CN113469505A - Multi-main-body collaborative transportation resource scheduling method for express non-standard service - Google Patents

Multi-main-body collaborative transportation resource scheduling method for express non-standard service Download PDF

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CN113469505A
CN113469505A CN202110650671.2A CN202110650671A CN113469505A CN 113469505 A CN113469505 A CN 113469505A CN 202110650671 A CN202110650671 A CN 202110650671A CN 113469505 A CN113469505 A CN 113469505A
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孟凡超
纪青然
初佃辉
郑宏珍
周学权
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Abstract

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.

Description

Multi-main-body collaborative transportation resource scheduling method for express non-standard service
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, piE, P represents the ith express flow organization; si＝{sik|k＝1,2,…,liIs express logistics quotient piTransport service set of liFor express logistics business piThe number of transportation service modes; v ═ Vj1,2, …, n is a set of backbone city nodes, vjE, V is a jth backbone city node, and n is the number of nodes;
Figure BDA0003111060910000031
a collection of lines is served for the backbone network,
Figure BDA0003111060910000032
Figure BDA0003111060910000033
representing slave backbone nodes vjTo vj'Adopting express logistics business piService mode sikThe 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. thej＝{aji|i＝1,2,…,mjIs located at a backbone city node vjInternal collection of transit centres ajiExpress logistics business piThe 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, mjM is not more than m and is positioned at a trunk city node vjNumber 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 vjInternal slave express logistics quotient piA transfer center ofjiTo a shared transfer center wjOf transit service line of, e'j,iIs shown at node vjFrom a shared transit centre wjTo express logistics quotient piA transfer center ofjiThe transit service line of (1).
The multi-agent sharing express logistics network SEN further comprises: from the backbone node vjTo backbone node vj'Distance d ofj,j'(ii) a Backbone node vjWhen internal transport occurs, from shared transport centre wjTo express logistics business piA transfer center ofjiDistance d ofi,j(ii) a Express logistics business piFrom the backbone node vjTo vj'By means of transport services sikPrice per unit distance per unit weight of goods
Figure BDA0003111060910000034
At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjPrice per unit distance of weight of goods ci,j(ii) a Express logistics business piOwned mode of transportation service sikVelocity u ofi,k(ii) a At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjMedium rotational speed ui,j(ii) a Express logistics business piFrom the backbone node vjTo vj'Transport service mode sikQuality of service of
Figure BDA0003111060910000035
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 is
Figure BDA0003111060910000041
Wherein v isje.V is the j (j is 1, …, n) th task pathr) A node, v1Starting node b, v representing a client tasknrRepresenting the terminating node e, nrIs the number of nodes that are traversed through,
Figure BDA0003111060910000042
is node vjAnd node vj+1A service line therebetween; setting a binary decision variable
Figure BDA0003111060910000043
Indicating lines of transportation service
Figure BDA0003111060910000044
Whether it belongs to task path r, and node vjAnd vj’Whether the task belongs to the task path r; if it is not
Figure BDA0003111060910000045
To represent
Figure BDA0003111060910000046
R, otherwise, it does not belong to r;
the transport line r'jAs a backbone node vjInternal transport line, r'j＝{aji,e'i,j,wj,e'j,i',aji’Denotes a slave express logistics quotient piA transfer center ofjiThrough a shared transit centre wjTo express logistics quotient pi'A transfer center ofji'(ii) a Setting a binary decision variable
Figure BDA0003111060910000047
Is shown at node vjWhether or not to follow express logistics business piA transfer center ofjiThrough a shared transit centre wjTo express logistics business pi’A transfer center ofji'And e'i,jAnd e'j,iWhether the task belongs to the task path; if it is not
Figure BDA0003111060910000048
Is shown at node vjHas occurred from express logistics provider piA transfer center ofjiThrough a shared screen point wjTo express logistics business pi'A transfer center ofji'Otherwise, it is indicated at node vjNo transport occurs.
Since there are multiple task paths for a client task, set RρSet of all task paths representing customer task ρ, pass r, r'j∈RρAnd 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:
Figure BDA0003111060910000049
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:
Figure BDA0003111060910000051
C2(r) is a transit network task path r'jThe total cost of the conversion is calculated according to the following formula:
Figure BDA0003111060910000052
s3.2: setting a calculation formula of the transportation time; the transit time calculation formula is as follows:
Figure BDA0003111060910000053
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:
Figure BDA0003111060910000054
T2(r) is a transit network task path r'jThe sum of the above conversion times is calculated as follows:
Figure BDA0003111060910000055
s3.3: setting a transportation service quality calculation formula; the transport quality of service calculation formula is as follows:
Figure BDA0003111060910000056
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)
Figure BDA0003111060910000061
Figure BDA0003111060910000062
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 pcProbability of variation pmPopulation 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.
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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, piE, P represents the ith express flow organization; si＝{sik|k＝1,2,…,liIs express logistics quotient piTransport service set of liFor express logistics business piThe number of transportation service modes; v ═ V j1,2, …, n is a set of backbone city nodes, vjE, V is a jth backbone city node, and n is the number of nodes;
Figure BDA0003111060910000101
a collection of lines is served for the backbone network,
Figure BDA0003111060910000102
representing slave backbone nodes vjTo vj'Adopting express logistics business piService mode sikThe 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. thej＝{aji|i＝1,2,…,mjIs located at a backbone city node vjInternal collection of transit centres ajiExpress logistics business piThe 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, mjM is not more than m and is positioned at a trunk city node vjNumber 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 vjInternal slave express logistics quotient piA transfer center ofjiTo a shared transfer center wjOf transit service line of, e'j,iIs shown at node vjFrom a shared transit centre wjTo express logistics quotient piA transfer center ofjiThe transit service line of (1).
S1.1.3: acquiring the shared service network SEN further comprises: from the backbone node vjTo backbone node vj'Distance d ofj,j'(ii) a Backbone node vjWhen internal transport occurs, from shared transport centre wjTo express logistics business piA transfer center ofjiDistance d ofi,j(ii) a Express logistics business piFrom the backbone node vjTo vj'By means of transport services sikPrice per unit distance per unit weight of goods
Figure BDA0003111060910000103
At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjPrice per unit distance of weight of goods ci,j(ii) a Express logistics business piOwned mode of transportation service sikVelocity u ofi,k(ii) a At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjMedium rotational speed ui,j(ii) a Express logistics business piFrom the backbone node vjTo vj'Transport service mode sikQuality of service of
Figure BDA0003111060910000104
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 is
Figure BDA0003111060910000111
Wherein v isje.V is the j (j is 1, …, n) th task pathr) A node, v1Starting node b, v representing a client tasknrRepresenting the terminating node e, nrIs the number of nodes that are traversed through,
Figure BDA0003111060910000112
is node vjAnd node vj+1A service line between. Setting a binary decision variable
Figure BDA0003111060910000113
Indicating lines of transportation service
Figure BDA0003111060910000114
Whether it belongs to task path r, and node vjAnd vj’Whether it belongs to task path r.
S2.3：r'j＝{aji,e'i,j,wj,e'j,i',aji’Is a backbone node vjInternal transfer lines, representing logistics p from the expressiA transfer center ofjiThrough a shared transit centre wjTo express logistics quotient pi'A transfer center ofji'. Setting a binary decision variable and
Figure BDA0003111060910000115
is shown at node vjWhether or not to follow express logistics business piA transfer center ofjiThrough a shared transit centre wjTo express logistics business pi’A transfer center ofji'And e'i,jAnd e'j,iWhether it belongs to a task path. If it is not
Figure BDA0003111060910000116
To represent
Figure BDA0003111060910000117
R, otherwise, it does not; if it is not
Figure BDA0003111060910000118
Is shown at node vjHas occurred from express logistics provider piA transfer center ofjiThrough a shared screen point wjTo express logistics business pi'A transfer center ofji'Otherwise, it is indicated at node vjNo transport occurs.
S2.4: since there are multiple task paths for a client task, set RρSet of all task paths representing customer task ρ, pass r, r'j∈RρAnd 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:
Figure BDA0003111060910000119
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:
Figure BDA0003111060910000121
C2(r) is a transit network task path r'jThe total cost of the conversion is calculated according to the following formula:
Figure BDA0003111060910000122
s3.2: and setting a calculation formula of the transportation time. The transit time calculation formula is as follows:
Figure BDA0003111060910000123
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:
Figure BDA0003111060910000124
T2(r) is a transit network task path r'jThe sum of the above conversion times is calculated as follows:
Figure BDA0003111060910000125
s3.3: and setting a transportation service quality calculation formula. The transport quality of service calculation formula is as follows:
Figure BDA0003111060910000126
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)
Figure BDA0003111060910000131
Figure BDA0003111060910000132
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, E
Figure BDA0003111060910000133
And
Figure BDA0003111060910000134
and 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 pcProbability of variation pmPopulation 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:
Figure BDA0003111060910000141
the probability of selection of a single individual is PjCumulative probability of SjThe calculation formula is as follows:
Figure BDA0003111060910000142
Figure BDA0003111060910000143
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,Sj]Selecting jth(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:
Figure BDA0003111060910000151
respectively storing the backbone node information, the shared transfer center information, the transportation service mode information and the express logistics organization information into tauv、τpAnd τsI.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 viTransfer to backbone city vjFor τv，
Figure BDA0003111060910000152
The transfer probability of the a th ant; for taus，
Figure BDA0003111060910000153
The transfer probability of adopting a certain transportation service mode for the a-th ant; for taup，
Figure BDA0003111060910000154
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:
Figure BDA0003111060910000155
wherein, tauij(t) represents a path (v)i,vj) The intensity of the pheromone at time t, 1-p, represents the degree of pheromone attenuation,
Figure BDA0003111060910000156
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,…,vnrIs 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, vnrTo terminate a node, nrIs the number of passing nodes; p ═ P1,…,pnr-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 viAnd vi+1Number p of the space betweeniThe express logistics merchant; s ═ S1,…,snr-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 viAnd vi+1Select express logistics business p betweeniTransport service mode si；W＝{w1,…,wnrThe 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 vnrAll are not transported, then w10 and wnr＝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 pcProbability of variation pmPopulation 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.

Claims (10)

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 ═ Pi1,2, …, m is the set of express logistics quotient, m is the number of express logistics quotient, piE, P represents the ith express flow organization; si＝{sik|k＝1,2,…,liIs express logistics quotient piTransport service set of liFor express logistics business piThe number of transportation service modes; v ═ Vj1,2, …, n is a set of backbone city nodes, vjE, V is a jth backbone city node, and n is the number of nodes;
Figure FDA0003111060900000011
a collection of lines is served for the backbone network,
Figure FDA0003111060900000012
Figure FDA0003111060900000013
representing slave backbone nodes vjTo vj'Adopting express logistics business piService mode sikThe transportation service line of (1);
in the shared transit network STN ═ (W, a, E'), W ═ Wj1,2, …, 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. thej＝{aji|i＝1,2,…,mjIs located at a backbone city node vjInternal collection of transit centres ajiExpress logistics business piThe 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, mjM is not more than m and is positioned at a trunk city node vjNumber 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 vjInternal slave express logistics quotient piA transfer center ofjiTo a shared transfer center wjOf transit service line of, e'j,iIs shown at node vjFrom a shared transit centre wjTo express logistics quotient piA transfer center ofjiA transit service line of (a);
the multi-agent sharing express logistics network SEN further comprises: from the backbone node vjTo backbone node vj'Distance d ofj,j'(ii) a Backbone node vjWhen internal transport occurs, from shared transport centre wjTo express logistics business piA transfer center ofjiDistance d ofi,j(ii) a Express logistics business piFrom the backbone node vjTo vj'By means of transport services sikPrice per unit distance per unit weight of goods
Figure FDA0003111060900000021
At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjPer unit weight of cargoPrice per unit distance ci,j(ii) a Express logistics business piOwned mode of transportation service sikVelocity u ofi,k(ii) a At backbone node vjInternal, from express logistics provider piA transfer center ofjiTo a shared transfer center wjMedium rotational speed ui,j(ii) a Express logistics business piFrom the backbone node vjTo vj'Transport service mode sikQuality of service of
Figure FDA0003111060900000022
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 is
Figure FDA0003111060900000023
Wherein v isje.V is the j (j is 1, …, n) th task pathr) A node, v1Starting node b, v representing a client tasknrRepresenting the terminating node e, nrIs the number of nodes that are traversed through,
Figure FDA0003111060900000031
is node vjAnd node vj+1A service line therebetween; setting a binary decision variable
Figure FDA0003111060900000032
Indicating lines of transportation service
Figure FDA0003111060900000033
Whether it belongs to task path r, and node vjAnd vj’Whether the task belongs to the task path r; if it is not
Figure FDA0003111060900000034
To represent
Figure FDA0003111060900000035
R, otherwise, it does not belong to r;
the transport line r'jAs a backbone node vjInternal transport line, r'j＝{aji,e'i,j,wj,e'j,i',aji’Denotes a slave express logistics quotient piA transfer center ofjiThrough a shared transit centre wjTo express logistics quotient pi'A transfer center ofji'(ii) a Setting a binary decision variable
Figure FDA0003111060900000036
Is shown at node vjWhether or not to follow express logistics business piA transfer center ofjiThrough a shared transit centre wjTo express logistics business pi’A transfer center ofji'And e'i,jAnd e'j,iWhether the task belongs to the task path; if it is not
Figure FDA0003111060900000037
Is shown at node vjHas occurred from express logistics provider piA transfer center ofjiThrough a shared screen point wjTo express logistics business pi'A transfer center ofji'Otherwise, is indicated inNode vjNo transport occurs;
since there are multiple task paths for a client task, set RρSet of all task paths representing customer task ρ, pass r, r'j∈RρAnd 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:
Figure FDA0003111060900000038
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:
Figure FDA0003111060900000039
C2(r) is a transit network task path r'jThe total cost of the conversion is calculated according to the following formula:
Figure FDA00031110609000000310
s3.2: setting a calculation formula of the transportation time; the transit time calculation formula is as follows:
Figure FDA0003111060900000041
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:
Figure FDA0003111060900000042
T2(r) is a transit network task path r'jThe sum of the above conversion times is calculated as follows:
Figure FDA0003111060900000043
s3.3: setting a transportation service quality calculation formula; the transport quality of service calculation formula is as follows:
Figure FDA0003111060900000044
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)
Figure FDA0003111060900000045
Figure FDA0003111060900000046
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 pcProbability of variation pmPopulation 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.
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