Computer-implemented method for production planning and / or controlling a production system and production planning and / or production control system for production optimization

Abstract

The invention relates to a computer-implemented method for production planning and / or production control of a production system comprising a plurality of production sections (PA1, PA2) and production lines (line 1, line 2, line 3). Furthermore, the invention relates to a production planning and / or production control system (APO) for production optimization.

Description

Technical Field

[0001] This invention relates to a computer-implemented method for production planning and / or production control of a production system, a production planning and / or production control system for production optimization, and a computer program. Background Technology

[0002] In production, products, including material goods and services, are created based on factors of production, including raw materials and means of production. For example, the production of transmission devices. In the production of transmission devices, additional material goods, such as driven shafts, are created. Production planning and / or production control optimize the entire production system.

[0003] Various measures for production planning and / or production control are known in the prior art. For example, traditional systems include step-by-step planning for basic data management, production process planning, quantity planning, date planning, shop floor control, order monitoring, and sales control. Furthermore, integrated IT systems that include production planning and / or production control are also known.

[0004] Furthermore, optimization methods for production planning and / or production control are known, such as constraint-based methods using linear programming. However, such methods cannot be scaled up to the scale of real-world problems. Additionally, local search or branch-and-bound algorithms are known for optimization. Furthermore, classical scheduling algorithms, such as multiprocessor scheduling, are also known, but they can only be applied to simplified models. Moreover, evolutionary algorithms for optimization are known, but they require significant resources, such as time or computational power, and a good initial solution.

[0005] Currently, human controllers plan the production process of specific products, workpieces, or semi-finished components (such as the production of driven shafts) with or without the support of known optimization methods. Here, production consists of multiple production segments that the components must sequentially pass through. Therefore, the controller must consider various input parameters. For example, planning which components to produce at which time on which production line / sub-production line should be optimal in terms of multiple optimization criteria. Another complicating factor is that, on the one hand, the parameters determining the existing production process often change over time, as do the optimization criteria. This leads to the need for frequent replanning, which must be done as quickly as possible to ensure production proceeds without interruption or in a suboptimal manner. Summary of the Invention

[0006] Based on this, the objective of the present invention is to determine how to create production sequences, worker allocations, and supplier orders according to given requirements, and how to evaluate and optimize production sequences based on predefined criteria.

[0007] For clarity, the present invention will be introduced first.

[0008] This invention addresses this task through a method and an adaptive system that optimizes complex processes, such as production processes, based on a given evaluation and utilizing a virtual representation of production. The system adapts to changes impacting production in the shortest possible time and ensures production plans can be implemented at any time. Furthermore, the system generates solutions for highly complex production conditions.

[0009] Compared to existing technologies, this invention allows for longer planning horizons, such as weeks instead of days. For example, this invention has been used in the production of driven shafts, where planning horizons of several weeks have been achieved. For instance, a planning horizon of two weeks has been achieved. With the length of this planning horizon, the duration of the method is correspondingly longer. However, in the course of this invention, it has been found that the method according to the invention advantageously exhibits a linear relationship with the planning horizon only compared to known optimization methods, while known optimization methods generally exhibit an exponential relationship with it. Along with this, costs are significantly reduced due to a more efficient planning process, assembly output is increased, and capital investment is reduced due to decreased storage.

[0010] Furthermore, this invention supports the increasing complexity of products in the future, such as greater variability or additional edge conditions that cannot be or cannot be adequately reproduced by known control tools. For example, in the applicant's factory, approximately 500 different transmission devices are manufactured. With the development of more generations and more transmission devices, the variability will again increase significantly. The resulting costs, such as those incurred from weekend work or production downtime until delivery to the customer, can be avoided by this invention.

[0011] A concise description of the invention is embodied by focusing on the input values ​​or inputs into the system and the output values ​​or outputs provided by the system. Inputs into the system include both direct and indirect inputs. System outputs include control-related outputs and information-providing outputs.

[0012] Therefore, this method and system provide all control-related information for optimal production sequences, worker allocation, and supplier orders. The production sequence is implemented automatically or only after approval by the controller. Within the scope of this invention, optimal means optimal with respect to a given total cost function. The controller may additionally have the possibility to influence the outcome by initiating new runs with changed inputs. To support such decisions, the system provides outputs with detailed information regarding production line allocation, inventory development, and predicted completion times.

