阅读说明：本技术 一种面向快件非标准服务的多主体协同运输资源调度方法 (Multi-main-body collaborative transportation resource scheduling method for express non-standard service ) 是由 孟凡超 纪青然 初佃辉 郑宏珍 周学权 于 2021-06-10 设计创作，主要内容包括：本发明提出的一种面向快件非标准服务的多主体协同运输资源调度方法,首先获取多主体共享快件物流网络和某客户任务；然后建立多主体协同运输方案模型；同时设定运输费用、运输时间和运输质量计算公式；在上述基础上,建立多主体协同运输资源调度模型,配置用于求解上述模型的三信息素蚁群-遗传混合算法；最后对协同运输方案进行可视化展示。本发明能够整合各家快件物流公司的运输资源,制定个性化的协同运输调度方案,解决单个快递物流公司无法满足用户对快件非标准服务的个性化需求的难题,降本增效。(The invention provides a multi-subject collaborative transportation resource scheduling method facing express nonstandard service, which comprises the steps of firstly obtaining a multi-subject shared express logistics network and a certain client task; then establishing a multi-subject collaborative transportation scheme model; meanwhile, a calculation formula of the transportation cost, the transportation time and the transportation quality is set; on the basis, a multi-subject cooperative transportation resource scheduling model is established, and a three-pheromone ant colony-genetic hybrid algorithm for solving the model is configured; and finally, visually displaying the collaborative transportation scheme. The invention can integrate the transportation resources of each express logistics company, formulate an individualized collaborative transportation scheduling scheme, solve the problem that a single express logistics company cannot meet the individualized requirement of a user on express nonstandard service, reduce cost and improve efficiency.)

1. A multi-subject collaborative transportation resource scheduling method for express non-standard service is characterized by comprising the following steps:

s1: acquiring a multi-subject shared express logistics network and a client task;

s2: establishing a multi-subject collaborative transportation scheme model;

s3: setting a calculation formula of transportation cost, transportation time and transportation quality;

s4: establishing a multi-subject collaborative transportation resource scheduling model;

s5: configuring a three-pheromone ant colony-genetic hybrid algorithm for solving a multi-main-body collaborative transportation scheme model and a multi-main-body collaborative transportation resource scheduling model, and generating a corresponding collaborative transportation scheme;

s6: and visually displaying the collaborative transportation scheme.

2. The dispatch method of multi-agent collaborative transportation resources for express non-standard services according to claim 1, characterized in that:

the multi-main-body shared express logistics network is SEN (SBN, STN), wherein SBN (P, V, E) is a shared trunk network, and STN (W, A, E') is a shared transport network;

in the shared backbone network SBN ═ (P, V, E), P ═ P_i1,2, …, m is the set of express logistics quotient, m is the number of express logistics quotient, p_iE, P represents the ith express flow organization; s_i＝{s_ik|k＝1,2,…,l_iIs express logistics quotient p_iTransport service set of l_iFor express logistics business p_iThe number of transportation service modes; v ═ V_j1,2, …, n is a set of backbone city nodes, v_jE, V is a jth backbone city node, and n is the number of nodes;a collection of lines is served for the backbone network, representing slave backbone nodes v_jTo v_j'Adopting express logistics business p_iService mode s_ikThe transportation service line of (1);

in the shared transit network STN ═ (W, a, E'), W ═ W_j1,2, …, n is a shared transit center set located inside a trunk city node, the number of the shared transit centers located in a certain trunk city node is 1 by default, and n is the number of the shared transit centers; a. the_j＝{a_ji|i＝1,2,…,m_jIs located at a backbone city node v_jInternal collection of transit centres a_jiExpress logistics business p_iThe number of the transfer centers of which the default express logistics quotient is positioned at a certain trunk city node is less than or equal to 1, m_jM is not more than m and is positioned at a trunk city node v_jNumber of internal transit centers; e '═ E'_i,j,e'_j,i|i∈[1,m],j∈[1,n]Is a collection of transit network service lines, transit mode only, e'_i,jIs shown at node v_jInternal slave express logistics quotient p_iA transfer center of_jiTo a shared transfer center w_jOf transit service line of, e'_j,iIs shown at node v_jFrom a shared transit centre w_jTo express logistics quotient p_iA transfer center of_jiA transit service line of (a);

the multi-agent sharing express logistics network SEN further comprises: from the backbone node v_jTo backbone node v_j'Distance d of_j,j'(ii) a Backbone node v_jWhen internal transport occurs, from shared transport centre w_jTo express logistics business p_iA transfer center of_jiDistance d of_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'By means of transport services s_ikPrice per unit distance per unit weight of goodsAt backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jPer unit weight of cargoPrice per unit distance c_i,j(ii) a Express logistics business p_iOwned mode of transportation service s_ikVelocity u of_i,k(ii) a At backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jMedium rotational speed u_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'Transport service mode s_ikQuality of service of

3. The method for scheduling multi-agent collaborative transportation resources for express nonstandard service according to claim 2, wherein the customer task ρ ═ is (b, e, w, C, T, Q), where b and e are respectively a start node and an end node of freight transportation, w is freight weight, C is a maximum value of total price of transportation service, T is a maximum value of service time, and Q is a minimum value of service quality.

