Production scheduling optimization method based on man-machine cooperation and related equipment

Abstract

The embodiment of the invention belongs to the technical field of production scheduling, and relates to a production scheduling optimization method based on man-machine cooperation, and the method comprises the steps: obtaining production plan data; establishing a production schedule optimization model according to the production plan data and preset configuration information; and solving the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan. The invention further provides a production schedule optimization device based on man-machine cooperation, computer equipment and a storage medium. According to the method, the mixed integer programming model related to the workshop production schedule is automatically established, the mathematical optimization solver is used for solving, the characteristics of man-machine cooperation and the production process are combined, optimization of the production schedule is achieved, the production schedule can achieve the optimal resource configuration, and therefore the production efficiency is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of production scheduling, and in particular to a production scheduling optimization method based on human-machine collaboration and related equipment. Background Art

[0002] In workshop production planning, advanced planning and scheduling (APS) is usually the key link. Reasonable and efficient production scheduling can effectively allocate production resources, such as equipment, workers and materials, to achieve the goal of improving production efficiency and reducing production costs.

[0003] However, the current workshop production scheduling in the printed circuit board (PCB) industry generally relies on manual experience to arrange production tasks. When faced with emergencies (such as equipment failure or order changes), it is easy to have difficulty adjusting production plans, which has the disadvantage of insufficient flexibility. In addition, due to the lack of systematic data analysis and optimization, production scheduling often cannot achieve the optimal resource allocation, which easily leads to conflicts or idleness of equipment and manpower, resulting in low production efficiency. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a production scheduling optimization method and related equipment based on human-machine collaboration to solve the technical problem of low production efficiency caused by manual workshop production scheduling.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a production scheduling optimization method based on human-machine collaboration, which adopts the following technical solutions:

[0006] A production scheduling optimization method based on human-machine collaboration includes the following steps:

[0007] Obtain production planning data;

[0008] Establishing a production scheduling optimization model based on the production plan data and preset configuration information;

[0009] The production scheduling optimization model is solved according to a preset solver to obtain a target production scheduling plan.

[0010] Furthermore, the step of establishing a production scheduling optimization model according to the production plan data and the preset configuration information specifically includes:

[0011] Preprocessing the production plan data to obtain a data set corresponding to the production scheduling optimization model;

[0012] Determining parameter information and decision variables of the production scheduling optimization model according to the data set and the configuration information, and determining an objective function of the production scheduling optimization model;

[0013] The constraint conditions of the production scheduling optimization model are determined according to the objective function to obtain the production scheduling optimization model.

[0014] Furthermore, the optimization objectives of the objective function include maximizing the on-time delivery rate and minimizing the waiting time, wherein the maximizing the on-time delivery rate represents maximizing the proportion of on-time delivery orders to all orders, and the minimizing the waiting time represents minimizing the waiting time of all machines.

[0015] Furthermore, the constraints include basic constraints on drilling rig processing tasks, constraints on the end of drilling rig processing tasks, constraints on total order demand, constraints on the upper limit of the production quantity of a single drilling rig task, constraints on drilling rig processing time, constraints on the earliest time when order processing can start, constraints on time for drilling rig pre- and post-processing tasks, personnel conflict constraints, constraints on order completion time, constraints on quasi-delivery of orders, and constraints on the earliest available time of the drilling rig.

[0016] Furthermore, the drilling rig processing task basic constraint represents that each processing task of each drilling rig corresponds to at most one order;

[0017] The drilling rig processing task end constraint means that if the i-th processing task of each drilling rig is no longer assigned orders, then no subsequent tasks will be assigned orders;

[0018] The total order demand constraint means that the product processing quantity of all drilling rigs assigned to the same order is equal to the total product demand of the order;

[0019] The upper limit constraint on the production quantity of a single task of a drilling rig represents that the number of products allocated to each processing task of any drilling rig shall not be greater than the upper limit of the single production capacity of the drilling rig;

[0020] The drilling rig processing time constraint represents each processing task of any drilling rig, and the processing end time is equal to the processing start time plus the single-trip processing time of the order;

[0021] The earliest processing start time constraint of the order means that for any order, the processing start preparation time shall not be earlier than the estimated arrival time of the order;

[0022] The time constraint of the drilling rig front and back processing tasks means that for any drilling rig front and back two processing tasks, the time relationship should satisfy that the processing start time of the current task must not be less than the processing preparation start time of the current task plus the auxiliary processing time of the current task, and the processing preparation start time of the next task must not be less than the processing end time of the current task plus the waiting time of the next task;

[0023] The personnel conflict constraint represents the time relationship that should be satisfied for any two processing preparation tasks of all drilling machines in the area of ​​responsibility of any personnel;

[0024] The order completion time constraint means that the actual completion time of the order is greater than or equal to the time it takes for all drilling rigs assigned to the order to complete the order;

[0025] The order quasi-delivery constraint means that if the final completion time of the order is not greater than the upper limit of the delivery period required by the order, the order is a quasi-delivery order;

[0026] The earliest available time constraint of the drilling machine means that for the first task of any drilling machine, the processing preparation time must be later than the earliest available time of the drilling machine.