[0013] Direct inputs include the inputs expected in each optimization run of the system. An optimization run is typically triggered when production parameters change. Another reason is, for example, the change in weights of different optimization criteria by the controller. The controller is the human operator who has independently completed the planning so far. However, each different change in the input conditions usually results in a new system startup. For example, the following inputs are direct inputs:

[0014] • Production parameters: worker availability, machine capacity, material availability, initial warehouse and buffer stock levels, and / or supplier capacity;

[0015] • Material requirements: Which materials / semi-finished products must be produced at what time, and / or the weighted / prioritized order of the parts to be produced;

[0016] • Optimal criteria: Maximize the utilization of all machinery and labor, minimize delays, minimize warehouse inventory, minimize material flow from geographically dispersed areas within the plant, and / or weight them together, and

[0017] • Edge conditions: These edge conditions must be strictly adhered to compared to optimal standards for the optimization operation to commence. For example, these edge conditions include priority requirements of level 1 demand that must be produced within a specified timeframe under all circumstances, not exceeding warehouse / intermediate warehouse size, and not transporting parts from one manufacturing line / warehouse to another, which is currently unreasonable due to logistical or other reasons. These edge conditions can be changed by the controller. Planning lead times, such as how many hours or days in advance production planning should be planned, also fall under edge conditions.

[0018] Indirect inputs are only integrated into the system when structural changes occur during production or the production process.

[0019] This invention simulates a production system and thus provides a virtual representation of the production process and / or the production system. This virtual representation is a digital twin of the entire production process and / or production system. The digital twin models all dependencies in production. This model also includes production parameters as variables. This invention always keeps the model synchronized with the actual conditions and dependencies in the actual production process and / or the actual production system.

[0020] Control-related outputs must be used for implementation in the plant's production planning and / or production control systems, and must include:

[0021] • Optimized production sequence: Which production line needs which material at what time?

[0022] • Worker allocation: Which production line needs or will need how many workers in which shift?

[0023] • Supplier orders: When, what type of material, and how much material to supply?

[0024] The information provided by the output adds value in terms of explanatory power, such as explaining why material delays occur, and facilitates the controller's own evaluation of the optimization results. The information provided includes:

[0025] For example, demand coverage related to logistics and the expected manufacturing completion date;

[0026] • Display utilization, bottlenecks, and critical path; and

[0027] • The projected timeframe for the development of semi-finished and / or finished parts and / or inventory levels.

[0028] A further summary of the invention indicates that the method and system optimize the total cost function of a production system. The cost function determines the lowest-cost production process based on a technically efficient production flow. The cost function represents the total cost of the production process, derived from the factors of production used, multiplied by their respective market prices or weights. For example, the total cost function is defined as follows:

[0029] • Demand fulfillment: Delay time, with each demand having a weight;

[0030] • Production utilization rate: the time of production downtime;

[0031] Secondary production conditions: transportation between production lines, and commissioning time.

[0032] These criteria are combined into a single value using mathematical functions, where each criterion can be weighted differently.

[0033] For example: Total cost = α * ∑_delay"(b) * weight(b) + β * production downtime + γ * debugging time + ..., where α≥0, β≥0, γ≥0, ... represent the weights of different components and are variable. All material requirements are summed.

[0034] For clarity, the method according to the present invention is as follows:

[0035] The current production parameters, demand, optimality criteria, and marginal conditions are, for example, inputs obtained from the data. An initial production sequence is then created using a fast optimization method, meaning it takes only a few seconds to run. This production sequence is then incorporated into subsequent, more thorough, and longer-running optimization methods, such as as the initial population, i.e., as the initialization in an evolutionary algorithm. It is possible to perform even more thorough but more time-consuming optimization methods, such as genetic optimizers with larger populations and other hyperparameters.

[0036] The initial production sequence is also incorporated into the actual production system or actual factory for implementation. Once a better result regarding the total cost function is provided from a thorough optimization of one of the downstream systems, this result is output and implemented directly by the system in actual production, or output to a human controller to support that controller. This ensures that the better result is consistent with the production sequence that has already begun. This is guaranteed by making each plan that has just been put into production by the preceding optimizer in the current time a marginal condition for the downstream optimizer.

[0037] When an event occurs that affects production, such as a machine malfunction or a change in worker conditions, a new initial production sequence is created, and the process starts from scratch again.

[0038] According to one aspect, the present invention provides a computer-implemented method for production planning and / or production control of a production system. The production system includes multiple production sections and production lines. The method includes the following steps:

[0039] • Simulate the production system, production planning, and / or production control.

[0040] In the simulation, the first sub-method and the second sub-method are executed, where,

[0041] The first sub-method includes the following steps:

[0042] The material requirements in the production segment are prioritized based on the optimization of the cost function of the production system.

[0043] Select one material requirement in priority order, adjust at least one requirement quantity and / or requirement time point of the material in the preceding production segment to implement the material requirement, and retain the material and the adjusted requirement quantity and / or requirement time point respectively.

[0044] Select other material requirements and repeat the previous step until materials and adjusted quantities and / or timings are reserved for all priority material requirements, and a production sequence is obtained.

[0045] The second sub-method includes the following steps:

[0046] The first production time segment in the fixed production sequence, and

[0047] The production sequence outside the fixed first production time period is optimized to further optimize the cost function, where,

[0048] • Adjust and / or control the production system based on the optimized production sequence obtained in the second sub-method.

[0049] The first sub-method is equivalent to a fast optimization method, which provides an initial production sequence as the first result within seconds. This first result is meaningful in a very short time because production should not be interrupted after a sudden event. The optimization objective is to combine demand coverage with maximizing production utilization, which means minimizing production downtime.

[0050] The second sub-method is equivalent to a more thorough optimization method.

[0051] The first sub-method of the method according to the invention provides a first result more quickly than the second sub-method.

[0052] Simulation provides a virtual representation of the production system, production planning, and / or production control, wherein the entire production system is implemented as a digital twin. For example, bottlenecks or critical paths are simulated in the simulation. According to one aspect of the invention, the simulation projects the future state of the production system. Therefore, it is possible to extend the planning outlook period arbitrarily into the future, for example, within a range of several weeks. Through simulation, the optimization achieved by the method according to the invention, and consequently the entire production system, is adapted advantageously to production changes.

[0053] Material requirements include the types or categories of materials. Material types include raw materials such as iron, auxiliary materials such as screws, means of production such as energy, unfinished products such as pre-assembled components that still need to be assembled, and finished products such as finished goods and commodities ready for shipment.

[0054] In product planning, sequencing or sequence planning (also known as ordering and scheduling) involves the formation of a manufacturing sequence for product orders.

[0055] Fixing ensures that the output of the second sub-method can also be implemented. Fixing prevents changes to the portion of the production sequence determined by fixing within the second sub-method. According to one aspect of the invention, all input parameters are fixed in time for a given period. This fixing is achieved, for example, by prioritizing the fixing of the production sequence obtained by the first sub-method, worker conditions, and / or supply. Due to fixing, the time required for the second sub-method can be at most as long as the time covered by fixing. For example, the time until the end of the current shift is considered a fixed time. The fast optimizer optimizes across all production periods. The first production period optimized by the fast optimizer is then implemented in the actual plant and cannot be changed. Therefore, the slower but more thorough optimizer optimizes other production periods outside the fixed time period.

[0056] According to one aspect of the invention, material requirements are prioritized based on material type, required quantity, required time point, priority and / or weight.

[0057] When sufficient input materials are available to fully meet demand, prioritized material requirements are implemented. In this case, input materials are reserved for that demand. Reserving input materials ensures that the production sequence determined in this way can be implemented; in other words, orders created in this way can be implemented under any circumstances.

[0058] According to another aspect, the present invention provides a production planning and / or production control system. The system includes a processing unit configured to implement the method according to the present invention.

[0059] According to another aspect, the present invention provides a computer program. This program contains instructions that, when run on a system, cause the system according to the invention to implement the method according to the invention.

[0060] Other design options of the present invention can be derived from the description of the dependent claims, drawings, and preferred embodiments.

[0061] According to one aspect of the invention, if no significant optimization of the production sequence is achieved, the continuation of the second sub-method is terminated, and further continuation of the second sub-method begins. This termination criterion accelerates the method, thereby further optimizing the production system.

[0062] The fixed time period does not need to be fully utilized. For example, if the second sub-method makes no progress or only minimal progress in optimizing the task within a time period, such as less than the fixed time period, then the current continuation of the second sub-method ends. If the second sub-method finds a significantly better optimization within a time period less than the fixed time period, then that optimization will be output earlier and accepted directly. This further accelerates the method and improves the optimization. According to one aspect of the invention, the controller actively requests new optimizations from the second sub-method.

[0063] If the improvement in the cost function optimization is too small and no change in the constraints occurs during this period, according to one aspect of the invention, the second sub-method will resume the search again with a further time unit postponed. The time lookout period can be extended backward.

[0064] According to another aspect of the invention, in order to execute the second sub-method, an evolutionary algorithm is executed, which is initialized with the production sequence or its mutation obtained in the first sub-method.

[0065] Evolutionary algorithms are inspired by the natural evolutionary process of organisms and are processed in the following way;

[0066] • Initialization: Generating the first generation of candidate solutions. According to the invention, the first generation is the initial production sequence. The initial production sequence is generated by the method according to the invention, that is, the fast optimizer.

[0067] • Evaluation: Each candidate solution in this generation is assigned a fitness function value based on its quality. The fitness function is the objective function of the evolutionary algorithm. A template for the fitness function is biological fitness, which describes how well an organism adapts to its environment. In evolutionary algorithms, the fitness of the production sequence describes how well the production sequence solves the basic optimization problem.

