CN103310310A - Multi-type steel batch rolling dynamic production planning system - Google Patents

Abstract

The invention discloses a multi-type steel batch rolling dynamic production planning system which is composed of a production plan information collection subsystem, a material supply information collection subsystem, a rolling production plan optimization subsystem and an output optimization information feedback subsystem. An output result of the system is fed back to an upstream production plan optimization subsystem through the output optimization information feedback subsystem, and dynamic change of materials and orders in the production process is combined to conduct production plan adjustment to ensure operation of the production planning system under the condition of minimum production cost. According to the system, correlation between available capacity, replaceability of the steel materials, blank and finished goods inventory, blank and production cost, batch production, tardy production and other factors are comprehensively considered in the rolling process, flexibility is improved, and the system meets the production status and the requirements of modern steel enterprises for multiple types and small batch.

Description

The in batches rolling schedule with dynamic of a kind of many kinds iron and steel system

Technical field

The invention belongs to iron and steel Rolling plan and material resource planning technique field, be specifically related to the in batches rolling schedule with dynamic of a kind of many kinds iron and steel system.

Background technology

Iron and steel is rolling to be the most frequently used modes of production of steel, is intensive production along with iron and steel enterprise is gradated by extensive style, and more and more pays attention to variety and quality, thereby increase the benefit, and its Production planning and scheduling problem also more and more receives people's concern.In addition, market is more and more abundanter to the demand type of steel in recent years, the trend of customer order short run and many kindizations is also more and more obvious, for iron and steel enterprise, for the individual demand that reduces the production cost in the operation of rolling and satisfy the client, be necessary to realize by rational optimization system the efficient formulation of iron and steel operation of rolling production schedule under a plurality of time cycles, to guarantee the optimal utilization of resource.

Many kinds iron and steel in batches Rolling plan take " day " or " week " as planning cycle chronomere, supply with plan and steel order plan of needs according to blank, take into full account the operation of rolling in each production cycle productive capacity balance, stock's turnover, production and blank cost, drag the factor such as phase production, turn to target with customer satisfaction maximization and enterprise's production cost economy, by optimizing the batch production strategy, obtain the rational production schedule in the planning cycle.Planning cycle is unit calculating usually take the sky, and some days interior production schedules of planning cycle are carried out layout.Because blank is supplied with and steel demand possibility temporal evolution, therefore this plan is also along with the time is dynamically adjusted.Many kinds iron and steel in batches rolling Short term Planning is being born the vital role that contacts the market demand and production operation, and the multiplicity of the mass of the variation of the polytrope of the market demand, rolling shapes, Rolling Production and slab selection has all been brought difficulty to modeling process.

Chinese scholars has been carried out a large amount of research to the manufacturing industry production schedule and scheduling at present, has also carried out certain research for iron and steel Production Scheduling problem simultaneously.Neureuther etc. utilize the layer-stepping production schedule (HPP) method that steel production is divided into three respectively modelings of plan layer of passing rank, and have analyzed Existential Space and the contact of time between each plan layer for the steel plant that produce towards order.The Adoption Network structures such as Vanhoucke have been processed the steel production planning problem of multiple operation multipath, and this model is considered as the mode of production of each operation identical, but do not consider the batch production of the operation of rolling.Research for the rolling scheduling of iron and steel, Tang etc. and the angle from job scheduling such as Chen, the scheduling of iron and steel hot rolling is carried out modeling as many travelling salesmans and Vehicle Routing Problems, adopt respectively simultaneously improved genetic algorithm and quantum particle swarm-simulated annealing to find the solution.Yet the day by day raising that product variety and delivery date is required along with the client also seems more important to the tracking production management of order.Zanoni etc. are optimized scheduling by setting up linear programming model to the production of blank, considered production cost, dragged phase order, inventory cost etc., have realized the production schedule of blank under the JIT environment.As ' ad etc. have segmented the kind of blank and steel on this basis, have set up the MIXED INTEGER bilinear programming model of iron and steel Rolling plan, and utilize approximate linearization strategy that master mould is converted to linear programming model and find the solution.Cowling etc. introduce a kind of for the adjustable commercial decision support system (DSS) of the parameter of operation of rolling Optimized Operation, have realized the semi-automation of the operation of rolling.

The formulation process of iron and steel enterprise's Rolling plan need to be considered the factor of numerous complicated, and present research is considered mainly for the partial element in the production schedule, suppose that the available production capacity between each cycle is separate, and the order delivery is also relatively fixing.In fact, the available production capacity in each cycle is subject to the impact of cycle production, and namely the available production capacity between each cycle is to be mutually related.Simultaneously, order delivery can select the residue steel close with order requirements to mate from the stock.The existence of these coupling conditions has brought more uncertain factor to the system modelling process, has increased scale and the complexity of Rolling plan system.

Summary of the invention

The present invention has considered the operation of rolling can use the relevance between the production capacity, the substitutability of steel, and blank and finished goods inventory, blank and production cost, produce, drag the factors such as phase production in batches, for the shortcoming that the optimization system model was too fixing, dirigibility is not enough in the past, the in batches rolling schedule with dynamic of a kind of many kinds iron and steel system has been proposed, to improve prediction accuracy and dirigibility, adapt to market for the demand trend of rolling shapes variation, order short run.

In order to achieve the above object, the present invention adopts following technical scheme:

The in batches rolling schedule with dynamic of a kind of many kinds iron and steel system is comprised of production schedule information acquisition subsystem, material supply information acquisition subsystem, Rolling plan optimization subsystem and output optimization information feedback subsystem.

Described production schedule information acquisition subsystem comprises a plurality of order demand data collecting units and a storage unit.Order demand data collecting unit is used for gathering market for the demand information and the order data demand information that derives from the client of steel, and the order demand data that these collect is stored in the storage unit, forms the related data library information.

Described material supply information acquisition subsystem comprises a plurality of material supply information collecting units and a storage unit.Material supply information collecting unit is used for gathering the blank quantity delivered information that iron and steel is produced, and these information are stored in the storage unit, forms the related data library information.

The information of related memory cell passes to the Database Unit in the following Rolling plan optimization system in described production schedule information acquisition subsystem and the material supply information acquisition subsystem, inputs as system.

Described Rolling plan is optimized subsystem and is comprised Database Unit, Rolling plan Optimization Modeling unit and optimum results output unit.Database Unit is used for receiving the related data information in the memory cell that comes from described production schedule information acquisition subsystem and material supply information acquisition subsystem, inputs as system; Rolling plan Optimization Modeling unit produces blank according to iron and steel and supplies with amount of plan and order demand information, it is system's input, use the method for operational research, consider the coupling of each cycle production capacity balance and the substitutability of rolling shapes, and combine the stock, produce to prepare, drag the other influences factor such as phase, set up in batches rolling production planning optimization model of many kinds iron and steel; The data of Database Unit draw the optimum results of the steel production schedule in conjunction with the production planning optimization model, just exported by the optimum results output unit.

Described output is optimized the information feedback subsystem and is comprised the optimization information acquisition unit and optimize the information analysis processing unit.Wherein optimize the information acquisition unit collection and input to optimization information analysis processing unit through the optimum results of upstream Rolling plan optimization subsystem; Optimize that the information analysis processing unit carries out appropriate combination with this optimum results and data are processed, analyze and optimum blank coupling, in batches rolling strategy and stock's turnover strategy etc.The information feedback subsystem is optimized in described output, the processing policy of analysis and processing unit is fed back to upstream Rolling plan optimization subsystem, and in conjunction with the dynamic change of material in the production run and order volume, carry out the dynamic adjustment of the production schedule, the assurance system is in the situation that minimize the cost production run.

Beneficial effect of the present invention: take iron and steel enterprise's Rolling Production process as research object, be based on the expanding system of MPS (MPS), consider the operation of rolling and can use the relevance between the production capacity, the substitutability of steel, and blank and finished goods inventory, blank and production cost, produce, drag the factors such as phase production in batches, strengthen dirigibility, more met production status and the demand of the many kinds of modern steel enterprise, short run.

Description of drawings

Fig. 1 is the basic process figure that steel are produced.

Fig. 2 is in batches rolling schedule with dynamic structured flowchart of many kinds iron and steel.

Fig. 3 is in batches Rolling plan Optimized model figure of many kinds iron and steel.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is described in detail.Obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that obtains under the creative work prerequisite.

Rolling is committed step in the steel production run.Fig. 1 has described the basic process that steel are produced, and molten iron is through pneumatic steelmaking with after different refining furnaces are removed impurity, molten steel is transported to blank that conticaster pours into different size take " stove " as unit.Front usually need in heating furnace, be heated to high temperature as raw-material blank rolling, then send in batches rolling line, through obtaining different steel after the operations such as rolling, cold bed, shearing, steel are placed in the finished room according to the order classifying packing the most at last.Each production cycle supplies with according to blank and the order plan of needs is formulated Rolling plan.

The invention provides the in batches rolling schedule with dynamic of a kind of many kinds iron and steel system, its structured flowchart as shown in Figure 2.System of the present invention comprises four sub-systems, is respectively production schedule information acquisition subsystem, material supply information acquisition subsystem, Rolling plan optimization subsystem and output optimization information feedback subsystem.Wherein production schedule information acquisition subsystem is comprised of a plurality of order demand data collecting units and a storage unit; Material supply information acquisition subsystem is comprised of a plurality of material supply information collecting units and a storage unit; Rolling plan is optimized subsystem and is comprised of Database Unit, Rolling plan Optimization Modeling unit and optimum results output unit; Output optimization information feedback subsystem forms by optimizing information acquisition unit and optimizing the information analysis processing unit.

One, production schedule information acquisition subsystem

Described production schedule information acquisition subsystem comprises a plurality of order demand data collecting units and a storage unit.

A plurality of order demand data collecting units are used for gathering market for the demand information and the order data demand information that derives from the client of steel;

Storage unit is used for storing related data, forms the related data library information.

Two, material supply information acquisition subsystem

Described material supply information acquisition subsystem comprises a plurality of material supply information collecting units and a storage unit.

A plurality of material supply information collecting units are used for gathering the blank quantity delivered that iron and steel is produced;

Storage unit is used for storing related data, forms the related data library information.

Three, Rolling plan is optimized subsystem

Described Rolling plan is optimized subsystem and is comprised Database Unit, Rolling plan Optimization Modeling unit and optimum results output unit.

Database Unit is used for receiving the related data information in the memory cell that comes from described production schedule information acquisition subsystem and material supply information acquisition subsystem, inputs as system;

Rolling plan Optimization Modeling unit, produce blank according to iron and steel and supply with amount of plan and order demand information, it is system's input, use the method for operational research, consider the coupling of each cycle production capacity balance and the substitutability of rolling shapes, and combine the stock, produce to prepare, drag the other influences factor such as phase, set up in batches rolling production planning optimization model of many kinds iron and steel.

The optimum results output unit is used for the iron and steel Rolling plan optimum results of Optimization Modeling unit is exported.

Described many kinds iron and steel in batches rolling production planning optimization model as shown in Figure 3, implementation is as follows:

Suppose: 1, each order comprises a kind of product; 2, the steel grade of steel and blank is identical in the planning cycle, but dimensions is different with grade of steel; 3, blank and steel are divided into two class grade of steels, and " first-class grade of steel " finished product that grade of steel is high can substitute low " second-class grade of steel " finished product of grade of steel " with compensation "; 4, each order is finished by the processing of some compact material, i.e. batch production;

Step 1, the relevant mathematic sign of definition;

Symbol definition in the Optimized model is as follows.Wherein, in the production cycle relevant time take " hour " as unit, the blank weight relevant with finished product (being steel) is take " ton " as unit, relevant expense is take " unit " as unit.

Constants/parameters:

I: trimmed size numbering;

J: blank specification number;

G: grade of steel numbering;

D: production cycle;

T: each production cycle time scale;

TNi: trimmed size sum;

TNj: blank specification sum;

TNg: grade of steel sum;

TNd: production cycle sum;

α _d: the d cycle, idle unit can use the rejection penalty of production time;

S _d: the d cycle production schedule repair time;

ν _d: the efficiency in d cycle;

σ _d: the remaining available production time upper limit of d cycle;

The production efficiency that d cycle blank manufactures a finished product;

The demand of d cycle order finished product;

The weight of every compact material;

The weight of d cycle planning supplying blank;

The safety stock of d cycle blank;

The safety stock of d cycle finished product;

Cs _d: the expense of production of units setup time in d cycle;

The d cycle is by the expense of blank production unit finished product;

The expense of storage cell weight finished product in the d cycle stock;

The expense of storage cell weight blank in the d cycle stock;

Fixed expense when needs are produced blank;

The unit cost of d cycle blank;

The d cycle unit weight finished product phase of dragging is finished the rejection penalty of unit interval;

D cycle grade of steel is the unit price of the finished product i of g;

The production setup time of finished product;

Variable:

u _d: the available production time in d cycle;

Δ u _d: the remaining available production time in d cycle;

D cycle finished product drag the phase performance;

The d cycle is the finished weight of product as an alternative;

The tank farm stock of finished product during the d end cycle;

The tank farm stock of blank during the d end cycle;

The d cycle is the weight of the finished product i that produces of the blank j of g by grade of steel;

The d cycle is produced the weight that the last cycle drags the phase finished product;

The billet weight of d cycle need of production;

The lot number of d cycle need of production;

The d cycle finishes the last cycle and drags the required production time of phase output;

The production time of d cycle blank processed finished products;

Ts _d: the production setup time summation in d cycle;

Step 2, set up the objective function of Optimized model;

The described many kinds iron and steel in batches target of Rolling plan Optimized model is to minimize operation cost and inventory cost, the objective function of Optimized model is comprised of 7 parts, be respectively productive capacity balance rejection penalty, production setting up expenses, stock's occupancy expenses, finished product processing charges, grade of steel substituted expenses, blank cost and order production and drag phase rejection penalty etc., respectively by f ₁, f ₂F ₇Expression.

Minimum cost is objective function: Min(f ₁+ f ₂+ f ₃+ f ₄+ f ₅+ f ₆+ f ₇);

Wherein, productive capacity balance rejection penalty f ₁The rejection penalty that the actual production time that refers to the iron and steel operation of rolling produces when departing from the available production time, $f_1=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}{\mathrm{\α}}_d[u_d-\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g)];$

Produce setting up expenses f ₂Refer in the batch Rolling Production situation, the expense that is produced by rolling front production setup time of each compact material, $f_2=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}{\mathrm{cs}}_d{\mathrm{ts}}_d;$

Stock's occupancy expenses f ₃Comprise blank stock and finished goods inventory, $f_3=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}(\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}{\mathrm{cf}}_{\mathrm{id}}^{g}I{f}_{\mathrm{id}}^g+\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}{\mathrm{cb}}_{\mathrm{jd}}^g{\mathrm{Ib}}_{\mathrm{jd}}^g);$

Finished product processing charges f ₄Mainly refer to the every production operation expense summation in the operation of rolling, comprising: phase finished product processing is processed and dragged to the plan finished product, $f_4=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}{\mathrm{cp}}_{\mathrm{ijd}}^g({\mathrm{mx}}_{\mathrm{ijd}}^g+{\mathrm{me}}_{\mathrm{ijd}}^g);$

Because the present invention considers the different size specification product of two class grade of steels, be denoted as quality higher " first-class grade of steel " with g=1, the unit price of " first-class grade of steel " finished product is denoted as quality lower " second-class grade of steel " with g=2, therefore correspondingly will be higher than " second-class grade of steel " product." first-class grade of steel " finished product allows to substitute the delivery of " second-class grade of steel " finished product, but can produce grade of steel substituted expenses f ₅, this expense determines jointly by substituting amount and finished product price difference: $f_5=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}{r}_{\mathrm{id}}^1({\mathrm{sp}}_{\mathrm{id}}^1-{\mathrm{sp}}_{\mathrm{id}}^2);$

Blank cost and demand f ₆Take a certain fixed expense as starting point, increase with billet weight is linear.Because blank can be produced by the upstream steel smelting-continuous casting, also can purchase by the outsourcing mode, and the weight of blank is increase, then blank cost and demand take " criticizing " or " piece " as unit $f_6=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}({\mathrm{co}}_{\mathrm{jd}}^g{y}_{\mathrm{jd}}^g+{\mathrm{cr}}_{\mathrm{jd}}^g{\mathrm{my}}_{\mathrm{jd}}^g);$

In order to guarantee that order delivers goods on schedule, the situation that should avoid the finished product phase of dragging to produce occurs, and the finished product that the phase of dragging is produced applies rejection penalty $f_7=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\Σ}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}{\mathrm{ct}}_{\mathrm{id}}^g{\mathrm{qt}}_{\mathrm{id}}^g.$

Step 3, be three modules with described steel rolling production planning optimization model partition, be respectively in batches Rolling Production module, supplydemand relationship module and inventory management module.Set up respectively the constraint condition of its correlated variables according to three modules.

1) the rolling module of batch

Set up the productive capacity Constraints of Equilibrium according to operation of rolling actual production time restriction condition and the constraint of available production time;

$\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g)\≤u_d\∀d---\left(1\right)$

$u_d=(T-S_d-{\mathrm{ts}}_d)v_d+{\mathrm{\Δu}}_{d-1}\∀d---\left(2\right)$

${\mathrm{\Δu}}_d=\min [u_d-\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\Σ}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g),{\mathrm{\σ}}_d]\∀d---\left(3\right)$

$p_{\mathrm{ijd}}^g=\frac{{\mathrm{mx}}_{\mathrm{ijd}}^g}{{\mathrm{\γ}}_{\mathrm{ijd}}^g}\∀d,i,j,g---\left(4\right)$

${\mathrm{pe}}_{\mathrm{ijd}}^g=\frac{{\mathrm{me}}_{\mathrm{ijd}}^g}{{\mathrm{\γ}}_{\mathrm{ijd}}^g}\∀d,i,j,g---\left(5\right)$

$\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}{\mathrm{me}}_{\mathrm{ijd}}^g={\mathrm{qt}}_{\mathrm{id}-1}^g\∀d,i,g---\left(6\right)$

Wherein, formula (1) the expression actual production time can not be greater than the available production time, and wherein the actual production time has comprised the finished product production time and the finished product production time of dragging the phase of plan.Formula (2) has provided the d cycle can be with the calculating formula of production time, the time of personnel and device ready calculated with T hour in production cycle, had considered the scheduled overhaul time, had produced setup time, efficiency and remaining available production time in last cycle.And formula (3) has limited the upper limit σ that remains the available production time _dFormula (4), (5) represent respectively the finished weight of planned production and the phase of dragging produces finished weight and the relation between the corresponding production time.Formula (6) has represented phase of dragging that the d cycle need to produce weight that manufactures a finished product.

Set up production setup time constraint according to the operation of rolling:

${\mathrm{ts}}_d={\mathrm{tb}}_i^g\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\Σ}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\Σ}}}{y}_{\mathrm{jd}}^g\∀d---\left(7\right)$

$S_d+{\mathrm{ts}}_d<T\&ForAll;d---\left(8\right)$

Rolling production setup time refers to finish a kind of blank and is machined to and begins lower a kind of blank and process the needed time, and it comprises the picking and placeing and the operation such as loading and unloading of adjustment, blank of roll mill.Rolling total production setup time is directly related with rolling batch, and needs more than the rolling blank batch, and total production setup time is longer.Formula (7) has then represented to produce the relation between setup time and rolling batch, for wherein production setup time of some batch is thought fixed value.Scheduled overhaul time in formula (8) the expression one-period and production setup time sum were less than T hour.

2) supplydemand relationship module

Set up blank and finished product relation constraint according to blank and finished product relation:

${\mathrm{my}}_{\mathrm{jd}}^g={\mathrm{nb}}_{\mathrm{jd}}^g{\mathrm{mb}}_{\mathrm{jd}}^g\&ForAll;d,j,g---\left(9\right)$

$\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^g+{\mathrm{me}}_{\mathrm{ijd}}^g)={\mathrm{my}}_{\mathrm{jd}}^g\&ForAll;d,j,g---\left(10\right)$

$y_{\mathrm{jd}}^g=\left\{\begin{array}{c}1,\mathrm{when}{\mathrm{nb}}_{\mathrm{jd}}^g>0\\ 0,\mathrm{when}{\mathrm{nb}}_{\mathrm{jd}}^g=0\end{array}\right.\&ForAll;d,j,g---\left(11\right)$

Because iron and steel is rolling to be produced in batches take " criticizing " as unit, be the integral multiple of every compact material weight therefore need rolling billet weight, as the formula (9), therefore rolling billet weight also is an integer.Formula (10) represents respectively the relation of identity between the weight of rolling blank and finished product.Relation between formula (11) the expression decision variable.

3) inventory management module

Drag the phase the relationship between quantities to set up tank farm stock and drag phase amount constraint according to product library storage and production:

${\mathrm{Ib}}_{\mathrm{jd}}^g={\mathrm{Ib}}_{\mathrm{jd}-1}^g+{\mathrm{mp}}_{\mathrm{jd}}^g-{\mathrm{my}}_{\mathrm{jd}}^g\&ForAll;d,j,g---\left(12\right)$

${\mathrm{If}}_{\mathrm{id}}^1=\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^1+{\mathrm{me}}_{\mathrm{ijd}}^1)+{\mathrm{If}}_{\mathrm{id}-1}^1-({\mathrm{mo}}_{\mathrm{id}}^1-{\mathrm{qt}}_{\mathrm{id}}^1)-r_{\mathrm{id}}^1-{\mathrm{qt}}_{\mathrm{id}-1}^1\&ForAll;d,i---\left(13\right)$

${\mathrm{If}}_{\mathrm{id}}^2=\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^2+{\mathrm{me}}_{\mathrm{ijd}}^2)+{\mathrm{If}}_{\mathrm{id}-1}^2+r_{\mathrm{id}}^1-({\mathrm{mo}}_{\mathrm{id}}^2-{\mathrm{qt}}_{\mathrm{id}}^2)-{\mathrm{qt}}_{\mathrm{id}-1}^2\&ForAll;d,i---\left(14\right)$

The stock has comprised blank stock and finished goods inventory, produce or the blank of outsourcing is positioned over the blank stock, and the finished product of blank after rolling is positioned over finished goods inventory, and this two classes warehouse all needs to guarantee certain tank farm stock, i.e. safety inventory.The stock bank storage is according to the turnover amount of blank and dynamic change, as the formula (12).Because there is substitutability in finished product, therefore the warehouse for finished product storage account form of different grade of steels is different, suc as formula (13), (14).Considered the contingent phase production phenomenon of dragging in the formula, dragged phase amount to refer to fail the finished weight finished according to the production schedule, it in time and dynamic change.Arbitrary cycle was all preferentially produced the finished product that a upper cycle do not finish on time, and the phase of namely dragging measures.

Step 4, set up its dependent variable in the process according to model, such as safety inventory, time, quality, expense etc., set up constraints of variable ranges:

${\mathrm{Ib}}_{\mathrm{jd}}^g\&GreaterEqual;{\mathrm{Ibs}}_{\mathrm{jd}}^g---\left(15\right)$

${\mathrm{If}}_{\mathrm{id}}^g\&GreaterEqual;{\mathrm{Ifs}}_{\mathrm{id}}^g---\left(16\right)$

$u_d,{\mathrm{\&Delta;u}}_d,p_{\mathrm{ijd}}^g,{\mathrm{mx}}_{\mathrm{ijd}}^g,{\mathrm{my}}_{\mathrm{jd}}^g,{\mathrm{ts}}_d,r_{\mathrm{id}}^g,{\mathrm{qt}}_{\mathrm{id}}^g\&GreaterEqual;0\&ForAll;d,i,j,g---\left(17\right)$

$y_{\mathrm{jd}}^g\&Element;\left\{\mathrm{0,1}\right\}\&ForAll;d,j,g---\left(18\right)$

${\mathrm{nb}}_{\mathrm{jd}}^g\&Element;N\&ForAll;d,j,g---\left(19\right)$

Because may there be some uncertain factors in production ﹠ marketing, such as production equipment catastrophic discontinuityfailure, rush order, change at delivery date etc., therefore need blank and finished room need to reserve certain minimum stock, i.e. safety inventory is suc as formula (15), (16).In addition, other continuous variables in the model are non-negative, as the formula (17).Relevant 0-1 decision variable is produced in formula (18) expression.The piece number of the rolling blank of formula (19) expression is natural number.

Step 5, this Optimized model carried out the initialization of constraint condition:

${\mathrm{Ib}}_{j0}^g={\mathrm{Ibs}}_{j0}^g---\left(20\right)$

${\mathrm{If}}_{i0}^g={\mathrm{Ifs}}_{i0}^g---\left(21\right)$

${\mathrm{\&Delta;u}}_0={\mathrm{qt}}_{i0}^g=0\&ForAll;i,j,g---\left(22\right)$

Because the production capacity balance between each cycle exists between coupling and each the grade of steel steel and has substitutability, formula (2), formula (12)～(14) are the iteration formulas, need to provide the initialization condition of tank farm stock, available production time and the phase of dragging amount.Be without loss of generality, formula (20), (21) make that blank stock and finished goods inventory initial value are corresponding safety stock, and formula (22) then makes the residue production time and drags the initial value of phase variable all is 0.

Find the solution in step 6, the data substitution Optimized model with Database Unit in the described Rolling plan optimization subsystem, draw the production planning optimization result.

Four, output optimization information feedback subsystem

Described output is optimized the information feedback subsystem and is comprised the optimization information acquisition unit and optimize the information analysis processing unit.

Optimize information acquisition unit, be used for gathering the optimization Output rusults of optimizing subsystem through the upstream Rolling plan;

Optimize the information analysis processing unit, be used for this optimum results is carried out appropriate combination and data processing, analyze and optimum blank coupling, in batches rolling strategy and stock's turnover strategy etc., and it is fed back to the upstream Rolling plan optimize subsystem, in conjunction with the dynamic change of material in the production run and order volume, carry out the adjustment of the production schedule.

Claims (10)

1. the in batches rolling schedule with dynamic of kind iron and steel more than kind system is characterized in that, comprising:

Production schedule information acquisition subsystem is used for collecting effective market demand information and customer order demand information;

Material supply information acquisition subsystem is used for collecting the correlative supply information that steel are produced;

Rolling plan is optimized subsystem, is used for setting up the Rolling plan model according to the information that gathers, and considers simultaneously the Rough-cut Capacity Planning of enterprise's steel production and the raw materials inventory plan of production;

Output optimization information feedback subsystem is used for the Output rusults of system is fed back to production upstream planning optimization system, and in conjunction with the dynamic change of material in the production run and order volume, carries out the adjustment of the production schedule.

2. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 1 system is characterized in that, described production schedule information acquisition subsystem comprises:

A plurality of order demand data collecting units are used for gathering market for the demand information and the order data demand information that derives from the client of steel;

Storage unit is used for storing related data, forms the related data library information.

3. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 1 system is characterized in that, described material supply information acquisition subsystem comprises:

A plurality of material supply information collecting units are used for gathering the blank quantity delivered that iron and steel is produced;

Storage unit is used for storing related data, forms the related data library information.

4. the in batches rolling schedule with dynamic of described many kinds iron and steel system according to claim 2-3, it is characterized in that, the memory cell of described production schedule information acquisition subsystem and material supply information acquisition system receives and comes from the separately image data of relevant data acquisition unit, form database information, pass to following iron and steel Rolling plan and optimize subsystem.

5. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 4 system is characterized in that, described Rolling plan is optimized subsystem and comprised:

Database Unit is used for receiving the related data information in the memory cell that comes from described production schedule information acquisition subsystem and material supply information acquisition subsystem, inputs as system;

Rolling plan Optimization Modeling unit, produce blank according to iron and steel and supply with amount of plan and order demand information, it is system's input, use the method for operational research, consider the coupling of each cycle production capacity balance and the substitutability of rolling shapes, and combine the stock, produce to prepare, drag the other influences factor such as phase, set up in batches rolling production planning optimization model of many kinds iron and steel;

The optimum results output unit is used for the iron and steel Rolling plan optimum results of Optimization Modeling unit is exported.

6. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 5 system is characterized in that, Rolling plan Optimization Modeling unit is based on following assumed condition:

(1) each order comprises a kind of product;

(2) steel grade of steel and blank is identical in the planning cycle, but dimensions is different with grade of steel;

(3) blank and steel are divided into two class grade of steels, and the finished product that grade of steel is high substitutes the low finished product of grade of steel with compensation;

(4) each order is finished by the processing of some compact material, i.e. batch production.

7. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 5 system is characterized in that, the main part of Rolling plan Optimization Modeling unit is in batches Rolling plan Optimized model of many kinds iron and steel, and its content comprises:

Step 1, the relevant mathematic sign of definition;

Symbol definition in the Optimized model is as follows: wherein, the blank weight relevant with finished product is take ton as unit take hour as unit relevant time in the production cycle, and the expense of being correlated with is take unit as unit;

Constants/parameters:

I: trimmed size numbering;

J: blank specification number;

G: grade of steel numbering;

D: production cycle;

T: each production cycle time scale;

TNi: trimmed size sum;

TNj: blank specification sum;

TNg: grade of steel sum;

TNd: production cycle sum;

α _d: the d cycle, idle unit can use the rejection penalty of production time;

S _d: the d cycle production schedule repair time;

ν _d: the efficiency in d cycle;

σ _d: the remaining available production time upper limit of d cycle;

The production efficiency that d cycle blank manufactures a finished product;

The demand of d cycle order finished product;

The weight of every compact material;

The weight of d cycle planning supplying blank;

The safety stock of d cycle blank;

The safety stock of d cycle finished product;

Cs _d: the expense of production of units setup time in d cycle;

The d cycle is by the expense of blank production unit finished product;

The expense of storage cell weight finished product in the d cycle stock;

The expense of storage cell weight blank in the d cycle stock;

Fixed expense when needs are produced blank;

The unit cost of d cycle blank;

The d cycle unit weight finished product phase of dragging is finished the rejection penalty of unit interval;

D cycle grade of steel is the unit price of the finished product i of g;

The production setup time of finished product;

Variable:

u _d: the available production time in d cycle;

Δ u _d: the remaining available production time in d cycle;

D cycle finished product drag the phase performance;

The d cycle is the finished weight of product as an alternative;

The tank farm stock of finished product during the d end cycle;

The tank farm stock of blank during the d end cycle;

The d cycle is the weight of the finished product i that produces of the blank j of g by grade of steel;

The d cycle is produced the weight that the last cycle drags the phase finished product;

The billet weight of d cycle need of production;

The lot number of d cycle need of production;

The d cycle finishes the last cycle and drags the required production time of phase output;

The production time of d cycle blank processed finished products;

Ts _d: the production setup time summation in d cycle;

Step 2, set up the objective function of Optimized model: Min(f ₁+ f ₂+ f ₃+ f ₄+ f ₅+ f ₆+ f ₇), f wherein ₁, f ₂F ₇Be respectively productive capacity balance rejection penalty, production setting up expenses, stock's occupancy expenses, finished product processing charges, grade of steel substituted expenses, blank cost and order production and drag phase rejection penalty etc.; Wherein,

$f_1=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_d[u_d-\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g)]$

$f_2=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}{\mathrm{cs}}_d{\mathrm{ts}}_d$

$f_3=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}(\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}{\mathrm{cf}}_{\mathrm{id}}^g{\mathrm{If}}_{\mathrm{id}}^g+\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}{\mathrm{cb}}_{\mathrm{jd}}^g{\mathrm{Ib}}_{\mathrm{jd}}^g)$

$f_4=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}{\mathrm{cp}}_{\mathrm{ijd}}^g({\mathrm{mx}}_{\mathrm{ijd}}^g+{\mathrm{me}}_{\mathrm{ijd}}^g)$

$f_5=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}{r}_{\mathrm{id}}^1({\mathrm{sp}}_{\mathrm{id}}^1-{\mathrm{sp}}_{\mathrm{id}}^2)$

$f_6=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}({\mathrm{co}}_{\mathrm{jd}}^g{y}_{\mathrm{jd}}^g+{\mathrm{cr}}_{\mathrm{jd}}^g{\mathrm{my}}_{\mathrm{jd}}^g)$

$f_7=\underset{d=1}{\overset{\mathrm{TNd}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}{\mathrm{ct}}_{\mathrm{id}}^g{\mathrm{qt}}_{\mathrm{id}}^g$

Step 3, be three modules with described steel rolling production planning optimization model partition, be respectively in batches Rolling Production module, supplydemand relationship module and inventory management module, set up respectively the constraint condition of its correlated variables according to three modules;

1) the rolling module of batch

Set up the productive capacity Constraints of Equilibrium according to Rolling Production process actual production time restriction condition and the constraint of available production time:

$\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g)\&le;u_d\&ForAll;d$

$u_d=(T-S_d-{\mathrm{ts}}_d)v_d+{\mathrm{\&Delta;u}}_{d-1}\&ForAll;d$

${\mathrm{\&Delta;u}}_d=\min [u_d-\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}({\mathrm{pe}}_{\mathrm{ijd}}^g+p_{\mathrm{ijd}}^g),{\mathrm{\&sigma;}}_d]\&ForAll;d$

$p_{\mathrm{ijd}}^g=\frac{{\mathrm{mx}}_{\mathrm{ijd}}^g}{{\mathrm{\&gamma;}}_{\mathrm{ijd}}^g}\&ForAll;d,i,j,g$

${\mathrm{pe}}_{\mathrm{ijd}}^g=\frac{{\mathrm{me}}_{\mathrm{ijd}}^g}{{\mathrm{\&gamma;}}_{\mathrm{ijd}}^g}\&ForAll;d,i,j,g$

$\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}{\mathrm{me}}_{\mathrm{ijd}}^g={\mathrm{qt}}_{\mathrm{id}-1}^g\&ForAll;d,i,g$

Set up production setup time constraint according to the operation of rolling:

${\mathrm{ts}}_d={\mathrm{tb}}_i^g\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}\underset{g=1}{\overset{\mathrm{TNg}}{\mathrm{\&Sigma;}}}{y}_{\mathrm{jd}}^g\&ForAll;d$

$S_d+{\mathrm{ts}}_d<T\&ForAll;d$

2) supplydemand relationship module

Set up blank and finished product relation constraint according to blank and finished product relation:

${\mathrm{my}}_{\mathrm{jd}}^g={\mathrm{nb}}_{\mathrm{jd}}^g{\mathrm{mb}}_{\mathrm{jd}}^g\&ForAll;d,j,g$

$\underset{i=1}{\overset{\mathrm{TNi}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^g+{\mathrm{me}}_{\mathrm{ijd}}^g)={\mathrm{my}}_{\mathrm{jd}}^g\&ForAll;d,j,g$

$y_{\mathrm{jd}}^g=\left\{\begin{array}{c}1,\mathrm{when}{\mathrm{nb}}_{\mathrm{jd}}^g>0\\ 0,\mathrm{when}{\mathrm{nb}}_{\mathrm{jd}}^g=0\end{array}\right.\&ForAll;d,j,g$

3) inventory management module

Drag the phase the relationship between quantities to set up tank farm stock and drag phase amount constraint according to product library storage and production:

${\mathrm{Ib}}_{\mathrm{jd}}^g={\mathrm{Ib}}_{\mathrm{jd}-1}^g+{\mathrm{mp}}_{\mathrm{jd}}^g-{\mathrm{my}}_{\mathrm{jd}}^g\&ForAll;d,j,g$

${\mathrm{If}}_{\mathrm{id}}^1=\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^1+{\mathrm{me}}_{\mathrm{ijd}}^1)+{\mathrm{If}}_{\mathrm{id}-1}^1-({\mathrm{mo}}_{\mathrm{id}}^1-{\mathrm{qt}}_{\mathrm{id}}^1)-r_{\mathrm{id}}^1-{\mathrm{qt}}_{\mathrm{id}-1}^1\&ForAll;d,i$

${\mathrm{If}}_{\mathrm{id}}^2=\underset{j=1}{\overset{\mathrm{TNj}}{\mathrm{\&Sigma;}}}({\mathrm{mx}}_{\mathrm{ijd}}^2+{\mathrm{me}}_{\mathrm{ijd}}^2)+{\mathrm{If}}_{\mathrm{id}-1}^2+r_{\mathrm{id}}^1-({\mathrm{mo}}_{\mathrm{id}}^2-{\mathrm{qt}}_{\mathrm{id}}^2)-{\mathrm{qt}}_{\mathrm{id}-1}^2\&ForAll;d,i$

Step 4, set up its dependent variable in the process according to model, such as safety inventory, time, quality, expense etc., set up constraints of variable ranges:

${\mathrm{Ib}}_{\mathrm{jd}}^g\&GreaterEqual;{\mathrm{Ibs}}_{\mathrm{jd}}^g$

${\mathrm{If}}_{\mathrm{id}}^g\&GreaterEqual;{\mathrm{Ifs}}_{\mathrm{id}}^g$

$u_d,{\mathrm{\&Delta;u}}_d,p_{\mathrm{ijd}}^g,{\mathrm{mx}}_{\mathrm{ijd}}^g,{\mathrm{my}}_{\mathrm{jd}}^g,{\mathrm{ts}}_d,r_{\mathrm{id}}^g,{\mathrm{qt}}_{\mathrm{id}}^g\&GreaterEqual;0\&ForAll;d,i,j,g$

$y_{\mathrm{jd}}^g\&Element;\left\{\mathrm{0,1}\right\}\&ForAll;d,j,g$

${\mathrm{nb}}_{\mathrm{jd}}^g\&Element;N\&ForAll;d,j,g$

Step 5, this Optimized model carried out the initialization of constraint condition:

${\mathrm{Ib}}_{j0}^g={\mathrm{Ibs}}_{j0}^g$

${\mathrm{If}}_{i0}^g={\mathrm{Ifs}}_{i0}^g$

${\mathrm{\&Delta;u}}_0={\mathrm{qt}}_{i0}^g=0\&ForAll;i,j,g$

Find the solution in step 6, the data substitution Optimized model with Database Unit in the described Rolling plan optimization subsystem, draw the production planning optimization result.

8. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 5 system, it is characterized in that, described optimum results output unit output system optimum results, and with the input as output optimization information feedback subsystem of the iron and steel Rolling plan optimum results of described Optimization Modeling unit.

9. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 8 system is characterized in that, described output is optimized the information feedback subsystem and comprised:

Optimize information acquisition unit, be used for gathering the optimization Output rusults of optimizing subsystem through the upstream Rolling plan;

Optimize the information analysis processing unit, be used for that this optimum results is carried out appropriate combination and data are processed, analyze and optimum blank coupling, in batches rolling strategy and stock's turnover strategy etc.

10. the in batches rolling schedule with dynamic of many kinds iron and steel according to claim 8 system, it is characterized in that, the information feedback subsystem is optimized in described output, the processing policy of analysis and processing unit is fed back to upstream Rolling plan optimization subsystem, and in conjunction with the dynamic change of material in the production run and order volume, carry out the adjustment of the production schedule, the assurance system is in the situation that minimize the cost production run.

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