Cigarette production scheduling method and device and electronic equipment

Abstract

The invention provides a cigarette production scheduling method and device and electronic equipment. The cigarette production scheduling method comprises the steps of obtaining production basic data of a target cigarette factory in a future preset time period; based on the production basic data, generating an initial task sequence containing a plurality of ordered tasks for each cigarette device; wherein each task is associated with a group of variables to be solved; on the basis of a production scheduling model taking minimization of collaborative completion time as an optimization target, solving to-be-solved parameters by following a task time sequence constraint condition, a channel time window coverage constraint condition, a process aging constraint condition and a maintenance time period aggregation constraint condition to obtain an optimal time arrangement and resource allocation scheme of each task; according to the scheme, a complete constraint system covering resources, time sequences and processes is constructed by obtaining multiple production basic data, multiple types of variables are synchronously solved on the basis of the optimization model for minimizing the collaborative completion time, and synchronous improvement of global optimization quality and calculation solving efficiency of the scheduling scheme is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of cigarette industrial production technology, and in particular to a cigarette production scheduling method, apparatus and electronic equipment. Background Technology

[0002] Cigarette production, as a typical process manufacturing industry, involves the coordination of multiple equipment, the connection of multiple processes, and the scheduling of multiple resources. With the increasing demand for diversified cigarette products and the expansion of production scale, production scheduling needs to simultaneously meet multiple constraints such as equipment maintenance, process switching, and resource allocation, which places extremely high demands on the global optimization capability and computational efficiency of scheduling methods.

[0003] Existing cigarette production scheduling technologies mainly employ rule-based heuristic algorithms or phased optimization methods. The former allocates tasks by pre-setting priority rules, such as prioritizing the shortest equipment idle time or the lowest process changeover cost, but lacks global coordination capabilities and struggles to handle multivariate coupling problems under complex constraints. The latter decomposes scheduling into two independent stages: task sequencing and resource allocation. It first generates an initial task sequence and then allocates resources, resulting in a separation between temporal and resource constraints, making it prone to getting trapped in local optima.

[0004] The existing technology has three core defects: First, the constraint system is incomplete, which leads to the disconnect between the scheduling scheme and the actual production conditions; second, the optimization objective is singular, making it difficult to achieve global optimal efficiency; third, the solution method has great limitations, and phased optimization or heuristic algorithms cannot simultaneously solve for strongly coupled variables such as task time, tobacco type, and feeding channel allocation, which easily leads to conflict scheduling, resulting in increased equipment idle waiting time and asynchronous production rhythm, ultimately restricting the achievement of overall production efficiency and collaborative completion goals. Summary of the Invention

[0005] This application provides a cigarette production scheduling method, apparatus, and electronic device, which constructs a complete constraint system covering resources, timing, and processes by acquiring multiple production basic data, and simultaneously solves multiple types of variables based on an optimization model that minimizes collaborative completion time, thereby achieving a simultaneous improvement in the global optimization quality and computational efficiency of the scheduling scheme.

[0006] In a first aspect, embodiments of this application provide a cigarette production scheduling method, the method comprising:

[0007] When a production scheduling request for a target cigarette factory is received, the production basic data of the target cigarette factory within a future preset time period is obtained; wherein, the production basic data includes the connection relationship between at least one cigarette equipment and at least one feeding channel, the tobacco product conversion time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product conversion production plan, and target production volume of various types of tobacco products.

[0008] Based on the aforementioned production data, an initial task sequence containing multiple ordered tasks is generated for each cigarette-making machine; wherein each task is associated with a set of variables to be solved, including at least one of the following: start time to be solved, end time to be solved, type of tobacco to be solved, and feeding channel to be solved.

[0009] Based on a production scheduling model with the goal of minimizing collaborative completion time, and following constraints on task timing, channel time window coverage, process efficiency, and maintenance period aggregation, the parameters to be solved are obtained to arrive at the optimal time arrangement and resource allocation scheme for each task.

[0010] Secondly, embodiments of this application also provide a cigarette production scheduling device, the device comprising:

[0011] The data acquisition module is used to acquire the basic production data of the target cigarette factory within a future preset time period when a production scheduling request for the target cigarette factory is received; wherein, the basic production data includes the connection relationship between at least one cigarette equipment and at least one feeding channel, the tobacco product conversion time cost matrix, the equipment maintenance calendar, the equipment forced interruption event, the tobacco product conversion production plan, and the target production volume of various types of tobacco products.

[0012] The task sequence generation module is used to generate an initial task sequence containing multiple ordered tasks for each cigarette-making machine based on the production basic data; wherein each task is associated with a set of variables to be solved, including at least one of the following: start time to be solved, end time to be solved, type of tobacco to be solved, and feeding channel to be solved.

[0013] The scheduling solution module is used to solve the parameters to be solved based on the production scheduling model with the optimization objective of minimizing collaborative completion time, following the constraints of task sequence, channel time window coverage, process efficiency, and maintenance period aggregation, so as to obtain the optimal time arrangement and resource allocation scheme for each task.

[0014] Thirdly, embodiments of this application also provide an electronic device, which includes:

[0015] One or more processors;

[0016] Storage device for storing one or more programs.

[0017] When one or more programs are executed by one or more processors, the one or more processors implement a cigarette production scheduling method as described in any of the embodiments of this application.

[0018] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a cigarette production scheduling method as described in any of the embodiments of this application.

[0019] This application provides a cigarette production scheduling method, apparatus, and electronic device. The method includes: when a production scheduling request for a target cigarette factory is received, acquiring basic production data including the connection relationship between at least one cigarette equipment and at least one feeding channel, a tobacco product conversion time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product conversion production plans, and target production volumes for various types of tobacco products; then, based on the basic production data, generating an initial task sequence containing multiple ordered tasks for each cigarette equipment, wherein each task is associated with a set of variables to be solved, including: a start time to be solved, an end time to be solved, a tobacco product type to be solved, and at least one of a feeding channel to be solved; thereby, based on a production scheduling model with the optimization objective of minimizing collaborative completion time, following task timing constraints, channel time window coverage constraints, process time constraints, and maintenance period aggregation constraints, solving the parameters to be solved, and obtaining the optimal time arrangement and resource allocation scheme for each task. The technical solution of this application constructs a complete constraint system covering resource allocation, timing coordination, and process requirements by acquiring basic production data such as equipment-channel connection relationships, tobacco product switching time costs, and equipment maintenance calendars. Based on an optimization model aimed at minimizing collaborative completion time, it simultaneously solves multiple variables such as task start / end time, tobacco product type, and tobacco feeding channel allocation, while satisfying complex constraints such as task timing, channel time windows, process efficiency, and maintenance aggregation. This improves equipment utilization and the synchronization of production rhythm, effectively reduces idle waiting time caused by tobacco product switching, equipment maintenance, or forced interruptions, enhances the adaptability of the scheduling scheme to actual production dynamic disturbances, and ultimately achieves overall production efficiency optimization and the goal of collaborative completion. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the embodiments to be described in this application, and not all of them. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0021] Figure 1 A schematic flowchart illustrating a cigarette production scheduling method provided in an embodiment of this application;

