CN113762573A - Logistics network optimization method and device - Google Patents
Logistics network optimization method and device Download PDFInfo
- Publication number
- CN113762573A CN113762573A CN202011290378.1A CN202011290378A CN113762573A CN 113762573 A CN113762573 A CN 113762573A CN 202011290378 A CN202011290378 A CN 202011290378A CN 113762573 A CN113762573 A CN 113762573A
- Authority
- CN
- China
- Prior art keywords
- candidate
- route
- transportation
- line
- constructing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005457 optimization Methods 0.000 title claims abstract description 31
- 230000032683 aging Effects 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims description 22
- 238000013507 mapping Methods 0.000 claims description 19
- 238000003860 storage Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 6
- 230000003068 static effect Effects 0.000 claims description 4
- 230000032258 transport Effects 0.000 description 74
- 238000010586 diagram Methods 0.000 description 30
- 230000006870 function Effects 0.000 description 29
- 238000004891 communication Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000011160 research Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 4
- 230000005641 tunneling Effects 0.000 description 4
- 238000012821 model calculation Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000001502 supplementing effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A logistics network optimization method and device relate to the technical field of warehouse logistics. The specific implementation mode of the method comprises the following steps: adding a reverse transportation line aiming at a transportation line with a single open flow direction in the existing transportation line, and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate route; based on the candidate route information table and the candidate vehicle type information table of the candidate line, performing route planning on a target waybill by adopting a pre-constructed route planning model to obtain an optimal route; the routing planning model takes the minimum total cost of the target freight note as an objective function and restrains the routing timeliness. The implementation method can optimize the existing logistics network planning while considering the aging requirement, not only can meet the customer experience, but also can reduce the total logistics cost.
Description
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a logistics network optimization method and device.
Background
In the logistics distribution of personal consumer electronic commerce (i.e., C2C), the goods are collected from the seller, transported to the initial site, passed through several sorting centers to the final site, and then distributed to the buyer by the distributor. The existing logistics network is mainly planned through manual calculation or a simple greedy algorithm.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
(1) the logistics network planning involves more variables (including lines, nodes, shifts and the like of the logistics network), the logic is complex, and the optimal solution is difficult to obtain through manual evaluation; (2) the model of the logistics network planning mainly considers the time-efficiency optimization during modeling, and although the user experience is better, the problem of logistics cost is not emphasized sufficiently.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for optimizing a logistics network, which can optimize an existing logistics network plan while considering an aging requirement, and can not only meet customer experience but also reduce total logistics cost.
In order to achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for optimizing a logistics network, including:
adding a reverse transportation line aiming at a transportation line with a single open flow direction in the existing transportation line, and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line;
constructing a candidate vehicle type information table of the candidate route;
based on the candidate route information table and the candidate vehicle type information table of the candidate line, performing route planning on a target waybill by adopting a pre-constructed route planning model to obtain an optimal route; the routing planning model takes the minimum total cost of the target freight note as an objective function and restrains the routing timeliness.
Optionally, the method further comprises:
and before the route planning is carried out on the target freight note by adopting a pre-constructed route planning model, constructing a transportation cost function of the candidate vehicle type based on the candidate line.
Optionally, the adding a reverse transportation route for a transportation route with a single open flow direction in existing transportation routes, and constructing a candidate route based on the existing transportation routes and the added reverse transportation route includes:
and aiming at the existing single-opening flow direction transportation line, when the load in the reverse flow direction is more than or equal to the preset proportion of the load in the forward flow direction, adding a reverse transportation line for the single-opening flow direction transportation line.
Optionally, constructing the candidate route information table of the target waybill based on the candidate route includes:
for a target freight note with a transport starting point and a transport destination located in the same area, constructing a first candidate route of all nodes among the transport starting point, the transport node and the transport destination in series according to the transport starting point and the transport destination of the target freight note; expanding the first candidate route according to the departure time of the transport route to obtain a second candidate route; expanding the second candidate route according to the transportation shift of the transportation starting point to obtain the same-area candidate route of the target freight note; for target freight notes of which the transportation starting points and the transportation destination points are located in different areas, first determining a first part, a second part and a trunk line of a cross-region route, and constructing a first candidate route of all nodes connected in series among the first part, the trunk line and the second part; and expanding the first candidate route according to the departure time of the trunk line to obtain a cross-region candidate route of the target freight note.
Optionally, the candidate vehicle type information table includes: mapping relation between the candidate line information and the candidate vehicle type information; wherein the candidate line information includes: the vehicle-mounted system comprises a candidate line first identification and a candidate line second identification, wherein the candidate line first identification is obtained by numbering each candidate line, and the candidate line second identification is obtained by numbering each transport vehicle type of each candidate line.
Optionally, the total cost of the target waybill includes: operating costs, transportation costs for full car transportation and transportation costs for part load transportation.
Optionally, the route planning model constrains route aging such that the route aging of each route must be smaller than a standard static route aging, and the route model further includes the following constraint conditions: constraining the result to enable the result to only output the optimal solution of each route; the cargo volume is constrained so that all transport vehicles cannot be overloaded.
According to still another aspect of the embodiments of the present invention, there is provided a logistics network optimization apparatus, including:
a logistics network optimization apparatus, comprising:
the data processing module is used for adding a reverse transportation line aiming at the transportation line with single open flow direction in the existing transportation lines and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line;
the data processing module is used for constructing a candidate vehicle type information table of the candidate line;
the model construction module is used for constructing a route planning model, wherein the route planning model takes the minimum total cost of a target freight note as a target function and restrains route timeliness;
and the optimization module is used for carrying out route planning on the target waybill by adopting a pre-constructed route planning model based on the candidate route information table and the candidate vehicle type information table of the candidate line so as to obtain an optimal route.
