CN103246969B - A kind of implementation method of logistics deployment and device - Google Patents

Abstract

The invention discloses a kind of implementation method and device of logistics deployment, generate the Mathematical Modeling for realizing logistics deployment by the modeling of logistics deployment constraints, distribution station apart from modeling, the modeling of logistics deployment algorithm; On described Mathematical Modeling basis, apply ant group algorithm allotment logistics. The technology that the present invention realizes logistics deployment can generate the Mathematical Modeling for realizing logistics deployment apart from modeling, the modeling of logistics deployment algorithm by the modeling of logistics deployment constraints, distribution station; And on described Mathematical Modeling basis, apply ant group algorithm allotment logistics, and therefore can in minimum number, minimum zone, generate the best logistics deployment scheme that approaches, this can improve logistics deployment efficiency to a great extent, saves logistics deployment cost.

Description

Method and device for realizing logistics allocation

Technical Field

The invention relates to the technology of Internet of things, in particular to a method and a device for realizing logistics allocation.

Background

The optimization of the logistics allocation scheme belongs to the category of combinatorial optimization, and is a Non-deterministic polynomial (NP) problem, and the solution of the problem is a big research hotspot. In addition, since the actual logistics deployment state is in dynamic change, the combinatorial optimization algorithm is required to have good performance and to be capable of adapting to the change characteristic of dynamic deployment. Some current logistics allocation algorithms adopt non-heuristic traditional algorithms, and have the problem of low efficiency in performance, while other heuristic algorithms with better performance do not consider the change characteristic of allocation state, so that the scheme cannot be dynamically adjusted in time, and the low efficiency of the logistics allocation scheme is caused.

Disclosure of Invention

In view of the above, the main objective of the present invention is to provide a method and an apparatus for implementing logistics allocation, which are used for implementing high-efficiency logistics allocation and adapting to dynamic change characteristics in a logistics allocation process.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a method for realizing logistics allocation comprises the following steps:

generating a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling; and (4) applying an ant colony algorithm to allocate logistics on the basis of the mathematical model.

The method for modeling the logistics allocation constraint condition comprises the following steps:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n), at time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)。

The method further comprises the following steps:

the management of logistics deployment is carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, the state machine is subjected to state conversion by starting a logistics allocation scheme so as to meet the state constraint condition.

The distance modeling of the distribution station comprises absolute distance modeling and relative distance modeling; wherein,

when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site k in between, then d_ij＝d_ik+d_kj；

By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between them is expressed as the relative value of the distance between two minimum stations.

The logistics allocation algorithm modeling is used for predicting a future state, and comprises a future state function definition and a logistics allocation matrix definition; wherein,

when defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t))，y_i(T(t))，S_i(T(t)))；

If Num_i> 0, indicating deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, indicating a blending stationa_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i< 0, indicating the allocation station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d);

when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

The method further comprises the following steps:

by usingIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of the future state machine.

By usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

The process of applying the ant colony algorithm to allocate the logistics on the basis of the mathematical model comprises the following steps:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; putting nodes in all destination node sets into a node dynamic adjustment table tabv_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

The method further comprises the following steps:

corresponding to the generated optimal logistics allocation scheme, determining whether the scheme needs to be dynamically adjusted or not by applying real-time perception information, deleting corresponding nodes from the starting node set and the target node set and deleting corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.

A realization device for logistics allocation comprises a mathematical model generation module and a logistics allocation module; wherein,

the mathematical model generation module is used for generating a mathematical model for realizing logistics distribution through logistics distribution constraint condition modeling, distribution station distance modeling and logistics distribution algorithm modeling;

and the logistics allocation module is used for allocating logistics by applying an ant colony algorithm on the basis of the mathematical model.

The mathematical model generation module comprises a logistics allocation constraint condition modeling unit used for:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n) and at a future time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)。

The logistics allocation constraint modeling unit is further configured to:

the management of logistics deployment is carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, carrying out state conversion on the state machine.

The mathematical model generation module comprises a distribution station distance modeling unit and is used for carrying out absolute distance modeling and relative distance modeling; wherein,

when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site in betweenk, then d_ij＝d_ik+d_kj；

By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between them is expressed as the relative value of the distance between two minimum stations.