[0022] Figure 2This is a schematic diagram of the conversion time cost matrix among the nine types of tobacco products involved in the embodiments of this application;

[0023] Figure 3 This is a schematic diagram of the equipment maintenance calendar involved in the embodiments of this application;

[0024] Figure 4 This is a schematic diagram illustrating a list of forced device interruption events as described in the embodiments of this application;

[0025] Figure 5 A flowchart illustrating yet another cigarette production scheduling method provided in this application embodiment;

[0026] Figure 6 This is a schematic diagram of a cigarette production scheduling device provided in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0029] Before introducing the technical solutions provided in the embodiments of this application, the application scenarios of the solutions can be described first. This embodiment is applicable to various scenarios that require refined scheduling of cigarette production. Currently, although rule-based heuristic algorithms or phased optimization methods are widely used in production planning, traditional methods have obvious limitations. In practical applications, cigarette production has characteristics such as multi-process collaboration, strong equipment heterogeneity, high brand switching losses, and complex constraints. Moreover, the scheduling process needs to take into account multiple resource constraints such as equipment maintenance and channel collaboration. Traditional scheduling methods are difficult to optimize at the global level, which can easily lead to problems such as low equipment utilization, long production cycles, and high product switching costs. Therefore, there is an urgent need for a scheduling method that can coordinate multiple equipment and channel resources and comprehensively consider various complex constraints to improve the global optimization quality and executability of the scheduling scheme. This embodiment aims to minimize the collaborative completion time. Under the premise of strictly adhering to constraints such as task sequence, channel time window, process efficiency, and maintenance period, it solves the optimal time arrangement and resource allocation scheme for each task, thereby ensuring the mathematical optimality of the scheduling scheme and effectively improving the overall efficiency and resource coordination level of cigarette production.

[0030] Example 1

[0031] Figure 1This is a flowchart illustrating a cigarette production scheduling method provided in an embodiment of this application. This embodiment is applicable to various situations requiring refined scheduling of cigarette production. The method can be executed by a cigarette production scheduling device, which can be implemented in the form of software and / or hardware. The hardware can be a controller, such as a mobile terminal, a PC, or a server.

[0032] like Figure 1 As shown, the method includes:

[0033] S110. When a production scheduling request for a target cigarette factory is received, obtain the basic production data of the target cigarette factory for a future preset time period.

[0034] In this context, the target cigarette factory refers to a specific cigarette manufacturing plant that serves as the object of implementation and application of the scheduling method of this invention; it is the entity to which the production scheduling request is directed. A production scheduling request is a clear instruction initiated by the production management system or authorized personnel of the target cigarette factory and submitted to the scheduling decision system to trigger scheduling calculations. The future preset time period refers to a pre-defined time interval extending backward from the current time point. This time period, as the planning period covered by the production scheduling request, is the time range boundary for acquiring the target cigarette factory's basic production data, generating the initial task sequence, and solving for the optimal time arrangement and resource allocation scheme.

[0035] Production base data refers to the set of all input information reflecting the specific production conditions and planned requirements of the target cigarette factory. Production base data includes the connection relationship between at least one cigarette-making machine and at least one feeding channel, the cigarette product changeover time-cost matrix, equipment maintenance calendar, equipment forced interruption events, cigarette product changeover production plans, and the target production volume for various types of cigarette products.

[0036] In this context, "cigarette equipment" refers to physical devices or machines that directly participate in the cigarette manufacturing process within the target cigarette factory. It is a production resource entity with a specific connection to the tobacco feeding channel. For example, cigarette equipment can be cigarette making and packaging equipment. The tobacco feeding channel refers to a dedicated logistics path responsible for conveying raw tobacco materials to the cigarette equipment. The definition of "the connection between at least one cigarette device and at least one tobacco feeding channel" clarifies which specific cigarette devices in the target cigarette factory can be assigned to which specific tobacco feeding channels to obtain raw materials. This relationship is the fundamental basis for constructing the channel time window coverage constraints and determining the tobacco feeding channel variables to be solved for each task.

[0037] The tobacco product conversion time cost matrix is ​​a data structure organized in matrix form. Its function is to quantify the time consumption corresponding to the conversion production between different tobacco product types. Figure 2The following is an example of the conversion time cost matrix between the nine types of tobacco products: the value "0" indicates that the same type of product is produced and there is no conversion time consumption; the values ​​"1", "4", etc., indicate the time consumption required for conversion between different products (for example, "1" corresponds to the consumption of P1→P2, and "4" corresponds to the consumption of P1→P3); the symbol "-" indicates that there is no conversion setting between two types of tobacco products.

[0038] The equipment maintenance calendar refers to a pre-defined schedule that outlines all planned maintenance work for the cigarette equipment in the target cigarette factory within a predetermined future timeframe. Figure 3 This is a diagram of an equipment maintenance calendar. A forced equipment interruption event refers to a pre-set, non-adjustable event within the preset future time period that will cause the production activities of the cigarette equipment to be forcibly suspended. Figure 4 This is a diagram illustrating forced interruption events of the device.

[0039] The tobacco product conversion production plan refers to a pre-formulated guiding scheme that specifies the various types of tobacco products that the target cigarette factory needs to produce within the pre-set future time period, along with their production sequence and timing. The target production volume refers to the total production task for each type of tobacco product that the target cigarette factory is required to complete within the pre-set future time period.

[0040] Specifically, the cigarette production scheduling method of this embodiment can be integrated into the scheduling decision system. When the scheduling decision system receives a trigger signal from the target cigarette factory to initiate a production scheduling request, it immediately extracts all the original information directly related to the scheduling calculation from the factory's internal information systems and databases. This information is limited to the future preset time period specified in the scheduling request. Its content includes the dynamically changing topological connection relationship between cigarette equipment and feeding channels, the time cost required for switching between different cigarette products, i.e., the cigarette product conversion time cost matrix, the pre-scheduled equipment maintenance calendar, unpredictable forced equipment interruption events, cigarette product conversion production plans driven by sales or inventory strategies, and the target production volume of various types of cigarette products subdivided by brand or specification. The above elements together constitute the production base data for subsequent modeling and solving.

[0041] S120. Based on production data, generate an initial task sequence containing multiple ordered tasks for each cigarette-making machine; wherein each task is associated with a set of variables to be solved, including at least one of the following: start time to be solved, end time to be solved, type of tobacco to be solved, and feeding channel to be solved.

[0042] The initial task sequence refers to a set of tasks arranged in a logical order that are pre-planned for each cigarette machine based on production data. Each task in the sequence is associated with a set of variables to be solved, including the start time, end time, type of tobacco, and feeding channel. It is the basis for building a production scheduling model and optimizing it to obtain the final resource allocation scheme.

[0043] Among them, the start time and end time to be solved are unknown time variables that define the start and end times of a task in the time dimension. The tobacco type to be solved is an unknown category variable that identifies the specific cigarette variety produced corresponding to a task. The feed path to be solved is an unknown resource variable that identifies the specific raw material transport path bound to the execution of a task. Together, they constitute a set of decision variables that are predefined when generating the initial task sequence and need to be optimized and solved by the subsequent scheduling model to be finally determined.

[0044] In this embodiment, based on the collected connection relationships, tobacco product conversion time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product conversion production plan, and target production volume of various types of tobacco products, a scheduling chain consisting of several tasks arranged in chronological order can be constructed for each cigarette equipment according to the logic of process sequence and capacity balance. Each task is assigned a set of variables to be solved at the same time as it is generated. These variables include at least the start time, end time, tobacco product type, and feeding channel, which are used to determine their unique values ​​through optimization calculation in the subsequent scheduling model, thereby transforming the initial task sequence into an executable time and resource allocation scheme.

[0045] More specifically, based on production data, the specific implementation steps for generating an initial task sequence containing multiple ordered tasks for each cigarette-making machine may include:

[0046] (1) Identify all non-production periods for each cigarette machine within a future preset time period based on the equipment maintenance calendar and equipment forced interruption events.

[0047] In this embodiment, the start and end times of scheduled maintenance, repair, and rotation maintenance activities can be read from the equipment maintenance calendar, and the start and end times of unexpected interruptions such as sudden failures, energy supply disruptions, and unplanned shutdowns can be extracted from equipment forced interruption events. The two are then merged and mapped onto the time axis of a future preset period, thereby accurately marking the entire time interval during which each cigarette equipment cannot perform any production tasks, forming a set of non-production periods to be avoided in the subsequent scheduling model.

[0048] For example, based on the equipment maintenance calendar (such as monthly rotational maintenance, planned weekly maintenance) and forced equipment downtime events (such as known holiday shutdowns, temporary power outages for maintenance), all non-production periods during the next month when the equipment will not be able to work can be identified. For example, there is an 8-hour rotational maintenance on Wednesday of the second week, a 24-hour holiday shutdown between the third and fourth days, and a 4-hour temporary maintenance on the 15th day.

[0049] (2) Aggregate the non-production time periods of working hours that are adjacent in time or have an interval of less than the threshold to form a continuous maintenance time block in working hours.

[0050] Among them, the continuous maintenance time block refers to the continuous and indivisible equipment unavailability period on the time axis. It is the direct basis for constructing maintenance period aggregation constraints and ensuring that the task time arrangement in the initial task sequence can effectively avoid all maintenance periods.

[0051] In this technical solution, the identified non-production periods, with working hours as the smallest recording unit, can be scanned sequentially according to the timeline. When any two periods are found to be directly connected or have only a gap of less than a set threshold of working hours in between, they are immediately merged into a new, longer segment. The process continues to move forward until merging is no longer possible, ultimately resulting in several segments of continuous maintenance time blocks with no production tasks inserted within them, with working hours as the granularity, and which are treated as a unified unavailable window. These blocks are then used for the subsequent scheduling model to avoid all unavailable time blocks at once.

[0052] For example, non-production periods can be converted from calendar time into uniform working hours (e.g., if each day is calculated as 16 production hours, Wednesday morning of the second week can be converted to the (7×1 + 2)×16 + 8 = 152nd working hour of the month). Non-production periods that are adjacent in time or have an interval of less than a preset threshold (e.g., 4 working hours) are aggregated. For example, the holiday shutdown at the end of the third day (48th working hour) and the weekly maintenance on the morning of the fourth day (52nd working hour) are only 4 hours apart and will be merged into a continuous maintenance time block from the 48th working hour to the 60th working hour.

[0053] (3) Based on the tobacco product conversion production plan and the target production volume of each type of tobacco product, determine the types of tobacco products that need to be produced on each cigarette machine and their production order.

[0054] In this embodiment, the types of tobacco products required to be started within a preset time period, the switching order, and the switching frequency can be read from the tobacco product conversion production plan. Combined with the target production volume of each type of tobacco product, the output quota of each product should be allocated to each cigarette machine under capacity balance. Then, by integrating the equipment connection relationship and the tobacco product conversion time cost matrix, the output quota is converted into a sequence of tobacco product types arranged in chronological order. This allows each cigarette machine to obtain a production sequence that satisfies the market switching rhythm and minimizes conversion losses, which serves as the direct basis for the tobacco product type field of each task in the initial task sequence.

[0055] For example, based on the tobacco product conversion production plan (this month's instruction requires the sequential production of "Brand A", "Brand B" and "Brand C") and the target production volume of each type of tobacco product (e.g., "Brand A" needs to produce 2000 cartons, "Brand B" needs to produce 1500 cartons, and "Brand C" needs to produce 1800 cartons), the types of tobacco products to be produced on this equipment and their production order are determined, that is, the production of "Brand A", "Brand B" and "Brand C" is arranged in sequence.

[0056] (4) Based on the conversion time data between different tobacco products provided by the tobacco conversion time cost matrix, insert the corresponding conversion task between adjacent tobacco product types in the production sequence.

[0057] Among them, the product conversion task refers to the task link in the initial task sequence that is specially set up to complete the production conversion from one type of tobacco product to another. Its execution time is defined by the tobacco product conversion time cost matrix. This task does not directly produce tobacco products, and its time consumption directly affects the interval setting of adjacent production tasks in the task timing constraints.

[0058] In this technical solution, according to the time required to switch from one type of tobacco product to another, as recorded in the tobacco product conversion time cost matrix, a product conversion task is generated at the junction of these two tobacco product types in the pre-arranged production sequence. This task is specifically used to complete activities such as brand change, material clearing, parameter adjustment, and quality confirmation. This allows the equipment to automatically reserve a gap in the operating sequence that is equal to the conversion time data, ensuring that the subsequent tobacco product type can start production at the time point that meets the process requirements.

[0059] For example, by querying the tobacco product switching time cost matrix, it is found that switching from "Brand A" to "Brand B" requires 4 working hours, and switching from "Brand B" to "Brand C" requires 3 working hours. Therefore, a 4-hour product switching task is inserted between the production tasks of "Brand A" and "Brand B", and a 3-hour product switching task is inserted between the production tasks of "Brand B" and "Brand C".

[0060] (5) Take the continuous maintenance time block as maintenance task, combine it with the initial production task sequence containing the product transfer task, arrange them in time order, and generate the initial task sequence for each cigarette equipment.

[0061] Each task in the initial task sequence has reserved parameter variables for recording the start time, end time, tobacco type, and binding the feeding channel.

[0062] In this embodiment, the aggregated continuous maintenance time blocks can be directly regarded as non-producible maintenance tasks. They are then merged with the production task segments that were previously arranged in production order and inserted with product transfer tasks. They are uniformly sorted on the same time axis, so that maintenance tasks and production tasks alternate, forming a complete task chain that is connected end to end and has no conflicts. In this chain, each task, whether it is a maintenance task, a product transfer task, or a production task, is assigned the same parameter variable slots to record the start time, end time, tobacco type, and the bound feeding channel that need to be determined for subsequent optimization, thereby forming an initial task sequence that can be directly called and solved by the scheduling model.