According to another aspect of the embodiments of the present invention, there is provided a logistics network optimization electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the logistics network optimization method provided by the invention.
According to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the logistics network optimization method provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: because the technical means of opening the transport line in the single-opening flow direction into the transport line in the split flow direction and matching all transport vehicle types for each transport line and constructing the route planning model to obtain the optimal route are developed aiming at the existing transport line data under the condition that the information such as the node position is known, the technical problems of low efficiency of the existing logistics manual evaluation and insufficient attention on the logistics cost are solved, the problem of optimizing the existing logistics network planning while considering the aging requirement is further solved, the customer experience can be met, and the total logistics cost can be reduced.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a diagram illustrating an exemplary system architecture to which a logistics network optimization method or a logistics network optimization apparatus according to an embodiment of the present invention can be applied;
fig. 2(a) is a schematic diagram of a main flow of a logistics network optimization method according to an embodiment of the invention, and fig. 2(b) is a schematic diagram of transportation lines and routes;
fig. 3(a) is a schematic diagram of a detailed flow of a logistics network optimization method according to an embodiment of the invention, fig. 3(b) is a schematic diagram of a same-zone route and a cross-zone route, fig. 3(c) is a schematic diagram of an OD route according to an embodiment of the invention, fig. 3(d) is a schematic diagram of a mapping relationship between a candidate route id and a candidate line id, fig. 3(e) is a schematic diagram of a specific example of a candidate route information table, fig. 3(f) is a schematic diagram of a mapping relationship between a candidate line id and a line vehicle type identifier id, and fig. 3(g) is a schematic diagram of a specific example of a candidate vehicle type information table;
fig. 4 is a schematic diagram of main blocks of a logistics network optimization apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a diagram illustrating an exemplary system architecture to which a logistics network optimization method or a logistics network optimization apparatus according to an embodiment of the present invention may be applied, and as shown in fig. 1, the exemplary system architecture of the logistics network optimization method or logistics network optimization apparatus according to an embodiment of the present invention includes:
as shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the product information query request, and feed back the product information to the terminal devices 101, 102, and 103.
It should be noted that the logistics network optimization method provided by the embodiment of the invention is generally executed by the server 105, and accordingly, the logistics network optimization device is generally disposed in the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2(a) is a schematic diagram of a main flow of a logistics network optimization method according to an embodiment of the invention, and fig. 2(b) is a schematic diagram of transportation lines and routes. As shown in fig. 2a, the method for optimizing a logistics network of the present invention comprises:
step S201, adding a reverse transportation line aiming at a transportation line with a single open flow direction in the existing transportation lines, and constructing a candidate line based on the existing transportation line and the added reverse transportation line; and constructing a candidate route information table of the target waybill based on the candidate line.
The route is a path formed by connecting all transportation nodes from a transportation starting point to a transportation destination point during transportation of the cargo.
Illustratively, the origin of transportation, the destination of transportation, and the transportation node are all sorting centers. The transportation line is a passage formed by connecting two adjacent transportation nodes when goods are transported. As shown in FIG. 2(B), O is the start point OF transportation, D is the end point OF transportation, B, C, E, F, G is the transportation node, O-B-C-E-D, O-F-G-D is the route, OB, BC, OF, FG, etc. are the transportation routes. Wherein O, D, B, C, E, F, G are transportation nodes.
The whole vehicle transportation means that a transport vehicle loads and transports a batch of goods from a transportation starting point to a transportation terminal point, and point-to-point transportation is realized. The unit transportation means that when the weight or volume of one batch of goods is less than one transportation vehicle, one transportation vehicle can be used for carrying and transporting with other batches or even hundreds of batches of goods.
Illustratively, according to previous research, it is known that the logistics cost of the waybill can be reduced by opening up a transportation line with a single flow direction as a transportation line with a split flow direction. Therefore, according to the existing transportation line data searched from the flow direction data table, the transportation line with the single-opening flow direction is searched, the reverse transportation line is added to the transportation line with the single-opening flow direction, and the transportation line can be opened up to be the transportation line with the double-opening flow direction. And constructing a candidate route according to the existing transport route and the added reverse transport route, and constructing a mapping relation between the candidate route and the candidate route according to the candidate route to obtain a candidate route information table of the target freight note. Wherein the candidate routing information table may include: each route is composed of which transport lines.
Further, a tunneling condition may be added to determine whether to tunnel a single flow direction transportation line to a double flow direction transportation line, for example, the tunneling condition may be: whether the load of the reverse haul route is greater than a predetermined fraction of the load of the haul route in the single open flow direction.
Furthermore, the single amount of each route can be added into the mapping relation to construct the mapping relation between the route and the transportation line and the single amount, so that the subsequent operation cost can be conveniently processed.
And step S202, constructing a candidate vehicle type information table of the candidate route.
