Production scheduling method and system for flexible workshop with double-track type guide vehicle

Abstract

The invention belongs to the related technical field of workshop scheduling, and discloses a flexible workshop production scheduling method and system with a double-track type guide vehicle, and the method comprises the steps: building a flexible workshop production scheduling model with the double-track type guide vehicle in consideration of the machine constraint of a flexible workshop machining process and the carrying constraint of the track type guide vehicle; performing iterative solution on the flexible workshop production scheduling model through an optimization algorithm to obtain a flexible workshop production scheduling method; during iterative solution, the procedure processing sequence, the procedure processing machine selection and the procedure guide vehicle selection are coded respectively to form chromosomes, and the three chromosomes correspond to one solution; and a decoding operator based on motion conflict repair is executed on a solution generated by each iteration. According to the method, processing machine constraints and track guided vehicle constraints of processes are considered, a three-layer coding structure is provided, and a decoding mode based on motion conflict repair is designed, so that collaborative optimization scheduling of a flexible manufacturing system with double RGVs can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field related to workshop scheduling, and more specifically, relates to a flexible workshop production scheduling method and system with a double-rail guided vehicle. Background Art

[0002] With the wide application of numerical control machining equipment in the discrete manufacturing industry and the rapid growth of the demand for small and medium batch customized products, the flexible manufacturing system production mode has gradually become one of the most commonly used processing modes in addition to the assembly line operation. For example, the machining workshops of typical parts of aerospace equipment usually configure multiple different types of equipment for machining various parts with different process routes. Multiple devices can be selected for the same process during machining, which is an important feature of the flexible manufacturing system production mode. The related scheduling problem, that is, the flexible manufacturing system scheduling problem (Flexible Manufacturing System, FMS), has become one of the research focuses in the scheduling field in the past two decades due to its complexity and extensive practical application scenarios.

[0003] In the context of intelligent manufacturing, enterprises rely on automated production lines to improve production efficiency. Among them, the double-RGV (Rail Guided Vehicle) system, as an automated logistics and production line technical solution, is regarded as the key technology to solve efficient logistics and automated processing. RGV (Rail Guided Vehicle) is a device responsible for material transportation in a manufacturing environment, and the double-RGV system consists of two independently operating RGVs and can achieve simultaneous operation. However, the current utilization efficiency of the double-RGV system in actual applications is relatively low, mainly reflected in the insufficient optimization of task allocation and path planning. When the double-RGVs cooperate, they often lack an efficient scheduling mechanism, resulting in uneven task allocation and even frequent conflicts. In addition, due to the inability to fully combine the rhythm and priority of processing tasks in a complex production environment, transportation resources are not reasonably utilized, further increasing the time cost of material transportation. This problem of mismatch between logistics and processing rhythm not only reduces the overall efficiency of the double-RGV system but also restricts the potential of the flexible manufacturing system production mode.

[0004] Especially when considering the scheduling problem of the double-RGV system in combination with the flexible manufacturing system, the problem complexity increases significantly. The FMS problem itself already includes complex factors such as multi-device selection, multi-process arrangement, and multi-constraint conditions. After introducing the double-RGV system, scheduling optimization needs to take into account multiple dimensions such as logistics scheduling, processing task allocation, and path planning at the same time. This systematic optimization requirement greatly increases the complexity of the solution, not only posing higher requirements for algorithm performance but also needing to solve the problems of multi-objective optimization and resource scheduling in a dynamic environment.

[0005] In summary, although the prior art has made great progress in the research on the flexible manufacturing system FMS, there is a lack of relevant research on the collaborative optimization of the double-RGV system and the FMS problem. In order to achieve the collaborative optimization of the double-RGV system and the flexible manufacturing system, enterprises urgently need a new and efficient scheduling method for the flexible manufacturing system with double rail-guided vehicles to solve the problems in actual production. Summary of the Invention

[0006] In view of the above deficiencies or improvement requirements of the prior art, the present invention provides a flexible workshop production scheduling method and system with double rail-guided vehicles, which is used to solve the problem that there is a lack of a collaborative optimization scheduling method for the double rail-guided vehicle system and the flexible manufacturing system in the prior art.

[0007] To achieve the above object, according to the first aspect of the present invention, a flexible workshop production scheduling method with double rail-guided vehicles is provided, including:

[0008] Considering the machine constraints of the flexible workshop processing procedures and the handling constraints of the rail-guided vehicles, a flexible workshop production scheduling model with double rail-guided vehicles is constructed with the goal of minimizing the makespan; the flexible workshop production scheduling model is iteratively solved through an optimization algorithm to obtain a flexible workshop production scheduling method;

[0009] During iterative solution, the processing sequence of the procedures, the selection of the processing machines for the procedures, and the selection of the guided vehicles for the procedures are respectively encoded to form chromosomes, and three chromosomes correspond to one solution; and a decoding operator based on motion conflict repair is executed for the solution generated each time, and the decoding operator based on motion conflict repair is specifically:

[0010] When decoding the three chromosomes into a scheduling plan, for each procedure on the chromosome of the processing sequence of the procedures, it is judged whether there is a conflict between the motion trajectory of the guided vehicle selected for the procedure and that of another guided vehicle during the process of completing the handling task of the procedure. If there is no conflict, the guided vehicle selected for the procedure normally completes the handling task of the procedure; if there is a conflict, the position and / or handling time of the guided vehicle selected for the procedure are coordinated until the conflict is resolved and then the handling task of the procedure is executed.

