Integrated process planning and scheduling collaborative optimization method based on hybrid ZOA-VNS algorithm

Abstract

The invention provides an integrated process planning and scheduling collaborative optimization method and system based on a hybrid ZOA-VNS algorithm, a storage medium and electronic equipment, and relates to the field of process planning and production scheduling collaborative optimization. The method comprises the steps that firstly, a to-be-machined workpiece set, a to-be-machined machine set and available resources are obtained; secondly, under the limitation of the number of available resources, a mixed integer linear programming model is constructed by taking minimization of the maximum completion time as an optimization target; and then adopting a four-layer real number coding structure as a coding scheme, solving the model by adopting a hybrid zebra optimization-variable neighborhood search algorithm, outputting an optimal zebra individual, and decoding to obtain an optimal integrated process planning and scheduling collaborative optimization scheme. On the basis of a traditional integrated process planning and production scheduling problem, a real factor, namely continuous resource limitation, is introduced, so that the problem is more practical and applicable, meanwhile, the problem is effectively solved by the proposed hybrid zebra optimization-variable neighborhood search algorithm, and a good optimization effect is achieved.

Description

Technical Field

[0001] The present invention relates to the field of collaborative optimization of process planning and production scheduling, and in particular to an integrated collaborative optimization method, system, storage medium and electronic device for process planning and scheduling based on a hybrid ZOA-VNS algorithm. Background Art

[0002] Process planning and production scheduling are two indispensable links in the product development and manufacturing process. Process planning is responsible for determining the materials and processes required for production, while scheduling is responsible for allocating the production processes to different machines and optimizing specific goals based on meeting the requirements of process planning.

[0003] Although the relationship between process planning and scheduling is very close, in the traditional manufacturing model, they are often regarded as two independent links that are carried out in sequence. In fact, the separation between process planning and scheduling often leads to problems such as unbalanced machine load, lack of flexibility in process plans, and conflicts between process plans and scheduling plans.

[0004] In addition, modern manufacturing systems face the challenge of effectively utilizing bottleneck resources. Reasonable allocation of consumable resources required by different machines to perform work is one of the key issues in production. Since operations need to wait for resources to arrive before they can start, improper allocation of resources may cause the system to be unavailable and idle. This further leads to problems such as waste of resources and increased costs. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides an integrated process planning and scheduling collaborative optimization method, system, storage medium and electronic device based on a hybrid ZOA-VNS algorithm, which solves the technical problems of integrated process planning and workshop scheduling with continuous resource constraints.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An integrated process planning and scheduling collaborative optimization method based on a hybrid ZOA-VNS algorithm, comprising:

[0010] Obtain a set of workpieces to be processed, a set of machines, and available resources;

[0011] Based on the workpiece set and the machine set, and under the quantity restriction of the available resources, a mixed integer linear programming model is constructed with minimizing the maximum completion time as the optimization goal;

[0012] A four-layer real number coding structure of feature-link-machine-process is adopted as the coding scheme, and all initial zebra individual positions are randomly generated as the initial zebra population. The mixed integer linear programming model is solved by using a hybrid zebra optimization-variable neighborhood search algorithm, and the optimal zebra individual is output. Based on the corresponding decoding scheme, the optimal integrated process planning and scheduling collaborative optimization scheme is obtained.

[0013] Preferably, the mixed integer linear programming model includes:

[0014] minC max (1)

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Where J represents the set of artifacts; O i represents the process set of workpiece i; F i represents the set of machining features of workpiece i; M represents the set of machines;

[0028] i,i′ represents workpiece i,i′,1≤i,i′≤|J|; j,j′ represents process j,j′,1≤j,j′≤|O i |; f,f′ represents the feature f,f′,1≤f,f′≤|F i |; k, k′ represents machine k, kn, 1≤k, k′≤|M|; l, l′ represents process link l, l′;

[0029] Formula (1) is the objective function; min is the minimization function, Cmax represents the maximum completion time;

[0030] Constraint (2) means that for any workpiece, each processing feature must select a process link; F ifl is a decision variable, which is 1 if the lth process link of the processing feature f of workpiece i is selected, otherwise it is 0;

[0031] Constraint (3) means that for any workpiece, if the process link of the processing feature where a certain process is located is not selected, then the process will not be selected; X ij is a decision variable, if o ij If selected, it is 1, otherwise it is 0; N is a sufficiently large positive number; E ijfl If o ij If it is on the lth process link of the processing feature f, it is 1, otherwise it is 0;

[0032] Constraint (4) means that for any workpiece, if a process link of a certain processing feature is selected, then all the processes on this process link will be selected;

[0033] Constraint (5) indicates that when a process of a workpiece is selected, a selectable processing machine needs to be assigned to it, and if a process is not selected, no processing machine needs to be assigned; Z ijk is a decision variable, if o ij If it is processed on machine k, it is 1, otherwise it is 0;

[0034] Constraint (6) indicates that for any workpiece, the process priority relationship of any two processes selected from two processing features with a priority relationship must satisfy the priority relationship corresponding to its processing features; E ij′f′l If o ij′ If it is on the lth process link of the processing feature f′, it is 1, otherwise it is 0; ij′ is a decision variable, if o ij′ If selected, it is 1, otherwise it is 0; iji′j′ is a decision variable, if o ij In o i′j′ If it is pre-processed, it is 1, otherwise it is 0; P iff′ Indicates that if the machining feature f of workpiece i is the immediate predecessor of machining feature f′, it is 1, otherwise it is 0;

[0035] Constraint (7) indicates that for any workpiece, the process priority relationship between two processes on any process link of any processing feature of the workpiece should satisfy the priority relationship of the corresponding processes; E ij′fl If o ij′ If it is on the lth process link of the processing feature f, it is 1, otherwise it is 0; Yijij′ is a decision variable, if o ij In o ij′ If it is pre-processed, it is 1; It means that if the process j on the machining feature f of workpiece i is the immediate predecessor of j′, it is 1, otherwise it is 0;

[0036] Constraints (8) and (9) indicate that when the process priority relationship is determined, different processes of the same workpiece should have a certain processing order; ij , o ij′ They represent the process j and j′ of workpiece i respectively; S ij , S ij′ Respectively represent o ij , o ij′ Processing start time; Z ij′k is a decision variable, if o ij′ If it is processed on machine k, it is 1, otherwise it is 0; T ijk 、T ij′k Respectively represent o ij , o ij′ Processing time on machine k;

[0037] Constraints (10) and (11) indicate that when the process priority relationship is determined, the processes processed on the same machine should have a certain processing order; S i′j′ Indicates o i′j′ Processing start time; Z i′j′k is a decision variable, if o i′j′ If it is processed on machine k, it is 1, otherwise it is 0; T ijk 、T i′j′k Respectively represent o ij , o i′j′ Processing time on machine k;

[0038] Constraint (12) indicates that the cumulative resource consumption of all processing steps at any time cannot exceed the maximum total available resources at the same time; i (t) represents the set of processes being processed at time t; R ijk Indicates o ij Resource consumption on machine k; R max Indicates the maximum available amount of resources at the same time;

[0039] Constraint (13) specifies the maximum completion time.