[0027] Furthermore, the total order demand constraint is linearized by introducing an intermediate variable, and the intermediate variable is an integer variable and its value is a positive number.

[0028] Furthermore, after the step of solving the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan, the method further includes:

[0029] When update information corresponding to the production plan data is received, the production scheduling optimization model is updated according to the update information, and the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan is returned to be executed.

[0030] In order to solve the above technical problems, the embodiment of the present application further provides a production scheduling optimization device based on human-machine collaboration, which adopts the following technical solution:

[0031] A production scheduling optimization device based on human-machine collaboration, comprising:

[0032] An acquisition module is used to obtain production plan data;

[0033] An establishment module is used to establish a production scheduling optimization model according to the production plan data and preset configuration information;

[0034] The solving module is used to solve the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan.

[0035] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0036] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the production scheduling optimization method based on human-machine collaboration as described above are implemented.

[0037] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0038] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the production scheduling optimization method based on human-machine collaboration as described above.

[0039] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0040] The production scheduling optimization method based on human-machine collaboration disclosed in the present application obtains production plan data; then, according to the production plan data and preset configuration information, a production scheduling optimization model is established; thereby, the production scheduling optimization model is solved according to a preset solver to obtain a target production scheduling plan. The present application automatically establishes a mixed integer programming model related to workshop production scheduling and uses a mathematical optimization solver to solve it, thereby combining human-machine collaboration with the characteristics of the production process, realizing the optimization of production scheduling, so that production scheduling can achieve the optimal resource allocation, thereby greatly improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0043] Figure 2 is a flow chart of an embodiment of a production scheduling optimization method based on human-machine collaboration according to the present application;

[0044] Figure 3 It is a structural schematic diagram of an embodiment of a production scheduling optimization device based on human-machine collaboration according to the present application;

[0045] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0049] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0050] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0051] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0052] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0053] It should be noted that the production scheduling optimization method based on human-machine collaboration provided in the embodiment of the present application is generally executed by a server, and accordingly, the production scheduling optimization device based on human-machine collaboration is generally set in the server.

[0054] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0055] Continue to refer Figure 2 , shows a flow chart of an embodiment of a production scheduling optimization method based on human-machine collaboration according to the present application. The production scheduling optimization method based on human-machine collaboration comprises the following steps:

[0056] Step S201, obtaining production plan data.

[0057] In this embodiment, the production scheduling optimization method based on human-machine collaboration is run on an electronic device (e.g. Figure 1 The server shown in the figure) can send or receive data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0058] In this embodiment, it involves extracting key production planning data from various data sources (such as enterprise resource planning systems, manufacturing execution systems, etc.), including current order information (such as order quantity, type and delivery period), the status of production equipment (such as availability, maintenance status), human resource arrangements (such as employee availability and skills), raw material inventory status, etc. The accuracy and timeliness of the data directly affect the quality of subsequent analysis, so data verification and consistency checks may be required at this stage. Automated data interfaces or APIs can be used to capture data from different systems and integrate them into a unified database to ensure that all relevant information is up to date.

[0059] Step S202: establishing a production scheduling optimization model according to the production plan data and preset configuration information.

[0060] In this embodiment, after obtaining the production plan data, the next step is to establish a mathematical optimization model based on these data and preset configuration information (e.g., the production capacity of the factory, equipment characteristics, personnel configuration, etc.). Operations research methods can be used to define decision variables, objective functions and constraints to form a mathematical model that can be processed by the solver. For example, the task arrangement of the drilling rig can be regarded as a scheduling problem and modeled by linear programming or integer programming.

[0061] Step S203, solving the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan.

[0062] In this embodiment, it involves using a preset solver (such as CPLEX, Gurobi, etc.) to solve the established production scheduling optimization model. The solver will use advanced algorithms (such as simplex method, branch and bound method, etc.) to calculate the model to find the optimal solution. In this process, the solver will determine how to allocate resources and arrange the production sequence according to the objective function and constraints in the model, thereby generating an optimal production scheduling plan. During the solution process, the solver may perform multiple iterations and try different solutions to optimize production efficiency. For example, it may take into account the frequency of use of equipment, the workload of personnel, and the urgency of orders to derive the final scheduling plan.

[0063] This application automatically establishes a mixed integer programming model related to workshop production scheduling and uses a mathematical optimization solver to solve it, thereby combining human-machine collaboration with the characteristics of the production process, achieving optimization of production scheduling, and enabling production scheduling to achieve optimal resource allocation, thereby greatly improving production efficiency.

[0064] In some optional implementations of this embodiment, the step of establishing a production scheduling optimization model according to the production plan data and the preset configuration information specifically includes:

[0065] Preprocessing the production plan data to obtain a data set corresponding to the production scheduling optimization model;

[0066] Determining parameter information and decision variables of the production scheduling optimization model according to the data set and the configuration information, and determining an objective function of the production scheduling optimization model;

[0067] The constraint conditions of the production scheduling optimization model are determined according to the objective function to obtain the production scheduling optimization model.

[0068] In this embodiment, data preprocessing is an important step in optimizing model building, the purpose is to ensure that the data used is complete, accurate and consistent, and preprocessing includes the following aspects: data cleaning, identifying and eliminating invalid or erroneous data, for example, if there is an incorrect quantity or invalid equipment information in the order, it needs to be marked and removed; missing value processing, for missing data, you can choose to fill (such as using the average, mode, etc.) or delete the entire row of data, depending on the proportion and importance of the missing value; data standardization, converting data from different sources into a unified format and unit, such as converting all time data into hours to ensure consistency during subsequent processing; data integration, integrating data from different systems (such as ERP, MES) into a unified data set to facilitate the construction of subsequent models. For example, the system can use data processing tools (such as Python's Pandas library) to automatically perform data cleaning and formatting operations, thereby improving efficiency and accuracy. Parameters are constants in the model, such as the processing capacity of the production equipment, the required quantity of each order, the maximum number of stacks, production time, etc. These parameters reflect the constraints of the actual production environment; the objective function is used to quantify the optimization goals, such as minimizing the total machine waiting time or maximizing the on-time delivery rate. By setting the weights of different goals, flexible optimization directions can be achieved. All constraints are input into the model in the form of mathematical expressions so that the solver can effectively consider these restrictions. In this process, linear programming or integer programming methods are used to handle these constraints to ensure that the final production scheduling plan meets business needs and is practical.

[0069] This application can establish an accurate and effective production scheduling optimization model, fully consider the factors of human-machine collaboration, better adapt to the complexity of the production environment, improve the flexibility and efficiency of production scheduling, and ultimately achieve higher production efficiency and customer satisfaction.

[0070] In some optional implementations of the present embodiment, the optimization objectives of the above objective function include maximizing the on-time delivery rate and minimizing the waiting time, wherein the maximization of the on-time delivery rate represents maximizing the proportion of on-time delivery orders to all orders, and the minimization of the waiting time represents minimizing the waiting time of all machines.

[0071] In this embodiment, the on-time delivery rate refers to the proportion of orders delivered on time within the specified delivery period to all orders. The goal of maximizing the on-time delivery rate is to enable more orders to be completed within the agreed time to improve customer satisfaction and the reliability of on-time delivery of orders. By setting the on-time delivery constraint in the optimization model, the system will prioritize those orders that can be completed within the delivery period and try to meet their time requirements according to priority. Maximizing the on-time delivery rate can not only meet the delivery requirements of customers, but also improve the credibility and market competitiveness of the enterprise. A higher on-time delivery rate indicates that the production process is reasonably arranged and the production plan is executed efficiently, which can ensure that most orders are completed on time.

[0072] Waiting time refers to the idle time of equipment between tasks. The goal of minimizing waiting time is to reduce the idle time of production equipment between processing tasks to achieve efficient use of equipment. By optimizing the scheduling in the model, the time intervals between different tasks are compressed as much as possible so that the equipment can work continuously. During the optimization process, factors such as equipment switching time, worker operation time, and order priority are comprehensively considered to ensure that the idle time of equipment is reduced as much as possible without affecting the quality of the task. Minimizing waiting time not only improves the utilization rate of equipment, but also reduces energy waste caused by idle equipment and reduces production costs. Efficient equipment utilization means that more orders can be processed in the same time, increasing the production capacity of the production line.

[0073] Exemplarily, the objective function may be:

[0074]

[0075] Among them, w1 and w2 are weights, Q n To ensure that the order can be delivered on time, WT mi is the waiting time after the completion of the i-th task of drilling rig m, and N is the order set. The objective function in this embodiment includes two optimization directions, each corresponding to a weight w1 and w2:

[0076] Part I Q n is a 0-1 variable used to determine whether order n is delivered, and the number of orders delivered on time is Q n =1, otherwise, Q n =0;∑ n Q n is the total number of orders delivered on time among all orders;

[0077] Part 2 w2∑ m ∑ i WT mi : WT miIt represents the waiting time of equipment (drilling rig) m before entering the next task after completing task i. The purpose of this part is to reduce the waiting time of the equipment, that is, to minimize the idle time of the equipment after a task is completed, so as to improve the utilization rate of the equipment.

[0078] The weights w1 and w2 determine the relative importance of the two objectives in the optimization process. If the company is more concerned about on-time delivery of orders, then w1 should be set larger so that the optimization process pays more attention to the proportion of on-time delivery; if it is more concerned about equipment utilization, then w2 should be set larger so as to give priority to reducing equipment waiting time.

[0079] The system of this application can significantly improve production efficiency while meeting customer order requirements through dual optimization of maximizing on-time delivery rate and minimizing waiting time. This optimization method can fully utilize production resources and achieve low-cost and high-efficiency production scheduling on the basis of ensuring on-time delivery.

