High-end equipment production rescheduling optimization method under condition of considering machine fault

Abstract

The invention provides a high-end equipment production rescheduling optimization method under the condition of considering machine faults, and relates to the technical field of task scheduling. According to the method, input parameters are set based on workpiece data, machine data and rescheduling data in the production process of a high-end equipment workshop; after the machine fault occurs, the workpiece set, the machine set and the workpiece machining starting time are updated; and based on the updated input parameters, performing complete rescheduling on the workpieces by adopting a hybrid particle swarm algorithm based on neighborhood search to obtain a global optimal solution, and decoding to obtain the corresponding number of started standby machines, a production rescheduling scheme, rescheduling production cost, rescheduling standby machine starting cost and rescheduling conflict cost. The optimization targets of rescheduling processing time and cost are comprehensively considered, and a manufacturing interruption flexible workshop workpiece sequence scheduling model is constructed. The provided improved particle swarm algorithm can effectively reduce the production and manufacturing cost and enhance the ability of enterprises to cope with the sudden production interruption problem.

Description

Technical Field

[0001] The present invention relates to the field of task scheduling technology, and in particular to a method, system, storage medium and electronic device for optimizing the rescheduling of high-end equipment production under the condition of machine failure. Background Art

[0002] The production environment of high-end equipment is generally complex, and various factors such as natural disasters, emergency orders and equipment failures may cause production interruptions. Interruptions will cause the original scheduling plan of the workshop to no longer be applicable, forcing the company to readjust the scheduling arrangements.

[0003] When high-end equipment manufacturing companies carry out production operations, equipment failures often occur in the production workshop. The downtime caused by equipment failure will cause the workpieces originally scheduled to be produced on the equipment to be unable to continue processing, and the remaining normal machines may not be able to complete the workpieces within the delivery period, affecting the company's production progress and even causing losses to the company. In order to avoid the above situation as much as possible, companies usually reschedule production plans. Companies make comprehensive decisions on the number of machines invested and the order of processing workpieces to ensure that the workpieces are processed on schedule and reduce the total cost of rescheduling. At present, there are relatively few studies on the rescheduling problem of high-end equipment production due to equipment failure. Given the production needs of manufacturing companies for high efficiency and low energy consumption, the study of this rescheduling problem has important theoretical and practical value. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a method, system, storage medium and electronic device for optimizing the rescheduling of high-end equipment production in the event of machine failure, which solves the technical problem of rescheduling a workshop with spare machines.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A high-end equipment production rescheduling optimization method considering machine failures, comprising:

[0009] Based on the workpiece data, machine data and rescheduling data in the production process of high-end equipment workshops, input parameters are set, including at least workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time;

[0010] After a machine failure occurs, update the workpiece set, machine set and workpiece processing start time;

[0011] Based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0012] Preferably, the input parameters specifically include:

[0013] The workpiece set J = [J1, J2, ..., J n ];

[0014] A workpiece set consisting of l unprocessed workpieces

[0015] Machine set MM = {MM1, MM2, ..., MM u , B.M. u+1 , B.M. u+2 , ..., B.M. p}, where MM j is the machine for the initial production operation, u is the number of machines for the initial production operation, BM j is the newly enabled standby machine for rescheduling, and p is the number of newly enabled standby machines for rescheduling;

[0016] Assume CM j China Machine CM q Failure occurs, q∈[1, 2, ..., u];

[0017] CM q The time interval t from the start of processing to the occurrence of failure follows an exponential distribution, and the faulty machine CM q The maintenance time d follows an exponential distribution;

[0018] Machine CM as of time t j The set of processed workpieces F j , the set F of workpieces processed on all machines as of time t;

[0019] Non-faulty machine CM at time t j The workpiece set Z is being processed j , at time t, all non-faulty machines are processing the workpiece set Z;

[0020] P ij For workpiece J i On the machine MM j The time spent on processing, C i Represents the initial scheduling job J i Processing completion time, Represents the rescheduling job J i Processing completion time; Sj MM for the rescheduled machine j The start time of the first workpiece processing;

[0021] c ij For workpiece J i On the machine MM j The processing cost per unit time;

[0022] ε BM Start-up cost for each spare machine;

[0023] c0 is the conflict cost per unit time;

[0024] a j is a binary decision variable. If the backup machine BM is enabled j , then a j The value is 1, otherwise it is 0;

[0025] x ij is a binary decision variable. If workpiece J i Select the machine MM j If processing is performed on ij The value is 1, otherwise it is 0;

[0026] y jki is a binary decision variable, if That is, workpiece J k Workpiece J i Process first, then y jki The value is 1 if yes, otherwise 0.

