CN105512756A - System for distributing and conveying emergency supplies - Google Patents

Abstract

The invention discloses a system for distributing and conveying emergency supplies, and the system comprises a distributing and conveying vehicle and a navigator disposed on the distributing and conveying vehicle. The navigator comprises a signal module, a processing module, and a generation module. The system employs an optimized path algorithm, gives consideration to all cost factors in a distributing and conveying process, is good in optimizing effect, is high in solving efficiency, is stable in performance, improves the overall searching capability, can save the operation cost of distributing and conveying to maximum degree, and can achieve a good energy-saving effect.

Description

Emergency material distribution system

Technical Field

The invention relates to the field of emergency material distribution, in particular to an emergency material distribution system.

Background

The emergency materials are necessary security materials in emergency disposal process for dealing with sudden public events such as severe natural disasters, sudden public health events, public safety events, military conflicts and the like. In a broad sense, all materials used in the process of dealing with the emergent public events can be called emergency materials. The distribution of emergency materials is an important link of the emergency system in the modern society.

Since each distribution point of the emergency material distribution work is often scattered in a region with a long distance, how to select a path capable of saving the operation cost of the distribution vehicle to the maximum extent according to different destinations and required time for reaching each destination is a problem to be solved urgently.

Disclosure of Invention

In order to solve the problems, the invention provides an emergency material distribution system.

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

an emergency material distribution system is used for the remote emergency material delivery of a plurality of destinations and is characterized by comprising a distribution vehicle and a navigator installed on the distribution vehicle; the navigator specifically comprises a signal module, a processing module and a generating module;

the signal module is used for receiving a plurality of delivery 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 distribution destination of the round and the geographical environment information input in advance, and specifically comprises the following steps:

a simulation module:

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

wherein minS is the lowest cost in the distribution process; m is the total number of the current delivery vehicles; 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 distribution vehicle; h is the maximum load capacity of the distribution vehicle; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the delivery vehicle to arrive at the loss factor in advance,for cost penalty in arriving at destination i earlier at time G, T₂In order for the delivery vehicle to arrive late for the loss factor,for cost losses when the destination i is reached at the delay 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 delivery vehicle 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 small number)Constant of (d) and the transfer direction is selected by the delivery vehicle during movement according to the intensity of the tracker, the probability that the delivery vehicle k (k ═ 1, 2.. times, m) transfers from node i to node j is:

wherein, g ∈ A_k；A_k＝{0，1，...，R-1}-B_kRepresenting the set of points which the delivery vehicle k is allowed to select next, which is dynamic over time, B_k(k is 1, 2.. multidot.m) is a taboo table of the kth delivery vehicle and is used for recording the points delivered by the delivery vehicle 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_kthe length of a path taken by a kth delivery vehicle in the current 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 delivery vehicle 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 delivery vehicles in the current cycle, wherein 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_kIn the method, the steps are repeated until all the node tasks are completed, and the initial point of the simulation algorithm is obtainedBeginning set S_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 delivery vehicle is used as the 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 of υ ∈ [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 distribution process, has good optimization effect, high solving efficiency and stable performance, enhances the global search capability, can save the distribution 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.

An emergency material distribution system as shown in fig. 1 is used for remotely delivering emergency materials at multiple destinations, and comprises a distribution vehicle and a navigator installed on the distribution vehicle; the navigator specifically comprises a signal module, a processing module and a generating module;

the system comprises a signal module 1, a plurality of dispatching destinations and expected required time for reaching each destination, wherein the dispatching destinations are input by a user;

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

and (3) an analog module:

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

wherein minS is the lowest cost in the distribution process; m is the total number of the current delivery vehicles; 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 distribution vehicle; h is the maximum load capacity of the distribution vehicle; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the delivery vehicle to arrive at the loss factor in advance,for cost penalty in arriving at destination i earlier at time G, T₂In order for the delivery vehicle to arrive late for the loss factor,for cost losses when the destination i is reached at the delay 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 delivery vehicle arrives at each destination₁And T₂A coefficient set artificially;

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) If the transfer direction of the delivery vehicle is selected according to the intensity of the tracker during the movement process, the probability that the delivery vehicle 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 which the delivery vehicle k is allowed to select next, which is dynamic over time, B_k(k is 1, 2.. multidot.m) is a taboo table of the kth delivery vehicle and is used for recording the points delivered by the delivery vehicle 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_kthe length of a path taken by a kth delivery vehicle in the current 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 delivery vehicle 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 delivery vehicles in the current cycle, wherein 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 newFeasible 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 delivery vehicle is used as the 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 of υ ∈ [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 distribution process, has good optimization effect, high solving efficiency and stable performance, enhances the global search capability, can save the distribution 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. An emergency material distribution system is used for the remote emergency material delivery of multiple destinations and comprises a distribution vehicle and a navigator installed on the distribution vehicle, 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 delivery 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 distribution destination of the round and the geographical environment information input in advance, and specifically comprises the following steps:

a simulation module:

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

wherein minS is the lowest cost in the distribution process; m is the total number of the current delivery vehicles; 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 distribution vehicle; h is the maximum load capacity of the distribution vehicle; phi^*Fuel consumption per unit distance when fully loaded;

T₁in order for the delivery vehicle to arrive at the loss factor in advance,for cost penalty in arriving at destination i earlier at time G, T₂In order for the delivery vehicle to arrive late for the loss factor,for cost losses when the destination i is reached at the delay 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 delivery vehicle arrives at each destination₁And T₂Is a personIs a set coefficient;

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) If the transfer direction of the delivery vehicle is selected according to the intensity of the tracker during the movement process, the probability that the delivery vehicle 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 which the delivery vehicle k is allowed to select next, which is dynamic over time, B_k(k is 1, 2.. multidot.m) is a taboo table of the kth delivery vehicle and is used for recording the points delivered by the delivery vehicle 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, ${\mathrm{\&Delta;\&gamma;}}_{ij}\left(t\right)={\mathrm{\&Sigma;}}_{k=1}^m{\mathrm{\&Delta;\&gamma;}}_{ij}^k\left(t\right),$

F_kthe length of a path taken by a kth delivery vehicle in the current 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 delivery vehicle 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 delivery vehicles in the current cycle, wherein 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 according to the summaryThe probability formula in the rate module selects the next node j, adds j to 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: 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 delivery vehicle is used as the 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.