The mathematical model generation module comprises a logistics allocation algorithm modeling unit which is used for predicting future states, including future state function definition and logistics allocation matrix definition; wherein,

when defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t))，y_i(T(t))，S_i(T(t)))；

If Num_i> 0, indicating deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i< 0, indicating the allocation station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d);

when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

The logistics allocation algorithm modeling unit is further configured to:

by usingIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of the future state machine.

By usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

The logistics allocation module is used for executing the following steps when the ant colony algorithm is applied to allocate logistics on the basis of the mathematical model:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; dynamic adjustment of nodes in all destination node setsTabv in whole table_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

The logistics allocation module is further configured to:

corresponding to the generated optimal logistics allocation scheme, determining the execution degree of the scheme by using real-time perception information, deleting corresponding nodes from the starting node set and the destination node set and deleting corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.

The technology for realizing logistics allocation can generate a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling; and the ant colony algorithm is applied to allocate the logistics on the basis of the mathematical model, so that the dynamic change characteristic in the logistics allocation process can be adapted, and a nearly optimal logistics allocation scheme is generated in the minimum quantity and the minimum range, so that the logistics allocation efficiency can be improved to a great extent, the high-efficiency logistics allocation is realized, and the logistics allocation cost is saved.

Drawings

FIG. 1 is a simplified flow chart of a logistics allocation according to an embodiment of the present invention;

fig. 2 is a diagram of an apparatus for implementing logistics deployment according to an embodiment of the present invention.

Detailed Description

The invention adopts a dynamic and improved ant colony algorithm to carry out dynamic optimization of the allocation scheme, and the algorithm is a heuristic algorithm. Heuristic algorithms, also known as intelligent computing and also called soft computing, are algorithms inspired by natural laws of people and simulating and solving problems according to the natural laws, have the advantages of good global optimization performance, strong robustness, strong universality, suitability for parallel processing and the like, and have been widely used in the fields of computer science, optimized scheduling, transportation problems, combined optimization and the like due to the advantages of high-efficiency optimization performance, no need of problem special information and the like. The ant colony algorithm (antcolonyaltgorithm) was first proposed by the italian scholaro, Maniezzo et al in the early 90 s of the 20 th century. The ant colony algorithm is a heuristic search algorithm applied to the combinatorial optimization problem after meta-heuristic search algorithms such as simulated annealing algorithm, genetic algorithm, tabu search algorithm, artificial neural network algorithm and the like. Due to the characteristics of the ant colony algorithm, the ant colony optimization method can better adapt to the dynamic problem in the optimization process.

The establishment of an intelligent management system based on the internet of things technology enables the real-time acquisition of information such as the commodity stock quantity, the commodity name, the type, the real-time demand quantity of commodities, the specific position of a commodity circulation allocation vehicle and the like of a commodity circulation allocation station to be possible, the sensing data acquired in real time are analyzed and predicted, a nearly optimal commodity circulation allocation scheme can be generated, and the real-time updating and the dynamic adjustment of the commodity circulation allocation scheme can be realized.

The invention establishes a mathematical model and improves based on the ant colony algorithm, and provides a logistics allocation scheme with good optimization effect and high efficiency. The invention researches the logistics allocation problem based on the technology of the Internet of things. Firstly, a mathematical model for realizing logistics allocation is designed through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling, and then the logistics allocation problem is researched on the basis of the mathematical model, wherein the research comprises the research of an ant colony algorithm on a static logistics allocation algorithm and the research of a dynamic logistics allocation algorithm.

Specifically, the ant colony algorithm is a heuristic optimization algorithm (also called as intelligent computing, or soft computing), has the characteristics of high-efficiency optimization performance, global optimization performance, strong robustness, strong universality and the like, and is widely used in the fields of computer science, optimization scheduling, transportation problems, combination optimization and the like.

It should be noted that logistics allocation is very important, because only the required goods are allocated in time, normal operation can be ensured, and the quality of logistics allocation is improved. The application of the technology of the Internet of things enables the acquisition of real-time perception information and the prediction of the information to be possible. The invention applies the technologies of prediction technology, real-time perception and the like to generate a nearly optimal logistics allocation scheme in the minimum quantity and the minimum range, and can save the logistics allocation cost to a great extent.

In performing mathematical modeling, the following operations may be performed:

modeling logistics allocation constraint conditions:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n) at a future time t (t), deployment station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(t)), in order not to affect the smooth progress of the production and transaction, the system must satisfy the following condition constraints:

S_i(T(t))≥y_i(T(t))；(i＝1…n)(1)

to better illustrate and solve the problem, finite state machine theory is introduced. The logistics deployment is managed by establishing and maintaining a finite state machine for each logistics deployment station. The information contained and maintained by the finite state machine includes: time, the logistics goods inventory of the corresponding logistics dispatching station at a certain time point, and the logistics goods demand of the corresponding logistics dispatching station at the time point. When the state machine can not satisfy the state constraint condition (1) of the corresponding time point in the future, the state machine needs to be subjected to state transition. The condition functions which can promote the state change of the state machine of the logistics allocation station comprise f, g and the like; wherein,

g: a logistics deployment function;

f: other influencing factors.

Logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n).

Distance modeling of a delivery station:

modeling of the distance between logistics dispatching stations can be divided into absolute distance modeling and relative distance modeling.

1) Absolute distance modeling

Setting a station a_iAnd site a_jA distance d between_ijAnd establishing a two-dimensional matrix of the distances between different stations. Station a due to the road peculiarities_iTo site a_jAnd station a, and_jto site a_iThe distances between may be different, and thus the matrix formed by the modeling is not a symmetric matrix:

2) relative distance modeling

All logistics deployment sites form a large interconnected network, and some of the sites may be directly connected with each other, and other sites may exist among the sites.

In order to more intuitively represent the relative distance between different stations, a relative distance matrix is additionally established, and the following rules are set:

if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij，d_ijThe distance value may be an absolute distance value or a relative distance value.

If site a_iTo site a_jThere is a third site k in between, then d_ij＝d_ik+d_kj。

By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between them is expressed as the relative value of the distance between two minimum stations.

Modeling a logistics allocation algorithm:

the logistics deployment scheme is correctly and reasonably determined, and factors such as future inventory, demand, state change and the like need to be considered. Therefore, a future state function needs to be established:

1) future state function definition

The quantity of the goods to be allocated at a certain time in the future can be predicted through characteristics such as empirical values, historical data and future changes. The future state function is defined as: num_i＝fc(st_i(T(t))，y_i(T(t))，S_i(T(t)))；

If Num_i> 0, indicating deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iThere is no logistics cargo remaining. Num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i< 0, indicating the allocation station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (a).

2) Logistics deployment matrix definition

Establishing a two-dimensional matrix for the distribution of the material flow among the distribution stations if the distribution station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

Can useIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of the future state machine.

Can useIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

During specific logistics allocation, a static logistics allocation algorithm or a dynamic logistics allocation algorithm can be applied. Specifically, when the target state machine of the logistics deployment station cannot meet the constraint condition, the logistics deployment system needs to be started for deploying the goods within a specified time. The static logistics allocation algorithm is an allocation algorithm which is established when logistics allocation is not started; the dynamic logistics allocation algorithm is an adjusting algorithm which determines that the situation changes through real-time sensed information when an allocation task is carried out, so that the original allocation scheme is modified in real time.

The static logistics allocation algorithm generally comprises the steps of starting, solving allocation schemes and optimizing the schemes:

1) startup of static logistics deployment algorithm

And detecting whether the condition that the corresponding state machine does not meet the constraint condition occurs in the future time of the allocation station according to the real-time sensed information, and accordingly determining whether to start a static logistics allocation algorithm.

2) Solution of deployment scenario

The static logistics allocation algorithm solves the logistics allocation matrix under the constraint condition, and simultaneously, the generated allocation scheme is required to be minimum in related range and minimum in transportation cost, namely, the optimization problem of the allocation scheme is solved. The specific operation is as follows: and for each group of solutions meeting the conditions of the logistics allocation matrix meeting the constraint conditions of the target state machine, performing logistics allocation scheme optimization calculation on the group of solutions to obtain an optimal allocation scheme under the group of solutions, and finally comparing the optimal allocation schemes of all the solutions to obtain a globally optimal allocation scheme.

