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

Abstract

Computer-implemented method for production planning and / or control of a production system comprising several production sections (PA1, PA2) and production lines (Line 1, Line 2, Line 3), the method comprising the steps • Simulating the production system, production planning and / or control (V1), • in the simulation, performing a first sub-process (V2-V7) and a second sub-process (V8, V9) where • the first sub-process (V2-V7) the steps o Prioritizing material requirements in the production stages (PA1, PA2) depending on an impact on an optimization of a cost function of the production system (V2), o Selecting one of the material requirements in order of prioritization (V3), adjusting at least one required quantity and / or one required time of materials in preceding production stages (PA1, PA2) to execute the material requirement (V4) and reserving the materials and the respectively adjusted required quantity and / or the required time (V5), o Selecting another of the material requirements (V6), repeating the previous step (V4, V5) until the materials and the adjusted required quantities and / or required times are reserved for all of the prioritized material requirements, and obtaining an initial production sequence across all production periods (V7), • and the second sub-process (V8, V9) the steps ◯ Fixing a first production period in the initial production sequence, whereby a part of the production sequence determined by the fixation is fixed and the first production period is executed in the real production system (V8) and o Optimizing the initial production sequence outside the fixed first production period to further optimize the cost function (V9), wherein the production system is regulated and / or controlled according to the optimized production sequence obtained in the second sub-process (V8, V9) as soon as a better result with regard to the cost function is available from the further optimization, wherein it is ensured that the better result corresponds to the already started first production period of the initial production sequence (V10).

Description

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

[0002] During production, products (comprising tangible goods) and services are created based on production factors (comprising materials and resources). For example, gearboxes are produced. Within gearbox production, other tangible goods are created, such as output shafts. Production planning and / or control optimizes the entire production system.

[0003] Several methods for production planning and / or control are known in the state of the art. For example, traditional systems include successive planning of master data management, production program planning, quantity planning, scheduling, workshop control, order monitoring, and sales control. Integrated IT systems are also known that encompass production planning and / or control.

[0004] Furthermore, optimization methods for production planning and / or control are known, for example, constraint-based approaches using linear programming. However, such approaches do not scale to real-world problem sizes. Local search or branch-and-bound algorithms are also known for optimization. Classical scheduling algorithms, such as multiprocessor scheduling, are also known, although these are only applicable to simplified models. Evolutionary optimization algorithms are also known, but these require large amounts of resources, such as time or computing power, and a good initial solution.

[0005] For example, US 2014 / 0 031 965 A1 discloses a method for production planning management that implements a genetic algorithm. The initial population is randomly started.

[0006] Currently, a human controller plans the production processes for a specific product, workpiece, or semi-finished part, such as the production of an output shaft, with or without the support of known optimization methods. Production consists of several production steps that the part must pass through sequentially. To do this, the controller must consider a multitude of input variables. The planning, for example, which parts are to be produced on which line / sub-line at what time, should be optimal with regard to a multitude of optimality criteria. To make matters worse, the parameters that determine the existing production processes frequently change over time, as do the optimality criteria. This necessitates frequent replanning, which, however, must be carried out as quickly as possible to avoid a standstill in production or suboptimal production.

[0007] Based on this, the invention is based on the task of how production sequences, worker assignments and supplier orders can be created from given requirements and how the production sequences can be evaluated and optimized based on previously defined criteria.

[0008] The invention is first presented for the sake of clarity.

[0009] The invention solves this problem through a method and an adaptive system that optimizes complex processes, such as production workflows, based on given evaluations and a virtual representation of production. The system adapts to changes that impact production within a very short time and guarantees a production plan that can be implemented at any time. At the same time, the system generates solutions for highly complex production conditions.

[0010] The invention allows for a longer planning horizon to be implemented compared to the prior art, for example several weeks instead of a few days. For example, the invention was used in the production of an output shaft, and a planning horizon of several weeks was realized. For example, a planning horizon of two weeks is realized. With the length of the planning horizon, the runtime of the method becomes correspondingly longer. However, it was discovered during the invention that the method according to the invention advantageously only scales linearly with the planning horizon, in contrast to known optimization methods, which generally scale exponentially. This is accompanied by a significant cost reduction through a more efficient planning process, an increase in assembly output, and lower capital tied up through inventory reductions.

[0011] Furthermore, the invention supports the increasing complexity of products in the future, for example, increased variance or additional constraints that cannot be adequately represented by existing control tools. For example, approximately 500 different types of transmissions are manufactured in the applicant's factories. With the development of further generations and additional transmissions, this variance will increase significantly again. The invention avoids the resulting costs, for example, for weekend work or production downtime, or even delivery bottlenecks to customers.

[0012] A concise description of the invention is presented by considering input values or inputs that are input into the system and output values or outputs that the system provides. Inputs to the system include direct inputs and indirect inputs. Outputs of the system include control-relevant and informational outputs.

