CN113762573B - Logistics network optimization method and device - Google Patents
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Abstract
A logistics network optimization method and a 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 for 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; constructing a candidate route information table of the target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate line; based on the candidate route information table and the candidate vehicle type information table of the candidate line, carrying out route planning on a target waybill by adopting a pre-constructed route planning model so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and restrains route timeliness. According to the embodiment, the existing logistics network planning can be optimized while the aging requirement is considered, so that customer experience can be met, and the total logistics cost can be reduced.
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), a distributor typically receives goods from sellers, conveys the goods to a starting site, then passes through a plurality of sorting centers to reach an end site, and finally distributes the goods to buyers by the distributor. The existing logistics network is mainly planned through manual calculation or a simple greedy algorithm.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
(1) The physical distribution network planning involves more variables (including lines, nodes, shifts and the like of the physical distribution network), has complex logic, and is difficult to obtain an optimal solution through manual evaluation; (2) The model of logistics network planning mainly considers that the time effect is optimal in modeling, and the problem of logistics cost is paid attention to the defect though the user experience is better.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for optimizing a logistics network, which can optimize the existing logistics network plan while considering the aging requirement, thereby not only meeting the customer experience, but also reducing the total cost of logistics.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a method for optimizing a logistics network, including:
Adding a reverse transportation line for 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; constructing a candidate route information table of the target waybill based on the candidate line;
constructing a candidate vehicle type information table of the candidate line;
based on the candidate route information table and the candidate vehicle type information table of the candidate line, carrying out route planning on a target waybill by adopting a pre-constructed route planning model so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and restrains route timeliness.
Optionally, the method further comprises:
before a pre-constructed route planning model is adopted to carry out route planning on a target waybill, constructing a transportation cost function of the candidate vehicle type based on the candidate route.
Optionally, adding a reverse transportation route for a transportation route of a single open flow direction in the existing transportation route, and constructing a candidate route based on the existing transportation route and the added reverse transportation route includes:
for the existing single-opening-flow-direction transportation line, when the cargo quantity in the reverse flow direction is larger than or equal to the preset proportion of the cargo quantity in the forward flow direction, adding the 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 waybill with a transportation starting point and a transportation destination in the same area, constructing a first candidate route of all nodes among the serial transportation starting point, the transportation node and the transportation destination according to the transportation starting point and the transportation destination of the target waybill; expanding the first candidate route according to the departure time of the transportation 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 regional candidate route of the target waybill; for a target waybill with a transportation starting point and a transportation terminal point in different areas, 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 between the first part, the trunk line and the second part in series; and expanding the first candidate route according to the departure time of the trunk line to obtain the cross-region candidate route of the target waybill.
Optionally, the candidate vehicle type information table includes: mapping relation between candidate line information and candidate vehicle type information; wherein the candidate line information includes: the system comprises a first identification of a candidate line and a second identification of the candidate line, wherein the first identification of the candidate line is obtained by numbering each candidate line, and the second identification of the candidate line is obtained by numbering each transportation vehicle type of each candidate line.
Optionally, the total cost of the target waybill includes: the operation cost, the transportation cost of whole car transportation and the transportation cost of part transportation.
Optionally, the routing model constrains the routing timeliness such that the routing timeliness of each route must be less than a standard static routing timeliness, and the routing model further includes the following constraints: constraint is carried out on the result, so that the result only outputs the optimal solution of each route; the cargo quantity is constrained so that all transport vehicles cannot be overloaded.
According to still another aspect of the embodiment of the present invention, there is provided a logistic network optimization device, including:
a logistic network optimization device, comprising:
the data processing module is used for adding a reverse transportation line aiming at a transportation line with a single 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 the target waybill based on the candidate line;
the data processing module constructs 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 waybill as a target function and constrains 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 embodiment of the present invention, there is provided a logistics network optimization electronic apparatus, including:
one or more processors;
storage means 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 are enabled to realize 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 having stored thereon a computer program, which when executed by a processor, implements the method for optimizing a logistics network provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: because the technical means of opening up the single-flow-direction transportation line into the split-flow-direction transportation line and matching all transportation vehicle types for each transportation line to obtain the optimal route is adopted under the condition of knowing the information such as the node position and the like, the technical problems of low efficiency of manual evaluation of the existing logistics and insufficient importance on the logistics cost are overcome, and the technical effects of optimizing the existing logistics network planning while considering the aging requirement are achieved, so that the customer experience is met, and the total cost of the logistics is reduced.
Further effects of the above-described non-conventional alternatives are 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 of an exemplary system architecture to which a logistics network optimization method or logistics network optimization apparatus of an embodiment of the present invention may 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 present invention, and fig. 2 (b) is a schematic diagram of a transportation route and a route;
fig. 3 (a) is a schematic diagram of a detailed flow of a logistics network optimization method according to an embodiment of the present invention, fig. 3 (b) is a schematic diagram of an area route and a cross-area route, fig. 3 (c) is a schematic diagram of an OD route according to an embodiment of the present invention, fig. 3 (d) is a schematic diagram of a mapping relationship between candidate route ids and candidate line ids, 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 candidate line ids and line model identifiers ids, and fig. 3 (g) is a schematic diagram of a specific example of a candidate vehicle model information table;
FIG. 4 is a schematic diagram of the main modules of a logistic network optimization device according to an embodiment of the present invention;
Fig. 5 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered 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 shows an exemplary system architecture diagram to which the logistic network optimization method or the logistic network optimization device according to the embodiment of the present invention may be applied, and as shown in fig. 1, the exemplary system architecture of the logistic network optimization method or the logistic network optimization device according to the embodiment of the present invention includes:
as shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user 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 method for optimizing a physical distribution network provided in the embodiment of the present invention is generally executed by the server 105, and accordingly, the physical distribution network optimizing 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 main flow of a logistics network optimization method according to an embodiment of the present invention, and fig. 2 (b) is a schematic diagram of a transportation route and a route. As shown in fig. 2a, the method for optimizing the logistics network of the present invention comprises:
step S201, adding a reverse transportation route aiming at a transportation route with a single opening flow direction in the existing transportation route, and constructing a candidate route based on the existing transportation route and the added reverse transportation route; 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 start point to a transportation end point during the transportation of goods.
Illustratively, the origin of transportation, the destination of transportation, and the transportation node are all sorting centers. The transportation line refers to a passage formed by connecting two adjacent transportation nodes during cargo transportation. As shown in fig. 2 (B), O is a transport start point, D is a transport end point, B, C, E, F, G is a transport node, O-B-C-E-D, O-F-G-D is a route, OB, BC, OF, FG, etc. are transport routes. Wherein O, D, B, C, E, F, G are all transportation nodes.
The whole vehicle transportation refers to the transportation of a batch of cargos transported by a transportation vehicle from a transportation starting point to a transportation destination, so as to realize point-to-point transportation. The transportation of the parts refers to that when the weight or the volume of one batch of goods is less than one transportation vehicle, the goods can be transported by sharing one transportation vehicle with other batches or even hundreds of batches of goods.
For example, according to previous investigation, the logistics cost of the bill can be reduced by opening the single-flow-direction transportation line into the split-flow-direction transportation line. Therefore, according to the existing transportation route data searched from the flow direction data table, the transportation route with the flow direction being the single-opening flow direction is searched, and the reverse transportation route is added for the transportation route with the single-opening flow direction, so that the transportation route can be opened up into the transportation route with the double-opening flow direction. And constructing a candidate line according to the existing transportation line and the added reverse transportation line, and constructing a mapping relation between a candidate route and the candidate line according to the candidate line to obtain a candidate route information table of the target waybill. Wherein the candidate route information table may include: each route consists 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 amount of cargo of the reverse transport route is greater than a predetermined proportion of the amount of cargo of the single open flow direction transport route.
Furthermore, the single quantity of each route can be added into the mapping relation to construct the mapping relation of the route, the transportation line and the single quantity, thereby being convenient for the processing of the subsequent operation cost.
Step S202, constructing a candidate vehicle type information table of the candidate line.
Illustratively, according to previous investigation, 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 model of each candidate route is checked according to the candidate route information table of the destination waybill generated in step S201, and compared with all existing transportation models, and according to the comparison result, the transportation model that is missing compared with all transportation models is added to the candidate route that is not configured with all transportation models. 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: each line can have several transportation vehicle types, and each transportation vehicle type comprises several transportation vehicles, cargo carrying capacity and the like.