Specifically, the solution of the allocation scheme is to add the value which is calculated by the state prediction function and is a negative number to 0, and record the transformation process to generate a corresponding transformation scheme, which is a logistics allocation scheme. The record of the conversion process is realized by a two-dimensional matrix with the initialized state of all zero, corresponding addition operation only occurs between positive and negative values, and the vector is subjected to one-time addition operation, so that the corresponding position in the deployment matrix is labeled correspondingly, and the corresponding deployment matrix is generated when one-time conversion is finished. Different transformation processes will produce different deployment matrices, i.e. different deployment scenarios, and as to which deployment scenario is the optimal deployment scenario, further deployment scenario optimization algorithm needs to be applied to further optimize, and finally a near-optimal deployment scenario is generated.

3) Deployment scenario optimization

An optimal transportation scenario is first generated for each deployment scenario. For the sake of simplicity, it is assumed that the logistics distribution cost is only related to the distribution distance, and the same distribution vehicle distributes one cargo and distributes multiple cargoes at the same time with the same cost. Firstly, a distance matrix and a relative distance matrix are applied to a generated allocation scheme matrix to calculate whether repeated routes exist in the scheduling of goods among different stations, the goods on the repeated routes can take the same-route vehicles, so that the road cost is saved, the repeated routes of the taking same-route vehicles are merged, and the optimal allocation scheme problem is converted into a special condition-limited traveler problem (TSP) after the repeated routes are merged.

The optimal solution exists in the TSP theoretically, but an effective method for solving the value is not found in practical application, the optimal solution can only be gradually approached, namely, only the suboptimal solution closest to the theoretical optimal solution can be obtained, and the TSP solving problem is a typical NP problem. The scheduling scheme optimization problem is a special and conditional TSP, because the solving process of the TSP has no limitation of the access sequence of the nodes, and the scheduling scheme optimization problem has limitation of the access sequence and needs to start from the station for calling out goods and reach the station for calling in the goods.

The ant colony algorithm is applied below to solve the above special, conditionally restricted TSP.

And setting m 'as the number of nodes in the starting node set, correspondingly setting the number of ants as m', and placing m 'ants on m' starting nodes. The ant is a variable used for optimizing in the ant colony algorithm, n' represents the number of all nodes in the starting node set and the destination node set, d_ijIs the distance between node i and node j, τ_ij(t) is the quantity of pheromones on the path (i, j) at time t, the initial time τ_ij(t) taking the constant const, τ_ij(t) determines the calculation of the state transition probability. The following is the state transition probability calculation:

$p_{\mathrm{ij}}^k\left(t\right)=\left\{\begin{array}{cc}\frac{{\left[{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)\right]}^{\mathrm{\&alpha;}}\&times;{\left[{\mathrm{\&eta;}}_{\mathrm{ik}}\left(t\right)\right]}^{\mathrm{\&beta;}}}{\underset{s\&Subset;\mathrm{allowedk}}{\mathrm{\&Sigma;}}{\left[{\mathrm{\&tau;}}_{\mathrm{is}}\left(t\right)\right]}^{\mathrm{\&alpha;}}\&times;{\left[{\mathrm{\&eta;}}_{\mathrm{is}}\left(t\right)\right]}^{\mathrm{\&beta;}}},& j\&Element;{\mathrm{allowed}}_k\\ 0,& \mathrm{else}\end{array}\right.---\left(2\right)$

in the formula (2), the reaction mixture is,representing the state transition probability, allowed, of the kth ant from node i to node j connected to the edge of i at time t_kα is an information heuristic factor representing the relative importance of the track, reflecting the action of the information accumulated by the ant during the movement process when the ant moves, the bigger the value, the more the ant tends to select the path that other ants pass, the stronger the cooperation between ants, β is an expectation heuristic factor representing the relative importance of visibility, reflecting the degree of importance of the ant in inspiring the information in the ant selection path during the movement process η_ij(t) is a heuristic function if the calculation sectionPoint no history information η_ij(t) taking empirical values.

