Simulation method and system for urban logistics operation environment

Abstract

The present invention discloses a method and system for simulating an urban logistics operation environment. The method includes three steps: basic information collection, data processing, and operation environment simulation. By collecting geographical related information, customer demand related information, vehicle related information, and freight traffic management policy related information, and processing urban road network information, customer demand information, and vehicle information, the simulation of three different urban logistics operation environments, namely, deterministic type, uncertain customer demand type, and uncertain vehicle travel time type, is realized. The present invention generates many calculation examples that consider different urban logistics operation environments by comprehensively and realistically simulating the urban logistics operation environment, and can provide rich and reliable research data for optimizing urban logistics operations and scientifically formulating freight traffic management policies.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics system simulation, and in particular relates to a simulation implementation method and system for an urban logistics operation environment. Background Art

[0002] Cities are the main places for production and life, and also the places where logistics activities are most concentrated. The two-layer logistics network is being adopted by many large and medium-sized cities at home and abroad as a feasible urban logistics solution. How to optimize the urban logistics operation of the two-layer network structure (such as minimizing logistics costs and minimizing vehicle carbon emissions) and how to evaluate the impact of the current freight traffic management policy on urban logistics operations have become urgent tasks.

[0003] A large amount of data is needed to carry out research on urban logistics operation optimization and freight traffic management policy evaluation. However, there is currently no simulation research on the urban logistics operation environment with a two-layer network structure at home and abroad. Most of the data in the existing literature on urban logistics operation optimization and freight traffic management policy evaluation do not come from real simulations of the urban logistics operation environment, making most research conclusions difficult to verify and put into practice. Summary of the invention

[0004] Purpose of the invention: In view of the current situation that there is a lack of real data in the research on urban logistics operation optimization and freight traffic management policy evaluation, one purpose of the present invention is to provide a simulation implementation method for the urban logistics operation environment.

[0005] Another object of the present invention is to provide a simulation implementation system for an urban logistics operation environment.

[0006] Technical solution: The method for simulating the urban logistics operation environment of the present invention comprises the following steps:

[0007] S1. Basic data collection;

[0008] Basic data includes geographical information, customer demand information, vehicle information, and freight transportation management policy information;

[0009] S2, data processing: processing the basic data collected in step S1;

[0010] S3, operating environment simulation: Based on the data of step S1 and step S2, simulations are performed for deterministic operating environments, uncertain customer demand environments, and uncertain vehicle travel time environments, respectively, to obtain a number of calculation examples.

[0011] Furthermore, the geographic related information in step S1 includes urban road network information, location information of logistics parks, location information of candidate distribution centers and location information of potential customers. The urban road network information is obtained through map vendors, and the location information of logistics parks, candidate distribution centers and potential customers is obtained from logistics companies; customer demand related information mainly refers to historical data on potential customers' freight demand, and customer demand related information is obtained from logistics companies; vehicle related information includes the vehicle's rated load, fuel consumption level, and the average speed of the vehicle passing through each section in the road network. The vehicle's rated load and fuel consumption level are obtained from logistics companies or automobile manufacturers, and the average speed of the vehicle passing through each section in the road network is obtained from the traffic management department; freight traffic management policy related information mainly refers to the name of the section where restricted traffic measures are imposed on freight vehicles, and freight traffic management policy related information is obtained from the traffic management department.

[0012] Further, step S2 includes the following steps:

[0013] S21, processing urban road network information, logistics park location information, candidate distribution center location information, potential customer location information, and restricted road section information;

[0014] S22. Processing of customer demand information: fitting the probability distribution of the potential customer's freight demand based on the historical data of the potential customer's freight demand, and calculating the parameter value of the probability distribution function;

[0015] S23, vehicle information processing: the rated load and fuel consumption level data of the vehicle are stored separately according to the vehicle type; according to the average speed V of the vehicle passing through the road section in the road network i and the segment length L in the segment feature attribute table i , calculate the average time t for vehicles to pass through a section in the road network i , where t i =L i / V i , i is any road section.