[0080] In some optional implementations of the present embodiment, the above-mentioned constraints include basic constraints on drilling rig processing tasks, constraints on the end of drilling rig processing tasks, constraints on total order demand, constraints on the upper limit of the production quantity of a single drilling rig task, constraints on drilling rig processing time, constraints on the earliest time when order processing can start, time constraints on drilling rig pre- and post-processing tasks, personnel conflict constraints, order completion time constraints, order quasi-delivery constraints, and constraints on the earliest available time of the drilling rig.

[0081] In this embodiment, the basic constraint of drilling rig processing tasks ensures that each drilling rig can only perform its designated processing tasks and ensures that the tasks of different drilling rigs will not conflict. This constraint can ensure the rationality of task allocation and avoid resource waste; the drilling rig processing task end constraint stipulates that the drilling rig can only start the next task after completing the previous task. This constraint prevents task overlap or omission and ensures that all tasks are completed in order; the total order demand constraint ensures that the total production quantity of each order meets the customer's demand. That is, it ensures that the order quantity can meet the customer's ordering requirements, and will not lead to insufficient delivery quantity due to unreasonable scheduling; the upper limit constraint of the production quantity of a single task of the drilling rig limits the maximum production quantity of each drilling rig to perform tasks, preventing too many or too few tasks from affecting the processing efficiency. This constraint is set according to the load capacity of the drilling rig to protect the equipment and prevent overload operation; the drilling rig processing time constraint requires that the processing time of each task be completed within the specified time to ensure that the task time is controllable. This constraint ensures that the task arrangement is reasonable and avoids time exceeding or insufficient affecting the overall scheduling; the earliest processing time constraint of the order, the order can only be scheduled after its earliest processing time, to prevent early production and waste of resources. This ensures the reasonable allocation of production time, so that each order can be processed according to the planned time; the time constraint of the processing tasks before and after the drilling rig limits the time interval between adjacent tasks and ensures that there is enough conversion time between tasks, so as to avoid conflicts when the drilling rig switches between tasks and ensure the smoothness of scheduling; the personnel conflict constraint avoids the problem of insufficient personnel due to too many tasks being scheduled at the same time, that is, to ensure that there are enough personnel to allocate when executing each task, so as to avoid affecting production due to insufficient personnel resources; the order completion time constraint ensures that each order is completed before the planned completion time to meet the delivery requirements. This constraint helps to avoid order delays and ensure that customer needs are met; the order punctuality constraint ensures that each order is completed within the specified delivery time range to improve the on-time delivery rate, which not only meets customer needs, but also improves the company's reputation and customer satisfaction; the earliest available time constraint of the drilling rig ensures that the task can be scheduled only when the drilling rig is idle or available, avoiding equipment occupation or repeated scheduling of tasks. This constraint ensures the rational use of resources and reduces conflicts in scheduling.

[0082] The setting of these constraints in this application can optimize scheduling, improve production efficiency and reduce resource waste while meeting customer needs.

[0083] In some optional implementations of this embodiment, the above drilling rig processing task basic constraint represents that each processing task of each drilling rig corresponds to at most one order;

[0084] The above drilling rig processing task end constraint means that if the i-th processing task of each drilling rig is no longer assigned orders, then no subsequent tasks will be assigned orders;

[0085] The above order total demand constraint means that the number of products processed by all drilling rigs assigned to the same order is equal to the total product demand of the order;

[0086] The above-mentioned upper limit constraint on the production quantity of a single task of a drilling rig means that for each processing task of any drilling rig, the number of products allocated shall not exceed the upper limit of the single production capacity of the drilling rig;

[0087] The above drilling rig processing time constraint represents each processing task of any drilling rig, and the processing end time is equal to the processing start time plus the single-trip processing time of the order;

[0088] The earliest processing time constraint of the above order means that for any order, the processing start preparation time shall not be earlier than the estimated arrival time of the order;

[0089] The above-mentioned time constraints of the drilling rig's front and back processing tasks mean that for any drilling rig's front and back two processing tasks, the time relationship should satisfy that the processing start time of the current task must not be less than the processing preparation start time of the current task plus the auxiliary processing time of the current task, and the processing preparation start time of the next task must not be less than the processing end time of the current task plus the waiting time of the next task;

[0090] The above personnel conflict constraint represents the time relationship that should be satisfied for any two processing preparation tasks of all drilling machines in the area responsible for any personnel;

[0091] The above order completion time constraint means that the actual completion time of the order is greater than or equal to the time it takes for all the rigs assigned to the order to complete the order;

[0092] The above-mentioned order quasi-delivery constraint means that if the final completion time of the order is not greater than the upper limit of the delivery period required by the order, the order is a quasi-delivery order;

[0093] The above-mentioned earliest available time constraint of a drilling machine means that for the first task of any drilling machine, the processing preparation time must be later than the earliest available time of the drilling machine.

