A production planning and scheduling method and system

Abstract

The application belongs to the technical field of production planning and scheduling system, and discloses a production planning and scheduling method and system; the method comprises the following steps: obtaining process capacity data of each machine tool and process demand data of each order; based on the process capacity data and the process demand data, a process matching degree matrix of each machine tool to each order is calculated; based on the process matching degree matrix, the orders are distributed to each machine tool; based on a pre-collected basic changeover time database, the process capacity data and the process demand data, a changeover time matrix of changeover time between any two orders of each machine tool is generated; based on the changeover time matrix of each machine tool, the order sequence on each machine tool is optimized, the total changeover time is minimized, and a final order distribution table is obtained; the overall changeover total time length is accurately evaluated and minimized, the machine tool utilization rate and the production capacity are significantly improved under the production mode of multiple varieties and small batches, the delivery cycle is shortened, and the planning and execution deviation is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production planning and scheduling systems, and more particularly, to a production planning and scheduling method and system. BACKGROUND

[0002] In the field of manufacturing production, it is crucial to reasonably allocate orders to machine tools and optimize the sequence to improve production efficiency. Traditional production scheduling methods usually only consider matching the process requirements of orders with the basic capacity of machine tools, ignoring the changeover time cost when machine tools switch between different orders. Especially in the production mode of multiple varieties and small batches, the changeover time ratio increases significantly, which seriously affects the utilization rate of machine tools and overall productivity.

[0003] The existing patent application with publication number CN118095572A proposes a battery scheduling method, device, electronic equipment and storage medium, which includes: gradient splitting and demand merging of battery product demand data to obtain target demand data, the target demand data including the production demand of each level of battery product; performing scheduling calculation according to the target demand data to obtain a first scheduling scheme set; optimizing the first scheduling scheme set according to a plurality of preset optimization targets to obtain a second scheduling scheme set; determining the production process corresponding to each second scheduling scheme in the second scheduling scheme set according to a preset sorting reference index to obtain a target scheduling scheme set; and outputting the target scheduling scheme set.

[0004] The existing patent application with publication number CN113627759B proposes a dynamic scheduling method for manufacturing resources of a mixed-line manufacturing system, which includes: using data structures to implement the construction of a process model indicator, completing the data modeling of the actual process model of the product; completing the data modeling of the actual process-manufacturing resource correspondence and the corresponding theoretical process duration; completing the construction and maintenance of various manufacturing resource numbers, manufacturing resource preparation time, product switching time, manufacturing resource utilization interval time and related necessary information; constructing a dynamic scheduling program encoder and decoder; designing and constructing a complete optimization program and setting program parameters; completing the construction of a scheduling scheme solver and scheme solving; completing the release of the scheduling manufacturing scheme; constructing a detector; constructing a manufacturing system operation core data monitor; and triggering a rearrangement mechanism.

[0005] The patent application with the existing publication number CN118569555A proposes a production plan automatic scheduling method, device, equipment and storage medium, which comprises the following steps: obtaining a target sales order created for a discrete manufacturing product, which comprises delivery priority of the discrete manufacturing product, combined production mode identification and combined production identification; determining the sub-product types of each sub-product in the product, and determining the production line capable of producing each sub-product based on the sub-product types to obtain a target production line; obtaining the created scheduling orders on each target production line; determining the production time interval of each target production line capable of producing sub-products according to the delivery priority based on the created scheduling orders; and using digital twin technology in combination with heuristic algorithm and combined production mode identification to forward schedule and rearrange the production time interval on the target production line to obtain a production plan scheduling result.

[0006] However, the above patent application has the following defects: the order allocation and sorting are processed separately, and a static and single-factor tool change time assumption is adopted, ignoring dynamic factors such as real-time state of equipment, and finally only relying on local heuristic algorithm iteration, easily falling into local optimum, resulting in low machine tool utilization, high tool change cost and frequent delivery delay.

[0007] In view of this, the present application proposes a production plan scheduling method and system to solve the above defects. SUMMARY

[0008] In order to overcome the above defects of the prior art and achieve the above purpose, the present application provides the following technical scheme: a production plan scheduling method, comprising:

[0009] obtaining process capability data of each machine tool and process demand data of each order;

[0010] based on the process capability data and the process demand data, calculating a process matching degree matrix of each machine tool to each order;

[0011] based on the process matching degree matrix, allocating orders to each machine tool;

[0012] based on the pre-collected basic tool change time database, process capability data and process demand data, generating a tool change time matrix of tool change time between any two orders of each machine tool;

[0013] based on the tool change time matrix of each machine tool, optimizing the order sorting on each machine tool to minimize the total tool change time, and obtaining a final order allocation table.

[0014] Further, the process capability data comprises machine tool supported process type, process parameter range and real-time state data; and the process demand data comprises order process type, process parameter requirement and delivery deadline weight.

[0015] The process parameters include temperature, pressure, rotation speed and feeding amount; and the real-time state data include die wear degree and load rate.

[0016] Further, the method for obtaining the process matching degree matrix of each machine tool for each order comprises:

[0017] determining whether the process types supported by the machine tools contain the process types of the orders, and generating a process type matching matrix;

[0018] comparing the process parameter requirements of the orders with the process parameter ranges of the machine tools, and generating a parameter compatibility matrix;

[0019] element-wise multiplying the process type matching matrix and the parameter compatibility matrix to generate the process matching degree matrix of each machine tool for each order.

[0020] Further, the method for generating the process type matching matrix comprises:

[0021] if the process types supported by the machine tools contain the process types of the orders, the corresponding elements of the process type matrix are 1;

[0022] if the process types supported by the machine tools do not contain the process types of the orders, the corresponding elements of the process type matrix are 0;

[0023] wherein the rows of the process type matrix represent the machine tools, and the columns represent the orders.

[0024] Further, the method for generating the parameter compatibility matrix comprises:

[0025] obtaining the midpoint values of the process parameter ranges of the machine tools;

[0026] calculating the absolute values of the differences between the process parameters of the orders and the midpoint values of the process parameter ranges of the machine tools;

[0027] calculating the corresponding elements in the parameter compatibility matrix based on the absolute values and the widths of the corresponding process parameter ranges of the machine tools;

[0028] wherein the rows of the parameter compatibility matrix represent the machine tools, and the columns represent the orders.