4. The dispatch method for multi-agent collaborative transportation resources oriented to express nonstandard service of claim 3, wherein the multi-agent collaborative transportation scheme is a sequence of a trunk node and a transit node passed by a starting node to a terminating node of a client task, participating express logistics merchants and transportation modes used, and comprises a trunk transportation line and a transit transportation line;

the trunk transportation line isWherein v is_je.V is the j (j is 1, …, n) th task path_r) A node, v₁Starting node b, v representing a client task_nrRepresenting the terminating node e, n_rIs the number of nodes that are traversed through,is node v_jAnd node v_j+1A service line therebetween; setting a binary decision variableIndicating lines of transportation serviceWhether it belongs to task path r, and node v_jAnd v_j’Whether the task belongs to the task path r; if it is notTo representR, otherwise, it does not belong to r;

the transport line r'_jAs a backbone node v_jInternal transport line, r'_j＝{a_ji,e'_i,j,w_j,e'_j,i',a_ji’Denotes a slave express logistics quotient p_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics quotient p_i'A transfer center of_ji'(ii) a Setting a binary decision variableIs shown at node v_jWhether or not to follow express logistics business p_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics business p_i’A transfer center of_ji'And e'_i,jAnd e'_j,iWhether the task belongs to the task path; if it is notIs shown at node v_jHas occurred from express logistics provider p_iA transfer center of_jiThrough a shared screen point w_jTo express logistics business p_i'A transfer center of_ji'Otherwise, is indicated inNode v_jNo transport occurs;

since there are multiple task paths for a client task, set R_ρSet of all task paths representing customer task ρ, pass r, r'_j∈R_ρAnd ensuring the legality of the task path.

5. The dispatch method for multi-subject collaborative transportation resources oriented to express non-standard services as claimed in claim 4, wherein the step S3 specifically comprises the following steps:

s3.1: setting a transportation cost calculation formula; the transportation cost calculation formula is as follows:

wherein, C₁(r) is the total cost of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

C₂(r) is a transit network task path r'_jThe total cost of the conversion is calculated according to the following formula:

s3.2: setting a calculation formula of the transportation time; the transit time calculation formula is as follows:

wherein, T₁(r) is the time sum of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

T₂(r) is a transit network task path r'_jThe sum of the above conversion times is calculated as follows:

s3.3: setting a transportation service quality calculation formula; the transport quality of service calculation formula is as follows:

6. the dispatch method for multi-subject collaborative transportation resources oriented to express non-standard services according to claim 5, wherein the step S4 includes:

establishing a multi-main-body collaborative transportation resource scheduling model, wherein the objective function is as follows:

minC(r,r') (8)

minT(r,r') (9)

maxQ(r) (10)

the constraint conditions of the multi-subject collaborative transportation resource scheduling model comprise the following formulas:

C(r,r′)≤C (11)

T(r,r′)≤T (12)

Q(r)≥Q (13)

in the above formal description, formula (8) -formula (10) are optimization targets of the problem, and represent that the total cost of the transportation service is minimum, the total time is minimum, and the service quality is highest; equation (11) is a cost constraint that indicates that the cost of freight transportation on the mission path must be less than the cost specified by the customer mission; equation (12) is a time constraint that indicates that the shipment of goods on the task path must be completed within the time specified by the customer task; equation (13) is a quality of service constraint that indicates that the average transport quality of service for the nodes on the task path must meet the quality of service specified by the task customer.

7. The dispatch method for multi-subject collaborative transportation resources oriented to express non-standard services as claimed in claim 6, wherein the step S5 specifically comprises the following steps:

s5.1: importing a shared express network SEN and a client task rho; initializing genetic algorithm crossover probability p_cProbability of variation p_mPopulation number P, evolution algebra K₁(ii) a Initializing an ant colony algorithm pheromone influence factor alpha, a priori influence factor beta, a minimum information volatilization factor rho, the number of ants P and the iteration number K₂；

S5.2: generating an initial chromosome;

s5.3: generating a plurality of better individuals by using a genetic algorithm;

s5.4: updating a path prior matrix, a transportation mode matrix and an express logistics organization prior matrix;

s5.5: performing ant colony algorithm operation;

s5.6: outputting the best solution of the calendar;

s5.7: the three pheromone ant colony-genetic hybrid algorithm ends.

8. The dispatch method for multi-agent collaborative transportation resources for express non-standard services as claimed in claim 7, wherein the step S5.2 specifically comprises:

storing a client task starting node, randomly selecting a next trunk node, an express logistics provider and a transportation service mode within an optional range, and storing the next trunk node, the express logistics provider and the transportation service mode respectively;

calculating the total transportation service cost, the total transportation service time and the transportation service quality on the current transportation service path, judging whether constraint conditions are met, if so, performing the next round of random selection, if not, withdrawing the selected transportation service mode, judging after reselecting, if not, withdrawing the logistics quotient selected in the previous step, reselecting the logistics quotient and the transportation service mode, judging, if not, withdrawing the node selected in the previous step, reselecting the node, the logistics quotient and the transportation service mode in an optional range, judging again, and the like.

9. The dispatch method for multi-agent collaborative transportation resources for express non-standard services as claimed in claim 8, wherein the step S5.3 specifically comprises the steps of:

s5.3.1: setting the current evolution algebra k to be 0;

s5.3.2: calculating the fitness, and selecting by adopting a roulette selection method;

s5.3.3: performing a crossover operation according to the crossover probability, and performing a mutation operation according to the mutation probability;

s5.3.4: adding 1 to the value of the current evolution algebra K, and judging whether K reaches K₁If not, go to step S5.3.2.

10. The dispatch method for multi-agent collaborative transportation resources for express non-standard services as claimed in claim 9, wherein the step S5.5 specifically comprises the steps of:

s5.5.1: setting the current iteration times k' as 0, and setting the ants at the starting point;

s5.5.2: calculating the state transition probability and selecting the next node;

s5.5.3: judging whether the terminal is reached, if not, turning to step S5.5.3;

s5.5.4: keeping the pheromone of the best solution and the volatilization path of the current generation;

s5.5.5: adding 1 to the value of the current iteration times K ', and judging whether K' reaches K₂If not, go to step S5.5.2.

Technical Field

The invention relates to the technical field of intelligent logistics, in particular to a multi-subject collaborative transportation resource scheduling method for express non-standard service.

Background

In recent years, the express delivery and logistics industries in China are developed at a high speed, the types and the number of express delivery logistics companies are increased year by year, the service coverage is enlarged continuously, and the express delivery service volume is increased continuously. The express logistics industry also faces new challenges while promoting the development of socioeconomic performance. With the continuous release of the consumption potential of remote areas, the express transportation pressure from economically developed areas (middle east areas) to remote areas (middle west areas) is increasing day by day, and a single express company has the problems that the remote areas cannot be covered, or the transportation time is long, the transportation cost is high, and the personalized express logistics service requirements of users cannot be met, so that the express logistics task needs to be completed together by coordinating the transportation resources of a plurality of express logistics organizations. The scheduling problem of the multi-main-body collaborative transportation resources is essentially based on a shared express logistics service network, and express logistics organization, transportation routes and transportation modes are selected and combined, so that the personalized express logistics service requirements of users are met.

Aiming at the problem of current multi-subject collaborative transportation resource scheduling, although related departments and enterprises research and adjust express logistics transportation service modes, the following 4 defects still exist:

(1) the existing express logistics transportation service mode only considers the combination of a single express logistics organization and a plurality of transportation service modes. Due to the lack of cooperation and integration of a plurality of express logistics organizations, the traditional express logistics transportation service mode is difficult to meet the individual requirements of customers on express logistics services.

(2) The relevant studies do not take into account the actual situation. Because the transfer center is independently operated by each express logistics organization, and the vehicle labels of different express logistics organizations are different, the direct transfer between different express logistics organizations cannot be realized.

(3) Most of the objective functions of the existing mathematical models are cost reduction and efficiency improvement, and the improvement of the service quality is less concerned, and most of the objective functions are single objective functions.

(4) Most researches only use traditional optimization algorithms such as genetic algorithm, ant colony algorithm, simulated annealing algorithm, immune algorithm and the like to solve problems, and no innovative idea is introduced.

Disclosure of Invention

Aiming at the problems in the prior art, the invention aims to provide a multi-main-body collaborative transportation resource scheduling method facing express nonstandard service, which can establish a multi-main-body collaborative transportation resource scheduling model, utilize a three-pheromone ant colony-genetic hybrid algorithm to solve, select and combine express logistics organization, transportation routes and transportation modes, and formulate multi-main-body collaborative transportation resource scheduling so as to meet the personalized express logistics service requirements of users.

In order to achieve the purpose, the invention is realized by the following technical scheme:

a multi-subject collaborative transportation resource scheduling method for express non-standard service comprises the following steps:

s1: acquiring a multi-subject shared express logistics network and a client task;

s2: establishing a multi-subject collaborative transportation scheme model;

s3: setting a calculation formula of transportation cost, transportation time and transportation quality;

s4: establishing a multi-subject collaborative transportation resource scheduling model;

s5: configuring a three-pheromone ant colony-genetic hybrid algorithm for solving a multi-main-body collaborative transportation scheme model and a multi-main-body collaborative transportation resource scheduling model, and generating a corresponding collaborative transportation scheme;

s6: and visually displaying the collaborative transportation scheme.

Further, the multi-agent shared express logistics network is SEN ═ SBN, STN, where SBN ═ P, V, E) is the shared backbone network, and STN ═ W, a, E' is the shared transit network.

In the shared backbone network SBN ═ (P, V, E), P ═ P_i1,2, …, m is express mailSet of physical distribution merchants, m is the number of express physical distribution merchants, p_iE, P represents the ith express flow organization; s_i＝{s_ik|k＝1,2,…,l_iIs express logistics quotient p_iTransport service set of l_iFor express logistics business p_iThe number of transportation service modes; v ═ V_j1,2, …, n is a set of backbone city nodes, v_jE, V is a jth backbone city node, and n is the number of nodes;a collection of lines is served for the backbone network, representing slave backbone nodes v_jTo v_j'Adopting express logistics business p_iService mode s_ikThe transportation service line of (1).

In the shared transit network STN ═ (W, a, E'), W ═ W_j1,2, …, n is a shared transit center set located inside a trunk city node, the number of the shared transit centers located in a certain trunk city node is 1 by default, and n is the number of the shared transit centers; a. the_j＝{a_ji|i＝1,2,…,m_jIs located at a backbone city node v_jInternal collection of transit centres a_jiExpress logistics business p_iThe number of the transfer centers of which the default express logistics quotient is positioned at a certain trunk city node is less than or equal to 1, m_jM is not more than m and is positioned at a trunk city node v_jNumber of internal transit centers; e '═ E'_i,j,e'_j,i|i∈[1,m],j∈[1,n]Is a collection of transit network service lines, transit mode only, e'_i,jIs shown at node v_jInternal slave express logistics quotient p_iA transfer center of_jiTo a shared transfer center w_jOf transit service line of, e'_j,iIs shown at node v_jFrom a shared transit centre w_jTo express logistics quotient p_iA transfer center of_jiThe transit service line of (1).

The multi-agent sharing express logistics network SEN further comprises: from the backbone node v_jTo backbone node v_j'Distance d of_j,j'(ii) a Backbone node v_jWhen internal transport occurs, from shared transport centre w_jTo express logistics business p_iA transfer center of_jiDistance d of_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'By means of transport services s_ikPrice per unit distance per unit weight of goodsAt backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jPrice per unit distance of weight of goods c_i,j(ii) a Express logistics business p_iOwned mode of transportation service s_ikVelocity u of_i,k(ii) a At backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jMedium rotational speed u_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'Transport service mode s_ikQuality of service of

Further, the customer task is ρ ═ b, e, w, C, T, Q, where b and e are respectively the start node and the end node of the freight transportation, w is the freight weight, C is the maximum value of the total price of the transportation service, T is the maximum value of the service time, and Q is the minimum value of the service quality.

Further, the multi-subject cooperative transportation scheme is a sequence of a trunk node and a transit node passed by a starting node to a terminating node of a customer task, participating express logistics merchants and a used transportation mode, and comprises a trunk transportation line and a transit transportation line.

The trunk transportation line isWherein v is_je.V is the j (j is 1, …, n) th task path_r) A node, v₁Starting node b, v representing a client task_nrRepresenting the terminating node e, n_rIs the number of nodes that are traversed through,is node v_jAnd node v_j+1A service line therebetween; setting a binary decision variableIndicating lines of transportation serviceWhether it belongs to task path r, and node v_jAnd v_j’Whether the task belongs to the task path r; if it is notTo representR, otherwise, it does not belong to r;

the transport line r'_jAs a backbone node v_jInternal transport line, r'_j＝{a_ji,e'_i,j,w_j,e'_j,i',a_ji’Denotes a slave express logistics quotient p_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics quotient p_i'A transfer center of_ji'(ii) a Setting a binary decision variableIs shown at node v_jWhether or not to follow express logistics business p_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics business p_i’A transfer center of_ji'And e'_i,jAnd e'_j,iWhether the task belongs to the task path; if it is notIs shown at node v_jHas occurred from express logistics provider p_iA transfer center of_jiThrough a shared screen point w_jTo express logistics business p_i'A transfer center of_ji'Otherwise, it is indicated at node v_jNo transport occurs.

Since there are multiple task paths for a client task, set R_ρSet of all task paths representing customer task ρ, pass r, r'_j∈R_ρAnd ensuring the legality of the task path.

Further, the step S3 specifically includes the following steps:

s3.1: setting a transportation cost calculation formula; the transportation cost calculation formula is as follows:

wherein, C₁(r) is the total cost of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

C₂(r) is a transit network task path r'_jThe total cost of the conversion is calculated according to the following formula:

s3.2: setting a calculation formula of the transportation time; the transit time calculation formula is as follows:

wherein, T₁(r) is the time sum of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

T₂(r) is a transit network task path r'_jThe sum of the above conversion times is calculated as follows:

s3.3: setting a transportation service quality calculation formula; the transport quality of service calculation formula is as follows:

further, the step S4 includes:

establishing a multi-main-body collaborative transportation resource scheduling model, wherein the objective function is as follows:

min C(r,r') (8)

min T(r,r') (9)

max Q(r) (10)

the constraint conditions of the multi-subject collaborative transportation resource scheduling model comprise the following formulas:

C(r,r′)≤C (11)

T(r,r′)≤T (12)

Q(r)≥Q (13)

in the above formal description, formula (8) -formula (10) are optimization targets of the problem, and represent that the total cost of the transportation service is minimum, the total time is minimum, and the service quality is highest; equation (11) is a cost constraint that indicates that the cost of freight transportation on the mission path must be less than the cost specified by the customer mission; equation (12) is a time constraint that indicates that the shipment of goods on the task path must be completed within the time specified by the customer task; equation (13) is a quality of service constraint that indicates that the average transport quality of service for the nodes on the task path must meet the quality of service specified by the task customer.

Further, the step S5 specifically includes the following steps:

s5.1: importing a shared express network SEN and a client task rho; initializing genetic algorithm crossover probability p_cProbability of variation p_mPopulation number P, evolution algebra K₁(ii) a Initializing an ant colony algorithm pheromone influence factor alpha, a priori influence factor beta, a minimum information volatilization factor rho, the number of ants P and the iteration number K₂；

S5.2: generating an initial chromosome;

s5.3: generating a plurality of better individuals by using a genetic algorithm;

s5.4: updating a path prior matrix, a transportation mode matrix and an express logistics organization prior matrix;

s5.5: performing ant colony algorithm operation;

s5.6: outputting the best solution of the calendar;

s5.7: the three pheromone ant colony-genetic hybrid algorithm ends.

Further, the step S5.2 specifically includes:

storing a client task starting node, randomly selecting a next trunk node, an express logistics provider and a transportation service mode within an optional range, and storing the next trunk node, the express logistics provider and the transportation service mode respectively;

calculating the total transportation service cost, the total transportation service time and the transportation service quality on the current transportation service path, judging whether constraint conditions are met, if so, performing the next round of random selection, if not, withdrawing the selected transportation service mode, judging after reselecting, if not, withdrawing the logistics quotient selected in the previous step, reselecting the logistics quotient and the transportation service mode, judging, if not, withdrawing the node selected in the previous step, reselecting the node, the logistics quotient and the transportation service mode in an optional range, judging again, and the like.

Further, the step S5.3 specifically includes the following steps:

s5.3.1: setting the current evolution algebra k to be 0;

s5.3.2: calculating the fitness, and selecting by adopting a roulette selection method;

s5.3.3: performing a crossover operation according to the crossover probability, and performing a mutation operation according to the mutation probability;

s5.3.4: adding 1 to the value of the current evolution algebra K, and judging whether K reaches K₁If not, go to step S5.3.2.

Further, the step S5.5 specifically includes the following steps:

s5.5.1: setting the current iteration times k' as 0, and setting the ants at the starting point;

s5.5.2: calculating the state transition probability and selecting the next node;

s5.5.3: judging whether the terminal is reached, if not, turning to step S5.5.3;

s5.5.4: keeping the pheromone of the best solution and the volatilization path of the current generation;

s5.5.5: adding 1 to the value of the current iteration times K ', and judging whether K' reaches K₂If not, go to step S5.5.2.

Compared with the prior art, the invention has the beneficial effects that: the invention provides a multi-subject collaborative transportation resource scheduling method for express non-standard service. Firstly, a multi-subject collaborative transportation scheme model is established based on a multi-subject shared express logistics network. Secondly, aiming at the lowest total cost, the shortest total time and the highest service quality, a multi-main-body collaborative transportation resource scheduling model is established, and express logistics organizations, transportation routes and transportation modes are selected and combined by collaborating transportation resources of a plurality of express logistics organizations to jointly complete express logistics tasks so as to meet the personalized express logistics service requirements of users. Then, an efficient solving algorithm, namely a three-pheromone ant colony-genetic hybrid algorithm, is provided, a problem model is solved, and a multi-main-body collaborative transportation resource scheduling scheme is formulated. And finally, returning the obtained result to a user interface, so that the user can conveniently view the result in a visual mode.

Aiming at the defects in the website integration research, the invention introduces a shared transfer center when a coordinated transportation resource scheduling scheme model is used, and realizes the coordinated transportation among a plurality of main bodies by virtue of the shared characteristics. Meanwhile, the selection combination of multi-express logistics organization, multiple transportation service modes and multiple transportation routes is considered, a multi-main-body collaborative transportation resource scheduling model with the aims of lowest total cost, shortest total time and highest service quality is established, a three-pheromone ant colony-genetic hybrid algorithm for solving the model is designed, and a multi-main-body collaborative transportation resource scheduling scheme is formulated. And finally, returning the collaborative transportation resource scheduling scheme output by the algorithm to the user interface in a visual method. Aiming at the problem of scheduling multi-main-body collaborative transportation resources, the multi-main-body collaborative transportation resource scheduling is formulated through the novel solving model and the solving algorithm provided by the invention, so that the personalized express logistics service requirements of users can be met, and the method has practical value.

Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 is a schematic diagram of a shared express logistics service network of the present invention;

FIG. 3 is a schematic representation of a multi-subject collaborative transportation scenario model of the present invention;

FIG. 4 is a schematic cross-operation of the three pheromone ant colony-genetic hybrid algorithm of the present invention;

FIG. 5 is a schematic diagram of the variant operation of the three pheromone ant colony-genetic hybrid algorithm of the present invention;

FIG. 6 is a flow chart for solving using the three pheromone ant colony-genetic hybrid algorithm of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.

The first embodiment is as follows:

as shown in fig. 1, this embodiment provides a method for scheduling resources for multi-agent collaborative transportation for express non-standard service, including the following steps:

s1: and acquiring a multi-subject shared express logistics network and a client task.

S2: and establishing a multi-subject collaborative transportation scheme model.

S3: and setting a calculation formula of the transportation cost, the transportation time and the transportation quality.

S4: and establishing a multi-main-body collaborative transportation resource scheduling model.

S5: and configuring a three-pheromone ant colony-genetic hybrid algorithm for solving the multi-main-body collaborative transportation scheme model and the multi-main-body collaborative transportation resource scheduling model, and generating a corresponding collaborative transportation scheme.

S6: and visually displaying the collaborative transportation scheme.

In the present embodiment, the specific steps of step S1 are as follows:

s1.1: and acquiring a multi-subject shared express logistics network. The shared service network SEN is (SBN, STN), (P, V, E) is a shared backbone network, and (W, a, E') is a shared transit network.

S1.2: a certain client task is obtained. And (b, e, w, C, T and Q), wherein b and e are respectively a starting node and an ending node of freight transportation, w is the weight of the freight, C is the maximum value of the total price of the transportation service, T is the maximum value of the service time, and Q is the minimum value of the service quality.

The specific steps of shared service network SEN acquisition in step S1.1 are as follows:

s1.1.1: the acquisition shared backbone network SBN is (P, V, E). Wherein, P ═ { P ═ P_i1,2, …, m is the set of express logistics quotient, m is the number of express logistics quotient, p_iE, P represents the ith express flow organization; s_i＝{s_ik|k＝1,2,…,l_iIs express logistics quotient p_iTransport service set of l_iFor express logistics business p_iThe number of transportation service modes; v ═ V_j1,2, …, n is a set of backbone city nodes, v_jE, V is a jth backbone city node, and n is the number of nodes;a collection of lines is served for the backbone network,representing slave backbone nodes v_jTo v_j'Adopting express logistics business p_iService mode s_ikThe transportation service line of (1).

S1.1.2: and acquiring the shared transit network STN ═ W, A and E'). Wherein W ═ { W ═ W_j1,2, …, n is a shared transit center set located inside a trunk city node, the number of the shared transit centers located in a certain trunk city node is 1 by default, and n is the number of the shared transit centers; a. the_j＝{a_ji|i＝1,2,…,m_jIs located at a backbone city node v_jInternal collection of transit centres a_jiExpress logistics business p_iThe number of the transfer centers of which the default express logistics quotient is positioned at a certain trunk city node is less than or equal to 1, m_jM is not more than m and is positioned at a trunk city node v_jNumber of internal transit centers; e '═ E'_i,j,e'_j,i|i∈[1,m],j∈[1,n]Is a transfer netSet of network service lines, transit mode unique, e'_i,jIs shown at node v_jInternal slave express logistics quotient p_iA transfer center of_jiTo a shared transfer center w_jOf transit service line of, e'_j,iIs shown at node v_jFrom a shared transit centre w_jTo express logistics quotient p_iA transfer center of_jiThe transit service line of (1).

S1.1.3: acquiring the shared service network SEN further comprises: from the backbone node v_jTo backbone node v_j'Distance d of_j,j'(ii) a Backbone node v_jWhen internal transport occurs, from shared transport centre w_jTo express logistics business p_iA transfer center of_jiDistance d of_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'By means of transport services s_ikPrice per unit distance per unit weight of goodsAt backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jPrice per unit distance of weight of goods c_i,j(ii) a Express logistics business p_iOwned mode of transportation service s_ikVelocity u of_i,k(ii) a At backbone node v_jInternal, from express logistics provider p_iA transfer center of_jiTo a shared transfer center w_jMedium rotational speed u_i,j(ii) a Express logistics business p_iFrom the backbone node v_jTo v_j'Transport service mode s_ikQuality of service of

In the present embodiment, the specific steps of step S2 are as follows:

s2.1: establishing a multi-main-body collaborative transportation scheme model, wherein the transportation scheme refers to a sequence of a main node and a transfer node which pass from a starting node to a terminating node of a client task, participating express logistics merchants and a used transportation mode and comprises a main transportation line and a transfer transportation line.

S2.2: the trunk transportation line isWherein v is_je.V is the j (j is 1, …, n) th task path_r) A node, v₁Starting node b, v representing a client task_nrRepresenting the terminating node e, n_rIs the number of nodes that are traversed through,is node v_jAnd node v_j+1A service line between. Setting a binary decision variableIndicating lines of transportation serviceWhether it belongs to task path r, and node v_jAnd v_j’Whether it belongs to task path r.

S2.3：r'_j＝{a_ji,e'_i,j,w_j,e'_j,i',a_ji’Is a backbone node v_jInternal transfer lines, representing logistics p from the express_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics quotient p_i'A transfer center of_ji'. Setting a binary decision variable andis shown at node v_jWhether or not to follow express logistics business p_iA transfer center of_jiThrough a shared transit centre w_jTo express logistics business p_i’A transfer center of_ji'And e'_i,jAnd e'_j,iWhether it belongs to a task path. If it is notTo representR, otherwise, it does not; if it is notIs shown at node v_jHas occurred from express logistics provider p_iA transfer center of_jiThrough a shared screen point w_jTo express logistics business p_i'A transfer center of_ji'Otherwise, it is indicated at node v_jNo transport occurs.

S2.4: since there are multiple task paths for a client task, set R_ρSet of all task paths representing customer task ρ, pass r, r'_j∈R_ρAnd ensuring the legality of the task path.

In the present embodiment, the specific steps of step S3 are as follows:

s3.1: and setting a transportation cost calculation formula. The transportation cost calculation formula is as follows:

wherein, C₁(r) is the total cost of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

C₂(r) is a transit network task path r'_jThe total cost of the conversion is calculated according to the following formula:

s3.2: and setting a calculation formula of the transportation time. The transit time calculation formula is as follows:

wherein, T₁(r) is the time sum of all the transport lines on the task path r of the backbone network, and the calculation formula is as follows:

T₂(r) is a transit network task path r'_jThe sum of the above conversion times is calculated as follows:

s3.3: and setting a transportation service quality calculation formula. The transport quality of service calculation formula is as follows:

in this embodiment, the step S4 specifically includes:

establishing a multi-main-body collaborative transportation resource scheduling model, wherein the objective function is as follows:

min C(r,r') (8)

min T(r,r') (9)

max Q(r) (10)

the constraint conditions of the multi-subject collaborative transportation resource scheduling model comprise the following formulas:

C(r,r′)≤C (11)

T(r,r′)≤T (12)

Q(r)≥Q (13)

in the above formal description, formula (8) -formula (10) are optimization targets of the problem, and represent that the total cost of the transportation service is minimum, the total time is minimum, and the service quality is highest; equation (11) is a cost constraint that indicates that the cost of freight transportation on the mission path must be less than the cost specified by the customer mission; equation (12) is a time constraint that indicates that the shipment of goods on the task path must be completed within the time specified by the customer task; equation (13) is a quality of service constraint that indicates that the average transport quality of service for the nodes on the task path must meet the quality of service specified by the task customer.

In this embodiment, step S5 further includes: based on the shared service network SEN ═ SBN, (SBN, STN), the shared backbone network SBN ═ P, V, E, the shared transit network STN ═ W, a, E'), the client task ρ ═ b, E, W, C, T, Q, the decision variables are set to (b, E, W, C, T, Q), the decision variables are set to (b, E, W, E, Q), the client task ρ is set to (b, T, Q), the decision variables are set to (b, E, C, T, Q), the client task is set to (P, EAndand configuring a three-information ant colony-genetic hybrid algorithm for solving the model.

Specifically, the step of configuring the three-pheromone ant colony-genetic hybrid algorithm based on the multi-subject shared express logistics network information and certain client task information comprises the following steps:

s5.1: importing data, including a shared express network SEN and a client task rho; initializing genetic algorithm crossover probability p_cProbability of variation p_mPopulation number P, evolution algebra K₁(ii) a Initializing an ant colony algorithm pheromone influence factor alpha, a priori influence factor beta, a minimum information volatilization factor rho, the number of ants P and the iteration number K₂；

S5.2: generating an initial chromosome;

s5.3: generating a plurality of better individuals by using a genetic algorithm;

s5.4: updating a path prior matrix, a transportation mode matrix and an express logistics organization prior matrix;

s5.5: performing ant colony algorithm operation;

s5.6: outputting the best solution of the calendar;

s5.7: the algorithm ends.

In this embodiment, the method for generating the initial solution in step S5.2 is as follows:

s5.2.1: storing a client task starting node, randomly selecting a next trunk node (a transfer node is positioned in the trunk node), an express logistics provider and a transportation service mode in an optional range, and storing the next trunk node, the express logistics provider and the transportation service mode respectively;

s5.2.2: calculating the total transportation service cost, the total transportation service time and the transportation service quality on the current transportation service path, judging whether constraint conditions are met, if so, performing the next round of random selection, if not, withdrawing the selected transportation service mode, judging after reselecting, if not, withdrawing the logistics quotient selected in the previous step, reselecting the logistics quotient and the transportation service mode, judging, if not, withdrawing the node selected in the previous step, reselecting the node, the logistics quotient and the transportation service mode in an optional range, judging again, and the like.

In this embodiment, the specific steps of generating a plurality of better individuals by using a genetic algorithm in step S5.3 are as follows:

s5.3.1: setting the current evolution algebra k to be 0;

s5.3.2: calculating the fitness, performing selection operation, and adopting a roulette selection method;

s5.3.3: performing a crossover operation according to the crossover probability, and performing a mutation operation according to the mutation probability;

s5.3.4: k is K +1, and whether K reaches K is judged₁If not, go to step S5.3.2.

The fitness calculation formula adopted in step S5.3.2 is as follows:

the probability of selection of a single individual is P_jCumulative probability of S_jThe calculation formula is as follows:

produce a bit at [0,1 ]]If ω is within the interval [0, S ]₁]Selecting the 1 st individual; if ω is in the interval (S)_j-1,S_j]Selecting j_th(ii) an individual.

In the present invention, the ant colony algorithm in step S5.5 specifically includes the following steps:

s5.5.1: setting the current iteration times k' as 0, and setting the ants at the starting point;

s5.5.2: calculating the state transition probability and selecting the next node;

s5.5.3: judging whether the terminal is reached, if not, turning to step S5.5.3;

s5.5.4: keeping the pheromone of the best solution and the volatilization path of the current generation;

s5.5.5: k '+ 1, and determining whether K' reaches K₂If not, go to step S5.5.2.

The transition probability calculation formula used in step S5.5.2 is as follows:

respectively storing the backbone node information, the shared transfer center information, the transportation service mode information and the express logistics organization information into tau_v、τ_pAnd τ_sI.e. the initial pheromone value, so that the ants have good alternative initialization schemes. Due to shared transportThe centers are associated with the backbone nodes so the pheromone values are consistent. The ant individual moves from the starting point to the end point. From the trunk city v_iTransfer to backbone city v_jFor τ_v，The transfer probability of the a th ant; for tau_s，The transfer probability of adopting a certain transportation service mode for the a-th ant; for tau_p，Probability of selecting a certain courier organization for the a-th ant.

In this embodiment, the formula for calculating pheromone volatilization in step S5.5.4 is as follows:

wherein, tau_ij(t) represents a path (v)_i,v_j) The intensity of the pheromone at time t, 1-p, represents the degree of pheromone attenuation,representing the inverse of the best solution for this iteration.

In this embodiment, step S6 further includes: returning the multi-subject collaborative transportation resource scheduling scheme to a user interface in a visual mode; the user can view the multi-subject collaborative transportation resource scheduling scheme.

Example two:

based on the embodiment, the embodiment provides a method for scheduling multi-subject collaborative transportation resources for express nonstandard service, which specifically includes the following steps:

step one, acquiring a multi-subject shared express logistics network and a certain client task.

Step 1.1: and acquiring a multi-subject shared express logistics network. The shared service network SEN is (SBN, STN), (P, V, E) is a shared backbone network, and (W, a, E') is a shared transit network.

The schematic diagram of the shared express logistics network is shown in fig. 2, and comprises 5 backbone nodes (v)₁,v₂,v₃,v₄,v₅) 3 express logistics organizations (A, B, C) and 3 transportation service modes. In the figure, open circles represent main trunk city nodes, and double-line open ellipses represent start nodes (v)₁) And a termination node (v)₅) (ii) a Solid circles represent transfer nodes owned by express logistics organizations, different colors represent transfer nodes belonging to different express logistics organizations, and red solid circles represent shared transfer centers; connecting lines among the trunk city nodes represent transportation service lines, different colors represent that the nodes belong to different express logistics organizations, different linear shapes represent different transportation service modes, and red connecting lines inside the trunk nodes represent transfer lines; triplets are used to represent speed, price per unit of weight per unit of cargo, and quality of transportation service for a particular transportation service line. As in the figure at the backbone city node, v₂And v₃And 2 (10,16,7) circled in red, which indicates that the speed of the transportation service mode 2 provided by the express logistics organization C is 10, the price per unit weight and unit distance is 16, and the service quality is 7 between the two trunk nodes. Inside the main city node, the connecting lines between the solid circles represent the transfer service lines between the transfer centers belonging to different express logistics organizations and the shared transfer center, and binary groups are used for representing the transfer price of the medium rotation speed and the unit cargo weight. At node v in the figure₄4 (2,1) with the interior circled by red is indicated at v₄Inside, the speed of rotation is 2 in the transfer between express mail logistics organization C's the center of transportation and the shared center of transportation, and the transfer price of unit goods weight is 1.