For example, according to previous research, it is known that the logistics cost of the waybill can be reduced by adjusting the transportation vehicle type of the transportation vehicle of the transportation line. Therefore, in step S202 in the embodiment of the present invention, the existing transportation vehicle type of each candidate route is searched from the candidate route information table of the destination manifest generated in step S201, and compared with all existing transportation vehicle types, and according to the comparison result, a transportation vehicle type which is lacking compared with all transportation vehicle types is added to the candidate route which is not configured with all transportation vehicle types. And after supplementing the transport vehicle types lacking in the candidate route, constructing a candidate vehicle type information table of the candidate route. The candidate vehicle type information table may include the following information: the number of types of transportation vehicles can be arranged on each line, and each type of transportation vehicle comprises several types, the cargo capacity and the like.
Furthermore, after the transportation vehicle types of the candidate routes are supplemented, the walking method of the original candidate routes is unchanged, and possible transportation schemes are changed.
Step S203, based on the candidate route information table and the candidate vehicle type information table of the candidate route, adopting a pre-constructed route planning model to carry out route planning on a target waybill so as to obtain an optimal route; the routing planning model takes the minimum total cost of the target freight note as an objective function and restrains the routing timeliness.
Illustratively, the route planning model is constructed with the minimum total cost of the target freight bill as an objective function, wherein the total cost comprises operation cost, transportation cost of whole vehicle transportation and transportation cost of part transportation. The route planning model includes three constraints: result constraints, inventory constraints, and aging constraints. And the result constraint condition only requires to select an optimal route, the cargo quantity constraint condition requires not to be overloaded, and the aging constraint condition requires not to exceed the preset standard aging. Based on the candidate route information table obtained in step S201 and the candidate vehicle type information table obtained in step S202, a pre-established route planning model is adopted, and the information of the target order is input into the model for route planning, so that an optimal route of the optimized target order can be obtained.
In the embodiment of the invention, a reverse transportation line is added aiming at a transportation line with a single open flow direction in the existing transportation lines, and a candidate line is constructed based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate route; based on the candidate route information table and the candidate vehicle type information table of the candidate line, performing route planning on a target waybill by adopting a pre-constructed route planning model to obtain an optimal route; the routing planning model takes the minimum total cost of a target freight note as a target function, and restricts the routing timeliness, so that the existing logistics network planning can be optimized while considering the timeliness requirement, the customer experience can be met, and the total logistics cost can be reduced.
Fig. 3(a) is a schematic diagram of a detailed flow of a logistics network optimization method according to an embodiment of the invention, fig. 3(b) is a schematic diagram of a same-zone route and a cross-zone route, fig. 3(c) is a schematic diagram of an OD route according to an embodiment of the invention, fig. 3(d) is a schematic diagram of a mapping relationship between a candidate route id and a candidate line id, fig. 3(e) is a schematic diagram of a specific example of a candidate route information table, fig. 3(f) is a schematic diagram of a mapping relationship between a candidate line id and a line vehicle type identifier id, and fig. 3(g) is a schematic diagram of a specific example of a candidate vehicle type information table. As shown in fig. 3, the method for optimizing a logistics network of the present invention includes:
step S301, basic data cleaning.
Illustratively, based on the requirements of subsequent algorithm and model construction, data of a predefined time period before the data processing date is selected, a flow direction data table is obtained, and the data in the flow direction data table is cleaned, wherein the predefined time period can be 15 days or half a month. The cleansing data may include: and processing the data of the freight note quantity, the transport line, the transport node, the transport class and the like in the flow direction data table by referring to the routing data of the standard template.
Further, the data in the traffic flow data table can be obtained in a prediction mode; the predefined time period can be any time period and is selected according to the requirement of data processing.
Step S302, line data processing.
Illustratively, through the results of previous line transportation research, the transportation cost can be optimized by adding a plurality of pairs of open lines. The method for adding the split line can comprise the following steps: and searching the existing transportation line with the single-opening flow direction, and adding a reverse transportation line with the flow direction opposite to that of the existing transportation line, so that the existing transportation line with the single-opening flow direction is developed into the transportation line with the split flow direction.
Illustratively, adding a split line includes: and searching the existing single-opening flow transportation line from the flow direction data table, counting the forward quantity of the goods from the transportation starting point to the transportation terminal point of the single-opening flow direction and the reverse quantity of the goods from the transportation starting point to the transportation terminal point opposite to the single-opening flow direction of each transportation line, and opening the reverse transportation line of the existing single-opening flow transportation line to open the existing single-opening flow transportation line into a split flow transportation line if the reverse quantity of the goods is more than or equal to a preset proportion of the forward quantity of the goods.
Further, it is specified that if the reverse cargo volume is 80% or more of the forward cargo volume, the reverse transportation route of the existing single-opening flow direction transportation route is opened. For example, the existing single-opening flow direction transportation route is from a transportation starting point a to a transportation ending point B, and is obtained by statistics according to the flow direction data table: the total cargo quantity of A- > B is 3000 bills, the total cargo quantity of B- > A is 2500 bills, the reverse cargo quantity is more than or equal to 80 percent of the forward cargo quantity, then the reverse transportation line BA of the existing transportation line AB with single open flow is opened, and the transportation line with split flow is arranged between the AB.
Illustratively, candidate routes are constructed based on existing transport routes and added reverse transport routes. And numbering each candidate line to obtain the id corresponding to each candidate line, and using the id for subsequent routing line combination and model calculation.
Furthermore, it can be known from the previous line transportation research that the cost is saved by adopting the line in the open flow direction in the transportation process compared with the line in the single open flow direction.
Step S303, route data generation.