[0068] Continue with the following steps until the termination criteria are met:

[0069] o Selection: Select a number of individuals for recombination.

[0070] ο Recombination: Combine the selected individuals

[0071] ο Mutation: Randomly altering offspring

[0072] Evaluation: Each candidate solution in this generation is assigned a fitness function value based on its quality.

[0073] ο Choice: Determine the new generation.

[0074] Typical termination criteria are further mentioned below.

[0075] Evolutionary algorithms have the advantage that they can represent solutions in different forms for better processing and output them again in their original form later, similar to genotype-phenotype mapping or artificial embryogenesis. This is particularly useful when the representation of possible solutions can be greatly simplified and does not need to be processed in memory in terms of its complexity. Evolutionary algorithms include genetic algorithms. Genetic algorithms use a binary problem representation and therefore typically require genotype-phenotype mapping. With evolutionary algorithms, candidate solutions are found only through mutation, without recombination. Genetic algorithms take recombination into account. According to one aspect of the invention, an evolutionary algorithm is implemented based on one of the following evolutionary strategies:

[0076] • Adaptive Adjustment or the 1 / 5 Success Rule: The 1 / 5 success rule states that the quotient of effective mutations (i.e., mutations that lead to improvements in the production process) in the initial production sequence should be approximately one-fifth of all mutations. If this quotient is large, the magnitude of mutation changes should be increased; if the quotient is small, it should be decreased.

[0077] • Self-adaptation: Each individual possesses additional genes for the intensity of mutations themselves. While this is impossible in biology, evolution in computers has found a suitable range of variation in this way, without human constraints. Here, in computers, recombination and mutation are adjusted accordingly based on the intensity of the mutation.

[0078] For example, the genotype used for thorough optimization is comprised of the data structure employed by the fast optimizer. The fast optimizer's solution is used as the initial population, and the order of material requirements in the data structure is then altered through recombination and mutation. In this case, the mutation operator changes the order of randomly selected material requirements for randomly selected production regions. The recombination operator takes two chromosomes from the parents and produces two chromosomes for the offspring. This is achieved, for example, through recombination. The phenotype is derived from the genotype by implementing the fast optimizer on the modified data structure.

[0079] This further improved adaptive production optimization.

[0080] According to another aspect of the invention, production parameters, optimization criteria, and / or edge conditions are simulated. Production parameters include worker availability, machine capacity, material availability, material buffers, and / or supplier capacity. Optimization criteria include maximum machine and / or worker utilization, minimum latency, minimum warehouse inventory, and / or minimum material flow. Edge conditions include material demand priority, maximum warehouse and / or material buffer size, transportation conditions, planning outlook period, and / or supplier capacity. Therefore, the entire production system is further optimized. According to one aspect of the invention, these data form the inputs for the simulation.

[0081] According to another aspect of the invention, a worker shift pattern is simulated, and workers are assigned to production lines in the simulation, with the worker allocation to production lines varying at least depending on material demand and / or material inventory. Therefore, the entire production system is further optimized. According to one aspect of the invention, each production line is initially fully allocated to maximize utilization based on production parameters. If the number of assigned workers exceeds the number of available workers (see production parameters), the number of assigned workers is reduced accordingly. The decision of which production line to reduce can take into account various factors, such as material inventory, production line capacity, and demand.

[0082] According to another aspect of the invention, it is checked whether the missing materials in the material requirements can be supplied in accordance with the required time point. If the check is affirmative, the supply is ordered. The supplied materials are reserved. If the check is negative, the additional material requirements are reserved as specified. Materials include materials produced in the previous production segment, which form the input materials for the subsequent production segment. In addition, materials also include supplied materials, such as supplied input materials. If there are not enough input materials to supply during production, for example, in each production process, a second check is performed to check whether it is possible to supply these materials at the current time point, especially under edge conditions such as supplier capacity, supply time point, and / or supplier control. Thus, the entire production system is further optimized. If the input materials can both be produced and supplied, according to another aspect of the invention, the supply and buffer stock are reduced at the beginning as described above, except that, unlike the initial buffer stock, the supply time point must be considered. According to one aspect of the invention, the second check, supplier orders, supplier capacity, supply time point, and / or supplier control are all incorporated into the simulation.

[0083] According to another aspect of the invention, material requirements in production segments are prioritized to optimize the slack time of the production system. Slack time is detected in the cost function via delay. Therefore, the delay in minutes is optimized. Slack time refers to the remaining time of an order. Within the scope of the invention, the meaning of material requirements includes the meaning of an order. This is the time interval from the current processing time to the rated end time, minus the remaining processing time. For example, the slack time for an order is determined as follows: January 20th: delivery date, January 10th: date of priority determination, 4 days remaining from production start to delivery time → 20 - 10 - 4 = 6 days of slack time. When optimizing slack time, according to one aspect of the invention, order priority is determined not only in cases of production disruption but also in cases of undisrupted production. To optimize slack time, according to one aspect of the invention, a least-slack-time-scheduling algorithm is integrated into the method, which is implemented when the method is executed. According to another aspect of the invention, the optimization of relaxation time is incorporated into the simulation.

