CN105844354A - Stochastic model predictive control-based logistics enterprise coal selective delivery optimization method - Google Patents

Abstract

The invention relates to a stochastic model predictive control-based logistics enterprise coal selective delivery optimization method and belongs to the logistics enterprise coal selective delivery technical field. The method includes the following steps that: coal selective delivery information is obtained; stochastic model predictive control is adopted to obtain the optimal selective delivery schemes of each supply cycle within in a prediction length according to the obtained coal selective delivery information; and the optimal selective delivery scheme of the first supply cycle within in the prediction length T is adopted as the coal selective delivery scheme of a logistics enterprise, and the logistics enterprise carries out coal selective delivery according to the above scheme until a supply cycle is terminated. With the method of the invention adopted, the logistics enterprise can ensure maximum benefits with demands of coal power plants satisfied; and relatively low coal total inventory can be kept constantly, the demands of the coal power plants can be satisfied more frequently. The method can bring more profits compared with other decision-making methods.

Description

Optimization method is sent in loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL apolegamy

Technical field

The invention belongs to the Coal Logistics Enterprise apolegamy feed technique field to coal, be specifically related to a kind of based on stochastic model Optimization method is sent in the loglstics enterprise coal apolegamy of PREDICTIVE CONTROL.

Background technology

Coal selects delivery process mainly to include tripartite, i.e. coal supply side, loglstics enterprise and coal demand side；Due to coal Being bulk supply tariff, loglstics enterprise is in occupation of increasingly consequence in coal selects delivery process, and therefore government vigorously supports The development of Coal Logistics industry；Ordinary circumstance, loglstics enterprise needs to be responsible for seeking coal demand enterprise by all means, such as electricity Factory, steel mill, cement plant etc., then both sides decide through consultation coal supply amount, and the coal that final decision is concrete selects distribution project, including choosing Select coal supply scheme, traffic program, decision Coal Blending Schemes etc.；Different cultivars coal is compounded in together by coal blending exactly, so Both the strictest environment protection emission index and the different requirements to coal index of the different parties in request boiler it be adapted to, The coal-fired cost of party in request can also be reduced.

Coal apolegamy send optimization to be how by finding coal-fired party in request's (power plant, steel mill etc.), selecting coal to supply Side, arrangement Coal Transport mode and route, formulation coal blending plan reduce whole coal as far as possible and select the operation cost of delivery process, Make resource obtain more rationally effective configuration simultaneously；Therefore, optimizing coal selects distribution project can reach to increase business economic The purpose of benefit, can make again coal apolegamy send system high efficiency to operate, and energy-saving and emission-reduction adapt to the requirement of social sustainable development.

Existing coal selects means of distribution, set up coal apolegamy send system optimization model during, due to coal match Pass through journey complicated, cause optimized variable and various constraints too much, such as demand constraint, coal blending capacity consistency and supply Constraint etc.；Sending the stage in each apolegamy, the price fluctuation of coal and coal demand have random nature, and each rank Section coal quantity in stock is also dynamic, and existing coal selects delivery process not account for price and the change of power plant's demand Change so that loglstics enterprise keeps the highest warehouse amount of storage during charcoal is delivered coal in whole apolegamy, but often occur to meet The situation of power plant's coal demand, and cause enterprise income to reduce.

Summary of the invention

For the deficiencies in the prior art, the present invention proposes the choosing of a kind of loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL Dispensing optimization method, can remain the total quantity in stock of relatively low coal reaching loglstics enterprise, meet power plant's coal demand, carry The purpose of high enterprise income.

Optimization method is sent in the apolegamy of a kind of loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL, comprises the following steps:

Step 1, obtain coal and select distribution information, specifically include following steps:

Step 1.1, obtain the coal supply type of each coal supply side, if discount type coal supply, then obtain folding Button coal supply information, if no allowance type coal supply, then obtains no allowance coal supply information；

Described discount coal supply information includes: the supply kind of coal in each supply cycle, every kind of coal Q factor, the additivity Q factor number of every kind of coal, the nonadditivity Q factor number of every kind of coal, every kind of coal Price, the Maximum Supply Quantity of every kind of coal, coal minimum total supply, obtain the coal minimum total supply of discount, discount Rate, Coal Procurement amount are less than compensation payment during coal minimum total supply and supply cycle total number；

Described no allowance coal supply information includes: the supply kind of coal, the every kind of coal in each supply cycle Additivity Q factor number, the nonadditivity Q factor number of every kind of coal, the Q factor of every kind of coal, every kind of coal The price of charcoal, the Maximum Supply Quantity of every kind of coal, coal minimum total supply and supply cycle total number；

Step 1.2, the logistics information of acquisition loglstics enterprise, particularly as follows:

The number of discount coal supply information, the number of no allowance coal supply information in each supply cycle, it is used for Storage mixes the warehouse capacity of front variety classes coal, for storing the warehouse capacity of coal, the maximum of coal distributor after mixing Mix quantity, be distributed to the coal minimum supply of Mei Jia power plant and be distributed to the coal Maximum Supply Quantity of Mei Jia power plant；

Step 1.3, obtain the coal demand information of power plant in each supply cycle, particularly as follows:

The Q factor of coal is required and Mei Jia power plant coal demand by power plant's number of demand coal, Mei Jia power plant Probability distribution；

Step 2, select distribution information according to the coal obtained, each in using stochastic model PREDICTIVE CONTROL to obtain prediction length The optimum coal of supply cycle selects distribution project, specifically comprises the following steps that

Step 2.1, set prediction length as T；

The parameter of the stochastic model PREDICTIVE CONTROL of each supply cycle in step 2.2, initialization prediction length T, particularly as follows:

Set serial number i of coal supply side；Serial number j of coal kind；Serial number k of power plant；Time serial number t； Current time is t₀；KJia power plant is D to the total demand of coal_kt；The coal demand upper limit of the Mei Jia power plant obtained isThe coal demand lower limit of the Mei Jia power plant obtained isAt t₀Moment, the demand to prediction length Nei Meijia power plant D_ktRandom distribution be predicted asLoglstics enterprise can meet the general of the coal demand of kJia power plant in whole prediction length The upper bound of rate isThe lower bound of the probability that loglstics enterprise can meet the coal demand of kJia power plant in whole prediction length isRandom distribution is predicted?Supremum value under probabilistic confidence isRandom distribution is predicted?Probability Minimum floor value under confidence level is

Step 2.3, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise jth of each supply cycle Plant the quantity in stock of quality coal；

Formula is as follows:

${\mathrm{\Ψ}}_{j(t+1)}={\mathrm{\Ψ}}_{jt}+\underset{i\∈I}{\Σ}{u}_{ijt}-\underset{k\∈K}{\Σ}{B}_{jkt}-\underset{k\∈K}{\Σ}{f}_{jkt}---\left(1\right)$

Wherein, Ψ_j(t+1)Represent t+1 moment, the quantity in stock of loglstics enterprise jth kind quality coal；Ψ_jtWhen representing t Carve, the quantity in stock of loglstics enterprise jth kind quality coal；I represents the set of all coal supply sides；u_ijtRepresent loglstics enterprise from The coal total amount of i-th coal supply side buying jth kind coal；K represents the set of all power plant k；B_jktRepresent that loglstics enterprise is The consumption of jth kind coal during kth man coal blending at power plant；f_jktRepresent that loglstics enterprise is that kJia power plant directly prepares to need not mixed processing The total amount of jth kind coal；

Step 2.4, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise of each supply cycle is Power plant of k family carries out the quantity in stock of the coal provided and delivered after preparing to need mixed processing；

Formula is as follows:

$C_{k(t+1)}=C_{kt}+\underset{j\∈J}{\Σ}{B}_{jkt}-Y_{kt},\mathrm{\Δ}t=0,1,...,T-1;---\left(2\right)$

Wherein, C_k(t+1)Representing the t+1 moment, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；C_ktRepresent T, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；J represents the set of all coal kind j；Y_ktRepresent Loglstics enterprise is the coal amount carrying out after kJia power plant prepares mixed processing providing and delivering；Δ t represents current time t₀With prediction length Interval supply cycle number between interior future time instance t；

Step 2.5, the loglstics enterprise jth kind quality coal of each supply cycle in prediction length T obtained according to prediction The quantity in stock of charcoal and the loglstics enterprise of each supply cycle are the coal carrying out after kJia power plant prepares to need mixed processing providing and delivering Quantity in stock, in using the mode of founding mathematical models to describe prediction length T the coal of each supply cycle select delivery process:

Described mathematical model, sets up process as follows:

Step 2.5.1, the parameter of mathematical model is set, including:

Serial number l of coal quality parameter is set；Loglstics enterprise is from the coal of i-th coal supply side buying jth kind coal Charcoal total amount purchase cost is A_ijt；In t, the unit cost that coal is transported to loglstics enterprise from coal supply side i is Φ_it；In t, loglstics enterprise, the unit carrying cost of coal is F_t；In each moment, it is impossible to meet kJia power plant demand Unit shortage cost be G_k；In t, the unit coal blending cost needed for the coal distributor of loglstics enterprise runs is H_t；When t Carving, the unit income obtained by Mei Jia power plant dispensing coal is N_t；In t, loglstics enterprise pays i-th discount moulded coal charcoal The down payment of supplier is O_it；

Step 2.5.2, the Distribution logistics that selects according to the coal of supply cycle each in prediction length T limit, and set mathematics The decision variable of model is:

[t, t+1) in supply cycle, loglstics enterprise is from the coal total amount of i-th coal supply side buying jth kind coal u_ijt；Loglstics enterprise is total amount f that kJia power plant directly prepares need not the jth kind coal of mixed processing_jkt；Loglstics enterprise is kth What power plant of family prepared to carry out after mixed processing to provide and deliver coal amount Y_kt；The use of jth kind coal when loglstics enterprise is kth man coal blending at power plant Amount B_jkt；Whether the coal amount that loglstics enterprise is purchased from i-th discount moulded coal charcoal supplier exceedes each supply cycle minimum supply The 0-1 variable s of amount_itIf, equal to 1 when exceeding, otherwise equal to 0；Whether loglstics enterprise is from i-th coal supply side buying coal 0-1 variable Z_itIf, equal to 1 during buying, otherwise equal to 0；

Step 2.5.3, set up prediction length T in the coal of each supply cycle match the mathematical model that send；

Object function is as follows:

Set current time as t₀, work as t₀During≤Z-T+1:

$X=\underset{\mathrm{\Δ}t=0}{\overset{T-1}{\Σ}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\Δ}t}}+U_{t_0},\mathrm{\Δ}t=t-t_0---\left(3\right)$

As Z-T+2≤t₀During≤Z:

$X=\underset{\mathrm{\Δ}t=0}{\overset{Z-t_0}{\Σ}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\Δ}t}}+U_{t_0},\mathrm{\Δ}t=t-t_0---\left(4\right)$

Wherein, Z represents supply cycle total number；In X represents prediction length T, loglstics enterprise is required in coal apolegamy is sent The totle drilling cost wanted；P_tIn representing each supply cycle, the Coal Procurement cost of loglstics enterprise；Q_tIn representing each supply cycle, thing Stream enterprise Coal Procurement amount is less than the compensation of required payment during minimum supply；R_tIn representing each supply cycle, coal from Coal supply side is transported to the cost of transportation required for loglstics enterprise；S_tRepresent the carrying cost of coal in each supply cycle；V_t Represent the coal distributor operating cost of loglstics enterprise in each supply cycle；W_tIn representing each supply cycle, loglstics enterprise is The income that power plant's dispensing coal is obtained；R represents the discount rate in each supply cycle；Represent [t₀, t₀+ 1) supply cycle In, the shortage cost summation that loglstics enterprise causes owing to cannot meet the demand of each power plant, wherein can not meet kth household electrical appliances The shortage cost of factory's demand is unit shortage cost G_kProduct with total amount in short supply；

Step 2.5.4, the coal supply restriction of each coal supply side according to cycle each in prediction length T, logistics The coal saving of enterprise limits and the coal demand of Mei Jia power plant limits, and sets the constraints of mathematical model, specific as follows:

As follows to the constraints of coal supply side:

(1) within each supply cycle, loglstics enterprise supplies less than coal to the Coal Procurement total amount of any coal supply side Should the minimum supply of square each supply cycle, then this coal supply side does not provide coal；

(2) within each supply cycle, loglstics enterprise exceedes to the Coal Procurement total amount of any discount moulded coal charcoal supplier The coal minimum total supply of the acquisition discount of this coal supply side, then this coal supply side provides coal according to discounted cost, Otherwise this coal supply side provides coal according to original cost；

As follows to the constraints of loglstics enterprise:

(3) within each supply cycle, the coal backlog total for the mixing of kJia power plant mixes coal less than or equal to storage Storage capacity；

(4) within each supply cycle, the coal backlog total of jth kind coal is less than or equal to the storage capacity of this kind of coal；

(5) within each supply cycle, if the coal of a kind is unsatisfactory for power plant's requirement to coal quality parameter, then This coal can not be routed directly to power plant；

(6) within each supply cycle, the coal total amount of any kind of buying provides less than or equal to coal supply side Maximum quantity；

(7) within each supply cycle, the demand model that the coal total amount that loglstics enterprise is provided and delivered to power plant is predicted in this power plant In enclosing；

(8) within each supply cycle, nonadditivity Q factor does not meets the coal of the requirement of power plant and can not mix Conjunction processes；

(9) within each supply cycle, mixed coal every additivity Q factor is satisfied by power plant to coal Requirement；

(10) within each supply cycle, to the coal blending total amount of all power plant less than or equal to the maximum mixed number of coal distributor Amount；

As follows to the constraints of power plant:

(11) within each supply cycle, the coal actual demand amount of power plant is in the range of needs that it is predicted；

The constraint that whole coal selects delivery process is as follows:

(12) decision variable in whole prediction length is more than or equal to 0；

(13) loglstics enterprise supply cycle [t, t+1) in the constraints of coal of power plant's dispensing be: in each confession Should be in the cycle, the coal total amount that loglstics enterprise is provided and delivered to power plant is in the supremum of power plant's coal demand correspondence probabilistic confidence Between value and minimum floor value；

Step 2.6, according to relation between general expenses and decision variable in the mathematical model set up, in constraints The optimum coal of each supply cycle in distribution project corresponding during lower acquisition object function minimum, i.e. acquisition prediction length T Select distribution project；

In step 3, employing prediction length T, the optimum coal of first supply cycle selects distribution project as loglstics enterprise Coal selects distribution project, and loglstics enterprise carries out coal apolegamy according to the program and send；

Step 4, judge whether the supply cycle number sent of coal apolegamy reaches supply cycle total number of coal supply side, The most then the apolegamy of loglstics enterprise stopping coal being sent, and otherwise, returns and performs step 2.

In each supply cycle described in step 2.5.3, the Coal Procurement cost P of loglstics enterprise_t, employing below equation:

$P_t=\underset{i\∈m}{\Σ}{\mathrm{\η}}_i\underset{j\∈J}{\Σ}{A}_{ijt}\·u_{ijt}+\underset{i\∈n}{\Σ}\underset{j\∈J}{\Σ}{A}_{ijt}\·u_{ijt}---\left(5\right)$

Wherein, m represents coal supply side's set that coal supply type is discount type；η_iRepresent that the coal of loglstics enterprise is adopted When the amount of purchasing is more than the coal minimum total supply obtaining discount, the special discount rate given by coal supply side；A_ijtRepresent logistics Enterprise is from the unit procurement cost of the coal total amount of i-th coal supply side buying jth kind coal；N represents coal supply type Coal supply side for no allowance type gathers.

Carrying cost S of coal in each supply cycle described in step 2.5.3_t, employing below equation:

$S_t=F_t\·\{\underset{k\∈K}{\Σ}\frac{C_{kt}+C_{k(t+1)}}{2}+\underset{j\∈J}{\Σ}\frac{{\mathrm{\Ψ}}_{jt}+{\mathrm{\Ψ}}_{j(t+1)}}{2}\}---\left(6\right)$

Wherein, F_tRepresent the unit carrying cost of coal in t, loglstics enterprise.

In step 2.5.4 described in constraints (13) within each supply cycle, the coal that loglstics enterprise is provided and delivered to power plant Charcoal total amount, between the supremum value and minimum floor value of power plant's coal demand correspondence probabilistic confidence, uses following public Formula:

${\underset{\‾}{\mathrm{\γ}}}_{t|t_0}\≤Y_{kt}+\underset{j\∈J}{\Σ}{f}_{ikt}\≤{\stackrel{\‾}{\mathrm{\γ}}}_{t|t_0},\∀k\∈K,\mathrm{\Δ}t=1,2,...,T-1---\left(7\right)$

Wherein,WithObtained by following equation:

${\underset{\‾}{p}}_k\≤\mathrm{Pr}\{Y_{kt}+\underset{j\∈J}{\Σ}{f}_{jkt}\≥D_{kt}\}\≤{\stackrel{\‾}{p}}_k,\∀k\∈K,\mathrm{\Δ}t=1,2,...,T-1---\left(8\right)$

$\mathrm{Pr}\{D_{kt}\≤{\underset{\‾}{\mathrm{\γ}}}_{t|t_0}\}={\underset{\‾}{p}}_k,\∀k\∈K,\mathrm{\Δ}t=1,2,...,T-1---\left(9\right)$

$\mathrm{Pr}\{D_{kt}\≤{\stackrel{\‾}{\mathrm{\γ}}}_{t|t_0}\}={\stackrel{\‾}{p}}_k,\∀k\∈K,\mathrm{\Δ}t=1,2,...,T-1---\left(10\right).$

Advantages of the present invention:

The present invention provides the apolegamy of a kind of loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL to send optimization method, use with Machine Model Predictive Control processes loglstics enterprise coal and selects the decision content optimization in delivery process, and general according to power plant's coal demand Rate distribution shifts is soft-constraint condition, so that loglstics enterprise ensures in the case of meeting power plant's coal demand as far as possible Big income；Loglstics enterprise can remain the total quantity in stock of relatively low coal simultaneously, and meets power plant's coal demand Number of times be greatly improved, compared to other decision methods, there is higher income.

Accompanying drawing explanation

Fig. 1 is that optimization side is sent in loglstics enterprise coal based on the stochastic model PREDICTIVE CONTROL apolegamy of an embodiment of the present invention Method flow chart；

Fig. 2 is the stochastic model PREDICTIVE CONTROL Principle of Process figure of an embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings an embodiment of the present invention is described further.

In the embodiment of the present invention, optimization method is sent in the apolegamy of a kind of loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL, Method flow diagram is as it is shown in figure 1, comprise the following steps:

Step 1, obtain coal and select distribution information, specifically include following steps:

Step 1.1, obtain the coal supply type of each coal supply side, if discount type coal supply, then obtain folding Button coal supply information, if no allowance type coal supply, then obtains no allowance coal supply information；

Described discount coal supply information includes: the supply kind of coal in each supply cycle, every kind of coal Q factor, the additivity Q factor number of every kind of coal, the nonadditivity Q factor number of every kind of coal, every kind of coal Price, the Maximum Supply Quantity of every kind of coal, coal minimum total supply, obtain the coal minimum total supply of discount, discount Rate, Coal Procurement amount are less than compensation payment during coal minimum total supply and supply cycle total number；

Described no allowance coal supply information includes: the supply kind of coal, the every kind of coal in each supply cycle Additivity Q factor number, the nonadditivity Q factor number of every kind of coal, the Q factor of every kind of coal, every kind of coal The price of charcoal, the Maximum Supply Quantity of every kind of coal, coal minimum total supply and supply cycle total number；

Step 1.2, the logistics information of acquisition loglstics enterprise, particularly as follows:

The number of discount coal supply information, the number of no allowance coal supply information in each supply cycle, it is used for Storage mixes the warehouse capacity of front variety classes coal, for storing the warehouse capacity of coal, the maximum of coal distributor after mixing Mix quantity, be distributed to the coal minimum supply of Mei Jia power plant and be distributed to the coal Maximum Supply Quantity of Mei Jia power plant；

Step 1.3, obtain the coal demand information of power plant in each supply cycle, particularly as follows:

The Q factor of coal is required and Mei Jia power plant coal demand by power plant's number of demand coal, Mei Jia power plant Probability distribution；

Step 2, select distribution information according to the coal obtained, each in using stochastic model PREDICTIVE CONTROL to obtain prediction length The optimum coal of supply cycle selects distribution project, stochastic model PREDICTIVE CONTROL Principle of Process figure as in figure 2 it is shown, concrete steps such as Under:

Step 2.1, set prediction length as T；

The parameter of the stochastic model PREDICTIVE CONTROL of each supply cycle in step 2.2, initialization prediction length T, particularly as follows:

Set serial number i of coal supply side；Serial number j of coal kind；Serial number k of power plant；Time serial number t； Current time is t₀；KJia power plant is D to the total demand of coal_kt；The coal demand upper limit of the Mei Jia power plant obtained isThe coal demand lower limit of the Mei Jia power plant obtained isAt t₀Moment, the demand to prediction length Nei Meijia power plant D_ktRandom distribution be predicted asLoglstics enterprise can meet the general of the coal demand of kJia power plant in whole prediction length The upper bound of rate isThe lower bound of the probability that loglstics enterprise can meet the coal demand of kJia power plant in whole prediction length isRandom distribution is predicted?Supremum value under probabilistic confidence isRandom distribution is predicted?Probability Minimum floor value under confidence level is

Step 2.3, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise jth of each supply cycle Plant the quantity in stock of quality coal；

Formula is as follows:

${\mathrm{\Ψ}}_{j(t+1)}={\mathrm{\Ψ}}_{jt}+\underset{i\∈I}{\Σ}{u}_{ijt}-\underset{k\∈K}{\Σ}{B}_{jkt}-\underset{k\∈K}{\Σ}{f}_{jkt}---\left(1\right)$

Wherein, Ψ_j(t+1)Represent t+1 moment, the quantity in stock of loglstics enterprise jth kind quality coal；Ψ_jtWhen representing t Carve, the quantity in stock of loglstics enterprise jth kind quality coal；I represents the set of all coal supply sides；u_ijtRepresent loglstics enterprise from The coal total amount of i-th coal supply side buying jth kind coal；K represents the set of all power plant k；B_jktRepresent that loglstics enterprise is The consumption of jth kind coal during kth man coal blending at power plant；f_jktRepresent that loglstics enterprise is that kJia power plant directly prepares to need not mixed processing The total amount of jth kind coal；

Step 2.4, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise of each supply cycle is Power plant of k family carries out the quantity in stock of the coal provided and delivered after preparing to need mixed processing；

Formula is as follows:

$C_{k(t+1)}=C_{kt}+\underset{j\∈J}{\Σ}{B}_{jkt}-Y_{kt},\mathrm{\Δ}t=0,1,...,T-1;---\left(2\right)$

Wherein, C_k(t+1)Representing the t+1 moment, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；C_ktRepresent T, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；J represents the set of all coal kind j；Y_ktRepresent Loglstics enterprise is the coal amount carrying out after kJia power plant prepares mixed processing providing and delivering；Δ t represents current time t₀With prediction length Interval supply cycle number between interior future time instance t；

Step 2.5, the loglstics enterprise jth kind quality coal of each supply cycle in prediction length T obtained according to prediction The quantity in stock of charcoal and the loglstics enterprise of each supply cycle are the coal carrying out after kJia power plant prepares to need mixed processing providing and delivering Quantity in stock, in using the mode of founding mathematical models to describe prediction length T the coal of each supply cycle select delivery process:

Described mathematical model, sets up process as follows:

Step 2.5.1, the parameter of mathematical model is set, including:

Serial number l of coal quality parameter is set；Loglstics enterprise is from the coal of i-th coal supply side buying jth kind coal Charcoal total amount purchase cost is A_ijt；In t, the unit cost that coal is transported to loglstics enterprise from coal supply side i is Φ_it；In t, loglstics enterprise, the unit carrying cost of coal is F_t；In each moment, it is impossible to meet kJia power plant demand Unit shortage cost be G_k；In t, the unit coal blending cost needed for the coal distributor of loglstics enterprise runs is H_t；When t Carving, the unit income obtained by Mei Jia power plant dispensing coal is N_t；In t, loglstics enterprise pays i-th discount moulded coal charcoal The down payment of supplier is O_it；

Step 2.5.2, the Distribution logistics that selects according to the coal of supply cycle each in prediction length T limit, and set mathematics The decision variable of model is:

[t, t+1) in supply cycle, loglstics enterprise is from the coal total amount of i-th coal supply side buying jth kind coal u_ijt；Loglstics enterprise is total amount f that kJia power plant directly prepares need not the jth kind coal of mixed processing_jkt；Loglstics enterprise is kth What power plant of family prepared to carry out after mixed processing to provide and deliver coal amount Y_kt；The use of jth kind coal when loglstics enterprise is kth man coal blending at power plant Amount B_jkt；Whether the coal amount that loglstics enterprise is purchased from i-th discount moulded coal charcoal supplier exceedes each supply cycle minimum supply The 0-1 variable s of amount_itIf, equal to 1 when exceeding, otherwise equal to 0；Whether loglstics enterprise is from i-th coal supply side buying coal 0-1 variable Z_itIf, equal to 1 during buying, otherwise equal to 0；

Step 2.5.3, set up prediction length T in the coal of each supply cycle match the mathematical model that send；

Object function is as follows:

Set current time as t₀, work as t₀During≤Z-T+1:

$X=\underset{\mathrm{\Δ}t=0}{\overset{T-1}{\Σ}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\Δ}t}}+U_{t_0},\mathrm{\Δ}t=t-t_0---\left(3\right)$

As Z-T+2≤t₀During≤Z:

$X=\underset{\mathrm{\Δ}t=0}{\overset{Z-t_0}{\Σ}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\Δ}t}}+U_{t_0},\mathrm{\Δ}t=t-t_0---\left(4\right)$

Wherein, Z represents supply cycle total number；In X represents prediction length T, loglstics enterprise is required in coal apolegamy is sent The totle drilling cost wanted；P_tIn representing each supply cycle, the Coal Procurement cost of loglstics enterprise；Q_tIn representing each supply cycle, thing Stream enterprise Coal Procurement amount is less than the compensation of required payment during minimum supply；R_tIn representing each supply cycle, coal from Coal supply side is transported to the cost of transportation required for loglstics enterprise；S_tRepresent the carrying cost of coal in each supply cycle；V_t Represent the coal distributor operating cost of loglstics enterprise in each supply cycle；W_tIn representing each supply cycle, loglstics enterprise is The income that power plant's dispensing coal is obtained；R represents the discount rate in each supply cycle；Represent [t₀, t₀+ 1) supply cycle In, the shortage cost summation that loglstics enterprise causes owing to cannot meet the demand of each power plant, wherein can not meet kth household electrical appliances The shortage cost of factory's demand is unit shortage cost G_kProduct with total amount in short supply；

In above-mentioned object function:

$P_t=\underset{i\∈m}{\Σ}{\mathrm{\η}}_i\underset{j\∈J}{\Σ}{A}_{ijt}\·u_{ijt}+\underset{i\∈n}{\Σ}\underset{j\∈J}{\Σ}{A}_{ijt}\·u_{ijt}---\left(5\right)$

Wherein, m represents coal supply side's set that coal supply type is discount type；η_iRepresent that the coal of loglstics enterprise is adopted When the amount of purchasing is more than the coal minimum total supply obtaining discount, the special discount rate given by coal supply side；A_ijtRepresent logistics Enterprise is from the unit procurement cost of the coal total amount of i-th coal supply side buying jth kind coal；N represents coal supply type Coal supply side for no allowance type gathers；

$Q_t=\underset{i\∈m}{\Σ}{O}_{it}\·(1-s_{it})---\left(11\right);$

$R_t=\underset{i\∈I}{\Σ}{\mathrm{\Φ}}_{it}\·\underset{j\∈J}{\Σ}{u}_{ijt}---\left(12\right);$

$S_t=F_t\·\{\underset{k\∈K}{\Σ}\frac{C_{kt}+C_{k(t+1)}}{2}+\underset{j\∈J}{\Σ}\frac{{\mathrm{\Ψ}}_{jt}+{\mathrm{\Ψ}}_{j(t+1)}}{2}\}---\left(6\right)$

Wherein, F_tRepresent the unit carrying cost of coal in t, loglstics enterprise；

$V_t=H_t\·\underset{k\∈K}{\Σ}\underset{j\∈J}{\Σ}{B}_{jkt}---\left(13\right);$

$W_t=-N_t\·\underset{k\∈K}{\Σ}(Y_{kt}+\underset{j\∈J}{\Σ}{f}_{jkt})---\left(14\right);$

$U_t=\underset{k\∈K}{\Σ}{G}_k\·(D_{kt}-Y_{kt}-\underset{j\∈J}{\Σ}{f}_{jkt})---\left(15\right)$

Wherein, U_tRepresenting within each supply cycle, what loglstics enterprise caused owing to cannot meet the demand of power plant lacks Goods expense；

Step 2.5.4, the coal supply restriction of each coal supply side according to cycle each in prediction length T, logistics The coal saving of enterprise limits and the coal demand of Mei Jia power plant limits, and sets the constraints of mathematical model, specific as follows:

As follows to the constraints of coal supply side:

(1) within each supply cycle, loglstics enterprise supplies less than coal to the Coal Procurement total amount of any coal supply side Should the minimum supply of square each supply cycle, then this coal supply side does not provide coal, and formula is as follows:

$z_{it}{\underset{\‾}{u}}_{it}\≤\underset{j\∈J}{\Σ}{u}_{ijt}\≤z_{it}\underset{j\∈J}{\Σ}{\stackrel{\‾}{u}}_{ijt},z_{it}\∈\{0,1\},\∀i\∈I,\mathrm{\Δ}t=0,1,\mathrm{..},T-1---\left(16\right)$

Wherein, Z_itRepresent [t, t+1) in supply cycle coal supply side i ∈ I whether to loglstics enterprise supply coal 0-1 variable, carries out supplying its value and is equal to 1, otherwise equal to 0；Represent that the minimum coal of coal supply side's each supply cycle supplies Ying Liang；Represent u_ijtMaximum upper limit；

(2) within each supply cycle, loglstics enterprise exceedes to the Coal Procurement total amount of any discount moulded coal charcoal supplier The coal minimum total supply of the acquisition discount of this coal supply side, then this coal supply side provides coal according to discounted cost, Otherwise this coal supply side provides coal according to original cost, and formula is as follows:

$\left\{\begin{array}{c}{\tilde{u}}_{it}\&le;\underset{j\&Element;J}{\&Sigma;}{u}_{ijt}+M(1-s_{it})\\ \underset{j\&Element;J}{\&Sigma;}{u}_{ijt}<{\tilde{u}}_{it}+{\mathrm{Ms}}_{it}\end{array}\right.,s_{it}\&Element;\{0,1\},\mathrm{\&Delta;}t=0,1,\mathrm{...},T-1---\left(17\right)$

Wherein,Represent the coal minimum total supply obtaining discount；M represents and is much larger thanPositive number；

As follows to the constraints of loglstics enterprise:

(3) within each supply cycle, the coal backlog total for the mixing of kJia power plant mixes coal less than or equal to storage Storage capacity, formula is as follows:

$0\&le;C_{kt}\&le;C_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=0,1,\mathrm{...},T-1---\left(18\right)$

Wherein, C_kRepresent the capacity in the warehouse that loglstics enterprise is kJia power plant storage mixing coal；

(4) within each supply cycle, the coal backlog total of jth kind coal is less than or equal to the storage capacity of this kind of coal, Formula is as follows:

$0\&le;{\mathrm{\&Psi;}}_{jt}\&le;{\mathrm{\&Psi;}}_j,\&ForAll;j\&Element;J,\mathrm{\&Delta;}t=0,1,...,T-1---\left(19\right)$

Wherein, Ψ_jRepresent the capacity in the warehouse of loglstics enterprise storage jth kind coal；

(5) within each supply cycle, if the coal of a kind is unsatisfactory for power plant's requirement to coal quality parameter, then This coal can not be routed directly to power plant, and formula is as follows:

$f_{jkt}=0,if{e}_{jl}\&NotElement;\&lsqb;{\underset{\&OverBar;}{E}}_{k1},{\stackrel{\&OverBar;}{E}}_{kl}\&rsqb;,\&ForAll;l\&Element;L,\mathrm{\&Delta;}t=1,2,...,T-1---\left(20\right)$

Wherein, e_jlRepresent the value of l Q factor of jth type coal；Represent that kJia power plant is to the l coal The lower limit requirement of charcoal Q factor；Represent the upper limit requirement to the l coal quality parameter of the kJia power plant；L represents all coals The set of charcoal Q factor；

(6) within each supply cycle, the coal total amount of any kind of buying provides less than or equal to coal supply side Maximum quantity, formula is as follows:

$u_{ijt}\&le;{\stackrel{\&OverBar;}{u}}_{ijt},\&ForAll;i\&Element;I,j\&Element;J,\mathrm{\&Delta;}t=0,1,...,T-1---\left(21\right);$

(7) within each supply cycle, the demand model that the coal total amount that loglstics enterprise is provided and delivered to power plant is predicted in this power plant In enclosing, formula is as follows:

${\underset{\&OverBar;}{D}}_{kt}\&le;Y_{kt}+\underset{j\&Element;J}{\&Sigma;}{f}_{jkt}\&le;{\stackrel{\&OverBar;}{D}}_{kt},\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=0,1,\mathrm{..},T-1---\left(22\right);$

(8) within each supply cycle, nonadditivity Q factor does not meets the coal of the requirement of power plant and can not mix Conjunction processes, and formula is as follows:

$B_{jkt}=0,if{e}_{jl}\&NotElement;\&lsqb;{\underset{\&OverBar;}{E}}_{kl},{\stackrel{\&OverBar;}{E}}_{kl}\&rsqb;,\&ForAll;l\&Element;y,\mathrm{\&Delta;}t=1,2,...,T-1---\left(23\right)$

Wherein, y represents the set of nonadditivity Q factor of coal；

(9) within each supply cycle, mixed coal every additivity Q factor is satisfied by power plant to coal Requirement, formula is as follows:

${\underset{\&OverBar;}{E}}_{kl}\&le;\frac{\underset{j\&Element;J}{\&Sigma;}{B}_{jkt}\&CenterDot;e_{jl}}{\underset{j\&Element;J}{\&Sigma;}{B}_{jkt}}\&le;{\stackrel{\&OverBar;}{E}}_{kl},\&ForAll;l\&Element;x,\mathrm{\&Delta;}t=1,2,...,T-1---\left(24\right)$

Wherein, x represents the set of additivity Q factor of coal；

(10) within each supply cycle, to the coal blending total amount of all power plant less than or equal to the maximum mixed number of coal distributor Amount, formula is as follows:

$\underset{k\&Element;K}{\&Sigma;}\underset{j\&Element;J}{\&Sigma;}{B}_{jkt}\&le;\stackrel{\&OverBar;}{B},\mathrm{\&Delta;}t=0,1,...,T-1---\left(25\right)$

Wherein,In representing each supply cycle, the maximum of coal distributor mixes quantity；

As follows to the constraints of power plant:

(11) within each supply cycle, the coal actual demand amount of power plant is in the range of needs that it is predicted, formula is as follows:

${\underset{\&OverBar;}{D}}_{kt}\&le;D_{kt}\&le;{\stackrel{\&OverBar;}{D}}_{kt},\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=0---\left(26\right);$

The constraint that whole coal selects delivery process is as follows:

(12) decision variable in whole prediction length is more than or equal to 0, and formula is as follows:

$u_{ijt}\&GreaterEqual;0,f_{jkt}\&GreaterEqual;0,Y_{kt}\&GreaterEqual;0,B_{jkt}\&GreaterEqual;0,\&ForAll;j\&Element;J,k\&Element;K,\mathrm{\&Delta;}t=0,1,...,T-1---\left(27\right);$

(13) loglstics enterprise supply cycle [t, t+1) in the constraints of coal of power plant's dispensing be: in each confession Should be in the cycle, the coal total amount that loglstics enterprise is provided and delivered to power plant is in the supremum of power plant's coal demand correspondence probabilistic confidence Between value and minimum floor value, formula is as follows:

${\underset{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0}\&le;Y_{kt}+\underset{j\&Element;J}{\&Sigma;}{f}_{jkt}\&le;{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0},\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,...,T-1---\left(7\right)$

Wherein,WithObtained by following equation:

${\underset{\&OverBar;}{p}}_k\&le;\mathrm{Pr}\{Y_{kt}+\underset{j\&Element;J}{\&Sigma;}{f}_{jkt}\&GreaterEqual;D_{kt}\}\&le;{\stackrel{\&OverBar;}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,...,T-1---\left(8\right)$

$\mathrm{Pr}\{D_{kt}\&le;{\underset{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0}\}={\underset{\&OverBar;}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,...,T-1---\left(9\right)$

$\mathrm{Pr}\{D_{kt}\&le;{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0}\}={\stackrel{\&OverBar;}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,\mathrm{...},T-1---\left(10\right);$

Step 2.6, according to relation between general expenses and decision variable in the mathematical model set up, in constraints The optimum coal of each supply cycle in distribution project corresponding during lower acquisition object function minimum, i.e. acquisition prediction length T Select distribution project；

In step 3, employing prediction length T, the optimum coal of first supply cycle selects distribution project as loglstics enterprise Coal selects distribution project, and loglstics enterprise carries out coal apolegamy according to the program and send；

Step 4, judge whether the supply cycle number sent of coal apolegamy reaches supply cycle total number of coal supply side, The most then the apolegamy of loglstics enterprise stopping coal being sent, and otherwise, returns and performs step 2.

Claims (4)

1. optimization method is sent in a loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL apolegamy, it is characterised in that: include with Lower step:

Step 1, obtain coal and select distribution information, specifically include following steps:

Step 1.1, obtain the coal supply type of each coal supply side, if discount type coal supply, then obtain discount coal Charcoal information provision, if no allowance type coal supply, then obtains no allowance coal supply information；

Described discount coal supply information includes: the supply kind of coal, the quality of every kind of coal in each supply cycle Parameter, the additivity Q factor number of every kind of coal, the nonadditivity Q factor number of every kind of coal, the valency of every kind of coal Lattice, the Maximum Supply Quantity of every kind of coal, coal minimum total supply, acquisition the coal minimum total supply of discount, discount rate, coal Charcoal amount of purchase is less than compensation payment during coal minimum total supply and supply cycle total number；

Described no allowance coal supply information includes: in each supply cycle supply the kind of coal, every kind of coal can Additivity Q factor number, the nonadditivity Q factor number of every kind of coal, the Q factor of every kind of coal, every kind of coal Price, the Maximum Supply Quantity of every kind of coal, coal minimum total supply and supply cycle total number；

Step 1.2, the logistics information of acquisition loglstics enterprise, particularly as follows:

The number of discount coal supply information, the number of no allowance coal supply information in each supply cycle, it is used for storing The warehouse capacity of variety classes coal, the maximum mixing of the warehouse capacity of coal, coal distributor after storing mixing before mixing Quantity, it is distributed to the coal minimum supply of Mei Jia power plant and is distributed to the coal Maximum Supply Quantity of Mei Jia power plant；

Step 1.3, obtain the coal demand information of power plant in each supply cycle, particularly as follows:

The Q factor of coal is required and the probability of Mei Jia power plant coal demand by power plant's number of demand coal, Mei Jia power plant Distribution；

Step 2, select distribution information according to the coal obtained, use stochastic model PREDICTIVE CONTROL to obtain each supply in prediction length The optimum coal in cycle selects distribution project, specifically comprises the following steps that

Step 2.1, set prediction length as T；

The parameter of the stochastic model PREDICTIVE CONTROL of each supply cycle in step 2.2, initialization prediction length T, particularly as follows:

Set serial number i of coal supply side；Serial number j of coal kind；Serial number k of power plant；Time serial number t；Currently Moment is t₀；KJia power plant is D to the total demand of coal_kt；The coal demand upper limit of the Mei Jia power plant obtained isObtain The coal demand lower limit of the Mei Jia power plant obtained isD _kt；At t₀Moment, demand D to prediction length Nei Meijia power plant_ktWith Machine forecast of distribution isLoglstics enterprise can meet the upper bound of the probability of the coal demand of kJia power plant in whole prediction length ForThe lower bound of the probability that loglstics enterprise can meet the coal demand of kJia power plant in whole prediction length isp _k；Random point Cloth is predicted?Supremum value under probabilistic confidence isRandom distribution is predicted?p _kUnder probabilistic confidence Minimum floor value is

Step 2.3, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise jth kind product of each supply cycle The quantity in stock of matter coal；

Formula is as follows:

${\mathrm{\&Psi;}}_{j(t+1)}={\mathrm{\&Psi;}}_{jt}+\underset{i\&Element;I}{\mathrm{\&Sigma;}}{u}_{ijt}-\underset{k\&Element;K}{\mathrm{\&Sigma;}}{B}_{jkt}-\underset{k\&Element;K}{\mathrm{\&Sigma;}}{f}_{jkt}---\left(1\right)$

Wherein, Ψ_j(t+1)Represent t+1 moment, the quantity in stock of loglstics enterprise jth kind quality coal；Ψ_itRepresent t, thing The quantity in stock of stream enterprise jth kind quality coal；I represents the set of all coal supply sides；u_ijtRepresent that loglstics enterprise is from i-th The coal total amount of coal supply side's buying jth kind coal；K represents the set of all power plant k；B_jktRepresent that loglstics enterprise is kth man The consumption of jth kind coal during coal blending at power plant；f_jktRepresent that loglstics enterprise is the jth that kJia power plant directly prepares need not mixed processing Plant the total amount of coal；

Step 2.4, parameter according to step 2.2, it was predicted that in prediction length T, the loglstics enterprise of each supply cycle is kth man Power plant carries out the quantity in stock of the coal provided and delivered after preparing to need mixed processing；

Formula is as follows:

$C_{k(t+1)}=C_{kt}+\underset{j\&Element;J}{\mathrm{\&Sigma;}}{B}_{jkt}-Y_{kt},\mathrm{\&Delta;}t=0,1,...,T-1;---\left(2\right)$

Wherein, C_k(t+1)Representing the t+1 moment, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；C_ktWhen representing t Carving, loglstics enterprise is the quantity in stock of the coal of kJia power plant dispensing；J represents the set of all coal kind j；Y_ktRepresent logistics Enterprise is the coal amount carrying out after kJia power plant prepares mixed processing providing and delivering；Δ t represents current time t₀With in prediction length not Carry out the interval supply cycle number between moment t；

Step 2.5, the loglstics enterprise jth kind quality coal of each supply cycle in prediction length T obtained according to prediction The loglstics enterprise of quantity in stock and each supply cycle is the storehouse of coal carrying out after kJia power plant prepares to need mixed processing providing and delivering Storage, in using the mode of founding mathematical models to describe prediction length T the coal of each supply cycle select delivery process:

Described mathematical model, sets up process as follows:

Step 2.5.1, the parameter of mathematical model is set, including:

Serial number l of coal quality parameter is set；Loglstics enterprise is total from the coal of i-th coal supply side buying jth kind coal Amount purchase cost is A_ijt；In t, the unit cost that coal is transported to loglstics enterprise from coal supply side i is Ф_it；? T, in loglstics enterprise, the unit carrying cost of coal is F_t；In each moment, it is impossible to meet the unit of kJia power plant demand Shortage cost is G_k；In t, the unit coal blending cost needed for the coal distributor of loglstics enterprise runs is H_t；In t, for often The unit income that power plant of family dispensing coal is obtained is N_t；In t, loglstics enterprise pays i-th discount moulded coal charcoal supplier Down payment be O_it；

Step 2.5.2, the Distribution logistics that selects according to the coal of supply cycle each in prediction length T limit, and set mathematical model Decision variable be:

[t, t+1) in supply cycle, loglstics enterprise is from coal total amount u of i-th coal supply side buying jth kind coal_ijt； Loglstics enterprise is total amount f that kJia power plant directly prepares need not the jth kind coal of mixed processing_jkt；Loglstics enterprise is kth household electrical appliances What factory prepared to carry out after mixed processing to provide and deliver coal amount Y_kt；The consumption of jth kind coal when loglstics enterprise is kth man coal blending at power plant B_jkt；Whether the coal amount that loglstics enterprise is purchased from i-th discount moulded coal charcoal supplier exceedes each supply cycle minimum supply 0-1 variable s_itIf, equal to 1 when exceeding, otherwise equal to 0；Whether loglstics enterprise is from the 0-of i-th coal supply side buying coal 1 variable z_itIf, equal to 1 during buying, otherwise equal to 0；

Step 2.5.3, set up prediction length T in the coal of each supply cycle match the mathematical model that send；

Object function is as follows:

Set current time as t₀, work as t₀During≤Z-T+1:

$X=\underset{\mathrm{\&Delta;}t=0}{\overset{T-1}{\&Sigma;}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\&Delta;}t}}+U_{t_0},\mathrm{\&Delta;}t=t-t_0---\left(3\right)$

As Z-T+2≤t₀During≤Z:

$X=\underset{\mathrm{\&Delta;}t=0}{\overset{Z-t_0}{\&Sigma;}}\frac{P_t+Q_t+R_t+S_t+V_t+W_t}{{(1+r)}^{\mathrm{\&Delta;}t}}+U_{t_0},\mathrm{\&Delta;}t=t-t_0---\left(4\right)$

Wherein, Z represents supply cycle total number；In X represents prediction length T, loglstics enterprise is required in coal apolegamy is sent Totle drilling cost；P_tIn representing each supply cycle, the Coal Procurement cost of loglstics enterprise；Q_tIn representing each supply cycle, logistics is looked forward to Industry Coal Procurement amount is less than the compensation of required payment during minimum supply；R_tIn representing each supply cycle, coal is from coal Supplier is transported to the cost of transportation required for loglstics enterprise；S_tRepresent the carrying cost of coal in each supply cycle；V_tRepresent The coal distributor operating cost of loglstics enterprise in each supply cycle；W_tIn representing each supply cycle, loglstics enterprise is power plant The income that dispensing coal is obtained；R represents the discount rate in each supply cycle；Represent [t₀, t₀+ 1) in supply cycle, thing The shortage cost summation that stream enterprise causes owing to cannot meet the demand of each power plant；

Step 2.5.4, the coal supply restriction of each coal supply side according to cycle each in prediction length T, loglstics enterprise Coal saving limit and Mei Jia power plant coal demand limit, set mathematical model constraints, specific as follows:

As follows to the constraints of coal supply side:

(1) within each supply cycle, loglstics enterprise is less than coal supply side to the Coal Procurement total amount of any coal supply side The minimum supply of each supply cycle, then this coal supply side does not provide coal；

(2) within each supply cycle, loglstics enterprise exceedes this coal to the Coal Procurement total amount of any discount moulded coal charcoal supplier The coal minimum total supply of the acquisition discount of charcoal supplier, then this coal supply side provides coal according to discounted cost, otherwise This coal supply side provides coal according to original cost；

As follows to the constraints of loglstics enterprise:

(3) within each supply cycle, the coal backlog total for the mixing of kJia power plant is less than or equal to store the storehouse of mixing coal Capacity；

(4) within each supply cycle, the coal backlog total of jth kind coal is less than or equal to the storage capacity of this kind of coal；

(5) within each supply cycle, if the coal of a kind is unsatisfactory for power plant's requirement to coal quality parameter, the most this Coal can not be routed directly to power plant；

(6) within each supply cycle, the maximum that the coal total amount of any kind of buying provides less than or equal to coal supply side Quantity；

(7) within each supply cycle, the coal total amount that loglstics enterprise is provided and delivered to power plant is in the range of needs that this power plant predicts；

(8) within each supply cycle, nonadditivity Q factor does not meets the coal of the requirement of power plant can not be carried out at mixing Reason；

(9) within each supply cycle, mixed coal every additivity Q factor is satisfied by power plant's requirement to coal；

(10) within each supply cycle, the coal blending total amount of all power plant is mixed quantity less than or equal to the maximum of coal distributor；

As follows to the constraints of power plant:

(11) within each supply cycle, the coal actual demand amount of power plant is in the range of needs that it is predicted；

The constraint that whole coal selects delivery process is as follows:

(12) decision variable in whole prediction length is more than or equal to 0；

(13) loglstics enterprise supply cycle [t, t+1) in the constraints of coal of power plant's dispensing be: in each supply week In phase, the coal total amount that loglstics enterprise is provided and delivered to power plant power plant's coal demand correspondence probabilistic confidence supremum value and Between minimum floor value；

Step 2.6, according to relation between general expenses and decision variable in the mathematical model set up, obtain under constraints Object function minimum time corresponding distribution project, i.e. obtain the optimum coal apolegamy of each supply cycle in prediction length T Send scheme；

In step 3, employing prediction length T, the optimum coal of first supply cycle selects distribution project as the coal of loglstics enterprise Selecting distribution project, loglstics enterprise carries out coal apolegamy according to the program and send；

Step 4, judge whether the supply cycle number sent of coal apolegamy reaches supply cycle total number of coal supply side, if It is that then the apolegamy of loglstics enterprise stopping coal being sent, and otherwise, returns and performs step 2.

Optimization method is sent in loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL the most according to claim 1 apolegamy, its It is characterised by: in each supply cycle described in step 2.5.3, the Coal Procurement cost P of loglstics enterprise_t, employing below equation:

$P_t=\underset{i\&Element;m}{\mathrm{\&Sigma;}}{\mathrm{\&eta;}}_i\underset{j\&Element;J}{\&Sigma;}{A}_{ijt}\&CenterDot;u_{ijt}+\underset{i\&Element;n}{\&Sigma;}\underset{j\&Element;J}{\&Sigma;}{A}_{ijt}\&CenterDot;u_{ijt}---\left(5\right)$

Wherein, m represents coal supply side's set that coal supply type is discount type；η_iRepresent the Coal Procurement amount of loglstics enterprise When being more than the coal minimum total supply obtaining discount, the special discount rate given by coal supply side；A_ijtRepresent loglstics enterprise Unit procurement cost from the coal total amount of i-th coal supply side buying jth kind coal；N represents that coal supply type is nothing Coal supply side's set of discount type.

Optimization method is sent in loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL the most according to claim 1 apolegamy, its It is characterised by: carrying cost S of coal in each supply cycle described in step 2.5.3_t, employing below equation:

$S_t=F_t\&CenterDot;\{\underset{k\&Element;K}{\&Sigma;}\frac{C_{kt}+C_{k(t+1)}}{2}+\underset{j\&Element;J}{\&Sigma;}\frac{{\mathrm{\&Psi;}}_{jt}+{\mathrm{\&Psi;}}_{j(t+1)}}{2}\}---\left(6\right)$

Wherein, F_tRepresent the unit carrying cost of coal in t, loglstics enterprise.

Optimization method is sent in loglstics enterprise coal based on stochastic model PREDICTIVE CONTROL the most according to claim 1 apolegamy, its Be characterised by: in step 2.5.4 described in constraints (13) within each supply cycle, the coal that loglstics enterprise is provided and delivered to power plant Charcoal total amount, between the supremum value and minimum floor value of power plant's coal demand correspondence probabilistic confidence, uses following public Formula:

${\underset{-}{\mathrm{\&gamma;}}}_{t|t_0}\&le;Y_{\mathrm{kt}}+\underset{j\&Element;J}{\mathrm{\&Sigma;}}{f}_{\mathrm{jkt}}\&le;{\stackrel{-}{\mathrm{\&gamma;}}}_{t|t_0},\&ForAll;k\&Element;K,\mathrm{\&Delta;t}=1,2,...,T-1---\left(7\right)$

Wherein,WithObtained by following equation:

${\underset{-}{p}}_k\&le;\mathrm{Pr}\{Y_{\mathrm{kt}}+\underset{j\&Element;J}{\mathrm{\&Sigma;}}{f}_{\mathrm{jkt}}\&GreaterEqual;D_{\mathrm{kt}}\}\&le;{\stackrel{-}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;t}=\mathrm{1,2},...,T-1---\left(8\right)$

$\mathrm{Pr}\{D_{kt}\&le;{\underset{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0}\}={\underset{\&OverBar;}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,...,T-1---\left(9\right)$

$\mathrm{Pr}\{D_{kt}\&le;{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{t|t_0}\}={\stackrel{\&OverBar;}{p}}_k,\&ForAll;k\&Element;K,\mathrm{\&Delta;}t=1,2,...,T-1---\left(10\right).$