Illustratively, the routing data includes a peer route and a cross-peer route. Planning the area according to the actual service, or directly utilizing the existing area division data, wherein the existing area division comprises the following steps: north China, south China, east China, northwest, southwest and northeast China. During the transportation of the goods, the routing data in the area, i.e. the routing data in the same area, for example, the routing in the northward area, i.e. the same area routing. As shown in the left diagram of fig. 3(b), the OD is the same-zone routing. Routing data between regions, i.e., routing data across regions, such as a route between the north-china region and the south-west region, i.e., a cross-region route. As shown in the right diagram of fig. 3(b), OD, OAD, OBD, and OABD are cross-regional routes. Wherein, the transport lines in the same area are branch lines, the transport lines across the area are trunk lines, OA and BD are branch lines, and OD, OB, AD and AB are trunk lines.
Illustratively, the start of transportation and the end of transportation for each route are obtained from the traffic flow data table. Obtaining all transport nodes between the transport starting point and the transport destination according to the candidate line data obtained in the steps S301-302, and constructing a first candidate route of all nodes between the serial transport starting point, the transport nodes and the transport destination; obtaining departure time of candidate routes among the transport nodes from the flow direction data table, and expanding the first candidate route according to the departure time of the candidate routes to obtain a second candidate route; and acquiring the transportation shift of the transportation starting point from the flow direction flow data table, and expanding the second candidate route according to the transportation shift of the transportation starting point to obtain the candidate route of the target freight note.
Illustratively, each candidate route is numbered based on the generated candidate routes, and an id corresponding to each candidate route is obtained and used for subsequent route line combination and model calculation.
Further, as shown in fig. 3(c), O is a transportation start point, D is a transportation end point, B, C, E is a transportation node between the transportation start point and the transportation end point, OB, BC, CE, and ED are candidate lines, and a candidate route OD is obtained by connecting O, B, C, E, D in series; obtaining departure times of the candidate routes OB, BC, CE, ED, for example, the candidate route OB includes 3 departure times: 01:00, 09:00, 13:30, candidate routes BC include 2 departure times: 15:00, 20:00, the departure time of the candidate line CE is only 19:30, and the candidate line ED includes 2 departure times: 08:00 and 14: 20. Obtaining the routing of the candidate route OD after obtaining the departure time of the candidate route according to the departure time of the candidate routes OB, BC, CE and ED between the transportation starting point O and the transportation terminal D, wherein 3 times 2 times 1 times 2 times 12 times in total; the transport shift of the transport origin O of the candidate route OD is obtained, e.g. the candidate route OD comprises 3 transport shifts starting from the transport origin O: o _ S0100, O _ S0900, O _ S1630, and each transport shift of the transport origin O may be used for 12 walks of the candidate route OD, so that, according to the transport shift of the transport origin O of the candidate route OD, a walk of the candidate route OD after obtaining the transport shift of the transport origin O is obtained, and 3 × 12 is total to 36.
Further, as shown in fig. 3(c), OB may be a cargo line, ED may be a bulk line, BC and CE are intermediate lines, and a route OD is obtained by connecting the cargo line OB, the intermediate line BC, the intermediate line CE and the bulk line ED in series.
Furthermore, when there is only one transportation node B between the transportation start point O and the transportation end point D, OB may be a cargo collection line, BD may be an intermediate line, and the serial cargo collection line OB and the intermediate line BD obtain a route OD; or OB can be an intermediate line, BD can be a bulk cargo line, and the intermediate line OB and the bulk cargo line BD are connected in series to obtain a route OD; alternatively, OB may be a cargo line, BD may be a bulk cargo line, and route OD is obtained by connecting cargo line OB and bulk cargo line BD in series. When there are two transportation nodes B, C between the transportation start point O and the transportation end point D, OB may be a cargo collection line, CD may be a bulk cargo line, and the route OD is obtained by connecting the cargo collection line OB, the intermediate line BC, and the bulk cargo line CD in series. And when no transport node exists between the transport starting point O and the transport destination D, serially connecting the transport starting point O and the transport destination D to obtain a route OD.
Further, the route data generation includes the same-zone candidate route generation and the cross-zone candidate route generation. For a target freight note with a transport starting point and a transport destination located in the same area, constructing a first candidate route of all nodes among the transport starting point, the transport node and the transport destination in series according to the transport starting point and the transport destination of the target freight note; obtaining departure time of candidate routes among the transport nodes from the flow direction data table, and expanding the first candidate route according to the departure time of the candidate routes to obtain a second candidate route; and acquiring the transportation shift of the transportation starting point from the flow direction flow data table, and expanding the second candidate route according to the transportation shift of the transportation starting point to obtain the candidate route of the target freight note, namely generating the candidate route of the same area. The number of the sorting centers in the same area is not more than 5 at most.
And for the target freight notes of which the transportation starting points and the transportation destination points are positioned in different areas, determining the candidate route segments of the same area in the same area with the transportation starting points according to the generated candidate routes of the same area, and obtaining a first part of the cross-area candidate route. And determining the candidate route segment in the same area with the transportation destination to obtain a second part of the cross-regional candidate route. And connecting the end point of the first part of the cross-region candidate route and the starting point of the second part in series to obtain the trunk line of the cross-region candidate route. Constructing a first candidate route of all nodes among a first part, a trunk and a second part of the serial connection cross-region candidate route; and obtaining the departure time of the trunk from the traffic flow data table, and expanding the first candidate route according to the departure time of the trunk to obtain the candidate route of the target waybill, namely generating the cross-region candidate route. The number of the cross-district sorting centers is not more than 6 at most.
Step S304, a candidate routing information table is constructed.
Illustratively, according to the candidate route id obtained in step S303 and the candidate line id obtained in step S302, a mapping relationship between the candidate route id and the candidate line id is constructed, so as to obtain a candidate route information table. As shown in fig. 3(d), "1" indicates that the candidate line is used, and "0" indicates that the candidate line is not used, for example, candidate route 1 uses candidate line 1, candidate route 3 uses candidate line 2 and candidate line 3, … …, and candidate route m uses candidate line 1, candidate line 2, and candidate line 3.
Illustratively, information such as a starting point, an end point, a single quantity and the like of different route ids is searched from the traffic flow data table, and a mapping relation between the route id and the line id, the starting point, the end point, the single quantity is constructed to obtain a candidate route information table. And obtaining the composition line, the starting point, the end point and the single quantity of each route from the candidate route information table for processing the subsequent operation cost. For example, as shown in fig. 3(e), route 3 is composed of line 2 and line 3, and the single quantity is 250 from Xinjiang to Sichuan.
And step S305, adjusting the model of the line vehicle.
For example, through the results of the previous line transportation investigation, the transportation cost can be optimized by adjusting the vehicle type. The method for adjusting the vehicle type may include: for the transportation line with larger goods and the vehicle type being a small vehicle or a medium vehicle, the vehicle type is adjusted to be a large vehicle; and for the transportation line with smaller cargo quantity and the vehicle type of a medium-sized vehicle or a large-sized vehicle in the transportation line, the vehicle type is adjusted to be a small-sized vehicle.
Illustratively, adjusting the vehicle type includes: and referring to all the transportation vehicle types in the flow direction data table, searching the existing transportation vehicle type of each candidate line from the flow direction data table, matching the existing transportation vehicle type and the existing transportation vehicle type, and adding the transportation vehicle type which is not matched for each candidate line, so that all the transportation vehicle types are matched for each candidate line.
Illustratively, after each candidate route is matched with all the transportation vehicle types, each transportation vehicle type of each candidate route is numbered, and a route vehicle type identifier id of each transportation vehicle type of each candidate route is obtained and used for subsequent route combination and model calculation.
And step S306, constructing a candidate vehicle type information table.
Illustratively, according to the candidate line id obtained in step S302 and the line vehicle type identifier id obtained in step S305, a mapping relation between the candidate line id and the line vehicle type identifier id is constructed, and a candidate vehicle type information table is obtained. As shown in fig. 3(f), the line model identifier "1" indicates that the line 1 uses a model of 9.6 meters, and the line model identifier "4" indicates that the line 2 uses a model of 17.5 meters.
Illustratively, looking up the line data for different candidate line ids from the traffic flow data table, the line data may include: the method comprises the steps of establishing a mapping relation between candidate line ids, line vehicle type identifiers and line data to obtain a candidate vehicle type information table, wherein the mapping relation comprises an origin, a destination, a transportation distance, a transportation vehicle type, a loading capacity, a transportation cost and the like. And obtaining the route information of the candidate route from the candidate vehicle type information table. For example, as shown in fig. 3(g), the 3 rd candidate line has its origin in beijing, its destination in shenzhen, and a vehicle model of 17.5 meters.
Step S307, a transportation cost function is constructed.
Illustratively, according to the candidate vehicle type information table of the candidate route, a single-kilometer transportation cost function of different vehicle types is constructed. Considering cost parameters of transport vehicles of different vehicle types during transportation, the cost parameters may include: the method comprises the steps of manual work, distance, oil consumption, vehicle damage, vehicle insurance, cargo insurance and the like, and a single-kilometer transportation cost function of transportation vehicles of different vehicle types is constructed based on the existing cost accounting method.
Further, the transportation cost function comprises a cost function of the whole vehicle transportation line and a cost function of the part transportation line, the cost function of the whole vehicle transportation line is the single-trip transportation cost of the whole vehicle transportation line, and the cost function of the part transportation line is the square average cost of the part transportation line.
Furthermore, according to the candidate vehicle type information table of the candidate route, obtaining information such as a transport vehicle type and a transport distance of each candidate route, wherein the transport vehicle type information comprises vehicle type full-load capacity of different types of transport vehicles of the candidate route, and the transport distance information is transport length of the candidate route; and after the transportation distance information of the candidate line is obtained, calculating the single-kilometer transportation cost according to the single-kilometer transportation cost function, and multiplying the single-kilometer transportation cost by the transportation length to obtain the single-trip transportation cost.
Further, the cost function construction may include a custom function, and different cost parameters are selected according to cost accounting key points to be concerned, and different parameter factors are given to construct the cost function.
Step S308, a route planning model is constructed.
Illustratively, the route planning model is constructed as follows:
wherein the content of the first and second substances,
riji belongs to ODB, a mapping relation between a transport shift B and a route OD set, wherein the route OD set is all possible walks of the route OD, the transport shift B is i, and the possible walks of the route OD are j;
nijthe number of the sorting centers passed by the route is equal, and each sorting center comprises a transportation starting point, a transportation node and a transportation end point;
Cijsingle amount of route;
c, the bill average operation cost can be preset in advance according to needs and is input by a user;
lzmthe number of vehicles in the mth whole vehicle transportation line;
tzcmthe single-trip transportation cost of the mth whole vehicle transportation line;
Vjquantity of goods routed in m3；
zn＝∑jVj*rijN belongs to line _ id, and the quantity of goods in the unit of m is the route of the nth part transport line3；
tlcnMean square cost of the nth part transport line in units of yuan/m3。
The above equation represents the minimum value of the total cost. The total cost can comprise operation cost, transportation cost of whole vehicle transportation and transportation cost of part transportation, and the operation cost is the product of the number of the sorting centers passed by the route, the single average operation cost and the single quantity; the transportation cost of the whole vehicle transportation is the product of the number of vehicles of the whole vehicle transportation line and the single transportation cost of the whole vehicle transportation line; the transportation cost of the piece goods transportation is the product of the cargo volume of the route of the piece goods transportation line and the average cost of the party using the piece goods transportation line.
