CN111967828A - Whole-process logistics-oriented road-rail combined transport product collaborative optimization method - Google Patents

Whole-process logistics-oriented road-rail combined transport product collaborative optimization method Download PDF

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CN111967828A
CN111967828A CN202010843683.2A CN202010843683A CN111967828A CN 111967828 A CN111967828 A CN 111967828A CN 202010843683 A CN202010843683 A CN 202010843683A CN 111967828 A CN111967828 A CN 111967828A
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李海鹰
王莹
苗建瑞
魏鸿亮
贺茂盛
张家瑞
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CRRC Qiqihar Rolling Stock Co Ltd
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Abstract

The invention provides a whole-course logistics-oriented collaborative optimization method for a highway-railway combined transportation product, which comprises the following steps: acquiring highway and railway logistics data information, and establishing a collaborative optimization model based on cargo flow distribution, train bottom connection and access and delivery resource allocation; establishing a time-space-work shift three-dimensional service network according to the collaborative optimization model and the highway and railway logistics data information; and performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and performing iteration according to the obtained minimum cost path to obtain the optimal railway train operation scheme, the cargo flow distribution plan, the railway vehicle bottom application plan and the road access and delivery plan. The method effectively realizes the cooperative optimization of the highway transportation link and the railway transportation link of the highway and railway combined transportation product, can reduce the transportation cost of the highway and railway combined transportation product, and improves the operation benefit.

Description

Whole-process logistics-oriented road-rail combined transport product collaborative optimization method
Technical Field
The invention relates to the technical field of logistics, in particular to a method for collaborative optimization of a highway-railway combined transportation product for whole-course logistics.
Background
With the rapid development of the logistics industry, the matching between the railway trunk line and the highway branch line is more and more compact, and the success cases of the railway trunk line and the highway branch line for completing access and delivery and trunk line transportation in cooperation are increasing. Meanwhile, most of the research in the field of highway-railway transportation focuses on transportation organization under economic feasibility or macroscopic viewing angles, and a transportation organization optimization scheme under mesoscopic and microscopic viewing angles is lacked. Under the background, how to reasonably formulate a 'road-rail transport product optimization plan facing whole-course logistics' becomes a problem worthy of quantification and study.
The whole-course logistics for the road-rail combined transportation product comprises a road receiving and delivering link and a railway goods transportation link. In terms of road access, cargo access plan, enterprise own access and delivery vehicle operation route arrangement, work shift arrangement of own access and delivery resources and outsourcing plan of outsourcing logistics need to be considered. In the field of railway freight transportation, factors to be considered include: the method comprises the steps of designing a shift train running scheme, stopping a station, utilizing the passing capacity of a section of a station, making a railway bottom turnover plan, making a cargo flow distribution plan, a cargo flow transfer plan, a cargo flow loading and unloading plan and making a cargo transshipment plan between a road and a railway.
On the road bureau implementation level, the plan of the whole transportation process is manually established in stages. Firstly, the information of the shift train running path, the shift train starting arrival time, the shift train stop scheme and the like is clarified by the running chart agreed by each road bureau and the inside of the road bureau. The freight transportation scheduling of the road bureau makes a freight flow distribution plan according to the content; the vehicle scheduling determines a shift queue turnover plan according to the content; the dispatching of each station makes a loading and unloading operation plan and an access and delivery plan according to the above contents. The method considers the splitting of the operation of the railway class and the operation of the train bottom and the splitting of the railway transportation link and the road transportation link, breaks the association between the top plan and the bottom resource and the association between the transportation link and the transportation link, and leads the optimization result to be difficult to be practically implemented.
In the prior art, a mode of manual work in stages in the actual operation level of a road bureau or an existing mathematical optimization method in the theoretical research field is difficult to organically coordinate a road transportation link and a railway transportation link in the road-railway combined transportation process. In addition, in the field of optimization of the railway express train running scheme, the research is usually to split a railway train bottom turnover plan, a train running scheme and a cargo flow distribution plan; meanwhile, the existing theoretical research field has too rough description on the receiving and sending links, neglects the characteristics of high unit mileage cost, large influence on the aging and the like of the receiving and sending links, and further leads the optimization plan not to be directly applied to the actual production. Therefore, there is a need for a method for optimizing a highway-railway combined transportation product for whole-course logistics, which can improve the universality of the optimization method and the practicability of the optimization result.
Disclosure of Invention
The invention provides a method for collaborative optimization of a highway-railway combined transportation product for whole-course logistics, which aims to overcome the defects in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A method for collaborative optimization of road-rail transport products facing whole-course logistics comprises the following steps:
acquiring highway and railway logistics data information, and establishing a collaborative optimization model based on cargo flow distribution, train bottom connection and access and delivery resource allocation;
establishing a time-space-work shift three-dimensional service network according to the collaborative optimization model and the highway and railway logistics data information;
and performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and performing iteration according to the obtained minimum cost path to obtain an optimal railway train operation scheme, a cargo flow distribution plan, a railway vehicle bottom application plan and a road access and delivery plan, namely an optimal road-rail transport product scheme.