Step 1.2: a certain client task is obtained. The client task ρ is (1,5,0.02,20,30,2), and the start node of the task is v₁The termination node is v₅The unit mass of the goods is 0.02, the total cost is required to be not more than 20, the total time is not more than 30, and the total service quality is not less than 2.

And step two, establishing a multi-subject collaborative transportation scheme model. The transportation scheme refers to a sequence of a main node and a transit node which are passed from a starting node to a terminating node of a customer task, participating express logistics providers and used transportation modes.

A schematic diagram of a multi-subject collaborative transportation scheme model is shown in FIG. 3. Defined as E ═ (V, P, S, W). V ═ V₁,…,v_nrIs a sequence of backbone nodes for storing the number of the backbone nodes that pass through in the task path, where v₁For the starting node of the client task, v_nrTo terminate a node, n_rIs the number of passing nodes; p ═ P₁,…,p_nr-1The express organization sequence is used for storing express organization numbers selected between the front trunk node and the rear trunk node on the task path, and the express organization numbers are expressed in v_iAnd v_i+1Number p of the space between_iThe express logistics merchant; s ═ S₁,…,s_nr-1The transport mode sequence is used for storing transport mode numbers selected after the degree between two trunk nodes on the task path determines express organization and indicates the transport mode numbers in v_iAnd v_i+1Select express logistics business p between_iTransport service mode s_i；W＝{w₁,…,w_nrThe shared transit center sequence is used for storing the number of the shared transit center on the trunk node where the transit occurs, and the number of the shared transit center on the trunk node where the transit does not occur is 0, for example, the starting node v₁And a termination node v_nrAll are not transported, then w₁0 and w_nr＝0。

And step three, setting a calculation formula of the transportation cost, the transportation time and the transportation quality.

And step four, establishing a multi-main-body collaborative transportation resource scheduling model.

Step five, configuring a three-pheromone ant colony-genetic hybrid algorithm for solving the model, and configuring a three-pheromone ant colony-genetic hybrid algorithm for solving the model, wherein the three-pheromone ant colony-genetic hybrid algorithm comprises the following steps of:

step 5.1: importing data, including a shared express network SEN and a client task rho; initializing genetic algorithm crossover probability p_cProbability of variation p_mPopulation number P, evolutionary generationNumber K₁(ii) a Initializing an ant colony algorithm pheromone influence factor alpha, a priori influence factor beta, a minimum information volatilization factor rho, the number of ants P and the iteration number K₂。

Step 5.2: an initial chromosome is generated.

Step 5.2.1: storing a client task starting node, randomly selecting a next trunk node (a transfer node is positioned in the trunk node), an express logistics provider and a transportation service mode in an optional range, and storing the next trunk node, the express logistics provider and the transportation service mode respectively;

step 5.2.2: calculating the total transportation service cost, the total transportation service time and the transportation service quality on the current transportation service path, judging whether constraint conditions are met, if so, performing the next round of random selection, if not, withdrawing the selected transportation service mode, judging after reselecting, if not, withdrawing the logistics quotient selected in the previous step, reselecting the logistics quotient and the transportation service mode, judging, if not, withdrawing the node selected in the previous step, reselecting the node, the logistics quotient and the transportation service mode in an optional range, judging again, and the like.

Step 5.3: a number of superior individuals are generated using genetic algorithms.

Step 5.3.1: setting the current evolution algebra k to be 0;

step 5.3.2: calculating the fitness, performing selection operation, and adopting a roulette selection method;

step 5.3.3: performing a crossover operation according to the crossover probability, and performing a mutation operation according to the mutation probability;

the cross-over operation is schematically shown in fig. 4. And randomly generating a cross point, carrying out cross operation on the trunk node sequence (V) to generate a new trunk node sequence, and then carrying out twice correction to generate a legal and better individual. The first correction makes the trunk node sequence legal, and the second correction updates the shared transit center sequence (W), the express logistics organization sequence (P) and the transportation service mode sequence (S) according to the objective function.

The variant operation diagram is shown in fig. 5, and only organizes the sequence and the transportation mode sequence for express logistics, and does not relate to the trunk node sequence and the shared transit center sequence.

Step 5.3.4: k is K +1, and whether K reaches K is judged₁If not, go to step 5.3.2.

Step 5.4: and updating the path prior matrix, the transportation mode matrix and the express logistics organization prior matrix.

Step 5.5: and performing ant colony algorithm operation.

Step 5.5.1: setting the current iteration times k' as 0, and setting the ants at the starting point;

step 5.5.2: calculating the state transition probability and selecting the next node;

step 5.5.3: judging whether the terminal point is reached, if not, turning to the step 5.5.3;

step 5.5.4: keeping the pheromone of the best solution and the volatilization path of the current generation;

step 5.5.5: k '+ 1, and determining whether K' reaches K₂If not, go to step 5.5.2.

Step 5.6: outputting the best solution of the calendar;

step 5.7: the algorithm ends.

FIG. 6 shows a flow chart of the three pheromone ant colony-genetic hybrid algorithm.

And sixthly, visually displaying the collaborative transportation scheme.

Returning the multi-subject collaborative transportation resource scheduling scheme to a user interface in a visual mode; the user can view the multi-subject collaborative transportation resource scheduling scheme.

The invention is further described with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and these equivalents also fall within the scope of the present application.