[0027] Preferably, based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces, obtain the global optimal solution, and decode to obtain the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost; including:

[0028] S31, setting execution parameters, including at least: the maximum number of iterations Wl1 of the hybrid particle swarm algorithm based on neighborhood search, the population size Popsize of the algorithm, and the maximum number of iterations Wl2 of the local search;

[0029] S32, using a real number-based encoding method to generate an initial population X0, generating a code with a length of l, assigning workpieces to machines, using the weighted rescheduling total goal as the individual fitness value, and obtaining and recording the current optimal solution;

[0030] S33, updating the position of each individual particle based on the position update formula of the particle swarm optimization algorithm PSO;

[0031] S34, calculate the fitness value of each particle, the best pbest of each individual in the population is the current position, the particle individual with the best scheduling target in the population is the group's best position gbest, the group's best particle individual gbest is recorded as π, and the neighborhood structure NS is selected based on the roulette method k , and based on the neighborhood structure NS k Generate a neighborhood solution π′ of π

[0032] S35, based on a two-stage adaptive neighborhood search strategy, using the neighborhood structure NS k Perform local search on solution π′ to obtain the local optimal solution π″, compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm, if π″ is better than π, let π=π″, and record the number of successful searches for the local optimal solution by the neighborhood structure, and update the neighborhood structure NS k , determine whether wl2<Wl2 is true, if so, assign wl2+1 to wl2 and return to step S34, otherwise execute step S36;

[0033] S36, compare the optimal solution π of the variable neighborhood search algorithm with the worst individual in the initial population X0. If π is better than the worst individual in the worst initial population, replace the solution with π and compare π with the global optimal solution π best Compare, if π is better than πbes t , then let π best =π;

[0034] S37, determine whether wl1≤Wl1 holds. If so, assign wl1+1 to wl1, and use the updated population as the input for the next iteration, and return to step S33. Otherwise, output the global optimal solution π best , and decode to obtain the corresponding number of standby machines started, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0035] Preferably, the coding rules of the hybrid particle swarm algorithm based on neighborhood search include:

[0036] The length of the code LS is l, and the code LS means the assignment order of the workpiece on the initial operation machine or the backup machine. The real number in the code LS contains an integer part and a decimal part, where the integer part The number of the processing machine, decimal part Determine the processing order of the workpieces, the workpiece with a smaller decimal part has a closer processing position, wherein the real number encoding values ​​corresponding to different workpieces are not equal;

[0037] The decoding rules of the hybrid particle swarm algorithm based on neighborhood search include:

[0038] S101. The processing order of the workpiece on each machine in the initial scheduling is known. represents the initial schedule to produce the cth job on the jth machine, represents the processing time of the cth job produced by the jth machine in the initial schedule, F j is the machine CM at the end time t j The collection of processed workpieces, Z j is the non-faulty machine CM at time t j The set of workpieces being processed, S j Represents the machine MM after rescheduling j The time when the first workpiece processing of the machine started, the CM of the faulty machine j The estimated maintenance time is d;

[0039] S 102, let j=1;

[0040] S103, let m = 1, for the processing machine CM j , judging machine CM j Whether a fault occurs, if it is a faulty machine, execute step S104, otherwise, execute step S106;

[0041] S104, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute step S104, otherwise execute step S105;

[0042] S105, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , at this time S j = t + d, execute step S108, otherwise, the workpiece being processed on the faulty machine will be reassigned. j =t+d, execute step S108;

[0043] S106, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute S106, otherwise execute step S107;

[0044] S107, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , Machine CM j The workpiece set Z is being processed j is an empty set, then S j = t, execute step S108, otherwise output machine CM j The workpiece set Z is being processed j ,at this time Execute step S108;

[0045] S108, when j≠u, assign j+1 to j, and return to step S103; when j=u, execute step S109;

[0046] S109, the set F of workpieces processed by all machines as of time t, and the set Z of workpieces being processed by all non-faulty machines at time t, then the set F=F1∪...∪F j ∪...∪F u , j = [1, 2, ..., u], set Z = Z1∪...∪Z j ∪...∪Z u , j=[1, 2, ..., u] and j≠q, execute step S110;

[0047] S110, generating a to-be-scheduled workpiece set J from the initial workpiece set J, the set F and the set Z R , where the set J R = J - (F ∪ Z);

[0048] S111, order the standby machine BM j The first workpiece processing start time S j = t, execute step S112;

[0049] S112, respectively generate three components of the overall rescheduling target:

[0050] (1) Rescheduling production cost f1:

[0051]

[0052] Among them, represents the workpiece On the machine MM j The unit time processing cost, P ij Representation of workpiece On the machine MM j The processing time on

[0053]

[0054] (2) Energy consumption f2 for rescheduling standby machines:

[0055]

[0056] in

[0057] (3) Rescheduling deviation conflict cost f3:

[0058]

[0059] Represents a rescheduling artifact Completion time, C i Represents the initial scheduling artifact Completion time,

[0060]

[0061] y jki is a decision variable, if δ k -[δ k 」<δ i -[δ i "That is, the machine MM j Loading workpiece Workpiece Process first, then y jki The value is 1, otherwise it is 0. i The value is known.

[0062] Preferably, the process of obtaining the weighted total rescheduling target as the individual fitness value in S32 is as follows:

[0063] Get the optimal solution f of each particle 1max 、f 2max and f 3max ;

[0064] Weighted calculation of population fitness value:

[0065] f(x)=λ1f 1max +λ2f 2max +λ3f 3max

[0066] Among them, f(x) is the fitness function, λ1, λ2, λ3 are the weights of production cost, rescheduling standby machine startup cost and rescheduling deviation conflict cost, and λ1+λ2+λ3=1.