Preferably, the collaborative optimization model based on the cargo flow distribution, the base connection of the shift trains and the allocation of the access and delivery resources takes the maximum operation income as an objective function, and takes the cargo flow constraint, the cargo flow balance constraint, the vehicle bottom flow balance constraint, the coupling constraint of the vehicle bottom and the cargo flow, the road vehicle flow balance constraint, the coupling constraint of the road vehicle flow and the cargo flow and the decision variable value constraint as constraint conditions.
Preferably, the objective function is shown in the following formula (1):
Figure BDA0002642317970000031
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set used for representing benefits brought by successful transportation in a three-dimensional service network, V is a railway vehicle underflow, V is a railway vehicle underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, and r isfFor the purpose of the transport income of the cargo flow f,
Figure BDA0002642317970000032
the cost of transporting cargo flow f through service arc a,
Figure BDA0002642317970000033
the running cost of the railroad car underflow v through the service arc a,
Figure BDA0002642317970000034
transport of a road vehicle stream t through a service arc aLine cost;
Figure BDA0002642317970000035
a variable of 0 or 1, the flow f is via the service arc a, then
Figure BDA0002642317970000036
If not, then,
Figure BDA0002642317970000037
Figure BDA0002642317970000038
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure BDA0002642317970000039
If not, then,
Figure BDA00026423179700000310
Figure BDA00026423179700000311
the cost of transporting the road vehicle stream t through service arc a,
Figure BDA00026423179700000312
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure BDA00026423179700000313
Otherwise
Figure BDA00026423179700000314
Preferably, the cargo flow constraint, the cargo flow balance constraint, the vehicle bottom flow balance constraint, the coupling constraint of vehicle bottom and cargo flow, the road vehicle flow balance constraint, the coupling constraint of road vehicle flow and cargo flow and the decision variable value constraint are respectively as follows:
the cargo flow constraint is shown in equation (2) below:
Figure BDA00026423179700000315
the cargo flow balance constraint is shown in equation (3) below:
Figure BDA00026423179700000316
the underflow flow balance constraint is shown in equation (4) below:
Figure BDA0002642317970000041
the coupling constraint of the vehicle bottom and the cargo flow is shown as the following formula (5):
Figure BDA0002642317970000042
the highway vehicle flow balance constraint is shown in equation (6) below:
Figure BDA0002642317970000043
the constraint on the coupling of road vehicle flow to cargo flow is given by the following equation (7):
Figure BDA0002642317970000044
the decision variable value constraints are shown in the following formulas (8) to (10):
Figure BDA0002642317970000045
Figure BDA0002642317970000046
Figure BDA0002642317970000047
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set used for representing benefits brought by successful transportation in a three-dimensional service network, V is a railway vehicle underflow, V is a railway vehicle underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, and r isfFor the purpose of the transport income of the cargo flow f,
Figure BDA0002642317970000048
the cost of transporting cargo flow f through service arc a,
Figure BDA0002642317970000049
the running cost of the railroad car underflow v through the service arc a,
Figure BDA00026423179700000410
the cost of running a highway traffic stream t through service arc a;
Figure BDA00026423179700000411
a variable of 0 or 1, the flow f is via the service arc a, then
Figure BDA00026423179700000412
If not, then,
Figure BDA00026423179700000413
Figure BDA00026423179700000414
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure BDA00026423179700000415
If not, then,
Figure BDA00026423179700000416
Figure BDA00026423179700000417
the cost of transporting the road vehicle stream t through service arc a,
Figure BDA00026423179700000418
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure BDA0002642317970000051
Otherwise
Figure BDA0002642317970000052
otIs the starting node of the flow t, dtIs an arrival node of the cargo flow t; t is road vehicle flow, V is road vehicle flow set, EtIs the flow of the vehicle stream T, which is the set of railroad car bottom streams.
Preferably, the highway-railway logistics data information comprises: cargo transportation demand data, vehicle bottom information data, access and delivery resource information data and transportation expense data;
the freight transportation demand data includes: the system comprises a goods starting station, a goods final station, goods flow, the departure time of goods and the latest arrival time of goods;
the vehicle bottom information data comprises: the number of matching stations and grouped vehicles at the bottom of the vehicle;
the road vehicle information data includes: the number of road vehicles at the matching station and the matching station of the road vehicles;
the transportation cost data comprising: freight transportation revenue, freight transportation cost, shift line driving fixed cost, and vehicle route usage fees.
Preferably, the establishing of the time-space-work shift three-dimensional service network according to the collaborative optimization model and the highway-railway logistics data information comprises: generating all service network nodes according to three dimensions of time, space and working class, and respectively constructing an operation service arc, a waiting service arc, a virtual outgoing arc of a cargo flow, a virtual final arc of the cargo flow, a virtual super arc of the cargo flow, a virtual outgoing arc of the road vehicle flow, a virtual final arc of the road vehicle flow, a virtual super arc of the road vehicle flow, a transit service arc and a continuous service arc, a virtual outgoing arc of a railway vehicle underflow, a virtual final arc of the railway vehicle underflow and a virtual super arc of the railway vehicle underflow according to the constraint conditions of the collaborative optimization model and the data information of the road and railway logistics.
Preferably, the dual decomposition is performed on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks, the minimum cost path of each three-dimensional service sub-network is determined, iteration is performed according to the obtained minimum cost path to obtain an optimal railway train operation scheme, a cargo flow distribution plan, a railway bottom operation plan and a road access and delivery plan, and the method comprises the following steps:
performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate three-dimensional service sub-networks with the number equal to the number of the flows to be optimized; taking the shortest path of the goods flow and the shortest path of the railway vehicle bottom flow in the three-dimensional service sub-network as the minimum cost path of the three-dimensional service sub-network; and traversing the shortest paths of a plurality of cargo flows, the shortest paths of a plurality of railway vehicle bottom flows and the shortest paths of a plurality of road vehicle flows in a plurality of three-dimensional service sub-networks, and taking the paths meeting the arc section transportation capacity as an optimal railway shift driving scheme, a cargo flow distribution plan, a railway vehicle bottom operation plan and a road access plan, namely an optimal road-rail transport product scheme.
Preferably, the railway banquet operation scheme comprises: the shift train running time interval, the shift train grouping content, the shift train running path and the shift train acting as the train bottom;
the cargo flow distribution plan comprises: a cargo flow departure time period, a cargo flow arrival time period and a loading shift train;
the railway vehicle bottom operation plan comprises the following steps: the section-out time period of the vehicle bottom, the section-back time of the vehicle bottom, the running path of the vehicle bottom and the connection relationship of the vehicle bottom;
the road access and delivery plan comprises: and receiving the running path of the delivery vehicle and receiving the scheduling condition of the delivery task.
The present invention also provides an electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the processor is used for calling logic instructions in the memory so as to execute the method for the collaborative optimization of the highway-railway combined transportation product facing the whole-course logistics.
The invention also provides a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the method for collaborative optimization of the whole-journey logistics-oriented combined transportation product.
According to the technical scheme provided by the cooperative optimization method of the highway-railway combined transportation product facing the whole-course logistics, the method can effectively reduce the use cost of the transportation capacity of the station-free highway by coordinating the plans such as the cargo flow distribution plan, the class-train running plan and the like in the railway transportation; aiming at the condition that the free road transport capacity can not completely meet the requirements of access and delivery, a logistics outsourcing plan for access and delivery can be formulated, so that cost is effectively reduced and efficiency is improved; the shift running cost can be effectively reduced under the conditions of vehicle bottom turnover and timely goods transportation; the occupation of the interval passing capacity and the station passing/stopping capacity of the shift train running can be described on the mesoscopic level, and the feasibility of optimizing plan implementation is improved; by coordinating the road and railway resource allocation scheme, the transportation income maximization is realized on the basis of completing the transportation task.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a coordinated optimization method for a highway-railway combined transportation product facing whole-course logistics according to the embodiment;
fig. 2 is a schematic specific flow chart of the co-optimization method for the road-rail transport products facing the whole-course logistics in this embodiment;
FIG. 3 is a schematic structural diagram of an electronic device according to the embodiment;
FIG. 4 is a graph showing the profit from the comparison of the method of the present embodiment and the prior art.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Examples
The highway-railway combined transport product is a multi-type combined transport product which can realize the whole-course transport service by two transport modes of road and railway, and is characterized in that a transport contract is signed by a multi-type combined transport operator and a shipper, the whole-course transport is uniformly organized, and one-time consignment, one-time ordering, one-time charging, one-time claim settlement and whole-course responsibility of the whole-course transport are realized.
In this embodiment, the whole-course logistics faced by the road-rail transport product mainly includes: a road receiving and delivering link and a railway goods transportation link. In terms of road access, cargo access plan, enterprise own access and delivery vehicle operation route arrangement, work shift arrangement of own access and delivery resources and outsourcing plan of outsourcing logistics need to be considered. In the field of railway freight transportation, consideration is needed to be given to: the method comprises the steps of designing a shift train running scheme, utilizing station stop capacity/interval passing capacity, making a railway bottom turnover plan, making a cargo flow distribution plan/transfer plan/loading and unloading plan, and making a cargo reloading plan between a road and a railway.
Fig. 1 is a schematic view of a method for collaborative optimization of a utility transport product for whole-course logistics according to this embodiment, and fig. 2 is a schematic view of a specific flow of the method for collaborative optimization of a utility transport product for whole-course logistics according to this embodiment, with reference to fig. 1 and 2, the method includes:
s1, acquiring the data information of the highway and railway logistics, and establishing a collaborative optimization model based on the allocation of cargo flow, the bottom connection of the train and the allocation of access and delivery resources.
The highway-railway logistics data information comprises: cargo transportation demand data, vehicle bottom information data, access and delivery resource information data and transportation expense data.
Wherein the freight transportation demand data includes: the system comprises a goods starting station, a goods final station, goods flow, the departure time of goods and the latest arrival time of goods;
vehicle bottom information data, including: the number of matching stations and grouped vehicles at the bottom of the vehicle;
road vehicle information data comprising: the number of road vehicles at the matching station and the matching station of the road vehicles;
transportation expense data, comprising: freight transportation revenue, freight transportation cost, shift line driving fixed cost, and vehicle route usage fees.
According to the cargo transportation demand data, the vehicle bottom information data and the access and delivery resource information data, a collaborative optimization model based on cargo flow distribution, train bottom connection and access and delivery resource allocation is a maximum objective function with the operation income, and the objective function is shown as the following formula (1):
Figure BDA0002642317970000091
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set used for representing benefits brought by successful transportation in a three-dimensional service network, V is a railway vehicle underflow, V is a railway vehicle underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, and r isfFor the purpose of the transport income of the cargo flow f,
Figure BDA0002642317970000092
the cost of transporting cargo flow f through service arc a,
Figure BDA0002642317970000093
the running cost of the railroad car underflow v through the service arc a,
Figure BDA0002642317970000094
the cost of running a highway traffic stream t through service arc a;
Figure BDA0002642317970000095
a variable of 0 or 1, the flow f is via the service arc a, then
Figure BDA0002642317970000101
If not, then,
Figure BDA0002642317970000102
Figure BDA0002642317970000103
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure BDA0002642317970000104
If not, then,
Figure BDA0002642317970000105
Figure BDA0002642317970000106
the cost of transporting the road vehicle stream t through service arc a,
Figure BDA0002642317970000107
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure BDA0002642317970000108
Otherwise
Figure BDA0002642317970000109
The cooperative optimization model based on the cargo flow distribution, the base connection of the shift trains and the allocation of the access resources takes cargo flow constraint, cargo flow balance constraint, vehicle bottom flow balance constraint, coupling constraint of vehicle bottom and cargo flow, road vehicle flow balance constraint, coupling constraint of road vehicle flow and cargo flow and decision variable value constraint as constraint conditions.
Wherein the cargo flow constraint indicates that the sum of the cargo flows serviced by the class must be less than or equal to the total cargo flow. Due to uncertainty of the cargo transportation demand of the shift, the demand which cannot be met can be completed by the ordinary cargo train, and the cargo flow constraint is as shown in the following formula (2):
Figure BDA00026423179700001010
the cargo flow balance constraint means that the cargo inflow and outflow of each node satisfy the flow conservation relation, and simultaneously
Figure BDA00026423179700001011
The non-resolvability of the same cargo flow on the unique space path is ensured for the variable of 0-1, as shown in the following formula (3):
Figure BDA00026423179700001012
the balance constraint of the vehicle bottom flow indicates that the inflow and outflow of the vehicle bottom of each node need to satisfy the flow conservation relation, and simultaneously
Figure BDA00026423179700001013
The variable of 0-1 ensures that the bottom flow of the railway vehicle keeps continuous on a unique spatial path, namely a vehicle bottom fixed marshalling, as shown in the following formula (4):
Figure BDA00026423179700001014
the coupling constraint of the vehicle bottom and the cargo flow represents that the sum of the cargo flow on the service arc section is smaller than the capacity of the arc section (determined by the programmed vehicle number of the vehicle bottom passing through the arc section and the load capacity of a unit vehicle), and is shown as the following formula (5):
Figure BDA0002642317970000111
the road vehicle flow balance constraint indicates that the road vehicle inflow and outflow of each node need to satisfy the flow conservation relation, and simultaneously
Figure BDA0002642317970000112
The variable 0-1 ensures that the road vehicle flow remains continuous on a unique spatial path, i.e., a fixed train at the bottom of the vehicle, as shown in equation (6) below:
Figure BDA0002642317970000113
the constraint of coupling the road vehicle flow to the cargo flow means that the sum of the cargo flows over the service arc is smaller than the capacity over its arc (determined by the load of the road vehicles passing through the arc), as shown in the following equation (7):
Figure BDA0002642317970000114
the decision variable value constraints are shown in the following formulas (8) to (10):
Figure BDA0002642317970000115
Figure BDA0002642317970000116
Figure BDA0002642317970000117
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set for expressing the benefits brought by successful transportation in a three-dimensional service network, and v is the bottom of the railway vehicleFlow, V is a railway underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, rfFor the purpose of the transport income of the cargo flow f,
Figure BDA0002642317970000118
the cost of transporting cargo flow f through service arc a,
Figure BDA0002642317970000119
the running cost of the railroad car underflow v through the service arc a,
Figure BDA00026423179700001110
the cost of running a highway traffic stream t through service arc a;
Figure BDA00026423179700001111
a variable of 0 or 1, the flow f is via the service arc a, then
Figure BDA00026423179700001112
If not, then,
Figure BDA00026423179700001113
Figure BDA00026423179700001114
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure BDA00026423179700001115
If not, then,
Figure BDA00026423179700001116
Figure BDA00026423179700001117
the cost of transporting the road vehicle stream t through service arc a,
Figure BDA00026423179700001118
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure BDA00026423179700001119
Otherwise
Figure BDA00026423179700001120
otIs the starting node of the flow t, dtIs an arrival node of the cargo flow t; t is road vehicle flow, V is road vehicle flow set, EtIs the flow of the vehicle stream T, which is the set of railroad car bottom streams.