Further, after the transportation vehicle type of the candidate route is supplemented, the running method of the original candidate route is unchanged, and the possible transportation scheme is increased.
Step S203, based on the candidate route information table and the candidate vehicle type information table of the candidate line, adopting a pre-constructed route planning model to carry out route planning on a target waybill so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and restrains route timeliness.
Illustratively, the route planning model is constructed with the total cost of the target manifest as an objective function, the total cost including the cost of operation, the cost of transportation for the whole vehicle transportation, and the cost of transportation for the part transportation. The route planning model includes three constraints: result constraints, cargo quantity constraints, and aging constraints. The result constraint condition requires that only the optimal route is selected, the cargo quantity constraint condition cannot be overloaded, and the aging constraint condition cannot exceed the preset standard aging. Based on the candidate route information table obtained in the step S201 and the candidate vehicle type information table obtained in the step S202, a pre-built route planning model is adopted, and 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 route is added for a single-opening-flow transportation route in the existing transportation route, and a candidate route is constructed based on the existing transportation route and the added reverse transportation route; constructing a candidate route information table of the target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate line; based on the candidate route information table and the candidate vehicle type information table of the candidate line, carrying out route planning on a target waybill by adopting a pre-constructed route planning model so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and the route aging is restrained, so that the existing logistics network planning can be optimized while considering the aging requirement, customer experience can be met, and the total cost of logistics 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 present invention, fig. 3 (b) is a schematic diagram of an area route and a cross-area route, fig. 3 (c) is a schematic diagram of an OD route according to an embodiment of the present invention, fig. 3 (d) is a schematic diagram of a mapping relationship between candidate route ids and candidate line ids, 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 candidate line ids and line model identifiers ids, and fig. 3 (g) is a schematic diagram of a specific example of a candidate vehicle model information table. As shown in fig. 3, the method for optimizing the logistics network of the present invention comprises:
step S301, cleaning the basic data.
Illustratively, based on the requirements of the subsequent algorithm and model construction, data of a predefined period of time before the date of data processing is selected, a flow-to-flow data table is obtained, the data in the flow-to-flow data table is purged, and the predefined period of time may be 15 days or half a month. The cleaning data may include: and processing the data such as the quantity of the waybills, the transportation routes, the transportation nodes, the transportation shifts and the like in the traffic flow 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 predictive manner; the predefined time period can be any time period, and is selected according to the requirement of data processing.
Step S302, processing the line data.
Illustratively, the method of adding a plurality of split lines can be adopted to optimize the transportation cost after the earlier line transportation investigation result. The method for adding the split line can comprise the following steps: and searching the existing transportation line with the single flow direction, and adding a reverse transportation line with the opposite flow direction to the existing transportation line, so that the existing transportation line with the single flow direction is opened up into the transportation line with the opposite flow direction.
Illustratively, adding a split line includes: searching the existing single-flow-direction transportation lines from the flow-direction data table, counting the forward cargo quantity from the transportation start point to the transportation end point of the existing single-flow-direction transportation lines, and the reverse cargo quantity from the transportation start point to the transportation end point, which are opposite to the single-flow-direction transportation lines, if the reverse cargo quantity is greater than or equal to the preset proportion of the forward cargo quantity, opening the reverse transportation lines of the existing single-flow-direction transportation lines, and opening the reverse transportation lines into the opposite-flow-direction transportation lines.
Further, it is specified that if the reverse cargo amount is 80% or more of the forward cargo amount, the reverse transportation route of the existing transportation route of the single open flow direction is opened. For example, the existing transportation route with single flow direction is from the transportation start point a to the transportation end point B, and is obtained according to statistics of the flow direction data table: the total goods quantity A- > B is 3000 list, the total goods quantity B- > A is 2500 list, the reverse goods quantity is more than or equal to 80% of the forward goods quantity, the reverse transportation route BA of the existing single-open-flow transportation route AB is opened, and the transportation route between AB is the opposite-flow transportation route.
Illustratively, candidate routes are constructed based on existing routes and added reverse routes. Numbering each candidate line to obtain the id corresponding to each candidate line for subsequent route line combination and model calculation.