[0011] According to the flexible workshop production scheduling method with double rail-guided vehicles provided by the present invention, during iterative solution, the specific coding structure of the solution is as follows:

[0012] The first-layer coding represents the sequence of all procedures corresponding to the workpieces processed in the flexible workshop. The gene positions of the coding are composed of the workpiece numbers for processing. The number of times the workpiece numbers for processing appear in the coding is the number of procedures of the workpiece, and the order in which the same workpiece number for processing appears is the procedure order of the workpiece;

[0013] The second - layer coding represents the selection of processing machines for all processes corresponding to the processed workpieces. The coding corresponds to the processes of the processed workpieces numbered from small to large from left to right, and the processes of each processed workpiece are arranged in sequence from left to right according to the order of precedence.

[0014] The third - layer coding represents the selection of guiding vehicles for all processes corresponding to the processed workpieces. The gene positions of the coding are composed of guiding vehicle numbers. The coding corresponds to the processes of the processed workpieces numbered from small to large from left to right, and the processes of each processed workpiece are arranged in sequence from left to right according to the order of precedence.

[0015] According to the flexible job - shop production scheduling method with double - track guided vehicles provided by the present invention, the decoding operator based on motion conflict repair specifically includes:

[0016] For the process - processing - order chromosome, starting from the left end, each process is decoded based on motion conflict repair in sequence to obtain a scheduling plan without motion conflicts.

[0017] According to the flexible job - shop production scheduling method with double - track guided vehicles provided by the present invention, in the decoding operator based on motion conflict repair, for each process on the process - processing - order chromosome, it is judged whether there is a conflict between the motion trajectory of the guiding vehicle selected for the process and that of another guiding vehicle during the process of completing the process handling task. Specifically, it includes:

[0018] For each process on the process - processing - order chromosome, the guiding vehicle selected for the current process is denoted as Rgv1, and it is judged whether the current process belongs to the first process of the processed workpiece. If so, it is judged that there is no motion conflict;

[0019] If not, then the position of the previous - process processing machine corresponding to the current process is denoted as Pos1; the position of the target processing machine corresponding to the current process is denoted as Pos2; and the current position of Rgv1 is denoted as Pos current ; At the same time, on the premise of ignoring motion conflicts, the moments when Rgv1 moves to Pos1 and Pos2 are respectively denoted as t1 and t2;

[0020] Determine the minimum value Pos min and the maximum value Pos max of the position where another guiding vehicle Rgv2 is located during the time period [t1, t2]; judge whether there is a path overlap between [Pos min , Pos max and [Pos1, Pos2]. If so, it is judged that there is a motion conflict; if not, it is judged that there is no motion conflict.

[0021] According to the flexible workshop production scheduling method with a double-rail guided vehicle provided by the present invention, in the motion conflict repair decoding operator, if there is a conflict, the position and / or handling time of the guided vehicle selected for the process are coordinated, and the handling task of the process is executed after the conflict is resolved. Specifically, it includes:

[0022] If there is a conflict, judge whether Pos current is within the range of [Pos min , Pos max ;

[0023] If then keep Rgv1 stationary and stay at Pos current until Rgv2 completes the handling task of the corresponding process;

[0024] If Pos current ∈[Pos min , Pos max , then move Rgv1 to a safe distance outside [Pos min , Pos max and stay until Rgv2 completes the handling task of the corresponding process.

[0025] According to the flexible workshop production scheduling method with a double-rail guided vehicle provided by the present invention, the time constraint conditions of the rail-guided vehicle in the flexible workshop production scheduling model are as follows:

[0026]

[0027]

[0028] Among them, Ts i,j represents the transportation start time of process O i,j ; Take 1 when the handling start time of process O i′,j′ is later than the handling start time of process O i,j , otherwise take 0; Tc i′,j′ represents the transportation end time of process O i′,j′ ; Tc i,j represents the transportation end time of process O i,j ; Take 1 when the handling completion time of process O i′,j′ is later than the handling start time of process O i,j , otherwise take 0; Take 1 when the handling start time of process O i′,j′ is later than the handling completion time of process O i,j , otherwise take 0; Take 1 when the handling completion time of process O i′,j′ is later than the handling completion time of process Oi,j Take 1 when the handling completion time of

[0029] According to the flexible workshop production scheduling method with double - track guided vehicles provided by the present invention, the position constraint conditions of the guided vehicles in the flexible workshop production scheduling model are as follows:

[0030]

[0031] Among them, represents the transportation start position of process O i,j ; represents the transportation start position of process O i′,j′ ; represents the transportation end position of process O i,j ; represents the transportation end position of process O i′,j′ ; S d represents the safety distance that needs to be maintained between RGVs; z i,j,r Take 1 when processing on the guided vehicle r in process O i,j , otherwise take 0; r = 1 or 2.