It should be noted that the nodes in the three-dimensional service network represent work stations, the arcs represent transportation services, the transportation process of goods can be regarded as distributing goods flow on the service network, the operation arrangement of vehicle bottoms can be regarded as distributing railroad vehicle underflow on the service network, and the road access and delivery process can be regarded as distributing road vehicle flow on the service network.
S2, establishing a time-space-work shift three-dimensional service network according to the collaborative optimization model and the highway and railway logistics data information.
And constructing a time-space-work shift three-dimensional service network according to the highway and railway logistics data information and based on the model constraint conditions. And storing the data information of the male and the female logistics by means of the csv file, and reading the known data in the csv file into a computer program by using a C # language under the Net platform. Generating all service network nodes according to three dimensions of time, space and work class, and respectively constructing a time-space-work class three-dimensional service network according to constraint conditions of a collaborative optimization model and highway and railway logistics data information, wherein the service operation arc, the service waiting arc, the virtual outgoing arc of a cargo flow, the virtual final arc of the cargo flow, the virtual super arc of the cargo flow, the virtual outgoing arc of the road vehicle flow, the virtual final arc of the road vehicle flow, the virtual super arc of the road vehicle flow, the transit service arc and the continuous service arc, the virtual outgoing arc of a railway vehicle underflow, the virtual final arc of the railway vehicle underflow and the virtual super arc of the railway vehicle underflow.
S3 dually decomposing the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and iterating according to the obtained minimum cost path to obtain the optimal railway train operation scheme, cargo flow distribution scheme, railway bottom application scheme and road access and delivery scheme, namely the optimal road-rail transport product scheme.
Performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate three-dimensional service sub-networks with the number equal to the number of the flows to be optimized; taking the shortest path of the goods flow and the shortest path of the railway vehicle bottom flow in the three-dimensional service sub-network as the minimum cost path of the three-dimensional service sub-network; and traversing the shortest paths of a plurality of cargo flows, the shortest paths of a plurality of railway vehicle bottom flows and the shortest paths of a plurality of road vehicle flows in a plurality of three-dimensional service sub-networks, and taking the paths meeting the arc section transportation capacity as an optimal railway shift driving scheme, a cargo flow distribution plan, a railway vehicle bottom operation plan and a road access plan, namely an optimal road-rail transport product scheme.
Specifically, the method comprises the following steps:
(1) when the dual decomposition is performed on the three-dimensional service network established in step S2 by using the lagrangian relaxation method, the iteration number k is set to 0, all lagrangian multiplier values are initialized to 0, the iteration step length is set to a positive number, the model of this embodiment is set to 1.0, and the optimal lower bound solution LB is set*Infinity, optimal upper bound solution UB*＝+∞。
(2) And solving a Lagrangian dual, wherein the Lagrangian dual is as follows:
MaxH(D)'；
wherein the content of the first and second substances,
Figure BDA0002642317970000131
ρa、σa、λaare lagrange multipliers on arc segment a.
Further, the solving step includes:
to pair
Figure BDA0002642317970000132
Solving the sub-problem of shortest path of cargo flow, and storing the shortest path and shortest path of cargo flowRadial length
Figure BDA0002642317970000133
The calculation formula is as follows:
Figure BDA0002642317970000134
to pair
Figure BDA0002642317970000135
Solving the sub-problem of shortest path of the railway vehicle underflow, and storing the shortest path and the shortest path length of the vehicle underflow
Figure BDA0002642317970000136
The calculation formula is as follows:
Figure BDA0002642317970000137
to pair
Figure BDA0002642317970000138
Solving the sub-problem of the shortest path of the road vehicle flow, and storing the shortest path and the length of the shortest path of the vehicle flow
Figure BDA0002642317970000139
Figure BDA0002642317970000141
Updating lagrange multipliers ρa、σa、λaThe update formula is shown in the following formulas (11) to (13):
Figure BDA0002642317970000142
Figure BDA0002642317970000143
Figure BDA0002642317970000144
where k is the number of iterations, αkDenotes the iteration step size, alpha, at the kth iterationkThe value of (A) is set according to actual needs, and the following formula (14) needs to be satisfied:
Figure BDA0002642317970000145
and k → ∞, αk→0 (14)
(3) Calculate the lower bound solution LBkAnd generates the optimal lower bound solution LB*＝max{LB*,LBk}; calculate the upper bound solution UBkAnd generating an optimal upper bound solution UB*＝min{UB*,UBk}。
Using the calculation of the lower bound solution LBkFormula (2) is as follows:
Figure BDA0002642317970000146
wherein the content of the first and second substances,
Figure BDA0002642317970000147
for the shortest path length of the flow f at the kth iteration,
Figure BDA0002642317970000148
for the shortest path length of the railroad car underflow v at the kth iteration,
Figure BDA0002642317970000149
is the shortest path length of the road vehicle flow t at the kth iteration.