Further, through earlier line transportation investigation, the cost is saved by adopting the line with the opposite flow direction in the transportation process compared with the line with the single flow direction.
Step S303, route data generation.
Illustratively, the routing data includes a region route and a cross-region route. According to the actual service planning area or directly using the existing area division data, the existing area division includes: north China region, south China region, east China region, middle China region northwest, southwest, northeast. During cargo transportation, the routing data in the area, i.e., the routing data in the area, such as the routing in the North China area, i.e., the area route. As shown in the left diagram of fig. 3 (b), OD is a region route. The routing data between the regions is inter-regional routing data, such as the routing between the north-south-China region and the southwest region, i.e. trans-regional routing. As shown in the right diagram of fig. 3 (b), OD, OAD, OBD, OABD is a cross-zone route. The transportation lines of the areas are branch lines, the transportation lines of the cross areas are trunk lines, the OA and BD are branch lines, and the OD, OB, AD, AB is trunk line.
Illustratively, the transport start point and the transport end point for each route are obtained from a traffic flow data table. According to the candidate route data obtained in the steps S301-302, all transportation nodes between the transportation starting point and the transportation destination are obtained, and a first candidate route of all nodes among the transportation starting point, the transportation nodes and the transportation destination are constructed; obtaining departure time of candidate lines between transportation nodes from a flow stream data table, and expanding the first candidate route according to the departure time of the candidate lines to obtain a second candidate route; and obtaining the transportation shift of the transportation starting point from the 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 waybill.
Illustratively, each candidate route is numbered based on the generated candidate route, 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 transport start point, D is a transport end point, B, C, E is a transport node between the transport start point and the transport end point, OB, BC, CE, ED is a candidate route, and the candidate route OD is obtained by connecting O, B, C, E, D in series; get departure times for candidate line OB, BC, CE, ED, for example, candidate line OB includes 3 departure times: 01:00, 09:00, 13:30, candidate route BC comprises 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, 14:20. According to the departure time of the candidate route OB, BC, CE, ED between the transportation starting point O and the transportation ending point D, obtaining the travel method of the candidate route OD after obtaining the departure time of the candidate route, wherein the total number is 3×2×1×2=12; the transportation shift of the transportation origin O of the candidate route OD is obtained, for example, the candidate route OD includes 3 transportation shifts from the transportation origin O: o_s0100, o_s0900, o_s1630, and each transportation shift of the transportation origin O can be used for 12 routes of the candidate route OD, so, according to the transportation shift of the transportation origin O of the candidate route OD, the routes of the candidate route OD after the transportation shift of the transportation origin O is obtained are obtained, 3×12=36 in total.
Further, as shown in fig. 3 (c), OB may be a collection line, ED may be a bulk line, BC and CE are intermediate lines, and the series collection line OB, intermediate line BC, intermediate line CE and bulk line ED obtain the route OD.
Further, when there is only one transport node B between the transport start point O and the transport end point D, OB may be a cargo collecting line, BD may be an intermediate line, and the cargo collecting line OB and the intermediate line BD are connected in series to obtain the route OD; alternatively, OB may be an intermediate line, BD may 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 collection line, BD may be a bulk line, and the collection line OB and bulk line BD are connected in series to obtain the route OD. When there are two transportation nodes B, C between the transportation start point O and the transportation end point D, OB may be a collection line, CD may be a bulk line, and the collection line OB, the intermediate line BC, and the bulk line CD connected in series obtain the route OD. When no transport node exists between the transport starting point O and the transport destination D, the transport starting point O and the transport destination D are connected in series to obtain a route OD.
Further, the route data generation includes a region candidate route generation and a cross region candidate route generation. For a target waybill with a transportation starting point and a transportation destination in the same area, constructing a first candidate route of all nodes among the serial transportation starting point, the transportation node and the transportation destination according to the transportation starting point and the transportation destination of the target waybill; obtaining departure time of candidate lines between transportation nodes from a flow stream data table, and expanding the first candidate route according to the departure time of the candidate lines to obtain a second candidate route; and obtaining the transportation shift of the transportation starting point from the 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 waybill, namely generating the candidate route of the region. The number of sorting centers in the area is not more than 5 at most.
And determining the region candidate route fragments in the same region with the transportation starting point according to the generated region candidate route for the target waybills with the transportation starting point and the transportation ending point in different regions, and obtaining a first part of the cross-region candidate route. And determining the region candidate route fragments in the same region as the transportation end point to obtain a second part of the cross-region candidate route. And connecting the end point of the first part and the start point of the second part of the cross-region candidate route 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 series cross-region candidate route; obtaining departure time of the trunk line from the flow stream data table, and expanding the first candidate route according to the departure time of the trunk line to obtain a candidate route of the target waybill, namely generating a cross-region candidate route. The number of sorting centers across the area is no more than 6 at maximum.
Step S304, constructing a candidate route information table.
Illustratively, a mapping relationship between the candidate route ids and the candidate route ids is constructed according to the candidate route ids obtained in the step S303 and the candidate route ids obtained in the step S302, so as to obtain a candidate route information table. As shown in fig. 3 (d), a "1" indicates that the candidate line is used, and a "0" indicates that the candidate line is not used, for example, candidate line 1 is used for candidate line 1, candidate line 2 and candidate lines 3, … … are used for candidate line 3, and candidate line 1, candidate line 2 and candidate line 3 are used for candidate line m.
Illustratively, information such as a starting point, a finishing point, a single quantity and the like of different route ids is searched from a flow stream data table, and a mapping relation between the route ids and the route ids, the starting point, the finishing point and the single quantity is constructed to obtain a candidate route information table. The component lines, the starting points, the ending points and the single quantity of each route can be obtained from the candidate route information table and used for processing the subsequent operation cost. For example, as shown in fig. 3 (e), route 3 consists of line 2 and line 3, from Xinjiang to Sichuan, with a single quantity of 250.
Step S305, the line vehicle type is adjusted.
For example, the method of adjusting the vehicle type can be adopted to optimize the transportation cost after the earlier line transportation investigation result. The method for adjusting the vehicle type can comprise the following steps: for a transportation line with larger cargo quantity and a small or medium vehicle type, the vehicle type is adjusted to be a large vehicle; for the transportation line with smaller cargo quantity and the vehicle type of medium-sized or large-sized vehicle, the vehicle type is adjusted to be a small-sized vehicle.
Illustratively, adjusting the vehicle model includes: referring to all the transportation vehicles included in the flow direction data table, searching the existing transportation vehicles of each candidate line from the flow direction data table, matching the two transportation vehicles, and adding unmatched transportation vehicles for each candidate line, so that all the transportation vehicles are matched for each candidate line.
Illustratively, after each candidate line is matched to all the transportation vehicles, each transportation vehicle type of each candidate line is numbered to obtain a line vehicle type identifier id of each transportation vehicle type of each candidate line for subsequent route line combination and model calculation.
Step S306, constructing a candidate vehicle type information table.
Illustratively, a mapping relationship between the candidate line id and the line model identifier id is constructed according to the candidate line id obtained in the step S302 and the line model identifier id obtained in the step S305, so as to obtain a candidate model information table. As shown in fig. 3 (f), the line model identifier "1" indicates that the line 1 uses a 9.6 meter model, and the line model identifier "4" indicates that the line 2 uses a 17.5 meter model.
Illustratively, looking up line data for different candidate line ids from the traffic flow data table, the line data may include: the method comprises the steps of constructing a mapping relation between candidate line ids and line model identifiers ids and line data to obtain a candidate vehicle model information table, wherein the mapping relation comprises an origin, a destination, a transportation distance, a transportation vehicle model, a loading capacity, transportation cost and the like. The line information of the candidate line can be obtained from the candidate vehicle type information table. For example, as shown in fig. 3 (g), the origin of the 3 rd candidate line is Beijing, the destination is Shenzhen, 17.5 m car type is used, etc.
Step S307, transportation cost function construction.
Illustratively, a single kilometer transportation cost function of different vehicle types is constructed according to the candidate vehicle type information table of the candidate line. Considering cost parameters of transport vehicles of different vehicle types during transportation, the cost parameters may include: and constructing a single kilometer transportation cost function of transportation vehicles of different vehicle types based on the existing cost accounting method by manpower, distance, oil consumption, vehicle loss, vehicle risk, cargo risk and the like.