[0032] According to the flexible workshop production scheduling method with double - track guided vehicles provided by the present invention, the flexible workshop production scheduling model is iteratively solved by a genetic algorithm, which specifically includes:

[0033] Adopt a random initialization strategy to generate an initial population;

[0034] Execute a motion - conflict repair decoding operator on the individuals in the initial population;

[0035] Calculate the fitness of the individuals in the population and select the individuals in the population according to the selection strategy to generate a new population;

[0036] Perform crossover between the chromosomes of the population individuals;

[0037] Execute a mutation operator on the individuals in the population;

[0038] Execute a motion - conflict repair decoding operator on the individuals in the population;

[0039] When the set termination condition is met, stop the iteration and output the individual with the maximum fitness in the population at this time as the optimal scheduling method; otherwise, return to the selection strategy step.

[0040] According to a second aspect of the present invention, there is provided a flexible workshop production scheduling system with a double-rail guided vehicle. The system includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it executes the flexible workshop production scheduling method with a double-rail guided vehicle described in any one of the above.

[0041] According to a third aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the flexible workshop production scheduling method with a double-rail guided vehicle described in any one of the above.

[0042] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the flexible workshop production scheduling method and system with a double-rail guided vehicle provided by the present invention:

[0043] 1. Considering the processing machine constraints and rail-guided vehicle constraints of the processes, a three-layer coding structure is proposed, and a decoding method based on motion conflict repair is designed. During the iteration process, decoding based on motion conflict repair is performed. This method can not only ensure that the rail-guided vehicles do not have overlapping paths during movement, guaranteeing the feasibility of the chromosome; at the same time, it can ensure that the relative order of the underlying machining processes does not change during the repair process, minimizing the damage to the initial solution while satisfying feasibility, thereby realizing a more efficient exploration of the solution space; it can realize the collaborative optimization scheduling of the flexible manufacturing system with double RGVs;

[0044] 2. For the problem of allocating handling guided vehicles for double-rail guided vehicles, a three-layer coding method is designed, so that the algorithm will not produce infeasible solutions during operation, improving the search efficiency of the algorithm;

[0045] 3. It can provide an excellent production scheduling plan for the production environment of the flexible manufacturing system with double-rail guided vehicles, combining the advantages of flexibility and economy, and it is also convenient to change the scheduling plan according to actual needs; it can improve production stability and production efficiency, reduce the product manufacturing cycle, and improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic flowchart of generating a scheduling model by a genetic algorithm in an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of the corresponding coding chromosome in an embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of motion conflicts occurring in the corresponding rail-guided vehicle in an embodiment of the present invention;

[0049] Figure 4 is a schematic diagram of the movement of the guided vehicle based on the motion conflict repair decoding operator in an embodiment of the present invention. Specific implementation mode

[0050] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Please refer to Figure 1 , this embodiment provides a flexible workshop production scheduling method with a double-rail guided vehicle. The flexible workshop production scheduling method includes:

[0052] Considering the machine constraints of the flexible workshop processing procedures and the handling constraints of the rail-guided vehicle, with the goal of minimizing the makespan, a flexible workshop production scheduling model with a double-rail guided vehicle is constructed; the flexible workshop production scheduling model is iteratively solved through an optimization algorithm to obtain a flexible workshop production scheduling method;

[0053] During iterative solution, the processing sequence of the procedures, the selection of the processing machine for the procedures, and the selection of the guided vehicle for the procedures are respectively encoded to form chromosomes, and the three chromosomes correspond to one solution; and a decoding operator based on motion conflict repair is executed on the solution generated each time. The decoding operator based on motion conflict repair is specifically:

[0054] When decoding the three chromosomes into a scheduling plan, for each procedure on the chromosome of the processing sequence of the procedures, it is judged whether there is a conflict between the motion trajectories of the guided vehicle selected for the procedure and another guided vehicle during the process of completing the handling task of the procedure. If there is no conflict, the guided vehicle selected for the procedure normally completes the handling task of the procedure; if there is a conflict, the position and / or handling time of the guided vehicle selected for the procedure are coordinated, and the handling task of the procedure is executed after the conflict is resolved.

[0055] The flexible workshop production scheduling method with a double-rail guided vehicle provided in this embodiment encodes the selection of the guided vehicle for the procedure as a chromosome as part of the solution, so that the scheduling plan has a guided vehicle allocation plan and can be optimized together with the guided vehicle allocation plan through an optimization algorithm. And it is proposed that when decoding the three chromosomes, the motion conflict between two guided vehicles within the same time period is considered, and the motion conflict is resolved by adjusting the positions and / or working times between the guided vehicles, thereby realizing decoding based on motion conflict repair and obtaining a scheduling plan without motion conflict.