[0016] Furthermore, step S21 specifically includes:

[0017] Processing of urban road network information: Based on the longitude, latitude and road type of the selected urban area, a set of data files containing urban road network information is calculated, and then the urban road network map is obtained, and then the node element attribute table and the road section element attribute table are obtained;

[0018] Processing of location information of logistics parks, candidate distribution centers and potential customers: adding the location information of logistics parks, candidate distribution centers and potential customers to the node feature attribute table and marking them with different legends in the urban road network map;

[0019] Processing of restricted road section information: Retrieve the corresponding road section from the road section feature attribute table according to the name of the road section where the restricted measures are taken, hide it in the road section feature attribute table, and mark it with different legends in the urban road network map;

[0020] The node feature attribute table and the road segment feature attribute table are stored separately.

[0021] Furthermore, step S22 is specifically as follows: finding the maximum value q of freight demand from the historical data of freight demand of potential customers max and the minimum value q min , divide the potential customer freight demand into [q min ,q min +(q max -q min ) / N]、(q min ,q min +2(q max -q min ) / N],…,(q max -(q max -q min ) / N,q max ]N groups, and count the frequency of each group of freight demands respectively; generate a histogram with freight demand as the horizontal axis and frequency as the vertical axis, and judge the probability distribution of potential customers' freight demands according to the shape of the histogram, and use this probability distribution function as the probability distribution fit of potential customers' freight demands, and calculate the parameter value of the probability distribution function according to the historical data of potential customers' freight demands.

[0022] Furthermore, the simulation of the deterministic operating environment in step S3 is specifically as follows: loading the node element attribute table and the road section element attribute table, calculating the distance matrix between the logistics park and the candidate distribution center, the candidate distribution center and the potential customers, and the potential customers and the potential customers; loading the data file containing the vehicle rated load and fuel consumption level; generating the freight demand data of the potential customers based on the probability distribution function of the freight demand of the potential customers; randomly selecting some customers from the potential customers as customers who generate freight demand; by respectively loading the distance matrix between the logistics park and the candidate distribution center, the candidate distribution center and the selected customers, and the selected customers and the selected customers, the vehicle rated load and fuel consumption level data, and the freight demand data of the selected customers, the simulation of the deterministic operating environment is realized (different selected customer sets represent different calculation examples).

[0023] Further, the simulation of the operation environment with uncertain customer demand in step S3 is specifically as follows: load the node element attribute table and the road section element attribute table, and calculate the shortest paths between the logistics park and the candidate distribution centers, between the candidate distribution centers and the potential customers, and between the potential customers and the potential customers; load the data file containing the average time t i of the vehicle passing through each road section in the road network, and calculate the travel time matrix between the logistics park and the candidate distribution centers, between the candidate distribution centers and the potential customers, and between the potential customers and the potential customers; load the data file containing the rated load and fuel consumption level of the vehicle; generate the freight demand data of the potential customers based on the probability distribution function of the freight demand of the potential customers; randomly select some customers from the potential customers as the customers with known freight demand, set the time range for the generation of the freight demand of the remaining potential customers as [1, H], with minutes as the time unit, and the probability P j t that the remaining potential customer j generates freight demand at time t follows a binomial distribution, where 1 ≤ t < H, and its service time window is [e j , l j , where 1 < e j < l j ≤ H, sample the generation probability of the freight demand of all the remaining potential customers at each moment within [1, H] to obtain the random matrix of the generation of the freight demand of all the remaining potential customers within [1, H]; by respectively loading the travel time matrix between the logistics park and the candidate distribution centers, between the candidate distribution centers and the customers to be served, and between the customers to be served and the customers to be served, the data of the rated load and fuel consumption level of the vehicle, and the known and random freight demand data of the customers to be served, realize the simulation of the operation environment with uncertain customer demand (different random matrices of the generation of freight demand represent different cases), where the customers to be served include the customers with known freight demand and the customers generating random freight demand.