Further, the transportation cost function comprises a cost function of the whole transportation line and a cost function of the spare part transportation line, wherein the cost function of the whole transportation line is the single-pass transportation cost of the whole transportation line, and the cost function of the spare part transportation line is the square average cost of the spare part transportation line.
Further, according to the candidate vehicle type information table of the candidate lines, obtaining information such as a transportation vehicle type, a transportation distance and the like of each candidate line, wherein the transportation vehicle type information comprises vehicle type full load capacity of different types of transportation vehicles of the candidate line, and the transportation distance information is the transportation length of the candidate line; 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 can comprise a self-defined function, different cost parameters are selected according to cost accounting key points which need to be focused, and different parameter factors are given to construct the cost function.
Step S308, constructing a route planning model.
Illustratively, the route planning model is constructed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
r ij i is E ODB, the mapping relation between transport shift B and a route OD set, the route OD set is all possible routes of the route OD, the transport shift B is i, and the possible routes of the route OD are j;
n ij the number of the sorting centers through which the route passes, wherein the sorting centers comprise a transportation starting point, a transportation node and a transportation terminal point;
C ij a single quantity of routes;
c, the single-average operation cost can be preset in advance according to the requirement and input by a user;
lz m the number of vehicles in the mth whole vehicle transportation line;
tzc m the single-pass transportation cost of the m-th whole transportation line;
V j the unit of the amount of goods routed is m 3 ；
z n ＝∑ j V j *r ij N epsilon line_id, the amount of goods routed using the nth part transport line in m 3 ；
tlc n Square average cost of nth piece of transportation line in yuan/m 3 。
The above equation represents that the minimum of the total cost is calculated. The total cost can comprise operation cost, transportation cost of whole-vehicle transportation and transportation cost of part-vehicle transportation, wherein the operation cost is the product of the number of sorting centers through which the route passes, single-average operation cost and single quantity; the transportation cost of the whole vehicle transportation is the product of the number of vehicles in the whole vehicle transportation line and the single-pass transportation cost of the whole vehicle transportation line; the transportation cost of the part transportation is the product of the amount of the goods routed by the part transportation route and the party cost of using the part transportation route.
Illustratively, the result constraint 1 is as follows:
the above formula indicates that, for the mapping relationship between transport class B and the set of route ODs, one travel j of route OD under transport class i is selected from among them.
Illustratively, the cargo quantity constraint 2 is as follows:
z m ≤lz m *cap m ，m∈linez_id
z n ≤ll n *cap n ，n∈linel_id
wherein, the liquid crystal display device comprises a liquid crystal display device,
z m ＝∑ j V j *r ij m is epsilon linez_id, and the unit of the goods quantity of the route using the mth whole vehicle transportation line is m 3 ；
cap m Full load of the vehicle of the mth whole vehicle transportation line;
ll n the number of vehicles in the nth piece of the part transport line;
cap n full load of vehicles on the nth piece of the part transport route.
The above indicates that the cargo capacity of each whole vehicle transportation line is smaller than the full cargo capacity of each whole vehicle transportation line, and the cargo capacity of each spare part transportation line is smaller than the full cargo capacity of each spare part transportation line.
Illustratively, the aging constraint 3 is as follows:
r ij *tnt ij ≤sti，i∈ODB
wherein, the liquid crystal display device comprises a liquid crystal display device,
tnt ij routing aging;
st i static 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 flow direction data table.
By way of example, the route planning model obtained above, 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 the step S304 and the candidate vehicle type information table obtained in the step S306, the information of the target order is input into the model for route planning according to the route planning model, so that the optimal route of the optimized target order can be obtained, the total cost is optimized under the condition of ensuring aging, and the most suitable transportation scheme of each route in the route can be obtained, including the transportation vehicle type, the transportation mode (whole vehicle transportation or spare part transportation), the cargo quantity, the transportation shift and the like.
In the embodiment of the invention, the basic data is used for cleaning; processing line data; generating route data; constructing a candidate route information table; adjusting the model of the line vehicle; constructing a candidate vehicle type information table; constructing a transportation cost function; and constructing a route planning model and the like, and optimizing the existing logistics network planning while considering the aging requirement, so that customer experience can be met, and the total cost of logistics can be reduced.