Illustratively, the resulting constraint 1 is as follows:
the above equation represents that one route j of the route OD under one transport shift i is selected according to the mapping relation between the transport shift B and the route OD set.
Illustratively, the cargo quantity constraint 2 is as follows:
zm≤lzm*capm，m∈linez_id
zn≤lln*capn，n∈linel_id
wherein the content of the first and second substances,
zm＝∑jVj*rijm belongs to linez _ id, and the cargo volume of the route using the mth whole vehicle transportation line is in the unit of m3；
capmThe full load capacity of the vehicle of the mth whole vehicle transportation line;
llnthe number of vehicles in the nth part transport route;
capnthe full capacity of the vehicles of the nth piece of the transportation line.
The above formula shows that the cargo volume of each entire vehicle transportation line is less than the full cargo volume thereof, and the cargo volume of each part transportation line is less than the full cargo volume thereof.
Illustratively, the aging constraint 3 is as follows:
rij*tntij≤sti，i∈ODB
wherein the content of the first and second substances,
tntijrouting aging;
stistatic route aging.
The above formula indicates that, for each route, the route aging of the route needs to be less than or equal to the static route aging, where the static route aging is standard aging and can be calculated according to the existing data in the traffic flow data table.
Illustratively, by using the route planning model obtained above and the result constraint condition 1, the cargo quantity constraint condition 2 and the aging constraint condition 3, based on the candidate route information table obtained in step S304 and the candidate vehicle type information table obtained in step S306, according to the route planning model, the information of the target order is input into the model for route planning, so as to obtain the optimal route of the optimized target order, optimize the total cost under the condition of ensuring the aging, and obtain the most suitable transportation scheme for each route in the route, including the vehicle type, the transportation mode (whole vehicle transportation or part load transportation), the cargo quantity, the transportation class and the like.
In the embodiment of the invention, the basic data is used for cleaning; processing the line data; generating routing data; constructing a candidate routing information table; adjusting the model of the line vehicle; constructing a candidate vehicle type information table; constructing a transportation cost function; the steps of constructing a routing planning model and the like can optimize the existing logistics network planning while considering the aging requirement, thereby not only meeting the customer experience, but also reducing the total logistics cost.
Fig. 4 is a schematic diagram of main modules of a logistics network optimization system according to an embodiment of the invention, and as shown in fig. 4, a logistics network optimization apparatus 400 of the invention includes:
the data processing module 401 adds a reverse transportation route to a transportation route with a single open flow direction in the existing transportation routes, and constructs a candidate route based on the existing transportation route and the added reverse transportation route; constructing a candidate route information table of a target waybill based on the candidate line; and the data processing module is used for constructing a candidate vehicle type information table of the candidate line.
Illustratively, according to previous research, it is known that the logistics cost of the waybill can be reduced by opening up a transportation line with a single flow direction as a transportation line with a split flow direction. Therefore, the data processing module 401 searches the transportation line with the single-open flow direction according to the existing transportation line data found from the flow direction data table, adds a reverse transportation line to the transportation line with the single-open flow direction, and can open up the transportation line with the double-open flow direction. Constructing a candidate route according to the existing transportation route and the added reverse transportation route, and constructing a mapping relationship between the candidate route and the candidate route according to the candidate route to obtain a candidate route information table of the target waybill, which may include: each route is composed of which transport lines.
Further, a tunneling condition may be added to determine whether to tunnel a single flow direction transportation line to a double flow direction transportation line, for example, the tunneling condition may be: whether the load of the reverse haul route is greater than a predetermined fraction of the load of the haul route in the single open flow direction.
Further, the single amount of each route can be added into the mapping relationship, and the mapping relationship between the route and the transportation line and the single amount can be constructed for the treatment of the subsequent operation cost.
For example, according to previous research, it is known that the logistics cost of the waybill can be reduced by adjusting the transportation vehicle type of the transportation vehicle of the transportation line. Therefore, the data processing module 401 searches the existing transportation vehicle type of each candidate route according to the candidate route information table of the target waybill, compares the existing transportation vehicle type with all the existing transportation vehicle types, and adds the transportation vehicle type which is lacked compared with all the transportation vehicle types for the candidate route which is not configured with all the transportation vehicle types according to the comparison result. After supplementing the transportation vehicle types lacking in the candidate route, constructing a candidate vehicle type information table of the candidate route may include: the number of types of transportation vehicles can be arranged on each line, and each type of transportation vehicle comprises several types, the cargo capacity and the like.
Furthermore, after the transportation vehicle types of the candidate routes are supplemented, the walking method of the original candidate routes is unchanged, and possible transportation schemes are changed.
And a model building module 402, which builds a route planning model, wherein the route planning model takes the minimum total cost of the target freight note as an objective function and constrains the route aging.