[0040] Preferably, each layer of the coding scheme is represented by a single vector, and the vector π 1 Represents the feature sequence encoding, vector π 2 represents the link sequence encoding, vector π 3Represents the machine sequence encoding, vector π 4 Represents the process sequence code; the elements in the vector are random numbers between 0 and 1 that obey a uniform distribution, where the length of the feature sequence code and the link sequence code is equal to the sum of the number of processing features of all workpieces, and the length of the machine sequence code and the process sequence code is equal to the sum of the number of processes of all workpieces.

[0041] Preferably, the corresponding decoding scheme includes:

[0042] The first stage performs feature sequence decoding: for the feature sequence encoding vector π 1 The number at the jth position of the i-th workpiece in the feature sequence encoding vector is used as the feature set to be assigned, and the number at the jth position of the i-th workpiece in the feature sequence encoding vector is multiplied by the length of the feature set to be assigned and rounded down to obtain the feature index to be assigned. According to the index, the corresponding feature in the feature set to be assigned is selected and added to the decoded feature sequence;

[0043] The second stage performs link sequence decoding: for the link sequence encoding vector π 2 The number at the jth position of the ith workpiece in the figure, find the feature F of workpiece i j The optional links are taken as the link set to be allocated, the number at the jth position of the i-th workpiece in the link sequence encoding vector is multiplied by the length of the link set to be allocated and rounded down to obtain the link index to be allocated, and the corresponding link in the link set to be allocated is selected according to the index and added to the decoded link sequence;

[0044] The third stage performs machine sequence decoding: for the machine sequence encoding vector π 3 The number at the jth position of the ith workpiece in process O ij The optional machines are taken as the machine set to be allocated, the number at the jth position of the i-th workpiece in the machine encoding vector is multiplied by the length of the machine set to be allocated and rounded down to get the machine index to be allocated, and the corresponding machine in the machine set to be allocated is selected according to the index and added to the decoded machine sequence;

[0045] The fourth stage performs process sequence decoding: for the workpiece sequence encoding vector π 4 The jth process O of the i-th workpiece ij, if it is not in the process route of the i-th workpiece, then remove the number at the j-th position of the i-th workpiece from the process sequence encoding vector, and arrange the encoding vectors after the removal in ascending order. If the number in the arranged encoding vector belongs to the number of the i-th workpiece in the encoding vector before arrangement, then add the number i to the decoded process sequence. The number of numbers i in the decoded process sequence is equal to the number of processes in the process route of the i-th workpiece, and the order of numbers i in the decoded process sequence corresponds to the order of processes in the process route of the i-th workpiece.

[0046] The fifth stage determines the processing start and end time of each process according to the decoding rules embedded in the resource-based left shift strategy, including:

[0047] S100, for process O ij , find the process route of workpiece i in O ij The completion time OT of the previous process;

[0048] S200, for process O ij Assigned machine M i , find machine M i The completion time MT of the last assigned process and the machine M before MT i Free time in It's Machine M i The idle start time of the i-th idle time period, It's Machine M i The idle end time of the i-th idle time period;

[0049] S300: If there is an idle time period satisfy Then process O ij Earliest possible start time Where PT is process O ij On machine M i Processing time on

[0050] S400, judge Is there a sub-time period of length PT within Make the remaining resource available in this sub-time period greater than or equal to process O ij The resource consumption of process O ij Actual processing start time in They are the start and end time of the sub-time period with a length of PT.

[0051] Preferably, the method of using a hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model and outputting the optimal zebra individual comprises:

[0052] S31, initializing the parameters of the hybrid zebra optimization-variable neighborhood search algorithm, including the number of zebras N, the maximum number of iterations T, the number of neighborhood structures k_max, and the maximum number of iterations l_max between two improvements of a single neighborhood;

[0053] S32, randomly generate all initial zebra individual positions as the initial zebra population, each zebra individual is [π 1 ,π 2 ,π 3 ,π 4 ];

[0054] S33: let i=1, t=1;

[0055] S34, calculating the fitness values ​​of all zebra individuals in the population and sorting them, selecting the individual with the best fitness as the initial position of the pioneer zebra PZ, where the fitness value is the maximum completion time;

[0056] S35, simulate the foraging behavior of zebras. The i-th zebra in the population moves towards the pioneer zebra. The position of the i-th zebra is calculated using the formula renew;

[0057] S36. For the position of the i-th zebra after the update, an elite retention strategy is adopted, that is, if the fitness of the i-th zebra after the update is less than the fitness of the i-th zebra before the update, the updated position is retained, otherwise the position of the i-th zebra remains unchanged;

[0058] S37, randomly generate the position of the predator AZ, simulate the defense strategy taken by the zebra when facing the predator, assume that the zebra will choose to escape to the nearby or fight back against the predator, and the probability of the two choices is equal. Let P S =rand(0,1);

[0059] S38, if P S ≤0.5, the zebra chooses the first strategy, and the i-th zebra position in the population uses the formula Update, otherwise the zebra chooses the second strategy, and the i-th zebra position in the population adopts the formula renew;

[0060] S39. For the position of the i-th zebra after the update, an elite retention strategy is adopted, that is, if the fitness of the i-th zebra after the update is less than the fitness of the i-th zebra before the update, the updated position is retained, otherwise the position of the i-th zebra remains unchanged;

[0061] S310. If i < N, set i = i + 1, return to S34; otherwise, go to S311.