[0029] Further, the method for distributing the orders to the machine tools comprises:

[0030] obtaining a matching degree threshold value based on historical data analysis;

[0031] for each order, screening out a set of machine tools with a matching degree greater than the matching degree threshold value according to the process matching degree matrix, and forming an order-machine tool distribution pair;

[0032] for each order-machine tool distribution pair, calculating a distribution priority coefficient in combination with the matching degree of the corresponding distribution pair, the load rate of the machine tool and the deadline weight of the order;

[0033] According to the allocation priority coefficient from high to low, the orders are sequentially allocated to the corresponding machine tools.

[0034] Further, the method for generating the changeover time matrix of each machine tool comprises:

[0035] Extracting the process parameter requirements of the allocated orders of each machine tool to form a process parameter group of each machine tool;

[0036] Calculating the Euclidean distance of the process parameter groups of any two orders in the allocated orders of each machine tool to generate a parameter difference matrix of each machine tool;

[0037] Collecting real-time state data of the machine tool, and weighting and summing the die wear degree and load rate in the real-time state data to obtain a state correction value matrix of each machine tool with the same matrix dimension as the parameter difference matrix;

[0038] From the pre-collected basic changeover time database, the corresponding basic changeover time is retrieved according to the machine tool model and the order process type to form a diagonal matrix with the same matrix dimension as the number of allocated orders of each machine tool;

[0039] Through the preset parameter difference weight coefficient and state correction weight coefficient, the diagonal matrix of the basic changeover time of each machine tool is linearly combined with the parameter difference matrix and the state correction value matrix to generate a changeover time matrix of each machine tool.

[0040] Further, the method for obtaining the final order allocation table comprises:

[0041] For the allocated orders of each machine tool, a random permutation or heuristic rule is used to generate an order sequence of each machine tool;

[0042] Based on the changeover time matrix, a target function is constructed;

[0043] Through a search algorithm, a neighborhood search is performed on the order sequence of each machine tool, and the order sequence with the minimum total changeover time is retained;

[0044] After a preset number of iterations or the convergence condition is met, the optimal order sequence of each machine tool is output, and a final order allocation table is obtained.

[0045] Further, the target function is to minimize the changeover time of all adjacent orders in the order sequence.

[0046] Further, when generating the changeover time matrix of each machine tool, the basic changeover time is corrected in combination with the operator team skill level, including:

[0047] According to the preset operator team skill level, a skill disturbance coefficient is calculated, and the skill disturbance coefficient is multiplied by the basic changeover time to obtain an operator skill correction matrix;

[0048] The diagonal matrix of the basic changeover time, the parameter difference matrix, the state correction value matrix and the operator skill correction matrix are linearly combined by the preset parameter difference weight coefficient and the state correction weight coefficient to generate the changeover time matrix of each machine tool.

[0049] Compared with the prior art, the technical effects and advantages of the production plan scheduling method and system of the application are:

[0050] The application calculates a process matching degree matrix to accurately match orders and machine tools by obtaining machine tool process capacity data and order process demand data, and after distributing orders based on the matching degree, generates a changeover time matrix in combination with real-time state data of the machine tools and process parameter differences, and then optimizes order sequencing through 2-opt and simulated annealing search algorithms to minimize the total changeover time.

[0051] The scheme introduces a distribution priority coefficient and continuously generates matching degree, order distribution, changeover time matrix and sequencing optimization to ensure that the distribution and sequencing results are synergistically optimal; the changeover time does not use a static constant, but integrates multi-dimensional data such as process parameter differences, real-time states of equipment and skill levels of operator teams, to generate a dynamic changeover time matrix in real time; at the solution level, a hierarchical heuristic strategy of 2-opt local improvement and simulated annealing global jumping is introduced to retain the advantages of fast convergence and increase the ability to jump out of local optimum. The overall changeover time can be accurately evaluated and minimized, and the machine tool utilization rate and production capacity can be significantly improved in a multi-variety and small-batch production mode, thereby shortening the delivery cycle and reducing the planning and execution deviation. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A production plan scheduling system according to an embodiment of the application;

[0053] Figure 2 A production plan scheduling method according to an embodiment of the application;

[0054] Figure 3 A method for obtaining a process matching degree matrix according to an embodiment of the application;

[0055] Figure 4 A 2-opt neighborhood search algorithm flowchart according to an embodiment of the application;

[0056] Figure 5 A simulated annealing algorithm flowchart according to an embodiment of the application. DETAILED DESCRIPTION

[0057] To further illustrate the technical problems to be solved by the present application and their background, before the specific embodiments are described, the related reaction mechanisms, limitations of the prior art and practical difficulties faced by those skilled in the art in the solving process will be described in detail.

[0058] Specifically, in the field of discrete manufacturing, changeover time usually refers to the downtime adjustment time required for a production line to switch from processing one product model to another, including post-downtime device adjustment, process parameter setting, and necessary mold and tooling replacement, etc. During the changeover period, the production line cannot produce products, so this period of time is considered as a non-value-added production gap, which is one of the production wastes that is currently difficult to completely avoid. The greater the process difference between different product batches, the more complex the molds and fixtures needed to be replaced, the longer the changeover time tends to be; on the contrary, if the types of products produced continuously are similar, less adjustment is needed, and the changeover time is relatively short. This means that changeover time directly affects the production rhythm and line capacity utilization: too long changeover time will reduce the effective production time, delay the production cycle, and even affect order delivery.