At time t + n, the update rule of the pheromone on the node (i, j) arc segment is as follows:

τ_ij(t+n)＝(1-ρ)×τ_ij(t)+ρ×Δτ_ij；

$\mathrm{\&Delta;}{\mathrm{\&tau;}}_{\mathrm{ij}}=\underset{k=1}{\overset{m^{\&prime;}}{\mathrm{\&Sigma;}}}{\mathrm{\&Delta;\&tau;}}_{\mathrm{ij}}^k;$

$\mathrm{\&Delta;}{\mathrm{\&tau;}}_{\mathrm{ij}}^k=\left\{\begin{array}{c}\frac{Q}{L_k}\\ 0\end{array}\right.;$

the kth ant passes through the nodes i and j at the time t and the time t + n, and is 0 if not. Wherein, tau_ij(t) represents the value of the pheromone at time t over the arc segment (i, j),and (3) a value representing the pheromone quantity on the calculation node (i, j) newly sensed by a certain ant at the time of t + n. P denotes the pheromone update factor and 1-p denotes the pheromone residual factor, whereρ represents how much a computing node borrows from a newly experienced τ_ij(t) value, 1-p, indicates how much to use the previous τ_ij(t) value.

According to the one-to-one correspondence relationship between the nodes in the starting node set and the destination node set, the destination node corresponding to the starting node is placed into the corresponding tabv_k(k ═ 1,2, …, m').

In order to obtain a globally optimized deployment scenario, the following steps may be performed:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) being const, wherein const is constant, the value of const is preferably not to influence the operation result of the subsequent algorithm, all nodes in the destination node set are put into a node dynamic adjustment table tabv_k(k ═ 1,2, …, m');

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node, namely selecting the node and a calculation node determined by the next node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the newly added pheromone sum of the ant selection nodes in the round, wherein the newly added pheromone sum is the sum of the newly added pheromones of each sub-node; if all ants in the round are routed, jumping to the step 9, otherwise, jumping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: the deployment route represented by the pheromone maximum path is the optimal solution of the deployment scheme.

And finally obtaining the optimal scheduling scheme of the scheduling by solving the optimal transportation route and the lowest transportation cost of all the scheduling schemes.

As can be seen from the above description, the static logistics allocation algorithm is started before the logistics allocation program is started, and is used to generate the optimal allocation scheme for the current allocation. However, since the state of the system is always in a change, even in the process of generating and executing the logistics deployment plan, dynamic and real-time adjustment needs to be performed according to the change of the information to generate a dynamic logistics deployment plan. The dynamic logistics allocation algorithm is realized by adding dynamic design of the algorithm, dynamically deleting or adding nodes and changing corresponding algorithm flows on the basis of a static logistics allocation algorithm.

The dynamic logistics allocation algorithm may include the following operations:

corresponding to the generated optimal logistics allocation scheme, determining the execution degree of the scheme by using real-time perception information, deleting corresponding nodes from the starting node set and the destination node set and deleting corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state;

in addition, all k ants are placed at the current position node of the dispatching car during initialization, and the static logistics allocation algorithm is applied to carry out optimization solution again.

In combination with the above description, the operation idea of the present invention for realizing logistics allocation can be represented as a flow shown in fig. 1, and the flow comprises the following steps:

step 110: generating a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling;

step 120: and (4) applying an ant colony algorithm to allocate logistics on the basis of the mathematical model.

In order to ensure that the above operations can be smoothly carried out, a device as shown in fig. 2 can be provided, and the device comprises a mathematical model generation module and a logistics allocation module which can be connected; the mathematical model generation module can generate a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling; the logistics deployment module can deploy ant colony algorithm to deploy logistics based on the mathematical model.

Therefore, the mathematical model for realizing logistics distribution can be generated through the logistics distribution constraint condition modeling, the distribution station distance modeling and the logistics distribution algorithm modeling; and (4) applying an ant colony algorithm to allocate logistics on the basis of the mathematical model.

The method for modeling the logistics allocation constraint condition can be as follows:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n), at time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)。

The management of logistics deployment can be carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, the state machine is subjected to state conversion by starting a logistics allocation scheme so as to meet the state constraint condition.

The distribution station distance modeling may include absolute distance modeling and relative distance modeling; wherein,

when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site k in between, then d_ij＝d_ik+d_kj；

By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between them is expressed as the relative value of the distance between two minimum stations.

The logistics deployment algorithm modeling can be used for predicting future states, including future state function definition and logistics deployment matrix definition; wherein,

when defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t))，y_i(T(t))，S_i(T(t)))；

If Num_i> 0, indicating deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i< 0, indicating the allocation station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d);

when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

In addition, can useIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of the future state machine.

By usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

The process of applying the ant colony algorithm to allocate the logistics on the basis of the mathematical model can comprise the following steps:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; putting all nodes in the destination node set into the node dynamic adjustment table tabv_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If Nc > N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

The method can also apply real-time perception information corresponding to the generated optimal logistics allocation scheme to determine whether the scheme needs to be dynamically adjusted, and for the allocated tasks, corresponding nodes are deleted from the starting node set and the target node set, and corresponding paths are deleted from the optimal scheduling paths; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.

The logistics allocation implementation device can comprise a mathematical model generation module and a logistics allocation module; wherein,

the mathematical model generation module is used for generating a mathematical model for realizing logistics distribution through logistics distribution constraint condition modeling, distribution station distance modeling and logistics distribution algorithm modeling;

and the logistics allocation module is used for allocating logistics by applying an ant colony algorithm on the basis of the mathematical model.

The mathematical model generation module may include a logistics allocation constraint modeling unit configured to:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n) and at a future time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)。

The logistics allocation constraint modeling unit may be further configured to:

the management of logistics deployment is carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, carrying out state conversion on the state machine.

The mathematical model generation module can comprise a distribution station distance modeling unit which is used for carrying out absolute distance modeling and relative distance modeling; wherein,

when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site k in between, then d_ij＝d_ik+d_kj；

By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between them is expressed as the relative value of the distance between two minimum stations.

The mathematical model generation module can comprise a logistics allocation algorithm modeling unit which is used for predicting future states, including future state function definition and logistics allocation matrix definition; wherein,

when defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t))，y_i(T(t))，S_i(T(t)))；

If Num_i> 0, indicating deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i< 0, indicating the allocation station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d);

when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

The logistics allocation algorithm modeling unit may be further configured to:

by usingIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jShould be full of logistics goodsThe requirements of the future state machine are satisfied.

By usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

When the logistics deployment module applies the ant colony algorithm to deploy logistics based on the mathematical model, the following steps can be executed:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; putting nodes in all destination node sets into a node dynamic adjustment table tabv_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDelete, otherwise jump toNext, carrying out the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

The logistics allocation module can also apply real-time perception information to the generated optimal logistics allocation scheme, determine the execution degree of the scheme, delete corresponding nodes from the starting node set and the target node set and delete corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.

In summary, the technology for realizing logistics allocation can generate a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling, regardless of the method or the device for realizing the method; and the ant colony algorithm is applied to allocate the logistics on the basis of the mathematical model, so that the dynamic change characteristic in the logistics allocation process can be adapted, and a nearly optimal logistics allocation scheme is generated in the minimum quantity and the minimum range, so that the logistics allocation efficiency can be improved to a great extent, the high-efficiency logistics allocation is realized, and the logistics allocation cost is saved.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (10)

1. A method for realizing logistics deployment is characterized by comprising the following steps:

generating a mathematical model for realizing logistics allocation through logistics allocation constraint condition modeling, allocation station distance modeling and logistics allocation algorithm modeling; applying an ant colony algorithm to allocate logistics on the basis of the mathematical model;

the step of applying the ant colony algorithm to allocate the logistics on the basis of the mathematical model comprises the following steps: on the basis of the mathematical model, an ant colony algorithm is applied to obtain the optimal scheduling scheme of the scheduling; the method further comprises the following steps: when the optimal scheduling scheme of the current scheduling is executed, the ant colony algorithm is applied to adjust the optimal scheduling scheme of the current scheduling;

the logistics allocation constraint condition modeling method comprises the following steps:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n), at time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)；

The allocation station distance modeling comprises absolute distance modeling and relative distance modeling; wherein, when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site k in between, then d'_ij＝d_ik+d_kj(ii) a By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between the two stations is expressed as the relative value of the distance between every two minimum stations;

the logistics allocation algorithm modeling is used for predicting a future state, and comprises a future state function definition and a logistics allocation matrix definition; wherein, go intoWhen defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t)),y_i(T(t)),S_i(T (t)); wherein S is_i(T (t)) represents the inventory of goods in a certain stream at a certain dispatching station at a certain future time T (t), y_i(T (t)) represents the demand for said certain logistics goods at said T (t) said certain dispatching station, S_i(T), (t) and y_i(T (t)) satisfies the State constraint S_i(T(t))≥y_i(T(t))；St_i(t)) is a state machine function of said certain flow allocation station; fc () function is S_i(T(t))-y_i(T), (t)), and (st) is simultaneously performed_i(t)) satisfies the state constraint at the time t (t); wherein (i ═ 1 … n); if Num_i>0, denotes the deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i<0, denotes the deployment station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d); when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

2. The method of claim 1, further comprising:

the management of logistics deployment is carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, the state machine is subjected to state conversion by starting a logistics allocation scheme so as to meet the state constraint condition.

3. The method of claim 1, further comprising:

by usingIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of a future state machine;

by usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

4. The method according to any one of claims 1 to 3, wherein the process of formulating the logistics using the ant colony algorithm based on the mathematical model comprises the steps of:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), pathsTable path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; putting nodes in all destination node sets into a node dynamic adjustment table tabv_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

5. The method of claim 4, further comprising:

corresponding to the generated optimal logistics allocation scheme, determining whether the scheme needs to be dynamically adjusted or not by applying real-time perception information, deleting corresponding nodes from the starting node set and the target node set and deleting corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.

6. A realization device for logistics allocation is characterized by comprising a mathematical model generation module and a logistics allocation module; wherein,

the mathematical model generation module is used for generating a mathematical model for realizing logistics distribution through logistics distribution constraint condition modeling, distribution station distance modeling and logistics distribution algorithm modeling;

the logistics allocation module is used for allocating logistics by applying an ant colony algorithm on the basis of the mathematical model; the step of applying the ant colony algorithm to allocate the logistics on the basis of the mathematical model comprises the following steps: on the basis of the mathematical model, an ant colony algorithm is applied to obtain the optimal scheduling scheme of the scheduling; when the optimal scheduling scheme of the current scheduling is executed, the ant colony algorithm is applied to adjust the optimal scheduling scheme of the current scheduling;

the mathematical model generation module comprises a logistics allocation constraint condition modeling unit and is used for:

let the logistics dispatching system have n total logistics dispatching stations, each dispatching station is denoted as a_i(i-1 … n) and at a future time t (t), the dispatching station a_iHas a certain logistics cargo inventory of S_i(T (t)), and at this time the demand of the distribution station for the physical distribution goods is y_i(T (t)) satisfying a state constraint condition S_i(T(t))≥y_i(T(t))；(i＝1…n)；

The mathematical model generation module comprises a distribution station distance modeling unit and is used for carrying out absolute distance modeling and relative distance modeling; wherein, when modeling absolute distance, setting a site a_iAnd site a_jA distance d between_ijEstablishing a two-dimensional matrix of distances between different stations:

when modeling relative distance, if site a_iTo site a_jIf there is no other station on the path between the two, then set station a_iTo site a_jA distance d between_ij(ii) a If site a_iTo site a_jThere is a third site k in between, then d'_ij＝d_ik+d_kj(ii) a By analogy, the distance between all different sites is expressed as the sum of two non-resolvable minimum inter-site distances:

wherein, d'_ijIndicating site a_iTo site a_jThe relative distance between the two stations is expressed as the relative value of the distance between every two minimum stations;

the mathematical model generation module comprises a logistics allocation algorithm modeling unit which is used for predicting future states, including future state function definition and logistics allocation matrix definition; when defining the future state function, the future state prediction function is defined as: num_i＝fc(st_i(T(t)),y_i(T(t)),S_i(T (t)); wherein S is_i(T (t)) represents the inventory of goods in a certain logistics at a certain deployment site at a certain future time T (t),y_i(T (t)) represents the demand for said certain logistics goods at said T (t) said certain dispatching station, S_i(T), (t) and y_i(T (t)) satisfies the State constraint S_i(T(t))≥y_i(T(t))；St_i(t)) is a state machine function of said certain flow allocation station; fc () function is S_i(T(t))-y_i(T), (t)), and (st) is simultaneously performed_i(t)) satisfies the state constraint at the time t (t); wherein (i ═ 1 … n); if Num_i>0, denotes the deployment station a_iWith the remaining logistics goods in Num number_i(ii) a If Num_i0 denotes the order station a_iNo logistics goods remain; num_iWhen the value is more than or equal to 0, the value indicates a blending station a_iThe logistics goods do not need to be supplemented through a logistics allocation program; if Num_i<0, denotes the deployment station a_iStarting a logistics deployment program and deploying to a deployment station a_iThe number of the logistics goods is Num_iAbsolute value of (d); when defining the logistics allocation matrix, establishing a logistics allocation two-dimensional matrix between allocation stations, if the allocation station a_iWithout going to the allocating station a_jWhen the goods are prepared, 0 is used for representing; if the allocation is needed, the value is expressed as the allocated logistics cargo value t_ij：

7. The apparatus of claim 6, wherein the logistics deployment constraint modeling unit is further configured to:

the management of logistics deployment is carried out by establishing and maintaining a finite state machine for each logistics deployment station; logistics allocation station a_iThe state machine function of (a) is defined as: st_i(T (t)); wherein (i ═ 1 … n);

and when the state machine can not meet the state constraint condition of the corresponding future time point, carrying out state conversion on the state machine.

8. The apparatus of claim 6, wherein the logistics deployment algorithm modeling unit is further configured to:

by usingIndicating transport to dispatch station a_jThe sum of all the logistics cargo of (a) needs to satisfy the condition:

transported to the allocating station a_jThe logistics goods need to meet the requirements of a future state machine;

by usingIndicating a slave deployment station a_iThe total of logistics goods transported to all other allocation stations needs to meet the following conditions:namely the dispatching station a_iThe total sum of the logistics goods transported to all other allocation stations needs to meet the requirement of the allocation station a_iFuture state machine constraints.

9. The apparatus according to any one of claims 6 to 8, wherein the logistics allocation module is configured to perform the following steps when applying the ant colony algorithm to allocate logistics based on the mathematical model:

step 1: setting a maximum number of cycles N_cmaxNumber of ants m', initialization time slice t is 0, and loop control variable N_c0, 0 for ant circulation variable k, tabu_k(k ═ 1,2, …, m'), node dynamic adjustment table tabv_k(k ═ 1,2, …, m'), path table path_k(k ═ 1,2, …, m '), placing m ' ants on m ' starting nodes, making different nodes connected to pheromone tau on arc_ij(t) const, wherein const is a constant; putting nodes in all destination node sets into node dynamicsTabv of regulation table_k(k ═ 1,2, …, m'); the ants are variables used for optimizing in the ant colony algorithm;

step 2: number of cycles N_c＝N_c+1If N is present_c＞N_cmaxExiting the loop and jumping to step 11;

and step 3: if k is larger than m, quitting k circulation and jumping to step 8;

and 4, step 4: determining the node where the ant is located at present, detecting whether the node belongs to a destination node set, and if so, dynamically adjusting a destination node slave node table tabv corresponding to the node_kDeleting, otherwise, skipping to the next step;

and 5: searching a path connected with a node where the ant is located, calculating a transition probability according to a state transition probability formula, and selecting a next node from the node;

step 6: tabu modification_kPutting the newly selected node into a tabu table; and updating pheromones;

and 7: if tabu is contraindicated_kIf all the nodes in all the starting node set and all the nodes in the target node set are included, the ant finishes the route selection, and the step 9 is skipped, otherwise, the step 5 is skipped;

and 8: calculating and recording the sum of newly added pheromones of the ants in the round of selection nodes, if all ants in the round are finished selecting the route, skipping to the step 9, otherwise skipping to the step 3;

and step 9: comparing the newly increased pheromone sum of the combined calculation nodes selected by all ants in the round, taking the calculation node combination sequence with the maximum newly increased pheromone sum value, and updating pheromones on corresponding paths according to pheromone updating rules;

step 10: tabu for emptying tabu table_kPath table path_kSkipping to the step 2;

step 11: and taking the deployment route represented by the maximum pheromone path as the optimal solution of the deployment scheme.

10. The apparatus of claim 9, wherein the logistics deployment module is further configured to:

corresponding to the generated optimal logistics allocation scheme, determining the execution degree of the scheme by using real-time perception information, deleting corresponding nodes from the starting node set and the destination node set and deleting corresponding paths from the optimal scheduling paths for the allocated tasks; deleting and adding corresponding nodes in a starting node set and a target node set for the scheduling tasks to be deleted, added and modified currently; determining the current node position of the dispatching vehicle or the node position to be reached, and generating a corresponding node state diagram after updating the latest state; and (5) placing all k ants at the current position node of the dispatching car, and carrying out optimization solution again.