[0084] According to another aspect of the invention, material requirements in production sections are prioritized such that satisfying material requirements is combined with maximizing production utilization when optimizing the process planning of the production system. According to another aspect of the invention, satisfying material requirements is combined with maximizing production utilization when optimizing relaxation time. This advantageously achieves minimal production downtime.

[0085] According to another aspect of the invention, when adjusting the timing of demand, the production duration for the material demand is taken into account, and / or the material demand is selected based on the respective production line capacity on the production line.

[0086] Here, the necessary production time is deducted from the original demand time. For example, to produce 800 units of type B material by approximately 14:00, it would take 4 hours to produce this material in the second production section. To obtain 800 units of type B material, 700 units of type A material must also be produced in the first production section. This means that the demand time for the first production section is 10:00. By taking into account the production time of the preceding production sections, the entire production system is further optimized.

[0087] Production line capacity is an edge case and is related to the technical limitations of each individual production line. Material requirements that can run on the production line are not necessarily the highest priority material requirements, depending on the production line capacity. By taking production line capacity into account, the entire production system can be further optimized. According to one aspect of the invention, production line capacity is incorporated into the simulation.

[0088] According to another aspect of the invention, the production system includes material buffers between production sections. Material requirements are reduced by relying on these material buffers. Therefore, a production section includes one or more production lines and a material buffer. The material buffer contains the materials produced on the preceding production lines. The size of each material buffer is included in the production parameters. For example, if the required quantity of type B material is 1000 units, and the material buffer contains 200 units of type B material, then another 800 units of type B material must be produced. According to another aspect of the invention, the buffer storage capacity is incorporated into the simulation. Therefore, the entire production system is further optimized.

[0089] According to another aspect of the invention, a data structure is generated from the obtained material requirements, which includes at least the material type, required quantity, and required time point for each production segment. This data structure includes an index structure, which allows entries in the data structure to reference each other. A second sub-method is implemented to process the data structure. Production lines are allocated, workers are assigned, and / or supplier orders are generated based on the data structure. The material requirements are reflected in groups according to production segments through the data structure. For example, the data structure is provided as a database, such as an object-oriented database. Therefore, access to data including at least the material type, required quantity, and required time point is improved because this data is treated as objects. Furthermore, the semantic relationships between objects are known, for example, through the index structure. This knowledge can be used when querying the data using a query language (e.g., an object query language). The data structure also enables the provision of an overview of information about the production process to the controller. According to one aspect of the invention, the data structure is generated from material type, required quantity and required time point, priority, and weight.

[0090] According to another aspect of the invention, outputs related to regulation and / or control and / or informational outputs are provided. Outputs related to regulation and / or control include production sequences, worker allocations, and / or supplier orders. Informational outputs include material requirements coverage, completion dates, utilization rates, bottlenecks, critical paths, and / or the timeline of the production system. These outputs are provided, for example, via optical display devices or acoustic systems, and enable the controller to have a clear overview of the production process.

[0091] According to another aspect of the invention, a digital twin of a real factory is generated in a simulation, a planning outlook period is determined for the digital twin, and the real factory is controlled according to the planning outlook period.

[0092] Another embodiment of the production planning and / or production control system according to the present invention includes at least one interface via which communication is provided between the system and its controller. The system provides the controller with outputs related to regulation and / or control, and / or provides information outputs, via the interface. The interface provides the controller with optimization results. Therefore, the interface enables the controller to request optimization results from the system.

[0093] Another embodiment of the production planning and / or production control system according to the present invention includes cloud infrastructure. The cloud infrastructure includes cloud-based storage. Simulations of the production system, production planning, and / or production control are all performed in the cloud. With the help of the present invention, a digital twin of the entire production system is thus obtained in the cloud. According to one aspect of the present invention, the simulated and actual production systems are controlled in the cloud. Therefore, according to one aspect of the present invention, the method according to the present invention is provided as "Software as a Service". Inputs and outputs are provided via appropriate interfaces, such as wireless interfaces like WLAN interfaces.

[0094] According to another aspect, the system includes at least one display device that displays the system's outputs related to regulation and / or control and / or provides information. This facilitates an overview of the production process for the controller. Attached Figure Description

[0095] The present invention is described in the following embodiments. Wherein:

[0096] Figure 1 An example of a production model is shown;

[0097] Figure 2 An embodiment of the data structure generated according to the present invention is shown;

[0098] Figure 3 Show Figure 2 Another embodiment of the data structure;

[0099] Figure 4 A schematic diagram showing a fixed production sequence;

[0100] Figure 5 A diagram illustrating the timeline of the optimized production sequence according to the present invention;

[0101] Figure 6 A schematic diagram of the method according to the present invention is shown;

[0102] Figure 7A schematic embodiment of a production planning and / or production control system for adaptive production optimization according to the present invention is shown;

[0103] Figure 8 A graphical representation of the development of the material storage volume of supplied materials obtained by means of the method according to the invention is shown, and

[0104] Figure 9 A schematic diagram showing the material demand satisfaction obtained by means of the method according to the invention is shown.

[0105] In the accompanying drawings, the same reference numerals denote the same or similarly functional reference parts. For clarity, only the relevant reference parts are emphasized in each drawing. Detailed Implementation

[0106] Figure 1 A simplified production model of the production system is shown. The production model includes a first production section PA1 and a second production section PA2. The first production section PA1 and the second production section PA2 each include three production lines: line 1, line 2, and line 3. In addition, the first production section includes a first material buffer (buffer 1), and the second production section includes a second material buffer (buffer 2).

[0107] The first material buffer (buffer 1) includes materials manufactured in lines 1, 2, and 3 of the first production section PA1. The second material buffer (buffer 2) includes materials manufactured in lines 1, 2, and 3 of the second production section PA2. For example, the first material buffer (buffer 1) includes 100 units of type A material, and the second material buffer (buffer 2) includes 200 units of type B material and 100 units of type C material. These parameters are all included in the production parameters of the input data.

[0108] For example, one unit of type A material may be needed to produce one unit of type B material or one unit of type C material. Material requirements may include, for example, material type, quantity or required quantity and required timing. However, the method and system according to the invention can be used on more complex production models with arbitrary dependencies and material requirements, and also optimize such complex production models or the entire production system.

[0109] The process of the method according to the invention begins with initialization. This initialization takes the form of generating a data structure from material requirements, including, for example, material type, quantity or demand quantity, demand time point, priority, and weight. Using this data structure, material requirements are sorted according to their impact on the total cost function. Here, the material requirement with the highest impact, i.e., the highest priority in a given order, is placed first. Furthermore, the data structure groups material requirements according to production segments. If the total cost function is optimized, for example, in terms of delay minutes, then a minimum relaxation time scheduling algorithm is advantageously used, for example, to sort the material requirements. Material requirements are reduced, for example, based on the sorting according to the existing initial buffer storage. This... Figure 2 As shown in the image.

[0110] In the second production segment PA2, the earliest demand time for type B material is 14:00, and therefore it is placed in the first position, that is, the first row. Since the second material buffer 2 contains 200 type B materials, only 800 type B materials need to be produced when the demand quantity is 1000. Since the second material buffer 2 contains 100 type C materials, only 400 type B materials need to be produced when the demand quantity is 500. The demand time for type C materials is 18:00, and therefore it is placed after type B materials. In this example, there is no initial material demand for the first production segment PA1. Therefore, the data structure for the first production segment PA1 is initially empty.

[0111] After initialization, material requirements propagate backward through production sections. Here, material requirements are projected onto materials necessary for production in the next production section. For example, to produce materials of types B and C in the second production section PA2, materials of type A need to be obtained from the first production section PA1. Besides material type, the required quantity and demand timing are adjusted. The required quantity is reduced based on the initial buffer storage. The production duration is subtracted from the original demand timing. This is... Figure 3 The explanation is as follows.

[0112] For the first material demand of 800 units of material type B at the demand time point of 14:00, there are already 100 units of material type A in the first material buffer 1. Therefore, only 700 units of material type A need to be produced. For example, it takes four hours in the second production section PA2 to produce the first material demand. Therefore, the demand time point in the first production section PA1 is 10:00. A similar consideration applies to the second material demand of 400 units of material type C at the demand time point of 18:00. For the second material demand, the demand time point in the first production section PA1 is therefore 15:00.

[0113] Based on the demand data structure, the algorithm according to the present invention allocates production lines, assigns workers, and generates supplier orders. The following instructions are implemented for this purpose:

[0114] The virtual production system or virtual factory is simulated from the start point in time. Whenever a production line is running idle, i.e., when there are no more orders, the next highest priority demand that can run on that production line is selected based on the data structure described above. This demand is not necessarily the first demand in the data structure due to secondary conditions such as production line capacity.