[0067] Preferably, the position update formula of the particle swarm optimization algorithm PSO in S33 updates the position of each individual particle; including:

[0068]

[0069]

[0070] in, are the velocities of the mth individual particle in the kth and k+1th iterations respectively; is the best historical position of the mth particle; gbest k is the global optimal position; are the positions of the mth particle in the kth and k+1th iterations respectively; ω represents the inertia coefficient; c1 and c2 are acceleration constants; r1 and r2 are random numbers.

[0071] Preferably, the neighborhood structure NS is used in S35 k Perform local search on the solution π′ to obtain the local optimal solution π″, compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm, if π″ is better than π, let π=π″; including:

[0072] S2501, neighborhood structure NS of variable neighborhood search algorithm k , a two-stage adaptive neighborhood search method is used for optimization in the local search process of the variable neighborhood search algorithm:

[0073] S2502: In the first stage, probability initialization is performed to record the number of times each neighborhood structure successfully searches for the local optimal solution N i , update the selection probability of each neighborhood according to the following formula: δ is a sufficiently small positive number;

[0074] S2503. In the second stage, the number of successful update individuals in each neighborhood search counted in the first stage is N. i Clear to zero and use incremental learning to learn the preset K max The probability model of the neighborhood structure is updated:

[0075]

[0076] Among them, SPi(s) and SPi(s+1) are the probabilities of the i-th neighborhood structure being selected in the s-th and s+1-th neighborhood searches respectively.

[0077] A high-end equipment production rescheduling optimization system considering machine failures, comprising:

[0078] A setting module is used to set input parameters based on workpiece data, machine data and rescheduling data in the production process of a high-end equipment workshop, including at least workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time;

[0079] An update module, used for updating a workpiece set, a machine set and a workpiece processing start time after a machine failure occurs;

[0080] The solution module is used to completely reschedule the workpieces based on the updated input parameters and adopt a hybrid particle swarm algorithm based on neighborhood search to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0081] A storage medium stores a computer program for optimizing the rescheduling of high-end equipment production in consideration of machine failure situations, wherein the computer program enables a computer to execute the above-described method for optimizing the rescheduling of high-end equipment production in consideration of machine failure situations.

[0082] An electronic device, comprising:

[0083] 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 high-end equipment production rescheduling optimization method considering machine failure scenarios as described above.

[0084] (III) Beneficial effects

[0085] The present invention provides a method, system, storage medium and electronic device for optimizing the production rescheduling of high-end equipment in consideration of machine failure. Compared with the prior art, the present invention has the following beneficial effects:

[0086] In the present invention, firstly, input parameters are set based on workpiece data, machine data and rescheduling data in the production process of high-end equipment workshops; then, after a machine failure occurs, the workpiece set, machine set and workpiece processing start time are updated; finally, based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpiece, obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling scheme, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost. The problem of multi-machine production collaborative rescheduling under the situation of workshop production machine failure is considered, and the optimization objectives of rescheduling processing time and cost are comprehensively considered. A model for flexible workshop workpiece sequential scheduling of manufacturing interruption is constructed, and an improved particle swarm algorithm is proposed to solve the problem, which can effectively reduce production and manufacturing costs and enhance the ability of enterprises to cope with sudden production interruptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] 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.

[0088] Figure 1 A block diagram of a high-end equipment production rescheduling optimization method considering machine failure provided by an embodiment of the present invention;

[0089] Figure 2 A flow chart of a hybrid particle swarm algorithm based on neighborhood search provided by an embodiment of the present invention;

[0090] Figure 3 An encoding example provided for 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 problem of rescheduling a workshop with spare machines by providing a method, system, storage medium and electronic device for optimizing the rescheduling of high-end equipment production in consideration of machine failure.

[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] The embodiment of the present invention first obtains task data in the high-end equipment manufacturing rescheduling process, and sets the input parameters of the hybrid particle swarm algorithm based on neighborhood search according to the task data in the high-end equipment supply manufacturing rescheduling process; then sets the execution parameters of the algorithm and initializes the initial solution of the algorithm; then updates the position of each particle based on the position update formula of the PSO algorithm; calculates the fitness value of each individual, selects the neighborhood structure to generate the neighborhood solution of the optimal particle of the group; generates the neighborhood solution based on the neighborhood structure, and performs a local search operation.

[0095] The embodiments of the present invention reschedule the interrupted multi-machine production process of high-end equipment manufacturing, shorten the enterprise's production time while reducing production energy consumption, thereby effectively improving the production scheduling management level of high-end equipment enterprises and the enterprise's ability to cope with sudden production interruptions.

[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 a method for optimizing production rescheduling of high-end equipment considering machine failure, including:

[0099] S1. Based on the workpiece data, machine data and rescheduling data in the production process of the high-end equipment workshop, set the input parameters, including at least the workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and machine maintenance fixed time;

[0100] S2. After a machine failure occurs, update the workpiece set, machine set and workpiece processing start time;

[0101] S3. Based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost, and rescheduling conflict cost.