Illustratively, the model building module 402 builds the route planning model with a minimum total cost of the target waybill as an objective function, the total cost including operating costs, transportation costs for full vehicle transportation, and transportation costs for part transportation. The route planning model includes three constraints: result constraints, inventory constraints, and aging constraints. And the result constraint condition only requires to select an optimal route, the cargo quantity constraint condition requires not to be overloaded, and the aging constraint condition requires not to exceed the preset standard aging.
And the optimization module 403 performs route planning on the target waybill by using a pre-constructed route planning model based on the candidate route information table and the candidate vehicle type information table of the candidate route to obtain an optimal route.
Based on the candidate route information table and the candidate vehicle type information table obtained by the data processing module 401, a pre-constructed route planning model is adopted, and the information of the target order is input into the model to optimize the route of the target freight order, so that the optimal route of the optimized target order can be obtained.
In the embodiment of the invention, a reverse transportation line is added by a data processing module aiming at the transportation line with single open flow direction in the existing transportation lines, and a candidate line is constructed based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line; the data processing module is used for constructing a candidate vehicle type information table of the candidate line; constructing a route planning model through a model construction module, wherein the route planning model takes the minimum total cost of a target freight note as a target function and restrains route timeliness; the optimization module is used for carrying out route planning on the target freight note by adopting a pre-constructed route planning model based on the candidate route information table and the candidate vehicle type information table of the candidate line so as to obtain modules such as an optimal route, and can optimize the existing logistics network planning while considering the timeliness requirement, thereby not only meeting the customer experience, but also reducing the total logistics cost.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a data processing module, a model building module, and a computation module. The names of these modules do not in some cases constitute a limitation on the modules themselves, and for example, a data processing module may also be described as a "module that processes data obtained from a data table".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: adding a reverse transportation line aiming at a transportation line with a single open flow direction in the existing transportation line, and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate route; based on the candidate route information table and the candidate vehicle type information table of the candidate line, performing route planning on a target waybill by adopting a pre-constructed route planning model to obtain an optimal route; the routing planning model takes the minimum total cost of the target freight note as an objective function and restrains the routing timeliness.
According to the technical scheme of the embodiment of the invention, the existing logistics network planning can be optimized while the aging requirement is considered, so that the customer experience can be met, and the total logistics cost can be reduced.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for optimizing a logistics network is characterized by comprising the following steps:
adding a reverse transportation line aiming at a transportation line with a single open flow direction in the existing transportation line, and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line;
constructing a candidate vehicle type information table of the candidate route;
based on the candidate route information table and the candidate vehicle type information table of the candidate line, performing route planning on a target waybill by adopting a pre-constructed route planning model to obtain an optimal route; the routing planning model takes the minimum total cost of the target freight note as an objective function and restrains the routing timeliness.
2. The method of claim 1, wherein the method further comprises:
and before the route planning is carried out on the target freight note by adopting a pre-constructed route planning model, constructing a transportation cost function of the candidate vehicle type based on the candidate line.
3. The method of claim 1, wherein adding a reverse transportation route for a single-open-flow transportation route of existing transportation routes, and constructing a candidate route based on the existing transportation routes and the added reverse transportation route comprises:
and aiming at the existing single-opening flow direction transportation line, when the load in the reverse flow direction is more than or equal to the preset proportion of the load in the forward flow direction, adding a reverse transportation line for the single-opening flow direction transportation line.
4. The method of claim 1, wherein constructing a candidate routing information table for a target waybill based on the candidate routes comprises:
for a target freight note with a transport starting point and a transport destination located in the same area, constructing a first candidate route of all nodes among the transport starting point, the transport node and the transport destination in series according to the transport starting point and the transport destination of the target freight note; expanding the first candidate route according to the departure time of the transport route to obtain a second candidate route; expanding the second candidate route according to the transportation shift of the transportation starting point to obtain the same-area candidate route of the target freight note; for target freight notes of which the transportation starting points and the transportation destination points are located in different areas, first determining a first part, a second part and a trunk line of a cross-region route, and constructing a first candidate route of all nodes connected in series among the first part, the trunk line and the second part; and expanding the first candidate route according to the departure time of the trunk line to obtain a cross-region candidate route of the target freight note.
5. The method of claim 1, wherein the candidate vehicle type information table includes: mapping relation between the candidate line information and the candidate vehicle type information; wherein the candidate line information includes: the vehicle-mounted system comprises a candidate line first identification and a candidate line second identification, wherein the candidate line first identification is obtained by numbering each candidate line, and the candidate line second identification is obtained by numbering each transport vehicle type of each candidate line.
6. The method of claim 1, wherein the total cost of the target waybill comprises: operating costs, transportation costs for full car transportation and transportation costs for part load transportation.
7. The method of claim 1, wherein the route planning model constrains route aging such that the route aging of each route must be less than a standard static route aging, and further comprising the following constraints: constraining the result to enable the result to only output the optimal solution of each route; the cargo volume is constrained so that all transport vehicles cannot be overloaded.
8. A logistics network optimization apparatus, comprising:
the data processing module is used for adding a reverse transportation line aiming at the transportation line with single open flow direction in the existing transportation lines and constructing a candidate line based on the existing transportation line and the added reverse transportation line; constructing a candidate route information table of a target waybill based on the candidate line;
the data processing module is used for constructing a candidate vehicle type information table of the candidate line;
the model construction module is used for constructing a route planning model, wherein the route planning model takes the minimum total cost of a target freight note as a target function and restrains route timeliness;
and the optimization module is used for carrying out route planning on the target waybill by adopting a pre-constructed route planning model based on the candidate route information table and the candidate vehicle type information table of the candidate line so as to obtain an optimal route.
