CN105277197A - River way transportation ship - Google Patents

Abstract

The invention provides a river way transportation ship. The river way transportation ship comprises a transportation ship and a navigator mounted on the transportation ship, wherein the navigator specifically comprises a signal module, a processing module and a generation module. By adopting an optimized path algorithm, various cost factors in a transportation process are considered; the optimizing effect is good, the solving efficiency is high and the performance is stable; a global search capability is enhanced and the operation cost of transportation can be saved to the greatest extent; and very good energy-saving effect is realized.

Description

A kind of river course cargo ship

Technical field

The present invention relates to river course transport field, be specifically related to a kind of river course cargo ship.

Background technology

River course transport is all a kind of important means of transportation by Gu so far, even if in transportation system highly developed today, river course cargo ship remains main means of transport.

Each transportation point due to river course transport is often scattered in each mutually distant region, therefore, how according to different destinations and arrive each destination want seeking time to select a path saving cargo ship operating cost to greatest extent, be a problem needing solution badly.

Summary of the invention

For the problems referred to above, the invention provides a kind of river course cargo ship.

Object of the present invention realizes by the following technical solutions:

A kind of river course cargo ship, for the Material Transportation of remote multiple destination, is characterized in that, comprises cargo ship and is arranged on the navigating instrument on cargo ship; Navigating instrument specifically comprises signaling module, processing module and generation module;

Signaling module, wants seeking time for multiple transport destination of receiving this round of user's input and the expectation that arrives each destination;

Processing module, for selecting optimal path according to the transport destination of this round and the geographical environment information of input in advance, specifically comprises:

Analog module:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}{b}_0{\mathrm{\ω}}_0{\mathrm{\Φ}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}{b}_0{\mathrm{\ω}}_0\frac{{\mathrm{\Φ}}^{*}-{\mathrm{\Φ}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\Σ}}(G_i-t_i)\\ +T_2\underset{i=0}{\overset{U}{\Σ}}(t_i-O_i)\end{array}$

Wherein, minS is the least cost in transportation; M is the sum of current transportation ship; Ground quantity for the purpose of U; b ₀for unit distance carbon emission cost; ω ₀for carbon emission coefficient; Ф ₀for unit distance Fuel Consumption during zero load; f _ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of cargo ship; H is the dead weight of cargo ship; Ф ^*for full load unit distance Fuel Consumption;

t ₁for cargo ship arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T ₂for cargo ship is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering cargo ship in advance, T ₁and T ₂for the coefficient artificially set;

Probabilistic module: suppose total R node, γ _ijt () represents the tracking element intensity between t node i and node j, γ _ij(0)=K (K is the constant that numerical value is less), cargo ship selects shift direction according to the plain intensity of tracking in motion process, then cargo ship k (k=1,2 ..., probability m) transferring to node j from node i is:

Wherein, g ∈ A _k; A _k=0,1 ..., R-1}-B _krepresent the set of the point that next step permission of cargo ship k is selected, in time in dynamic change, B _k(k=1,2 ..., be m) taboo list of a kth cargo ship, be used for recording the point that cargo ship k had transported; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;

Update module: introduce optimized variable X _ijt (), it meets X _ij(t+1)=σ X (t) [1-X _ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:

γ _ij(t+1)＝(1-ζ)γ _ij(t)+Δγ _ij(t)+чX _ij(t)

Wherein, ${\mathrm{\Δ\γ}}_{ij}\left(t\right)={\Σ}_{k=1}^m{\mathrm{\Δ\γ}}_{ij}^k\left(t\right),$

f _kby a kth cargo ship is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth cargo ship stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ _minit is the minimum value of artificial setting; Δ γ _ijt () represents the summation of the tracking element intensity that all cargo ships stay on path (i, j) in this circulation; ч is adjustable coefficient;

Initial module: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p ₀, select next node j according to the following formula: wherein l ∈ A _k; Otherwise select next node j according to the new probability formula in probabilistic module, j is added array B _kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm _i;

Optimum solution module: generate one group of new feasible solution S from current initial set _j, desired value variation delta S=S _j-S _iif Δ S < 0, then accept new feasible solution S _jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S _jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S _i;

Judge module: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B _kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e ₁+ e ₂+ ..., e _i-1< u < e ₁+ e ₂+ ..., e _i, then select probability is e _icandidate's cargo ship as next destination node;

Generation module: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD _max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v _klist, wherein v ∈ [0,1], gets back to initial module, regenerates random number p; If DD=DD _max, then optimum solution is exported as optimal path.

Beneficial effect of the present invention is: the present invention adopts the routing algorithm of optimization, consider the various cost factors in transportation, optimizing is effective, solution efficiency is high, stable performance, enhance ability of searching optimum, the operating cost of transport can be saved to greatest extent, good energy-saving effect can be played.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not form any limitation of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, can also obtain other accompanying drawing according to the following drawings.

Fig. 1 is structured flowchart of the present invention.

Reference numeral: signaling module-1; Analog module-3; Probabilistic module-5; Update module-7; Initial module-9; Optimum solution module-10; Judge module-12; Generation module-14.

Embodiment

The invention will be further described with the following Examples.

A kind of river course cargo ship as shown in Figure 1, for the Material Transportation of remote multiple destination, is characterized in that, comprises cargo ship and is arranged on the navigating instrument on cargo ship; Navigating instrument specifically comprises signaling module, processing module and generation module;

Signaling module 1, wants seeking time for multiple transport destination of receiving this round of user's input and the expectation that arrives each destination;

Processing module, select optimal path for the transport destination according to this round with prior input geographical environment information, specifically comprise:

Analog module 3:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0{\mathrm{\&Phi;}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0\frac{{\mathrm{\&Phi;}}^{*}-{\mathrm{\&Phi;}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\&Sigma;}}(G_i-t_i)\\ +T_2\underset{i=0}{\overset{U}{\&Sigma;}}(t_i-O_i)\end{array}$

Wherein, minS is the least cost in transportation; M is the sum of current transportation ship; Ground quantity for the purpose of U; b ₀for unit distance carbon emission cost; ω ₀for carbon emission coefficient; Ф ₀for unit distance Fuel Consumption during zero load; f _ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of cargo ship; H is the dead weight of cargo ship; Ф ^*for full load unit distance Fuel Consumption;

t ₁for cargo ship arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T ₂for cargo ship is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering cargo ship in advance, T ₁and T ₂for the coefficient artificially set;

Probabilistic module 5: suppose total R node, γ _ijt () represents the tracking element intensity between t node i and node j, γ _ij(0)=K (K is the constant that numerical value is less), cargo ship selects shift direction according to the plain intensity of tracking in motion process, then cargo ship k (k=1,2 ..., probability m) transferring to node j from node i is:

Wherein, g ∈ A _k; A _k=0,1 ..., R-1}-B _krepresent the set of the point that next step permission of cargo ship k is selected, in time in dynamic change, B _k(k=1,2 ..., be m) taboo list of a kth cargo ship, be used for recording the point that cargo ship k had transported; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;

Update module 7: introduce optimized variable X _ijt (), it meets X _ij(t+1)=σ X (t) [1-X _ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:

γ _ij(t+1)＝(1-ζ)γ _ij(t)+Δγ _ij(t)+чX _ij(t)

Wherein, ${\mathrm{\&Delta;\&gamma;}}_{ij}\left(t\right)={\&Sigma;}_{k=1}^m{\mathrm{\&Delta;\&gamma;}}_{ij}^k\left(t\right),$

f _kby a kth cargo ship is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth cargo ship stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ _minit is the minimum value of artificial setting; Δ γ _ijt () represents the summation of the tracking element intensity that all cargo ships stay on path (i, j) in this circulation; ч is adjustable coefficient;

Initial module 9: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p ₀, select next node j according to the following formula: wherein l ∈ A _k; Otherwise select next node j according to the new probability formula in probabilistic module 5, j is added array B _kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm _i;

Optimum solution module 10: generate one group of new feasible solution S from current initial set _j, desired value variation delta S=S _j-S _iif Δ S < 0, then accept new feasible solution S _jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S _jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S _i;

Judge module 12: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B _kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e ₁+ e ₂+ ..., e _i-1< u < e ₁+ e ₂+ ..., e _i, then select probability is e _icandidate's cargo ship as next destination node;

Generation module 14: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD _max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v _klist, wherein v ∈ [0,1], gets back to initial module 9, regenerates random number p; If DD=DD _max, then optimum solution is exported as optimal path.

The present invention adopts the routing algorithm of optimization, consider the various cost factors in transportation, optimizing be effective, solution efficiency is high, stable performance, enhance ability of searching optimum, the operating cost of transport can be saved to greatest extent, good energy-saving effect can be played.

Finally should be noted that; above embodiment is only in order to illustrate technical scheme of the present invention; but not limiting the scope of the invention; although done to explain to the present invention with reference to preferred embodiment; those of ordinary skill in the art is to be understood that; can modify to technical scheme of the present invention or equivalent replacement, and not depart from essence and the scope of technical solution of the present invention.