[0102] The embodiment of the present invention takes into account the problem of coordinated rescheduling of multi-machine production under the situation of workshop production machine failure, and comprehensively considers the optimization goals of rescheduling processing time and cost, constructs a flexible workshop workpiece sequence scheduling model for manufacturing interruption, and proposes an improved particle swarm algorithm to solve the problem, which can effectively reduce production and manufacturing costs and improve the ability of enterprises to cope with sudden production interruptions.

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

[0104] In step S1, based on the workpiece data, machine data and rescheduling data in the production process of the high-end equipment workshop, the input parameters are set, including at least the workpiece set, machine set, workpiece processing start and end time, backup machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time.

[0105] In this step, the workpiece data, machine data and rescheduling data before the interruption of high-end equipment production are obtained through manual entry and other methods, and the input parameters of the particle swarm and variable neighborhood search hybrid algorithm are set based on the obtained data.

[0106] Specifically, the input parameters include:

[0107] The workpiece set J = [J1, J2, ..., J n ];

[0108] A workpiece set consisting of l unprocessed workpieces

[0109] Machine set MM = {MM1, MM2, ..., MM u , B.M. u+1 , B.M. u+2 , ..., B.M. p}, where MM j is the machine for the initial production operation, u is the number of machines for the initial production operation, BM j is the newly enabled standby machine for rescheduling, and p is the number of newly enabled standby machines for rescheduling;

[0110] Assume CM j China Machine CM q Failure occurs, q∈[1, 2, ..., u];

[0111] CM q The time interval t from the start of processing to the occurrence of failure follows an exponential distribution, and the faulty machine CM q The maintenance time d follows an exponential distribution;

[0112] Machine CM as of time t j The set of processed workpieces F j , the set F of workpieces processed on all machines as of time t;

[0113] Non-faulty machine CM at time t j The workpiece set Z is being processed j , at time t, all non-faulty machines are processing the workpiece set Z;

[0114] P ij For workpiece J i On the machine MM j The time spent on processing, C i Represents the initial scheduling job J i Processing completion time, Represents the rescheduling job J i Processing completion time; S j MM for the rescheduled machine j The start time of the first workpiece processing;

[0115] c ij For workpiece J i On the machine MM j The processing cost per unit time;

[0116] ε BM Start-up cost for each spare machine;

[0117] c0 is the conflict cost per unit time;

[0118] a j is a binary decision variable. If the backup machine BM is enabled j , then a j The value is 1, otherwise it is 0;

[0119] x ij is a binary decision variable. If workpiece J i Select the machine MM j If processing is performed on ij The value is 1, otherwise it is 0;

[0120] y jki is a binary decision variable, if That is, workpiece J k Workpiece J i Process first, then y jki The value is 1 otherwise, 0.

[0121] In step S2, after a machine failure occurs, the workpiece set, the machine set and the workpiece processing start time are updated.

[0122] It should be noted that the embodiment of the present invention is set to restart processing after the machine failure occurs and the failed machine is repaired. The workpiece being processed on the failed machine is reassigned, and the workpiece being processed on the non-faulty machine continues to be processed. The workpiece is completely rescheduled, and the workpiece set, machine set and workpiece processing start time are set and updated accordingly.

[0123] In step S3, based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0124] The embodiment of the present invention proposes an improved particle swarm algorithm, namely a hybrid particle swarm algorithm based on neighborhood search, to solve the problem of collaborative rescheduling of multi-machine production in the event of a workshop production machine failure, which can effectively reduce production and manufacturing costs.

[0125] like Figure 2 As shown, Figure 2 The flowchart of the hybrid particle swarm algorithm based on neighborhood search is given. Figure 2 , this step specifically includes:

[0126] S31, setting execution parameters, including at least: the maximum number of iterations Wl1 of the hybrid particle swarm algorithm based on neighborhood search, the population size Popsize of the algorithm, and the maximum number of iterations Wl2 of the local search;

[0127] S32, using a real number-based encoding method to generate an initial population X0, generating a code with a length of l, assigning workpieces to machines, using the weighted rescheduling total goal as the individual fitness value, and obtaining and recording the current optimal solution;

[0128] S33, updating the position of each individual particle based on the position update formula of the particle swarm optimization algorithm PSO;

[0129] S34, calculate the fitness value of each particle, the best pbest of each individual in the population is the current position, the particle individual with the best scheduling target in the population is the group's best position gbest, the group's best particle individual gbest is recorded as π, and the neighborhood structure NS is selected based on the roulette method k , and based on the neighborhood structure NS k Generate a neighborhood solution π′ of π;

[0130] S35, based on a two-stage adaptive neighborhood search strategy, using the neighborhood structure NS k Perform local search on solution π′ to obtain the local optimal solution π″, compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm, if π″ is better than π, let π=π″, and record the number of successful searches for the local optimal solution by the neighborhood structure, and update the neighborhood structure NS k , determine whether wl2<Wl2 is true, if so, assign wl2+1 to wl2 and return to step S34, otherwise execute step S36;

[0131] S36, compare the optimal solution π of the variable neighborhood search algorithm with the worst individual in the initial population X0. If π is better than the worst individual in the worst initial population, replace the solution with π and compare π with the global optimal solution π best Compare, if π is better than π best , then let π best =π;

[0132] S37, determine whether wl1≤Wl1 holds. If so, assign wl1+1 to wl1, and use the updated population as the input for the next iteration, and return to step S33. Otherwise, output the global optimal solution π best , and decode to obtain the corresponding number of standby machines started, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0133] In an optional implementation manner, the encoding rules of the hybrid particle swarm algorithm based on neighborhood search provided by the embodiment of the present invention specifically include:

[0134] The length of the code LS is l, and the code LS means the assignment order of the workpiece on the initial operation machine or the backup machine. The real number in the code LS contains an integer part and a decimal part, where the integer part The number of the processing machine, decimal part The processing sequence of the workpieces is determined, and the workpiece with a smaller decimal part is processed closer to the front, wherein the real number encoding values ​​corresponding to different workpieces are not equal.

[0135] In addition, the completed workpieces and the workpieces being processed by the non-faulty machines are eliminated from the workpiece set and their number is recorded as nl, a code with a length of l is generated, and the workpiece is assigned to the initial operation machine or the spare machine.

[0136] like Figure 3 As shown, Figure 3 A coding example is given.

[0137] Correspondingly, the decoding process of the hybrid particle swarm algorithm based on neighborhood search provided by the embodiment of the present invention specifically includes:

[0138] S101. The processing order of the workpiece on each machine in the initial scheduling is known. represents the initial schedule to produce the cth job on the jth machine, represents the processing time of the cth job produced by the jth machine in the initial schedule, F j is the machine CM at the end time t j The collection of processed workpieces, Z j is the non-faulty machine CM at time t j The set of workpieces being processed, S j Represents the machine MM after rescheduling j The time when the first workpiece processing of the machine started, the CM of the faulty machine j The estimated maintenance time is d;

[0139] S102, let j=1;

[0140] S103, let m = 1, for the processing machine CM j , judging machine CM j Whether a fault occurs, if it is a faulty machine, execute step S104, otherwise, execute step S106;

[0141] S104, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute step S104, otherwise execute step S105;

[0142] S105, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , at this time S j = t + d, execute step S108, otherwise, the workpiece being processed on the faulty machine will be reassigned. j =t+d, execute step S108;

[0143] S106, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute S106, otherwise execute step S107;

[0144] S107, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , Machine CM j The workpiece set Z is being processed j is an empty set, then S j = t, execute step S108, otherwise output machine CM j The workpiece set Z is being processed j ,at this time Execute step S108;

[0145] S108, when j≠u, assign j+1 to j, and return to step S103; when j=u, execute step S109;

[0146] S109, the set F of workpieces processed by all machines as of time t, and the set Z of workpieces being processed by all non-faulty machines at time t, then the set F=F1∪...∪F j ∪...∪F u , j = [1, 2, ..., u], set Z = Z1∪...∪Z j ∪...∪Z u , j=[1, 2, ..., u] and j≠q, execute step S110;

[0147] S110, generating a to-be-scheduled workpiece set J from the initial workpiece set J, the set F and the set Z R , where the set J R = J - (F ∪ Z);

[0148] S111, order the standby machine BMj The first workpiece processing start time S j = t, execute step S112;

[0149] S112, respectively generate three components of the overall rescheduling target:

[0150] (1) Rescheduling production cost f1:

[0151]

[0152] Among them, represents the workpiece On the machine MM j The unit time processing cost, P ij Representation of workpiece On the machine MM j The processing time on

[0153]

[0154] (2) Energy consumption f2 for rescheduling standby machines:

[0155]

[0156] in

[0157] (3) Rescheduling deviation conflict cost f3:

[0158]

[0159] Represents a rescheduling artifact Completion time, C i Represents the initial scheduling artifact Completion time,

[0160]

[0161] y jki is a decision variable, if That is, machine MM j Loading workpiece Workpiece Process first, then y jki The value is 1, otherwise it is 0. i The value is known.

[0162] It is particularly noted that the deviation conflict cost after rescheduling is an important indicator to measure the robustness of rescheduling. A higher value means more moving processes, which makes it more inconvenient to reschedule the production of workpieces.

[0163] Based on the aforementioned encoding and decoding rules, in an optional implementation, the process of obtaining the weighted rescheduling total target as the individual fitness value in S32 is as follows:

[0164] Get the optimal solution f of each particle 1max 、f 2max and f 3max ;

[0165] Weighted calculation of population fitness value:

[0166] f(x)=λ1f 1max +λ2f 2max ++λ3f 3max

[0167] Among them, f(x) is the fitness function, λ1, λ2, λ3 are the weights of production cost, rescheduling standby machine startup cost and rescheduling deviation conflict cost, and λ1+λ2+λ3=1.

[0168] In an optional implementation, the position of each individual particle is updated based on the position update formula of the particle swarm optimization algorithm PSO in S33; including:

[0169]

[0170]

[0171] in, are the velocities of the mth individual particle in the kth and k+1th iterations respectively; is the best historical position of the mth particle; gbest k is the global optimal position; are the positions of the mth particle in the kth and k+1th iterations respectively; ω represents the inertia coefficient; c1 and c2 are acceleration constants; r1 and r2 are random numbers.

[0172] The embodiment of the present invention selects a neighborhood structure NS for a feasible solution π. k Transformation is performed to make the initial solution jump to another solution in the feasible domain, avoiding repeated iterations and causing the algorithm to fall into local optimality. Considering the complexity of the high-end equipment production rescheduling process and the requirements of the constraint relationship.

[0173] Exemplarily, the following four neighborhood structures are designed:

[0174] Neighborhood structure 1: Randomly generate the real number encoding value of the workpiece with the largest deviation in processing completion time before and after rescheduling.

[0175] Neighborhood Structure 2: Define a variable a, randomly generate an integer in the interval [1, l], and assign it to a. Assign the workpiece at position a in the sequence LS to the machine with the shortest processing time.

[0176] Neighborhood Structure 3: Define variables a and b, randomly generate two integers in the interval [1, l], assign them to a and b, where a < b. Swap the encodings at positions a and b in the sequence LS.

[0177] Neighborhood Structure 4: Define variables a and b, randomly generate two integers in the interval [1, l], assign them to a and b, where a < b, and reverse the encoding between positions a and b in the sequence LS.

[0178] In addition, the embodiment of the present invention introduces an adaptive incremental learning process in the variable neighborhood search algorithm and continuously updates it during the iteration process. Specifically:

[0179] In an optional embodiment, the neighborhood structure NS is used in S35 k to perform local search on the solution π′ to obtain the local optimal solution π″, and compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm. If π″ is better than π, then let π = π″; including:

[0180] S2501. The neighborhood structure NS of the variable neighborhood search algorithm k is optimized by adopting a two-stage adaptive neighborhood search method during the local search process of the variable neighborhood search algorithm:

[0181] S2502. In the first stage, perform probability initialization, and record the number of times N that each neighborhood structure successfully searches for the local optimal solution i and update the selection probability of each neighborhood according to the following formula: δ is a sufficiently small positive number;

[0182] S2503. In the second stage, clear the number of times N that each neighborhood searches successfully and updates the individual in the first stage, and update the probability model of the preset K i types (which can be the 4 types listed above) of neighborhood structures in an incremental learning manner: max where SPi(s) and SPi(s + 1) are the probabilities that the i-th neighborhood structure is selected in the s-th and s + 1-th neighborhood searches respectively.

[0183]

[0184]

[0185] So far, the embodiment of the present invention has completed all the processes of the high-end equipment production rescheduling optimization method considering machine failure situations.

[0186] ​Embodiment 2:

[0187] The embodiment of the present invention provides a high-end equipment production rescheduling optimization system considering machine failure, including:

[0188] A setting module is used to set input parameters based on workpiece data, machine data and rescheduling data in the production process of a high-end equipment workshop, including at least workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time;

[0189] An update module, used for updating a workpiece set, a machine set and a workpiece processing start time after a machine failure occurs;

[0190] The solution module is used to completely reschedule the workpieces based on the updated input parameters and adopt a hybrid particle swarm algorithm based on neighborhood search to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

[0191] Embodiment 3:

[0192] An embodiment of the present invention provides a storage medium storing a computer program for optimizing the rescheduling of high-end equipment production in the context of machine failures, wherein the computer program enables a computer to execute the method for optimizing the rescheduling of high-end equipment production in the context of machine failures as described in Example 1.

[0193] Embodiment 4:

[0194] An embodiment of the present invention provides an electronic device, including:

[0195] 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 a method for executing the high-end equipment production rescheduling optimization method considering machine failure scenarios as described in Example 1.

[0196] It can be understood that the high-end equipment production rescheduling optimization system, storage medium and electronic device provided in the embodiments of the present invention correspond to the high-end equipment production rescheduling optimization method considering machine failure provided in the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the high-end equipment production rescheduling optimization method considering machine failure, and will not be repeated here.

[0197] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0198] The embodiment of the present invention takes into account the problem of coordinated rescheduling of multi-machine production under the situation of workshop production machine failure, and comprehensively considers the optimization goals of rescheduling processing time and cost, constructs a flexible workshop workpiece sequence scheduling model for manufacturing interruption, and proposes an improved particle swarm algorithm to solve the problem, which can effectively reduce production and manufacturing costs and improve the ability of enterprises to cope with sudden production interruptions.

[0199] 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.

[0200] 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. A method for optimizing the production rescheduling of high-end equipment considering machine failure, characterized in that: include: Based on the workpiece data, machine data and rescheduling data in the production process of high-end equipment workshops, input parameters are set, including at least workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time; After a machine failure occurs, update the workpiece set, machine set and workpiece processing start time; Based on the updated input parameters, a hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

2. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 1, characterized in that: The input parameters specifically include: The workpiece set J = [J1, J2, ..., J n ]; A workpiece set consisting of l unprocessed workpieces Machine set MM = {MM1, MM2, ..., MM u ,BM u+1 ,BM u+2 ,…,BM p }, where MM j is the machine for the initial production operation, u is the number of machines for the initial production operation, BM j is the newly enabled standby machine for rescheduling, and p is the number of newly enabled standby machines for rescheduling; Assume CM j China Machine CM q Failure occurs, q∈[1,2,…,u]; CM q The time interval t from the start of processing to the occurrence of failure follows an exponential distribution, and the faulty machine CM q The maintenance time d follows an exponential distribution; Machine CM as of time t j The set of processed workpieces F j , the set F of workpieces processed on all machines as of time t; Non-faulty machine CM at time t j The workpiece set Z is being processed j ,At time t, all non-faulty machines are processing the workpiece set Z; P ij For workpiece J i On the machine MM j The time spent on processing, C i Represents the initial scheduling job J i Processing completion time, Represents the rescheduling job J i Processing completion time; S j MM for the rescheduled machine j The start time of the first workpiece processing; c ij For workpiece J i On the machine MM j The processing cost per unit time; ε BM Start-up cost for each spare machine; c0 is the conflict cost per unit time; a j is a binary decision variable. If the backup machine BM is enabled j , then a j The value is 1, otherwise it is 0; x ij is a binary decision variable. If workpiece J i Select the machine MM j If processing is performed on ij The value is 1, otherwise it is 0; y jki is a binary decision variable, if That is, workpiece J k If y is processed before the workpiece Ji, jki The value is 1 otherwise, 0.

3. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 2, characterized in that: Based on the updated input parameters, the hybrid particle swarm algorithm based on neighborhood search is used to completely reschedule the workpieces, obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost; including: S31, setting execution parameters, including at least: the maximum number of iterations Wl1 of the hybrid particle swarm algorithm based on neighborhood search, the population size Popsize of the algorithm, and the maximum number of iterations Wl2 of the local search; S32, using a real number-based encoding method to generate an initial population X0, generating a code with a length of l, assigning workpieces to machines, using the weighted rescheduling total goal as the individual fitness value, and obtaining and recording the current optimal solution; S33, updating the position of each individual particle based on the position update formula of the particle swarm optimization algorithm PSO; S34, calculate the fitness value of each particle individual, the best pbest of each individual in the population is the current position of the individual, the particle individual with the best scheduling target in the population is the group's best position gbest, the group's best particle individual gbest is recorded as π, and the neighborhood structure NS is selected based on the roulette method k , and based on the neighborhood structure NS k Generate a neighborhood solution π′ of π S35. Based on the two-stage adaptive neighborhood search strategy, use the neighborhood structure NS k Perform local search on the solution π′ to obtain the local optimal solution π″. Compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm. If π″ is better than π, then let π = π″, and at the same time record the successful times of the local optimal solution searched by this neighborhood structure, and update the neighborhood structure NS k The selection probability of, judge whether wl2 < Wl2 holds. If so, assign wl2 + 1 to wl2, return to step S34, otherwise execute step S36; S36, compare the optimal solution π of the variable neighborhood search algorithm with the worst individual in the initial population X0. If π is better than the worst individual in the worst initial population, replace the solution with π and compare π with the global optimal solution π best Compare, if π is better than π best , then let π best =π; S37, determine whether wl1≤Wl1 holds. If so, assign wl1+1 to wl1, and use the updated population as the input for the next iteration, and return to step S33. Otherwise, output the global optimal solution π best , and decode to obtain the corresponding number of started standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

4. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 3, characterized in that: The coding rules of the hybrid particle swarm algorithm based on neighborhood search include: The length of the code LS is l, and the code LS means the assignment order of the workpiece on the initial operation machine or the backup machine; the real number in the code LS contains an integer part and a decimal part, where the integer part The number of the processing machine, decimal part Determine the processing order of the workpieces, the workpiece with a smaller decimal part has a closer processing position, wherein the real number encoding values ​​corresponding to different workpieces are not equal; The decoding process is as follows: S2202, the decoding process is to calculate the obtained feasible solution according to the actual scenario to obtain the rescheduling plan, the set of enabled standby machines, the rescheduling production cost and the rescheduling standby machine startup energy consumption; including the following operations: S101. The processing order of the workpiece on each machine in the initial scheduling is known. represents the initial schedule to produce the cth job on the jth machine, represents the processing time of the cth job produced by the jth machine in the initial schedule, F j is the machine CM at the end time t j The collection of processed workpieces, Z j is the non-faulty machine CM at time t j The set of workpieces being processed, S j Represents the machine MM after rescheduling j The time when the first workpiece processing of the machine started, the CM of the faulty machine j The estimated maintenance time is d; S102, let j=1; S103, let m = 1, for the processing machine CM j , judging machine CM j Whether a fault occurs, if it is a faulty machine, execute step S104, otherwise, execute step S106; S104, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute step S104, otherwise execute step S105; S105, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , at this time S j = t + d, execute step S108, otherwise, the workpiece being processed on the faulty machine will be reassigned. j =t+d, execute step S108; S106, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , assign m+1 to m, and continue to execute S106, otherwise execute step S107; S107, for processing machine CM j ,judge Is it true? If so, update the machine CM j The completed artifact set F j , Machine CM j The workpiece set Z is being processed j is an empty set, then S j = t, execute step S108, otherwise output machine CM j The workpiece set Z is being processed j ,at this time Execute step S108; S108, when j≠j, assign j+1 to j, and return to step S103; when j=u, execute step S109; S109. As of time t, the set F of workpieces that have been processed by all machines, and the set Z of workpieces that are being processed by all non-faulty machines at time t, then the set D=D1∪…∪Z j ∪…∪F u , j = [1, 2, ..., u], set Z = Z1∪ ...∪F j ∪…∪Z u , j=[1,2,…,u] and j≠q, execute step S110; S110, generating a to-be-scheduled workpiece set J from the initial workpiece set J, the set F and the set Z R , where the set J R = J - (F ∪ Z); S111, order the standby machine BM j The first workpiece processing start time S j = t, execute step S112; S112, respectively generate three components of the overall rescheduling target: (1) Rescheduling production cost f1: Among them, represents the workpiece On the machine MM j The unit time processing cost, P ij Representation of workpiece On the machine MM j The processing time on (2) Energy consumption f2 for rescheduling standby machines: in (3) Rescheduling deviation conflict cost f3: Represents a rescheduling artifact Completion time, C i Represents the initial scheduling artifact Completion time, y jki is a decision variable, if That is, machine MM j Loading workpiece Workpiece Process first, then y jki The value is 1, otherwise it is 0. i The value is known.

5. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 4, characterized in that: The process of obtaining the weighted rescheduling total target in S32 as the individual fitness value is as follows: Get the optimal solution f for each target of each particle 1max 、f 2max and f 3max ; Weighted calculation of population fitness value: f(x)=λ1f 1max +λ2f 2max +λ3f 3max Among them, f(x) is the fitness function, λ1, λ2, λ3 are the weights of rescheduling production cost, rescheduling standby machine startup cost and rescheduling deviation conflict cost, and λ1+λ2+λ3=1.

6. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 3, characterized in that: The position update formula of the particle swarm optimization algorithm PSO in S33 updates the position of each individual particle; including: in, are the velocities of the mth individual particle in the kth and k+1th iterations respectively; is the best historical position of the mth particle; gbest k is the global optimal position; are the positions of the mth particle in the kth and k+1th iterations respectively; ω represents the inertia coefficient; c1 and c2 are acceleration constants; r1 and r2 are random numbers.

7. The high-end equipment production rescheduling optimization method considering machine failure as claimed in claim 3, characterized in that: The neighborhood structure NS is used in S35 k Perform local search on the solution π′ to obtain the local optimal solution π″, compare the local optimal solution π″ with the optimal solution π of the variable neighborhood search algorithm, if π″ is better than π, let π=π″; including: S2501, neighborhood structure NS of variable neighborhood search algorithm k , a two-stage adaptive neighborhood search method is used for optimization in the local search process of the variable neighborhood search algorithm: S2502: In the first stage, probability initialization is performed to record the number of times each neighborhood structure successfully searches for the local optimal solution N i , update the selection probability of each neighborhood according to the following formula: δ is a sufficiently small positive number; S2503. In the second stage, the number of successful update individuals in each neighborhood search counted in the first stage is N. i Clear to zero and use incremental learning to learn the preset K max The probability model of the neighborhood structure is updated: Among them, SP i (s), SP i (s+1) is the probability of the i-th neighborhood structure being selected in the s-th and s+1-th neighborhood searches respectively.

8. A high-end equipment production rescheduling optimization system considering machine failure, characterized in that: include: A setting module is used to set input parameters based on workpiece data, machine data and rescheduling data in the production process of a high-end equipment workshop, including at least workpiece set, machine set, workpiece processing start and end time, standby machine startup cost, conflict cost, machine failure time point and fixed machine maintenance time; An update module, used for updating a workpiece set, a machine set and a workpiece processing start time after a machine failure occurs; The solution module is used to completely reschedule the workpieces based on the updated input parameters and adopt a hybrid particle swarm algorithm based on neighborhood search to obtain the global optimal solution, and decode the corresponding number of startup standby machines, production rescheduling plan, rescheduling production cost, rescheduling standby machine startup cost and rescheduling conflict cost.

9. A storage medium, characterized in that: It stores a computer program for optimizing the rescheduling of high-end equipment production in consideration of machine failure situations, wherein the computer program enables the computer to execute the method for optimizing the rescheduling of high-end equipment production in consideration of machine failure situations as described in any one of claims 1 to 7.

10. 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, and the programs include programs for executing the high-end equipment production rescheduling optimization method considering machine failure scenarios as described in any one of claims 1 to 7.

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