×(cos(ΔHV×π))+τ1 p m =μ2×(sin(ΔHV×π))+τ2 Among them, the sine and cosine functions are used to ensure that the probability value is within the appropriate interval; HV1 = 0, u∈[1,2,…,maxcycle] is the iteration number index, μ1 and μ2 are random values ​​in the intervals [0.4,0.8] and [0.1,0.3] respectively; τ1, τ2 are random values ​​in the interval [0.05,0.1], here, they are 0.1 and 0.05 respectively.

5. The energy-saving batch flow scheduling optimization method for a flexible job shop considering machine tool deterioration and preventive maintenance as described in claim 4 is characterized in that: Knowledge-driven neighborhood strategies, including: Step 1: Determine the maintenance time nodes of each machine tool under the existing scheduling scheme to form a machine tool maintenance information set A; Step 2: Select the maintenance machine tool based on the maintenance trigger time in descending order; Step 3: Based on the LBD formula k ≤D k ×IT k ±σ 2 ≤UBD k and Determine whether the machine tool should be adjusted. If so, move the subsequent affected processes on the machine tool to the left according to the greedy rule. Otherwise, remove the machine tool from set A. Step 4: Repeat the above steps until set A is an empty set; Step 5: Traverse the idle positions of the machine tools in descending order of processing time, and obtain the adjusted completion time based on the greedy rule; Step 6: Adjust the tooling selection based on the post-tooling adjustment strategy of the adjacent process. Where σ is the diffusion coefficient, which obeys the normal distribution [0.0005, 0.001]; and They are respectively machine tool M k The first process Q after the bth maintenance i,j The initial processing time, the start time of the first process and the end time of the last process before the bth maintenance.

Images