[0056] In some specific embodiments, during iterative solution, a solution includes a three-layer encoded chromosome. The three-layer encoding method is used to encode the processing sequence of each process for each workpiece, the selection of processing machines for each workpiece in each process, and the selection of transfer track guided vehicles for each workpiece in each process. The specific encoding structure of the solution is as follows:

[0057] The first-layer encoding represents the sequence of all processes corresponding to the workpieces processed in the flexible workshop. The gene positions of the encoding are composed of the workpiece numbers for processing. The number of times the workpiece numbers for processing appear in the encoding is the number of processes of the workpiece, and the order in which the same workpiece number for processing appears is the process order of the workpiece; the first-layer encoding represents the processing sequence of each workpiece in each process, and the sequence length is where n is the number of workpieces, and N i is the total number of processes of workpiece i. Each gene position of the encoding is composed of numbers from 1 to n;

[0058] The second-layer encoding represents the selection of processing machines for all processes corresponding to the workpieces processed. The encoding corresponds to the processes of the workpieces numbered from small to large from left to right, and the processes of each workpiece are arranged in sequence from left to right according to the order of precedence; the second-layer encoding specifies the selection of processing machines for each workpiece in each process, and the sequence length is where n is the number of workpieces, and N i is the total number of processes of workpiece i.

[0059] The third-layer encoding represents the selection of guided vehicles for all processes corresponding to the workpieces processed. The gene positions of the encoding are composed of the guided vehicle numbers. The encoding corresponds to the processes of the workpieces numbered from small to large from left to right, and the processes of each workpiece are arranged in sequence from left to right according to the order of precedence. The third-layer encoding specifies the selection of transfer track guided vehicles for each workpiece in each process, and the sequence length is where n is the number of workpieces, and N i is the total number of processes of workpiece i. Each gene position is composed of 1 and 2, representing Rgv1 and Rgv2 respectively.

[0060] Thus, integer encoding operations are performed on the processing sequence, the selection of processing machines for all processes, and the selection of transfer track guided vehicles for all processes of the entire production plan.

[0061] Specifically, considering the characteristics of the constructed production scheduling model, an integer encoding method is adopted for the processing sequence, the selection of processing machines for processes, and the selection of transfer track guided vehicles for processes respectively. For the sake of easy understanding, by way of example, such as Figure 2As shown, it is the encoded chromosome. This example contains 3 workpieces, with workpiece numbers 1, 2, and 3 respectively. Workpiece 1 contains 3 processes, workpiece 2 contains 3 processes, and workpiece 3 contains 4 processes. In the chromosome of the process processing sequence, the number of times the workpiece number appears in the encoding represents the corresponding machining process of the workpiece. For example, the first appearance of the number 1 represents machining process 1-1, that is, the first process of workpiece 1, and the second appearance of the number 3 represents machining process 3-2, that is, the second process of workpiece 3.

[0062] In the chromosome of the machining machine selection and the RGV (Rail Guided Vehicle) selection for handling, the length of the chromosome is the total number of processes, which successively represents the machining machines and the RGVs selected for the first process of workpiece 1 to the last process of workpiece 3. For example, in the machining machine selection encoding, the first gene being 2 represents that for machining process 1-1, the second available machining machine is selected as its machining machine, and the second gene being 3 represents that for machining process 1-2, the third available machining machine is selected as its machining machine.

[0063] During decoding, each digit in the process processing sequence encoding is read from left to right. In order to obtain a feasible scheduling plan, the decoding in this embodiment adopts motion conflict repair decoding. Since the traditional semi-active decoding method will result in infeasible solutions during the decoding process, the reason is that the traditional semi-active decoding method will cause path overlap phenomena for the rail-guided vehicle during the actual movement process; as Figure 3 shown; therefore, in order to ensure the feasibility of the chromosome, a motion conflict repair decoding operator is introduced to repair the chromosome.

[0064] The motion conflict repair decoding operator specifically includes:

[0065] For the chromosome of the process processing sequence, starting from the left end, each process is decoded based on motion conflict repair in turn to obtain a scheduling plan without motion conflicts.

[0066] In some specific embodiments, in the motion conflict repair decoding operator, for each process on the chromosome of the process processing sequence, it is judged whether there is a conflict between the motion trajectory of the guided vehicle selected for the process and that of another guided vehicle during the process of completing the handling task. Specifically, it includes:

[0067] Step 1: Retrieve the genes on the process sequence chromosome from the left end. For each process on the process sequence chromosome, determine the guiding vehicle Rgv1 selected for the current process and the current processing process of the workpiece, and mark its position in the chromosome as Ad1. Then, determine whether the current process belongs to the first process of the workpiece being processed. If so, it is determined that there is no movement conflict. That is, for the first process of any workpiece, considering the need to go to the raw material area for handling, when the current process belongs to the first process of the workpiece, the current process does not need to consider movement conflicts.