9. A logistics network optimization electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011290378.1A CN113762573B (en) | 2020-11-17 | 2020-11-17 | Logistics network optimization method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011290378.1A CN113762573B (en) | 2020-11-17 | 2020-11-17 | Logistics network optimization method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113762573A true CN113762573A (en) | 2021-12-07 |
| CN113762573B CN113762573B (en) | 2023-11-03 |
Family
ID=78786135
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011290378.1A Active CN113762573B (en) | 2020-11-17 | 2020-11-17 | Logistics network optimization method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113762573B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757629A (en) * | 2022-06-16 | 2022-07-15 | 北京京东振世信息技术有限公司 | Article distribution method, vehicle type information transmission method, apparatus, device, and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020082893A1 (en) * | 2000-02-29 | 2002-06-27 | Dennis Barts | Delivery system and method for vehicles and the like |
| CN108921483A (en) * | 2018-07-16 | 2018-11-30 | 深圳北斗应用技术研究院有限公司 | A kind of logistics route planing method, device and driver arrange an order according to class and grade dispatching method, device |
| CN109934532A (en) * | 2019-02-21 | 2019-06-25 | 深圳市北斗智能科技有限公司 | The route planning method and relevant apparatus of Bidirectional transporting |
| CN110555640A (en) * | 2018-05-31 | 2019-12-10 | 北京京东振世信息技术有限公司 | Method and device for route planning |
| CN111340431A (en) * | 2020-03-27 | 2020-06-26 | 上海东普信息科技有限公司 | Method, device and equipment for planning transportation route and storage medium |
| CN111667085A (en) * | 2019-03-08 | 2020-09-15 | 北京京东尚科信息技术有限公司 | Method, device, medium and electronic equipment for determining logistics routing network |
-
2020
- 2020-11-17 CN CN202011290378.1A patent/CN113762573B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020082893A1 (en) * | 2000-02-29 | 2002-06-27 | Dennis Barts | Delivery system and method for vehicles and the like |
| CN110555640A (en) * | 2018-05-31 | 2019-12-10 | 北京京东振世信息技术有限公司 | Method and device for route planning |
| CN108921483A (en) * | 2018-07-16 | 2018-11-30 | 深圳北斗应用技术研究院有限公司 | A kind of logistics route planing method, device and driver arrange an order according to class and grade dispatching method, device |
| CN109934532A (en) * | 2019-02-21 | 2019-06-25 | 深圳市北斗智能科技有限公司 | The route planning method and relevant apparatus of Bidirectional transporting |
| CN111667085A (en) * | 2019-03-08 | 2020-09-15 | 北京京东尚科信息技术有限公司 | Method, device, medium and electronic equipment for determining logistics routing network |
| CN111340431A (en) * | 2020-03-27 | 2020-06-26 | 上海东普信息科技有限公司 | Method, device and equipment for planning transportation route and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| WU, ZHIHUA: "Bi-level programming based layout optimization of water-land intermodal transport hub", 《APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY》 * |
| 毕明凯: "考虑运输时间和改编费用的铁路路网车流分配优化研究", 《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
| 王力锋;刘双双;刘抗英;: "物流运输快速配送路径规划仿真", 计算机仿真, no. 08 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757629A (en) * | 2022-06-16 | 2022-07-15 | 北京京东振世信息技术有限公司 | Article distribution method, vehicle type information transmission method, apparatus, device, and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113762573B (en) | 2023-11-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110555640B (en) | Route planning method and device | |
| CN107235276B (en) | Cargo method for carrying and device | |
| CN110371548B (en) | Goods warehousing method and device | |
| Ji et al. | An integrated model for the production-inventory-distribution problem in the Physical Internet | |
| Ji et al. | A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics | |
| US10223649B2 (en) | System and method of multi-objective optimization for transportation arrangement | |
| WO2020164319A1 (en) | Goods pickup method and device | |
| CN113259144A (en) | Storage network planning method and device | |
| CN110276466A (en) | The method and apparatus of packet cluster are built in a kind of determining distribution network | |
| Faulin et al. | The ALGACEA‐1 method for the capacitated vehicle routing problem | |
| Jiang et al. | A scheme for determining vehicle routes based on Arc-based service network design | |
| Zhang et al. | Design of limited-stop service based on the degree of unbalance of passenger demand | |
| CN113762573B (en) | Logistics network optimization method and device | |
| CN107085754B (en) | Information output method and device | |
| CN113255950B (en) | Method and device for optimizing logistics network | |
| CN111507497B (en) | Distributed point and neutral point site selection system and method | |
| CN113222205A (en) | Path planning method and device | |
| CN110321399A (en) | The method and apparatus for selecting address | |
| CN111860918B (en) | Distribution method and device, electronic equipment and computer readable medium | |
| CN111461430A (en) | Method and device for generating route information | |
| Wang et al. | Minimum Spanning Tree Method for Sparse Graphs | |
| Li et al. | Scheduled service network design of long-haul freight transportation based on the segment subcontract | |
| CN116402432B (en) | Route planning method, device, equipment and storage medium | |
| CN114969059B (en) | Method and device for generating order information, electronic equipment and storage medium | |
| Raggl et al. | Network on demand planning of Supply Chains towards Physical Internet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |