A workshop scheduling method, device and equipment

Abstract

The present invention provides a workshop scheduling method, device and equipment, which relate to the field of manufacturing technology. The workshop scheduling method includes: obtaining a process set corresponding to an order; determining the number of batches included in each process and the arrangement quantity corresponding to each batch according to the process set; searching the process path sequence corresponding to the order using an ant algorithm to obtain a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and the process route constraints; and selecting the optimal scheduling result in the scheduling result sequence. The scheme of the present invention is based on the flow rules between processes, realizes the dynamic batching of order processes, forms a more refined scheduling result, and can provide guidance for workshop scheduling personnel when performing job planning and production scheduling.

Description

Technical Field

[0001] The present invention belongs to the field of manufacturing technology, and in particular relates to a workshop scheduling method, device and equipment. Background Art

[0002] With the development of the manufacturing industry and the market's demand for product diversity, today's manufacturing production methods are characterized by multiple varieties, variable batches, and mixed-line production. In the actual production process, the types and quantities of orders in the workshop are not fixed each month, and urgent tasks are unavoidable. Sometimes the workshop has to suspend the current relatively non-urgent orders in order to complete urgent orders, which will cause order stagnation and accumulation. Therefore, more and more workshops adopt batch production, that is, divide the orders into batches, and process them in different batches. The processing between batches is parallel processing. Studies have shown that in flexible workshops, batch processing of orders can effectively shorten the production cycle of the entire order and effectively reduce the idle time of the workshop's equipment, thereby improving equipment utilization. However, the batch division of orders is not blindly the finer the better. Since the switching production between multiple varieties on the same equipment requires a certain amount of preparation time, if the batch division of orders is extremely fine, it will not only fail to reduce the production cycle of the order, but also make the idle time of the workshop equipment fragmented, but it will not be able to insert batches of other orders.

[0003] Since enterprise equipment has different capabilities for different types of orders, that is, the same equipment has different capacities for similar processes of different workpieces, and the capacities of different processes of the same workpiece on different equipment are also different, in some cases, different batches can be divided for different processes of the same workpiece to obtain better scheduling results.

[0004] In summary, how to reasonably divide the multi-variety and variable batches of workpieces in the current flexible job shop scheduling problem into batches according to different processes, and reasonably arrange optional equipment for orderly processing, so as to obtain the maximum completion time of the workpiece while minimizing the order delivery time, has become a more urgent problem that needs to be solved in the current workshop. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a shop scheduling method, device and equipment, so as to solve the shop scheduling problem of dividing different batches of different processes of the same workpiece in the prior art.

[0006] In order to achieve the above object, an embodiment of the present invention provides a workshop scheduling method, comprising:

[0007] Get the process set corresponding to the order;

[0008] According to the process set, determine the number of batches included in each process and the arrangement quantity corresponding to each batch;

[0009] Using an ant algorithm to search for a process path sequence corresponding to the order, and obtaining a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and process route constraints;

[0010] In the scheduling result sequence, the optimal scheduling result is selected.

[0011] Optionally, obtain the process set corresponding to the order, including:

[0012] According to the process route constraint conditions, obtain a process set corresponding to the order;

[0013] The process route constraints include the processing sequence of the processes corresponding to the order and the flow rules between the processes.

[0014] Optionally, according to the process set, determining the number of batches included in each process and the arrangement quantity corresponding to each batch includes:

[0015] Obtaining a minimum start-up margin matrix set corresponding to the process set; wherein the minimum start-up margin is the minimum processing quantity calculated based on the single-piece working time of the process to ensure that the subsequent process can process the previous process without interruption within one shift;

[0016] The number of batches included in each process in the process set and the arrangement quantity corresponding to each batch are determined according to the minimum start-up margin matrix set.

[0017] Optionally, obtaining a minimum start margin matrix set corresponding to the process set includes:

[0018] Obtaining a set of optional equipment corresponding to each process in the process set;

[0019] Obtain the effective duration of each optional device in the optional device set within a default shift;

[0020] According to the effective time, a minimum start margin matrix of each process and a minimum start margin matrix set corresponding to the process set are obtained.

[0021] Optionally, determining the number of batches included in each process in the process set and the arrangement quantity corresponding to each batch according to the minimum start-up margin matrix set includes:

[0022] According to the minimum start-up margin matrix set and the number of tasks to be arranged in the current process, determining whether the current process needs to be divided into batches;

[0023] When the current process needs to be batched, the dynamic batching results of the process are obtained according to the minimum start-up margin matrix of the subsequent process and the current unscheduled quantity;

[0024] The process set determines the batch result of the current process according to the dynamic batch result of the process and updates the unscheduled quantity of the current process in the process set.

[0025] Optionally, searching the process path sequence corresponding to the order by using an ant algorithm to obtain the scheduling result sequence corresponding to the order includes:

[0026] Using the ant algorithm to search the process path sequence corresponding to the order, calculate the state transition probability of each batch of each process;

[0027] Selecting the next target batch according to the state transition probability;

[0028] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed, and obtain the scheduling result sequence corresponding to the order.

[0029] Optionally, the method further comprises:

[0030] Follow the steps below to use the ant algorithm to search for the process path sequence corresponding to the order and calculate the state transition probability of each batch of each process:

[0031] Selecting a first process from a set of first processes corresponding to the order as the first process to be arranged;

[0032] Establish a taboo process table, and add the selected process and its corresponding batches to the taboo process table;

[0033] Initialize parameters and place ants on the first process to start searching; the parameters include the pheromone content, number of ants and preset number of iterations corresponding to each process;

[0034] The state transition probability of each batch of each process is calculated, the next target batch for ant search is selected, and the target batch is added to the taboo process table.

[0035] Optionally, the state transition probability of each batch of each process is calculated, including:

[0036] According to the pheromone content of each process, the expected degree of each batch of each process, the pheromone heuristic factor and the expected heuristic factor, the state transition probability of each batch of each process is calculated.

[0037] Optionally, according to the equipment selection rule, arranging equipment for the target batch until the equipment arrangement of the process set is completed, and obtaining a scheduling result sequence corresponding to the order includes:

[0038] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed;

[0039] After all the ants of the number of ants have completed the search, updating the pheromone content corresponding to each process;

[0040] Restart the search, and after the ant algorithm reaches the preset number of iterations, obtain the scheduling result sequence corresponding to the order.

[0041] Optionally, updating the pheromone content corresponding to each process includes:

[0042] Sort the pheromone contents corresponding to the process set from large to small to obtain the sorting result;

[0043] The pheromone content in the sorting result that is at a preset percentage is increased by a preset concentration.

[0044] Optionally, selecting the optimal scheduling result in the scheduling result sequence includes:

[0045] In the scheduling result sequence, the minimized maximum completion time is selected as the optimal scheduling result.

[0046] Another embodiment of the present invention further provides a workshop scheduling device, comprising:

[0047] The first acquisition module is used to acquire the process set corresponding to the order;

[0048] A determination module, used for determining the number of batches included in each process and the arrangement quantity corresponding to each batch according to the process set;

[0049] A second acquisition module is used to search the process path sequence corresponding to the order by using an ant algorithm to obtain a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and the order process route;

[0050] The selection module is used to select the optimal scheduling result in the scheduling result sequence.

[0051] Another embodiment of the present invention further provides a workshop scheduling device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the workshop scheduling method described above when executed by the processor.

[0052] Another embodiment of the present invention further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the workshop scheduling method described above are implemented.

[0053] The above technical solution of the present invention has at least the following beneficial effects:

[0054] In the above scheme, the workshop scheduling method obtains the process set corresponding to the order, and determines the number of batches included in each process and the arrangement quantity corresponding to each batch based on the process set, so as to realize dynamic batching of processes based on the flow rules between processes, and then uses the ant algorithm to search the process path sequence corresponding to the order to obtain the scheduling result sequence corresponding to the order; wherein, the process path sequence is determined according to the number of batches included in each process and the process route constraints; in the scheduling result sequence, the optimal scheduling result is selected to form a more refined workshop scheduling plan, so as to provide guidance for the work planning and production scheduling of workshop scheduling personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of the steps of a workshop scheduling method according to an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a flow chart of an embodiment of a workshop scheduling method according to an embodiment of the present invention;

[0057] Figure 3 Schematic diagram of a workshop scheduling device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] The embodiments of the present invention provide a workshop scheduling method, device and equipment for the workshop scheduling problem of dividing different batches according to different working procedures of the same workpiece in the prior art.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a workshop scheduling method, comprising:

[0061] Step S11, obtaining a process set corresponding to the order;

[0062] In this step, the process set includes multiple processes. The workpieces in different orders correspond to different processes, and the production capacity of different processes on different equipment is also different, so the order is bound to its corresponding process set.

[0063] Step S12, determining the number of batches included in each process and the arrangement quantity corresponding to each batch according to the process set;

[0064] In this step, during the order operation process, different batches need to be dynamically divided for different processes of the same workpiece according to actual conditions to obtain better scheduling results.

[0065] Step S13, using an ant algorithm to search for a process path sequence corresponding to the order, and obtaining a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and process route constraints;

[0066] In this step, the ant algorithm is used to search for orders based on process route constraints and dynamic batching of processes to obtain a sequence of scheduling results.

[0067] Step S14: selecting the optimal scheduling result in the scheduling result sequence.

[0068] In this step, the scheduling result sequence is a dynamic batch result based on the flow rules between processes, from which the optimal ant path is selected to determine the optimal scheduling solution.

[0069] In this embodiment of the present invention, the workshop scheduling method obtains a process set corresponding to an order, and determines the number of batches included in each process and the arrangement quantity corresponding to each batch based on the process set, thereby realizing dynamic batching of processes based on flow rules between processes, and then uses an ant algorithm to search for the process path sequence corresponding to the order to obtain a scheduling result sequence corresponding to the order; wherein the process path sequence is determined based on the number of batches included in each process and the process route constraints; in the scheduling result sequence, the optimal scheduling result is selected to form a more refined workshop scheduling plan, thereby providing guidance for workshop scheduling personnel in their work planning and production scheduling.

[0070] It should be noted that the workshop scheduling method is applicable to flexible operation workshops and also to other workshops that need scheduling.

[0071] Specifically, step S11, obtaining a process set corresponding to an order, includes:

[0072] According to the process route constraint conditions, obtain a process set corresponding to the order;

[0073] The process route constraints include the processing sequence of the processes corresponding to the order and the flow rules between the processes.

[0074] It should be noted that all orders that need to be arranged in the workshop are traversed, and all orders that need to be arranged are bound to the process set according to the process route constraints corresponding to the orders. The process route constraints of different orders may be different or the same, which is related to the processing order of the corresponding processes and the flow rules between processes. For example, the process route constraint of order one is process A first and process B second, but the flow rule between processes of order two may be process B first and process A second.

[0075] Specifically, step S12, according to the process set, determines the number of batches included in each process and the arrangement quantity corresponding to each batch, including:

[0076] Obtaining a minimum start-up margin matrix set corresponding to the process set; wherein the minimum start-up margin is the minimum processing quantity calculated based on the single-piece working time of the process to ensure that the subsequent process can process the previous process without interruption within one shift;

[0077] The number of batches included in each process in the process set and the arrangement quantity corresponding to each batch are determined according to the minimum start-up margin matrix set.

[0078] It should be noted that the current process of the order can only be started when the quantity to be arranged of the current process of the order is greater than the minimum start-up margin.

[0079] Furthermore, obtaining a minimum start margin matrix set corresponding to the process set includes:

[0080] Obtaining a set of optional equipment corresponding to each process in the process set;

[0081] Obtain the effective duration of each optional device in the optional device set within a default shift;

[0082] According to the effective time, a minimum start margin matrix of each process and a minimum start margin matrix set corresponding to the process set are obtained.

[0083] It should be noted that all orders that need to be arranged in the workshop are traversed, all orders that need to be arranged are bound to the process set, and the minimum start-up margin matrix of the optional equipment corresponding to each process is calculated according to the following formula:

[0084]

[0085] Among them, O ij represents the jth process of order i, l represents the lth optional equipment; w ij The minimum start-up margin coefficient of the j-th process of order i; Eft l Indicates the effective time of optional equipment l in one of its default shifts; WAijl It represents the minimum start-up allowance matrix of the j-th process of order i corresponding to the optional equipment l.

[0086] Traverse the process set and obtain the minimum start-up margin matrix set ∑(i,j)WA corresponding to the process set ij .

[0087] Specifically, according to the minimum start-up margin matrix set, determining the number of batches included in each process in the process set and the arrangement quantity corresponding to each batch includes:

[0088] According to the minimum start-up margin matrix set and the number of tasks to be arranged in the current process, determining whether the current process needs to be divided into batches;

[0089] When the current process needs to be batched, the dynamic batching results of the process are obtained according to the minimum start-up margin matrix of the subsequent process and the current unscheduled quantity;

[0090] According to the dynamic batching result of the process, the batching result of the current process is determined and the unscheduled quantity process set of the current process in the process set is updated.

[0091] It should be noted that the process set should be traversed, the minimum start-up margin matrix corresponding to each process should be arranged on the corresponding optional equipment, the arrangement quantity of the current batch of the current process should be determined, and the planned end time of the current batch should be less than or equal to the end time of the shift of the optional equipment, so as to realize the flow between processes.

[0092] In this embodiment, whether the current process needs to be divided into batches is determined according to the following formula:

[0093]

[0094] Among them, WA ij is the minimum start-up allowance matrix of the j-th process of order i; represents the current unscheduled quantity of the jth process of order i; IP ij Indicates whether the jth process of order i is divided into batches. ij =1, it means that the jth process of order i needs to be divided into batches. ij =0, it means that the j-th process of order i does not need to be divided into batches;

[0095] When the j-th process of order i needs to be divided into batches, the corresponding arrangement quantity of each batch is determined according to the minimum start-up margin matrix of the j+1-th process of order i and the quantity of the j-th process that has been completed and can be arranged to the j+1-th process.

[0096] Specifically, step S13, using an ant algorithm to search for a process path sequence corresponding to the order, and obtaining a scheduling result sequence corresponding to the order, includes:

[0097] Using the ant algorithm to search the process path sequence corresponding to the order, calculate the state transition probability of each batch of each process;

[0098] Selecting the next target batch according to the state transition probability;

[0099] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed, and obtain the scheduling result sequence corresponding to the order.

[0100] It should be noted that the ant selects the target batch of the subsequent process according to the state transition probability of the batch to be arranged in the process set, and arranges equipment for the target batch according to the equipment selection rule until the equipment arrangement is completed for all batches of all process sets.

[0101] The equipment selection rule is the optional equipment in the optional equipment set that can be arranged to the target batch earliest.

[0102] It should also be noted that the state transition probability of each batch is a combination of the pheromone content and probability selection corresponding to each process, so that the ant algorithm can establish a corresponding balance between using previous selection information and exploring unknown paths, so that batches of processes with higher pheromone content are easily selected, and those processes with lower pheromone content can also be selected with probability.

[0103] In an optional embodiment of the present invention, the method further includes:

[0104] Follow the steps below to use the ant algorithm to search for the process path sequence corresponding to the order and calculate the state transition probability of each batch of each process:

[0105] Selecting a first process from a set of first processes corresponding to the order as the first process to be arranged;

[0106] Here, since the pheromone content of each process is the same during the initial search, the state transition probability of each first process in the first process set is equal. Here, a first process is randomly selected as the first process for ant search and process arrangement. A taboo process table is established, and the selected processes and their corresponding batches are added to the taboo process table;

[0107] Initialize parameters and place ants on the first process to start searching; the parameters include the pheromone content, number of ants and preset number of iterations corresponding to each process;

[0108] The state transition probability of each batch of each process is calculated, the next target batch for ant search is selected, and the target batch is added to the taboo process table.