[0013] As a result, the method and system provide all control-relevant information for an optimal production sequence, worker scheduling, and supplier orders. This production sequence is implemented automatically or only after approval by the controller. In the context of the invention, optimal means optimal with respect to a given total cost function. The controller also has the option of influencing the result by starting a new run with modified inputs. To support such decisions, the system provides detailed informative outputs on line scheduling, inventory development, and predicted completion times.

[0014] Immediate inputs include inputs that are expected during each optimization run of the system. An optimization run is usually triggered when a change in the production parameters has occurred. Another cause is, for example, a change in the weighting of the various optimization criteria by the controller. The controller is a human operator who has previously handled this planning independently. However, any other change in the input conditions usually leads to a new start of the system. For example, the following inputs are immediate inputs: • Production parameters: worker situation, machine capabilities, material availability, initial inventory and buffer stocks and / or supplier capacity; • Material requirements: Which material / semi-finished product must be produced at what time and / or weighting / prioritization of the parts to be produced; • Optimality criteria: maximum utilization of all machines and workers, minimization of delays, lowest inventory levels, minimization of material flows from distant areas within the factory and / or weighting of these against each other and • Constraints: Unlike optimality criteria, these must be adhered to before an optimization run can begin. These include, for example, priorities for requirements with rank 1, which must be produced at a specified time, warehouse / intermediate storage sizes that must not be exceeded, and no transport of parts from one production line / warehouse to another production line / warehouse that is not currently practical for logistical or other reasons. The constraints can be changed by the controller. The planning horizon, for example, the number of hours or days over which production planning should be planned, is also part of the constraints.

[0015] The indirect inputs are only integrated into the system when structural things in production or the production process change.

[0016] The invention simulates the production system and thus provides a virtual representation of the production process and / or the production system. The virtual representation is a digital twin of the entire production process and / or production system. The digital twin models all dependencies within production. This model, in turn, contains the production parameters as variables. The invention keeps the model constantly up-to-date with the actual conditions and dependencies within the real production process and / or the real production system.

[0017] The control-relevant outputs are essential for implementation in the production planning and / or control system of a factory and include: • Optimized production sequence: Which material is needed on which line at what time? • Worker occupancy: How many workers are or will be needed on which line for which shift? • Supplier orders: Which and how much supplied material is available at what time?

[0018] The informative outputs provide added value in terms of explainability, for example, why a material is delayed, and facilitate the controller's own evaluation of the optimization result. The informative outputs include: • Demand coverage and forecast completion dates, relevant for example for logistics; • Display of utilization, bottlenecks and critical paths and • predicted temporal development of semi-finished / finished parts and / or inventory levels.

[0019] With regard to a further overview of the invention, it should be noted that the method and system optimize a total cost function of the production system. The cost function determines the cost-minimal production processes from the technically efficient production processes. The cost function represents the total costs of a production process, which result from the production factors used, multiplied by their respective market prices or weightings. For example, the total cost function is defined as: • Demand fulfillment: delay time, with weighting per demand; • Production capacity utilization: times with production downtime and • Production constraints: transport between lines, setup times.

[0020] Using mathematical functions, these criteria are summarized into a numerical value, whereby the above-mentioned criteria can be weighted differently.

[0021] Example: Total costs = α * ∑_Delay (b) * Weighting (b) + β * Production downtime + γ * Setup times + · · ·, where α≥0, β≥0, γ≥0,... represents a weighting of the various subterms and is variable. The summation is carried out over all material requirements.

[0022] For the sake of clarity, the method according to the invention proceeds as follows: The current production parameters, requirements, optimality criteria and boundary conditions are the inputs that are received as data, for example.

[0023] Subsequently, an initial production sequence is created using a fast optimization procedure (i.e., a few seconds runtime). This production sequence serves as input to a subsequent, more thorough and longer optimization procedure, for example, as the initial population (i.e., initialization) in an evolutionary algorithm. Potentially, even more thorough but more time-consuming optimizations can be initiated, for example, genetic optimizers with a larger population and different hyperparameters.

[0024] The initial production sequence is also transferred to the real production system or factory for implementation. As soon as a better result in terms of the total cost function is available from one of the subsequent thorough optimizations, it is output and implemented directly by the system in real production or output to a human controller to assist the human controller. It is ensured that the better result corresponds to the already initiated production sequence. This is ensured by ensuring that every plan currently put into production by a previous optimizer is a boundary condition of the subsequent optimizer for the time currently running.

[0025] If an event occurs that affects production, such as machine failure or a change in worker situation, a new initial production sequence is created and the process starts again from the beginning.

[0026] According to one aspect, the invention provides a computer-implemented method for production planning and / or control of a production system. The production system comprises several production sections and production lines. The method comprises the steps: • Simulating the production system, production planning and / or control, • in the simulation, performing a first sub-process and a second sub-process, where • the first sub-process the steps ◯ Prioritizing material requirements in the production stages depending on an impact on an optimization of a cost function of the production system, ◯ Selecting one of the material requirements in order of prioritization, adjusting at least one required quantity and / or time of materials in preceding production stages to execute the material requirement and reserving the materials and the respectively adjusted required quantity and / or time of requirement ◯ Selecting another of the material requirements, repeating the previous step until the materials and the adjusted required quantities and / or times are reserved for all of the prioritized material requirements, and obtaining a production sequence • and the second sub-process the steps ◯ Fixing a first production period in the production sequence and ◯ Optimizing the production sequence outside the fixed first production period to further optimize the cost function, where • the production system is regulated and / or controlled according to the optimized production sequence obtained in the second sub-process.

[0027] The first sub-procedure corresponds to the fast optimization procedure, which delivers the initial production sequence as the first result within a few seconds. An initial result within the shortest possible time is important because production must never be brought to a standstill after an incident. The optimization goal is to meet demand while maximizing production utilization, i.e., minimize production downtime.

[0028] The second sub-procedure corresponds to the more thorough optimization procedure.

[0029] The first sub-process according to the method according to the invention provides the first results quickly relative to the second sub-process.

[0030] The simulation provides a virtual representation of the production system, production planning, and / or production control, in which the entire production system is realized as a digital twin. For example, bottlenecks or critical paths are simulated in the simulation. According to one aspect of the invention, the simulation simulates a future state of the production system. This enables planning horizons extending any distance into the future, for example, over several weeks. The simulation advantageously adapts the optimization achieved by the method according to the invention and thus the entire production system to production changes.

[0031] Material requirements include material types or types. Material types include raw materials, such as iron; auxiliary materials, such as screws; operating materials, such as energy; unfinished products, such as pre-assembled components that still need to be assembled; finished products, such as finished products ready for shipment; and merchandise.

[0032] Sequencing or sequence planning, also called sequencing and scheduling, involves the creation of a production sequence of production orders in production planning.

[0033] The fixation ensures that the output of the second sub-process is also implementable. Due to the fixation, a part of the production sequence determined by the fixation can no longer be changed in the second sub-process. According to one aspect of the invention, all input parameters are fixed in time for a specific period of time. The fixation is implemented, for example, by a prefix in the production sequence, worker situation and / or in the deliveries obtained from the first sub-process. Due to the fixation, the second sub-process can require a maximum of as much time as is covered by the fixation. The fixation time is taken, for example, as the time until the end of the current shift. The fast optimizer optimizes across all production periods of the production. The first production period optimized by the fast optimizer is then executed in the real factory and can no longer be changed.Therefore, the slower but more thorough optimizer optimizes additional production periods outside the fixed period.

[0034] According to one aspect of the invention, the material requirements are prioritized according to material type, required quantity, required time, priority and / or weighting.

[0035] A prioritized material requirement is executed when sufficient input materials are available to completely meet the demand. In this case, the input materials are reserved for this requirement. Reserving the input materials ensures that the production sequences determined in this way can be implemented, meaning that orders created in this way can definitely be executed.

[0036] According to a further aspect, the invention provides a production planning and / or control system. The system comprises a processing unit configured to execute a method according to the invention.

[0037] According to a further aspect, the invention provides a computer program. The program comprises instructions that cause a system according to the invention to execute the method according to the invention when the program is run on the system.

[0038] Further embodiments of the invention emerge from the subclaims, the drawings and the description of preferred embodiments.

[0039] According to one aspect of the invention, a run of the second sub-process is terminated if no significant optimization of the production sequence is achieved, and another run of the second sub-process is started. This termination criterion accelerates the process and thus further optimizes the production system.

[0040] The time period determined by the fixation does not have to be fully utilized. If, for example, the second sub-process makes no or only minimal progress with respect to the optimization task within a time period that is, for example, shorter than the fixation time, a current run of the second sub-process is terminated. In the event that the second sub-process finds a significantly better optimization within a time period shorter than the fixation period, this optimization is output earlier and adopted directly. This further accelerates the process and improves the optimization. According to one aspect of the invention, the controller proactively requests new optimizations from the second sub-process.

[0041] If the improvement in the optimization of the cost function was too small and no change in the framework conditions has occurred in the meantime, according to one aspect of the invention, the second sub-process is run again, with the search now postponed by one more time unit. The time horizon can be extended backward.

[0042] According to a further aspect of the invention, to carry out the second sub-process, an evolutionary algorithm is executed which is initialized with the production sequence obtained in the first sub-process or a mutation thereof.

[0043] Evolutionary algorithms are inspired by the functioning of the evolution of natural organisms and are processed according to the following procedure: • Initialization: The first generation of solution candidates is generated. According to the invention, the first generation is the initial production sequence. The initial production sequence is generated by the inventive method, i.e., the fast optimizer. • Evaluation: Each candidate solution in the generation is assigned a value of a fitness function based on its quality. The fitness function is the objective function of the evolutionary algorithm. The fitness function is modeled on biological fitness, which indicates the degree of adaptation of an organism to its environment. In the evolutionary algorithm, the fitness of a production sequence describes how well the production sequence solves the underlying optimization problem. • Go through the following steps until a termination criterion is met: ◯ Selection: Selection of individuals for recombination ◯ Recombination: Combination of selected individuals ◯ Mutation: Random change of the offspring ◯ Evaluation: Each solution candidate of the generation is assigned a value of the fitness function according to its quality. ◯ Selection: Determination of a new generation.

[0044] Typical termination criteria are listed below.

[0045] An evolutionary algorithm has the advantage of being able to represent a solution differently for better processing and later output it in its original form, comparable to genotype-phenotype mapping or artificial embryogenesis. This is particularly useful when the representation of a possible solution can be significantly simplified and does not need to be processed in its complexity in memory. Evolutionary algorithms include genetic algorithms. Genetic algorithms use binary problem representation and therefore usually require genotype-phenotype mapping. With evolutionary algorithms, a solution candidate is searched exclusively through mutation; recombination does not occur. Genetic algorithms consider recombination. According to one aspect of the invention, the evolutionary algorithm is implemented based on one of the following evolutionary strategies: • Adaptive adaptation or 1 / 5 success rule: The 1 / 5 success rule states that the ratio of successful mutations in the initial production sequence, i.e., mutations that improve the production process, to all mutations should be approximately one-fifth. If the ratio is larger, the variance of the mutations should be increased; if the ratio is smaller, it should be decreased. • Self-adaptability: Each individual has an additional gene for its own mutational strength. While this isn't possible in biology, computer-based evolution finds suitable variance in this way without human constraints. The computer adjusts recombination and mutation accordingly, depending on the mutational strength.

[0046] For example, the genotype for thorough optimization consists of the data structure used by the fast optimizer. The solution of the fast optimizer is used as the initial population, and the order of the material requirements in the data structure is then changed through recombination and mutation. In this case, the mutation operator changes the order of a randomly selected material requirement in a randomly selected production area. The recombination operator takes two chromosomes from parents and produces two chromosomes from children. This is achieved, for example, through a recombination of permutations. The phenotype is derived from the genotype by running the fast optimizer on the modified data structure.

[0047] This further improves adaptive production optimization.

[0048] According to a further aspect of the invention, production parameters, optimality criteria, and / or boundary conditions are simulated. The production parameters include worker situation, machine capabilities, material availability, material buffers, and / or supplier capacities. The optimality criteria include maximum utilization of machines and / or workers, minimization of delays, lowest inventory levels, and / or minimization of material flows. The boundary conditions include priorities of material requirements, maximum storage and / or material buffer sizes, transport conditions, planning horizon, and / or supplier capacities. This further optimizes the entire production system. According to one aspect of the invention, this data forms inputs for the simulation.

[0049] According to a further aspect of the invention, shift operation of workers is simulated and, in the simulation, the production lines are assigned workers. A change in the assignment of workers to the production lines occurs at least depending on the material requirements and / or material stocks. This further optimizes the entire production system. According to one aspect of the invention, each production line is initially fully assigned so that utilization is maximum according to the production parameters. If the assignment is greater than the number of available employees (see production parameters), the assignment is reduced accordingly. Various factors such as material stocks, line capability, requirements, etc. can be taken into account when deciding which line should be reduced.

[0050] According to a further aspect of the invention, a check is carried out to determine whether missing materials can be delivered within the required time. If the check is positive, a delivery is ordered. The delivered materials are reserved. If the check is negative, further material requirements are properly reserved. Materials include materials produced from a previous production stage, which form input materials for the following production stage. Furthermore, the materials include delivered materials, for example, delivered input materials.If during production, for example in individual production processes, there are insufficient input materials available, the second check checks whether it is possible to deliver them at the current time, in particular while adhering to boundary conditions such as supplier capacities, delivery times and / or supplier control. This further optimizes the entire production system. If an input material can be both produced and delivered, according to a further aspect of the invention the delivery and buffer stocks are reduced at the beginning as described above, with the difference that, in contrast to initial buffer stocks, the delivery time must be taken into account. According to one aspect of the invention, the second check, the supplier orders, supplier capacities, delivery times and / or supplier control are included in the simulation.

[0051] According to a further aspect of the invention, material requirements in the production sections are prioritized in such a way that slack times in the production system are optimized. Slack times are recorded in the cost function via delays. This optimizes the minutes of delay. The slack time refers to the remaining time of an order. Within the scope of the invention, the meaning of material requirements encompasses the meaning of the order. This is the time span from the current processing time to the target end date, minus the remaining processing times. The slack time of an order is determined, for example, as follows: January 20th: delivery date, January 10th: priority determination date, 4 days remaining lead time → 20 - 10 - 4 = 6 days slack time. When optimizing the slack time, according to one aspect of the invention, the order priority is determined, both in the event of disruptions in production and in disruption-free production.To optimize slack times, according to one aspect of the invention, a least-slack-time scheduling algorithm is integrated into the method, which is executed when the method is implemented. According to another aspect of the invention, the optimization of slack times is included in the simulation.

[0052] According to a further aspect of the invention, the material requirements in the production sections are prioritized such that, when optimizing a production system schedule, fulfilling the material requirements is combined with maximizing production utilization. According to one aspect of the invention, when optimizing the slack times, fulfilling the material requirements is combined with maximizing production utilization. This advantageously achieves minimal production downtime.

[0053] According to a further aspect of the invention, when adjusting the time of requirement, the production duration for the material requirements is taken into account and / or the material requirements are selected depending on a respective line capability on the production lines.

[0054] The time required for production is subtracted from the original demand time. For example, 800 materials of type B are to be ready at 2:00 p.m. Producing this material requirement in a second production phase takes 4 hours. To obtain 800 materials of type B, 700 materials of type A must be produced in a first production phase. This means the demand time for the first production phase is 10:00 a.m. By taking the production time in previous production phases into account, the entire production system is further optimized.

[0055] Line capability is a boundary condition and relates to the technical limitations of the respective production line. Depending on line capability, the material requirement that can be met on a production line is not necessarily the material requirement with the highest priority. By taking line capability into account, the entire production system is further optimized. According to one aspect of the invention, line capability is included in the simulation.

[0056] According to a further aspect of the invention, the production system comprises material buffers between the production sections. The material requirements are reduced depending on the material buffers. A production section thus comprises one or more production lines and a material buffer. The material buffers comprise the materials produced in the respective preceding production lines. The size of the respective material buffers is contained in the production parameters. For example, if a requirement quantity for material type B is 1,000 pieces and a material buffer comprises 200 pieces of material type B, then 800 pieces of material type B still need to be produced. According to a further aspect of the invention, the buffer stocks are included in the simulation. This further optimizes the entire production system.

[0057] According to a further aspect of the invention, a data structure is generated from the obtained material requirements, which data structure comprises at least the material type, required quantity, and required time for each production stage. The data structure comprises an index structure by means of which entries in the data structure are referenced to one another. The second sub-method is designed to process the data structure. Based on the data structure, the production lines are allocated, workers are distributed, and / or supplier orders are generated. The data structure maps the material requirements grouped by production stages. The data structure is provided, for example, as a database, for example as an object-oriented database. This enables improved access to the data, comprising at least the material type, required quantity, and required time, because the data is treated as objects.Furthermore, semantic relationships between the objects are known, for example, through the index structure. This knowledge can be used when querying the data using a query language, such as object query language. The data structure also provides the controller with an informative overview of the production processes. According to a further aspect of the invention, the data structure is generated from the material type, required quantity and time of requirement, priority, and weighting.

[0058] According to a further aspect of the invention, control-relevant and / or control-relevant outputs and / or informative outputs are provided. The control-relevant and / or control-relevant outputs include production sequences, worker occupancy, and / or supplier orders. The informative outputs include material requirement coverage, completion dates, capacity utilization, bottlenecks, critical paths, and / or the temporal development of the production system. The outputs are output, for example, via optical display devices or acoustic systems and provide the controller with a clear overview of the production processes.

[0059] According to a further aspect of the invention, a digital twin of a real factory is created in the simulation, a planning horizon is determined for the digital twin, and the real factory is controlled based on the planning horizon.

[0060] A further embodiment of the production planning and / or control system according to the invention comprises at least one interface via which communication is provided between the system and a controller of the system. The system provides the controller with control-relevant and / or open-loop control outputs and / or informative system outputs via the interface. The interface provides the controller with optimization results. The interface thus enables the controller to request optimization results from the system.

[0061] A further embodiment of the production planning and / or control system according to the invention comprises a cloud infrastructure. The cloud infrastructure comprises cloud-based storage. A simulation of the production system, production planning and / or control takes place in the cloud. By means of the invention, a digital twin of the entire production system is thus obtained in the cloud. According to one aspect of the invention, the simulation and the real production system are controlled in the cloud. Thus, according to one aspect of the invention, the method according to the invention is provided as software-as-a-service. The inputs and outputs are provided via corresponding interfaces, for example radio interfaces, for example WLAN interfaces.

[0062] According to a further aspect, the system comprises at least one display device that displays control- and / or regulation-relevant outputs and / or informative outputs of the system. This facilitates an overview of the production processes for the controller.

[0063] The invention is illustrated in the following exemplary embodiments. They show: Fig. 1 an embodiment of a production model, Fig. 2 an embodiment of a data structure generated according to the invention, Fig. 3 another embodiment of the data structure from Fig. 2, Fig. 4 a schematic representation of the fixation of a production sequence, Fig. 5 a representation of a time course of a production sequence optimized according to the invention, Fig. 6 a schematic representation of the method according to the invention, Fig. 7 shows a schematic embodiment of a production planning and / or control system according to the invention for adaptive production optimization, Fig. 8 a graphic representation of the development of material stocks for delivered materials obtained by means of the method according to the invention and Fig. 9 a schematic representation of the material requirement fulfillment obtained by means of the method according to the invention.

[0064] In the figures, identical reference symbols denote identical or functionally similar reference parts. For clarity, only the relevant reference parts are highlighted in the individual figures.

[0065] Fig. Figure 1 shows a production model of a simplified production system. The production model comprises a first production section PA1 and a second production section PA2. The first production section PA1 and the second production section PA2 each comprise three production lines: Line 1, Line 2, and Line 3. Furthermore, the first production section comprises a first material buffer, Buffer 1, and the second production section comprises a second material buffer, Buffer 2.

[0066] The first material buffer, Buffer 1, contains the materials produced in lines 1, 2, and 3 of the first production section PA1. The second material buffer, Buffer 2, contains the materials produced in lines 1, 2, and 3 of the second production section PA2. For example, the first material buffer, Buffer 1, contains 100 type A materials. The second material buffer, Buffer 2, contains 200 type B materials and 100 type C materials. These quantities are included in the production parameters of the entered data.

[0067] For example, exactly 1 piece of material of type A is required to produce either 1 piece of material of type B or 1 piece of material of type C. The material requirements include, for example, the material type, quantity or required quantity, and the time of requirement. However, the method and the system according to the invention are applicable to more complex production models with any dependencies and material requirements and also optimize such complex production models or entire production systems.

[0068] The process of the method according to the invention begins with initialization. This is as follows: A data structure is generated from the material requirements, which include, for example, material type, quantity or required quantity, time of requirement, priority, and weighting. The data structure sorts the material requirements according to their influence on the total cost function. The material requirements with the greatest influence, i.e., the highest priority, are listed first. Furthermore, the data structure groups the material requirements according to production stages. If the total cost function is optimized, for example, with regard to delay minutes, the least-slack-time scheduling algorithm is advantageous for ordering the material requirements. The material requirements are reduced, for example, after the order based on the existing initial buffer stocks. This is Fig. 2 shown.

[0069] In the second production section PA2, the materials of type B have the earliest requirement time of 2:00 p.m. and are therefore sorted first, i.e. in the first row. Since the second material buffer Buffer 2 contains 200 materials of type B, only 800 materials of type B need to be produced from the required quantity of 1,000. Since the second material buffer Buffer 2 contains 100 materials of type C, only 400 materials of type B need to be produced from the required quantity of 500. The materials of type C have a requirement time of 6:00 p.m. and are therefore sorted after the materials of type B. In this example, there is no initial material requirement for the first production section PA1. The data structure for the first production section PA1 is therefore initially empty.

[0070] After initialization, the material requirements are propagated backward through the production stages. In doing so, the material requirements are projected onto the materials required for production in the following production stage. For example, to produce materials of material types B and C in the second production stage PA2, material of material type A from the first production stage PA1 is required. In addition to the material type, both the required quantity and the required time are adjusted. The required quantity is reduced based on the initial buffer stocks. The duration required for production is deducted from the original required time. This is Fig. 3 illustrates.

[0071] For the first material requirement of 800 materials of material type B at the requirement time of 2:00 p.m., 100 materials of material type A are already in the first material buffer, Buffer 1. This means that only 700 materials of material type A need to be produced. For example, four hours are needed to produce the first material requirement in the second production section PA2. This means that the requirement time in the first production section PA1 is 10:00 a.m. An analogous consideration applies to the second material requirement of 400 materials of type C at the requirement time of 6:00 p.m. For the second material requirement, the requirement time in the first production section PA1 is therefore 3:00 p.m.

[0072] Based on the demand data structure, the algorithm of the invention allocates lines, assigns workers, and generates supplier orders. The following instructions are executed: The virtual production system or virtual factory is simulated from the start. Whenever a production line is running idle, i.e., it no longer has any orders, the next highest-priority demand that can be run on the line is selected based on the above data structure. Due to constraints such as line capability, this need not necessarily be the first demand in the data structure.

[0073] The requirement selected in this way is executed when enough input materials are available to completely meet the requirement. In this case, the input materials are reserved for this requirement. If there are not enough delivered input materials available, a check is carried out to see whether it is possible to deliver them at the current time. Constraints can include supplier capacities. If the result is positive, a corresponding delivery is ordered and the delivered material is reserved. If the result is negative, the next requirement is selected according to the data structure. If an input material can be both produced and delivered, the delivery and buffer stocks are initially reduced as described above, with the difference that, unlike initial buffer stocks, the delivery time must be taken into account.

[0074] By reserving the input materials, it is ensured that the production sequences determined in this way can be implemented, i.e. that orders created in this way can definitely be executed.

[0075] Fig. 4 shows a result of a rapid optimization of the production system obtained with a first sub-process, namely an initial production sequence. The first sub-process comprises steps V2 to V7. In a process step V8, a first production period is fixed in the second sub-process. For example, it is fixed up to the end of the current shift. Outside of this period, the initial production sequence is more thoroughly optimized in a process step V9, for example by means of genetic optimization. According to one aspect of the invention, further parameters are fixed analogously, for example deliveries and / or worker situation. According to a further aspect of the invention, the individual parameters are fixed with different horizons. For example, deliveries can only be changed up to twelve hours in advance.In a process step V10, the production system is regulated and / or controlled according to the production sequence optimized in the second sub-process.

[0076] Fig. Figure 5 shows the temporal progression of the optimization result. The end of the fixation is reached at time 7. Within the period from 3 to 7, the cost function is only minimally minimized in the second sub-process. Therefore, a current run of the second sub-process is terminated at time 4, and a new run of the second sub-process is started.

[0077] Fig. 6 shows the method according to the invention. Method step V1 comprises a simulation of the production system, production planning, and / or control. This simulation is the input to the production planning and / or control system APO according to the invention. Outputs of the production planning and / or control system APO according to the invention comprise control and / or regulation signals for producing in a real factory according to the optimized production sequence obtained in the second sub-method. Furthermore, the outputs of the production planning and / or control system APO according to the invention comprise informative outputs for a controller of the production system.

[0078] To obtain the outputs, the production planning and / or control system APO according to the invention executes a first sub-process and a second sub-process. The first sub-process comprises the steps: • V2: Prioritizing material requirements in the production sections PA1, PA2 depending on an impact on an optimization of a cost function of the production system, • V3: Select one of the material requirements in order of prioritization • V4: Adjustment of at least one required quantity and / or one required time of materials in preceding production stages PA1, PA2 to execute the material requirement, • V5: Reservation of materials and the respective adjusted required quantity and / or the required time, • V6: Select another of the material requirements, repeat the previous step until the materials and the adjusted required quantities and / or times are reserved for all of the prioritized material requirements, and • V7: Obtaining a production sequence.

[0079] Between the first sub-process and the second sub-process, if there are insufficient materials available to meet the material requirement, a process step is performed to check whether the missing materials can be delivered within the required time. If the check is positive, a delivery is ordered in process step V11. The delivered materials are reserved in process step V12. If the check is negative, an additional material requirement is duly reserved.

[0080] Furthermore, between the first sub-process and the second sub-process, a data structure is generated from the prioritization of material requirements in a process step V13. The data structure includes at least the material type, required quantity, and required time for each production section PA1, PA2. Furthermore, the data structure includes an index structure by means of which entries in the data structure are referenced. The second sub-process is designed to process the data structure and, based on the data structure, allocate work to the production lines Line 1, Line 2, and Line 3, allocate workers, and / or generate supplier orders.

[0081] Furthermore, in the first sub-process, an initial check is carried out to determine whether the adjusted required quantity of the respective materials is sufficient for the selected material requirement. If the initial check is positive, the respective materials are reserved for the production system and / or the selected material requirement is executed.

[0082] Fig. Figure 7 shows an overview of the inventive production planning and / or control system APO, with which adaptive production optimization is achieved by executing the inventive method. The inventive rapid optimization method, which is the first sub-method, is followed by a more thorough optimization method, which is the second sub-method. The more thorough optimization method includes, for example, genetic optimization.

[0083] Fig. Figure 8 shows the development of a first material stock B1 and a second material stock B2 for delivered materials. Furthermore, Fig. 8 the development of deliveries L which are planned by the method and system according to the invention.

[0084] In Fig. 9, each bubble represents a material requirement. There are three different categories: “most important,” “important,” and “less important.” In Fig. 9, a first category P1 is labeled "most important" and a second category is labeled "important." The abscissa indicates the times at which the material requirement is expected to be met. The size of the bubbles represents the quantity required. The ordinate indicates the delay at the time of requirement fulfillment. Anything above 0 would be considered late. Reference symbol V1-V14 procedural steps PA1 Production Section 1 PA2 Production Phase 2 Line 1,2,3 production lines Buffer 1 Material buffer Buffer 2 Material buffer A,B,C material types APO production planning and / or control system L Delivery quantity B1 first material inventory B2 second material stock P1 first category P2 second category

Claims

[1] Computer-implemented method for production planning and / or control of a production system comprising several production sections (PA1, PA2) and production lines (Line 1, Line 2, Line 3), the method comprising the steps • Simulating the production system, production planning and / or control (V1), • in the simulation, performing a first sub-process (V2-V7) and a second sub-process (V8, V9) where • the first sub-process (V2-V7) the steps o Prioritizing material requirements in the production stages (PA1, PA2) depending on an impact on an optimization of a cost function of the production system (V2), o Selecting one of the material requirements in order of prioritization (V3), adjusting at least one required quantity and / or one required time of materials in preceding production stages (PA1, PA2) to execute the material requirement (V4) and reserving the materials and the respectively adjusted required quantity and / or the required time (V5), o Selecting another of the material requirements (V6), repeating the previous step (V4, V5) until the materials and the adjusted required quantities and / or required times are reserved for all of the prioritized material requirements, and obtaining an initial production sequence across all production periods (V7), • and the second sub-process (V8, V9) the steps ◯ Fixing a first production period in the initial production sequence, whereby a part of the production sequence determined by the fixation is fixed and the first production period is executed in the real production system (V8) and o Optimizing the initial production sequence outside the fixed first production period to further optimize the cost function (V9), wherein the production system is regulated and / or controlled according to the optimized production sequence obtained in the second sub-process (V8, V9) as soon as a better result with regard to the cost function is available from the further optimization, wherein it is ensured that the better result corresponds to the already started first production period of the initial production sequence (V10). [2] Method according to claim 1, wherein a run of the second sub-process (V8, V9) is terminated if no substantial optimization of the production sequence is obtained, and a further run of the second sub-process (V8, V9) is started. [3] Method according to claim 1 or 2, wherein to carry out the second sub-method (V8, V9) an evolutionary algorithm is executed which is initialized with the production sequence obtained in the first sub-method or a mutation thereof. [4] Method according to one of claims 1 to 3, wherein production parameters, optimality criteria and / or boundary conditions are simulated, wherein the production parameters comprise worker situation, machine capabilities, material availability, material buffers and / or supplier capacities, the optimality criteria comprise maximum utilization of the machines and / or workers, minimization of delays, lowest inventory levels and / or minimization of material flows and the boundary conditions comprise priorities of material requirements, maximum storage and / or material buffer sizes, transport conditions, planning horizon and / or supplier capacities. [5] Method according to one of claims 1 to 4, wherein a shift operation of workers is simulated and in the simulation the production lines (line 1, line 2, line 3) are occupied with workers and a change in the occupancy of the production lines (line 1, line 2, line 3) with workers takes place at least as a function of the material requirements and / or material stocks. [6] Method according to one of claims 1 to 5, wherein, in the event that there are insufficient materials available to meet the material requirement, a check is carried out to determine whether the missing materials can be delivered for the material requirement while adhering to the required time, wherein, if the check is positive, a delivery is ordered (V11) and the delivered materials are reserved (V12) and, if the check is negative, a further material requirement is duly reserved. [7] Method according to one of claims 1 to 6, wherein a data structure is generated from the prioritization of the material requirements (V13), which data structure comprises at least material type, required quantity and required time for each production section (PA1, PA2), and the data structure comprises an index structure by means of which entries of the data structure are referenced to one another, wherein the second sub-method (V8, V9) is designed to process the data structure and, based on the data structure, the production lines (line 1, line 2, line 3) are allocated, workers are distributed and / or supplier orders are generated. [8] Method according to one of claims 1 to 7, wherein control-relevant and / or control-relevant outputs and / or informative outputs are provided (V14), wherein the control-relevant and / or control-relevant outputs comprise production sequences, worker occupancy and / or supplier orders and the informative outputs comprise material requirement coverage, completion dates, capacity utilization, bottlenecks, critical paths and / or temporal development of the production system. [9] Method according to one of claims 1 to 8, wherein a digital twin of a real factory is generated in the simulation, a planning horizon is determined for the digital twin and the real factory is controlled based on the planning horizon. [10] Production planning and / or control system (APO) for production optimization comprising a processing unit designed to carry out a method according to one of claims 1 to 9. [11] System (APO) according to claim 10, comprising at least one interface via which communication is provided between the system and a controller of the system, wherein the system provides the controller with control-relevant outputs and / or informative outputs of the system via the interface and the interface provides the controller with optimization results. [12] System (APO) according to claim 10 or 11 comprising a cloud infrastructure, the cloud infrastructure comprising a cloud-based storage, wherein a simulation of the production system, production planning and / or control takes place in the cloud.

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