CN1845152A - Rapid command control method of rapid low risk transport for war field wounded person - Google Patents

Abstract

The invention relates to a quick command control method for quickly transporting wounded person in low risk. Wherein, the commanded object the all wounded persons; according to the lengths from different receivers to different senders, the transmission risk probability, the receiving amount of receiver, the sent amount of sender, and the speed and load of transmission device, the command control mode purposed for transporting all wounded persons in minimum time or risk is built, with lower calculation complexity and high solvability; and using linear programming, and the pair rule of linear programming, to solve said mode, and improve the result via the two-dimension table, to obtain the command control method that meeting the demand of quick low-risk transporting. The invention can improve the battle effectiveness, with wider application. The invention also provides relative technique.

Description

Quick commander's control method of battlefield wounded's fast and low-risk transhipment

Technical field the present invention relates to national defence and association area, is used for battlefield wounded's fast and low-risk transhipment enforcement commander's control is fast realized the battlefield wounded's fast and low-risk is transported.

Background technology is implemented quick wounded's transhipment between the reciever in battlefield and the side of transporting commander's control is an important component part of operational commanding control, the wounded described here comprise the patient, the length of transporting the path to the different side of the transporting wounded according to different recievers, transportation meets with risk probability, the reciever wounded's the receiving amount and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, work out one with realize that the reciever and the side of transporting transport that all wounded expend time in or the risk minimum be target and the commander of the transhipment with low computational complexity and high solvability control plan be the battlefield commander to the battlefield wounded's fast and low-risk transhipment implement the key issue that commander's control fast must solution, the solution of this problem is for increasing substantially fighting capacity, minimizing is to the risk of battlefield wounded transhipment, expend time in, guarantee that the wounded are in time given treatment to, have crucial meaning.

Wounded's fast and low-risk turn-over capacity is most important for the triumph of capturing IT-based warfare, complicated battlefield surroundings may impact the experience risk of wounded's transportation, thereby reduce the security of wounded's transhipment, and commander's control of low-risk wounded transhipment is the key that improves mobile operations, and wherein the matter of utmost importance that must solve is commander's control plan of wounded's transhipment of formulation science.Can the quality of this plan not only be related to and implements what of risk that battlefield wounded transhipment faced and consumption of natural resource, in time obtain treatment but also be related to some severely injured peoples, descends to guarantee fighting capacity to be unlikely to the delay that member due to wound transports.

Time seems more important for commander's control of battlefield wounded's fast and low-risk transhipment and this transhipment, constraint condition that therefore must be by reducing commander's controlling models, analyzes the choose reasonable parameter by antithesis and improves solvability and come the battlefield wounded's fast and low-risk transhipment to implement to command fast to control with transhipment risk and time minimum as optimization aim.

The present invention relates to quick commander's control method of battlefield wounded's fast and low-risk transhipment, relate to military affairs and association area, the object of commander's control is all battlefield wounded, this method is according to the length of transporting the path to the difference side of the transporting wounded from different recievers, transportation meets with risk probability, the reciever wounded's the receiving amount and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model, by two-dimentional form solving result is updated again, meet the option control command that battlefield wounded's fast and low-risk transhipment requires until final acquisition, this method has efficiently, simply, objective, characteristics are widely used and obviously improve its combat capabilities etc., can be widely used in quick commander's control of all battlefield wounded's fast and low-risk transhipments, the invention further relates to the technology that realizes this method.

Summary of the invention the present invention is according to the length of transporting the path to the difference side of the transporting wounded from different recievers, transportation meets with risk probability, the reciever wounded's the receiving amount and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model, obtain to implement the scheme of commander's control with the transhipment to battlefield wounded's fast and low-risk of two-dimentional form description, and check whether this option control command meets and finish risk and the time requirement that the whole battlefield wounded transport task, if do not meet the demands, then by analysis to this two dimension commander control form, and according to shadow price, the risk bottleneck, wounded's receiving amount that the time bottleneck allows relevant reciever and means of transport etc. are adjusted, constantly repeat this and find the solution-check analytic process, meet the option control command that battlefield wounded's fast and low-risk transhipment requires until final acquisition.Therefore, the conception of quick commander's control of battlefield wounded's fast and low-risk transhipment is proposed, introduce the analytical approach that transportation expends time in and meets with risk probability, set up linear programming and the dual program model of seeking optimum option control command, come this model of rapid solving by reducing constraint condition, obtain to implement the scheme of commander's control with the transhipment to battlefield wounded's fast and low-risk of two-dimentional form description, and according to risk and the time requirement of finishing whole wounded's transhipment, finish risk and the time bottleneck that the whole battlefield wounded transport task by searching influence, reciever allows to accept the unreasonable configuration of wounded's amount and means of transport is adjusted, continue to optimize and improve this option control command, and final risk and the time requirement that obtains to satisfy battlefield wounded's fast and low-risk transhipment, option control command with two-dimentional form description becomes key character of the present invention.

The technical scheme of quick commander's control method of battlefield of the present invention wounded's fast and low-risk transhipment is:

At first, battlefield wounded's fast and low-risk transshipment problem is defined as by the wounded's reciever and the wounded's the supply and demand system that the side of transporting constituted, the feature of this system can be with the length of transporting the path to the difference side of the transporting wounded from different recievers, transportation meets with risk probability, the reciever wounded's the receiving amount and the side of the transporting wounded's upwards of movement, the speed and the carrying capacity of means of transport are described, and according to risk that the battlefield wounded are transported and time requirement, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model, obtain to implement the scheme of commander's control with the transhipment to battlefield wounded's fast and low-risk of two-dimentional form description, risk and time bottleneck by continuous searching supply and demand system, the quantity that relevant reciever allows to accept the wounded is carried out reasonable disposition, adopt methods such as different means of transports, final risk and the time requirement that obtains to satisfy battlefield wounded's fast and low-risk transhipment, the scheme of commander's control is implemented in transhipment to battlefield wounded's fast and low-risk, finishes the commander's control to the transhipment of battlefield wounded's fast and low-risk.

