EP2195779A1 - Procédé et système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille - Google Patents

Procédé et système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille

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Publication number
EP2195779A1
EP2195779A1 EP08785633A EP08785633A EP2195779A1 EP 2195779 A1 EP2195779 A1 EP 2195779A1 EP 08785633 A EP08785633 A EP 08785633A EP 08785633 A EP08785633 A EP 08785633A EP 2195779 A1 EP2195779 A1 EP 2195779A1
Authority
EP
European Patent Office
Prior art keywords
product
products
resource
optimized
sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP08785633A
Other languages
German (de)
English (en)
Inventor
Iiro Harjunkoski
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ABB Research Ltd Switzerland
ABB Research Ltd Sweden
Original Assignee
ABB Research Ltd Switzerland
ABB Research Ltd Sweden
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ABB Research Ltd Switzerland, ABB Research Ltd Sweden filed Critical ABB Research Ltd Switzerland
Publication of EP2195779A1 publication Critical patent/EP2195779A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the invention relates to a method for the optimized planning of complex production sequences in large-scale plant operations, in particular in the steel industry, the metalworking industry, the pharmaceutical and / or the chemical industry using program-technically implemented mixed-integer optimization method.
  • the invention also includes a corresponding system for carrying out the method.
  • the invention has for its object a possibility for improved planning of complex production sequences in large-scale plant operations, such as from the steel, pharmaceutical and / or chemical industries specify.
  • MILP Mixed Integer Linear Programming
  • a first step depending on product characteristics, such as delivery date, length, width, thickness, height, weight, density, quality, quality, purity, strength, elasticity and chemical composition of the respective product, as well as by equipment characteristics, which, for example Availability, Condition, Throughput, Consumption, Achievable Quality and / or Production Speed, Utilization, Capacities
  • product characteristics such as delivery date, length, width, thickness, height, weight, density, quality, quality, purity, strength, elasticity and chemical composition of the respective product
  • equipment characteristics which, for example Availability, Condition, Throughput, Consumption, Achievable Quality and / or Production Speed, Utilization, Capacities
  • Dependencies and / or shortcuts of equipment, operating, standstill and / or maintenance information and times, product and / or process paths and times, especially Pass or cycle times may relate, and / or other predetermined rules, which are in particular determined by the manufacturing process, carried out a sequential pre-sorting of the products to be produced.
  • the aforementioned rules essentially take into account geometric, chemical and / or physical requirements and / or limitations of the respective manufacturing process and / or the plant operation used for product production.
  • Geometric rules take significant account of restrictions and / or framework conditions regarding the equipment used for production or the available infrastructure.
  • Physical rules take into account, in particular, physical variables to be observed in relation to the production process, such as temperature, pressure and generally to be observed specifications and settings of the various resources used.
  • the presorted list is separated into individual product families and, based on at least one preselectable product parameter, for example the quality and / or width, a grouping of the products to be produced into individual product groups is performed rule-based.
  • a mathematical model is created for each product group, depending on the characteristic size and rule-based.
  • the mathematical model has to satisfy the aforementioned flexibility.
  • the execution of the method according to the invention is preferably carried out by means of a system for optimized planning of complex production sequences in large-scale plant operations, in particular the steel and / or pulp and / or paper and / or chemical / pharmaceutical industry, which means for programmatic application of, on Methods and algorithms of Mixed Integer Linear Programming (MILP) based, mixed-integer optimization method, wherein means are provided, which are adapted to processing and / or inclusion or compliance with predetermined rules and product and equipment characteristics gradually optimized grouping and optimized To perform sequencing of products to be produced in product families and product groups and to determine an optimized product sequence or production sequence.
  • MILP Mixed Integer Linear Programming
  • a computer program, in particular a computer program stored on a data carrier, which has the features of the method according to the invention, is therefore expressly included in the disclosure content of the present application.
  • Fig. 1 Exemplary underlying steelmaking process
  • Fig. 3 exemplary pre-sorting in sequences or product groups with the
  • Fig. 4 on the presorting of FIG. 3 based compatibility matrix Fig. 5 optimized grouping and sequencing of the products to be produced under the condition of exclusively decreasing widths within a sequence
  • Fig. 6 optimized grouping and sequencing of the products to be produced
  • FIG. 7 overall arrangement with exemplified inventive
  • the final casting of the liquid steel is carried out by means of modern continuous casting in a continuous casting S1, which ensure a constant solidification and an optimal microstructure of the slabs (steel blanks) and or round rods produced.
  • a continuous casting process usually comprises between 8-10 furnace fillings. Once these have been completed, regular maintenance and / or maintenance work will be carried out on the continuous caster. The maintenance cycle is thus at about 8-10 finished Ofenbe colllungen or melts.
  • Steelmaking is an extremely time-critical process with a comparatively high energy requirement, in which planning decisions concerning the course of the steelmaking process are generally still manual, that is to say made by an expert, which is especially true for highly complex process flows and a large number of considering the underlying conditions as difficult to almost impossible.
  • the last processing step namely the continuous casting of the melt in order slabs and / or round steels is here due to the variety to be considered and fulfilled rules and / or frameworks to achieve the required steel quality as the planning technically most difficult to implement process.
  • the aforementioned rules essentially include geometric, chemical and / or physical rules and / or requirements.
  • resource-specific rules and / or parameters may also be considered. These include, by themselves or in combination, for example, availability, condition, throughput, consumption, achievable quality and / or production speed, utilization, capacity dependencies and / or links, operating, standstill and / or maintenance information and times of resources. Also product and / or process paths and times, in particular also run or cycle times can be advantageously included
  • geometric rules are largely based on restrictions and / or framework conditions with regard to the equipment used for the production or the available infrastructure, so it is assumed, in particular in known methods, that the casting process in steel production takes place only in the sequence of decreasing width or width the molds and / or products is feasible.
  • Chemical rules for example, take into account the educts and / or admixtures required for the production of different steels, both
  • wash grades require thorough cleaning of the equipment to be used, for example impurities and residues of preceding melts, before the start of the production process or steels and / or castings.
  • Physical rules in this case relate in particular to temperatures to be maintained, for example, the furnaces, the melt and / or critical temperatures of the equipment used, as well as generally to be observed specifications and settings of the various resources used.
  • temperatures to be maintained for example, the furnaces, the melt and / or critical temperatures of the equipment used, as well as generally to be observed specifications and settings of the various resources used.
  • the change in thickness of cast slabs or slabs is a time-dependent process that should be avoided, if possible.
  • the equipment used also has limitations, so the caster, the "caster", tolerates only 8 furnace fillings ("heats ”) or melting batches before extensive maintenance must be performed.
  • Interruptions of the continuous casting process occur, for example, if for the order production of various products, especially steel blanks, for example, different geometrical dimensions conversions of resources, here the continuous casting required.
  • this is done in a first step S1, depending on order stock information and the product characteristics 2 contained, such as delivery date, width, quality, grade and thickness of the respective product, as well as by predetermined rules, which are co-determined in particular by the manufacturing process and / or the existing resources, a sequential pre-sorting of the products to be produced
  • the aforementioned rules essentially take into account geometric, chemical and / or physical requirements and / or limitations of the steelmaking process and / or the continuous casting plant used for product production and / or status, performance and / or operating information of the existing resources.
  • a second step 4 the sequentially presorted list of the products to be produced is subdivided into individual product families, with all products of the same thickness and quality being able to form one product family in each case.
  • each product family it is advantageously possible, based on at least one preselectable product parameter, in particular the quality and / or width, to regularly group the products to be produced into individual product groups, wherein no more than 8 products should be contained within a product group.
  • the casting sequences that is, the number of melts or furnace fillings required
  • CPM Core Performance Management
  • they can be determined using the specified method.
  • a restriction of the degrees of freedom, in particular by specifying the number of furnace loads, allows a more efficient optimization so that a specification of both an upper and a lower limit, in particular as a further characteristic or rule, appears advantageous.
  • the upper limit value can be taken directly from the presorting, wherein the lower limit value can be estimated theoretically.
  • a mathematical model and, as a preliminary stage of the mathematical model, first of all a compatibility matrix is created for each product group, which represents the complex and / or non-linear rules underlying the mixed-integer optimization method and maps all possible product pairings accordingly , Mainly chemical rules and parameters are considered and included.
  • the matrix elements can assume the values 0 or 1 here.
  • the matrix element P 1 , - has the value 1 if product / can be prepared by product /. If this is not the case, the matrix element P u has the value 0.
  • these compatibility information can also be stored in a suitably prepared database, a flat-file (hierarchical data structure) or a program memory, for example also in tabular form, for further processing and provided.
  • the mathematical model together with the objective functions to be optimized and / or their coefficients, for example minimum downtimes of the plant operation or a continuous production process, embedded in the MILP approach, provide all the information necessary for carrying out a successful sequencing or meet all the requirements for the Carrying out a successful sequencing.
  • an optimized product or production sequence is first determined within each product family. It is also advantageously possible to carry out the optimization only over partial areas, for example in the case of not exactly known (fuzzy) process parameters and / or resource information, an optimization for all production sequences (German expression) or for only a smaller part of the production sequences, in particular casting sequences, with complete parameter set and / or resource information.
  • Ch P 1 , 1, w, ⁇ w ,,, t, ⁇ t,
  • CA: P 1 , 1, w, ⁇ w ,,, t, ⁇ t " where w indicates the width of the respective product and t indicates a type number describing the quality of the respective product.
  • w indicates the width of the respective product
  • t indicates a type number describing the quality of the respective product.
  • Z 9 be a binary variable that takes the value 1 if a sequence g is used, that is, contains at least the product, otherwise it has the value 0.
  • the goal is to minimize the total number of sequences or product groups.
  • G gives an estimate of the maximum required number of groups.
  • the assignment of products i to product groups g is carried out by the binary variable Xj 9 .
  • Each product i must be uniquely assigned to exactly one product group g, as indicated by objective function or relation R2, whereby an upper limit Mmax, for which the maximum number of products in a product group is to be adhered to, is illustrated by relation R3.
  • the variables for unused product groups have the value 0.
  • I describe the quantity of the products to be produced.
  • ocg is a binary variable with the value 1 if within the product group or sequence g the product width w increases.
  • qi g is a variable that serves to soften or defuse some of the specified target functions for the last product in a sequence.
  • the target function R8 prevents the occurrence of products with a larger width and with a smaller type number, ie higher quality, as well as products with a smaller width and with a higher type number, ie lower quality, for increasing or increasing distances within a sequence or a product sequence of a product group.
  • the second objective function or relation R9 describes the corresponding case for decreasing distances within a sequence. It should be noted here notes that in the case of pre-sorting of the products depending on the quality in index comparison is sufficient.
  • the ' further objective functions or relations serve to further delimit the search space or to compact the model.
  • the flag for increasing widths within a sequence is set to 0 for nonexistent sequences, see relation R12.
  • the product groups are ordered according to the number of products contained in them, see relation R13.
  • equation R14 causes the active groups to be prepended.
  • , is achieved by presorting. It is common for some of these relations to appear redundant after optimization.
  • the exception variables for the first and last product of a sequence are real variables in the range between 0 and 1.
  • FIG. 3 shows an exemplary presorting in sequences or product groups with the product parameters quality, width, thickness. After presorting, there are 5 sequences separated by thicker horizontal lines. The prerequisite for this is that products of different quality must be produced in the order of 101A ⁇ 101B-> 101C ⁇ 101, and the maximum width change between two consecutive products may be 7.0 units.
  • FIG. 4 shows a compatibility matrix based on the presorting according to FIG. 3
  • FIG. 5 shows an optimized grouping and sequencing of the products to be produced, assuming only decreasing widths within a sequence, based on FIGS. 3 and 4.
  • FIG. 6 shows an optimized grouping and sequencing of the products to be produced according to FIG. 3 and FIG. 4.
  • FIG. 7 shows an overall concept or overall arrangement for a large-scale plant operation from the steel industry with several components and The inclusion of an exemplary trained system for the optimized planning of complex production sequences in large-scale plant operations shown, where systemically in interaction
  • Products with matching or corresponding product specifications can be determined by means of the CPM component 14 and the extent of the melting or casting process and in particular the number of slabs or ingots can be determined on the basis of the correspondences determined,
  • optimization model By means of the aforementioned system 16 and the information transmitted while processing and / or incorporating predetermined rules and product parameters, in particular also target specifications and resource characteristics corresponding optimization model (mathematical model) can be created and, in particular by means of a processing component in the form of a model solver 18 by application of the respective solution algorithm, the respective optimization model (mathematical model) solvable and optimized grouping and optimized sequencing of products to be produced in product families and product groups feasible and optimized Product sequence or production sequence can be determined,
  • the information underlying the optimization and thus the optimized production plan production can thereby be transmitted by means of XML or other known data structures-either file-based or memory-based, also via corresponding communication links and / or data carriers.
  • the optimization request or the corresponding request to the system and / or the initiation of the optimization process is effected by a corresponding "hosting system", in particular a CPM system or a corresponding Kkomponente, which all relevant information to the optimization process or the system producing an optimized production plan transmitted.

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Abstract

L'invention concerne un procédé et un système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille, notamment dans l'industrie de l'acier, par utilisation, à des fins de programmation, d'un procédé d'optimisation en numération mixte fondé sur des procédés et algorithmes de la programmation linéaire en numération mixte (MILP). Avec prise en compte de règles prédéfinies, de caractéristiques de produits et de caractéristiques de moyens d'exploitation, un groupage et un séquençage optimisés de produits à fabriquer, dans des familles et des groupes de produits, est réalisé par étapes, et une séquence de produits ou de production optimisée est déterminée.
EP08785633A 2007-08-31 2008-08-20 Procédé et système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille Withdrawn EP2195779A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102007041424A DE102007041424A1 (de) 2007-08-31 2007-08-31 Verfahren und System zur optimierten Planung komplexer Produktionsabfolgen in großtechnischen Anlagenbetrieben
PCT/EP2008/006820 WO2009030364A1 (fr) 2007-08-31 2008-08-20 Procédé et système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille

Publications (1)

Publication Number Publication Date
EP2195779A1 true EP2195779A1 (fr) 2010-06-16

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EP08785633A Withdrawn EP2195779A1 (fr) 2007-08-31 2008-08-20 Procédé et système de planification optimisée de séquences de production complexes dans des installations techniques de grande taille

Country Status (6)

Country Link
EP (1) EP2195779A1 (fr)
JP (1) JP5341090B2 (fr)
CN (1) CN101790746A (fr)
BR (1) BRPI0816060A2 (fr)
DE (1) DE102007041424A1 (fr)
WO (1) WO2009030364A1 (fr)

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DE102009039988A1 (de) 2009-09-03 2011-03-17 Siemens Aktiengesellschaft Verfahrwege für Lackierroboter
DE102010010551B4 (de) * 2010-03-05 2014-03-13 Abb Ag Verfahren und Vorrichtung zum Koordinieren von zwei aufeinanderfolgenden Herstellungsstufen eines Produktionsprozesses
DE102010015001A1 (de) * 2010-04-14 2011-10-20 Abb Ag Verfahren und Vorrichtung zum Erstellen eines Produktionsablaufplans
CN102682353A (zh) * 2011-03-16 2012-09-19 西门子（中国）有限公司 小型轧钢厂的生产排程方法及其系统
CN103257638B (zh) * 2013-04-18 2014-08-06 中国科学院沈阳自动化研究所 面向复杂制造过程的可重入工艺路径建模方法
JP6483373B2 (ja) 2014-08-07 2019-03-13 株式会社東芝 生産支援システムおよび生産支援方法
JP6950738B2 (ja) 2017-04-26 2021-10-13 富士通株式会社 生産計画生成装置、生産計画生成プログラム及び生産計画生成方法
EP3410363A1 (fr) * 2017-05-31 2018-12-05 Siemens Aktiengesellschaft Détermination d'un plan de production
EP3955187A1 (fr) * 2020-08-11 2022-02-16 Siemens Aktiengesellschaft Détermination continue des étapes critiques de production pour la fabrication de produits flexibles
DE102021203400A1 (de) * 2021-04-07 2022-10-13 Zf Friedrichshafen Ag Computerimplementiertes Verfahren und Computerprogramm zur Montagestückzahlplanung von Montageteilen für eine Produktionsoptimierung eines Produktionssystems, Montagestückzahlplanungssystem und Produktionsplanung und-steuerungssystem
CN115596410B (zh) * 2021-07-09 2024-10-11 大庆油田有限责任公司 一种适用于多井组抽油机的运行模式排序调整控制方法
CN114298567B (zh) * 2021-12-30 2024-06-21 重庆大学 连铸机浇次计划排程及开浇时间动态决策方法及系统
CN114742406A (zh) * 2022-04-11 2022-07-12 成都星云智联科技有限公司 自动排产方法、装置、电子设备及可读存储介质
CN118333793B (zh) * 2024-03-29 2024-10-25 江苏菲尔浦物联网有限公司 一种建筑模块生产进度优化方法及系统

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Also Published As

Publication number Publication date
WO2009030364A1 (fr) 2009-03-12
JP5341090B2 (ja) 2013-11-13
CN101790746A (zh) 2010-07-28
JP2010537328A (ja) 2010-12-02
DE102007041424A1 (de) 2009-03-05
BRPI0816060A2 (pt) 2015-03-31

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