[0024] Further, the simulation of the operation environment with uncertain vehicle travel time in step S3 is specifically as follows: load the node element attribute table and the road section element attribute table, load the data file containing the average time t i of the vehicle passing through each road section in the road network, generate a random quantity δ i for each road section, where -αt i ≤ δ i ≤ αt i , α is a proportionality coefficient, and use T i = t i + δ iCharacterize the random travel time of each road section; load the data file containing the rated load and fuel consumption level of the vehicle; generate the freight demand data of potential customers based on the probability distribution function of the freight demand of potential customers; randomly select some customers from the potential customers as customers who generate freight demand; by loading the random travel time of each section between the logistics park and the candidate distribution center, the candidate distribution center and the selected customers, and the selected customers and the selected customers, the vehicle rated load and fuel consumption level data, and the freight demand data of the selected customers, the simulation of the operation environment with uncertain travel time is realized (different selected customer sets represent different calculation examples).

[0025] In another embodiment of the present invention, a simulation implementation system for an urban logistics operating environment includes a data acquisition module, a data processing module, and an operating environment simulation module, wherein the data acquisition module is used to collect geographical related information, customer demand related information, vehicle related information, and freight traffic management policy related information, the data processing module processes the data information collected by the data acquisition module, and the operating environment simulation module simulates the data processed by the data processing module for a deterministic operating environment, a customer demand uncertain operating environment, and a vehicle travel time uncertain operating environment to obtain different examples.

[0026] In another embodiment of the present invention, a storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of the simulation implementation method of the urban logistics operation environment are implemented.

[0027] Beneficial effects: The present invention collects information related to urban geography, customer demand, vehicle, and freight traffic management policies, and processes urban road network information, customer demand information, and vehicle information to achieve a comprehensive and realistic simulation of three types of urban logistics operation environments: deterministic, uncertain customer demand, and uncertain vehicle travel time. It generates many calculation examples that consider different urban logistics operation environments, and can provide rich and reliable research data for optimizing urban logistics operations and scientifically formulating freight traffic management policies, which will help promote cost reduction, efficiency improvement, and quality improvement of urban logistics and serve the country's "dual carbon" strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a technical roadmap for simulating the urban logistics operation environment;

[0029] Figure 2 It is an electronic map of the urban road network in the studied area;

[0030] Figure 3 is the road network map of the study area;

[0031] Figure 4It is a statistical analysis chart of historical data on freight demand from potential customers. DETAILED DESCRIPTION

[0032] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1 As shown, the simulation implementation method of the urban logistics operation environment of the present invention generates many examples considering different urban logistics operation environments by comprehensively and realistically simulating the urban logistics operation environment, providing rich and reliable research data for optimizing urban logistics operations and scientifically formulating freight traffic management policies. Specifically, it includes the following steps:

[0034] S1. Basic data collection;

[0035] Basic data includes geographic information, customer demand information, vehicle information and freight traffic management policy information. Geographic information mainly includes urban road network information, logistics park location information, candidate distribution center location information and potential customer location information; urban road network information is obtained from the open source map provider (OpenStreetMap), and the location information of logistics parks, candidate distribution centers and potential customers is obtained from logistics companies. Customer demand information mainly refers to the historical data of potential customers' freight demand, and customer demand information is obtained from logistics companies. Vehicle-related information mainly includes the vehicle's rated load, fuel consumption level, and the average speed of the vehicle passing through each section of the road network; the vehicle's rated load and fuel consumption level are obtained from automobile manufacturers, and the average speed of the vehicle passing through each section of the road network is obtained from the traffic control department. Freight traffic management policy-related information mainly refers to the name of the section where restrictions are imposed on freight vehicles (such as regional restrictions, section restrictions, etc.), and freight traffic management policy-related information is obtained from the traffic control department. Specifically, it includes the following steps:

[0036] S11. Obtain the urban road network information of the studied area from OpenStreetMap. Figure 2 The figure shows the electronic map of the urban road network in the study area.

[0037] S12. Obtain location information of logistics parks, candidate distribution centers and potential customers from logistics companies.

[0038] As shown in Table 1, this is the location information of a logistics park obtained in an embodiment of the present invention.

[0039] Table 1. Location information of logistics parks

[0040] