CN107808213A - Dynamically-adjusted emergency transportation scheduling plan generation method - Google Patents

Dynamically-adjusted emergency transportation scheduling plan generation method Download PDF

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CN107808213A
CN107808213A CN201710981263.9A CN201710981263A CN107808213A CN 107808213 A CN107808213 A CN 107808213A CN 201710981263 A CN201710981263 A CN 201710981263A CN 107808213 A CN107808213 A CN 107808213A
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刘亚杰
吴志永
宋元明
张涛
雷洪涛
王锐
翟梦言
郑晓坤
明梦君
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National University of Defense Technology
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Abstract

the invention discloses a method for generating a dynamically adjusted emergency transportation scheduling plan, which comprises the steps of 1, uniformly dividing the whole transportation scheduling time into T periods, using r to represent the period sequence number of a current decision, wherein r ∈ { 1.,. T }, and the initial value of r is 1, 2, obtaining environmental information of E periods starting from a first period, operating an initial plan decision model, generating an initial plan facing the E scheduling periods, executing the plan of the first scheduling period, and then, making r equal to r +1, 3, collecting the actual execution condition of the decision generated from the first period to the (r-1) th period and the requirement updating information from the period r to the period min (r + E-1, T), 4, operating an adjustment decision model according to the information collected in the step 3, generating scheduling decision plans of a plurality of periods starting from the current period r, but only executing the plans belonging to the current period r, 5, updating r +1, if r is equal to T, turning to the step 3, otherwise, and optimizing the scheduling decision plan according to the advantages of the scheduling information of the scheduling plan and the corresponding resource optimization.

Description

A kind of emergent transportation dispatching scheduling method of dynamic adjustment
Technical field
The invention belongs to contingency management field, more particularly to a kind of emergent transportation dispatching plan generation side of dynamic adjustment Method.
Background technology
After the major natural disasters such as strong earthquakes occur, each disaster-stricken place material requirements, wounded's quantity disposal quantity etc. are general Can be in that blowout increases, farthest to reduce the Loss of Life and property of the disaster area people, it is necessary to which rapid tissue rises after calamity The disaster relief operations such as emergency resources transportation dispatching, so as to all kinds of goods and materials being badly in need of for disaster area transport point, shift what can not be treated on the spot The sick and wounded.But in these movement operations of organizational scheduling, for Post disaster relief commanding and decision-making personnel, not only need to consider calamity The dynamic that the factor of the influences such as area's rescue demand rescue decision-making is shown with time stepping method is not (for example, same disaster-stricken point is Typically can be different to the desired level of rescue on the same period), and also need to consider the body with rescue progress of these factors Reveal differentiation (for example, the desired level being expected when formulating decision scheme compared with real standard often exist it is poor It is different；And for example, in rescue operations because the generation of secondary disaster can cause actual demand and it is expected that demand big deviation occurs). The present invention takes into full account the dynamic and differentiation property that the factors such as Post disaster relief demand go out embodied in Post disaster relief progression, Emergency resources transportation dispatching plan generation and dynamic adjusting method after a kind of calamity are proposed, supports fast automatic generation emergency resources fortune Defeated operation plan, and Mobile state adjustment can be entered to emergent transportation dispatching plan according to the continuous renewal of the condition of a disaster information.
Because earthquake disaster takes place frequently in global range, obtained using Emergency Logistics of the emergency resources transportation dispatching as representative after calamity Domestic and foreign scholars it is widely studied.For the dynamic of relevant environmental conditions in emergency resources scheduling process after calamity, people is studied Member is proposed based on the research method based on multicycle planning, interference management, Bayesian decision etc..Based on answering for multicycle planning Anxious scheduling of resource only accounts for making the emergency resources operation plan in following one period based on current time, does not consider to plan Influence of the information " feedback " to operation plan in implementation procedure；Dynamic dispatching research based on interference management thought then concentrates on often The research of regular accident in the case of rule, stress to " local directed complete set " of original plan after interference incident occurs, to burst The achievement in research of property major natural disasters is less, and the research to Emergency Logistics after calamity is confined to vehicle path planning mostly (VRP) research of problem；Emergency resources transportation dispatching research based on Bayesian decision theory, is designed with to Bayes risk Rely in prior probability and the subjective consciousness of researcher with horizontal, objectivity relative deficiency.On the other hand, the present invention is based on relief goods Forward direction transports and the emergent transportation dispatching demands such as transfer is sent after the sick and wounded, considers the dynamic of the factors such as transportation demand, proposes A kind of emergency resources transportation dispatching scheduling method based on the multicycle, is conceived to the factors such as rescue demand on this basis Differentiation, further provide a kind of transportation dispatching Plan rescheduling method based on rolling optimization, it support according to information not The result of disconnected renewal carries out dynamic optimization and adjustment to emergent resource scheduling scheme plan, it is suitable for earthquake disaster rescue The features such as dynamic of the factors such as period demand, differentiation property, it is ensured that the decision-making made will more scientific and practicality.
The content of the invention
The technical problem to be solved in the present invention is that：For technical problem existing for prior art, the present invention provides one Kind considers the dynamic of the factors such as transportation demand, can be according to the result that information is constantly updated to resource scheduling scheme plan of meeting an urgent need Carry out a kind of emergent transportation dispatching scheduling method of dynamic adjustment of dynamic optimization and adjustment.
In order to solve the above technical problems, the present invention uses following technical scheme：
A kind of emergent transportation dispatching scheduling method of dynamic adjustment, it is characterised in that：Comprise the following steps：
Step 1：The whole transportation dispatching time was evenly dividing as T cycle, the cycle sequence number of current decision, r are represented with r ∈ { 1 ..., T }, r initial value are 1；
Step 2：Obtain since the ambient information in E dispatching cycle a cycle, operation is based on more all The emergent transportation dispatching original plan decision model of phase, generates the original plan towards E dispatching cycle.Perform first scheduling The plan in cycle, then makes r=r+1；
Step 3：The actual implementation status that decision-making is done from first dispatching cycle to (r-1) individual dispatching cycle is collected, And from cycle r to period m in (r+E-1, T) demand fresh information；
Step 4：According to the demand information after the result of decision performed collected in step 3 and renewal, base is run In the emergent transportation dispatching Plan rescheduling decision model of multicycle, the scheduling for generating multiple cycles since current period r is determined Plan plan, but only carry out the plan for belonging to current period r；
Step 5：R=r+1 is updated, if r≤T, goes to step 3, otherwise finishing scheduling.
As a further improvement on the present invention：
The emergent transportation dispatching original plan decision model based on the multicycle comprises the following steps described in step 2：
Step 2.1：Build the optimization aim of original plan decision model
Formula (1) represents to minimize the weighting sum that the non-meet volume of goods and materials and the wounded in the multicycle do not give treatment to quantity；Wherein,
CS represents that goods and materials set of types is closed；
DN represents demand nodes set；
T represents total length dispatching cycle；
Represent the preferential weight coefficient for meeting c type goods and materials；
Demand for c type goods and materials at t node p does not meet quantity；
Represent the weight coefficient of the preferential treatment h type wounded；
The h type wounded's quantity do not given treatment to for t node l；
Step 2.2：The constraints of original plan decision model is built, including：
Yopmt>=0 and be integer；
Formula (2) and formula (3) represent the Constraints of Equilibrium of relief goods stream, represent respectively in each dispatching cycle demand nodes and Supply the Material Transportation flow equilibrium of node.SN represents material supply node set；E represents decision rule length of window；Table Show quantity requireds of the t node p to c type goods and materials；δτoptFor 0-1 parameters：If any helicopter leaves node o at the τ moment, Node p is reached before t, then δτopt=1, otherwise δτopt=0；Represent supplies of the t node o to c type goods and materials Quantity；Represent that the τ moment transports the quantity of node p c type goods and materials, and variable to from node oWithRepresented Implication is similar.
Formula (4) defines wounded's quantity that the demand nodes within any dispatching cycle are not given treatment to yet, in this model, the wounded Treatment whether final medical node is reached as boundary using it；HN represents medical treatment node set；Represent the τ moment from Node o transports the quantity of the node p h type wounded for belonging to l nodes to, andImplication withImplication it is similar； The quantity of the h type wounded caused by t node l is represented, the value of the parameter has differentiation property, it is necessary to collect its renewal letter in time Breath.
Formula (5), formula (6) and formula (7) ensure the wounded in any cycle produce node, wounded's transporting pathway demand nodes, The flow equilibrium of medical node；Represent that the τ moment transports the quantity of the node p h type wounded for belonging to l nodes to from node l,The quantity of the h type wounded caused by t node l is represented, the value of the parameter has differentiation property, it is necessary to collect its renewal in time Information；HN represents medical treatment node set.
Formula (8) ensures wounded's quantity of any medical node processing in the range of the disposing capacity of hospital；Represent t Disposal capacity (ability) of the node o to the h type wounded；Represent that the τ moment transports the node o ' h for belonging to l nodes to from node o The quantity of the type wounded.
Formula (9) indicates whether to allow helicopter to navigate by water between two nodes；YopmtFly to node p's from node o for t M type helicopter quantity；topRepresent helicopter by the time required to path (o, p)；Represent big number.
The transhipment amount of goods and materials and the wounded are in the range of the transport capacity of helicopter between formula (10) represents any cycle interior nodes； θcRepresent the Unit Weight of c type goods and materials；Transport the quantity of node p c type goods and materials to from node o for t；For t Moment transports the quantity of the node p h type wounded for belonging to l nodes to from node o；Represent the maximum load of m type helicopters Loadage amount (ton)；Represent that m types helicopter can carry the maximum quantity (people) of the wounded；YopmtFlown to section from node o for t Point p m type helicopter quantity.
The purpose of formula (11) is the helicopter stream between each node in balance any cycle；avomtRepresent that t increases to Node o m type helicopter quantity, N represent node set, N=HN ∪ SN ∪ DN.
Formula (12) represents decision variable to be non-negative, and wounded's freight volume is with being discontented with the decision-makings such as enough, helicopter transport number Variable round numbers.
Further, the emergent transportation dispatching original plan decision model based on the multicycle is run described in step 2 to refer to The original plan decision model is solved, the method for solving is classical branch-bound algorithm or uses optimization software Instrument solves.
Further, the emergent transportation dispatching Plan rescheduling decision model based on the multicycle refers to described in step 4：
Step 4.1：The optimization aim of structure adjustment decision model
Step 4.2：The constraints of structure adjustment decision model：
Yopmt>=0 and be integer；
Wherein,With(τ ∈ 1,2 ..., r-1 }) represent the r moment before to decision variable respectivelyAnd YopmtThe actual implementing result made decision；In view of related uncertain before current dispatching cycle r Parameter has been made known in the actual value of history cycle, therefore, usesWith(τ ∈ 1,2 ..., r-1 }) represent that demand is not true Qualitative parameterWithIn the actual value that the τ moment has made known.
Further, the emergent transportation dispatching Plan rescheduling decision model based on the multicycle is run described in step 4 to refer to The adjustment decision model is solved, the method for solving is classical branch-bound algorithm or uses optimization software instrument Solve.
Compared with prior art, the advantage of the invention is that：
A kind of emergent transportation dispatching scheduling method of dynamic adjustment of the present invention, for formulating emergent transportation dispatching after calamity The dynamic of the environmental conditions such as the disaster area demand faced during plan and these environmental conditions are drilled with what time stepping method was showed Denaturation, the whole transportation dispatching time was evenly dividing as T cycle, it is a decision point to be carved at the beginning of each cycle； Carved at the beginning of each cycle, the ambient information in the E cycle that look to the future simultaneously makes the transport tune for including E cycle Degree plan, but only carry out first operation plan；When formulating the operation plan in following E cycle based on each cycle, no Only consider the actual deployment progress of preamble cycle Optimal Decision-making, it is also contemplated that the information updating of following E cycle relevant environmental conditions. Compared with existing scheme, it is preferably complete that this transportation dispatching optimization method rolled based on the multicycle make it that the result of decision had both had Office's property and sequential, while and can carries out dynamic optimization according to the result that information is constantly updated to emergent resource scheduling scheme plan And adjustment.
Brief description of the drawings
Fig. 1 is present system flow chart.
Fig. 2 is the emergent transport dynamic dispatching block schematic illustration that the present invention is rolled based on the multicycle.
Fig. 3 is demand place and supply place position distribution map.
Fig. 4 is transport project summary view and each moment delivery schedule.
Embodiment
The present invention is described in further details below with reference to Figure of description and specific embodiment.
Fig. 1 to Fig. 4 shows a kind of emergent transportation dispatching scheduling method of dynamic adjustment of the present invention, including step 1： The whole transportation dispatching time was evenly dividing as T cycle, the cycle sequence number of current decision, r ∈ { 1 ..., T }, r are represented with r Initial value be 1；Step 2：Acquisition is based on since the ambient information in E dispatching cycle a cycle, operation The emergent transportation dispatching original plan decision model of multicycle, generates the original plan towards E dispatching cycle.Perform first The plan of dispatching cycle, then makes r=r+1；Step 3：Collection is done from first dispatching cycle to (r-1) individual dispatching cycle The actual implementation status of decision-making, and from cycle r to period m in (r+E-1, T) demand fresh information；Step 4：According to step The demand information after the result of decision performed and renewal collected by 3, runs the emergent transportation dispatching based on the multicycle Plan rescheduling decision model, generates the scheduling decision plan in multiple cycles since current period r, but only carries out and belong to current Cycle r plan；Step 5：R=r+1 is updated, if r≤T, goes to step 3, otherwise finishing scheduling.The present invention formulates after being directed to calamity The dynamics of environmental condition such as the disaster area demand faced during emergent transportation dispatching plan and these environmental conditions are with time stepping method The differentiation showed, the whole transportation dispatching time was evenly dividing as T cycle, it is one to be carved at the beginning of each cycle Individual decision point；Carved at the beginning of each cycle, the ambient information in the E cycle that look to the future simultaneously is made comprising E The transportation dispatching plan in cycle, but only carry out first operation plan；The tune in following E cycle is being formulated based on each cycle During degree plan, the actual deployment progress of preamble cycle Optimal Decision-making is not only considered, it is also contemplated that following E cycle relevant environmental conditions Information updating.Compared with existing scheme, this transportation dispatching optimization method rolled based on the multicycle causes the result of decision both With preferable of overall importance and sequential, at the same and can according to the result that information is constantly updated to emergent resource scheduling scheme plan Carry out dynamic optimization and adjustment.