[0059] Although changeover time has an important impact on scheduling efficiency and capacity, there are still many limitations in existing scheduling techniques for handling it. Some existing solutions often set the time required for switching different products as a fixed constant, which is stored in the basic database as a scheduling parameter. This static processing method fails to reflect the dynamic fluctuations of changeover time in actual production. For example, in patent CN118095572A, although the tooling fixture replacement time and waiting time during switching of different battery products are considered to calculate the changeover time, these time parameters are fixed values based on experience, and there is no mechanism to adjust them according to real-time production conditions. In addition, existing scheduling models do not fully consider the impact of current state differences of machine tools and skill levels of operators on changeover efficiency. For example, traditional changeover process mainly relies on manual experience operation, which has the problems of low efficiency and high error rate; the time required for different operators to perform changeover may differ significantly, but this factor is often ignored in the scheduling model. More importantly, existing technologies generally lack a dynamic prediction mechanism for changeover time: the average of history or fixed estimates are usually used in scheduling, which cannot estimate the length of time required for upcoming changeover in real time and adjust the plan according to real-time data. These limitations result in a lack of flexibility and accuracy in scheduling solutions when changeover is involved, making it difficult to adapt to complex and variable production sites.

[0060] In summary, due to the great uncertainty and context dependence of changeover time, production planning personnel in the industry currently face many practical difficulties when scheduling. First, the time required for changeover is difficult to accurately estimate, and is affected by many factors such as equipment state, product difference, process adjustment complexity, and personnel operation proficiency. Changeover time fluctuates greatly in practice, and using a fixed value for evaluation often has significant errors. Existing evaluation methods are rough and cannot accurately quantify the impact of each changeover in advance. Second, it is difficult to optimize the scheduling sequence in a timely manner. Due to the lack of dynamic prediction of future changeover time, production personnel cannot adjust the production sequence in advance to avoid lengthy changeover or concentrate the production of similar products, missing the opportunity to optimize scheduling to reduce changeover loss. As a result, there are often discrepancies between the plan and the actual production process, either because the changeover time is underestimated and the production is delayed, or because it is too conservative and the production capacity is wasted. In addition, the delivery period becomes difficult to guarantee due to the uncertainty of changeover time, increasing the risk of manufacturing enterprises fulfilling their obligations. The above difficulties highlight the shortcomings of existing technology in handling changeover time, and an improved technical solution that can dynamically predict changeover time and optimize the scheduling sequence is urgently needed. Therefore, the present application proposes a production planning and scheduling method and system, which is designed to address the above-mentioned pain points and provide more accurate changeover time prediction and intelligent scheduling optimization in order to reduce changeover loss, improve production capacity utilization, and improve on-time delivery capability.

[0061] The technical solutions in the embodiments of the present application will be described in detail, clearly and completely below with reference to the drawings in the embodiments of the present application. It should be particularly noted that the specific embodiments described below are only used to better illustrate and explain the technical solutions of the present application, and are intended to enable those skilled in the art to better understand and implement the present application, and should not be understood as limiting the scope of protection of the present application. Those skilled in the art can modify, adjust or equivalently replace the present application according to the content disclosed in the present application without departing from the spirit and essence of the present application, and these should be considered as the protection scope of the present application.

[0062] Embodiment 1

[0063] Please refer to Figure 1 The present embodiment discloses a production planning and scheduling system, which includes a data acquisition module, an order matching module, an order allocation module, an order calculation module and an order optimization module. Each module is connected by wire and / or wireless connection to realize data transmission.

[0064] The data acquisition module is used to acquire the process capability data of each machine tool and the process requirement data of each order.

[0065] The process capability data includes machine tool supported process type, process parameter range and real-time state data; the process requirement data includes order process type, process parameter requirement and delivery period weight;

[0066] The process parameters include temperature, pressure, rotation speed and feeding amount; and the real-time state data includes die wear degree and load rate.

[0067] Exemplarily, the data acquisition module realizes data acquisition by integrating enterprise resource planning system, manufacturing execution system and industrial internet of things equipment. For static process capability data such as process type supported by the machine tool and process parameter range, the data is directly called from the equipment management database through a standardized API interface, and the equipment management database pre-stores machine tool factory parameters and process capability data. The die wear degree in the real-time state data is acquired by a pressure sensor installed on the die part of the machine tool to acquire the die use frequency in real time, and after analysis, the die wear degree is converted into a wear coefficient in the range of 0-1. The load rate is acquired by the control system of the machine tool in real time to acquire the spindle running time, feeding speed and other data, and the load rate is calculated by combining the rated capacity of the machine tool. In the process requirement data of the order, the order process type and process parameter requirement are input into the manufacturing execution system by the process design department in the product research and development stage, and then synchronized to the data acquisition module through a data integration interface. The delivery date weight is manually set in the manufacturing execution system by the planning personnel according to the order delivery date urgency, customer priority and other factors or automatically generated by a preset rule. After all the data is acquired, the data is processed by format standardization and stored in a unified production data center for calling by the data acquisition module.

[0068] The order matching module calculates a process matching degree matrix of each machine tool to each order based on the process capability data and the process requirement data.

[0069] Further, referring to Figure 3 The method for obtaining the process matching degree matrix of each machine tool to each order includes:

[0070] determining whether the process type supported by the machine tool contains the order process type, and generating a process type matching matrix;

[0071] comparing the order process parameter requirement with the machine tool process parameter range to generate a parameter compatibility matrix;

[0072] generating the process matching degree matrix of each machine tool to each order based on the process type matching matrix and the parameter compatibility matrix.

[0073] Exemplarily, in a manufacturing execution system of a certain automobile parts manufacturing enterprise, three machine tools M1, M2 and M3 need to process eight orders O1-O8. The machine tool M1 supports injection molding process, the temperature range is 180-220℃, the pressure range is 5-10MPa; the machine tool M2 supports milling process, the rotating speed range is 1000-3000rpm, the feed rate is 0.05-0.2mm / r; the machine tool M3 supports injection molding and welding process, the temperature range is 150-250℃, the pressure range is 4-12MPa. The process requirements of the orders O1-O8 are as follows:

[0074] Order 01: the process type is injection molding, the temperature requirement is 200℃, and the pressure requirement is 8MPa;

[0075] Order 02: the process type is milling, the rotating speed requirement is 2000rpm, and the feed rate requirement is 0.1mm / r;

[0076] Order 03: the process type is injection molding, the temperature requirement is 210℃, and the pressure requirement is 9MPa;

[0077] Order 04: the process type is welding, the temperature requirement is 180℃, and the pressure requirement is 6MPa;

[0078] Order 05: the process type is milling, the rotating speed requirement is 1500rpm, and the feed rate requirement is 0.15mm / r;

[0079] Order 06: the process type is injection molding, the temperature requirement is 190℃, and the pressure requirement is 6MPa;

[0080] Order 07: the process type is welding, the temperature requirement is 200℃, and the pressure requirement is 8MPa;

[0081] Order 08: the process type is milling, the rotating speed requirement is 2500rpm, and the feed rate requirement is 0.08mm / r.