Fig. 4 is a schematic diagram of main modules of a logistic network optimization system according to an embodiment of the present invention, and as shown in fig. 4, a logistic network optimization device 400 of the present invention includes:
the data processing module 401 adds a reverse transportation route for a transportation route of a single open flow direction in the existing transportation route, and constructs a candidate route based on the existing transportation route and the added reverse transportation route; constructing a candidate route information table of the target waybill based on the candidate line; and the data processing module constructs a candidate vehicle type information table of the candidate line.
For example, according to previous investigation, the logistics cost of the bill can be reduced by opening the single-flow-direction transportation line into the split-flow-direction transportation line. Therefore, the data processing module 401 searches for a transportation line with a single flow direction according to the existing transportation line data searched from the flow direction data table, adds a reverse transportation line for the transportation line with the single flow direction, and can open up the transportation line into a transportation line with a double flow direction. Constructing a candidate line according to the existing transportation line and the added reverse transportation line, and constructing a mapping relation between a candidate route and the candidate line according to the candidate line to obtain a candidate route information table of a target waybill, wherein the method can comprise the following steps: each route consists 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 amount of cargo of the reverse transport route is greater than a predetermined proportion of the amount of cargo of the single open flow direction transport route.
Further, the single quantity of each route can be added into the mapping relation to construct the mapping relation of the route, the transportation line and the single quantity for processing the subsequent operation cost.
Illustratively, according to previous investigation, 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 model of each candidate route according to the candidate route information table of the target waybill, compares the existing transportation model with all the existing transportation models, and adds the transportation model which is missing compared with all the transportation models for the candidate routes which are not configured with all the transportation models according to the comparison result. After supplementing the carrier vehicle type lacking in the candidate route, constructing a candidate vehicle type information table of the candidate route may include: each line can have several transportation vehicle types, and each transportation vehicle type comprises several transportation vehicles, cargo carrying capacity and the like.
Further, after the transportation vehicle type of the candidate route is supplemented, the walking method of the original candidate route is unchanged, and possible transportation schemes are increased.
The model building module 402 builds a routing model that uses the minimum total cost of the target waybill as an objective function and constrains the routing age.
Illustratively, the model building module 402 builds the route planning model with the overall cost of the target manifest as an objective function, the overall cost including the cost of operation, the cost of transportation for the entire vehicle transportation, and the cost of transportation for the part transportation. The route planning model includes three constraints: result constraints, cargo quantity constraints, and aging constraints. The result constraint condition requires that only the optimal route is selected, the cargo quantity constraint condition cannot be overloaded, and the aging constraint condition cannot exceed the preset standard aging.
And the optimizing module 403 performs 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.
Based on the candidate route information table and the candidate vehicle type information table obtained by the data processing module 401, a pre-built route planning model is adopted, the information input model of the target order is used for optimizing the route of the target freight bill, and the optimized optimal route of the target order can be obtained.
In the embodiment of the invention, a data processing module is used for adding a reverse transportation line for a transportation line with a single flow direction in the existing transportation line, 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 the target waybill based on the candidate line; the data processing module constructs 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 waybill as a target function and constrains route timeliness; 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 and other modules, so that the existing logistics network planning can be optimized while the aging requirement is considered, customer experience can be met, and the total logistics cost can be reduced.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which 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 required for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through 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 section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; 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 drive 510 is also connected to the I/O interface 505 as needed. 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 needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 501.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this document, 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, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 flowcharts 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 involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a data processing module, a model building module, and a computing module. The names of these modules do not constitute a limitation on the module itself in some cases, 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 present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: adding a reverse transportation line for 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; constructing a candidate route information table of the target waybill based on the candidate line; constructing a candidate vehicle type information table of the candidate line; based on the candidate route information table and the candidate vehicle type information table of the candidate line, carrying out route planning on a target waybill by adopting a pre-constructed route planning model so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and restrains route timeliness.
According to the technical scheme provided by the embodiment of the invention, the conventional logistics network planning can be optimized while considering the aging requirement, so that the customer experience can be met, and the technical effect of reducing the total cost of logistics can be achieved.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The logistics network optimization method is characterized by comprising the following steps of:
adding a reverse transportation route for a single-open-flow transportation route in an existing transportation route, constructing a candidate route based on the existing transportation route and the added reverse transportation route, comprising: for the existing single-opening-flow-direction transportation line, adding a reverse transportation line for the single-opening-flow-direction transportation line when the cargo quantity in the reverse flow direction is larger than or equal to the preset proportion of the cargo quantity in the forward flow direction; constructing a candidate route information table of the target waybill based on the candidate line, comprising: for a target waybill with a transportation starting point and a transportation destination in the same area, constructing a first candidate route of all nodes among the serial transportation starting point, the transportation node and the transportation destination according to the transportation starting point and the transportation destination of the target waybill; expanding the first candidate route according to the departure time of the transportation 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 regional candidate route of the target waybill; for a target waybill with a transportation starting point and a transportation terminal point in different areas, 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 between the first part, the trunk line and the second part in series; expanding the first candidate route according to the departure time of the trunk line to obtain a cross-region candidate route of the target waybill;
Constructing a candidate vehicle type information table of the candidate line;
based on the candidate route information table and the candidate vehicle type information table of the candidate line, carrying out route planning on a target waybill by adopting a pre-constructed route planning model so as to obtain an optimal route; the route planning model takes the minimum total cost of the target waybill as an objective function, and restrains route timeliness.
2. The method of claim 1, wherein the method further comprises:
before a pre-constructed route planning model is adopted to carry out route planning on a target waybill, constructing a transportation cost function of the candidate vehicle type based on the candidate route.
3. The method of claim 1, wherein the candidate vehicle type information table comprises: mapping relation between candidate line information and candidate vehicle type information; wherein the candidate line information includes: the system comprises a first identification of a candidate line and a second identification of the candidate line, wherein the first identification of the candidate line is obtained by numbering each candidate line, and the second identification of the candidate line is obtained by numbering each transportation vehicle type of each candidate line.
4. The method of claim 1, wherein the total cost of the target waybill comprises: the operation cost, the transportation cost of whole car transportation and the transportation cost of part transportation.
5. The method of claim 1, wherein the routing model constrains routing aging such that routing aging for each route must be less than standard static routing aging, and the routing model further comprises the constraint of: constraint is carried out on the result, so that the result only outputs the optimal solution of each route; the cargo quantity is constrained so that all transport vehicles cannot be overloaded.
6. A logistic network optimization device, comprising:
the data processing module adds reverse transportation route for the transportation route with single open flow direction in the existing transportation route, constructs candidate route based on the existing transportation route and the added reverse transportation route, and comprises: for the existing single-opening-flow-direction transportation line, adding a reverse transportation line for the single-opening-flow-direction transportation line when the cargo quantity in the reverse flow direction is larger than or equal to the preset proportion of the cargo quantity in the forward flow direction; constructing a candidate route information table of the target waybill based on the candidate line, comprising: for a target waybill with a transportation starting point and a transportation destination in the same area, constructing a first candidate route of all nodes among the serial transportation starting point, the transportation node and the transportation destination according to the transportation starting point and the transportation destination of the target waybill; expanding the first candidate route according to the departure time of the transportation 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 regional candidate route of the target waybill; for a target waybill with a transportation starting point and a transportation terminal point in different areas, 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 between the first part, the trunk line and the second part in series; expanding the first candidate route according to the departure time of the trunk line to obtain a cross-region candidate route of the target waybill;
The data processing module constructs 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 waybill as a target function and constrains 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.
7. A logistic network optimization electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
8. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1494697A (en) * | 2000-02-29 | 2004-05-05 | ��ϰ�� | Delivery system and method for vehicles and the like |
-
2020
- 2020-11-17 CN CN202011290378.1A patent/CN113762573B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
|---|
| Bi-level programming based layout optimization of water-land intermodal transport hub;Wu, Zhihua;《APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY》;全文 * |
| 物流运输快速配送路径规划仿真;王力锋;刘双双;刘抗英;;计算机仿真(08);全文 * |
| 考虑运输时间和改编费用的铁路路网车流分配优化研究;毕明凯;《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》;全文 * |
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