[0063] For example, all identified and aggregated continuous maintenance time blocks are treated as maintenance tasks and combined with the initial production task sequence (producing "Brand A" → producing "Brand B" → producing "Brand C") that has already been inserted into the product transfer task sequence. These are then arranged chronologically to generate the complete initial task sequence for this equipment for the month. In this sequence, each task has reserved corresponding parameter variables for recording and solving its specific start time, end time, tobacco type, and associated feed channel during subsequent optimization.

[0064] S130. Based on the production scheduling model with the optimization objective of minimizing collaborative completion time, and following the constraints of task sequence, channel time window coverage, process efficiency, and maintenance period aggregation, the parameters to be solved are obtained to achieve the optimal time arrangement and resource allocation scheme for each task.

[0065] Among them, the collaborative completion time refers to the cumulative number of working hours from the start of the future preset time period until the last task on all cigarette equipment is completed. This indicator covers production tasks, product transfer tasks and maintenance tasks, reflecting the limit of collaborative utilization of the entire plant's resources on the time axis. The optimization goal is to achieve simultaneous release of production capacity and the shortest overall cycle by compressing this value.

[0066] The optimal time schedule is the combination of start and end times for each task that minimizes the collaborative completion time. The resource allocation scheme refers to the feeding channel binding result generated synchronously with the optimal time schedule after being solved by the production scheduling model. Its content clearly defines the feeding channel number exclusively used by each cigarette machine during the execution of each task, the corresponding status of the channel opening time and the equipment connection relationship, ensuring that the tobacco supply, equipment capacity and task time are accurately matched and can be directly put into on-site execution.

[0067] Among them, the task timing constraint condition refers to the use of the pre-arranged order in the initial task sequence and the adjacent tobacco product conversion time cost matrix to stipulate that the next task can only start after the end time of the previous task on the same cigarette equipment is added to the corresponding product conversion time consumption. This ensures that various tasks such as production, conversion, and maintenance are connected end to end in time and do not overlap, ensuring that the equipment operation rhythm is synchronized with the brand switching process requirements.

[0068] Among them, the channel time window coverage constraint means that, based on the connection relationship between the cigarette equipment and the feeding channel and the preset channel opening time of each feeding channel, any production task must fall completely within the effective working window of its bound feeding channel during the execution period, ensuring that the task starts no earlier than the channel opening time and ends no later than the channel closing time, thereby avoiding mismatch or conflict between the equipment and the feeding channel in terms of time.

[0069] Among them, the process time constraint condition refers to setting a maximum allowable duration threshold for the duration of any task from start to finish, based on the process requirements specified in the tobacco product conversion production plan. The scheduling result must not exceed this upper limit to prevent tobacco from staying in the feeding channel or equipment for too long, causing indicators such as moisture, aroma, and temperature to deviate from the process standards, thereby ensuring that product quality and production rhythm are controlled synchronously.

[0070] Among them, the maintenance period aggregation constraint condition refers to treating the continuous maintenance time block formed by aggregation as an indivisible rigid time window, so that the scheduling model cannot arrange any production tasks or product transfer tasks within the window, and does not allow splitting, overlapping or moving forward or backward, ensuring that the equipment is in a completely stopped state during the complete and continuous maintenance period, thereby meeting the continuity and safety requirements of planned activities such as maintenance, repair and maintenance.

[0071] Next, we can elaborate on the construction methods of task timing constraints, channel time window coverage constraints, process efficiency constraints, and maintenance period aggregation constraints:

[0072] (1) Based on the task order and tobacco product conversion time cost matrix in the initial task sequence, construct task timing constraints to ensure that the start time of subsequent tasks on the same device is not earlier than the sum of the end time of the preceding task and the corresponding product conversion time.

[0073] In this embodiment, the time consumption required for switching between adjacent tobacco product types can be extracted from the tobacco product switching time cost matrix by utilizing the pre-arranged order of the initial task sequence. The next task can only start after the end time of the previous task is added to the time consumption of the switching product, thus forming an insurmountable lower time limit in the model.

[0074] Specifically, the formula for expressing the task timing constraints can be:

[0075] (1)

[0076] In the formula, This represents the start time of the i-th task of the m-th cigarette rolling machine. This represents the end time of the (i-1)th task. This represents the time consumed in switching products between the i-th task and the (i-1)-th task.

[0077] (2) Based on the connection relationship between at least one cigarette making equipment and at least one feeding channel and the preset channel opening time of each feeding channel, construct a channel time window coverage constraint to ensure that the production task execution period is within the effective working window of the bound feeding channel.

[0078] In this embodiment, the range of feeding channels that the equipment can access can be determined by the connection relationship, and the preset channel opening time of each channel can be read. It is mandatory that the start and end times of the production task must fall completely within the open range of the selected feeding channel, so that the task execution time is not earlier than the channel opening time and not later than the channel closing time, thereby preventing the equipment and the feeding channel from being mismatched or conflicting in time, and ensuring that the tobacco supply and production demand are synchronized.

[0079] Specifically, the formula for expressing the channel time window coverage constraint can be:

[0080] (2)

[0081] In the formula, It is a binary decision variable. This represents the start time of the i-th task of the m-th cigarette rolling machine. This represents the end time of the i-th task of the m-th cigarette rolling machine. This indicates the start time of the c-th wire feeding channel. This indicates the end time of the c-th wire feeding channel. This represents the set of optional channels for the m-th cigarette rolling device.

[0082] Formula (2) means: if the i-th task of the m-th cigarette rolling equipment determines the feeding channel c Then, the start time of the task on that device. It must be later than or equal to the start time of the corresponding transmission task on channel c. And the end time of the device's task. It must be earlier than or equal to the end time of the corresponding transmission task on channel c. This ensures that the equipment and the supply channel work in tandem in time; the equipment can only begin production after the supply channel has started supplying raw materials, and production must be completed before the supply channel stops supplying materials.

[0083] (3) Based on the process requirements specified in the tobacco product conversion production plan, construct process time constraints to limit the duration of the task from start to finish to be within the maximum allowable duration threshold.

[0084] In this embodiment, the maximum allowable duration threshold corresponding to each type of tobacco product can be extracted from the tobacco product conversion production plan, and the actual duration of any task can be set in the scheduling model to not exceed the threshold. This prevents the tobacco from staying in the feeding channel or inside the equipment for too long, causing process indicators such as moisture, aroma, and temperature to deviate from the qualified range, thereby ensuring that product quality and process rhythm are controlled synchronously.

[0085] Specifically, the formula for expressing the channel time window coverage constraint can be:

[0086] (3)

[0087] In the formula, This represents the maximum allowed duration threshold.

[0088] (4) Based on the continuous maintenance time blocks formed by the aggregation process, construct the aggregation constraints for the maintenance period.

[0089] In this embodiment, the merged continuous maintenance time block can be regarded as a rigid time window that is indivisible, immovable, and cannot overlap with any production task or product transfer task. In the scheduling model, it is mandatory to prohibit any equipment operation during this period and not to use it in segments or adjust it before or after. This ensures that planned maintenance activities such as maintenance, repair, and rotation maintenance can be completed in one go within a complete and continuous time, thus ensuring equipment safety and maintenance quality.

[0090] Specifically, the start time, end time, type of tobacco product, and feeding channel reserved for each task in the initial task sequence can be used as decision variables. The model can simultaneously satisfy multiple constraints such as the time consumption of product conversion between tasks, tasks can only fall within the open window of the bound feeding channel, the duration of tasks does not exceed the process limit, and the maintenance block cannot be intruded. Through optimization calculation, the end time of the last task on all devices is minimized, thereby outputting a set of task start and end time tables that are completely consistent with the constraints and can be directly executed, as well as the corresponding feeding channel occupancy status.

[0091] Based on the above embodiments, optionally, the cigarette production scheduling method provided in this embodiment further includes: converting the optimal time arrangement of each task into a natural time format of calendar date and specific hour time, and outputting a visual scheduling chart.

[0092] In this embodiment, the optimal time schedule obtained based on working hours can be mapped to a real calendar to generate the corresponding year, month, day, hour, minute, and second points. Then, it can be displayed in the form of a Gantt chart or time axis chart by equipment, so that the start and end intervals of each task, tobacco type, bound feeding channel, and maintenance block are presented intuitively in the same chart, allowing dispatchers to directly view, issue, and adjust.

[0093] This application provides a cigarette production scheduling method. When a production scheduling request for a target cigarette factory is received, basic production data is obtained, including the connection relationship between at least one cigarette equipment and at least one feeding channel, the cigarette product conversion time cost matrix, the equipment maintenance calendar, the equipment forced interruption event, the cigarette product conversion production plan, and the target production volume of various types of cigarette products. Then, based on the basic production data, an initial task sequence containing multiple ordered tasks is generated for each cigarette equipment. Each task is associated with a set of variables to be solved, including: the start time to be solved, the end time to be solved, the cigarette product type to be solved, and at least one of the feeding channel to be solved. Thus, based on a production scheduling model with the optimization objective of minimizing collaborative completion time, and following the task timing constraints, channel time window coverage constraints, process time constraints, and maintenance period aggregation constraints, the parameters to be solved are solved to obtain the optimal time arrangement and resource allocation scheme for each task. The technical solution of this application constructs a complete constraint system covering resource allocation, timing coordination, and process requirements by acquiring basic production data such as equipment-channel connection relationships, tobacco product switching time costs, and equipment maintenance calendars. Based on an optimization model aimed at minimizing collaborative completion time, it simultaneously solves multiple variables such as task start / end time, tobacco product type, and tobacco feeding channel allocation, while satisfying complex constraints such as task timing, channel time windows, process efficiency, and maintenance aggregation. This improves equipment utilization and the synchronization of production rhythm, effectively reduces idle waiting time caused by tobacco product switching, equipment maintenance, or forced interruptions, enhances the adaptability of the scheduling scheme to actual production dynamic disturbances, and ultimately achieves overall production efficiency optimization and the goal of collaborative completion.

[0094] Example 2

[0095] Figure 5 This is a schematic diagram of a cigarette production scheduling method provided in this application embodiment. Based on the foregoing embodiments, this embodiment provides a more detailed description of step S130. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0096] like Figure 5 As shown, the method specifically includes the following steps:

[0097] S210. When a production scheduling request for a target cigarette factory is received, obtain the basic production data of the target cigarette factory for a future preset time period.

[0098] The production base data includes the connection relationship between at least one cigarette equipment and at least one feeding channel, the tobacco product changeover time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product changeover production plan, and the target production volume of various types of tobacco products.

[0099] S220. Based on production data, generate an initial task sequence containing multiple ordered tasks for each cigarette-making machine.

[0100] Each task is associated with a set of variables to be solved, including: start time, end time, tobacco type, and feeding channel.

[0101] S230. Taking the minimization of the weighted collaborative completion time of all cigarette equipment as the objective function, the task timing constraints, channel time window coverage constraints, process efficiency constraints, and maintenance period aggregation constraints are used as constraints. The variables to be solved associated with each task in the initial task sequence are defined as model decision variables, and a mixed integer programming model is established.

[0102] Among them, the mixed integer programming model is a mathematical modeling framework that incorporates both integer and continuous variables into the decision space. By introducing integer variables into the linear objective function and linear constraints, it characterizes the discrete decision requirements in the scheduling problem. It also uses a mathematical programming solver to search for the optimal combination of variables that optimizes the objective function within a finite time, thereby simultaneously determining the optimal time arrangement and resource allocation scheme for each task.

[0103] In this embodiment, the function expression of the objective function is:

[0104] (4)

[0105] In the formula, This indicates the latest time among all the ending times of cigarette-making equipment. This represents the end time of the m-th cigarette rolling machine. Let represent the preset weight value of the m-th cigarette rolling device, p represent the current cigarette type, and q represent the cigarette type after conversion. This indicates a status value indicating whether a tobacco product type conversion has occurred. The conversion time cost for switching from tobacco product type p to tobacco product type q.

[0106] Specifically, the start time, end time, type of tobacco product, and feeding channel to be solved can all be included in the decision space. Under the premise of ensuring that the time consumption of the interval between tasks is converted into product consumption (corresponding to the task timing constraint), the task can only fall within the open window of the bound feeding channel (corresponding to the channel time window coverage constraint), the duration of the task does not exceed the process limit (corresponding to the process time constraint), and the maintenance block cannot be encroached upon by production (corresponding to the maintenance period aggregation constraint), a linear programming framework containing both integer and continuous variables is constructed. This allows the objective function to push the last task to end among all cigarette equipment to reach the minimum through a weighted approach, thereby obtaining the optimal time arrangement and resource allocation scheme that can be directly executed in one go.

[0107] S240. Call the mathematical programming solver to solve the mixed integer programming model and determine the optimal time arrangement and resource allocation scheme for each task in the initial task sequence.

[0108] Among them, the mathematical programming solver is a computational tool that automatically searches for the optimal solution within the framework of a mixed integer programming model.

[0109] The optimal time schedule includes the optimal start time and optimal end time, while the resource allocation scheme includes the optimal tobacco type and the optimal tobacco feeding channel. The optimal start time and optimal end time of each task together constitute a task interval that is free from time conflicts and executed continuously. The optimal tobacco type determines the specific brand produced in this interval, and the optimal tobacco feeding channel specifies the tobacco feeding channel number that provides tobacco for that brand. Both are determined simultaneously and used directly in the field.

[0110] Specifically, a mathematical programming solver can be used to search for the start time, end time, tobacco type, and feeding channel of the task within the feasible region formed by the task timing constraints, channel time window coverage constraints, process efficiency constraints, and maintenance period aggregation constraints. This minimizes the weighted collaborative completion time and outputs the optimal start time, optimal end time, and corresponding optimal tobacco type and optimal feeding channel combination for each task, which can then directly guide on-site production execution.

[0111] In this embodiment, optionally, the specific implementation steps of calling the mathematical programming solver to solve the mixed integer programming model may include:

[0112] S1. The mixed integer programming model is solved using the initial mathematical programming solver with default running parameters to obtain preliminary solution results.

[0113] The preliminary solution refers to the feasible solution obtained by first using the default running parameters of the initial mathematical programming solver to quickly solve the mixed integer programming model, which includes the start and end times of each task, as well as the tobacco type and feeding channel binding scheme.

[0114] Specifically, without adjusting any solver parameters, the model can be calculated directly using the solver's built-in default strategy, thereby obtaining a set of feasible solutions that satisfy the task timing constraints, channel time window coverage constraints, process aging constraints, and maintenance period aggregation constraints. This solution includes the initial start and end times of each task, as well as the preliminary binding schemes for tobacco types and feeding channels, which are used to evaluate the solution quality and provide a benchmark for the Bayesian optimization process.

[0115] S2. Based on the preliminary solution results, the default operating parameters of the initial mathematical programming solver are automatically tuned using the Bayesian optimization process to determine the optimal operating parameters, so as to obtain the optimized mathematical programming solver with updated parameters.

[0116] The optimal operating parameters refer to the set of key parameter values ​​that, after multiple iterations through a Bayesian optimization process, are determined in the parameter search space, enabling the initial mathematical programming solver to achieve the highest solution accuracy within a fixed time limit. This set is used to update the solver configuration, thereby obtaining an optimized mathematical programming solver that can output higher quality optimal time scheduling and resource allocation schemes within a preset time.

[0117] In this embodiment, the preliminary solution results can be used as an evaluation benchmark. In the defined parameter search space, different combinations of key parameters are repeatedly tried through the Gaussian process surrogate model and the expected improved acquisition function, so that the initial mathematical programming solver can reach the highest solution accuracy within a fixed time limit, thereby determining the optimal operating parameters. The solver is then reconfigured with these parameters to form an optimized mathematical programming solver that can output the optimal time schedule and resource allocation scheme faster and more accurately.

[0118] Optionally, the default operating parameters of the initial mathematical programming solver can be automatically tuned using a Bayesian optimization process to determine the optimal operating parameters. Specific implementation methods include:

[0119] (1) Define a parameter search space that includes multiple key parameters and the range of values ​​for each key parameter.

[0120] In this embodiment, before starting the Bayesian optimization process, a batch of key parameters that significantly affect the solution speed and accuracy can be defined based on the operating characteristics and experience of the initial mathematical programming solver. For each key parameter, an upper and lower limit range that allows for variation is set, thereby forming a finite and structured multidimensional value range. This allows the subsequent Gaussian process proxy model and the expected improved acquisition function to continuously explore and locate the optimal combination of operating parameters during the iteration process.

[0121] (2) Establish a Gaussian process proxy model and use the expected improved acquisition function to guide the parameter search process. Through multiple rounds of iterative optimization, find the optimal combination of operating parameters in the parameter search space that makes the initial mathematical programming solver solve the problem with the highest accuracy in a fixed time.

[0122] In this embodiment, a Gaussian process can be used to probabilistically model the unknown mapping relationship between key parameters and solution accuracy, forming a surrogate model. Then, the improvement potential of each candidate parameter combination can be measured by the expected improvement acquisition function, achieving a balance between exploration and utilization. The surrogate model is gradually updated, so that the search process continuously focuses on the region that may bring the greatest accuracy improvement. After multiple iterations, the optimal running parameter combination that achieves the highest solution accuracy within a preset time limit is output.

[0123] S3. Use an optimized mathematical programming solver to solve the mixed integer programming model, and obtain the optimal time arrangement and resource allocation scheme for each task in the optimized initial task sequence within the preset time limit.

[0124] In this embodiment, the solver configuration, optimized by the Bayesian optimization process, can be used to re-solve the same mixed integer programming model within a limited time. Through a more efficient search strategy, the combination of variables that minimizes the weighted collaborative completion time can be quickly located within the feasible region formed by the task timing constraints, channel time window coverage constraints, process efficiency constraints, and maintenance period aggregation constraints. This outputs the optimal start time, optimal end time, and corresponding optimal tobacco type and optimal tobacco feeding binding channel for each task, which can be used to directly guide on-site production execution.

[0125] The technical solution of this application embodiment, when solving for the parameters to be solved, takes minimizing the weighted collaborative completion time of all cigarette equipment as the objective function, and uses task timing constraints, channel time window coverage constraints, process efficiency constraints, and maintenance period aggregation constraints as constraints. The variables to be solved associated with each task in the initial task sequence are defined as model decision variables, and a mixed integer programming model is established. A mathematical programming solver is called to solve the mixed integer programming model to determine the optimal time arrangement and resource allocation scheme for each task in the initial task sequence. The optimal time arrangement includes the optimal start time and the optimal end time, and the resource allocation scheme includes the optimal tobacco type and the optimal tobacco feeding binding channel. The technical solution of this application aims to minimize the weighted collaborative completion time of all cigarette equipment. Under the premise of strictly meeting the constraints of task sequence, channel time window coverage, process efficiency, and maintenance period aggregation, it automatically calculates the optimal start time, optimal end time, optimal tobacco type, and optimal feeding binding channel for each task. This mathematically ensures global optimality, effectively coordinates the collaborative production of multiple equipment and channels, and significantly improves equipment utilization and the overall quality and feasibility of the scheduling scheme.

[0126] Example 3

[0127] Figure 6 This is a schematic diagram of a cigarette production scheduling device provided in an embodiment of this application. The device includes:

[0128] The data acquisition module 310 is used to acquire the basic production data of the target cigarette factory within a future preset period when a production scheduling request for the target cigarette factory is received; wherein, the basic production data includes the connection relationship between at least one cigarette equipment and at least one feeding channel, the tobacco product conversion time cost matrix, the equipment maintenance calendar, the equipment forced interruption event, the tobacco product conversion production plan, and the target production volume of various types of tobacco products.

[0129] The task sequence generation module 320 is used to generate an initial task sequence containing multiple ordered tasks for each cigarette-making machine based on the production basic data; wherein each task is associated with a set of variables to be solved, including at least one of the following: start time to be solved, end time to be solved, type of tobacco to be solved, and feeding channel to be solved.

[0130] The scheduling solution module 330 is used to solve the parameters to be solved based on the production scheduling model with the optimization objective of minimizing collaborative completion time, following the constraints of task sequence, channel time window coverage, process efficiency, and maintenance period aggregation, so as to obtain the optimal time arrangement and resource allocation scheme for each task.

[0131] This application provides a cigarette production scheduling device. When the device receives a production scheduling request for a target cigarette factory, it acquires basic production data including the connection relationship between at least one cigarette equipment and at least one feeding channel, a tobacco product conversion time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product conversion production plans, and target production volumes for various types of tobacco products. Based on this basic production data, it generates an initial task sequence containing multiple ordered tasks for each cigarette equipment. Each task is associated with a set of variables to be solved, including: a start time to be solved, an end time to be solved, a tobacco product type to be solved, and at least one feeding channel to be solved. Then, based on a production scheduling model with the optimization objective of minimizing collaborative completion time, and following task timing constraints, channel time window coverage constraints, process timeliness constraints, and maintenance period aggregation constraints, it solves for the parameters to be solved, obtaining the optimal time arrangement and resource allocation scheme for each task. The technical solution of this application constructs a complete constraint system covering resource allocation, timing coordination, and process requirements by acquiring basic production data such as equipment-channel connection relationships, tobacco product switching time costs, and equipment maintenance calendars. Based on an optimization model aimed at minimizing collaborative completion time, it simultaneously solves multiple variables such as task start / end time, tobacco product type, and tobacco feeding channel allocation, while satisfying complex constraints such as task timing, channel time windows, process efficiency, and maintenance aggregation. This improves equipment utilization and the synchronization of production rhythm, effectively reduces idle waiting time caused by tobacco product switching, equipment maintenance, or forced interruptions, enhances the adaptability of the scheduling scheme to actual production dynamic disturbances, and ultimately achieves overall production efficiency optimization and the goal of collaborative completion.

[0132] Based on the above-described device, optionally, the data acquisition module 310 includes:

[0133] The non-production period determination unit is used to identify all non-production periods for each cigarette-making machine within the future preset period based on the equipment maintenance calendar and the equipment forced interruption event;

[0134] The continuous time block determination unit is used to aggregate non-production time periods that are adjacent in time or have an interval of less than a threshold to form continuous maintenance time blocks in units of working hours.

[0135] The production sequence determination unit is used to determine the types of tobacco products that need to be produced on each cigarette machine and their production sequence based on the tobacco product conversion production plan and the target production volume of each type of tobacco product;

[0136] The product transfer task insertion unit is used to insert corresponding product transfer tasks between adjacent tobacco product types in the production sequence based on the conversion time data between different tobacco products provided by the tobacco product conversion time cost matrix.

[0137] The task sequence determination unit is used to combine the continuous maintenance time block as a maintenance task with the initial production task sequence containing the product transfer task, arrange them in chronological order, and generate an initial task sequence for each cigarette machine; wherein, each task in the initial task sequence reserves parameter variables for recording start time, end time, tobacco type, and binding feed channel.

[0138] Based on the above-mentioned device, optionally, the scheduling scheme solving module 330 includes:

[0139] The planning model determination unit is used to establish a mixed integer programming model with the objective function of minimizing the weighted collaborative completion time of all cigarette equipment, taking the task timing constraints, the channel time window coverage constraints, the process time constraints, and the maintenance period aggregation constraints as constraints, and defining the variables to be solved associated with each task in the initial task sequence as model decision variables.

[0140] The scheduling solution unit is used to call a mathematical programming solver to solve the mixed integer programming model and determine the optimal time arrangement and resource allocation scheme for each task in the initial task sequence; wherein, the optimal time arrangement includes the optimal start time and the optimal end time, and the resource allocation scheme includes the optimal tobacco type and the optimal tobacco feeding binding channel.

[0141] Based on the above-mentioned device, the cigarette production scheduling device may optionally include: a constraint determination module;

[0142] The constraint determination module is used to construct, based on the task order in the initial task sequence and the tobacco product conversion time cost matrix, task timing constraints to ensure that the start time of subsequent tasks on the same equipment is no earlier than the sum of the end time of the preceding task and the corresponding product conversion time consumption; to construct, based on the connection relationship between at least one cigarette equipment and at least one feeding channel and the preset channel opening time of each feeding channel, channel time window coverage constraints to ensure that the execution period of the production task is within the effective working window of the bound feeding channel; to construct, based on the process requirements specified in the tobacco product conversion production plan, process time constraints to limit the duration of the task from start to finish to within the maximum allowable duration threshold; and to construct maintenance period aggregation constraints based on the continuous maintenance time blocks formed by aggregation processing.

[0143] Based on the above-described apparatus, optionally, the function expression of the objective function is as follows:

[0144]

[0145] In the formula, This indicates the latest time among all the ending times of cigarette-making equipment. This represents the end time of the m-th cigarette rolling machine. Let represent the preset weight value of the m-th cigarette rolling device, p represent the current cigarette type, and q represent the cigarette type after conversion. This indicates a status value indicating whether a tobacco product type conversion has occurred. The conversion time cost for switching from tobacco product type p to tobacco product type q.

[0146] Based on the above-mentioned device, the optional scheduling scheme solving unit includes:

[0147] The preliminary solution subunit is used to solve the mixed integer programming model using an initial mathematical programming solver with default running parameters to obtain preliminary solution results;

[0148] The solver parameter optimization subunit is used to automatically adjust the default operating parameters of the initial mathematical programming solver based on the preliminary solution results using a Bayesian optimization process, and determine the optimal operating parameters to obtain an optimized mathematical programming solver with updated parameters.

[0149] The scheduling scheme solving subunit is used to solve the mixed integer programming model using the optimized mathematical programming solver, and obtain the optimal time arrangement and resource allocation scheme for each task in the optimized initial task sequence within a preset time limit.

[0150] Based on the above-mentioned device, optionally, a solver parameter optimization subunit is used to define a parameter search space containing multiple key parameters and the value range of each key parameter; establish a Gaussian process proxy model, and use an expected improved acquisition function to guide the parameter search process; through multiple rounds of iterative optimization, find the optimal combination of operating parameters in the parameter search space that makes the initial mathematical programming solver achieve the highest solution accuracy within a fixed time.

[0151] Based on the above-mentioned device, optionally, the cigarette production scheduling device also includes: a time format conversion module, used to convert the optimal time arrangement of each task into a natural time format of calendar date and specific hour time, and output a visual scheduling chart.

[0152] The cigarette production scheduling device provided in this application embodiment can execute the cigarette production scheduling method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0153] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0154] Example 4

[0155] Figure 7 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Figure 7 A block diagram is shown of an exemplary controller 40 suitable for implementing embodiments of the present application. Figure 7 The controller 40 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0156] like Figure 7 As shown, the controller 40 is presented in the form of a general-purpose computing device. The components of the controller 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0157] Bus 403 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0158] The controller 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the controller 40, including volatile and non-volatile media, and removable and non-removable media.

[0159] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. Controller 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 403 via one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0160] A program / utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 407 typically perform the functions and / or methods described in the embodiments of this application.

[0161] The controller 40 can also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), and with one or more devices that enable a user to interact with the controller 40, and / or with any device that enables the controller 40 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 411. Furthermore, the controller 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 412. As shown, network adapter 412 communicates with other modules of the controller 40 via bus 403. It should be understood that, although... Figure 7 As not shown, other hardware and / or software modules can be used in conjunction with controller 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0162] The processing unit 401 executes various functional applications and page processing by running programs stored in the system memory 402, such as implementing the cigarette production scheduling method provided in the embodiments of this application.

[0163] Example 5

[0164] This application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a cigarette production scheduling method, the method comprising:

[0165] When a production scheduling request for a target cigarette factory is received, the production basic data of the target cigarette factory within a future preset time period is obtained; wherein, the production basic data includes the connection relationship between at least one cigarette equipment and at least one feeding channel, the tobacco product conversion time cost matrix, equipment maintenance calendar, equipment forced interruption events, tobacco product conversion production plan, and target production volume of various types of tobacco products.