[0082] Further, the method for generating the process type matching matrix comprises:

[0083] If the supported process type of the machine tool includes the process type of the order, the corresponding element of the process type matrix is 1;

[0084] If the supported process type of the machine tool does not include the process type of the order, the corresponding element of the process type matrix is 0;

[0085] Wherein, the rows of the process type matrix represent machine tools, and the columns represent orders.

[0086] Exemplarily, for the process type of order O1, machine tools M1 and M3 support the process, the corresponding matrix element in the process type matching matrix is 1, and machine tool M2 does not support, and the corresponding element in the process type matching matrix is 0; similarly, only machine tool M2 supports orders O2, O5 and O8, only machine tool M3 supports orders O4 and O7, orders O3 and O6 are supported by machine tool M1 and machine tool M3, and finally a 3*8 process type matching matrix is generated:

[0087] .

[0088] Further, the method for generating the parameter compatibility matrix comprises:

[0089] obtaining the midpoint value of each process parameter range of the machine tool;

[0090] calculating the absolute value of the difference between each process parameter of the order and the midpoint value of each process parameter range of the machine tool;

[0091] the corresponding element in the parameter compatibility matrix = 1-absolute value / width of the corresponding process parameter range of the machine tool;

[0092] wherein the rows of the parameter compatibility matrix represent machine tools, and the columns represent orders.

[0093] Exemplarily, the temperature requirement of order O1 is 200℃, the temperature range of machine tool M1 is 180-220℃, and the parameter compatibility score is 1- |200-200| / (220-180)=1; the corresponding score of 8MPa pressure is 1- |8-7.5| / (10-5)=0.9, and the average value of the two is 0.95. Similarly, the parameter compatibility scores of other orders and machine tools are calculated to generate the parameter compatibility matrix:

[0094] .

[0095] The process type matching matrix and the parameter compatibility matrix are multiplied element by element to obtain the final process matching degree matrix:

[0096] ;

[0097] wherein the rows represent machine tools, and the columns represent orders.

[0098] The purpose of calculating the process matching degree matrix is to provide a quantitative basis for order allocation, ensuring that the order is allocated to the most suitable machine tool, reducing additional changeover costs or production delays caused by process mismatch. The reason for selecting support process types and process parameters to calculate the process matching degree matrix is that the support process type is the basic condition for whether the order can be processed on the machine tool. If the machine tool does not support the basic process type of the order, production cannot be carried out. The process parameter determines the difficulty and efficiency of the machine tool to execute the order. The higher the process parameter compatibility, the better the stability of the machine tool processing order and the shorter the changeover adjustment time. Therefore, the combination of the two can fully reflect the matching ability of the machine tool to the order.

[0099] The order allocation module allocates orders to each machine tool based on the process matching degree matrix.

[0100] Further, the method of allocating orders to each machine tool comprises:

[0101] Based on historical data analysis, a matching degree threshold is obtained;

[0102] For each order, the machine tool set with a matching degree greater than the matching degree threshold is screened out according to the process matching degree matrix, forming an order-machine tool allocation pair;

[0103] For each order-machine tool allocation pair, the allocation priority coefficient is calculated by combining the matching degree of the corresponding allocation pair, the load rate of the machine tool, and the delivery deadline weight of the order;

[0104] According to the allocation priority coefficient from high to low, the order is allocated to the corresponding machine tool in turn.

[0105] Further, the matching degree threshold is based on statistical analysis of historical production data. When the defective rate of the machine tool processing the order exceeds the predetermined threshold, and the average changeover time exceeds the predetermined threshold, the corresponding matching degree is taken as the matching degree threshold. For example, when the matching degree is less than 0.9, the defective rate of the machine tool processing the order increases significantly, and the average changeover time increases by more than 20%. Therefore, 0.9 is set as the threshold to ensure production quality and efficiency.

[0106] For example, for order O1, its corresponding machine matching degrees in the process matching matrix are M1 (0.95) and M3 (0.93), and the matching degree threshold is 0.9, so the machine M1 and the machine M3 are screened out to form the order-machine allocation pairs (O1, M1) and (O1, M3); similarly, order O2 only forms an allocation pair with machine M2, order O3 forms an allocation pair with machine M1 (0.92) and machine M3 (0.88), but the matching degree of M3 is lower than the matching degree threshold and is excluded, order O4 only forms an allocation pair with machine M3 (0.91), order O5 only forms an allocation pair with machine M2 (0.96), order O6 forms an allocation pair with machine M1 (0.97) and machine M3 (0.95), order O7 only forms an allocation pair with machine M3 (0.90), and order O8 only forms an allocation pair with machine M2 (0.94).

[0107] Further, the method for calculating the allocation priority coefficient of each order-machine allocation pair is to multiply the matching degree of the order and the machine, the current load condition of the machine, and the delivery deadline weight of the order, specifically: allocation priority coefficient = matching degree × (1 - machine current load rate) × order delivery deadline weight.

[0108] For example, the current load rates of machine M1, M2 and M3 are 70%, 65% and 80% respectively; the delivery deadline weights of orders O1-O8 are 0.8, 0.6, 0.9, 0.7, 0.5, 0.85, 0.65 and 0.75 respectively. For the allocation pair (O1, M1), the allocation priority coefficient is 0.95 (matching degree) multiplied by the load factor (1-0.7=0.3) and then multiplied by the delivery deadline weight 0.8, the result is 0.228; the coefficient of the allocation pair (O1, M3) is 0.93×(1-0.8)×0.8=0.1488. Similarly, the priority coefficients of other allocation pairs are calculated: (O2, M2) is 0.98×(1-0.65)×0.6=0.2058, (O3, M1) is 0.92×0.3×0.9=0.2484, (O4, M3) is 0.91×0.2×0.7=0.1274, (O5, M2) is 0.96×0.35×0.5=0.168 (O6, M1) is 0.97×0.3×0.85=0.24735, (O6, M3) is 0.95×0.2×0.85=0.1615, (O7, M3) is 0.90×0.2×0.65=0.117, and (O8, M2) is 0.94×0.35×0.75=0.24975.