Quick commander's control to the transhipment of battlefield wounded's fast and low-risk, the computational complexity and the needed computing time of finding the solution commander's linear programming of controlling models and dual program should not exerted an influence to the real-time of commander's control decision, therefore reducing unnecessary constraint condition is the important measures that improve commander's control decision real-time, for computational complexity that reduces commander's controlling models and the solvability that improves commander's controlling models, stipulate that the constraint condition relevant with the side of transporting is the constraint condition that equals the side's of transporting upwards of movement, the constraint condition relevant with reciever is the constraint condition that is not more than the maximum receiving amount of reciever.

Complicated battlefield surroundings may impact the current risk of wounded's transportation route, risk can make the wounded injured from carriage direction take over party's transport process, thereby reduce the transhipment battlefield wounded's security, for the wounded expend time in or the risk minimum is commander's control of target to transport, this reduction has been equivalent to increase the risk that the wounded transport experience, it can be with the function of time as variable that transportation meets with risk probability, also can be and irrelevant constant of time, the transportation in different paths meets with risk probability can be different.

Find the solution commander's controlling models by the method for finding the solution linear programming and finding the solution the dual program of linear programming, the minimum transportation that can obtain respectively from the difference side of the transporting transportation wounded to different take over partys meets with risk probability or the transportation route of least consume time, with different take over partys and the relevant shadow price of the different sides of transporting constraint condition, the result that will find the solution inserts in a kind of two dimension commander's control form again, according to analysis to this two dimension commander control form, and pass through according to shadow price, risk and time bottleneck are adjusted correlation parameter, constantly find the solution and update, meet the option control command that battlefield wounded's fast and low-risk transhipment requires until final acquisition.

Can by the quantity that each take over party transports the wounded is described as the zones of different in the two-dimentional form of option control command from each side of transporting, size, risk in transit, the quantity of means of transport, the transportation that each side of transporting needs transport power expends time in and relevant shadow price, the situation of change of quantity, residue reception wounded ability that each take over party receives the wounded is with relevant shadow price and transport all wounded's priming the pump and the minimum time that expends.

If the option control command of trying to achieve can not satisfy predetermined risk and time requirement, then can be by two dimension commander control table, result to former linear programming and dual program analyzes, determine to influence the risk of battlefield wounded transhipment and the bottleneck of T.T., carry out reasonable disposition, increase the quantity of means of transport and adopt different means such as means of transport by the quantity that take over party's permission is received the wounded again, eliminate risk and time bottleneck, and repeat this process, until the risk of finishing battlefield wounded transhipment with meet predetermined requirement T.T..

Quick commander's control method of battlefield wounded's fast and low-risk transhipment of the present invention's design is applicable to that all battlefield wounded's fast and low-risk transhipments are key characters of the present invention.

With risk minimum being analyzed as follows that be target to quick commander's control problem of battlefield wounded's fast and low-risk transhipment, it is the analysis of target to quick commander's control problem of battlefield wounded's fast and low-risk transhipment that this analysis is equally applicable to the minimum that expends time in, and only need objective function this moment $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$ Be replaced into $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ With constraint condition D _jy _j+ S _iy _N+i≤ p _IjBe replaced into D _jy _j+ S _iy _N+i≤ d _IjAnd similarly analyze and get final product.

Suppose that the battlefield wounded's transportation problem can transport wounded's node and describe in different receptions and the network that transports the path that has transportation wounded between the node with receiving wounded's node and n by m, receive from receiving node i that to transport wounded's quantity that node j transports be x _Ij, it is p that transportation meets with risk probability _Ij(t), the length of transportation route is d _IjTransportation meets with risk probability and is meant that complicated battlefield surroundings may impact the current risk of wounded's transportation route, risk can make the wounded injured from carriage direction take over party's transport process, thereby reduce the transhipment battlefield wounded's security, for the wounded expend time in or the risk minimum is commander's control of target to transport, this reduction has been equivalent to increase the risk that the wounded transport experience, it can be with the function of time as variable that transportation meets with risk probability, also can be and irrelevant constant of time, be expressed as p _Ij, the transportation in different paths meets with risk probability can be different,

The problem that need to solve is that one of design receives node from m and receives the individual wounded that transport node from n, make the diversion plan of transporting all wounded's risk minimums, the satisfied requirement of being scheduled to of consumed time simultaneously, and calculate each and transport the quantity that node transports the required means of transport of the wounded, it is as follows that the relevant battlefield wounded transport commander controlling models and linear programming equation:

Objective function: $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$

Upwards of movement constraint condition: $\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}=D_j,$ (j＝1，…，n)

Quantities received constraint condition: $\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤S_i,$ (i＝1，…，m)

Condition of Non-Negative Constrains: x _Ij〉=0, (i=1 ..., m; J=1 ..., n)

Arrival reception node i (i=1 ... m) means of transport quantity V _i:

Reception node i (i=1 ... m) receive from transport node j (j=1 ... n) time that the wounded are spent: $T_{\mathrm{ij}}=\frac{d_{\mathrm{ij}}}{C}$

Finish all battlefield wounded and transport spent minimum time: minT=max{T _Ij}

Transport the relevant maximum transportation experience risk probability of node with j: $p_j=\underset{p_{\mathrm{ij}}\∈P_{\mathrm{op}}}{\max }\left\{p_{\mathrm{ij}}\right\},$ j(j＝1，…n)