[0068] If not, that is, the current workpiece is already being processed on the processing machine, then determine the position of the previous processing machine of the workpiece corresponding to the current process, denoted as Pos1; the position of the target processing machine corresponding to the current process, denoted as Pos2; and the current position of Rgv1, denoted as Pos current ; At the same time, on the premise of ignoring movement conflicts, determine the times when Rgv1 moves to Pos1 and Pos2, denoted as t1 and t2 respectively;

[0069] Step 2: Determine the minimum value Pos min and the maximum value Pos max of the position where another guiding vehicle Rgv2 is located during the time period [t1, t2]; Determine whether there is a path overlap between [Pos min , Pos max and [Pos1, Pos2]. If so, it is determined that there is a movement conflict; if not, it is determined that there is no movement conflict. A movement conflict occurs between the two rail-guided vehicles, that is This situation is denoted as S1, as Figure 3 shown; if there is no movement conflict between the two rails, this situation is denoted as S2.

[0070] In the above-mentioned motion conflict repair decoding operator, if there is a conflict, the position and / or handling time of the guiding vehicle selected for the process are coordinated until the conflict is resolved and then the handling task of the process is executed. Specifically, it includes:

[0071] Step 3: When conflict situation S1 occurs, determine whether Pos current is within the range of [Pos min , Pos max ;

[0072] If then keep Rgv1 stationary at Pos current , until Rgv2 completes the corresponding process handling task, as Figure 4 shown in (a) of

[0073] If Pos vurrent ∈[Pos min , Pos max], then move Rgv1 to [Pos min ,Pos max ] and stay at a safe distance outside the vehicle until Rgv2 completes the corresponding process handling task and then starts Rgv1 to carry out the current process handling task to avoid collision between the two rail-guided vehicles. Figure 4 As shown in (b), after Rgv2 completes the corresponding process handling task, it can preferentially carry out the handling task of the next process without interfering with Rgv1 in the current process handling task.

[0074] Move Rgv1 to [Pos min ,Pos max ] and stay at a safe distance outside the device. Specifically, Rgv1 can be moved to a position close to one end and maintain a safe distance from the end.

[0075] Step 4: Starting from the current Ad1 position, repeat steps 1 to 3 until the entire chromosome is decoded and a repaired decoding solution is obtained.

[0076] After executing the motion conflict repair decoding operator, the solution obtained will not cause motion conflicts between rail guided vehicles. At the same time, the repair operator will not destroy the relative order between machining processes, and the repair operator will not damage the initial solution, and can maintain the diversity of the initial population in the process of repairing motion conflicts.

[0077] In some specific embodiments, considering the machine constraints of the flexible workshop processing procedures and the handling constraints of the track-guided vehicle, with the goal of minimizing the maximum completion time, a flexible workshop production scheduling model with dual track-guided vehicles is constructed as follows:

[0078] The preset conditions, basic parameters and constraints are set for the target flexible manufacturing system, and the optimization objective function is set based on minimizing the maximum completion time, and a production scheduling model of the flexible manufacturing system with a double-track guided vehicle is constructed.

[0079] In order to effectively describe the scheduling problem, the preset conditions set in this embodiment include:

[0080] 1) Each machine (RGV) intelligently processes (transports) one workpiece at any time;

[0081] 2) Each workpiece can only be processed (transported) by one machine at any time;

[0082] 3) Once each workpiece begins to be transported (processed), it cannot be interrupted;

[0083] 4) All workpieces can be processed (transported) at zero time;

[0084] 5) All machines (RGVs) are available at time zero;

[0085] 6) Each RGV is exactly the same, and the pickup and unloading times of the RGV are known. The RGV has no storage space. The moving speeds of the loaded and unloaded RGVs are constant.

[0086] 7) The equipment can operate continuously and there is an infinite buffer.

[0087] 8) The set basic parameters include:

[0088] Index:

[0089] i, i′: workpieces, i, i′ = 1, 2,..., n;

[0090] j, j′: processes, j, j′ = 1, 2,..., N i ;

[0091] k, k′: machines, for process O i,j , k, k′ = 1, 2,..., m i,j ;

[0092] r, r′: RGVs, r, r′ = 1, 2;

[0093] Parameters:

[0094] O i,j : The j-th process of workpiece i;

[0095] N i : The total number of processes that workpiece i contains;

[0096] U i,j,k : If process O i,j can be processed by machine k, it takes the value of 1, otherwise 0;

[0097] p i,j,k : The processing time required for process O i,j to be processed on machine k;

[0098] S d : The safety distance that needs to be maintained between RGVs;

[0099] V: The moving speed of the RGV;

[0100] t LU : The time required for the RGV to load / unload workpieces;

[0101] PM k : The location where machine k is located;