[0109] According to the order distribution priority coefficient from high to low, the orders are distributed: firstly, (O8, M2) (the distribution priority coefficient is 0.24975), the order O8 is distributed to M2; secondly, (O3, M1) (the distribution priority coefficient is 0.2484), the order O3 is distributed to M1; then (O6, M1), the distribution priority coefficient is 0.24735, which is higher than 0.1615 of (O6, M3), so the order O6 is distributed to M1; (O1, M1) (the distribution priority coefficient is 0.228) is distributed, which is higher than 0.1488 of (O1, M3), so the order O1 is distributed to M1; then (O2, M2), (O5, M2), (O4, M3), (O7, M3) are distributed in turn. Finally, the initial order distribution table is formed: the machine tool M1 is distributed to the orders O3, O6, O1, the machine tool M2 is distributed to the orders O8, O2, O5, and the machine tool M3 is distributed to the orders O4, O7.

[0110] The purpose of calculating the distribution priority coefficient is to comprehensively consider the process matching degree, the machine tool load state and the order delivery deadline urgency, to avoid the problem that some machine tools are overloaded and some machine tools are idle caused by simply distributing according to the matching degree, to ensure that the order distribution can meet the process requirements, balance the machine tool load and prioritize the processing of urgent orders, and thus optimize the overall production plan.

[0111] Further, the method for setting the delivery deadline weight of the order comprises:

[0112] obtaining the time difference between the required delivery date of the order and the current date , and calculating the total time of the order expected to occupy the machine tool , that is, the sum of the processing time and the changeover time, and the calculation method of the delivery deadline weight is:

[0113] ;

[0114] If the delivery deadline of a certain order = 10 days, = 6 days, then the delivery deadline weight is 0.4; if < , that is, the delivery deadline is urgent, then the delivery deadline weight is automatically set to 1.0 to trigger urgent processing. This way avoids the subjectivity of manual intervention and ensures the repeatability of the algorithm.

[0115] The order calculation module generates a changeover time matrix of the changeover time between any two orders of each machine tool based on the process capacity data and the process demand data.

[0116] The method for generating the changeover time matrix of each machine tool comprises:

[0117] extracting the process parameter requirements of the allocated orders of each machine tool to form a process parameter group of each machine tool;

[0118] The Euclidean distance of any two order process parameter groups in the allocated orders of each machine tool is calculated to generate a parameter difference matrix of each machine tool.

[0119] Real-time state data of the machine tool is collected, and the die wear degree and load rate in the real-time state data are weighted and summed to obtain a state correction value matrix of each machine tool, which has the same matrix dimension as the parameter difference matrix.

[0120] From the pre-collected basic changeover time database, the corresponding basic changeover time is retrieved according to the machine tool model and the order process type to form a diagonal matrix with the same dimension as the number of allocated orders of each machine tool.

[0121] The diagonal matrix of the basic changeover time of each machine tool, the parameter difference matrix, and the state correction value matrix are linearly combined by the preset parameter difference weight coefficient and the state correction weight coefficient to generate a changeover time matrix of each machine tool.

[0122] Exemplarily, for the initial order allocation table, the changeover time matrix of each machine tool is generated.

[0123] For machine tool M2, the process parameter groups of the allocated orders are first extracted: order O8 (2500 rpm, 0.08 mm / r), order O2 (2000 rpm, 0.1 mm / r), and order O5 (1500 rpm, 0.15 mm / r), forming a three-dimensional process parameter group matrix. The Euclidean distance of any two orders is calculated, such as the speed difference of order O8 and order O2 500 rpm, the feed rate difference 0.02 mm / r, and the difference degree ; the speed difference of order O2 and order O5 500 rpm, the feed rate difference 0.05 mm / r, and the difference degree ; similarly, the difference degree between order O8 and order O5 is about 0.45, and a 3×3 parameter difference matrix D is generated.

[0124] ;

[0125] wherein, represents the parameter difference degree from the i-th order to the j-th order.

[0126] Real-time state data of the machine tool M2 is collected to obtain a die wear degree MJMS of 0.7 and a load rate FZL of 75%, and a state correction value matrix S is generated. The calculation method of each element in the state correction value matrix S is as follows:

[0127] ;

[0128] Finally, a state correction value matrix S with the same dimension as the parameter difference matrix is obtained.

[0129] ;

[0130] wherein, represents the state correction value from the i-th order switching to the j-th order.

[0131] The mold wear degree coefficient 0.6 and the load rate coefficient 0.4 in the state correction value matrix are obtained by combining historical data correlation analysis and expert experience. For example, the enterprise records the past 100 times of retooling of machine tool M2 and performs regression analysis, and finds that the influence of mold wear degree on retooling time accounts for about 60%, that is, the retooling time increases by an average of 8 minutes for every 0.1 increase in wear degree, and the influence of load rate accounts for about 40%, that is, the debugging time increases by 5 minutes for every 10% exceeding the threshold value of load rate, so the weights are set to 0.6 and 0.4. This proportion can be dynamically optimized through machine learning algorithm in subsequent production.

[0132] The milling basic retooling time of machine tool M2 is 1.2 hours from the basic retooling time database, forming a 3x3 diagonal matrix, that is, the main diagonal is 1.2, and the rest is 0. The parameter difference weight coefficient α = 0.6 and the state correction weight coefficient β = 0.4 are set by experience value. Linear combination is performed on the basic retooling time matrix, the parameter difference matrix and the state correction value matrix to generate the retooling time matrix T, and each element of the retooling time matrix is calculated as follows:

[0133] ;

[0134] The final retooling time matrix T of machine tool M2 is obtained as follows:

[0135] ;

[0136] wherein, represents the retooling time from the i-th order switching to the j-th order.

[0137] The basic retooling time database collects historical retooling data of each machine tool through the manufacturing execution system, and stores the basic retooling time according to the classification of "machine tool model + process type". For example, the milling basic retooling time of machine tool M2 is 1.2 hours, which is derived from the average time consumption of switching of 50 past pure milling orders of the machine tool, and is corrected in combination with the theoretical retooling time in the machine tool manual. The data collection period is 3 months, and the basic retooling time is automatically updated once every 50 new records, so as to ensure that the basic retooling time database reflects the latest device performance.

[0138] Further, the setting method of the parameter difference weight coefficient and the state correction weight coefficient is as follows:

[0139] Based on production experience, analyze the influence coefficient of process parameter difference and machine tool real-time state on changeover time, determine the initial weight value, and give higher weight to the higher influence coefficient;

[0140] Through the stress test of the simulation scheduling system, verify and optimize the weight combination, select the weight combination that makes the changeover time prediction error rate lowest and the actual execution highest;

[0141] According to the needs of specific industry scenarios, adjust the weight appropriately to meet the special requirements of actual production.

[0142] For example, through multiple production tests, it is found that the process parameter difference between orders is the main driving factor of changeover time, for example, the changeover time increases by 12% for every 10% increase in milling speed difference, while the machine tool real-time state is a secondary adjusting factor, for example, the changeover time increases by 8% for every 10% increase in load rate, so the process parameter difference is given a higher weight. After setting the initial weight, stress test the simulation scheduling system: when α=0.6, β=0.4, the changeover time prediction error rate is the lowest, and the scheduling scheme has the highest degree of execution in actual production. The weight can be adjusted according to the characteristics of different industries, for example, in the precision machining scene, the state correction weight can be increased to 0.5.

[0143] Similarly, calculate the changeover time matrix for machine tools M1 and M3, and finally generate the changeover time matrix for each machine tool.

[0144] The parameter difference matrix reflects the actual difference degree of process parameters between orders. The larger the parameter difference, the longer the time-consuming of machine tool parameter adjustment and mold replacement, which is the core influencing factor of changeover time. For example, the speed difference of milling orders directly affects the spindle adjustment time, and the feed amount difference affects the tool position calibration time. By quantifying the difference with Euclidean distance, the basic time-consuming of process switching can be scientifically evaluated.

[0145] The state correction value matrix takes into account the dynamic influence of the real-time state of the machine tool, such as the higher the mold wear degree, the longer the preparation time for mold replacement; the higher the load rate, the longer the debugging time caused by the decrease in machine tool stability.

[0146] The combination of the two avoids the limitations of relying solely on historical average data, considering both the inherent differences in order process characteristics and the real-time changes in the current state of the machine tool, ensuring the accuracy and dynamic adaptability of changeover time calculation, and providing reliable time cost basis for subsequent order optimization.

[0147] The order optimization module optimizes the order sequencing on each machine tool based on the changeover time matrix of each machine tool, minimizes the total changeover time, and obtains the final order allocation table.

[0148] The method for obtaining the final order allocation table includes:

[0149] The orders assigned to each machine are arranged randomly or generated by heuristic rules to form the order sequence of each machine;

[0150] Based on the tool change time matrix, a target function is constructed;

[0151] The order sequence of each machine is searched by a search algorithm to calculate the total tool change time after exchanging adjacent orders, and the order sequence with smaller total tool change time is retained;

[0152] After a preset number of iterations or meeting the convergence condition, the optimal order sequence of each machine is output, and the final order allocation table is obtained.

[0153] For example, for the order sequence O8→O2→O5 of machine M2, the system generates an initial sequence using the NEH heuristic rule, i.e., arranging the orders in descending order of processing time, and constructs a function with the goal of minimizing the total tool change time. According to the tool change time matrix of M2:

[0154]

[0155] The total tool change time of the current sequence O8→O2→O5 is 1.692 (O8→O2) + 1.734 (O2→O5) = 3.426 hours.

[0156] Please refer to Figure 4 , the system exchanges adjacent orders through the 2-opt neighborhood search algorithm to generate a candidate sequence O2→O8→O5, whose total tool change time is 1.692 (O2→O8) + 1.83 (O8→O5) = 3.522 hours, which is greater than the original sequence, so the original sequence is retained. Similarly, check the sequence O8→O5→O2, whose total tool change time is 1.83 (O8→O5) + 1.734 (O5→O2) = 3.564 hours, which is still greater than the original sequence, so the optimal order sequence of M2 remains O8→O2→O5.

[0157] Please refer to Figure 5 , for the order sequence O3→O6→O1 of machine M1, the neighborhood search is performed using the simulated annealing algorithm. The total tool change time of the initial sequence is: O3→O6 (1.9 hours) + O6→O1 (1.81 hours) = 3.71 hours. Randomly exchange the positions of O1 and O6 to obtain the candidate sequence O3→O1→O6, and calculate its total tool change time as: O3→O1 (1.9 hours) + O1→O6 (1.81 hours) = 3.71 hours. After 50 iterations, the final sequence O1→O3→O6 is adopted, with a total tool change time of 3.740 hours, which is slightly higher than the initial sequence, but it is accepted during the annealing process due to the probability mechanism jumping out of the local optimum.

[0158] ​The strategy of accepting worse solutions essentially pays for the exploration cost of global optimization. In the high-temperature phase, worse solutions are widely accepted to traverse the solution space, and as the temperature decreases, it gradually converges to the high-quality solution domain. For example, after 50 iterations, the algorithm may find a better sequence with a total changeover time of less than 3.7 hours, which is difficult for a deterministic algorithm to achieve. This ability directly translates into production benefits: a global reduction of 1 hour in changeover time means that the machine tool can process more urgent orders during this period, improving overall production capacity utilization by 3-5%. Especially in large-scale order scenarios such as more than 10 orders per machine tool, simulated annealing can reduce the total changeover time by 10-15% compared to the 2-opt algorithm, while maintaining computational efficiency - balancing optimization quality and real-time needs through a preset number of iterations (such as 50), avoiding the impracticality of exhaustive methods. Therefore, this optimization mechanism of retreat for progress enables the production system to still approach the global optimal solution under complex constraints, ultimately achieving the dual goals of minimizing changeover costs and improving on-time delivery rates.

[0159] The order sequence of machine tool M3 is O4→O7, with only two orders and a changeover time of 2.8 hours, so no optimization is needed.

[0160] Finally, the optimal order sequence for each machine tool is output, forming the final order allocation table: M1(O1→O3→O6), M2(O8→O2→O5), M3(O4→O7). The total changeover time of the final order allocation table is 3.740(M1) + 3.426(M2) + 2.8(M3) = 9.966 hours.

[0161] In this embodiment, the 2-opt algorithm and the simulated annealing algorithm each have their own advantages in minimizing changeover time for different sizes of order sequences. The 2-opt algorithm generates candidate sequences by swapping adjacent orders and calculates the total changeover time, strictly retaining better solutions, quickly finding locally optimal order sequences in smaller solution spaces, avoiding complex calculations and efficiently converging, ensuring fast optimization of small-scale order sorting; the simulated annealing algorithm uses its probability acceptance mechanism to allow accepting worse solutions with a certain probability, thereby escaping from local optimal traps and exploring better solutions in a larger solution space, gradually approaching the global optimum through a preset number of iterations, effectively addressing the complex solution space problem caused by the increase in the number of orders. The combination of the two forms a hierarchical optimization strategy, with 2-opt handling simple small-scale sequences and simulated annealing tackling complex large-scale sequences, both based on the accurate cost data provided by the changeover time matrix, ensuring the efficiency of small-scale order sorting and improving the solution quality of large-scale order sorting, working together to help the production scheduling system output high-quality order allocation plans that balance process matching and changeover efficiency within a reasonable time, achieving significant reduction in total changeover time and improvement in production efficiency.

[0162] Example 2

[0163] The embodiment provides a production planning and scheduling system, and further comprises:

[0164] When the tool change time matrix of each machine tool is generated, the basic tool change time is corrected in combination with the skill level of the operator team, and the correction specifically comprises:

[0165] The skill disturbance coefficient is calculated according to the preset skill level of the operator team, the skill disturbance coefficient is multiplied by the basic tool change time, and an operator skill correction matrix is obtained;

[0166] The diagonal matrix of the basic tool change time, the parameter difference matrix, the state correction value matrix and the operator skill correction matrix are linearly combined through the preset parameter difference weight coefficient and the state correction weight coefficient, and the tool change time matrix of each machine tool is generated.

[0167] Further, the preset method of the skill level of the operator team comprises:

[0168] The tool change efficiency of the operator is tracked for a long time based on historical production data, the actual skill level of each team is determined through statistical analysis, the skill performance of the team is divided into a plurality of discrete levels, and the level division is based on the deviation amplitude of the actual tool change time of the team in the historical production process relative to the basic tool change time;

[0169] Specifically, the team whose historical tool change efficiency is higher than the basic tool change time is defined as a high-level skill team, the team whose efficiency is close to the basic tool change time is defined as a medium-level skill team, and the team whose efficiency is significantly lower than the basic tool change time is defined as a low-level skill team, each level corresponds to a preset skill disturbance coefficient, so that the basic tool change time is corrected by using the skill disturbance coefficient in the calculation process of the tool change time matrix.

[0170] Further, the preset method of the skill disturbance coefficient comprises:

[0171] The skill disturbance coefficient corresponding to the high-level skill team is set to a value less than 1, the skill disturbance coefficient of the medium-level skill team is set to 1, and the skill disturbance coefficient of the low-level skill team is set to a value greater than 1. For example, the operator team of high skill level is skilled and has high execution efficiency, so the actual changeover time is usually shorter than the basic changeover time, and the skill disturbance coefficient is set to be less than 1 to reflect the improvement of changeover efficiency; the team of low skill level is insufficient in experience or poor in proficiency, so the actual changeover time is significantly longer than the basic changeover time, and the skill disturbance coefficient is set to be greater than 1 to reflect the decrease of changeover efficiency; the team of medium-level skill actually performs close to the standard basic changeover time, so the coefficient is set to 1 and no additional correction is made. The main purpose of such setting is to quantify the actual influence of operator skills on changeover efficiency through the skill disturbance coefficient, so as to more accurately predict the changeover time in the actual production environment and improve the accuracy and practicality of the production scheduling scheme.

[0172] Further, the method for generating the changeover time matrix of each machine tool by linearly combining the diagonal matrix of the basic changeover time, the parameter difference matrix, the state correction value matrix, and the operator skill correction matrix through the preset parameter difference weight coefficient and the state correction weight coefficient comprises:

[0173] First, the parameter difference weight coefficient is multiplied by the parameter difference matrix, and the state correction weight coefficient is multiplied by the state correction value matrix, and then the diagonal matrix of the basic changeover time and the operator skill correction matrix are combined together, and the comprehensive changeover time matrix considering the parameter difference, the real-time state of the machine tool, and the skill level of the operator team is obtained through matrix addition operation, which is finally used to optimize the production scheduling scheme to minimize the total changeover time.

[0174] Combining the skill level of the operator team to correct the basic changeover time can more accurately reflect the changeover efficiency in the actual production environment. Since the skill level of the operator directly affects the execution efficiency of process adjustment and equipment changeover in the actual production process, by introducing the skill level of the team, the influence of human factors on the changeover time can be more comprehensively considered, thereby improving the accuracy of changeover time prediction. This method makes the production scheduling scheme closer to the actual production, which is conducive to reducing the deviation between production planning and execution and improving the stability and effectiveness of plan execution. In addition, this method can also clearly show the contribution of operator skill difference to production efficiency, providing an important basis for enterprises to improve employee skills and optimize human resource allocation.

[0175] Further, the operator team skill level can also be dynamically adjusted according to the order delivery weight in the changeover time correction, specifically including: in the order changeover process with high delivery weight, the system preferentially schedules the operator team with high skill level to perform the changeover task to improve the changeover efficiency and ensure the delivery deadline; when the high-skill team is unavailable, the system corrects the changeover time according to the skill level of the available team, and gives a higher scheduling priority combined with the order delivery weight, so as to reflect the potential delay risk caused by insufficient skills in the changeover time matrix.

[0176] Linking the operator team skill level with the order delivery weight can effectively enhance the sensitivity of the system to key orders, dynamically allocate high-skill human resources in the production scheduling process, and reduce the delivery deviation caused by human changeover delay from the source.

[0177] Embodiment 3

[0178] Referring to Figure 2 The embodiment provides a production plan scheduling method, which comprises the following steps of:

[0179] Obtaining process capability data of each machine tool and process demand data of each order;

[0180] Based on the process capability data and the process demand data, a process matching degree matrix of each machine tool to each order is calculated;

[0181] Based on the process matching degree matrix, the orders are distributed to each machine tool;

[0182] Based on the process capability data and the process demand data, a changeover time matrix of the changeover time between any two orders of each machine tool is generated;

[0183] Based on the changeover time matrix of each machine tool, the order sequence on each machine tool is optimized to minimize the total changeover time, and a final order distribution table is obtained.

[0184] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any modification or substitution within the technical range disclosed by the present application can be easily thought of by those skilled in the art, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0185] Finally: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, and any modification, equivalent substitution, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A production planning and scheduling method, characterized in that, include: Obtain the process capability data of each machine tool and the process requirement data of each order; Based on process capability data and process requirement data, the process matching degree matrix of each machine tool to each order is calculated; Based on the process matching degree matrix, orders are allocated to each machine tool; Extract the process parameter requirements of the assigned orders for each machine tool to form a process parameter group for each machine tool; For any two orders in the assigned orders of each machine tool, calculate the Euclidean distance to generate the parameter difference matrix of each machine tool; Collect real-time status data of machine tools, and sum the mold wear degree and load rate in the real-time status data by weighting, to obtain the status correction value matrix of each machine tool with the same matrix dimension as the parameter difference matrix; From the pre-collected basic changeover time database, the corresponding basic changeover time is retrieved according to the machine tool model and order process type, forming a diagonal matrix with the same matrix dimension as the number of each machine tool and the number of allocated orders; The skill disturbance coefficient is calculated based on the preset operator team skill level. The skill disturbance coefficient is then multiplied by the basic changeover time to obtain the operator skill correction matrix. By using preset parameter difference weighting coefficients and state correction weighting coefficients, the diagonal matrix of the basic changeover time, the parameter difference matrix, the state correction value matrix, and the operator skill correction matrix are linearly combined to generate the changeover time matrix for each machine tool. Based on the changeover time matrix of each machine tool, the order sorting on each machine tool is optimized to minimize the total changeover time, and the final order allocation table is obtained.

2. The production planning and scheduling method according to claim 1, characterized in that, The process capability data includes the machine tool supported process types, process parameter ranges, and real-time status data; the process requirement data includes the order process type, process parameter requirements, and delivery weight. The process parameters include temperature, pressure, rotational speed, and feed rate; the real-time status data includes mold wear and load rate.

3. The production planning and scheduling method according to claim 1, characterized in that, Methods for obtaining the process matching degree matrix of each machine tool for each order include: Determine whether the machine tool supports process types that include order process types, and generate a process type matching matrix; The order's process parameter requirements are compared with the machine tool's process parameter range to generate a parameter compatibility matrix; The process type matching matrix and the parameter compatibility matrix are multiplied element by element to generate the process matching degree matrix of each machine tool for each order.

4. The production planning and scheduling method according to claim 3, characterized in that, Methods for generating process type matching matrices include: If the machine tool supports process types that include the order's process types, then the corresponding element in the process type matrix is ​​1; If the machine tool's supported process types do not include the order's process types, then the corresponding element in the process type matrix is ​​0; In the process type matrix, rows represent machine tools and columns represent orders.

5. The production planning and scheduling method according to claim 3, characterized in that, Methods for generating parameter compatibility matrices include: Obtain the midpoint value of the range of various process parameters of the machine tool; Calculate the absolute value of the difference between each process parameter of the order and the midpoint value of each process parameter range of the machine tool; The elements in the parameter compatibility matrix are calculated based on the absolute value and the width of the range of corresponding process parameters of the machine tool. In the parameter compatibility matrix, rows represent machine tools and columns represent orders.

6. The production planning and scheduling method according to claim 1, characterized in that, Methods for allocating orders to machine tools include: The matching degree threshold is obtained based on historical data analysis; For each order, a set of machine tools with a matching degree greater than the matching degree threshold is selected based on the process matching degree matrix to form an order-machine tool allocation pair; For each order-machine tool allocation pair, the allocation priority coefficient is calculated by combining the matching degree of the corresponding allocation pair, the load rate of the machine tool, and the delivery weight of the order. Orders are assigned to the corresponding machine tools in descending order of priority coefficient.

7. The production planning and scheduling method according to claim 1, characterized in that, Methods for obtaining the final order allocation table include: For each machine tool with assigned orders, a sequence of machine tool orders is generated using random permutation or heuristic rules; Based on the transformation time matrix, construct the objective function; A neighborhood search is performed on each machine tool order sequence using a search algorithm to retain the order sequence with the minimum total changeover time. After a preset number of iterations or when the convergence condition is met, the optimal order sequence for each machine tool is output, and the final order allocation table is obtained.

8. The production planning and scheduling method according to claim 7, characterized in that, The objective function is to minimize the changeover time of all adjacent orders in the order sequence.

9. A production planning and scheduling system for implementing the production planning and scheduling method according to any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire the process capability data of each machine tool and the process requirement data of each order; The order matching module calculates the process matching degree matrix of each machine tool for each order based on process capability data and process requirement data. The order allocation module assigns orders to each machine tool based on the process matching degree matrix. The order calculation module generates a changeover time matrix for any two orders for each machine tool, based on process capability data and process requirement data. The order optimization module optimizes the order sorting on each machine tool based on the changeover time matrix of each machine tool, minimizes the total changeover time, and obtains the final order allocation table.

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