CN117151321A - Logistics transportation management method based on optimization algorithm - Google Patents
Logistics transportation management method based on optimization algorithm Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 26
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- 206010039203 Road traffic accident Diseases 0.000 claims abstract description 59
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- 239000003016 pheromone Substances 0.000 claims abstract description 27
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- 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
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The application relates to the technical field of data processing, in particular to a logistics transportation management method based on an optimization algorithm, which comprises the following steps: determining a plurality of transportation terminals and acquiring path data between each transportation terminal; calculating a vehicle depreciation index, a pollutant emission ratio index, and a traffic accident occurrence probability based on the path data between each transportation terminal; calculating a heuristic factor between each transportation terminal based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability; calculating the transportation probability of each vehicle between each transportation terminal based on heuristic factors between each transportation terminal and updating pheromones in an ant colony algorithm; an optimal logistics transportation path is determined based on the transportation probability of each vehicle between the respective transportation terminals, and the optimal logistics transportation path is sent to the driver of each vehicle. The method can obtain the optimal transportation path of logistics transportation by constructing the heuristic factor improved ant colony algorithm which is more in line with the characteristics of logistics transportation.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a logistics transportation management method based on an optimization algorithm.
Background
The logistics transportation management refers to the management of the whole processes of planning, organizing, coordinating, executing, monitoring and the like of logistics transportation links in the logistics management of a supply chain, and aims to improve the operation efficiency of a logistics supply chain. The difficulty of logistics transportation management is mainly that the difficulty of route selection and planning is high, the safety risk in the transportation process is high, and various factors are required to be considered in logistics transportation management to determine the optimal transportation route and plan.
The traditional path planning algorithm is based on the shortest path algorithm, has high calculation speed, and is not suitable for large-scale complex path planning in logistics transportation management. For example, the algorithm a is relatively fast, and can be applied to a large-scale network and a complex path planning problem, but when no feasible solution is found, the time complexity and the space complexity are high, and a local optimal solution may be trapped; the ant colony algorithm has strong global searching capability, and parallel solving is realized through parallel processing, so that the running efficiency is improved, but different heuristic factors are set in different scenes, the result can be influenced to a certain extent, and the global optimal solution cannot be ensured.
Disclosure of Invention
In view of the above problems, the present application provides a logistics transportation management method based on an optimization algorithm, which can obtain an optimal transportation path of logistics transportation by constructing a heuristic factor which is more in line with characteristics of logistics transportation to improve an ant colony algorithm.
The embodiment of the application provides a logistics transportation management method based on an optimization algorithm, which comprises the following steps:
determining a plurality of transportation terminals and acquiring path data between each transportation terminal;
calculating a vehicle depreciation index, a pollutant emission ratio index and a traffic accident probability between each transportation terminal based on the path data between each transportation terminal;
calculating a heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident probability between each of the transportation terminals;
calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and updating pheromones in an ant colony algorithm;
and determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, and sending the optimal logistics transportation path to a driver of each vehicle.
In one possible implementation, the determining path data between a plurality of transport terminals and each of the transport terminals includes:
and acquiring logistics order information from a logistics dispatching center, determining a plurality of transportation terminals according to the logistics order information, and acquiring path data between the transportation terminals.
In one possible implementation, the path data includes weather information, temperature, humidity, grade, traffic light quantity, and traffic flow data.
In one possible implementation manner, before calculating the vehicle depreciation index, the pollutant emission ratio index and the traffic accident probability between each transportation terminal based on the path data between each transportation terminal, the method further includes:
initializing the number of vehicles and pheromones between each of the transportation terminals.
In one possible implementation manner, the calculating a vehicle depreciation index, a pollutant emission ratio index, and a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating a vehicle depreciation index between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the vehicle depreciation index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>Representing from->Transport terminal to->Average temperature on the path of the individual transport terminals, +.>Indicating the optimal temperature when the vehicle is driving, +.>Representing from->Transport terminal to->Average humidity on the individual transport terminal paths, +.>Indicating the optimal humidity during driving of the vehicle, +.>Indicating humidity coordination coefficient, ">Representing from->Transport terminal to->Path length of individual transport terminals.
In one possible implementation manner, the calculating a vehicle depreciation index, a pollutant emission ratio index, and a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating a pollutant emission ratio index between each transportation terminal based on path data between each transportation terminal, wherein a calculation formula of the pollutant emission ratio index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Pollutant emission ratio index for individual transport terminals, < >>Representing traffic light waiting coefficient, < >>Indicating that the vehicle is from the%>Transport terminal to->Number of traffic lights encountered on the route of the individual transport terminals, < >>Represents the vehicle start pollutant emission rate, +.>Representing ideal traffic light waiting coefficient, +.>Indicating that the vehicle is from the%>Transport terminal to->The number of sections of the up-slope, down-slope and level road in the path of the individual transport terminals, +.>Indicating that the vehicle is from the%>Transport terminal to->In the route of the individual transport terminals +.>Length of individual road sections->Is indicated at +.>Gradient pollutant discharge rate during driving on individual road sections, < ->Indicate->Dynamic friction factor of individual road sections->Indicate->Included angle between each road section and the horizontal plane,>indicating the amount of pollutant emissions generated by the vehicle during normal travel on a flat road.
In one possible implementation manner, the calculating a vehicle depreciation index, a pollutant emission ratio index, and a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating the possibility of traffic accidents between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the possibility of traffic accidents is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->The possibility of traffic accidents at the individual transportation terminals,indicating that the vehicle is from the%>Transport terminal to->The number of sections traversed on the route of the individual transport terminals, < > and/or->Indicating that the vehicle is from the%>Transport terminal to->The first part of the transport terminal path>Traffic flow of individual road sections->Indicating traffic flow under normal conditions, +.>The influence degree of the weather with different ends on the traffic accident is shown.
In one possible implementation manner, the calculation formula for calculating the heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident probability between each of the transportation terminals is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Heuristic of individual transport terminals, +.>Represented asFirst, theThe individual vehicle is from the%>Transport terminal to->Traffic barrier index of individual transport terminals +.>Weight indicating the influence of vehicle depreciation on traffic barrier index, +.>Weight indicating the influence of pollutant emission ratio on the traffic barrier index, +.>Weight indicating the influence of the probability of a traffic accident on the traffic impediment index, < ->Indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>Indicating that the vehicle is from the%>Transport terminal to->The pollutant emission ratio index of each transportation terminal,indicating that the vehicle is from the%>Transport terminal to->And the possibility of traffic accidents of the individual transportation terminals.
In one possible implementation manner, the calculation formula for calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and updating pheromones in an ant colony algorithm is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Transport probability of individual transport terminals,/->Indicate->Transport terminal to->Pheromone contained on the route of the individual transport terminals, < >>Representing the impact weight of the pheromone on the search results,for the heuristic factor, < >>Representing the influence weight of the heuristic factors on the search results,/>Indicate->A collection of transport terminals to which the vehicle can travel next.
In one possible implementation manner, the determining an optimal logistics transportation path based on the transportation probability of each vehicle between the respective transportation terminals, and sending the optimal logistics transportation path to the driver of each vehicle includes:
and determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, uploading the optimal logistics transportation path to a logistics dispatching center, and sending the optimal logistics transportation path to a driver of each vehicle by the logistics dispatching center.
The application has the beneficial effects that: constructing a vehicle depreciation index according to the environmental temperature and the environmental humidity between each transport terminal, and reflecting the depreciation degree of the vehicle in the running process of the transport vehicle; constructing a pollutant emission ratio index according to the gradient and the traffic light number between each transportation terminal, and reflecting the pollutant emission ratio generated by the transportation vehicle when the transportation vehicle runs between each transportation terminal and the transportation vehicle runs normally; constructing traffic accident possibility according to weather conditions and traffic flow between each transport terminal, and reflecting the possibility of accident of the transport vehicle on each transport terminal path; and constructing a traffic obstruction index between each transportation terminal based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability, so that obstruction encountered by the transportation vehicle when driving between each transportation terminal can be more accurately described, further, a heuristic factor which is more in line with the characteristics of logistics transportation is constructed, and an ant colony algorithm is used for solving an optimal transportation path to obtain the optimal transportation path of logistics transportation.
Drawings
Fig. 1 is a step flowchart of a logistics transportation management method based on an optimization algorithm according to an embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings, and some, but not all of which are illustrated in the appended drawings. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the application, fall within the scope of protection of the application.
The terminology used in the description of the embodiments of the application herein is for the purpose of describing particular embodiments of the application only and is not intended to be limiting of the application.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
Embodiments of the present application are described below with reference to the accompanying drawings. As one of ordinary skill in the art can know, with the development of technology and the appearance of new scenes, the technical scheme provided by the embodiment of the application is also applicable to similar technical problems.
Referring to fig. 1, the embodiment of the application discloses a logistics transportation management method based on an optimization algorithm, which comprises the following steps:
step S11, determining a plurality of transportation terminals and acquiring path data between the transportation terminals;
step S12, calculating a vehicle depreciation index, a pollutant emission ratio index and a traffic accident probability between each transportation terminal based on the path data between each transportation terminal;
step S13 of calculating a heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index, and the traffic accident occurrence probability between each of the transportation terminals;
step S14, calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and updating pheromones in an ant colony algorithm;
and step S15, determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, and sending the optimal logistics transportation path to a driver of each vehicle.
In the above-described embodiment steps, first, a plurality of transportation terminals (i.e., destinations to which the respective transportation vehicles are to deliver packages) are determined, and route data between each of the transportation terminals is acquired through various data interfaces (step S11). The principle of the ant colony algorithm for finding the optimal path is that ants can leave pheromones when passing through one path, and the pheromones left in a shorter path are larger than those left in a longer path because the pheromones volatilize along time, and the ants behind can select the path to advance according to the pheromones left in each path and the value estimation of the target place. The steps of the embodiment construct heuristic factors based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability in the logistics transportation path, so as to obtain the optimal distribution path. The method comprises the following steps: calculating a vehicle depreciation index, a pollutant emission ratio index and a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals (step S12), wherein the vehicle depreciation index reflects the depreciation degree of the vehicle during the traveling of the transportation vehicle, the pollutant emission ratio index reflects the pollutant emission ratio generated when the transportation vehicle travels between each of the transportation terminals compared with the normal traveling, and the traffic accident probability reflects the probability of the vehicle accident on the path of each of the transportation terminals; and then calculating a heuristic factor between each transportation terminal based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability between each transportation terminal (step S13), and constructing the heuristic factor which is more in line with the logistics transportation characteristics by describing main indexes of main difficulties in the driving process of the transportation vehicle. Next, calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and the updating of the pheromone in the ant colony algorithm (step S14), and calculating the transportation probability by using the heuristic factors which are more in line with the logistics transportation characteristics as the heuristic factors in the ant colony algorithm and simultaneously combining the updating of the pheromone in the ant colony algorithm; and finally, determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, and sending the optimal logistics transportation path to a driver of each vehicle (step S15) to complete the optimal transportation path management of logistics transportation.
In an alternative embodiment of the present application, the determining path data between a plurality of transportation terminals and each of the transportation terminals includes:
and acquiring logistics order information from a logistics dispatching center, determining a plurality of transportation terminals according to the logistics order information, and acquiring path data between the transportation terminals.
In the steps of the above embodiment, the destination (i.e., a plurality of transportation terminals) where each transportation vehicle will send the package is determined by the logistics order information acquired from the logistics scheduling center, the path data between each transportation terminal may be acquired through various data interfaces, such as acquiring weather information, temperature, humidity, etc. between each transportation terminal on the same day according to a weather API interface, acquiring gradient, traffic light number, etc. between each transportation terminal according to a map API interface, acquiring traffic flow data between each transportation terminal through a local traffic management department website, etc.