[0102] M: A very large constant;

[0103] Decision variables:

[0104] C max : Makespan;

[0105] s i,j : The start processing time of operation O i,j ;

[0106] c i,j : The end processing time of operation O i,j ;

[0107] Ts i,j : The transportation start time of operation O; Ts i,j i′,j′ Indicates the transportation start time of operation O i′,j′ ;

[0108] Tc i,j : The transportation end time of operation O; Tc i,j i′,j′ Indicates the transportation end time of operation O i′,j′ ;

[0109] Operation O i,j 's transportation start position; Operation O i′,j′ 's transportation start position; Operation O i,j 's transportation end position; Operation O i′,j′ 's transportation end position;

[0110] x i,j,k : When processing on machine k, it takes the value of 1, otherwise 0; i,j

[0111] z k,i,j,i′,j′ : When operation O i,j is the immediate predecessor operation of operation O i′,j′ on machine k, it takes the value of 1, otherwise 0;

[0112] z i,j,r : When processed by the rail-guided vehicle r, it takes the value of 1, otherwise 0; i,j

[0113] g r,i,j,i′,j′ : When operation O i,j is the immediate predecessor handling operation of operation O i′,j′ on r, it takes the value of 1, otherwise 0;

[0114] Operation O i′,j′ 's handling start time is later than that of operation O i,j 's handling start time, it takes 1, otherwise 0; Operation Oi,j The handling start time is later than that of process O i′,j′ Take 1 when the handling start time is later than that of process O, otherwise 0;

[0115] Process O i′,j′ The handling completion time is later than that of process O i,j Take 1 when the handling start time is later than that of process O, otherwise 0; Process O i,j The handling completion time is later than that of process O i′,j′ Take 1 when the handling start time is later than that of process O, otherwise 0;

[0116] Process O i′,j′ The handling start time is later than that of process O i,j Take 1 when the handling completion time is later than that of process O, otherwise 0; Process O i,j The handling start time is later than that of process O i′,j′ Take 1 when the handling completion time is later than that of process O, otherwise 0;

[0117] Process O i′,j′ The handling completion time is later than that of process O i,j Take 1 when the handling completion time is later than that of process O, otherwise 0; Process O i,j The handling completion time is later than that of process O i′,j′ Take 1 when the handling completion time is later than that of process O, otherwise 0;

[0118] The set constraint conditions are as follows:

[0119]

[0120]

[0121] The time constraint condition of the rail-guided vehicle in the flexible job shop scheduling model, i.e., the RGV time constraint condition:

[0122]

[0123] The position constraint condition of the rail-guided vehicle in the flexible job shop scheduling model, i.e., the RGV position constraint condition:

[0124]

[0125] Equation (1) represents the calculation of the total completion time of the workpiece; Equations (2) and (3) are used to calculate the completion time of each operation in the workpiece; Equation (4) represents that only one workpiece can be processed by a machine within a time period; Equation (5) represents that a workpiece can only be processed on one machine within a time period; Equations (6) and (7) together represent that the workpieces are processed sequentially on a machine; Equation (8) represents that a workpiece can only be transported by one RGV within a time period; Equation (9) is used to calculate the transportation completion time of each operation in the workpiece; Equation (10) represents that each workpiece can only start processing after the transportation is completed; Equation (11) represents that only one workpiece can be transported by an RGV within a time period; Equations (12) and (13) together represent that the workpieces are transported sequentially by the RGV; Equations (14) and (15) are jointly used to calculate the transportation start position and transportation end position of operation O i,j ; Equations (16) are used to constrain the corresponding variables to be positive; Equations (17) and (18) are used to correctly define the decision variables together represent operation O i′,j′ 's handling start time and operation O i,j 's handling start time; Equations (19) and (20) are used to correctly define the decision variables together represent operation O i′,j′ 's handling completion time and operation O i,j 's handling start time; Equations (21) and (22) are used to correctly define the decision variables together represent operation O i′,j′ 's handling start time and operation O i,j 's handling completion time; Equations (23) and (24) are used to correctly define the decision variables together represent operation O i′,j′ 's handling completion time and operation O i,j 's handling completion time; Equations (25) and Equation (26) together represent that when the transportation start time and transportation completion time of operation O i′,j′ are both later than those of operation O i,j , ensure that the safety distance constraint is satisfied during the RGV transportation period; Equations (27) and (28) together represent that when the transportation start time of operation O i′,j′ is later than the transportation start time of operation O i,j , and the transportation end time of operation O i′,j′ is earlier than the transportation end time of operation O i,j , ensure that the safety distance constraint is satisfied during the RGV transportation period.

[0126] The optimization objective function is to minimize the makespan, which is specifically expressed as:

[0127] Minimize Obj = C max 。

[0128] In some specific embodiments, a meta - heuristic algorithm is used to solve the production scheduling model. For example, the flexible job - shop production scheduling model can be iteratively solved by a genetic algorithm, specifically including:

[0129] S21, adopting a random initialization strategy to generate an initial population; there are 4 main parameters affecting the genetic algorithm, namely the population size C, the maximum number of iterations N, the crossover rate R1, and the mutation rate R2. The optimal algorithm parameters of the population size C, the maximum number of iterations N, the crossover rate R1, and the mutation rate R2 can be determined through experimental tests.

[0130] S22, performing a motion - conflict - repair decoding operator on the individuals in the initial population to ensure the feasibility of each individual solution;

[0131] S23, calculating the fitness of the individuals in the population and selecting the individuals in the population according to the selection strategy to generate a new population; the selection strategy preferably adopts the elitist - retention strategy and the tournament - selection strategy. First, directly retain the top 10% of the individuals with excellent fitness in the population to the next population; then, for the remaining individuals, adopt the tournament - selection strategy. Each time, take a certain number of individuals from the original population (sampling with replacement), and then select the best one of them to enter the offspring population. Repeat this operation until the size of the new population reaches the size of the original population.

[0132] S24, performing crossover between the chromosomes of the population individuals;

[0133] S25, performing a mutation operator on the individuals in the population;

[0134] S26, performing a motion - conflict - repair decoding operator on the individuals in the population;

[0135] S27, when the set termination condition is met, such as meeting the set maximum number of iterations or other termination conditions, stop the iteration and output the individual with the maximum fitness in the population at this time as the optimal scheduling method; otherwise, return to the selection - strategy step, that is, S23.

[0136] Specifically, the selection strategy of the genetic algorithm enables individuals with higher fitness to be continuously retained. At the same time, due to the operations of the crossover and mutation operators, it avoids the premature convergence of the population to dominant individuals, increasing the global search ability of the algorithm to a certain extent.

[0137] Furthermore, this embodiment provides a flexible job - shop production scheduling system with a double - track guided vehicle. The system includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it executes the flexible job - shop production scheduling method with a double - track guided vehicle described in any one of the above.

[0138] Furthermore, this embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the flexible workshop production scheduling method with a double-rail guided vehicle as described in any one of the above.

[0139] The following are specific embodiments:

[0140] Taking 20 flexible manufacturing system scheduling task datasets as an example, the motion repair decoding method proposed by the present invention is applied to the genetic algorithm, the particle swarm algorithm, and the particle swarm algorithm combined with the simulated annealing algorithm. To ensure the fairness of the algorithms, the parameters are uniformly set as follows: the population size is set to 500, the mutation rate is set to 0.025, the crossover rate is set to 0.9, and the maximum number of iterations is set to 100. Each algorithm runs independently 10 times, and the comparison metrics are the minimum value and the average value of the algorithm running 10 times. The algorithm running results are shown in Table 1.

[0141] Table 1: Algorithm running results

[0142]

[0143] It can be seen from Table 1 that the algorithm proposed by the present invention performs excellently compared with the other two algorithms, which proves the rationality and effectiveness of the algorithm when solving such problems of flexible manufacturing systems.

[0144] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 steps of the flexible workshop production scheduling method with a double-rail guided vehicle provided.

[0145] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0147] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means, and the instruction means implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0149] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A flexible workshop production scheduling method with a double-track guided vehicle, characterized in that: include: Considering the machine constraints of the processing procedures in the flexible workshop and the handling constraints of the track-guided vehicle, a flexible workshop production scheduling model with double track-guided vehicles is constructed with the goal of minimizing the maximum completion time; the flexible workshop production scheduling model is iteratively solved through an optimization algorithm to obtain a flexible workshop production scheduling method; During iterative solving, the process processing sequence, process processing machine selection, and process guided vehicle selection are encoded to form chromosomes, and three chromosomes correspond to one solution; and the motion conflict repair decoding operator based on each iteration is executed on the solution generated, and the motion conflict repair decoding operator based on the motion conflict is specifically: When decoding the three chromosomes into a scheduling plan, for each process on the process processing sequence chromosome, it is determined whether the guided vehicle selected for the process conflicts with the motion trajectory of another guided vehicle in the process of completing the process handling task. If there is no conflict, the guided vehicle selected for the process completes the process handling task normally. If there is a conflict, the position and / or transportation time of the guided vehicle selected for the process will be coordinated until the conflict is resolved and then the transportation task of the process will be executed.

2. The flexible workshop production scheduling method with a double-track guided vehicle according to claim 1, characterized in that: When solving iteratively, the specific encoding structure of the solution is as follows: The first level of coding represents the order of all processes corresponding to the workpieces processed in the flexible workshop. The genetic position of the coding is composed of the workpiece number. The number of times the workpiece number appears in the coding is the number of processes for processing the workpiece, and the order in which the same workpiece number appears is the order of the processes for processing the workpiece. The second level of coding indicates the selection of processing machines for all processes corresponding to the workpiece. The codes correspond to the processes of the workpiece numbered from small to large from left to right, and the processes of each workpiece are arranged in order from left to right; The third-level code represents the guide vehicle selection for all processes corresponding to the workpiece processing. The genetic position of the code is composed of the guide vehicle number. The code corresponds to the processes of processing the workpiece numbered from small to large from left to right, and the processes of processing each workpiece are arranged in sequence from left to right.

3. The flexible workshop production scheduling method with a double-track guided vehicle according to claim 1, characterized in that: The motion conflict repair decoding operator specifically includes: For the process processing sequence chromosome, each process is decoded based on motion conflict repair starting from the left end to obtain a scheduling plan without motion conflict.

4. The flexible workshop production scheduling method with a double-track guided vehicle according to any one of claims 1 to 3, characterized in that: In the motion conflict repair decoding operator, for each process on the process processing sequence chromosome, it is determined whether the motion trajectory of the guided vehicle selected by the process conflicts with that of another guided vehicle in the process of completing the process handling task, specifically including: For each process on the process processing sequence chromosome, the guide vehicle selected for the current process is determined as Rgv1, and it is determined whether the current process is the first process for processing the workpiece. If so, it is determined that there is no motion conflict; If not, determine the position of the previous processing machine corresponding to the current process, recorded as Pos1; the position of the target processing machine corresponding to the current process, recorded as Pos2; and the current position of Rgv1, recorded as Pos current ; At the same time, ignoring the motion conflict, determine the time when Rgv1 moves to Pos1 and Pos2, which are recorded as t1 and t2 respectively; Determine the minimum value Pos of the position of another guided vehicle Rgv2 in the time period [t1, t2] min And the maximum value Pos max ; Judge [Pos min ,Pos max ] and [Pos1, Pos2] whether there is path overlap, if so, it is determined that there is a motion conflict; if not, it is determined that there is no motion conflict.

5. The flexible workshop production scheduling method with a double-track guided vehicle according to claim 4, characterized in that: In the motion conflict repair decoding operator, if there is a conflict, the position and / or handling time of the guided vehicle selected in the process is coordinated until the conflict is resolved before executing the handling task of the process, specifically including: If there is a conflict, determine Pos current Is it in [Pos min ,Pos max ] within the scope; like Then keep Rgv1 unchanged and stay at Pos current , until Rgv2 completes the corresponding process handling task; If Pos current ∈[Pos min ,Pos max ], then move Rgv1 to [Pos min ,Pos max ] and stay at a safe distance outside until Rgv2 completes the corresponding process handling task.

6. The flexible workshop production scheduling method with a double-track guided vehicle according to any one of claims 1 to 3, characterized in that: The time constraints of the rail-guided vehicle in the flexible workshop production scheduling model are as follows: Among them, Ts i,j Indicates process O i,j The transportation start time; In process O i′,j′ The transportation start time of process O is later than that of process O i,j The value is 1 when the transportation starts, otherwise it is 0; Tc i′,j′ Indicates process O i′,j′ The transportation end time; Tc i,j Indicates process O i,j The transportation end time; In process O i′,j′ The transportation completion time of process O is later than that of process O i,j The value is 1 when the transport start time is , otherwise it is 0; In process O i′,j′ The transportation start time of process O is later than that of process O i,j The value is 1 when the handling is completed, otherwise it is 0; In process O i′,j′ The transportation completion time of process O is later than that of process O i,j The value is 1 when the transportation is completed, otherwise it is 0; M is a constant.

7. The flexible workshop production scheduling method with a double-track guided vehicle according to claim 6, characterized in that: The position constraints of the rail-guided vehicle in the flexible workshop production scheduling model are as follows: in, Indicates process O i,j The transport start position; Indicates process O i′,j′ The transport start position; Indicates process O i,j The transport end location; Indicates process O i′,j′ The transport end position; S d Indicates the safe distance that needs to be maintained between RGVs; i,j,r In process O i,j The value is 1 when processing on a rail-guided vehicle r, otherwise it is 0; r = 1 or 2.

8. The flexible workshop production scheduling method with a double-track guided vehicle according to any one of claims 1 to 3, characterized in that: The flexible workshop production scheduling model is iteratively solved by a genetic algorithm, specifically including: Use random initialization strategy to generate the initial population; Execute the motion conflict repair decoding operator on the individuals in the initial population; Calculate the fitness of individuals in the population, and select individuals in the population according to the selection strategy to generate a new population; Perform crossover between chromosomes of individuals in the population; Execute mutation operators on individuals in the population; Execute the motion conflict repair decoding operator on the individuals in the population; When the set termination condition is met, the iteration stops and the individual with the largest fitness in the population is output as the optimal scheduling method; otherwise, it returns to the strategy selection step.

9. A flexible workshop production scheduling system with a double-track guided vehicle, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the flexible workshop production scheduling method with a dual-track guided vehicle described in any one of claims 1 to 8 is executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a flexible workshop production scheduling method with a dual-track guided vehicle is implemented as described in any one of claims 1-8.

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