[0094] In this embodiment, each processing task of each drilling machine can correspond to at most one order, which means that one task cannot serve multiple orders at the same time. The formula is as follows:

[0095]

[0096] Among them, X mni is a 0-1 variable indicating whether the i-th processing task of drilling machine m corresponds to order n. If X mni =1, it means that the task serves order n. This constraint ensures that each processing task is assigned to at most one order;

[0097] If the i-th processing task of a drilling machine is no longer assigned an order, then the subsequent tasks cannot be assigned an order. This shows that in the time sequence, the unassigned tasks will not appear after the assigned tasks, including:

[0098]

[0099] This constraint ensures the order of tasks. If there is no order for task i, then task i+1 will not be assigned an order either.

[0100] The constraint on the total order demand is that the number of products processed by all drilling rigs assigned to the same order must be equal to the demand for the order, including:

[0101]

[0102] Where: Y mi represents the number of products processed in the i-th task of drilling rig m; d n is the total demand for order n, AM n is the set of available rigs for order n. This constraint ensures that the demand for the order is fully satisfied.

[0103] The upper limit of the production quantity of a single task of a drilling rig. Specifically, each drilling rig has an upper limit of production capacity when performing each task. This constraint ensures that in each task, the number of products processed by the drilling rig does not exceed its maximum production capacity, including:

[0104]

[0105] The drilling machine processing time requirement is that the time arrangement of each processing task must meet the processing time requirement of the order. Specifically, the end time of each task is equal to the start time plus the processing time of the order. This constraint ensures that the processing task has enough time to be completed without rushing or delay, including:

[0106]

[0107] The earliest processing time constraint of an order, specifically, the processing start time of an order cannot be earlier than the estimated arrival time of the order. This means that the rig will not prepare for the order in advance before the order actually arrives, to ensure that the order is processed on time, including:

[0108]

[0109] The time arrangement of the tasks before and after the drilling rig, specifically on the same drilling rig, the time arrangement between the two tasks before and after needs to be reasonable. Specifically, the end time of the previous task plus a certain waiting time can start the preparation time for the next task, so as to avoid conflicts between tasks and ensure the smooth connection of tasks, including:

[0110]

[0111] Personnel conflict constraint: if different drilling rigs in the same operating area are managed by the same person, there should be no time conflict between the tasks of these drilling rigs to avoid personnel scheduling conflicts. This constraint ensures that the task time assigned to the same person does not overlap to prevent the situation where the person is too busy to handle all tasks. The time relationship should satisfy:

[0112]

[0113] The order completion time constraint specifies that the completion time of each order should be equal to or later than the last task time of all the rigs assigned to it to complete the order. This constraint ensures that the completion time of an order is consistent with the progress of all its tasks, avoiding the situation where an order is marked as completed but the actual tasks have not been completed, including:

[0114]

[0115] The order on-time delivery constraint is that if the actual completion time of the order is less than or equal to the specified delivery time, then the order is considered to be delivered on time. This constraint helps to measure whether the order is delivered on time and provides standards for on-time delivery, including:

[0116]

[0117] The earliest available time constraint of the drilling rig is that the preparation time of the first task of each drilling rig cannot be earlier than the earliest available time of the drilling rig. This constraint ensures that the drilling rig will not be assigned tasks before it is available, preventing conflicts or waste of resources caused by premature task assignment, including:

[0118]

[0119] w1 and w2 are weights, Q n To ensure that the order can be delivered on time, WT mi is the waiting time after the completion of the i-th task of drilling rig m, and N is the order set.

[0120] In the constraints, X mni is a 0-1 variable, representing whether the i-th processing task of drilling machine m corresponds to order n; Y mi is an integer variable, representing the number of processed products assigned to the i-th processing task of drilling machine m; PT mi is a continuous variable, representing the start preparation time of the i-th task of drilling rig m; ST mi is a continuous variable, representing the start time of the i-th processing task of drilling machine m, and also represents the end preparation time; ET miis a continuous variable, representing the end time of the i-th processing task of drilling machine m; WT mi is a continuous variable, representing the waiting time after the completion of the i-th task of drilling rig m; R mm′ii′ is a 0-1 variable, representing whether the start preparation time of the i-th task of drilling rig m is before the start preparation time of the i′th task of drilling rig m′; T n is a continuous variable, representing the actual processing completion time of order n; Q n is a 0-1 variable representing whether order n can be delivered on time; N is the order set, n∈N; M is the rig set, m∈M; K is the personnel set, k∈K; AM n is the set of available rigs corresponding to order n, which is a subset of M; BM k is the set of drilling machines that person k is responsible for, which is a subset of M; I is the set of processing tasks of the drilling machine, i∈I; s n is the maximum number of stacking required for order n; d n is the demand quantity corresponding to order n; dr m is the number of axes of the drilling rig m; arrive_time n The estimated arrival time of order n; delivery_time n The completion delivery time specified for order n; switch_time n The auxiliary processing time of order n; process_time n The single-trip processing time of order n; avail_time m is the earliest available start time of drilling rig m; max_value is the theoretical maximum value.

[0121] Through these constraints, the scheduling system can reasonably arrange tasks to ensure that resources are fully utilized, the production sequence is reasonable, the order requirements are met, and on-time delivery is achieved when possible.

[0122] In some optional implementations of this embodiment, the above-mentioned total order demand constraint is linearized by introducing an intermediate variable, and the intermediate variable is an integer variable and its value is a positive number.

[0123] In this embodiment, by introducing the intermediate variable α mni Linearization is achieved to make the model easier and more efficient to explain, including:

[0124]

[0125] Among them, the intermediate variable α mni is an integer variable and its value is positive. The following is a detailed explanation of each constraint: First, limit the number of tasks assigned. When a task is assigned to order n, the intermediate variable αmni The value cannot exceed the number Y assigned to the task mi This ensures that α mni It represents the actual quantity of order n produced in this task. If the task is not assigned to an order, that is, X mni = 0, then by setting a sufficiently large upper limit value, we can ensure that this condition no longer restricts α mni , that is, the impact of this task on the order can be ignored; second, to maintain consistency, further constrain the number of tasks. When tasks are assigned to orders, α mni The value cannot be less than the allocated amount Y mi This ensures that when a task is used for order n, α mni and Y mi If the task is not assigned to order n, the upper limit will make this condition automatically true, without limiting α mni value; third, ensure task relevance. If a task is not assigned to an order (i.e., X mni =0), then α mni must be zero. This condition ensures that the production quantity is not incorrectly allocated to unassigned tasks, and ensures that the resources of the task are only used for the orders related to it; fourth, to meet the total demand for orders, the α of all rig tasks assigned to order n mni Summarize and ensure that the summary value is equal to the total demand d of the order n This ensures that all requirements of the order are met, with no shortage or surplus.

[0126] By setting these constraints, the model of this application can avoid complex nonlinear relationships while meeting order requirements, ensure simpler linear expressions, and facilitate efficient optimization using a linear programming solver.

[0127] In some optional implementations of this embodiment, after the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan, the following steps are further included:

[0128] When update information corresponding to the production plan data is received, the production scheduling optimization model is updated according to the update information, and the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan is returned to be executed.

[0129] In this embodiment, when changes occur in the production process, such as new orders, cancellations, equipment failures, or resource reallocation, the system will receive these new production plan data. The update information may come from the monitoring system, warehouse management system, order management system, etc. at the production site to ensure that the production scheduling model reflects the latest production status. Then, the optimization model is adjusted based on the new production plan data. For example, if an urgent order is added, the system will add the demand and priority of the order to the model; if a certain device fails, the system will remove the availability of the device in the model or adjust its earliest available time; for resource reallocation or other changes in production conditions, the system will update the allocation strategy of production resources accordingly. Through these adjustments, the optimization model can ensure that the current schedule matches the actual production situation and avoid production bottlenecks or resource waste. After updating the optimization model, the system uses the preset solver to solve the model again to generate a new production schedule. The new schedule will take into account the changed production conditions and resource constraints to ensure the efficiency and feasibility of the schedule. After re-solving, the system will obtain a new production schedule and send it to the execution layer, such as to the production scheduling system or production workshop. The new schedule will take effect immediately, allowing the production line to operate according to the latest schedule instructions and ensure timely response to changes.

[0130] Through this dynamic adjustment and real-time update mechanism, the production scheduling system of this application has a high degree of flexibility and adaptability, and can better meet rapidly changing production needs.

[0131] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0132] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0134] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0135] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a production scheduling optimization device based on human-machine collaboration. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0136] like Figure 3 As shown, the production scheduling optimization device 300 based on human-machine collaboration described in this embodiment includes: an acquisition module 301, a creation module 302 and a solution module 303. Among them:

[0137] An acquisition module 301 is used to acquire production plan data;

[0138] Establishing module 302, for establishing a production scheduling optimization model according to the production plan data and preset configuration information;

[0139] The solving module 303 is used to solve the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan.

[0140] The production scheduling optimization device based on human-machine collaboration provided in the present application realizes the optimization of production scheduling by automatically establishing a mixed integer programming model related to workshop production scheduling and using a mathematical optimization solver to solve it, thereby combining human-machine collaboration with the characteristics of the production process. This allows the production scheduling to achieve the optimal resource allocation, thereby greatly improving production efficiency.

[0141] In some optional implementations of this embodiment, the establishing module 302 is further used to:

[0142] Preprocessing the production plan data to obtain a data set corresponding to the production scheduling optimization model;

[0143] Determining parameter information and decision variables of the production scheduling optimization model according to the data set and the configuration information, and determining an objective function of the production scheduling optimization model;

[0144] The constraint conditions of the production scheduling optimization model are determined according to the objective function to obtain the production scheduling optimization model.

[0145] The production scheduling optimization device based on human-machine collaboration provided in this application can establish an accurate and effective production scheduling optimization model, fully consider the factors of human-machine collaboration, can better adapt to the complexity of the production environment, improve the flexibility and efficiency of production scheduling, and ultimately achieve higher production efficiency and customer satisfaction.

[0146] In some optional implementations of the present embodiment, the optimization objectives of the objective function of the establishment module 302 include maximizing the on-time delivery rate and minimizing the waiting time, wherein the maximization of the on-time delivery rate represents maximizing the proportion of on-time delivery orders to all orders, and the minimization of the waiting time represents minimizing the waiting time of all machines.

[0147] The production scheduling optimization device based on human-machine collaboration provided by this application can significantly improve production efficiency while meeting customer order requirements through dual optimization of maximizing on-time delivery rate and minimizing waiting time. This optimization method can fully utilize production resources on the basis of ensuring on-time delivery and achieve low-cost and high-efficiency production scheduling.

[0148] In some optional implementations of this embodiment, the constraints in the establishment module 302 include basic constraints on drilling rig processing tasks, constraints on the end of drilling rig processing tasks, constraints on total order demand, constraints on the upper limit of the production quantity of a single drilling rig task, drilling rig processing time constraints, constraints on the earliest time when order processing can start, time constraints on drilling rig pre- and post-processing tasks, personnel conflict constraints, order completion time constraints, order quasi-delivery constraints, and constraints on the earliest available time of the drilling rig.

[0149] The production scheduling optimization device based on human-machine collaboration provided in this application can optimize scheduling, improve production efficiency and reduce resource waste while meeting customer needs by setting these constraints.

[0150] In some optional implementations of this embodiment, in the establishment module 302, the drilling rig processing task basic constraint represents that each processing task of each drilling rig corresponds to at most one order;

[0151] The drilling rig processing task end constraint means that if the i-th processing task of each drilling rig is no longer assigned orders, then no subsequent tasks will be assigned orders;

[0152] The total order demand constraint means that the product processing quantity of all drilling rigs assigned to the same order is equal to the total product demand of the order;

[0153] The upper limit constraint on the production quantity of a single task of a drilling rig represents that the number of products allocated to each processing task of any drilling rig shall not be greater than the upper limit of the single production capacity of the drilling rig;

[0154] The drilling rig processing time constraint represents each processing task of any drilling rig, and the processing end time is equal to the processing start time plus the single-trip processing time of the order;

[0155] The earliest processing start time constraint of the order means that for any order, the processing start preparation time shall not be earlier than the estimated arrival time of the order;

[0156] The time constraint of the drilling rig front and back processing tasks means that for any drilling rig front and back two processing tasks, the time relationship should satisfy that the processing start time of the current task must not be less than the processing preparation start time of the current task plus the auxiliary processing time of the current task, and the processing preparation start time of the next task must not be less than the processing end time of the current task plus the waiting time of the next task;

[0157] The personnel conflict constraint represents the time relationship that should be satisfied for any two processing preparation tasks of all drilling machines in the area of ​​responsibility of any personnel;

[0158] The order completion time constraint means that the actual completion time of the order is greater than or equal to the time it takes for all drilling rigs assigned to the order to complete the order;

[0159] The order quasi-delivery constraint means that if the final completion time of the order is not greater than the upper limit of the delivery period required by the order, the order is a quasi-delivery order;

[0160] The earliest available time constraint of the drilling machine means that for the first task of any drilling machine, the processing preparation time must be later than the earliest available time of the drilling machine.

[0161] The production scheduling optimization device based on human-machine collaboration provided in this application can reasonably arrange tasks through these constraints, ensure that resources are fully utilized, the production sequence is reasonable, meet order requirements, and achieve on-time delivery when possible.

[0162] In some optional implementations of this embodiment, in the establishment module 302, the above-mentioned total order demand constraint is linearized by introducing an intermediate variable, and the intermediate variable is an integer variable and its value is a positive number.

[0163] The production scheduling optimization device based on human-machine collaboration provided in this application, through the setting of these constraints, the model can avoid complex nonlinear relationships while meeting order requirements, ensure simpler linear expressions, and facilitate efficient optimization using a linear programming solver.

[0164] In some optional implementations of this embodiment, after the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan, the solving module 303 is further used to:

[0165] When update information corresponding to the production plan data is received, the production scheduling optimization model is updated according to the update information, and the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan is returned to be executed.

[0166] The production scheduling optimization device based on human-machine collaboration provided in the present application has a production scheduling system with high flexibility and adaptability through the mechanism of dynamic adjustment and real-time update, and can better meet the rapidly changing production needs.

[0167] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0168] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0169] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0170] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of a production scheduling optimization method based on human-machine collaboration, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0171] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the production scheduling optimization method based on human-machine collaboration.

[0172] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0173] The computer equipment provided in this application realizes the optimization of production scheduling by automatically establishing a mixed integer programming model related to workshop production scheduling and using a mathematical optimization solver to solve it, thereby combining human-machine collaboration with the characteristics of the production process, so that the production scheduling can achieve the optimal resource allocation, thereby greatly improving production efficiency.