Finish all battlefield wounded and transport the risk probability of experience: minP=max{p _j, j (j=1 ... n)

Transport the relevant risk carrying capacity of node with j: $\min Z_j=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ j(j＝1，…n)

The overall risk carrying capacity of battlefield wounded transportation: $\min Z=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\min Z_j$

Transport the relevant wounded's carrying capacity of node with j: $Z_j=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ j(j＝1，…n)

The total wounded's carrying capacity in battlefield: $Z=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j$

Wherein:

M is for receiving the wounded's node sum;

N is the node sum that transports the wounded;

P _OpBe commander's controlling models p by associated pathway when obtaining optimum solution _IjThe set of forming;

The value of objective function was called the risk carrying capacity when minZ obtained optimum solution for commander's controlling models, and this value is the smaller the better;

p _IjFor receive node i (i=1 ... m) with transport node j (j=1 ... n) transportation between meets with risk probability, is with the function of time t as variable;

d _IjFor receive node i (i=1 ... m) with transport node j (j=1 ... the length of the transportation route n) (unit: kilometer);

V _iFor arrive to receive wounded's node i (i=1 ... m) the means of transport quantity of transporting the wounded;

L transports the wounded's ability (unit: the people) for each means of transport;

C transports the wounded's speed (unit: kilometer/hour) for each means of transport;

S _iFor receive node i (i=1 ... m) can receive the wounded's quantity (unit: the people);

D _jFor transport node j (j=1 ... n) need transport the wounded's quantity (unit: the people);

Above-mentioned model shows: objective function be equivalent to ask probability-weighted and, on the basis of trying to achieve risk carrying capacity minZ value by linear programming, can calculate each and transport wounded's quantity x that node must transport to the correlation reception node _Ij, the p of associated pathway _Ij,, can calculate and arrive the means of transport quantity V that each receives node again according to the dead weight capacity L of means of transport _iAnd transport the means of transport quantity that node needs, transport the wounded's speed C and, can calculate the risk carrying capacity minZ that each transports node again according to means of transport at last at the longest path that receives and transport between the node _j, maximum transportation meets with risk probability p _jFinish all battlefield wounded and transport the risk probability minP of experience, the shortest time T that expends, thereby realize commander's control to the transhipment of battlefield wounded's fast and low-risk, for constraint condition rationally being set, improving solvability, utilizing above-mentioned linear programming model better, the dual linear programming model that provides this model is as follows:

Objective function: $\max G=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{D}_j{y}_j+\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{S}_i{y}_{n+i}$

Constraint condition: D _jy _j+ S _iy _N+i≤ p _Ij, (i=1 ..., m; J=1 ..., n)

Condition of Non-Negative Constrains: y _j, y _N+i〉=0, (i=1 ..., m; J=1 ..., n)

Wherein: y _j, y _N+iBe respectively the shadow price of transporting and receive wounded's constraint condition or the relevant decision variable of opportunity cost with former linear programming,

Since primal linear programming solves be with transport node j and receive node i (i=1 ..., m; J=1 ..., the resource optimal utilization problem that constraint condition n) is relevant, thus dual program solve then be estimate to make transport node j and receive node i (i=1 ..., m; J=1 ..., constraint condition n) satisfies the cost problem that must pay, promptly uses the valency problem, and shadow price y _jAnd y _N+iThe making just of reflection transport node j and receive node i (i=1 ..., m; J=1, n) constraint condition satisfies the cost that must pay, by making the target function value relevant minimize (or maximization) with cost, shadow price can be used for each constraint condition of comparison and carry out equivalence analysis to the contribution of target function value or to this contribution influence, shadow price is big more, show that this constraint condition is big more to the influence of the priming the pump delivery power of option control command, but it is also just difficult more to satisfy this condition, therefore, introducing shadow price just can be by comparing shadow price and realistic objective functional value, and can variation that study former linear programming constraint condition make objective function obtain gain.

Embodiment

Implementation example

In IT-based warfare, battlefield wounded's turn-over capacity of mechanization combat division is an important component part of its fighting capacity, to battlefield wounded's turn-over capacity and the demand of time, make commander's control of implementing battlefield wounded transhipment become vital task, the implementation example of quick commander's control of battlefield wounded's fast and low-risk transhipment that with the risk minimum is target is as follows, it is the implementation example analysis of target to quick commander's control of battlefield wounded's fast and low-risk transhipment that this implementation example is equally applicable to the minimum that expends time in, and only need objective function this moment $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$ Be replaced into $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ With constraint condition D _jy _j+ S _iy _N+i≤ p _IjBe replaced into D _jy _j+ S _iy _N+i≤ d _IjAnd similarly analyze and get final product, suppose that certain mechanization combat division must be that 16 people, average speed per hour are wounded's transfer car(buggy) of 70 kilometers with dead weight capacity, receive from 14 from 5 acceptance points and to transport the battlefield wounded that a little transport, it is as shown in table 1 to transport experience risk probability, quantities received and upwards of movement between receiving and transport a little.

Table 1: the wounded receive and transport a little between transportation experience risk probability, reception and upwards of movement (unit: probability, people)

	01 acceptance point	02 acceptance point	03 acceptance point	04 acceptance point	05 acceptance point	Transport quantity
							01 transports a little 02 transports a little 03 and transports a little 04 and transport a little 05 and transport a little 06 and transport a little 07 and transport a little 08 and transport a little 09 and transport a little and 10 transport a little	0.037 0.034 0.025 0.014 0.026 0.024 0.120 0.159 0.112 0.062	0.013 0.025 0.028 0.015 0.035 0.020 0.098 0.138 0.096 0.037	0.070 0.083 0.108 0.097 0.082 0.110 0.012 0.051 0.096 0.046	0.074 0.087 0.112 0.101 0.086 0.100 0.129 0.149 0.025 0.050	0.044 0.031 0.066 0.058 0.056 0.039 0.105 0.145 0.110 0.059	36.00 21.00 90.00 130.00 70.00 40.00 60.00 16.00 29.00 36.00

11 transport a little 12 transports a little 13 and transports a little and 14 transport a little	0.091 0.126 0.090 0.081	0.066 0.097 0.068 0.056	0.017 0.081 0.099 0.020	0.079 0.086 0.104 0.066	0.073 0.027 0.011 0.075	90.00 22.00 18.00 24.00
							Can connect quantity	250.00	200.00	300.00	400.00	150.00

Receive and transport a little between the length and the supply and demand amount of transportation route as shown in table 2,

Table 2: the wounded receive and transport a little between transportation route length, reception and upwards of movement (unit: kilometer, people)

	01 acceptance point	02 acceptance point	03 acceptance point	04 acceptance point	05 acceptance point	Transport quantity
							01 transports a little 02 transports a little 03 and transports a little 04 and transport a little 05 and transport a little 06 and transport a little 07 and transport a little 08 and transport a little 09 and transport a little 10 and transport a little 11 and transport a little 12 and transport a little 13 and transport a little and 14 transport a little	37.00 34.00 25.00 14.00 26.00 24.00 120.00 159.00 112.00 62.00 91.00 126.00 90.00 81.00	13.00 25.00 28.00 15.00 35.00 20.00 98.00 138.00 96.00 37.00 66.00 97.00 68.00 56.00	70.00 83.00 108.00 97.00 82.00 110.00 12.00 51.00 96.00 46.00 17.00 81.00 99.00 20.00	74.00 87.00 112.00 101.00 86.00 100.00 129.00 149.00 25.00 50.00 79.00 86.00 104.00 66.00	44.00 31.00 66.00 58.00 56.00 39.00 105.00 145.00 110.00 59.00 73.00 27.00 11.00 75.00	36.00 21.00 90.00 130.00 70.00 40.00 60.00 16.00 29.00 36.00 90.00 22.00 18.00 24.00
Can connect quantity	250.00	200.00	300.00	400.00	150.00

According to above-mentioned linear programming and commander controlling models and relevant dual linear programming model, calculating the minimum risk wounded of mechanization combat division by simplex algorithm, to transport option control command as shown in table 3, and wherein people's risk is to transport the risk carrying capacity minZ of node _j, risk probability is that the maximum transportation of transporting node meets with risk probability p _j, passenger-kilometer is the wounded's carrying capacity Z that transports node _j

Table 3: the minimum risk wounded of mechanization combat division transport option control command (unit: people, people's risk, probability, passenger-kilometer,, minute)

	01 acceptance point	02 acceptance point	03 acceptance point	04 acceptance point	05 acceptance point	People's risk	Risk probability	Passenger-kilometer	The fortune car	Expend time in	Shadow price
												01 transports a little 02 transports a little 03 and transports a little 04 and transport a little 05 and transport a little 06 and transport a little 07 and transport a little 08 and transport a little 09 and transport a little 10 and transport a little 11 and transport a little and 12 transport a little	90.00 90.00 70.00	36.00 21.00 40.00 40.00 36.00	60.00 16.00 90.00	29.00	22.00	0.468 0.525 2.250 1.860 1.820 0.800 0.720 0.816 0.725 1.332 1.530 0.594	0.013 0.025 0.025 0.015 0.026 0.020 0.012 0.051 0.025 0.037 0.017 0.027	468.00 525.00 2250.00 1860.00 1820.00 800.00 720.00 816.00 725.00 1332.00 1530.00 594.00	3 2 6 9 5 3 4 1 2 3 6 2	11.14 21.43 21.43 12.26 22.29 17.14 10.29 43.71 21.43 31.71 14.57 23.14	0.00 12.00 13.00 2.00 14.00 7.00 0.00 39.00 0.00 24.00 5.00 16.00

13 transport a little and 14 transport a little			24.00		18.00	0.198 0.480	0.011 0.020	198.00 480.00	2 2	9.43 17.14	0.00 8.00
												Add up to	250.00	173.00	190.00	29.00	40.00	14.118	0.051	14118.00	50	43.71 *
Can connect quantity	250.00	200.00	300.00	400.00	150.00
												Connect the back surplus	0.00	27.00	110.00	371.00	110.00
Shadow price	12.00	13.00	12.00	25.00	11.00

* finishing the minimum time that transport task expends is 43.71 minutes

By option control command (table 3) is analyzed as can be known; wounded's transfer car(buggy) that finishing the transhipment task needs adds up to 50; time is 43.71 minutes; wounded's transfer car(buggy) that 01～05 acceptance point receives is respectively 17; 14; 13; 2 and 4; therefore must be to 01; 02 and 03 acceptance point is implemented to lay special stress on protecting; further analyze as can be known; transporting 16 43.71 minutes that the wounded spent of a little transporting from the reception of 03 acceptance point from 08 is the bottleneck that the whole transhipment task of restriction is finished sooner; this transports also is simultaneously to reduce to finish the bottleneck that all battlefield wounded transport the risk probability of experience; if finish this part wounded's transhipment with helicopter; then can be shortened to 31.71 minutes the time that whole transport task is finished; reduction is 27.45%; risk probability is reduced to 0.037; reduction is 27.45%; and for example fruit is adopted the bottleneck that uses the same method and eliminated 31.71 minutes; then the transhipment time can be shortened to 23.14 minutes; reduction reaches 47.06%; risk probability is reduced to 0.027; reduction is 47.06%, almost only is half of former free and risk probability.

From to upwards of movement constraint condition D _j(j=1,14) analysis of shadow price as can be known, the size of price has truly reflected the complexity that the related constraint condition satisfies, shadow price is 0 to be meant in specific span, and relevant constraint condition does not constitute influence to target function value, the easiliest satisfies, again for example, in order to satisfy constraint condition D ₈, 08 transport the risk of a little transporting the wounded be 0.051,43.71 minutes consuming time, the shadow price of this constraint condition is a maximal value 39, illustrates that this condition is the most difficult to satisfy, can be by D with similar method _jThe complexity that satisfies, from difficulty to easy ordering: D ₈, D ₁₀, D ₁₂, D ₅..., to quantities received constraint condition S _i(i=1 ..., 5) analysis of shadow price as can be known, S _iThe complexity that satisfies, from difficulty to easy ordering: S ₄, S ₂, S ₁, S ₃, S ₅, i.e. constraint condition S ₄The most difficult satisfied.

In addition, from the residue quantities received of each acceptance point of back of finishing the work as can be seen, the permission quantities received of 01 acceptance point and 02 acceptance point is obviously on the low side, and particularly 01 acceptance point allows quantities received to exhaust, this statement of facts:, add S if 01 acceptance point has more permission quantities received ₁Constraint condition more easily satisfies, and just may obtain better diversion plan, therefore, can also carry out reasonable configuration to the permission quantities received of each acceptance point with said method, realizes the Optimal Management of permission quantities received.

Claims (9)

1, the present invention relates to quick commander's control method of battlefield wounded's fast and low-risk transhipment, relate to military affairs and association area, the object of commander's control is all battlefield wounded, this method is according to the length from different take over partys to the difference side of transporting wounded transportation route, transportation meets with risk probability, the take over party wounded's the quantities received and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model, by two-dimentional form solving result is updated again, meet the option control command that battlefield wounded's fast and low-risk transhipment requires until final acquisition, this scheme is applicable to commander's control of all battlefield wounded's fast and low-risk transhipments.

2, quick commander's control method of battlefield wounded's fast and low-risk transhipment according to claim 1, the object that it is characterized in that described commander control is meant the object as commander's control with all battlefield wounded and patient for all battlefield wounded, described commander's control is meant according to the actual demand of battlefield to wounded's transhipment, design is transported to different take over partys with the battlefield wounded from the different sides of transporting, and all transportations are expended time in or the probability-weighted that meets with risk for minimum, can be for the scheme of implementing.

3, quick commander's control method of battlefield wounded's fast and low-risk transhipment according to claim 1, it is characterized in that described this method is meant the supply and demand system that can set up a battlefield wounded transhipment by these parameters according to quantities received and the side of the transporting wounded's upwards of movement, the speed and the carrying capacity of means of transport that the length from different take over partys to the difference side of transporting wounded transportation route, transportation meet with risk probability, the take over party wounded, obtains the battlefield wounded are transported the method for implementing commander's control on this basis.

4, quick commander's control method of battlefield according to claim 1 wounded's fast and low-risk transhipment, it is characterized in that described transportation meets with risk probability and is meant that complicated battlefield surroundings may impact the current risk of wounded's transportation route, risk can make the wounded injured from carriage direction take over party's transport process, thereby reduce the transhipment battlefield wounded's security, for the wounded expend time in or the risk minimum is commander's control of target to transport, this reduction has been equivalent to increase the risk that the wounded transport experience, it can be with the function of time as variable that transportation meets with risk probability, also can be and irrelevant constant of time, the transportation in different paths meets with risk probability can be different.

5, quick commander's control method of battlefield wounded's fast and low-risk transhipment according to claim 1, it is characterized in that described structure to transport all wounded and expend time in or the risk minimum is that target and the commander's controlling models with low computational complexity and high solvability are meant for computational complexity that reduces this commander's controlling models and the solvability that improves this commander controlling models, stipulates that the constraint condition relevant with the side of transporting is that the constraint condition that equals the side's of transporting upwards of movement, the constraint condition relevant with the take over party are the constraint condition that is not more than take over party's maximum quantities received.

6, quick commander's control method of battlefield according to claim 1 wounded's fast and low-risk transhipment, it is characterized in that described and use linear programming, the dual program method of linear programming is found the solution this model, by two-dimentional form solving result is updated again, meeting option control command that wounded's fast and low-risk transhipment in battlefield requires until final acquisition is meant by the method for finding the solution linear programming and finding the solution the dual program of linear programming and finds the solution commander's controlling models, the minimum transportation that can obtain respectively from the difference side of the transporting transportation wounded to different take over partys meets with risk probability or the transportation route of least consume time, with different take over partys and the relevant shadow price of the different sides of transporting constraint condition, the result that will find the solution inserts in a kind of two dimension commander's control form again, according to analysis to this two dimension commander control form, and pass through according to shadow price, risk and time bottleneck are adjusted correlation parameter, constantly find the solution and update, meet the option control command that battlefield wounded's fast and low-risk transhipment requires until final acquisition.

7, quick commander's control method of battlefield according to claim 1 wounded's fast and low-risk transhipment, it is characterized in that described and use linear programming, the dual program method of linear programming is found the solution this model, by two-dimentional form solving result is updated again, the option control command that meets battlefield wounded's fast and low-risk transhipment time requirement until final acquisition is meant can be by describing the quantity of transporting the wounded to each take over party from each side of transporting as the zones of different in the two-dimentional form of option control command, each side of transporting needs the size of transport power, risk in transit, the quantity of means of transport, transportation expends time in and relevant shadow price, and each take over party receives the wounded's quantity, the situation of change of residue reception wounded ability is with relevant shadow price and transport all wounded's priming the pump and the minimum time that expends.

8, quick commander's control method of battlefield according to claim 1 wounded's fast and low-risk transhipment, it is characterized in that described this method is according to the length from different take over partys to the difference side of transporting wounded transportation route, transportation meets with risk probability, the take over party wounded's the quantities received and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model and is meant that following is the analysis of target to quick commander's control problem of battlefield wounded's fast and low-risk transhipment with the risk minimum, but it is the analysis of target to quick commander's control problem of battlefield wounded's fast and low-risk transhipment that this analysis is equally applicable to the minimum that expends time in, and only need objective function this moment $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$ Be replaced into $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ With constraint condition D _jy _j+ S _iy _N+i≤ p _IjBe replaced into D _jy _j+ S _jy _N+i≤ d _IjAnd similarly analyze and get final product, following mathematical formulae, derivation, result of calculation and application process are applicable to the quick commander's control to all battlefield wounded's fast and low-risk transhipments,

Suppose that battlefield wounded's fast and low-risk transshipment problem can transport wounded's node and describes in different receptions and the network that transports the path that has transportation wounded between the node with receiving wounded's node and n by m, receive from receiving node i that to transport wounded's quantity that node j transports be x _Ij, it is p that transportation meets with risk probability _Ij(t), the length of transportation route is d _IjTransportation meets with risk probability and is meant that complicated battlefield surroundings may impact the current risk of wounded's transportation route, risk can make the wounded injured from carriage direction take over party's transport process, thereby reduce the transhipment battlefield wounded's security, for the wounded expend time in or the risk minimum is commander's control of target to transport, this reduction has been equivalent to increase the risk that the wounded transport experience, it can be with the function of time as variable that transportation meets with risk probability, also can be and irrelevant constant of time, be expressed as p _Ij, the transportation in different paths meets with risk probability can be different,

The problem that need to solve is that one of design receives node from m and receives the individual wounded that transport node from n, make the diversion plan of transporting all wounded's risk minimums, the satisfied requirement of being scheduled to of consumed time simultaneously, and calculate each and transport the quantity that node transports the required means of transport of the wounded, it is as follows that the relevant battlefield wounded transport commander controlling models and linear programming equation:

Objective function: $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$

Upwards of movement constraint condition: $\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}=D_j,(j=1,\·\·\·,n)$

Quantities received constraint condition: $\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤S_i,(i=1,\·\·\·,m)$

Condition of Non-Negative Constrains: x _Ij〉=0, (i=1 ..., m; J=1 ..., n)

Arrival reception node i (i=1 ... m) means of transport quantity

Reception node i (i=1 ... m) receive from transport node j (j=1 ... n) time that the wounded are spent: $T_{\mathrm{ij}}=\frac{d_{\mathrm{ij}}}{C}$

Finish all battlefield wounded and transport spent minimum time: minT=max{T _Ij}

Transport the relevant maximum transportation experience risk probability of node with j: $p_j=\underset{p_{\mathrm{ij}}\∈P_{\mathrm{op}}}{\max }\left\{p_{\mathrm{ij}}\right\},j(j=1,\·\·\·n)$

Finish all battlefield wounded and transport the risk probability of experience: minP=max{p _j, j (j=1 ... n)

Transport the relevant risk carrying capacity of node with j: $\min Z_j=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}},j(j=1,\·\·\·n)$

The overall risk carrying capacity of battlefield wounded transportation: $\min Z=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\min Z_j$

Transport the relevant wounded's carrying capacity of node with j: $Z_j=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},j(j=1,\·\·\·n)$

The total wounded's carrying capacity in battlefield: $Z=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j$

Wherein:

M is for receiving the wounded's node sum;

N is the node sum that transports the wounded;

P _OpBe commander's controlling models p by associated pathway when obtaining optimum solution _IjThe set of forming;

The value of objective function was called the risk carrying capacity when minZ obtained optimum solution for commander's controlling models, and this value is the smaller the better;

p _IjFor receive node i (i=1 ... m) with transport node j (j=1 ... n) transportation between meets with risk probability, is with the function of time t as variable;

d _IjFor receive node i (i=1 ... m) with transport node j (j=1 ... the length of the transportation route n) (unit: kilometer);

V _iFor arrive to receive wounded's node i (i=1 ... m) the means of transport quantity of transporting the wounded;

L transports the wounded's ability (unit: the people) for each means of transport;

C transports the wounded's speed (unit: kilometer/hour) for each means of transport;

S _iFor receive node i (i=1 ... m) can receive the wounded's quantity (unit: the people);

D _jFor transport node j (j=1 ... n) need transport the wounded's quantity (unit: the people);

Above-mentioned model shows: objective function be equivalent to ask probability-weighted and, on the basis of trying to achieve risk carrying capacity minZ value by linear programming, can calculate each and transport wounded's quantity x that node must transport to the correlation reception node _Ij, the p of associated pathway _Ij,, can calculate and arrive the means of transport quantity V that each receives node again according to the dead weight capacity L of means of transport _iAnd transport the means of transport quantity that node needs, transport the wounded's speed C and, can calculate the risk carrying capacity minZ that each transports node again according to means of transport at last at the longest path that receives and transport between the node _j, maximum transportation meets with risk probability p _jFinish all battlefield wounded and transport the risk probability minP of experience, the shortest time T that expends, thereby realize commander's control to the transhipment of battlefield wounded's fast and low-risk, for constraint condition rationally being set, improving solvability, utilizing above-mentioned linear programming model better, the dual linear programming model that provides this model is as follows:

Objective function: $\max G=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{D}_j{y}_j+\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{S}_i{y}_{n+i}$

Constraint condition: D _jy _j+ S _iy _N+i≤ p _Ij, (i=1 ..., m; J=1 ..., n)

Condition of Non-Negative Constrains: y _j, y _N+i〉=0, (i=1 ..., m; J=1 ..., n)

Wherein: y _j, y _N+iBe respectively the shadow price of transporting and receive wounded's constraint condition or the relevant decision variable of opportunity cost with former linear programming,

Since primal linear programming solves be with transport node j and receive node i (i=1 ..., m; J=1 ..., the resource optimal utilization problem that constraint condition n) is relevant, thus dual program solve then be estimate to make transport node j and receive node i (i=1 ..., m; J=1 ..., constraint condition n) satisfies the cost problem that must pay, promptly uses the valency problem, and shadow price y _jAnd y _N+iThe making just of reflection transport node j and receive node i (i=1 ..., m; J=1, n) constraint condition satisfies the cost that must pay, by making the target function value relevant minimize (or maximization) with cost, shadow price can be used for each constraint condition of comparison and carry out equivalence analysis to the contribution of target function value or to this contribution influence, shadow price is big more, show that this constraint condition is big more to the influence of the priming the pump delivery power of option control command, but it is also just difficult more to satisfy this condition, therefore, introducing shadow price just can be by comparing shadow price and realistic objective functional value, and can variation that study former linear programming constraint condition make objective function obtain gain.

9, quick commander's control method of battlefield according to claim 1 wounded's fast and low-risk transhipment, it is characterized in that described this method is according to the length from different take over partys to the difference side of transporting wounded transportation route, transportation meets with risk probability, the take over party wounded's the quantities received and the side of the transporting wounded's upwards of movement, the speed of means of transport and carrying capacity, structure is to transport all wounded and expend time in or the risk minimum is target and the commander's controlling models with low computational complexity and high solvability, and use linear programming, the dual program method of linear programming is found the solution this model, by two-dimentional form solving result is updated again, the option control command that meets battlefield wounded's fast and low-risk transhipment requirement until final acquisition is meant if the option control command of trying to achieve can not satisfy predetermined risk and time requirement, then can be by two dimension commander control table, result to former linear programming and dual program analyzes, determine to influence the risk of battlefield wounded transhipment and the bottleneck of T.T., carry out reasonable disposition by the quantity that take over party's permission is received the wounded again, increase the quantity of means of transport and adopt different means such as means of transport, eliminate risk and time bottleneck, and repeat this process, until the risk of finishing battlefield wounded transhipment with meet predetermined requirement T.T., this process can be target with the risk minimum describe the example of quick commander's control of battlefield wounded's fast and low-risk transhipment with following, it is the instance analysis of target to quick commander's control of battlefield wounded's fast and low-risk transhipment that this example is equally applicable to the minimum that expends time in, and only need objective function this moment $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{p}_{\mathrm{ij}}{x}_{\mathrm{ij}}$ Be replaced into $\min Z=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ij}},$ With constraint condition D _jy _j+ S _iy _N+i≤ p _IjBe replaced into D _jy _j+ S _iy _N+i≤ d _IjAnd similarly analyze and get final product, but the mathematical formulae described in example, result of calculation, various form and application process are applicable to the quick commander's control to all battlefield wounded's fast and low-risk transhipments,

Suppose that certain mechanization combat division must be that 16 people, average speed per hour are wounded's transfer car(buggy) of 70 kilometers with dead weight capacity, transport the battlefield wounded that a little transport from 5 acceptance points receptions from 14, receive and transport a little between transportation experience risk probability, quantities received and upwards of movement as shown in table 1

Table 1: the wounded receive and transport a little between transportation experience risk probability, reception and upwards of movement (unit: probability, people) 01 acceptance point 02 acceptance point 03 acceptance point 04 acceptance point 05 acceptance point Transport quantity 01 transports a little 0.037 0.013 0.070 0.074 0.044 36.00 02 transports a little 03 transports a little 04 and transports a little 05 and transport a little 06 and transport a little 07 and transport a little 08 and transport a little 09 and transport a little 10 and transport a little 11 and transport a little 12 and transport a little 13 and transport a little and 14 transport a little 0.034 0.025 0.014 0.026 0.024 0.120 0.159 0.112 0.062 0.091 0.126 0.090 0.081 0.025 0.028 0.015 0.035 0.020 0.098 0.138 0.096 0.037 0.066 0.097 0.068 0.056 0.083 0.108 0.097 0.082 0.110 0.012 0.051 0.096 0.046 0.017 0.081 0.099 0.020 0.087 0.112 0.101 0.086 0.100 0.129 0.149 0.025 0.050 0.079 0.086 0.104 0.066 0.031 0.066 0.058 0.056 0.039 0.105 0.145 0.110 0.059 0.073 0.027 0.011 0.075 21.00 90.00 130.00 70.00 40.00 60.00 16.00 29.00 36.00 90.00 22.00 18.00 24.00 Can connect quantity 250.00 200.00 300.00 400.00 150.00

Receive and transport a little between the length and the supply and demand amount of transportation route as shown in table 2,

Table 2: the wounded receive and transport a little between transportation route length, reception and upwards of movement (unit: kilometer, people) 01 acceptance point 02 acceptance point 03 acceptance point 04 acceptance point 05 acceptance point Transport quantity 01 transports a little 02 transports a little 03 and transports a little 04 and transport a little 05 and transport a little 06 and transport a little 07 and transport a little 08 and transport a little 09 and transport a little 10 and transport a little 11 and transport a little 12 and transport a little 13 and transport a little and 14 transport a little 37.00 34.00 25.00 14.00 26.00 24.00 120.00 159.00 112.00 62.00 91.00 126.00 90.00 81.00 13.00 25.00 28.00 15.00 35.00 20.00 98.00 138.00 96.00 37.00 66.00 97.00 68.00 56.00 70.00 83.00 108.00 97.00 82.00 110.00 12.00 51.00 96.00 46.00 17.00 81.00 99.00 20.00 74.00 87.00 112.00 101.00 86.00 100.00 129.00 149.00 25.00 50.00 79.00 86.00 104.00 66.00 44.00 31.00 66.00 58.00 56.00 39.00 105.00 145.00 110.00 59.00 73.00 27.00 11.00 75.00 36.00 21.00 90.00 130.00 70.00 40.00 60.00 16.00 29.00 36.00 90.00 22.00 18.00 24.00 Can connect quantity 250.00 200.00 300.00 400.00 150.00

According to above-mentioned linear programming and commander controlling models and relevant dual linear programming model, calculating the minimum risk wounded of mechanization combat division by simplex algorithm, to transport option control command as shown in table 3, and wherein people's risk is to transport the risk carrying capacity minZ of node _j, risk probability is that the maximum transportation of transporting node meets with risk probability p _j, passenger-kilometer is the wounded's carrying capacity Z that transports node _j,

Table 3: the minimum risk wounded of mechanization combat division transport option control command (unit: people, people's risk, probability, passenger-kilometer,, minute) 01 acceptance point 02 acceptance point 03 acceptance point 04 acceptance point 05 acceptance point People's risk Risk probability Passenger-kilometer The fortune car Expend time in Shadow price 01 transports a little 02 transports a little and 03 transports a little 90.00 36.00 21.00 0.468 0.525 2.250 0.013 0.025 0.025 468.00 525.00 2250.00 3 2 6 11.14 21.43 21.43 0.00 12.00 13.00 04 transports a little 05 transports a little 06 and transports a little 07 and transport a little 08 and transport a little 09 and transport a little 10 and transport a little 11 and transport a little 12 and transport a little 13 and transport a little and 14 transport a little 90.00 70.00 40.00 40.00 36.00 60.00 16.00 90.00 24.00 29.00 2200 18.00 1.860 1.820 0.800 0.720 0.816 0.725 1.332 1.530 0.594 0.198 0.480 0.015 0.026 0.020 0.012 0.051 0.025 0.037 0.017 0.027 0.011 0.020 1860.00 1820.00 800.00 720.00 816.00 725.00 1332.00 1530.00 594.00 198.00 480.00 9 5 3 4 1 2 3 6 2 2 2 12.26 22.29 17.14 10.29 43.71 21.43 31.71 14.57 23.14 9.43 17.14 2.00 14.00 7.00 0.00 39.00 0.00 24.00 5.00 16.00 0.00 8.00 Add up to 250.00 173.00 190.00 29.00 40.00 14.118 0.051 14118.00 50 43.71 ^* Can connect quantity 250.00 200.00 300.00 400.00 150.00 Connect the back surplus 0.00 27.00 110.00 371.00 110.00 Shadow price 12.00 13.00 12.00 25.00 11.00

* finishing the minimum time that transport task expends is 43.71 minutes

By option control command (table 3) is analyzed as can be known; wounded's transfer car(buggy) that finishing the transhipment task needs adds up to 50; time is 43.71 minutes; wounded's transfer car(buggy) that 01～05 acceptance point receives is respectively 17; 14; 13; 2 and 4; therefore must be to 01; 02 and 03 acceptance point is implemented to lay special stress on protecting; further analyze as can be known; transporting 16 43.71 minutes that the wounded spent of a little transporting from the reception of 03 acceptance point from 08 is the bottleneck that the whole transhipment task of restriction is finished sooner; this transports also is simultaneously to reduce to finish the bottleneck that all battlefield wounded transport the risk probability of experience; if finish this part wounded's transhipment with helicopter; then can be shortened to 31.71 minutes the time that whole transport task is finished; reduction is 27.45%; risk probability is reduced to 0.037; reduction is 27.45%; and for example fruit is adopted the bottleneck that uses the same method and eliminated 31.71 minutes; then the transhipment time can be shortened to 23.14 minutes; reduction reaches 47.06%; risk probability is reduced to 0.027; reduction is 47.06%; almost only be half of former free and risk probability

From to upwards of movement constraint condition D _j(j=1,14) analysis of shadow price as can be known, the size of price has truly reflected the complexity that the related constraint condition satisfies, shadow price is 0 to be meant in specific span, and relevant constraint condition does not constitute influence to target function value, the easiliest satisfies, again for example, in order to satisfy constraint condition D ₈, 08 transport the risk of a little transporting the wounded be 0.051,43.71 minutes consuming time, the shadow price of this constraint condition is a maximal value 39, illustrates that this condition is the most difficult to satisfy, can be by D with similar method _jThe complexity that satisfies, from difficulty to easy ordering: D ₈, D ₁₀, D ₁₂, D ₅..., to quantities received constraint condition S _i(i=1 ..., 5) analysis of shadow price as can be known, S _iThe complexity that satisfies, from difficulty to easy ordering: S ₄, S ₂, S ₁, S ₃, S ₅, i.e. constraint condition S ₄It is the most difficult satisfied,

In addition, from the residue quantities received of each acceptance point of back of finishing the work as can be seen, the permission quantities received of 01 acceptance point and 02 acceptance point is obviously on the low side, and particularly 01 acceptance point allows quantities received to exhaust, this statement of facts:, add S if 01 acceptance point has more permission quantities received ₁Constraint condition more easily satisfies, and just may obtain better diversion plan, therefore, can also carry out reasonable configuration to the permission quantities received of each acceptance point with said method, realizes the Optimal Management of permission quantities received.