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

River transport ship

Technical Field

The invention relates to the field of river transportation, in particular to a river transport ship.

Background

River transport is an important transport mode from ancient times to present, and even today, a river transport ship is still a main transport tool.

Since each transportation point of the river transportation work is often scattered in each area with a long distance from each other, how to select a path capable of saving the operation cost of the transport ship to the maximum extent according to different destinations and required time for reaching each destination is a problem which needs to be solved urgently.

Disclosure of Invention

Aiming at the problems, the invention provides a river transport ship.

The purpose of the invention is realized by adopting the following technical scheme:

a river transport ship is used for long-distance material transportation at multiple destinations and is characterized by comprising a transport ship and a navigator arranged on the transport ship; the navigator specifically comprises a signal module, a processing module and a generating module;

the signal module is used for receiving a plurality of transportation destinations of the round and predicted required time for reaching each destination, which are input by a user;

the processing module is used for selecting an optimal path according to the transportation destination of the round and the geographical environment information input in advance, and specifically comprises the following steps:

a simulation 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 lowest cost in the transportation process; m is the total number of the current transport ship; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi (₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the carrying capacity of the transport vessel; h is the maximum carrying capacity of the transport ship; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the carrier to arrive at the loss factor in advance,to arrive at destination i earlier at time GCost loss, T₂In order for the carrier to arrive late for the loss factor,for cost losses when the destination i is reached at the delayed arrival time O, an early arrival loss factor and a late arrival loss factor are used to take into account the punctual situation, T, at which the transport vessel arrives at each destination₁And T₂A coefficient set artificially;

a probability module: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) K (K is a constant with a small value), the transfer direction of the transport ship is selected according to the intensity of the tracker during the movement process, and the probability that the transport ship K (K is 1, 2.. multidot.m) is transferred from the node i to the node j is as follows:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the carrier k is allowed to select next, which dynamically changes over time, B_k(k-1, 2, …, m) is a contraindication table of the kth transport ship and is used for recording the points transported by the transport ship k;the heuristic factor represents the expected degree of the t time from the node i to the node j, and is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

an update module: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

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_kis the length of the path taken by the kth transport ship in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth transport ship in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0,1 ]]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) represents the sum of the intensities of the tracers left on the paths (i, j) by all the carriers in the cycle, ч is an adjustable coefficient;

an initial module: let the iteration number DD equal to 0, proceed parameter initializationInitializing, and adjusting trackers of each path; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation: wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the probability module, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

An optimal solution module: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

A judging module: after finding out the optimal solution, judging whether the new path has an overload phenomenon, if so, regenerating a feasible solution, and if not, accepting the new feasible solution as the optimal solution; when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate transport ship of (2) is used as a next target node;

a generation module: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maxClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module to regenerate the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.

The invention has the beneficial effects that: the invention adopts an optimized path algorithm, considers various cost factors in the transportation process, has good optimization effect, high solving efficiency and stable performance, enhances the global searching capability, can save the transportation operation cost to the maximum extent and can play a good energy-saving effect.

Drawings

The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.

Fig. 1 is a block diagram of the present invention.

Reference numerals: a signal module-1; an analog module-3; a probability module-5; an update module-7; an initial module-9; an optimal solution module-10; a judgment module-12; generating a module-14.

Detailed Description

The invention is further described with reference to the following examples.

The river transport ship shown in fig. 1 is used for transporting materials at a plurality of long-distance destinations, and is characterized by comprising a transport ship and a navigator installed on the transport ship; the navigator specifically comprises a signal module, a processing module and a generating module;

the system comprises a signal module 1, a data processing module and a data processing module, wherein the signal module is used for receiving a plurality of transportation destinations of the round and predicted required time for reaching each destination, which are input by a user;

the processing module is used for selecting an optimal path according to the transportation destination of the round and the geographical environment information input in advance, and specifically comprises the following steps:

and (3) an analog module:

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

wherein minS is the lowest cost in the transportation process; m is the total number of the current transport ship; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi (₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the carrying capacity of the transport vessel; h is the maximum carrying capacity of the transport ship; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the carrier to arrive at the loss factor in advance,for cost penalty in arriving at destination i earlier at time G, T₂In order for the carrier to arrive late for the loss factor,for cost losses when the destination i is reached at the delayed arrival time O, an early arrival loss factor and a late arrival loss factor are used to take into account the punctual situation, T, at which the transport vessel arrives at each destination₁And T₂For artificial settingDetermining a coefficient;

the probability module 5: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) K (K is a constant with a small value), the transfer direction of the transport ship is selected according to the intensity of the tracker during the movement process, and the probability that the transport ship K (K is 1, 2.. multidot.m) is transferred from the node i to the node j is as follows:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the carrier k is allowed to select next, which dynamically changes over time, B_k(k-1, 2, …, m) is a contraindication table of the kth transport ship and is used for recording the points transported by the transport ship k;the heuristic factor represents the expected degree of the t time from the node i to the node j, and is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

the updating module 7: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

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_kis the length of the path taken by the kth transport ship in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth transport ship in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0,1 ]]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) represents the sum of the intensities of the tracers left on the paths (i, j) by all the carriers in the cycle, ч is an adjustable coefficient;

an initial module 9: initializing parameters and adjusting tracking elements of each path by setting the iteration number DD to be 0; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation: wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the probability module 5, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

The optimal solution module 10: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

The judging module 12: after finding out the optimal solution, judging whether the new path has an overload phenomenon, if so, regenerating a feasible solution, and if not, accepting the new feasible solution as the optimal solution; when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate transport ship of (2) is used as a next target node;

the generation module 14: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maxClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module 9, and regenerating the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.

The invention adopts an optimized path algorithm, considers various cost factors in the transportation process, has good optimization effect, high solving efficiency and stable performance, enhances the global searching capability, can save the transportation operation cost to the maximum extent and can play a good energy-saving effect.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (1)

1. A river transport ship is used for long-distance material transportation at multiple destinations and comprises a transport ship and a navigator installed on the transport ship, and is characterized in that the navigator specifically comprises a signal module, a processing module and a generating module;

the signal module is used for receiving a plurality of transportation destinations of the round and predicted required time for reaching each destination, which are input by a user;

the processing module is used for selecting an optimal path according to the transportation destination of the round and the geographical environment information input in advance, and specifically comprises the following steps:

a simulation 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 lowest cost in the transportation process; m is the total number of the current transport ship; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the carrying capacity of the transport vessel; h is the maximum carrying capacity of the transport ship; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the carrier to arrive at the loss factor in advance,for cost penalty in arriving at destination i earlier at time G, T₂In order for the carrier to arrive late for the loss factor,for cost losses when the destination i is reached at the delayed arrival time O, an early arrival loss factor and a late arrival loss factor are used to take into account the punctual situation, T, at which the transport vessel arrives at each destination₁And T₂A coefficient set artificially;

a probability module: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) K (K is a constant with a small value), the transfer direction of the transport ship is selected according to the intensity of the tracker during the movement process, and the probability that the transport ship K (K is 1, 2.. multidot.m) is transferred from the node i to the node j is as follows:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the carrier k is allowed to select next, which dynamically changes over time, B_k(k-1, 2, …, m) is a contraindication table of the kth transport ship and is used for recording the points transported by the transport ship k;the heuristic factor represents the expected degree of the t time from the node i to the node j, and is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

an update module: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

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_kis the length of the path taken by the kth transport ship in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth transport ship in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0,1 ]]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) represents the sum of the intensities of the tracers left on the paths (i, j) by all the carriers in the cycle, ч is an adjustable coefficient;

an initial module: initializing parameters and adjusting tracking elements of each path by setting the iteration number DD to be 0; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation: wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the probability module, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

An optimal solution module: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

A judging module: after finding out the optimal solution, judging whether the new path has an overload phenomenon, if so, regenerating a feasible solution, and if not, accepting the new feasible solution as the optimal solution; when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate transport ship of (2) is used as a next target node;

a generation module: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maxClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module to regenerate the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.