In the present embodiment, the emergent transportation dispatching original plan decision model based on the multicycle includes following step in step 2 Suddenly：
Step 2.1：Build the optimization aim of original plan decision model
Formula (1) represents to minimize the weighting sum that the non-meet volume of goods and materials and the wounded in the multicycle do not give treatment to quantity；Wherein,
CS represents that goods and materials set of types is closed；
DN represents demand nodes set；
T represents total length dispatching cycle；
Represent the preferential weight coefficient for meeting c type goods and materials；
Demand for c type goods and materials at t node p does not meet quantity；
Represent the weight coefficient of the preferential treatment h type wounded；
The h type wounded's quantity do not given treatment to for t node l；
Step 2.2：The constraints of original plan decision model is built, including：
Yopmt>=0 and be integer；
Formula (2) and formula (3) represent the Constraints of Equilibrium of relief goods stream, represent respectively in each dispatching cycle demand nodes and Supply the Material Transportation flow equilibrium of node.SN represents material supply node set；E represents the length of window of scheduling decision； Represent quantity requireds of the t node p to c type goods and materials；δτoptFor 0-1 parameters：If any helicopter leaves node at the τ moment O, node p is reached before t, then δτopt=1, otherwise δτopt=0；C type goods and materials can at expression t node o Supply quantity；Transport the quantity of node p c type goods and materials to from node o for the τ moment,WithImplication with it is such Seemingly.Formula (4) defines wounded's quantity that the demand nodes within any dispatching cycle are not given treatment to yet, and in this model, the wounded's rescues Whether control so that whether it reaches final medical node as boundary；HN represents medical treatment node set；Represent the τ moment from node O transports the quantity of the node p h type wounded for belonging to l nodes to, andImplication withImplication it is similar；Represent t The quantity of the h type wounded caused by moment node l.Formula (5), formula (6) and formula (7) ensure in any cycle the wounded produce node, The flow equilibrium of the demand nodes of wounded's transporting pathway, medical node；Represent that the τ moment transports belonging to for node p to from node l The quantity of the h type wounded of l nodes,Represent the quantity of the h type wounded caused by t node l；HN represents medical treatment section Point set.Formula (8) ensures wounded's quantity of any medical node processing in the range of the disposing capacity of hospital；Represent t Disposal capacity (ability) of the node o to the h type wounded；Represent that the τ moment transports the node o ' h for belonging to l nodes to from node o The quantity of the type wounded.Formula (9) indicates whether to allow helicopter to navigate by water between two nodes；YopmtFlown to for t from node o Node p m type helicopter quantity；topRepresent helicopter by the time required to path (o, p)；Represent big number.Formula (10) table The transhipment amount of goods and materials and the wounded are in the range of the transport capacity of helicopter between showing any cycle interior nodes；θcRepresent c type goods and materials Unit Weight；Transport the quantity of node p c type goods and materials to from node o for t；Transported for t from node o Toward the quantity of the node p h type wounded for belonging to l nodes；Represent the maximum pay load (ton) of m type helicopters；Represent that m types helicopter can carry the maximum quantity (people) of the wounded；YopmtFlown to for t from node o node p m types Helicopter quantity.The purpose of formula (11) is the helicopter stream between each node in balance any cycle；avomtRepresent t increase To node o m type helicopter quantity, N represents node set, N=HN ∪ SN ∪ DN.Formula (12) represents that decision variable is non- It is negative, and wounded's freight volume is with being discontented with the decision variable round numbers such as enough, helicopter transport number.In the present embodiment, classics are used Branch-bound algorithm or using the original plan decision model in the optimization software instrument solution procedure 2 such as CPLEX, obtain first The operation plan in individual dispatching cycle to the E cycle, but only carry out the plan of first dispatching cycle.
In the present embodiment, the emergent transportation dispatching Plan rescheduling decision model based on the multicycle refers to described in step 4：
Step 4.1：The optimization aim of structure adjustment decision model
Wherein,The preferential weight coefficient for meeting c type goods and materials is represented,Represent the weight of the preferential treatment h type wounded Coefficient.
Step 4.2：The constraints of structure adjustment decision model：
Yopmt>=0 and be integer；
Wherein,With(τ ∈ 1,2 ..., r-1 }) represent the r moment before to decision variable respectivelyAnd YopmtThe actual implementing result made decision；In view of related uncertain before current dispatching cycle r Parameter has been made known in the actual value of history cycle, usesWith(τ ∈ 1,2 ..., r-1 }) represent demand uncertainty ginseng NumberWithIn the actual value that the τ moment has made known.The emergent transportation dispatching Plan rescheduling decision model is collected in step 3 From first planning time section to the actual implementation status of the r-1 planning time section, and following E cycle relevant environment bar On the basis of the information updating of part, emergent transportation dispatching original plan decision model (1)-(12) are adjusted to emergent transportation dispatching Plan rescheduling decision model (13)-(24), and enter using the branch-bound algorithm of classics or using the optimization software instrument such as CPLEX Row solves, and obtains the result of decision from current period r to period m in (r+E-1, T), but only carry out current period r decision-making As a result.So that the present invention not only considers the preamble cycle when formulating the operation plan in following E cycle based on each cycle The actual deployment progress of Optimal Decision-making, it is also contemplated that the information updating of following E cycle relevant environmental conditions.Realization can be according to letter Cease the purpose that the result constantly updated carries out dynamic optimization and adjustment to resource scheduling scheme plan of meeting an urgent need.Below with a reality The example of operation illustrates.
The feasible of developed model and method is illustrated to the example rescued from Wenchuan earthquake using the present invention Property and validity.It is to arrive cycle T the cycle 1 that this method, which is evenly dividing Post disaster relief scheduling time for T cycle, numbering, and false If the rescue demand in each cycle remains relatively unchanged over.If system is r (r≤T) current decision-making period, during each decision-making most More E planning time sections that look to the future simultaneously.Fig. 2 gives disaster-stricken place and supplies the location map in place, wherein A~H (being represented for demand nodes with circle), totally 8, F1~F3 is material supply node and medical node (being represented with triangle), totally 3 It is individual.
In experiment, time interval is 2h between each planning time section, selects two kinds of rescue things being most badly in need of under actual conditions Money, including：Food, medicine, are represented with C1, C2 respectively, and its Unit Weight is 14kg, 16kg respectively；Select a type of wound Member H1.C1, C2, H1 weight coefficient are respectively 0.28,0.32,0.40.Tables 1 and 2 sets forth each demand at different moments Node increases the desired value of relief goods demand amount and wounded's quantity newlyEach disaster-stricken place goods and materials need on this basis The actual value of the amount of asking and wounded's quantity existsRandomly generated in section, to characterize The differentiation of disaster area desired level.Table 3 and table 4 illustrate at different moments the respectively newly-increased goods and materials quantity of supply (medical treatment) node and doctor Treat disposing capacity.Table 5 is the explanation to helicopter parameter, there is the helicopter of three types in experiment.Table 6 gives at different moments ((10,5,5) in such as the first row first row represent t=1 to the quantity of the newly-increased different type helicopter of each supply (medical treatment) node At the moment, it is respectively Z8s=10, S70g=5, M171s=5 that F1 nodes, which increase each model helicopter newly).It is soft that numerical experiment is based on optimization Part ILOG CPLEX12.5.1.0 are carried out, and experiment computer is configured to Intel (R) Core (TM) i7-5500UCPU 2.40GHz RAM=8.00GB.
Each cycle material requirements predicted value of the demand nodes of table 1
Each cycle wounded quantitative forecast value of the demand nodes of table 2
Table 3 supplies each cycle material supply amount of node
Each cycle wounded of 4 medical node of table dispose capacity (ability)
The helicopter parameter setting of table 5
6 each cycle of table increased helicopter quantity (avomt)
1 2 3 4 5 6
F1 (10,5,5) (0,5,8) (7,0,0) (5,0,7) (0,5,3) (0,0,0)
F2 (7,5,6) (0,7,0) (8,0,5) (0,3,0) (0,0,5) (0,0,0)
F3 (6,7,5) (0,0,8) (4,5,0) (0,0,7) (0,4,0) (3,0,0)
Fig. 3 gives transport project summary view and the delivery schedule at each moment that institute's extracting method of the present invention is generated.Its In, each demand place is listed in specific diversion plan of each moment in the form of delivery schedule.It is each in delivery schedule Individual form represents that goods and materials of the demand nodes within decision-making period are transferred to and produces plan with the wounded, and first row represents that the goods and materials turn Enter or carve at the beginning of the wounded produce, second and third, four row represent to be transferred to supply node or the reception of the demand nodes goods and materials respectively The medical node of the demand nodes wounded.Data represent goods and materials C1, C2 and the wounded H1 quantity respectively in form, turn on the occasion of expression Enter demand nodes, negative value represents to produce the demand nodes.Such as：Demand nodes C the t=13 moment with supply node (or medical treatment Node) diversion plan between F2 is：Transport C goods and materials C1=14.3 tons, C2=9.4 tons to by F2, transport F2 wounded H1=to by C 162 people；In addition, the transhipment that transport project figure is then given between each supply place and demand place within whole decision-making period is total Measure, the numerical value on oriented dotted line in bracket represents goods and materials C1, the C2 collected and the wounded H1 quantity respectively, and "-" represents transporter To with it is in opposite direction shown in arrow.Such as：Supply data (6.1 of the node (or medical node) between F1 and demand nodes B；0；- 75) represent, B goods and materials C1=6.1 tons are transported to (in for delivery schedule when t=10,20,21 by F1 within whole decision-making period Carve goods and materials C1 transhipment quantity sum), C2=0 tons, by B transport to F1 wounded H1=75 people (for t=7 in delivery schedule, 19, The 21 moment wounded transport quantity sum).
As shown in Figure 3, in addition to F, remaining demand nodes is by three supply node (or medical node) common supply things Money (or receiving the wounded).But different demand nodes are directed to, the direction of the input of its goods and materials and wounded's output but has differences.By table 1 understands that demand nodes C, E, G are severely afflicated area, and material requirements are larger with wounded's quantity, and observation Fig. 3 can have found have from it The large number of goods and materials of his three supply nodes, the C wounded transport three medical nodes to, but in the majority with F1, the E wounded by F1, F3 are received, and the G wounded are mainly received by F1, F2.In addition, A goods and materials are mainly derived from F2, F3, but the wounded are mainly connect by F1 Receive, a small amount of wounded are received by F3；B has the goods and materials from three supply nodes, but based on F2, F3, the wounded are then to transport F1 to Quantity it is in the majority, the wounded's quantity for transporting other two node to is identical；The C1 overwhelming majority of D demands come from F1, C2 then come from F1, F3, the wounded mainly transport F1 to；F goods and materials come from F1, F2, and the wounded are disposed by F1, F2；H nodes have from three nodes Goods and materials, but C2 goods and materials main source F2, the wounded have F1 and F2 to receive jointly.It is seen that transport project is broken to a certain extent The cognition supplied nearby in the past, such as：D distances F2 is nearest, but its goods and materials is mainly supplied by F1 and F3, and the wounded are mainly connect by F1 Receive；F distances F3 is nearest, but its material supply and wounded's disposal are mainly completed by F1, F2.
Examples detailed above shows, method proposed by the invention can well adapt to the factors such as disaster relief demand dynamic and Differentiation property, the transportation dispatching and its plan for adjustment that generation is adapted therewith.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment, All technical schemes belonged under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that for the general of the art For logical technical staff, some improvements and modifications without departing from the principles of the present invention, protection scope of the present invention should be regarded as.

Claims (5)

  1. A kind of 1. emergent transportation dispatching scheduling method of dynamic adjustment, it is characterised in that：Comprise the following steps：
    Step 1：The whole transportation dispatching time was evenly dividing as T cycle, the cycle sequence number of current decision, r ∈ are represented with r { 1 ..., T }, r initial value is 1；
    Step 2：Obtain since the ambient information in E dispatching cycle a cycle, run based on the multicycle Emergent transportation dispatching original plan decision model, generates the original plan towards E dispatching cycle.Perform first dispatching cycle Plan, then make r=r+1；
    Step 3：The actual implementation status that decision-making is done from first dispatching cycle to (r-1) individual dispatching cycle is collected, and The demand fresh information of (r+E-1, T) from cycle r to period m in；
    Step 4：According to the demand information after the result of decision performed collected in step 3 and renewal, operation is based on more The emergent transportation dispatching Plan rescheduling decision model in cycle, generate the scheduling decision meter in multiple cycles since current period r Draw, but only carry out the plan for belonging to current period r；
    Step 5：R=r+1 is updated, if r≤T, goes to step 3, otherwise finishing scheduling.
  2. A kind of 2. emergent transportation dispatching scheduling method of dynamic adjustment according to claim 1, it is characterised in that：Step The emergent transportation dispatching original plan decision model based on the multicycle comprises the following steps described in rapid 2：
    Step 2.1：Build the optimization aim of original plan decision model
    <mrow> <mtable> <mtr> <mtd> <mi>min</mi> </mtd> <mtd> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mi>T</mi> </mrow> </munder> <msubsup> <mi>&amp;rho;</mi> <mi>c</mi> <mi>R</mi> </msubsup> <msubsup> <mi>DEV</mi> <mrow> <mi>c</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>R</mi> </msubsup> <mo>+</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mi>T</mi> </mrow> </munder> <msubsup> <mi>&amp;rho;</mi> <mi>h</mi> <mi>W</mi> </msubsup> <msubsup> <mi>DEV</mi> <mrow> <mi>h</mi> <mi>l</mi> <mi>t</mi> </mrow> <mi>W</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    Formula (1) represents to minimize the weighting sum that the non-meet volume of goods and materials and the wounded in the multicycle do not give treatment to quantity；Wherein,
    CS represents that goods and materials set of types is closed；
    DN represents demand nodes set；
    T represents total length dispatching cycle；
    Represent the preferential weight coefficient for meeting c type goods and materials；
    Demand for c type goods and materials at t node p does not meet quantity；
    Represent the weight coefficient of the preferential treatment h type wounded；
    For the h type wounded's quantity do not given treatment at t node l；
    Step 2.2：The constraints of original plan decision model is built, including：
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <mo>&amp;lsqb;</mo> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>o</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>S</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> <mo>-</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>p</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <msup> <mi>cpp</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> </mrow> <mo>&amp;rsqb;</mo> <mo>&amp;GreaterEqual;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>c</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> <mo>-</mo> <msubsup> <mi>DEV</mi> <mrow> <mi>c</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>R</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> <mo>,</mo> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>S</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> <mo>&amp;le;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mi>s</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>o</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>S</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <msup> <mi>&amp;tau;o</mi> <mo>&amp;prime;</mo> </msup> <mi>o</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <msup> <mi>co</mi> <mo>&amp;prime;</mo> </msup> <mi>o</mi> <mi>&amp;tau;</mi> </mrow> <mi>R</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> <mo>,</mo> <mi>o</mi> <mo>&amp;Element;</mo> <mi>S</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <mo>&amp;lsqb;</mo> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>o</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>-</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>p</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <msup> <mi>hpp</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> </mrow> <mo>&amp;rsqb;</mo> <mo>&amp;GreaterEqual;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>h</mi> <mi>l</mi> <mi>&amp;tau;</mi> </mrow> <mi>W</mi> </msubsup> <mo>-</mo> <msubsup> <mi>DEV</mi> <mrow> <mi>h</mi> <mi>l</mi> <mi>t</mi> </mrow> <mi>W</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> <mo>,</mo> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>l</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>h</mi> <mi>l</mi> <mi>&amp;tau;</mi> </mrow> <mi>W</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> <mo>,</mo> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>p</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <msup> <mi>hpp</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>o</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> <mo>,</mo> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>,</mo> <mi>l</mi> <mo>&amp;NotEqual;</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>p</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <msup> <mi>hpp</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>o</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>p</mi> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> <mo>,</mo> <mi>p</mi> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> <mo>,</mo> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <mo>&amp;lsqb;</mo> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> <mo>&amp;cup;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>p</mi> <mi>o</mi> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>-</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>o</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <msup> <mi>hoo</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;tau;</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> </mrow> <mo>&amp;rsqb;</mo> <mo>&amp;le;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mi>s</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>&amp;tau;</mi> </mrow> <mi>W</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> <mo>,</mo> <mi>o</mi> <mo>&amp;Element;</mo> <mi>H</mi> <mi>N</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>Y</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>m</mi> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>lt</mi> <mrow> <mi>o</mi> <mi>p</mi> </mrow> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mo>(</mo> <mrow> <mi>o</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>)</mo> <mo>&amp;Element;</mo> <mi>A</mi> <mo>,</mo> <mi>m</mi> <mo>&amp;Element;</mo> <mi>M</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> </mrow> </munder> <msub> <mi>&amp;theta;</mi> <mi>c</mi> </msub> <msubsup> <mi>X</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>R</mi> </msubsup> <mo>/</mo> <msubsup> <mi>cap</mi> <mi>m</mi> <mi>R</mi> </msubsup> <mo>+</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>cap</mi> <mi>m</mi> <mi>W</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>Y</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>m</mi> <mi>t</mi> </mrow> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mo>(</mo> <mrow> <mi>o</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>)</mo> <mo>&amp;Element;</mo> <mi>A</mi> <mo>,</mo> <mi>m</mi> <mo>&amp;Element;</mo> <mi>M</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <msub> <mi>&amp;delta;</mi> <mrow> <mi>&amp;tau;</mi> <mi>p</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>Y</mi> <mrow> <mi>p</mi> <mi>o</mi> <mi>m</mi> <mi>&amp;tau;</mi> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msub> <mi>av</mi> <mrow> <mi>o</mi> <mi>m</mi> <mi>&amp;tau;</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <msub> <mi>Y</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>m</mi> <mi>&amp;tau;</mi> </mrow> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mi>o</mi> <mo>&amp;Element;</mo> <mi>N</mi> <mo>,</mo> <mi>m</mi> <mo>&amp;Element;</mo> <mi>M</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>E</mi> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    And it is integer；
    Formula (2) and formula (3) represent the Constraints of Equilibrium of relief goods stream, represent demand nodes and supply in each dispatching cycle respectively The Material Transportation flow equilibrium of node.SN represents material supply node set；E represents the length of window of scheduling decision；Represent T node p has differentiation property, it is necessary to collect its fresh information in time to the quantity requireds of c type goods and materials, the value of the parameter； δτoptFor 0-1 parameters：If any helicopter leaves node o at the τ moment and node p, δ is reached before tτopt=1, it is no Then δτopt=0；C type goods and materials supplies quantity at expression t node o；For the τ moment node p is transported to from node o C type goods and materials quantity,WithImplication it is similar.
    Formula (4) defines wounded's quantity that the demand nodes within any dispatching cycle are not given treatment to yet, and in this model, the wounded's rescues Whether control so that whether it reaches final medical node as boundary；HN represents medical treatment node set；Represent the τ moment from node O transports the quantity of the node p h type wounded for belonging to 1 node to, andImplication withImplication it is similar；Represent t The quantity of the h type wounded caused by moment node l, the value of the parameter have differentiation property, it is necessary to collect its fresh information in time.
    Formula (5), formula (6) and formula (7) ensure that the wounded produce node, the demand nodes of wounded's transporting pathway, medical treatment in any cycle The flow equilibrium of node；Represent that the τ moment transports the quantity of the node p h type wounded for belonging to 1 node to from node l, Represent the quantity of the h type wounded caused by t node l；HN represents medical treatment node set.
    Formula (8) ensures wounded's quantity of any medical node processing in the range of the disposing capacity of hospital；Represent t node Disposal capacity or ability of the o to the h type wounded；Represent that the τ moment transports the node o ' h types for belonging to 1 node to from node o The quantity of the wounded.
    Formula (9) indicates whether to allow helicopter to navigate by water between two nodes；YopmtFlown to for t from node o node p m types Helicopter quantity；topRepresent helicopter by the time required to path (o, p)；L represents big number.
    The transhipment amount of goods and materials and the wounded are in the range of the transport capacity of helicopter between formula (10) represents any cycle interior nodes；θcTable Show the Unit Weight of c type goods and materials；Transport the quantity of node p c type goods and materials to from node o for t；For t when Carve the quantity for the h type wounded for belonging to l nodes for transporting node p to from node o；Represent the maximum loading of m type helicopters Weight, unit are ton；Represent that m types helicopter can carry the maximum number of the wounded；YopmtFlown to section from node o for t Point p m type helicopter quantity.
    The purpose of formula (11) is the helicopter stream between each node in balance any cycle；avomtRepresent that t increases to node o M type helicopter quantity, N represent node set, N=HN ∪ SN ∪ DN.
    Formula (12) represents decision variable to be non-negative, and wounded's freight volume is with being discontented with the decision variables such as enough, helicopter transport number Round numbers.
  3. A kind of 3. emergent transportation dispatching scheduling method of dynamic adjustment according to claim 2, it is characterised in that：Step The emergent transportation dispatching original plan decision model based on the multicycle is run described in rapid 2 to refer to the original plan decision model Type is solved, and the method for solving is solved for classical branch-bound algorithm or using optimization software instrument.
  4. A kind of 4. emergent transportation dispatching scheduling method of dynamic adjustment according to claim 1, it is characterised in that：Step The emergent transportation dispatching Plan rescheduling decision model based on the multicycle refers to described in rapid 4：
    Step 4.1：The optimization aim of structure adjustment decision model
    <mrow> <mtable> <mtr> <mtd> <mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <mi>i</mi> <mi>m</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </mtd> <mtd> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mi>r</mi> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>+</mo> <mi>E</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>T</mi> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </munder> <msubsup> <mi>&amp;rho;</mi> <mi>c</mi> <mi>R</mi> </msubsup> <msubsup> <mi>DEV</mi> <mrow> <mi>c</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>R</mi> </msubsup> <mo>+</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mi>r</mi> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>+</mo> <mi>E</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>T</mi> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </munder> <msubsup> <mi>&amp;rho;</mi> <mi>h</mi> <mi>W</mi> </msubsup> <msubsup> <mi>DEV</mi> <mrow> <mi>h</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>W</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
    Wherein,The preferential weight coefficient for meeting c type goods and materials is represented,Represent the weight system of the preferential treatment h type wounded Number,Demand for c type goods and materials at t node p does not meet quantity；For the h not given treatment at t node l Type wounded's quantity.
    Step 4.2：The constraints of structure adjustment decision model：
    <mrow> <msub> <mi>Y</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>m</mi> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>lt</mi> <mrow> <mi>o</mi> <mi>p</mi> </mrow> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mo>(</mo> <mrow> <mi>o</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>)</mo> <mo>&amp;Element;</mo> <mi>A</mi> <mo>,</mo> <mi>m</mi> <mo>&amp;Element;</mo> <mi>M</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mi>r</mi> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>r</mi> <mo>+</mo> <mi>E</mi> <mo>-</mo> <mn>1</mn> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>21</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>c</mi> <mo>&amp;Element;</mo> <mi>C</mi> <mi>S</mi> </mrow> </munder> <msub> <mi>&amp;theta;</mi> <mi>c</mi> </msub> <msubsup> <mi>X</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mi>R</mi> </msubsup> <mo>/</mo> <msubsup> <mi>cap</mi> <mi>m</mi> <mi>R</mi> </msubsup> <mo>+</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>&amp;Element;</mo> <mi>D</mi> <mi>N</mi> </mrow> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>h</mi> <mo>&amp;Element;</mo> <mi>W</mi> <mi>S</mi> </mrow> </munder> <msubsup> <mi>X</mi> <mrow> <mi>h</mi> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> <mrow> <mi>W</mi> <mi>l</mi> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>cap</mi> <mi>m</mi> <mi>W</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>Y</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>m</mi> <mi>t</mi> </mrow> </msub> <mo>,</mo> <mrow> <mo>(</mo> <mo>&amp;ForAll;</mo> <mo>(</mo> <mrow> <mi>o</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>)</mo> <mo>&amp;Element;</mo> <mi>A</mi> <mo>,</mo> <mi>m</mi> <mo>&amp;Element;</mo> <mi>M</mi> <mo>,</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mo>{</mo> <mi>r</mi> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>min</mi> <mo>(</mo> <mrow> <mi>r</mi> <mo>+</mo> <mi>E</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>T</mi> </mrow> <mo>)</mo> <mo>}</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>22</mn> <mo>)</mo> </mrow> </mrow>
    And it is integer；
    Wherein,WithRepresent the r moment before to decision variable respectively And YopmtThe actual implementing result made decision；WithRepresent demand uncertainty parameter WithIn the actual value that the τ moment has made known.
  5. A kind of 5. emergent transportation dispatching scheduling method of dynamic adjustment according to claim 4, it is characterised in that：Step The emergent transportation dispatching Plan rescheduling decision model based on the multicycle is run described in rapid 4 to refer to enter the adjustment decision model Row is solved, and the method for solving is solved for classical branch-bound algorithm or using optimization software instrument.
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CN109190821A (en) * 2018-08-30 2019-01-11 中国联合网络通信集团有限公司 Disaster relief dispatching method based on edge calculations, device and system
CN112819323A (en) * 2021-01-29 2021-05-18 中核清原环境技术工程有限责任公司 Calculation method and system for nuclear power station spent fuel transportation plan formulation
CN113313331A (en) * 2021-07-30 2021-08-27 深圳市城市交通规划设计研究中心股份有限公司 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk
CN113344356A (en) * 2021-05-31 2021-09-03 烽火通信科技股份有限公司 Multi-target resource allocation decision-making method and device
CN116136898A (en) * 2023-04-19 2023-05-19 中国西安卫星测控中心 Aerospace measurement and control resource scheduling result fusion method and device and computer equipment
CN116976588A (en) * 2023-06-16 2023-10-31 浙江大学 Emergency material dynamic distribution method and system based on typhoon real-time information

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109190821A (en) * 2018-08-30 2019-01-11 中国联合网络通信集团有限公司 Disaster relief dispatching method based on edge calculations, device and system
CN109190821B (en) * 2018-08-30 2021-02-02 中国联合网络通信集团有限公司 Disaster rescue scheduling method, device and system based on edge calculation
CN112819323A (en) * 2021-01-29 2021-05-18 中核清原环境技术工程有限责任公司 Calculation method and system for nuclear power station spent fuel transportation plan formulation
CN113344356A (en) * 2021-05-31 2021-09-03 烽火通信科技股份有限公司 Multi-target resource allocation decision-making method and device
CN113313331A (en) * 2021-07-30 2021-08-27 深圳市城市交通规划设计研究中心股份有限公司 Emergency material multi-cycle segmentation distribution method and device based on inventory gap risk
CN116136898A (en) * 2023-04-19 2023-05-19 中国西安卫星测控中心 Aerospace measurement and control resource scheduling result fusion method and device and computer equipment
CN116976588A (en) * 2023-06-16 2023-10-31 浙江大学 Emergency material dynamic distribution method and system based on typhoon real-time information
CN116976588B (en) * 2023-06-16 2024-05-07 浙江大学 Emergency material dynamic distribution method and system based on typhoon real-time information

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