Calculate the upper bound solution UBkThe method specifically comprises the following steps:
a. and loading the ith branch into a service network according to the shortest path between the road vehicle flow and the railway vehicle underflow in the lower bound solution, if the flow violates the constraint, turning to b, otherwise, i is i +1, setting the transport capacity value of the arc section as the flow size of the road vehicle flow/the railway vehicle underflow, and repeating the step.
b. Unloading the highway vehicle flow/railway vehicle bottom flow i, updating the transport capacity of the arc section, setting the price of the arc section through which the existing flow passes as 2 x 10^9 (namely a positive number far greater than the model parameter) to solve the shortest path sub-problem of the highway vehicle flow/railway vehicle bottom flow i, recovering the vehicle flow price of the arc section after the completion, if all the vehicle flows are loaded, turning to c, otherwise, turning to a.
c. Planning priority coefficient w according to cargo flowfAnd sorting the goods flow in a descending order. Cargo flow planning priority coefficient wfThe calculation formula (2) is shown in the following formula (16):
Figure BDA0002642317970000151
in the formula (I), the compound is shown in the specification,
Figure BDA0002642317970000152
length of shortest path for flow f at kth iteration, QfThe flow rate of the cargo flow is m-0.2, and n-0.8.
d. And loading the ith cargo flow to a service network according to the shortest cargo flow path in the lower bound solution. If the cargo flow in the shortest cargo flow path exceeds the arc section transportation capacity, the process is switched to e, otherwise, i is i +1, and the step is repeated.
e. And unloading the cargo flow i from the service network, traversing the arc segment of the service network, and setting the price of the arc segment of which the loading cargo flow i exceeds the transportation capacity of the arc segment to be 2 x 10^9 (namely, a positive number far larger than the model parameter). And (4) solving the sub-problem of the shortest path of the goods flow i, recovering the goods flow price of the arc section after the sub-problem is solved, and turning to f if all the goods flows are completely loaded, or turning to d if all the goods flows are not completely loaded.
f. Saving the flow loading information and calculating the upper bound solution UBkThe calculation formula is as follows:
Figure BDA0002642317970000153
if the iteration times k are larger than the preset maximum iteration times or the dual gap of the upper and lower boundaries meets the requirement, the Lagrangian relaxation algorithm is ended; otherwise k is k +1, and the Lagrangian dual is continuously solved.
In the step, a Lagrange relaxation algorithm is adopted, the original problem is decomposed into a plurality of independent three-dimensional service network minimum cost path subproblems through dual decomposition, the minimum cost path subproblems of all the goods flows and the vehicle flows are solved, the solving result (lower bound) of the minimum cost path subproblems is taken as heuristic information and is brought into the Lagrange heuristic algorithm, the lower bound result can be changed according to the heuristic information, an upper bound solution is obtained, and meanwhile, the solving result is output.
The output solution result comprises: the system comprises a railway class running scheme, a cargo flow distribution plan, a railway vehicle bottom operation plan and a road access and delivery plan.
The cargo flow allocation plan includes: a flow departure time period, a flow arrival time period, and a load shift column.
The railway banbury running scheme comprises the following steps: the shift train running time interval, the shift train grouping content, the shift train running path and the shift train acting as the train bottom.
The railway vehicle bottom operation plan comprises the following steps: the section-out time period of the vehicle bottom, the section-back time of the vehicle bottom, the running path of the vehicle bottom and the vehicle bottom connection relationship.
The road access and delivery plan comprises: and receiving the running path of the delivery vehicle and receiving the scheduling condition of the delivery task.
Fig. 3 is a schematic structural diagram of the electronic device of the embodiment, and referring to fig. 3, the electronic device includes: processor 810, memory 830, communication interface 820 and communication bus 840, processor 810, communication interface 820 and memory 830 communicating with each other via communication bus 840.
The processor 810 is configured to call the logic instructions in the memory 830 to perform the co-optimization method for the through-the-road logistics of the co-transportation product.
Specifically, this embodiment is performed on a road network composed of 10 railway stations, and the computer randomly generates 5 railway car attached stations, 8 usable railway cars, 40 flows of goods to be transported, and the number of road transport vehicles per railway station (greater than 10 road transport vehicles per station) as input data. With 1 hour as the time granularity, the decision period is 144 hours.
The result of the co-optimization is solved by the algorithm used in this embodiment; and the non-collaborative optimization result is used for optimizing the railway transportation part by an algorithm in the prior art, and then a commercial solver Gurobi9.1 is used for optimizing the road transportation part according to the optimization result of the railway part. Under the condition of 200 iterations, the solution of cooperative optimization and non-cooperative optimization is compared, and the comparison condition is shown in fig. 4. As can be seen from fig. 4, in five random data tests, compared with a non-collaborative optimization method, the collaborative optimization method can effectively reduce the whole-process logistics cost of the highway-railway combined transportation product, increase the transportation profit, and further achieve the purpose of collaboratively optimizing the whole-process logistics of the highway-railway combined transportation product.
Another aspect of this embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause the method for collaborative optimization of the through-the-road logistics of a highway-railway intermodal product described above.
On one hand, the embodiment can enable the railway transportation link and the road transportation link to be connected more closely, and reduce the transportation cost; on the other hand, the contents of a complete turnover plan, a shift train operation plan and a cargo flow distribution plan in the railway transportation link are refined; meanwhile, the station passing/stopping capacity and the section passing capacity in a road network are considered in the design link of the shift train running scheme, so that the implementation of the shift train running scheme is stronger. In conclusion, the collaborative optimization result of the scheme can provide more reliable input information for compiling the operation schedule of the railway express train.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for collaborative optimization of road-rail transport products facing whole-course logistics is characterized by comprising the following steps:
acquiring highway and railway logistics data information, and establishing a collaborative optimization model based on cargo flow distribution, train bottom connection and access and delivery resource allocation;
establishing a time-space-work shift three-dimensional service network according to the collaborative optimization model and the highway and railway logistics data information;
and performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and performing iteration according to the obtained minimum cost path to obtain an optimal railway train operation scheme, a cargo flow distribution plan, a railway vehicle bottom application plan and a road access and delivery plan, namely an optimal road-rail transport product scheme.
2. The cooperative optimization method for road-rail transport products facing whole logistics of claim 1, wherein the cooperative optimization model based on cargo flow distribution, shift train bottom connection and access and delivery resource allocation takes the maximum operation income as an objective function, and takes cargo flow constraint, cargo flow balance constraint, vehicle bottom and cargo flow coupling constraint, road vehicle flow balance constraint, road vehicle flow and cargo flow coupling constraint and decision variable value constraint as constraint conditions.
3. The method for the collaborative optimization of the road-to-rail combined transportation products facing the whole-course logistics according to claim 2, wherein the objective function is shown as the following formula (1):
Figure FDA0002642317960000011
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set used for representing benefits brought by successful transportation in a three-dimensional service network, V is a railway vehicle underflow, V is a railway vehicle underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, and r isfFor the purpose of the transport income of the cargo flow f,
Figure FDA0002642317960000021
the cost of transporting cargo flow f through service arc a,
Figure FDA0002642317960000022
the running cost of the railroad car underflow v through the service arc a,
Figure FDA0002642317960000023
the cost of running a highway traffic stream t through service arc a;
Figure FDA0002642317960000024
a variable of 0 or 1, the flow f is via the service arc a, then
Figure FDA0002642317960000025
If not, then,
Figure FDA0002642317960000026
Figure FDA0002642317960000027
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure FDA0002642317960000028
If not, then,
Figure FDA0002642317960000029
Figure FDA00026423179600000210
the cost of transporting the road vehicle stream t through service arc a,
Figure FDA00026423179600000211
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure FDA00026423179600000212
Otherwise
Figure FDA00026423179600000213
4. The cooperative optimization method for road and railway intermodal products facing whole logistics according to claim 2, characterized in that the cargo flow constraint, the cargo flow balance constraint, the vehicle bottom flow balance constraint, the coupling constraint between vehicle bottom and cargo flow, the road vehicle flow balance constraint, the coupling constraint between road vehicle flow and cargo flow and the decision variable value constraint are respectively as follows:
the cargo flow constraint is shown in equation (2) below:
Figure FDA00026423179600000214
the cargo flow balance constraint is shown in equation (3) below:
Figure FDA00026423179600000215
the underflow flow balance constraint is shown in equation (4) below:
Figure FDA00026423179600000216
the coupling constraint of the vehicle bottom and the cargo flow is shown as the following formula (5):
Figure FDA00026423179600000217
the highway vehicle flow balance constraint is shown in equation (6) below:
Figure FDA00026423179600000218
the constraint on the coupling of road vehicle flow to cargo flow is given by the following equation (7):
Figure FDA0002642317960000031
the decision variable value constraints are shown in the following formulas (8) to (10):
Figure FDA0002642317960000032
Figure FDA0002642317960000033
Figure FDA0002642317960000034
wherein F is the goods flow in the highway-railway combined transportation product facing the whole-course logistics, F is the goods flow set in the highway-railway combined transportation product facing the whole-course logistics, a is the arc section in the three-dimensional service network, A is the arc section set in the three-dimensional service network, and A is the arc section set in the three-dimensional service networkvA service arc set used for representing benefits brought by successful transportation in a three-dimensional service network, V is a railway vehicle underflow, V is a railway vehicle underflow set, T is a highway access delivery vehicle flow, T is a highway access delivery vehicle flow set, and r isfFor the purpose of the transport income of the cargo flow f,
Figure FDA0002642317960000035
the cost of transporting cargo flow f through service arc a,
Figure FDA0002642317960000036
the running cost of the railroad car underflow v through the service arc a,
Figure FDA0002642317960000037
the cost of running a highway traffic stream t through service arc a;
Figure FDA0002642317960000038
a variable of 0 or 1, the flow f is via the service arc a, then
Figure FDA0002642317960000039
If not, then,
Figure FDA00026423179600000310
Figure FDA00026423179600000311
a variable of 0 or 1, if the railroad car underflow v is via the service arc a, then
Figure FDA00026423179600000312
If not, then,
Figure FDA00026423179600000313
Figure FDA00026423179600000314
the cost of transporting the road vehicle stream t through service arc a,
Figure FDA00026423179600000315
a variable of 0 or 1, if the road vehicle flow t is via service arc a
Figure FDA00026423179600000316
Otherwise
Figure FDA00026423179600000317
otIs the starting node of the flow t, dtIs an arrival node of the cargo flow t; t is road vehicle flow, V is road vehicle flow set, EtIs the flow of the vehicle stream T, which is the set of railroad car bottom streams.
5. The coordinated optimization method for the road-rail intermodal products facing the whole-course logistics according to claim 1, wherein the road-rail logistics data information comprises: cargo transportation demand data, vehicle bottom information data, access and delivery resource information data and transportation expense data;
the freight transportation demand data includes: the system comprises a goods starting station, a goods final station, goods flow, the departure time of goods and the latest arrival time of goods;
the vehicle bottom information data comprises: the number of matching stations and grouped vehicles at the bottom of the vehicle;
the road vehicle information data includes: the number of road vehicles at the matching station and the matching station of the road vehicles;
the transportation cost data comprising: freight transportation revenue, freight transportation cost, shift line driving fixed cost, and vehicle route usage fees.
6. The coordinated optimization method for the road-to-rail combined transportation products facing the whole logistics as claimed in claim 1, wherein the establishing of the time-space-work shift three-dimensional service network according to the coordinated optimization model and the road-to-rail logistics data information comprises: generating all service network nodes according to three dimensions of time, space and working class, and respectively constructing an operation service arc, a waiting service arc, a virtual outgoing arc of a cargo flow, a virtual final arc of the cargo flow, a virtual super arc of the cargo flow, a virtual outgoing arc of the road vehicle flow, a virtual final arc of the road vehicle flow, a virtual super arc of the road vehicle flow, a transit service arc and a continuous service arc, a virtual outgoing arc of a railway vehicle underflow, a virtual final arc of the railway vehicle underflow and a virtual super arc of the railway vehicle underflow according to the constraint conditions of the collaborative optimization model and the data information of the road and railway logistics.
7. The cooperative optimization method for the road-rail transport products facing to the whole-course logistics according to claim 1, wherein the lagrangian relaxation method is adopted to carry out dual decomposition on the time-space-work shift three-dimensional service network to generate a plurality of three-dimensional service sub-networks, the minimum cost path of each three-dimensional service sub-network is determined, iteration is carried out according to the obtained minimum cost path to obtain an optimal railway shift operation scheme, a cargo flow distribution plan, a railway bottom operation plan and a road access and delivery plan, and the method comprises the following steps:
performing dual decomposition on the time-space-work shift three-dimensional service network by adopting a Lagrange relaxation method to generate three-dimensional service sub-networks with the number equal to the number of the flows to be optimized; taking the shortest path of the goods flow and the shortest path of the railway vehicle bottom flow in the three-dimensional service sub-network as the minimum cost path of the three-dimensional service sub-network; and traversing the shortest paths of a plurality of cargo flows, the shortest paths of a plurality of railway vehicle bottom flows and the shortest paths of a plurality of road vehicle flows in a plurality of three-dimensional service sub-networks, and taking the paths meeting the arc section transportation capacity as an optimal railway shift driving scheme, a cargo flow distribution plan, a railway vehicle bottom operation plan and a road access plan, namely an optimal road-rail transport product scheme.
8. The full-range logistics-oriented coordinated optimization method for road-rail transport products according to claim 1, wherein the railway shift operation scheme comprises: the shift train running time interval, the shift train grouping content, the shift train running path and the shift train acting as the train bottom;
the cargo flow distribution plan comprises: a cargo flow departure time period, a cargo flow arrival time period and a loading shift train;
the railway vehicle bottom operation plan comprises the following steps: the section-out time period of the vehicle bottom, the section-back time of the vehicle bottom, the running path of the vehicle bottom and the connection relationship of the vehicle bottom;
the road access and delivery plan comprises: and receiving the running path of the delivery vehicle and receiving the scheduling condition of the delivery task.
9. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the processor is used for calling logic instructions in the memory to execute the method for collaborative optimization of the whole-journey logistics oriented co-transport products of any one of claims 1-8.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for co-optimizing whole-journey logistics oriented highway-railway transportation products according to any one of claims 1-8.
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