[0109] It should be noted that if Figure 2 As shown, a specific embodiment of the workshop scheduling method is described:

[0110] To start the ant algorithm, you first need to initialize the ant algorithm parameters;

[0111] Determine whether the number of iterations reaches the preset number of iterations. If so, end the search; if not, proceed to the next step;

[0112] Initialize the ant colony parameters, clear the taboo process table of each ant in the colony, and reset the maximum completion time of the ant to zero;

[0113] Initialize the process set. Since the ant algorithm cannot be directly applied to the workshop scheduling problem, the scheduling result is represented by character encoding in this embodiment. In the order to be arranged, according to the process route constraints mentioned above, the character encoding set of the process set is determined, and the batch corresponding to the process is cleared, and the pheromone content is set to a constant. Here, τ is used. ijk (0) = 1 means that the pheromone content of the jth process of the i-th order at the initial moment is 1;

[0114] Traverse the ant colony, place each ant on the first process, start searching, calculate the state transition probability of the first batch of each process, select the next target batch for the ant search, add the target batch to the taboo process table, and arrange the equipment, wherein the number of batches of each process can be known according to the above-mentioned process dynamic batching results;

[0115] Determine whether the equipment arrangement of all processes in the process set is completed, and determine the end time of the last process, that is, the maximum completion time;

[0116] Here, the path that the ant searches from the first process to the last process is a path.

[0117] After all ants of the number of ants have completed the search, a local optimal solution is obtained;

[0118] When the number of iterations of the ant algorithm reaches the number of iterations, a global optimal solution, that is, the scheduling result sequence, is obtained, and the ant algorithm is terminated.

[0119] Furthermore, the state transition probability of each batch of each process is calculated, including:

[0120] According to the pheromone content of each process, the expected degree of each batch of each process, the pheromone heuristic factor and the expected heuristic factor, the state transition probability of each batch of each process is calculated.

[0121] It should be noted that the state transition probability of each batch in each process is calculated according to the following formula:

[0122]

[0123] Among them, τ ijk (t) represents the pheromone content of the jth process of the i-th order at time t;

[0124] η ijk (t) is the heuristic function, which indicates the expected degree of the ant to choose the kth batch of the jth process of the i-th order at time t. It is generally taken as S ij represents the unit labor time of the jth process of the i-th order, C ijk It represents the scheduled quantity of the kth batch of the jth process of the i-th order;

[0125] α represents the pheromone heuristic factor;

[0126] β represents the expectation heuristic factor;

[0127] It represents the state transition probability of ant m in the ant algorithm selecting the kth batch of the jth process of the i-th order at time t;

[0128] s∈allpros represents all the processes in the process set;

[0129] q0 represents the degree of utilizing prior knowledge, q0∈(0,1), in this embodiment, q0=0.5;

[0130] q is a random number that follows a uniform distribution on (0, 1).

[0131] Specifically, according to the equipment selection rule, arranging equipment for the target batch until the equipment arrangement of the process set is completed, and obtaining the scheduling result sequence corresponding to the order, including:

[0132] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed;

[0133] After all the ants of the number of ants have completed the search, updating the pheromone content corresponding to each process;

[0134] Restart the search, and after the ant algorithm reaches the preset number of iterations, obtain the scheduling result sequence corresponding to the order.

[0135] It should be noted that after all the ants of the number of ants complete the search of all batches of the process set, a local optimal solution is obtained, and the pheromone content corresponding to each process is updated;

[0136] Restart the search, where the pheromone content of each process in the ant colony parameters is the updated pheromone content, and when the number of iterations of the ant algorithm reaches the number of iterations, obtain the global optimal solution, that is, the scheduling result sequence, and end the ant algorithm.

[0137] Furthermore, the updating of the pheromone content corresponding to each process includes:

[0138] Sort the pheromone contents corresponding to the process set from large to small to obtain the sorting result;

[0139] The pheromone content in the sorting result that is at a preset percentage is increased by a preset concentration.

[0140] It should be noted that the pheromone content corresponding to each process is updated according to the following formula:

[0141]

[0142] Among them, τ ij (t+1) represents the pheromone content of the jth process of the i-th order at time t+1;

[0143] τ ij (t) represents the pheromone content of the jth process of the i-th order at time t;

[0144] ρ is the pheromone volatility coefficient;

[0145] Q is the pheromone intensity corresponding to the process, is the working hours corresponding to the optimal path searched by the ants. The path searched by the ants is the path that the ants search from the first process to the last process in the process path sequence. The path with a higher pheromone content is the better path.

[0146] In order to prevent the pheromone content from decreasing over time, the above formula is used to strengthen the pheromone content of the better path. At the same time, in order to prevent the pheromone content of the path with weaker pheromone content from volatilizing too much, the pheromone content of the weaker path is also strengthened. The pheromone content of the weaker path is increased by a preset concentration. Here, the preset concentration is 0.25.

[0147] Among them, the method for determining the weaker path is:

[0148] The pheromone contents corresponding to the process set are sorted from large to small to obtain the sorting result;

[0149] The processes ranked in a preset percentage in the sorting results are set as weaker paths, for example, the preset percentage is the last 25% of the paths in the sorting results.

[0150] Specifically, step S14, selecting the optimal scheduling result in the scheduling result sequence, includes:

[0151] In the scheduling result sequence, the minimized maximum completion time is selected as the optimal scheduling result.

[0152] It should be noted that the maximum completion time is the time from the start of processing of the first process to the end of processing of the last process in the process set of the order, that is, the maximum completion time, so the optimal scheduling result is the scheduling result with the smallest maximum completion time.

[0153] Another embodiment of the present invention further provides a workshop scheduling device, comprising:

[0154] The first acquisition module 31 is used to acquire a process set corresponding to an order;

[0155] A determination module 32, for determining the number of batches included in each process and the arrangement quantity corresponding to each batch according to the process set;

[0156] The second acquisition module 33 is used to search the process path sequence corresponding to the order by using the ant algorithm to obtain the scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and the order process route;

[0157] The selection module 34 is used to select the optimal scheduling result from the scheduling result sequence.

[0158] In this embodiment of the present invention, the workshop scheduling device is suitable for a flexible job workshop to provide guidance for the work planning and production scheduling of the workshop scheduling personnel. By obtaining the process set corresponding to the order and determining the number of batches included in each process and the arrangement quantity corresponding to each batch based on the process set, dynamic batching of processes based on the flow rules between processes is realized. Then, the process path sequence corresponding to the order is searched according to the ant algorithm to obtain the scheduling result sequence corresponding to the order; in the scheduling result sequence, the optimal scheduling result is selected to form a more refined workshop scheduling plan.

[0159] Specifically, the first acquisition module 31 is used for:

[0160] According to the process route constraint conditions, obtain a process set corresponding to the order;

[0161] Among them, the process route constraint condition is the flow rule between processes corresponding to the order.

[0162] And, the determination module 32 is used for:

[0163] Obtaining a minimum start-up margin matrix set corresponding to the process set;

[0164] The minimum start-up margin is the minimum processing quantity calculated based on the single-piece working time of the process to ensure that the subsequent process can process the previous process without interruption within one shift;

[0165] The number of batches included in each process in the process set and the arrangement quantity corresponding to each batch are determined according to the minimum start-up margin matrix set.

[0166] Furthermore, the determining module 32 is specifically configured to:

[0167] Obtaining a set of optional equipment corresponding to each process in the process set;

[0168] Obtain the effective duration of each optional device in the optional device set within a default shift;

[0169] According to the effective time, a minimum start margin matrix of each process and a minimum start margin matrix set corresponding to the process set are obtained.

[0170] Furthermore, the determining module 32 is further specifically configured to:

[0171] According to the minimum start-up margin matrix set and the number of tasks to be arranged in the current process, determining whether the current process needs to be divided into batches;

[0172] When the current process needs to be batched, the dynamic batching results of the process are obtained according to the minimum start-up margin matrix of the next process and the current unscheduled quantity;

[0173] According to the dynamic batching result of the process, the batching result of the current process is determined and the unscheduled quantity of the current process in the process set is updated.

[0174] Specifically, the second acquisition module 33 is used for:

[0175] Using the ant algorithm to search the process path sequence corresponding to the order, calculate the state transition probability of each batch of each process;

[0176] Selecting the next target batch according to the state transition probability;

[0177] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed, and obtain the scheduling result sequence corresponding to the order.

[0178] Furthermore, the device also includes:

[0179] According to the following modules, the ant algorithm is used to search the process path sequence corresponding to the order and calculate the state transition probability of each batch of each process:

[0180] A selection module, used for selecting a first process from a set of first processes corresponding to the order as the first process to be arranged;

[0181] A creation module is used to create a taboo process table and add the selected process and its corresponding batches to the taboo process table;

[0182] A search module is used to initialize parameters and place ants on the first process to start searching; the parameters include the pheromone content, the number of ants and the preset number of iterations corresponding to each process;

[0183] The calculation module is used to calculate the state transition probability of each batch of each process, select the next target batch for ant search, and add the target batch to the taboo process table.

[0184] Specifically, the calculation module is used for:

[0185] According to the pheromone content of each process, the expected degree of each batch of each process, the pheromone heuristic factor and the expected heuristic factor, the state transition probability of each batch of each process is calculated.

[0186] Specifically, the second acquisition module 33 is specifically used for:

[0187] Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed;

[0188] After all the ants of the number of ants have completed the search, updating the pheromone content corresponding to each process;

[0189] Restart the search, and after the ant algorithm reaches the preset number of iterations, obtain the scheduling result sequence corresponding to the order.

[0190] Furthermore, the second acquisition module 33 is also used for:

[0191] Sort the pheromone contents corresponding to the process set from large to small to obtain the sorting result;

[0192] The pheromone content in the sorting result that is at a preset percentage is increased by a preset concentration.

[0193] Specifically, the selection module 34 is used to:

[0194] In the scheduling result sequence, the one with the smallest corresponding maximum completion time is selected as the optimal scheduling result.

[0195] Another embodiment of the present invention further provides a workshop scheduling device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the workshop scheduling method described above when executed by the processor.

[0196] It should be noted that the workshop scheduling device of the embodiment of the present invention is a workshop scheduling device capable of executing the above-mentioned workshop scheduling method, and all embodiments of the above-mentioned workshop scheduling method are applicable to the workshop scheduling device and can achieve the same or similar beneficial effects.

[0197] Another embodiment of the present invention further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the workshop scheduling method described above are implemented.

[0198] It should be noted that the readable storage medium stores programs or instructions, which, when executed by the processor, implement the various processes of the above-mentioned workshop scheduling method embodiment and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0199] The processor is the processor in the workshop scheduling device described in the above embodiment. The computer readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0200] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A workshop scheduling method, characterized in that: include: Get the process set corresponding to the order; According to the process set, determine the number of batches included in each process and the arrangement quantity corresponding to each batch; Using an ant algorithm to search for a process path sequence corresponding to the order, and obtaining a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and process route constraints; Selecting the optimal scheduling result from the scheduling result sequence; The ant algorithm is used to search the process path sequence corresponding to the order to obtain the scheduling result sequence corresponding to the order, including: Using the ant algorithm to search the process path sequence corresponding to the order, calculate the state transition probability of each batch of each process; Selecting the next target batch according to the state transition probability; Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed, and obtain the scheduling result sequence corresponding to the order; In the scheduling result sequence, selecting the optimal scheduling result includes: In the scheduling result sequence, the minimized maximum completion time is selected as the optimal scheduling result.

2. The workshop scheduling method according to claim 1, characterized in that: Get the process set corresponding to the order, including: According to the process route constraint conditions, obtain a process set corresponding to the order; The process route constraints include the processing sequence of the processes corresponding to the order and the flow rules between the processes.