[0174] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the production scheduling optimization method based on human-computer collaboration as described above.

[0175] The computer-readable storage medium provided in the present application optimizes the production schedule by automatically establishing a mixed integer programming model related to workshop production scheduling and using a mathematical optimization solver to solve it, thereby combining human-machine collaboration with the characteristics of the production process, so that the production schedule can achieve the optimal resource allocation, thereby greatly improving production efficiency.

[0176] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0177] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A production scheduling optimization method based on human-machine collaboration, characterized in that: The steps include: Obtain production planning data; Establishing a production scheduling optimization model based on the production plan data and preset configuration information; The production scheduling optimization model is solved according to a preset solver to obtain a target production scheduling plan.

2. The production scheduling optimization method based on human-machine collaboration according to claim 1 is characterized in that: The step of establishing a production scheduling optimization model according to the production plan data and the preset configuration information specifically includes: Preprocessing the production plan data to obtain a data set corresponding to the production scheduling optimization model; Determining parameter information and decision variables of the production scheduling optimization model according to the data set and the configuration information, and determining an objective function of the production scheduling optimization model; The constraint conditions of the production scheduling optimization model are determined according to the objective function to obtain the production scheduling optimization model.

3. The production scheduling optimization method based on human-machine collaboration according to claim 2 is characterized in that: The optimization goals of the objective function include maximizing the on-time delivery rate and minimizing the waiting time, wherein the maximizing the on-time delivery rate represents maximizing the proportion of on-time delivery orders to all orders, and the minimizing the waiting time represents minimizing the waiting time of all machines.

4. The production scheduling optimization method based on human-machine collaboration according to claim 2 is characterized in that: The constraints include basic constraints on drilling rig processing tasks, constraints on the end of drilling rig processing tasks, constraints on total order demand, constraints on the upper limit of the production quantity of a single drilling rig task, constraints on drilling rig processing time, constraints on the earliest time when order processing can start, constraints on the time of drilling rig pre- and post-processing tasks, personnel conflict constraints, constraints on order completion time, constraints on quasi-delivery of orders, and constraints on the earliest available time of the drilling rig.

5. The production scheduling optimization method based on human-machine collaboration according to claim 4 is characterized in that: The drilling rig processing task basic constraint means that each processing task of each drilling rig corresponds to at most one order; The drilling rig processing task end constraint means that if the i-th processing task of each drilling rig is no longer assigned orders, then no subsequent tasks will be assigned orders; The total order demand constraint means that the product processing quantity of all drilling rigs assigned to the same order is equal to the total product demand of the order; The upper limit constraint on the production quantity of a single task of a drilling rig represents that the number of products allocated to each processing task of any drilling rig shall not be greater than the upper limit of the single production capacity of the drilling rig; The drilling rig processing time constraint represents each processing task of any drilling rig, and the processing end time is equal to the processing start time plus the single-trip processing time of the order; The earliest processing start time constraint of the order means that for any order, the processing start preparation time shall not be earlier than the estimated arrival time of the order; The time constraint of the drilling rig front and back processing tasks means that for any drilling rig front and back two processing tasks, the time relationship should satisfy that the processing start time of the current task must not be less than the processing preparation start time of the current task plus the auxiliary processing time of the current task, and the processing preparation start time of the next task must not be less than the processing end time of the current task plus the waiting time of the next task; The personnel conflict constraint represents the time relationship that should be satisfied for any two processing preparation tasks of all drilling machines in the area of ​​responsibility of any personnel; The order completion time constraint means that the actual completion time of the order is greater than or equal to the time it takes for all drilling rigs assigned to the order to complete the order; The order quasi-delivery constraint means that if the final completion time of the order is not greater than the upper limit of the delivery period required by the order, the order is a quasi-delivery order; The earliest available time constraint of the drilling machine means that for the first task of any drilling machine, the processing preparation time must be later than the earliest available time of the drilling machine.

6. The production scheduling optimization method based on human-machine collaboration according to claim 5 is characterized in that: The total order demand constraint is linearized by introducing an intermediate variable, where the intermediate variable is an integer variable and its value is a positive number.

7. The production scheduling optimization method based on human-machine collaboration according to any one of claims 1 to 6, characterized in that: After the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan, the method further includes: When update information corresponding to the production plan data is received, the production scheduling optimization model is updated according to the update information, and the step of solving the production scheduling optimization model according to the preset solver to obtain the target production scheduling plan is returned to be executed.

8. A production scheduling optimization device based on human-machine collaboration, characterized in that: include: An acquisition module is used to obtain production plan data; An establishment module is used to establish a production scheduling optimization model according to the production plan data and preset configuration information; The solving module is used to solve the production scheduling optimization model according to a preset solver to obtain a target production scheduling plan.

9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the production scheduling optimization method based on human-machine collaboration as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the production scheduling optimization method based on human-machine collaboration as described in any one of claims 1 to 7.

Images