[0166] Based on the aforementioned production data, an initial task sequence containing multiple ordered tasks is generated for each cigarette-making machine; wherein each task is associated with a set of variables to be solved, including at least one of the following: start time to be solved, end time to be solved, type of tobacco to be solved, and feeding channel to be solved.

[0167] Based on a production scheduling model with the goal of minimizing collaborative completion time, and following constraints on task timing, channel time window coverage, process efficiency, and maintenance period aggregation, the parameters to be solved are obtained to arrive at the optimal time arrangement and resource allocation scheme for each task.

[0168] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0169] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0170] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0171] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method of scheduling a cigarette production, characterized by, The method comprises: When a production scheduling request for a target cigarette factory is received, production basic data of the target cigarette factory in a future preset period is acquired; wherein the production basic data comprises a connection relationship between at least one cigarette device and at least one feeding channel, a tobacco product conversion time cost matrix, a device maintenance calendar, a device forced interruption event, a tobacco product conversion production plan, and a target production amount of various types of tobacco products; Based on the production basic data, an initial task sequence comprising a plurality of ordered tasks is generated for each cigarette device; wherein each task is associated with a set of to-be-solved variables, including at least one of a to-be-solved start time, a to-be-solved end time, a to-be-solved tobacco product type, and a to-be-solved feeding channel; Based on a production scheduling model with the optimization objective of minimizing the collaborative completion time, the to-be-solved parameters are solved under the task timing constraint condition, the channel time window coverage constraint condition, the process time limit constraint condition, and the maintenance time period aggregation constraint condition, to obtain an optimal time arrangement and resource allocation scheme for each task.

2. The method of claim 1, wherein, The method comprises: According to the device maintenance calendar and the device forced interruption event, all non-production time periods of each cigarette device in the future preset period are identified; Work hour level non-production time periods that are adjacent in time or have a time interval less than a threshold value are aggregated to form continuous maintenance time blocks in units of work hours; Based on the tobacco product conversion production plan and the target production amount of each type of tobacco product, the types of tobacco products that need to be produced on each cigarette device and the production order thereof are determined; According to the conversion time data between different tobacco products provided by the tobacco product conversion time cost matrix, corresponding conversion tasks are inserted between adjacent tobacco product types in the production order; The continuous maintenance time blocks are combined with the initial production task sequence comprising the conversion tasks, and are arranged in time sequence to generate an initial task sequence for each cigarette device; wherein each task in the initial task sequence reserves parameter variables for recording start time, end time, tobacco product type, and binding feeding channel.

3. The method of claim 1, wherein, The method comprises: A mixed integer programming model is established, taking the weighted collaborative completion time of all cigarette devices as an objective function, taking the task timing constraint condition, the channel time window coverage constraint condition, the process time limit constraint condition, and the maintenance time period aggregation constraint condition as constraint conditions, and defining the to-be-solved variables associated with each task in the initial task sequence as model decision variables; solving the mixed integer programming model by calling a mathematical programming solver to determine an optimal time schedule and resource allocation scheme for each task in the initial task sequence, wherein the optimal time schedule comprises an optimal start time and an optimal end time, and the resource allocation scheme comprises an optimal tobacco product type and an optimal binding channel for feeding.

4. The method of claim 1, wherein, The method further comprises: constructing, based on the task order in the initial task sequence and the tobacco product conversion time cost matrix, the task timing constraint condition for ensuring that the start time of a subsequent task on the same device is not earlier than the sum of the end time of a preceding task and the corresponding product conversion time consumption; constructing, based on the connection relationship between at least one cigarette production device and at least one feeding channel and the preset channel opening time of each feeding channel, the channel time window coverage constraint condition for ensuring that the production task execution period is within the effective working window of the binding feeding channel; constructing, based on the process requirements specified in the tobacco product conversion production plan, the process aging constraint condition for limiting the duration of the task from start to end within the maximum allowed duration threshold; constructing, based on the continuous maintenance time block formed after aggregation, the maintenance period aggregation constraint condition.

5. The method of claim 3, wherein, The function expression of the objective function is: ； In the formula, represents the latest time among the end times of all cigarette devices, represents the end time of the mth cigarette device, represents the preset weight value of the mth cigarette device, p represents the current cigarette type, and q represents the cigarette type after the cigarette type conversion, represents a state value of whether the cigarette type conversion occurs, is the conversion time cost of the p cigarette type converted to the q cigarette type.

6. The method of claim 3, wherein, The solving of the mixed integer programming model by calling the mathematical programming solver to determine the optimal time schedule and resource allocation scheme for each task in the initial task sequence comprises: solving the mixed integer programming model by using an initial mathematical programming solver with default running parameters to obtain a preliminary solution result; based on the preliminary solution result, automatically tuning the default running parameters of the initial mathematical programming solver by using a Bayesian optimization process to determine optimal running parameters to obtain an optimized mathematical programming solver after parameter update; solving the mixed integer programming model by using the optimized mathematical programming solver to obtain the optimal time schedule and resource allocation scheme for each task in the initial task sequence within a preset time limit.

7. The method of claim 6, wherein, The automatic tuning of the default running parameters of the initial mathematical programming solver by using the Bayesian optimization process to determine the optimal running parameters comprises: defining a parameter search space containing multiple key parameters and the value range of each key parameter; establishing a Gaussian process proxy model and using an expected improvement acquisition function to guide the parameter search process to find the optimal combination of running parameters in the parameter search space that can achieve the highest solution accuracy of the initial mathematical programming solver within a fixed time through multiple rounds of iterative optimization.

8. The method of claim 1, wherein, The method further comprises: converting the optimal time schedule of each task into a natural time format of calendar date and specific clock time, and outputting a visual scheduling chart.

9. A cigarette production scheduling apparatus characterized by comprising: a cigarette production scheduling device according to any one of claims 1 to 8; and a cigarette production device. The device comprises: The data acquisition module is configured to acquire production base data of the target cigarette factory in a preset time period in the future when a production scheduling request for the target cigarette factory is received, wherein the production base data comprises a connection relationship between at least one cigarette device and at least one feeding channel, a tobacco product conversion time cost matrix, a device maintenance calendar, a device forced interruption event, a tobacco product conversion production plan, and a target production amount of various types of tobacco products. The task sequence generation module is configured to generate an initial task sequence comprising a plurality of ordered tasks for each cigarette device based on the production base data, wherein each task is associated with a group of to-be-solved variables, including at least one of a to-be-solved start time, a to-be-solved end time, a to-be-solved tobacco product type, and a to-be-solved feeding channel. The scheduling scheme solving module is configured to solve the to-be-solved parameters based on a production scheduling model with the optimization objective of minimizing the coordinated completion time, and to obtain an optimal time arrangement and a resource allocation scheme for each task by following a task timing constraint condition, a channel time window coverage constraint condition, a process time limit constraint condition, and a maintenance time period aggregation constraint condition.

10. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the cigarette production scheduling method according to any one of claims 1-8.

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