[0062] S311. Obtain the top 10% of the individuals with the highest fitness in the zebra population as elite zebras, and perform variable neighborhood search on the positions of the elite zebras using k_max neighborhood structures. The maximum number of iterations between two improvements in a single neighborhood is l_max.

[0063] S312. If t < T, set t = t + 1 and i = 1, return to S34; otherwise, end the algorithm and output the zebra individual with the optimal fitness.

[0064] Preferably, design the following four neighborhood structures:

[0065] (1) Neighborhood structure N1:

[0066] For the processes on the critical path, randomly select one process and assign it to another machine; the processes on the critical path are the processes on the processing route with the maximum completion time where the processes on different machines are connected end to end; update the element value of the corresponding process in the machine sequence code according to the above operation, and the new element value is 1 - the original element value.

[0067] (2) Neighborhood structure N2:

[0068] Traverse the processes on the critical path in sequence. If two adjacent processes on the critical path are processed on the same machine and do not belong to the same workpiece, add these two processes to the pair of processes to be selected. Randomly select a pair of processes from the pair of processes to be selected and exchange the processing order of the two processes; update the element value of the corresponding process in the process sequence code according to the above operation. First, arrange the element values corresponding to the processes of each workpiece in the process sequence in ascending order, and exchange the element values at the corresponding positions of the two selected processes.

[0069] (3) Neighborhood structure N3:

[0070] Randomly select an index from the feature sequence code, link sequence code, machine sequence code, and process sequence code respectively, and change the element value at the index position. The new element value is a random number between [0, 1).

[0071] (4) Neighborhood structure N4:

[0072] For each machine, calculate the load rate of the machine. For the machine with the highest load among all machines, randomly select a process assigned to this machine, find all the alternative machines for this process and arrange them in ascending order of load rate, and reassign the process to the machine with the minimum load rate; the element value at the corresponding position of the process O in the machine sequence code is ij corresponding to Where random represents a random function and index represents process O ij The index of the machine with the smallest load factor in the list of available machines. N represents process O. ij The number of machines available.

[0073] An integrated process planning and scheduling collaborative optimization system based on a hybrid ZOA-VNS algorithm, comprising:

[0074] An acquisition module, used to acquire a set of workpieces to be processed, a set of machines, and available resources;

[0075] A construction module is used to construct a mixed integer linear programming model based on the workpiece set and the machine set and with minimization of maximum completion time as the optimization goal under the quantity restriction of the available resources;

[0076] The solution module is used to adopt the four-layer real number coding structure of feature-link-machine-process as the coding scheme, randomly generate all initial zebra individual positions as the initial zebra population, and adopt the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model, output the optimal zebra individual, and based on the corresponding decoding scheme, obtain the optimal integrated process planning and scheduling collaborative optimization solution.

[0077] A storage medium stores a computer program for integrated process planning and scheduling collaborative optimization based on a hybrid ZOA-VNS algorithm, wherein the computer program enables a computer to execute the integrated process planning and scheduling collaborative optimization method as described above.

[0078] An electronic device, comprising:

[0079] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the integrated process planning and scheduling collaborative optimization method as described above.

[0080] (III) Beneficial effects

[0081] The present invention provides an integrated process planning and scheduling collaborative optimization method, system, storage medium and electronic device based on a hybrid ZOA-VNS algorithm. Compared with the prior art, it has the following beneficial effects:

[0082] The present invention first obtains the set of workpieces to be processed, the set of machines and the available resources; secondly, under the limit of the number of available resources, a mixed integer linear programming model is constructed with the minimization of the maximum completion time as the optimization goal; then, a four-layer real number coding structure of feature-link-machine-process is used as the coding scheme, and all initial zebra individual positions are randomly generated as the initial zebra population, and the hybrid zebra optimization-variable neighborhood search algorithm is used to solve the model, output the optimal zebra individual, and finally obtain the optimal integrated process planning and scheduling collaborative optimization scheme based on the corresponding decoding scheme. The realistic factor of continuous resource limitation is introduced on the basis of the traditional integrated process planning and production scheduling problem, making the problem more practical and applicable. At the same time, the proposed hybrid zebra optimization-variable neighborhood search algorithm effectively solves the problem and achieves good optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0084] Figure 1 A block diagram of an integrated process planning and scheduling collaborative optimization method based on a hybrid ZOA-VNS algorithm provided in an embodiment of the present invention;

[0085] Figure 2 A network diagram of three different artifact instances provided for an embodiment of the present invention;

[0086] Figure 3 A sample diagram of a feature-link-machine-process four-layer real number coding structure provided in an embodiment of the present invention;

[0087] Figures 4 to 7 The following are sample diagrams of a feature sequence decoding solution, a link sequence decoding solution, a machine sequence decoding solution, and a process sequence decoding solution provided in the embodiments of the present invention respectively;

[0088] Figure 8 A flow chart of a hybrid zebra optimization-variable neighborhood search algorithm provided in an embodiment of the present invention.

[0089] Fig. 9 A problem instance scheduling Gantt chart provided for an embodiment of the present invention;

[0090] Fig.10 A resource consumption diagram of a problem example provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] The embodiments of the present application solve the technical problems of integrated process planning and workshop scheduling with continuous resource constraints by providing an integrated process planning and scheduling collaborative optimization method, system, storage medium and electronic device based on a hybrid ZOA-VNS algorithm.

[0093] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0094] In order to solve the integrated process planning and workshop scheduling problems with continuous resource constraints, the present invention designs a hybrid zebra optimization-variable neighborhood search algorithm (ZOA-VNS) with NP-hard properties for solving the main problem. The continuous resource constraint is that the resources consumed by each process at the same time cannot exceed the total amount of resources. The Zebra Optimization Algorithm (ZOA) is a meta-heuristic search algorithm proposed by Trojovsky et al. (2022). It mathematically models the zebra's following the pioneer zebra for foraging and the escape and counterattack strategies when facing predators as an optimization process, and iteratively optimizes by continuously updating the position of the zebra in the population. The variable neighborhood search algorithm (VNS) is an improved local search algorithm. It uses the neighborhood structure composed of different actions for alternating searches to achieve a good balance between centralization and decentralization.