3. The workshop scheduling method according to claim 1, characterized in that: According to the process set, the number of batches included in each process and the arrangement quantity corresponding to each batch are determined, including: Obtaining a minimum start-up margin matrix set corresponding to the process set; The minimum start-up margin is the minimum processing quantity calculated based on the single-piece working time of the process to ensure that the subsequent process can process the previous process without interruption within one shift; The number of batches included in each process in the process set and the arrangement quantity corresponding to each batch are determined according to the minimum start-up margin matrix set.

4. The workshop scheduling method according to claim 3, characterized in that: Obtaining the minimum start-up allowance matrix set corresponding to the process set, including: Obtaining a set of optional equipment corresponding to each process in the process set; Obtain the effective duration of each optional device in the optional device set within a default shift; According to the effective time, a minimum start margin matrix of each process and a minimum start margin matrix set corresponding to the process set are obtained.

5. The workshop scheduling method according to claim 3, characterized in that: Determining the number of batches included in each process in the process set and the arrangement quantity corresponding to each batch according to the minimum start-up margin matrix set includes: According to the minimum start-up margin matrix set and the number of tasks to be arranged in the current process, determining whether the current process needs to be divided into batches; When the current process needs to be batched, the dynamic batching results of the process are obtained according to the minimum start-up margin matrix of the subsequent process and the current unscheduled quantity; The process set determines the batch result of the current process according to the dynamic batch result of the process and updates the unscheduled quantity of the current process in the process set.

6. The workshop scheduling method according to claim 1, characterized in that: The method further comprises: Follow the steps below to use the ant algorithm to search for the process path sequence corresponding to the order and calculate the state transition probability of each batch of each process: Selecting a first process from a set of first processes corresponding to the order as the first process to be arranged; Establish a taboo process table, and add the selected process and its corresponding batches to the taboo process table; Initialize parameters and place ants on the first process to start searching; the parameters include the pheromone content, number of ants and preset number of iterations corresponding to each process; The state transition probability of each batch of each process is calculated, the next target batch for ant search is selected, and the target batch is added to the taboo process table.

7. The workshop scheduling method according to claim 6, characterized in that: Calculate the state transition probability of each batch of each process, including: According to the pheromone content of each process, the expected degree of each batch of each process, the pheromone inspiration factor and the expected inspiration factor, the state transition probability of each batch of each process is calculated.

8. The workshop scheduling method according to claim 7, characterized in that: Arranging equipment for the target batch according to the equipment selection rule until the equipment arrangement of the process set is completed, and obtaining the scheduling result sequence corresponding to the order, including: Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed; After all the ants of the number of ants have completed the search, updating the pheromone content corresponding to each process; Restart the search, and after the ant algorithm reaches the preset number of iterations, obtain the scheduling result sequence corresponding to the order.

9. The workshop scheduling method according to claim 8, characterized in that: The updating of the pheromone content corresponding to each process includes: Sort the pheromone contents corresponding to the process set from large to small to obtain the sorting result; The pheromone content in the sorting result that is at a preset percentage is increased by a preset concentration.

10. A workshop scheduling device, characterized in that: include: The first acquisition module is used to acquire the process set corresponding to the order; A determination module, used for determining the number of batches included in each process and the arrangement quantity corresponding to each batch according to the process set; A second acquisition module is used to search the process path sequence corresponding to the order by using an ant algorithm to obtain a scheduling result sequence corresponding to the order; wherein the process path sequence is determined according to the number of batches included in each process and the order process route; A selection module, used for selecting the optimal scheduling result in the scheduling result sequence; Wherein, the second acquisition module is specifically used for: Using the ant algorithm to search the process path sequence corresponding to the order, calculate the state transition probability of each batch of each process; Selecting the next target batch according to the state transition probability; Arrange equipment for the target batch according to the equipment selection rule until the equipment arrangement for all batches is completed, and obtain the scheduling result sequence corresponding to the order; The selection module is specifically used for: In the scheduling result sequence, the one with the smallest corresponding maximum completion time is selected as the optimal scheduling result.

11. A workshop scheduling device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the workshop scheduling method according to any one of claims 1 to 9.

12. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the workshop scheduling method according to any one of claims 1 to 9 are implemented.

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