In an alternative embodiment of the application, the path data includes weather information, temperature, humidity, grade, traffic light number, and traffic flow data.
In the above embodiment, the path data includes weather information (e.g., heavy fog, heavy rain, ice and snow, strong wind, etc.), temperature, humidity, gradient, traffic light number, and traffic flow data (e.g., number of passing vehicles, etc.) of each transportation terminal.
In an optional embodiment of the present application, before calculating the vehicle depreciation index, the pollutant emission ratio index, and the traffic accident possibility between each of the transportation terminals based on the path data between each of the transportation terminals, the method further includes:
initializing the number of vehicles and pheromones between each of the transportation terminals.
In the above embodiment steps, initializing the number of vehicles and the pheromone between each of the transportation terminals; at the initial time, each vehicle for carrying out logistics transportation is located in a logistics dispatching center, and the pheromone content between each transportation terminal path is set to be the same, and is allThe empirical value 20 is usually taken, and may be appropriately adjusted, and the total number of the transport vehicles is denoted as K and the total number of the transport terminals is denoted as N.
In an optional embodiment of the present application, the calculating a vehicle depreciation index, a pollutant emission ratio index, a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating a vehicle depreciation index between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the vehicle depreciation index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>Representing from->Transport terminal to->Average temperature on the path of the individual transport terminals, +.>Indicating the optimal temperature when the vehicle is driving, +.>Representing from->Transport terminal to->Average humidity on the individual transport terminal paths, +.>Indicating the optimal humidity during driving of the vehicle, +.>Indicating humidity coordination coefficient, ">Representing from->Transport terminal to->Path length of individual transport terminals.
The depreciation of the vehicle is affected by various factors, wherein the main influencing factors are the use environment of the vehicle, namely the temperature, the humidity and the like on a path in the running process, the vehicle is in a high-temperature and high-humidity environment, rubber parts, wires and the like of the vehicle are easy to age and damage, and the depreciation speed of the vehicle can be rapidly increased. In the above embodiment step, the vehicle depreciation index between each of the transportation terminals is calculated based on the path data between each of the transportation terminals. The optimal temperature during running of the vehicleThe checked value can be taken to be 22.5 ℃; optimal humidity during driving of the vehicle->The checked value can be taken to be 50%; the humidity coordination coefficient is used for increasing the humidity level to the same level as the temperature, and can take a checked value of 50; the above-mentioned empirical values may be appropriately adjusted according to actual conditions, and are not particularly limited herein.
In an optional embodiment of the present application, the calculating a vehicle depreciation index, a pollutant emission ratio index, a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating a pollutant emission ratio index between each transportation terminal based on path data between each transportation terminal, wherein a calculation formula of the pollutant emission ratio index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Pollutant emission ratio index for individual transport terminals, < >>Representing traffic light waiting coefficient, < >>Indicating that the vehicle is from the%>Transport terminal to->Number of traffic lights encountered on the route of the individual transport terminals, < >>Represents the vehicle start pollutant emission rate, +.>Representing ideal traffic light waiting coefficient, +.>Indicating that the vehicle is from the%>Transport terminal to->The number of sections of the up-slope, down-slope and level road in the path of the individual transport terminals, +.>Indicating that the vehicle is from the%>Transport terminal to->In the route of the individual transport terminals +.>Length of individual road sections->Is indicated at +.>Gradient pollutant discharge rate during driving on individual road sections, < ->Indicate->Dynamic friction factor of individual road sections->Indicate->Included angle between each road section and the horizontal plane,>indicating the amount of pollutant emissions generated by the vehicle during normal travel on a flat road.
Similarly, the fuel consumption of the vehicle at the moment of starting is obviously larger than that of the vehicle during normal running, and more pollutants such as greenhouse gases are generatedNitrifying substance->And the number of traffic lights, uphill road sections and the like encountered by the vehicle in the driving path can have great influence on the emission of pollutants. In the above embodiment step, the pollutant emission ratio index between each of the transportation terminals is calculated based on the path data between each of the transportation terminals.
It should be noted that, traffic light waiting coefficientThe possibility that the traffic light encountered by the vehicle on the driving path is waited for is represented, and the checked value can be 0.8; vehicle start pollutant emission rate->The test value can be taken to be 2.5, which represents the multiple of the pollutant emission amount during the starting moment of the vehicle relative to the normal running moment; ideally, the vehicles run on green lights, and no red lights such as parking exist at the moment, so the vehicles do not existFuel consumption and pollutant emission increase during starting, so ideal traffic light waiting coefficient +.>The checked value 0 can be taken; first->The gradient pollutant discharge rate of each road section is the multiple of the pollutant discharge rate of the corresponding road section relative to the pollutant discharge rate generated during normal running of the flat road, and the corresponding road section is +.>Greater than 1 +.>Less than 1, when the road is level->Equal to 1; the above-mentioned individual empirical values may be appropriately adjusted according to actual conditions, and are not particularly limited herein.
In an optional embodiment of the present application, the calculating a vehicle depreciation index, a pollutant emission ratio index, a traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals includes:
calculating the possibility of traffic accidents between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the possibility of traffic accidents is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->The possibility of traffic accidents at the individual transportation terminals,indicating that the vehicle is from the%>Transport terminal to->The number of sections traversed on the route of the individual transport terminals, < > and/or->Indicating that the vehicle is from the%>Transport terminal to->The first part of the transport terminal path>Traffic flow of individual road sections->Indicating traffic flow under normal conditions, +.>The influence degree of the weather with different ends on the traffic accident is shown.
Further, the distribution efficiency is affected when a traffic accident occurs in the transport vehicle, and the traffic condition of the road section where the accident vehicle travels is also affected when traffic accidents occur in other vehicles, so that the distribution efficiency of the transport vehicle is also affected to some extent. Therefore, in the above-described embodiment step, the traffic accident occurrence probability between each of the transportation terminals is calculated based on the path data between each of the transportation terminals; the accident possibility refers to the possibility of traffic accidents of transportation vehicles and other vehicles in the driving process. Traffic accidents are affected by various factors, wherein weather conditions and traffic flow are main influencing factors, when the traffic accidents are in various extreme weather conditions such as ice and snow, heavy rain, heavy fog and the like, the judgment of road conditions and drivers on surrounding conditions is affected, so that traffic accidents are generated, when the traffic flow is large, the traffic accidents are easy to generate, and when the two influencing factors occur simultaneously, the occurrence probability of the traffic accidents is exponentially increased.
The vehicle is from the firstTransport terminal to->The number of sections traversed on the route of the individual transport terminals +.>The statistical mode of the method is that the paths with the same and continuous path names are regarded as one path, the paths with the same and discontinuous path names are regarded as ase:Sub>A plurality of paths, if ase:Sub>A vehicle runs from one section of the A path to the B path, ase:Sub>A distance is travelled in the B path, and the vehicle runs to the other section of the A path, and in the process, the vehicle is regarded as passing through three sections, namely A-B-A; traffic flow under normal conditions->For example, the traffic flow at the time of the afternoon of the previous month can be used for obtaining a predicted value of the current crown block traffic flow by a moving average method, the predicted value is used as the traffic flow under the normal condition of the current day, and other conventional methods can be used for determining the traffic flow under the normal condition>It is not particularly limited herein; in additionIndicate->The possibility of traffic accidents in individual road sections.
In an optional embodiment of the present application, the calculation formula for calculating the heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident probability between each of the transportation terminals is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Heuristic of individual transport terminals, +.>Denoted as the firstThe individual vehicle is from the%>Transport terminal to->Traffic barrier index of individual transport terminals +.>Indicating depreciation of vehicleInfluence weight on traffic barrier index, +.>Weight indicating the influence of pollutant emission ratio on the traffic barrier index, +.>Weight indicating the influence of the probability of a traffic accident on the traffic impediment index, < ->Indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>Indicating that the vehicle is from the%>Transport terminal to->The pollutant emission ratio index of each transportation terminal,indicating that the vehicle is from the%>Transport terminal to->And the possibility of traffic accidents of the individual transportation terminals.
In the conventional ant colony algorithm, the reciprocal of the path length between two transport terminals is used as a heuristic factor. In the steps of the embodiment, the traffic obstruction index is constructed based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability between the two transportation terminals, and the traffic obstruction index is used as the path length between the two places in the ant colony algorithm, so that the heuristic factor is constructed.
The weight of the influence of the vehicle depreciation on the traffic barrier indexCan take the tested value of 0.32, the influence weight of pollutant emission ratio on the passing obstruction index>The experimental value of 0.29 can be taken, and the influence weight of the possibility of traffic accidents on the traffic obstruction index is +.>The checked value can be 0.39; the above-mentioned individual empirical values may be appropriately adjusted according to actual conditions, and are not particularly limited herein.
In an optional embodiment of the present application, the calculation formula for calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and updating the pheromone in the ant colony algorithm is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Transport probability of individual transport terminals,/->Indicate->Transport terminal to->Pheromone contained on the route of the individual transport terminals, < >>Representing the impact weight of the pheromone on the search results,for the heuristic factor, < >>Representing the influence weight of the heuristic factors on the search results,/>Indicate->A collection of transport terminals to which the vehicle can travel next.
In the above embodiment, the first step is obtained according to the previous stepVehicle from the%>Transport terminal to->Heuristic factors of the individual transportation terminals can be calculated by combining with updating of pheromones in an ant colony algorithm (wherein the value of the volatile coefficient of the pheromones can be an empirical value of 0.5)>Vehicle from the%>Transport terminal to->Transportation probability of individual transportation terminals.
It should be noted that, the influence weight of pheromone on search resultsThe checked value can be 0.3; influence weight of heuristic factors on search results>The checked value can be 0.7; the heuristic factors are used for guiding the vehicle to find the optimal path in the solution space to obtain a high-quality solution; since a transport terminal can only be passed once, when a transport terminal is passed once by a vehicle, the transport terminal is not at +.>In the aggregate, as each vehicle is continuously driven, +.>Fewer and fewer elements in the collection; the above-mentioned individual empirical values may be appropriately adjusted according to actual conditions, and are not particularly limited herein.
In an optional embodiment of the present application, the determining an optimal logistics transportation path based on the transportation probability of each vehicle between the respective transportation terminals, and sending the optimal logistics transportation path to the driver of each vehicle, includes:
and determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, uploading the optimal logistics transportation path to a logistics dispatching center, and sending the optimal logistics transportation path to a driver of each vehicle by the logistics dispatching center.
In the foregoing embodiment, the optimal logistics transportation path is determined based on the transportation probability of each vehicle between the transportation terminals (for example, a path with the largest transportation probability of each transportation terminal is selected as the optimal logistics transportation path), the optimal logistics transportation path is uploaded to a logistics dispatching center, the logistics dispatching center sends the optimal logistics transportation path to the driver of each vehicle, and the sending of the optimal logistics transportation path can be performed by a wired communication manner such as a data line or a wireless communication manner such as bluetooth, 2G, 3G, 4G, 5G or WIFI, which is not specifically limited herein; the driver of the vehicle can receive the data information of the optimal logistics transportation path through intelligent terminals such as intelligent mobile phones, intelligent wearable intelligent instrument panels of the vehicle and the like, and the data information is not particularly limited; and finally, the optimal transportation path management of logistics transportation is finished.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above describes in detail a method for managing logistics transportation based on an optimization algorithm, and specific examples are applied to illustrate the principle and implementation of the present application, and the above examples are only used to help understand the method and core idea of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the idea of the present application, the present disclosure should not be construed as limiting the present application in summary.
Claims (10)
1. The logistics transportation management method based on the optimization algorithm is characterized by comprising the following steps of:
determining a plurality of transportation terminals and acquiring path data between each transportation terminal;
calculating a vehicle depreciation index, a pollutant emission ratio index and a traffic accident probability between each transportation terminal based on the path data between each transportation terminal;
calculating a heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident probability between each of the transportation terminals;
calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and updating pheromones in an ant colony algorithm;
and determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, and sending the optimal logistics transportation path to a driver of each vehicle.
2. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the determining path data between a plurality of transportation terminals and each of the transportation terminals comprises:
and acquiring logistics order information from a logistics dispatching center, determining a plurality of transportation terminals according to the logistics order information, and acquiring path data between the transportation terminals.
3. The method of claim 2, wherein the path data includes weather information, temperature, humidity, grade, traffic light number, and traffic flow data.
4. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein before calculating the vehicle depreciation index, the pollutant emission ratio index, the traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals, further comprises:
initializing the number of vehicles and pheromones between each of the transportation terminals.
5. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the calculating the vehicle depreciation index, the pollutant emission ratio index, the traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals comprises:
calculating a vehicle depreciation index between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the vehicle depreciation index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>The representation is from the firstTransport terminal to->Average temperature on the path of the individual transport terminals, +.>Indicating the optimal temperature when the vehicle is driving, +.>Representing from->Transport terminal to->Average humidity on the individual transport terminal paths, +.>Indicating the optimal humidity during driving of the vehicle, +.>Indicating humidity coordination coefficient, ">Representing from->Transport terminal to->Path length of individual transport terminals.
6. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the calculating the vehicle depreciation index, the pollutant emission ratio index, the traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals comprises:
calculating a pollutant emission ratio index between each transportation terminal based on path data between each transportation terminal, wherein a calculation formula of the pollutant emission ratio index is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Pollutant emission ratio index for individual transport terminals, < >>Representing traffic light waiting coefficient, < >>Indicating that the vehicle is from the%>Transport terminal to->Number of traffic lights encountered on the route of the individual transport terminals, < >>Represents the vehicle start pollutant emission rate, +.>Representing ideal traffic light waiting coefficient, +.>Indicating that the vehicle is from the%>Transport terminal to->The number of sections of the up-slope, down-slope and level road in the path of the individual transport terminals, +.>Indicating that the vehicle is from the%>Transport terminal to->In the route of the individual transport terminals +.>Length of individual road sections->Is indicated at +.>Gradient pollutant discharge rate during driving on individual road sections, < ->Indicate->Dynamic friction factor of individual road sections->Indicate->Included angle between each road section and the horizontal plane,>indicating the amount of pollutant emissions generated by the vehicle during normal travel on a flat road.
7. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the calculating the vehicle depreciation index, the pollutant emission ratio index, the traffic accident probability between each of the transportation terminals based on the path data between each of the transportation terminals comprises:
calculating the possibility of traffic accidents between each transportation terminal based on the path data between each transportation terminal, wherein the calculation formula of the possibility of traffic accidents is as follows:
wherein,indicating that the vehicle is from the%>Transport terminal to->Possibility of traffic accident occurrence of individual transportation terminals, < ->Indicating that the vehicle is from the%>Transport terminal to->The number of sections traversed on the route of the individual transport terminals, < > and/or->Indicating that the vehicle is from the%>Transport terminal to->The first part of the transport terminal path>Traffic flow of individual road sections->Indicating traffic flow under normal conditions, +.>The influence degree of the weather with different ends on the traffic accident is shown.
8. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the calculation formula for calculating the heuristic factor between each of the transportation terminals based on the vehicle depreciation index, the pollutant emission ratio index and the traffic accident occurrence probability between each of the transportation terminals is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Heuristic of individual transport terminals, +.>Denoted as +.>The individual vehicle is from the%>Transport terminal to->Traffic barrier index of individual transport terminals +.>Weight indicating the influence of vehicle depreciation on traffic barrier index, +.>Weight indicating the influence of pollutant emission ratio on the traffic barrier index, +.>Weight indicating the influence of the probability of a traffic accident on the traffic impediment index, < ->Indicating that the vehicle is from the%>Transport terminal to->Vehicle depreciation index of individual transport terminals, < +.>Indicating that the vehicle is from the%>Transport terminal to->Pollutant emission ratio index for individual transport terminals, < >>Indicating that the vehicle is from the%>Transport terminal to->And the possibility of traffic accidents of the individual transportation terminals.
9. The method for logistics transportation management based on the optimization algorithm according to claim 1, wherein the calculation formula for calculating the transportation probability of each vehicle between each transportation terminal based on the heuristic factors between each transportation terminal and the update of pheromone in the ant colony algorithm is as follows:
wherein,indicate->Vehicle from the%>Transport terminal to->Transport probability of individual transport terminals,/->Indicate->Transport terminal to->Pheromone contained on the route of the individual transport terminals, < >>Representing the influence weight of pheromone on search results, < ->For the heuristic factor, < >>Representing the influence weight of the heuristic factors on the search results,/>Indicate->A collection of transport terminals to which the vehicle can travel next.
10. The method for logistics transportation management based on the optimization algorithm of claim 1, wherein the determining an optimal logistics transportation path based on the transportation probability of each vehicle between the respective transportation terminals, and transmitting the optimal logistics transportation path to the driver of each vehicle, comprises:
and determining an optimal logistics transportation path based on the transportation probability of each vehicle between the transportation terminals, uploading the optimal logistics transportation path to a logistics dispatching center, and sending the optimal logistics transportation path to a driver of each vehicle by the logistics dispatching center.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040143560A1 (en) * | 2003-01-20 | 2004-07-22 | Chun Bao Zhu | Path searching system using multiple groups of cooperating agents and method thereof |
| WO2015154831A1 (en) * | 2014-04-07 | 2015-10-15 | Nec Europe Ltd. | Dynamic fleet routing |
| CN110119150A (en) * | 2019-05-22 | 2019-08-13 | 电子科技大学 | A kind of multi-user's real-time route method and system for planning based on ant group algorithm |
| CN116227773A (en) * | 2023-03-14 | 2023-06-06 | 广东电网有限责任公司广州供电局 | Distribution path optimization method based on ant colony algorithm |
-
2023
- 2023-10-31 CN CN202311424489.0A patent/CN117151321A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040143560A1 (en) * | 2003-01-20 | 2004-07-22 | Chun Bao Zhu | Path searching system using multiple groups of cooperating agents and method thereof |
| WO2015154831A1 (en) * | 2014-04-07 | 2015-10-15 | Nec Europe Ltd. | Dynamic fleet routing |
| CN110119150A (en) * | 2019-05-22 | 2019-08-13 | 电子科技大学 | A kind of multi-user's real-time route method and system for planning based on ant group algorithm |
| CN116227773A (en) * | 2023-03-14 | 2023-06-06 | 广东电网有限责任公司广州供电局 | Distribution path optimization method based on ant colony algorithm |
Non-Patent Citations (4)
| Title |
|---|
| 于雷;王东;: "模拟并行蚁群算法的绿色物流最小油耗VRPSDP", 计算机***应用, no. 07, pages 127 - 133 * |
| 张叶茂;杨晓武;: "基于改进蚁群算法的动态路径规划算法研究", 西部交通科技, no. 03, pages 89 - 93 * |
| 薛小虎;南振岐;赵文杰;: "基于改进的文化蚁群算法求解最优路径问题研究", 佳木斯大学学报(自然科学版), no. 01, pages 54 - 57 * |
| 马贵平;潘峰;: "基于改进蚁群算法的物流运输路径研究", 计算机工程与科学, no. 03, pages 523 - 528 * |
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