[0095] Specifically: First, a MILP mathematical model is established, with the goal of minimizing the maximum completion time. To solve the problem, a new four-layer real integrated encoding and decoding scheme is designed for the problem based on the model, and a left-shift strategy based on resources and machine idle time is designed to embed it into the decoding process. At the same time, the overall framework of the hybrid zebra optimization-variable neighborhood search algorithm (ZOA-VNS) algorithm is proposed, and four different neighborhood structures are designed to embed them into the ZOA algorithm, including neighborhood structures that consider critical routes and machine load balancing, thereby enhancing the local search ability of the algorithm.

[0096] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0097] Embodiment 1:

[0098] like Figure 1 As shown, an embodiment of the present invention provides an integrated process planning and scheduling collaborative optimization method based on a hybrid ZOA-VNS algorithm, including:

[0099] S1. Obtain a set of workpieces to be processed, a set of machines, and available resources;

[0100] S2. Based on the set of workpieces and the set of machines, and under the quantity restriction of the available resources, a mixed integer linear programming model is constructed with minimizing the maximum completion time as the optimization goal;

[0101] S3. Adopt the feature-link-machine-process four-layer real number coding structure as the coding scheme, randomly generate all initial zebra individual positions as the initial zebra population, and use the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model, output the optimal zebra individual, and based on the corresponding decoding scheme, obtain the optimal integrated process planning and scheduling collaborative optimization scheme.

[0102] The embodiment of the present invention introduces the realistic factor of continuous resource limitation on the basis of the traditional integrated process planning and production scheduling problem, making the problem more practical and applicable. At the same time, the proposed hybrid zebra optimization-variable neighborhood search algorithm effectively solves the problem and achieves good optimization effect.

[0103] Next, we will introduce the steps of the above scheme in detail:

[0104] First, the problem description of the embodiment of the present invention is introduced:

[0105] The embodiment of the present invention studies the integrated process planning and shop scheduling problem considering continuous resource constraints as follows: This problem combines process planning and shop scheduling, aiming to simultaneously optimize the process plan and scheduling plan of a set of workpieces. Specifically, the problem involves a set of n workpieces, represented as {J1, J2, …, J n}, processed on m machines, represented by {M1,M2,…,M m Each workpiece has multiple machining features, represented by {F1, F2, …, F f}, each feature can choose a different processing link. The features of a workpiece can have different processing orders under the constraints of the feature priority relationship. Each process of each workpiece can be processed on different machines, with different processing times and resource consumption. The number of continuous resources owned by the factory at any time is limited, and the resources consumed by the processing process at any time cannot exceed the total amount of resources at this time. The goal is to simultaneously determine the process route, machine allocation and processing order of the workpiece, and minimize the completion time under the conditions of satisfying the workpiece process route and resource constraints.

[0106] In step S1 , a set of workpieces to be processed, a set of machines, and available resources are obtained.

[0107] This step obtains data such as the set of processed workpieces, the set of machines, and available resources. In order to facilitate the explanation of subsequent model construction, the parameter symbols and descriptions shown in Table 1 are further given:

[0108] Table 1

[0109]

[0110]

[0111] In step S2, based on the set of workpieces and the set of machines, under the quantity restriction of the available resources, a mixed integer linear programming model is constructed with minimizing the maximum completion time as the optimization goal;

[0112] Before building the model, it is necessary to add the basic assumptions of the problem as follows:

[0113] (1) All workpieces can be processed at time zero;

[0114] (2) Once each process of the workpiece is started, it cannot be interrupted;

[0115] (3) A machine can only process one workpiece at a time;

[0116] (4) The workpiece process can only be processed by one machine at a time;

[0117] (5) After one process of processing is completed, the workpiece can be immediately sent to the next machine for processing the next process.

[0118] On this basis, the mixed integer linear programming model constructed in this step includes:

[0119] minC max (1)

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] in,

[0133] Formula (1) is the objective function; min is the minimization function;

[0134] Constraint (2) means that for any workpiece, a process link must be selected for each machining feature;

[0135] Constraint (3) means that for any workpiece, if the process link of the processing feature where a certain process is located is not selected, then the process will not be selected;

[0136] Constraint (4) means that for any workpiece, if a process link of a certain processing feature is selected, then all the processes on this process link will be selected;

[0137] Constraint (5) indicates that when a process of a workpiece is selected, it needs to be assigned an optional processing machine, and if a process is not selected, no processing machine needs to be assigned;

[0138] Constraint (6) indicates that for any workpiece, the process priority relationship of any two processes selected from two processing features with a priority relationship must satisfy the priority relationship corresponding to its processing features;

[0139] Constraint (7) indicates that for any workpiece, the process priority relationship between two processes on any process link of any processing feature of the workpiece should satisfy the priority relationship corresponding to the processes;

[0140] Constraints (8) and (9) indicate that when the process priority relationship is determined, different processes of the same workpiece should have a definite processing order;

[0141] Constraints (10) and (11) indicate that when the process priority relationship is determined, the processes processed on the same machine should have a definite processing order;

[0142] Constraint (12) indicates that the cumulative resource consumption of all ongoing processes at any time cannot exceed the maximum total available resources at the same time;

[0143] Constraint (13) specifies the maximum completion time.

[0144] In step S3, a four-layer real number coding structure of feature-link-machine-process is adopted as the coding scheme, and all initial zebra individual positions are randomly generated as the initial zebra population, so as to adopt a hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model, output the optimal zebra individual, and obtain the optimal integrated process planning and scheduling collaborative optimization scheme based on the corresponding decoding scheme.

[0145] Before introducing the hybrid zebra optimization-variable neighborhood search algorithm proposed in the embodiment of the present invention, the following encoding and decoding scheme is first innovatively proposed in this step:

[0146] (1) Coding scheme

[0147] As mentioned above, the four-layer real number coding structure of feature-link-machine-process is used as the coding scheme for the problem. Each layer of coding is represented by a vector, vector π 1 Represents the feature sequence encoding, vector π 2 represents the link sequence encoding, vector π 3 Represents the machine sequence encoding, vector π 4 Represents the process sequence code. The elements in the vector are random numbers between 0 and 1 that obey a uniform distribution. The length of the feature sequence code and link sequence code is equal to the sum of the number of processing features of all workpieces, and the length of the machine sequence code and process sequence code is equal to the sum of the number of processes of all workpieces.

[0148] For example, Table 2 and Figures 2-3 Together we give a sample encoding scheme:

[0149] Table 2

[0150]

[0151] (2) Decoding Scheme

[0152] The decoding scheme here includes five stages: feature sequence decoding, link sequence decoding, machine sequence decoding, process sequence decoding, and determining the processing start time and end time of each process. It should be noted that the fifth stage specifically uses the decoding rules embedded in the resource-based left shift strategy to maximize machine utilization and shorten the maximum completion time, as follows:

[0153] The first stage performs feature sequence decoding: for the feature sequence encoding vector π 1The number at the jth position of the ith workpiece in the vector is taken as the feature set to be assigned, and all the features of workpiece i without prior features or whose prior features have been assigned but have not been assigned themselves are taken as the feature set to be assigned. The number at the jth position of the ith workpiece in the feature sequence encoding vector is multiplied by the length of the feature set to be assigned and rounded down to obtain the feature index to be assigned. According to the index, the corresponding feature in the feature set to be assigned is selected and added to the decoded feature sequence.

[0154] For example, Figure 4 A sample signature sequence decoding scheme is given.

[0155] The second stage performs link sequence decoding: for the link sequence encoding vector π 2 The number at the jth position of the ith workpiece in the figure, find the feature F of workpiece i j The optional links are taken as the link set to be allocated, the number at the jth position of the i-th artifact in the link sequence encoding vector is multiplied by the length of the link set to be allocated and rounded down to obtain the link index to be allocated, and the corresponding link in the link set to be allocated is selected according to the index and added to the decoded link sequence.

[0156] For example, Figure 5 A sample link sequence decoding scheme is given.

[0157] It can be understood that, according to the decoding of the first stage and the second stage, the processing sequence of each workpiece can be determined. In this example, the processing sequence of workpiece 1 is O 11 -O 12 -O 13 , the processing step of workpiece 2 is O 21 -O 25 -O 23 , the processing step of workpiece 3 is O 31 -O 34 -O 32 -O 37 .

[0158] The third stage performs machine sequence decoding: for the machine sequence encoding vector π 3 The number at the jth position of the ith workpiece in process O ij The optional machines are taken as the machine set to be allocated, the number at the jth position of the ith workpiece in the machine encoding vector is multiplied by the length of the machine set to be allocated and rounded down to get the machine index to be allocated, and the corresponding machine in the machine set to be allocated is selected according to the index and added to the decoded machine sequence.

[0159] For example, Figure 6 A sample machine sequence decoding scheme is given.

[0160] It can be understood that, according to the decoding of the first three stages, the process route of each workpiece can be determined, that is, the processing steps of each workpiece and the processing machine of each step. 11 (M1)-O 12 (M4)-O 13 (M3), the process route of workpiece 2 is O 21 (M1)-O 25 (M4)-O 23 (M5), the process route of workpiece 3 is O 31 (M2)-O 34 (M2)-O 32 (M1)-O 37 (M4).

[0161] The fourth stage performs process sequence decoding: for the workpiece sequence encoding vector π 4 The jth process O of the i-th workpiece ij , if it is not in the process route of the ith workpiece, then remove the number at the jth position of the ith workpiece from the process sequence encoding vector, and arrange the encoding vectors after elimination in ascending order. If the number in the arranged encoding vector belongs to the number of the ith workpiece in the encoding vector before arrangement, then add the number i to the decoded process sequence. The number of numbers i in the decoded process sequence is equal to the number of processes in the process route of the ith workpiece. The order of numbers i in the decoded process sequence corresponds to the order of processes in the process route of the ith workpiece.

[0162] For example, Figure 7 A sample process sequence decoding scheme is given.

[0163] It can be understood that according to the decoding of the fourth stage, process O can be determined. ij In this example, the processing order of all processes is O 21 -O 25 -O 11 -O 12 -O 31 -O 34 -O 32 -O 37 -O 23 -O 13 .

[0164] After obtaining the processing order of all processes and the assigned machines, the fifth stage determines the processing start time and end time of each process according to the decoding rules embedded in the resource-based left shift strategy. The basic process is as follows:

[0165] S100, for process O ij , find the process route of workpiece i in Oij The completion time OT of the previous process;

[0166] S200, for process O ij Assigned machine M i , find machine M i The completion time MT of the last assigned process and the machine M before MT i Free time in It's Machine M i The idle start time of the i-th idle time period, It's Machine M i The idle end time of the i-th idle time period;

[0167] S300: If there is an idle time period satisfy Then process O ij Earliest possible start time Where PT is process O ij On machine M i Processing time on

[0168] S400, judge Is there a sub-time period of length PT within Make the remaining resource available in this sub-time period greater than or equal to process O ij The resource consumption of process O ij Actual processing start time in They are the start and end time of the sub-time period with a length of PT.

[0169] Based on the above encoding and decoding scheme, Figure 8 As shown in the figure, the hybrid zebra optimization-variable neighborhood search algorithm process proposed in this step is as follows:

[0170] S31, initializing the parameters of the hybrid zebra optimization-variable neighborhood search algorithm, including the number of zebras N, the maximum number of iterations T, the number of neighborhood structures k_max, and the maximum number of iterations l_max between two improvements of a single neighborhood;

[0171] S32, randomly generate all initial zebra individual positions as the initial zebra population, each zebra individual is [π 1 ,π 2 ,π 3 ,π 4 ];

[0172] S33: let i=1, t=1;

[0173] S34. Calculate the fitness values of all zebra individuals in the population and sort them. Select the individual with the optimal fitness as the initial position of the pioneer zebra PZ, where the fitness value is the makespan.

[0174] S35. Simulate the foraging behavior of zebras. The $i$-th zebra in the population moves towards the pioneer zebra, and the position of the $i$-th zebra is updated using the formula .

[0175] S36. For the updated position of the $i$-th zebra, adopt the elitist retention strategy, that is, if the fitness of the updated $i$-th zebra is less than the fitness of the $i$-th zebra before update, then retain the updated position; otherwise, the position of the $i$-th zebra remains unchanged.

[0176] S37. Randomly generate the position of the predator AZ. Simulate the defense strategy adopted by zebras when facing predators. Assume that zebras will choose two strategies: running away nearby or counterattacking the predator, and the probabilities of choosing these two strategies are equal. Let $P$ s = rand(0, 1);

[0177] S38. If $P$ S ≤ 0.5, then the zebra chooses the first strategy, and the position of the $i$-th zebra in the population is updated using the formula ; otherwise, the zebra chooses the second strategy, and the position of the $i$-th zebra in the population is updated using the formula .

[0178] S39. For the updated position of the $i$-th zebra, adopt the elitist retention strategy, that is, if the fitness of the updated $i$-th zebra is less than the fitness of the $i$-th zebra before update, then retain the updated position; otherwise, the position of the $i$-th zebra remains unchanged.

[0179] S310. If $i < N$, let $i = i + 1$, return to S34; otherwise, enter S311.

[0180] S311. Obtain the individuals in the zebra population with the top 10% fitness rankings as elite zebras. Use $k_{max}$ neighborhood structures to perform variable neighborhood search on the positions of the elite zebras, and the maximum number of iterations between two improvements in a single neighborhood is $l_{max}$.

[0181] S312. If $t < T$, let $t = t + 1$, $i = 1$, return to S34; otherwise, the algorithm ends and outputs the zebra individual with the optimal fitness.

[0182] Finally, based on the above decoding scheme, by decoding the optimal zebra individual, the optimal integrated process planning and scheduling collaborative optimization scheme can be obtained.

[0183] Exemplarily, Figures 9-10A problem instance scheduling Gantt chart and resource consumption chart are given respectively.

[0184] In an optional implementation manner, the embodiment of the present invention designs the following four neighborhood structures:

[0185] (1) Neighborhood structure N1:

[0186] For the process on the critical path, a process is randomly selected and assigned to another machine; the process on the critical path is the process on the processing route with the maximum completion time that is connected end to end with processes on different machines; according to the above operation, the element value of the corresponding process in the machine sequence code is updated, and the new element value is 1-the original element value;

[0187] (2) Neighborhood structure N2:

[0188] Traverse the processes on the critical path in sequence. If two adjacent processes on the critical path are processed by the same machine and do not belong to the same workpiece, add the two processes to the process pair to be selected, randomly select a pair of processes from the process pair to be selected, and exchange the processing order of the two processes; update the element value of the corresponding process in the process sequence code according to the above operation, first arrange the element value corresponding to the process of each workpiece in the process sequence in ascending order, and exchange the element value of the corresponding position of the two selected processes;

[0189] (3) Neighborhood structure N3:

[0190] Randomly select an index from the feature sequence code, link sequence code, machine sequence code and process sequence code respectively, change the element value at the index position, and the new element value is a random number between [0,1);

[0191] (4) Neighborhood structure N4:

[0192] For each machine, calculate the load rate of the machine. For the machine with the highest load among all machines, randomly select a process assigned to the machine, find all the selectable machines for the process and arrange them in ascending order of load rate, and reallocate the machine with the smallest load rate to the process; process O in the machine sequence code ij The element value at the corresponding position is Where random represents a random function and index represents process O ij The index of the machine with the smallest load factor in the list of available machines. N represents process O. ij The number of machines available.

[0193] So far, the embodiment of the present invention has completed the entire process of the integrated process planning and scheduling collaborative optimization method based on the hybrid ZOA-VNS algorithm.

[0194] Embodiment 2:

[0195] The embodiment of the present invention provides an integrated process planning and scheduling collaborative optimization system based on a hybrid ZOA-VNS algorithm, including:

[0196] An acquisition module, used to acquire a set of workpieces to be processed, a set of machines, and available resources;

[0197] A construction module is used to construct a mixed integer linear programming model based on the workpiece set and the machine set and with minimization of maximum completion time as the optimization goal under the quantity restriction of the available resources;

[0198] The solution module is used to adopt the four-layer real number coding structure of feature-link-machine-process as the coding scheme, randomly generate all initial zebra individual positions as the initial zebra population, and adopt the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model, output the optimal zebra individual, and based on the corresponding decoding scheme, obtain the optimal integrated process planning and scheduling collaborative optimization solution.

[0199] Embodiment 3:

[0200] An embodiment of the present invention provides a storage medium storing a computer program for integrated process planning and scheduling collaborative optimization based on a hybrid ZOA-VNS algorithm, wherein the computer program enables a computer to execute the integrated process planning and scheduling collaborative optimization method as described in Example 1.

[0201] Embodiment 4:

[0202] An embodiment of the present invention provides an electronic device, including:

[0203] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the integrated process planning and scheduling collaborative optimization method as described in Example 1.

[0204] It can be understood that the integrated process planning and scheduling collaborative optimization system based on the hybrid ZOA-VNS algorithm, storage medium and electronic device provided in the embodiments of the present invention correspond to the integrated process planning and scheduling collaborative optimization method based on the hybrid ZOA-VNS algorithm provided in the embodiments of the present invention. The explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the integrated process planning and scheduling collaborative optimization method, and will not be repeated here.

[0205] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0206] 1. The embodiment of the present invention introduces the realistic factor of continuous resource limitation on the basis of the traditional integrated process planning and production scheduling problem, making the problem more practical and applicable. At the same time, the proposed hybrid zebra optimization-variable neighborhood search algorithm effectively solves the problem and achieves good optimization effect.

[0207] 2. The embodiment of the present invention aims to minimize the maximum completion time and studies the integrated process planning and workshop scheduling problem considering continuous resource constraints. An integrated encoding and decoding solution is designed for this problem. A resource-based left-shift strategy is embedded in the decoding stage to maximize machine utilization and shorten the maximum completion time. A hybrid ZOA-VNS algorithm is proposed to effectively solve the above problem.

[0208] 3. The hybrid ZOA-VNS algorithm proposed in the present invention has excellent global search and local search capabilities. It is better than existing popular algorithms in solving large-scale integrated process planning and workshop scheduling problems, and is also highly applicable in solving other scheduling problems with NP-hard properties.

[0209] 4. The present invention introduces the realistic factor of continuous resource limitation on the basis of traditional integrated process planning and workshop scheduling problems, making the problem more practical and applicable. Furthermore, the problem can continue to add other constraints to expand into other problems, which has strong theoretical research significance.

[0210] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0211] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated process planning and scheduling collaborative optimization method based on a hybrid ZOA-VNS algorithm, characterized in that: Including: Obtain a set of workpieces to be processed, a set of machines, and available resources; Based on the workpiece set and the machine set, construct a mixed-integer linear programming model with the goal of minimizing the makespan under the quantity limit of the available resources; Adopt a four-layer real-number coding structure of feature-link-machine-operation as the coding scheme, randomly generate the positions of all initial zebra individuals as the initial zebra population, use the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed-integer linear programming model, output the optimal zebra individual, and obtain the optimal integrated process planning and scheduling collaborative optimization scheme based on the corresponding decoding scheme.

2. The integrated process planning and scheduling collaborative optimization method according to claim 1, characterized in that: The mixed-integer linear programming model includes: minC max (1) Where J represents the set of artifacts; O i represents the process set of workpiece i; F i represents the set of machining features of workpiece i; M represents the set of machines; i,i′ represents workpiece i,i′,1≤i,i′≤|J|; j,j′ represents process j,j′,1≤j,j′≤|O i |; f,f′ represents the feature f,f′,1≤f,f′≤|F i |; k, k′ represents machine k, k′, 1≤k, k′≤|M|; l, l′ represents process link l, l′; Formula (1) is the objective function; min is the minimization function, C max represents the maximum completion time; Constraint (2) means that for any workpiece, each processing feature must select a process link; F ifl is a decision variable, which is 1 if the lth process link of the processing feature f of workpiece i is selected, otherwise it is 0; Constraint (3) means that for any workpiece, if the process link of the processing feature where a certain process is located is not selected, then the process will not be selected; X ij is a decision variable, if o ij If selected, it is 1, otherwise it is 0; N is a sufficiently large positive number; E ijfl If o ij If it is on the lth process link of the processing feature f, it is 1, otherwise it is 0; Constraint (4) indicates that for any workpiece, if a certain process link of a certain processing feature is selected, then all operations on this process link will be selected; Constraint (5) indicates that when a process of a workpiece is selected, a selectable processing machine needs to be assigned to it, and if a process is not selected, no processing machine needs to be assigned; Z ijk is a decision variable, if o ij If it is processed on machine k, it is 1, otherwise it is 0; Constraint (6) indicates that for any workpiece, the process priority relationship of any two processes selected from two processing features with a priority relationship must satisfy the priority relationship corresponding to its processing features; E ij′f′l If o ij′ If it is on the lth process link of the processing feature f′, it is 1, otherwise it is 0; ij′ is a decision variable, if o ij′ If selected, it is 1, otherwise it is 0; iji′j′ is a decision variable, if o ij In o i′j′ If it is pre-processed, it is 1, otherwise it is 0; P iff′ Indicates that if the machining feature f of workpiece i is the immediate predecessor of machining feature f′, it is 1, otherwise it is 0; Constraint (7) indicates that for any workpiece, the process priority relationship between two processes on any process link of any processing feature of the workpiece should satisfy the priority relationship of the corresponding processes; E ij′fl If o ij′ If it is on the lth process link of the processing feature f, it is 1, otherwise it is 0; Y ijij′ is a decision variable, if o ij In o ij′ If it is pre-processed, it is 1; It means that if the process j on the machining feature f of workpiece i is the immediate predecessor of j′, it is 1, otherwise it is 0; Constraints (8) and (9) indicate that when the process priority relationship is determined, different processes of the same workpiece should have a certain processing order; ij , o ij′ They represent the process j and j′ of workpiece i respectively; S ij , S ij′ Respectively represent o ij , o ij′ Processing start time; Z ij′k is a decision variable, if o ij′ If it is processed on machine k, it is 1, otherwise it is 0; T ijk , T ij′k Respectively represent o ij , o ij′ Processing time on machine k; Constraints (10) and (11) indicate that when the process priority relationship is determined, the processes processed on the same machine should have a certain processing order; S i′j′ Indicates o i′j′ Processing start time; Z i′j′k is a decision variable, if o i′j′ If it is processed on machine k, it is 1, otherwise it is 0; T ijk , T i′j′k Respectively represent o ij , o i′j′ Processing time on machine k; Constraint (12) indicates that the cumulative resource consumption of all processing steps at any time cannot exceed the maximum total available resources at the same time; i (t) represents the set of processes being processed at time t; R ijk Indicates o ij Resource consumption on machine k; R max Indicates the maximum available amount of resources at the same time; Constraint (13) clarifies the definition of the makespan.

3. The integrated process planning and scheduling collaborative optimization method according to claim 2, characterized in that: Each layer of the coding scheme is represented by a single vector, vector π 1 Represents the feature sequence encoding, vector π 2 represents the link sequence encoding, vector π 3 Represents the machine sequence encoding, vector π 4 Represents the process sequence code; the elements in the vector are random numbers between 0 and 1 that obey a uniform distribution, where the length of the feature sequence code and the link sequence code is equal to the sum of the number of processing features of all workpieces, and the length of the machine sequence code and the process sequence code is equal to the sum of the number of processes of all workpieces.

4. The integrated process planning and scheduling collaborative optimization method according to claim 3, characterized in that: The corresponding decoding scheme includes: The first stage performs feature sequence decoding: for the feature sequence encoding vector π 1 The number at the jth position of the i-th workpiece in the feature sequence encoding vector is used as the feature set to be assigned, and the number at the jth position of the i-th workpiece in the feature sequence encoding vector is multiplied by the length of the feature set to be assigned and rounded down to obtain the feature index to be assigned. According to the index, the corresponding feature in the feature set to be assigned is selected and added to the decoded feature sequence; The second stage performs link sequence decoding: for the link sequence encoding vector π 2 The number at the jth position of the ith workpiece in the figure, find the feature F of workpiece i j The optional links are taken as the link set to be allocated, the number at the jth position of the i-th workpiece in the link sequence encoding vector is multiplied by the length of the link set to be allocated and rounded down to obtain the link index to be allocated, and the corresponding link in the link set to be allocated is selected according to the index and added to the decoded link sequence; The third stage performs machine sequence decoding: for the machine sequence encoding vector π 3 The number at the jth position of the ith workpiece in process O ij The optional machines are taken as the machine set to be allocated, the number at the jth position of the i-th workpiece in the machine encoding vector is multiplied by the length of the machine set to be allocated and rounded down to get the machine index to be allocated, and the corresponding machine in the machine set to be allocated is selected according to the index and added to the decoded machine sequence; The fourth stage performs process sequence decoding: for the workpiece sequence encoding vector π 4 The jth process O of the i-th workpiece ij , if it is not in the process route of the i-th workpiece, then remove the number at the j-th position of the i-th workpiece from the process sequence encoding vector, and arrange the encoding vectors after the removal in ascending order. If the number in the arranged encoding vector belongs to the number of the i-th workpiece in the encoding vector before arrangement, then add the number i to the decoded process sequence. The number of numbers i in the decoded process sequence is equal to the number of processes in the process route of the i-th workpiece, and the order of numbers i in the decoded process sequence corresponds to the order of processes in the process route of the i-th workpiece. In the fifth stage, according to the decoding rule embedded with the resource-based left shift strategy, determine the processing start time and end time of each operation, including: S100, for process O ij , find the process route of workpiece i in O ij The completion time OT of the previous process; S200, for process O ij Assigned machine M i , find machine M i The completion time MT of the last assigned process and the machine M before MT i Free time in It's Machine M i The idle start time of the i-th idle time period, It's Machine M i The idle end time of the i-th idle time period; S300: If there is an idle time period satisfy Then process O ij Earliest possible start time Where PT is process O ij On machine M i Processing time on S400, judge Is there a sub-time period of length PT within Make the remaining resource available in this sub-time period greater than or equal to process O ij The resource consumption of process O ij Actual processing start time in They are the start and end time of the sub-time period with a length of PT.

5. The integrated process planning and scheduling collaborative optimization method according to claim 4, characterized in that: The step of using the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed-integer linear programming model and output the optimal zebra individual; includes: S31. Initialize the parameters of the hybrid zebra optimization-variable neighborhood search algorithm, including the number of zebras N, the maximum number of iterations T, the number of neighborhood structures k_max, and the maximum number of iterations l_max between two improvements in a single neighborhood; S32, randomly generate all initial zebra individual positions as the initial zebra population, each zebra individual is [π 1 ,π 2 ,π 3 ,π 4 ]; S33: Let i = 1, t = 1; S34. Calculate the fitness values of all zebra individuals in the population and sort them, select the individual with the optimal fitness as the initial position of the pioneer zebra PZ, where the fitness value is the makespan; S35, simulate the foraging behavior of zebras. The i-th zebra in the population moves towards the pioneer zebra. The position of the i-th zebra is calculated using the formula renew; S36. For the updated position of the i-th zebra, adopt the elitist retention strategy, that is, if the fitness of the updated i-th zebra is less than the fitness of the i-th zebra before update, then retain the updated position, otherwise the position of the i-th zebra remains unchanged; S37, randomly generate the position of the predator AZ, simulate the defense strategy taken by the zebra when facing the predator, assume that the zebra will choose to escape to the nearby or fight back against the predator, and the probability of the two choices is equal. Let P S =rand(0,1); S38, if P S ≤0.5, the zebra chooses the first strategy, and the i-th zebra position in the population uses the formula Update, otherwise the zebra chooses the second strategy, and the i-th zebra position in the population adopts the formula renew; S39. For the updated position of the i-th zebra, adopt the elitist retention strategy, that is, if the fitness of the updated i-th zebra is less than the fitness of the i-th zebra before update, then retain the updated position, otherwise the position of the i-th zebra remains unchanged; S310. If i < N, let i = i + 1, return to S34, otherwise enter S311; S311. Obtain the individuals with the top 10% fitness rankings in the zebra population as elite zebras, use k_max neighborhood structures to perform variable neighborhood search on the positions of the elite zebras, and the maximum number of iterations between two improvements in a single neighborhood is l_max; S312. If t < T, let t = t + 1, i = 1, return to S34, otherwise the algorithm ends and outputs the zebra individual with the optimal fitness.

6. The integrated process planning and scheduling collaborative optimization method according to claim 5, characterized in that: Design the following four neighborhood structures: (1) Neighborhood structure N1: For the process on the critical path, a process is randomly selected and assigned to another machine; the process on the critical path is the process on the processing route with the maximum completion time that is connected end to end with processes on different machines; according to the above operation, the element value of the corresponding process in the machine sequence code is updated, and the new element value is 1-the original element value; (2) Neighborhood structure N2: Traverse the processes on the critical path in sequence. If two adjacent processes on the critical path are processed by the same machine and do not belong to the same workpiece, add the two processes to the process pair to be selected, randomly select a pair of processes from the process pair to be selected, and exchange the processing order of the two processes; update the element value of the corresponding process in the process sequence code according to the above operation, first arrange the element value corresponding to the process of each workpiece in the process sequence in ascending order, and exchange the element value of the corresponding position of the two selected processes; (3) Neighborhood structure N3: Randomly select an index from the feature sequence code, link sequence code, machine sequence code and process sequence code respectively, change the element value at the index position, and the new element value is a random number between [0,1); (4) Neighborhood structure N4: For each machine, calculate the load rate of the machine. For the machine with the highest load among all machines, randomly select a process assigned to the machine, find all the selectable machines for the process and arrange them in ascending order of load rate, and reallocate the machine with the smallest load rate to the process; process O in the machine sequence code ij The element value at the corresponding position is Where random represents a random function and index represents process O ij The index of the machine with the smallest load factor in the list of available machines. N represents process O. ij The number of machines available.

7. An integrated process planning and scheduling collaborative optimization system based on a hybrid ZOA-VNS algorithm, characterized in that: include: An acquisition module, used to acquire a set of workpieces to be processed, a set of machines, and available resources; A construction module is used to construct a mixed integer linear programming model based on the workpiece set and the machine set and with minimization of maximum completion time as the optimization goal under the quantity restriction of the available resources; The solution module is used to adopt the four-layer real number coding structure of feature-link-machine-process as the coding scheme, randomly generate all initial zebra individual positions as the initial zebra population, and adopt the hybrid zebra optimization-variable neighborhood search algorithm to solve the mixed integer linear programming model, output the optimal zebra individual, and based on the corresponding decoding scheme, obtain the optimal integrated process planning and scheduling collaborative optimization solution.

8. A storage medium, characterized in that: It stores a computer program for integrated process planning and scheduling collaborative optimization based on a hybrid ZOA-VNS algorithm, wherein the computer program enables a computer to execute the integrated process planning and scheduling collaborative optimization method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including a method for executing the integrated process planning and scheduling collaborative optimization method as described in any one of claims 1 to 6.

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