Claims (1)

1. a river course cargo ship, for the Material Transportation of remote multiple destination, comprise cargo ship and be arranged on the navigating instrument on cargo ship, it is characterized in that, navigating instrument specifically comprises signaling module, processing module and generation module;

Signaling module, wants seeking time for multiple transport destination of receiving this round of user's input and the expectation that arrives each destination;

Processing module, for selecting optimal path according to the transport destination of this round and the geographical environment information of input in advance, specifically comprises:

Analog module:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0{\mathrm{\&Phi;}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0\frac{{\mathrm{\&Phi;}}^{*}-{\mathrm{\&Phi;}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\&Sigma;}}\left(G_i-t_i\right)\\ +T_2\underset{i=0}{\overset{U}{\&Sigma;}}\left(t_i-O_i\right)\end{array}$

Wherein, minS is the least cost in transportation; M is the sum of current transportation ship; Ground quantity for the purpose of U; b ₀for unit distance carbon emission cost; ω ₀for carbon emission coefficient; Φ ₀for unit distance Fuel Consumption during zero load; f _ijfor the purpose of i (i=1,2 ..., U) to destination j (j=1,2 ..., U) between distance; C is the dead weight capacity of cargo ship; H is the dead weight of cargo ship; Ф ^*for full load unit distance Fuel Consumption;

t ₁for cargo ship arrives loss coefficient in advance, for the cost allowance when moment G arrives destination i in advance, T ₂for cargo ship is late loss coefficient, for being delayed to cost allowance during moment O arrival destination i, arrival loss coefficient and late loss coefficient arrive the situation on schedule of each destination for considering cargo ship in advance, T ₁and T ₂for the coefficient artificially set;

Probabilistic module: suppose total R node, γ _ijt () represents the tracking element intensity between t node i and node j, γ _ij(0)=K (K is the constant that numerical value is less), cargo ship selects shift direction according to the plain intensity of tracking in motion process, then cargo ship k (k=1,2 ..., probability m) transferring to node j from node i is:

Wherein, g ∈ A _k; A _k=0,1 ..., R-1}-B _krepresent the set of the point that next step permission of cargo ship k is selected, in time in dynamic change, B _k(k=1,2 ..., be m) taboo list of a kth cargo ship, be used for recording the point that cargo ship k had transported; for heuristic greedy method, represent that t is by the expected degree of node i to node j, generally gets ψ is information heuristic greedy method, and μ is for expecting heuristic factor; The time degree that α (i, j) is next destination; for the time degree relative importance of next destination;

Update module: introduce optimized variable X _ijt (), it meets X _ij(t+1)=σ X (t) [1-X _ij(t)], wherein σ is control variable, draws the tracking element update rule of optimization:

γ _ij(t+1)＝(1-ζ)γ _ij(t)+Δγ _ij(t)+чX _ij(t)

Wherein, ${\mathrm{\&Delta;\&gamma;}}_{ij}\left(t\right)={\&Sigma;}_{k=1}^m{\mathrm{\&Delta;\&gamma;}}_{ij}^k\left(t\right),$

f _kby a kth cargo ship is walked path in this circulation, I is the constant following the tracks of plain intensity, represent the tracking element intensity that a kth cargo ship stays on path (i, j) in this circulation; ζ is for following the tracks of plain overall volatilization factor, ζ ∈ [0,1], and ζ is the parameter according to following formula dynamic conditioning: wherein ζ _minit is the minimum value of artificial setting; Δ γ _ijt () represents the summation of the tracking element intensity that all cargo ships stay on path (i, j) in this circulation; ч is adjustable coefficient;

Initial module: make iterations DD=0, carries out parameter initialization, adjusts each path trace element; Produce the random number p that a scope is [0,1], if p < is given constant p ₀, select next node j according to the following formula: wherein l ∈ A _k; Otherwise select next node j according to the new probability formula in probabilistic module, j is added array B _kin, repeat until all node tasks complete, obtain the first initial set S of modeling algorithm _i;

Optimum solution module: generate one group of new feasible solution S from current initial set _j, desired value variation delta S=S _j-S _iif Δ S < 0, then accept new feasible solution S _jfor optimum solution; Otherwise consider the impact of deviation: r=exp (-Δ S/N (t)), wherein N is time dependent amount, if r > 1, then accepts S _jfor optimum solution, otherwise do not accept new feasible solution, optimum solution is still S _i;

Judge module: after finding out optimum solution, judges whether new path exists overloading, if overload, regenerate feasible solution, if non-overloading, accepting new feasible solution is optimum solution; When current optimum solution is less than a certain particular value, carries out following the tracks of element and upgrade; If epicycle list B _kmiddle without Data Update, then produce the random number u of [0, a 1] scope, if e ₁+ e ₂+ ..., e _i-1< u < e ₁+ e ₂+ ..., e _i, then select probability is e _icandidate's cargo ship as next destination node;

Generation module: for exporting the optimal path calculated, make iterations DD=DD+1, if DD < is DD _max, according to the plain update rule of tracking, carry out emptying B according to formula N (t+1)=N (t) .v _klist, wherein v ∈ [0,1], gets back to initial module, regenerates random number p; If DD=DD _max, then optimum solution is exported as optimal path.