[0115] When there are sufficient input materials available to fully meet the demand, the selected demand is implemented. In this case, the input materials are reserved for that demand. If there are not enough available input materials, it is checked whether it is possible to supply these input materials at the current point in time. Edge conditions may include supplier capacity. If yes, the corresponding supply is ordered, and the supplied materials are reserved. If no, the next demand is selected according to the data structure. If the input materials can be both produced and supplied, then the supply and buffer reserves will initially decrease as described above, except that the supply time point must be considered compared to the initial buffer reserves.

[0116] By retaining the input materials, it is ensured that the production sequence determined in this way can be implemented; that is, orders created in this way can be implemented under any circumstances.

[0117] Figure 4 The diagram illustrates the rapid optimization results of the production system obtained using the first sub-method, namely the initial production sequence. The first sub-method includes steps V2 to V7. In method step V8, a first production time period is fixed in the second sub-method. For example, the time until the end of the current shift is fixed. Outside this time period, the initial production sequence is further optimized in method step V9, for example, using genetic optimization. According to one aspect of the invention, other parameters, such as supplies and / or worker conditions, are similarly fixed. According to another aspect of the invention, the various parameters are fixed with different forecast periods. For example, supplies can only be changed at least twelve hours in advance. In method step V10, the production system is regulated and / or controlled according to the production sequence optimized in the second sub-method.

[0118] Figure 5 The timeline of the optimization results is shown. A fixed end is reached from time point 7. Within time periods 3 to 7, the cost function is minimized only to a minimum in the second sub-method. Therefore, the current continuation of the second sub-method ends at time point 4, and a new continuation of the second sub-method begins.

[0119] Figure 6A method according to the invention is illustrated. Method step V1 includes a simulation of a production system, production planning, and / or production control. This simulation is input to a production planning and / or production control system (APO) according to the invention. The output of the production planning and / or production control system (APO) according to the invention includes adjustment and / or control signals to produce in a real factory according to an optimized production sequence obtained in a second sub-method. Furthermore, the output of the production planning and / or production control system (APO) according to the invention includes information output for the controller of the production system.

[0120] To obtain output, the Production Planning and / or Production Control System (APO) according to the present invention executes a first sub-method and a second sub-method. The first sub-method includes the following steps:

[0121] V2: Prioritizes material requirements in production segments PA1 and PA2 based on the optimization of the cost function of the production system.

[0122] • V3: Select one of the material requirements in priority order.

[0123] • V4: Adjusts at least one required quantity and / or required time point for materials in the preceding production sections PA1 and PA2 to implement material requirements.

[0124] • V5: Retain materials and their adjusted quantities and / or timings.

[0125] • V6: Select other material requirements, repeat the previous step, until materials and adjusted quantities and / or timings are reserved for all priority material requirements, and

[0126] V7: Obtain the production sequence.

[0127] Between the first and second sub-methods, if there are insufficient materials to fulfill the material requirements, a check is performed in the method step to determine whether the missing materials in the material requirements can be supplied at the required time. If the check is affirmative, the supplies are ordered in method step V11. In method step V12, the supplied materials are reserved. If the check is negative, additional material requirements are reserved as stipulated.

[0128] Furthermore, in method step V13, between the first and second sub-methods, a data structure is generated based on the priority of material requirements. For each production segment PA1 and PA2, the data structure includes at least the material type, required quantity, and required time point. Additionally, the data structure includes an index structure that allows entries in the data structure to reference each other. The second sub-method is implemented to process the data structure and, based on the data structure, allocate production lines 1, 2, and 3, assign workers, and / or generate supplier orders.

[0129] In addition, a first check is performed in the first sub-method: whether the adjusted demand quantity of each material is sufficient to meet the selected material demand, wherein if the first check is affirmative, the respective materials are reserved for the production system, and / or the selected material demand is implemented.

[0130] Figure 7 An overview of the production planning and / or production control system (APO) according to the invention is shown, by which adaptive production optimization is achieved through the implementation of the method according to the invention using this production planning and / or production control system. The rapid optimization method according to the invention, i.e., the first sub-method, is followed by a more thorough optimization method, i.e., the second sub-method. This more thorough optimization method includes, for example, genetic optimization.

[0131] Figure 8 The development of the first material storage level B1 and the second material storage level B2 for the supplied materials is shown. Furthermore, Figure 8 The development of supply L is illustrated, which is planned by the method and system according to the invention.

[0132] exist Figure 9 In this diagram, each bubble represents a material requirement. There are three different categories: "Most Important," "Important," and "Less Important." Figure 9 The diagram shows the first category (P1) corresponding to "most important" and the second category corresponding to "important". The horizontal axis represents the expected time point to meet material demand. The size of the bubbles represents the quantity demanded. The vertical axis represents the delay in meeting demand; values ​​above 0 indicate delays.

[0133] List of reference numerals

[0134] V1-V14 Method Steps

[0135] PA1 Production Section 1

[0136] PA2 Production Section 2

[0137] Production lines 1, 2, and 3

[0138] Buffer 1 Material Buffer

[0139] Buffer 2 Material Buffer

[0140] Material types A, B, and C

[0141] APO production planning and / or production control system

[0142] L Supply Quantity

[0143] B1 First Material Storage Quantity

[0144] B2 Second Material Storage Quantity

[0145] P1 First Category

[0146] P2 Category 2

Claims

1. A computer-implemented method for production planning and / or production control of a production system, said production system comprising multiple production sections (PA1, PA2) and production lines, said method comprising the following steps: simulating the production system, the production plan and / or production control, In the simulation, a first sub-method (V2-V7) and a second sub-method (V8, V9) are executed, wherein The first sub-method (V2-V7) comprises the following steps ○ Prioritize the material requirements in the production sections (PA1, PA2) based on the optimization of the cost function of the production system (V2). ○ Select one of the material requirements (V3) in order of priority, adjust at least one requirement quantity and / or requirement time point of the material in the preceding production section (PA1, PA2) to implement the material requirement (V4), and retain the material and the adjusted requirement quantity and / or requirement time point (V5). ○ Select another material requirement (V6), and repeat the above steps (V4, V5) until the material and the adjusted requirement quantity and / or requirement time point are reserved for all priority-sorted material requirements, and the production sequence is obtained (V7). and the second sub-method (V8, V9) comprises the steps of ○ Fix the first production time segment (V8) in the production sequence, so that the fixed portion of the production sequence is not changed in the second sub-method (V8, V9), and ○ Optimize the production sequence outside the fixed first production time period to further optimize the cost function (V9), where, adjust and / or control the production system (V10) according to the optimized production sequence obtained in the second sub-method (V8, V9).

2. The method according to claim 1, wherein, If no significant optimization of the production sequence is achieved, the continuation of the second sub-method (V8, V9) is terminated, and another continuation of the second sub-method (V8, V9) is started.

3. The method according to claim 1 or 2, wherein, In order to execute the second sub-method (V8, V9), an evolutionary algorithm is implemented, which is initialized with the production sequence or its mutation obtained in the first sub-method.

4. The method according to any one of claims 1 to 2, wherein, The production parameters, optimal criteria, and / or edge conditions are simulated, wherein the production parameters include worker availability, machine capacity, material availability, material buffer, and / or supplier capacity; the optimal criteria include maximum utilization of machines and / or workers, minimum latency, minimum warehouse storage, and / or minimum material flow; and the edge conditions include priority of material demand, maximum warehouse and / or material buffer size, transportation conditions, planning outlook period, and / or supplier capacity.

5. The method according to any one of claims 1 to 2, wherein, The simulation involves worker shift patterns, and in the simulation, workers are assigned to the production line, with the worker assignments to the production line varying at least based on material requirements and / or material inventory levels.

6. The method according to any one of claims 1 to 2, wherein, When there are insufficient materials to fulfill material requirements, check whether the missing materials in the material requirements can be supplied in accordance with the required time point. If the check is yes, then order the supply (V11) and reserve the supplied materials (V12). If the check is no, then reserve the other material requirements as stipulated.

7. The method according to any one of claims 1 to 2, wherein, A data structure (V13) is generated from the priority of material requirements. For each production segment (PA1, PA2), the data structure includes at least the material type, the required quantity, and the required time point. The data structure includes an index structure, through which the entries of the data structure are mutually referenced. The second sub-method (V8, V9) is implemented to process the data structure and allocate the production line, assign workers, and / or generate supplier orders based on the data structure.

8. The method according to any one of claims 1 to 2, wherein, Provide outputs related to regulation and / or control and / or provide information outputs (V14), wherein the outputs related to regulation and / or control include production sequences, worker assignments and / or supplier orders, and the outputs providing information include material requirements coverage, completion dates, utilization rates, bottlenecks, critical paths and / or the time development of the production system.

9. The method according to any one of claims 1 to 2, wherein, In the simulation, a digital twin of the real factory is generated, a planning outlook period is determined for the digital twin, and the real factory is controlled according to the planning outlook period.

10. A production planning and / or production control system (APO) for production optimization, comprising a processing unit configured to implement the method according to any one of claims 1 to 9.

11. The system of claim 10 (APO), the system comprising at least one interface providing communication between the system and a controller of the system, wherein, The system provides the controller with outputs related to regulation and / or control and / or information outputs via the interface, and the interface provides the controller with optimization results.

12. The system (APO) of claim 10 or 11, wherein the system includes cloud infrastructure, the cloud infrastructure including cloud-based storage, wherein, Simulations of production systems, production planning, and / or production control are conducted in the cloud.

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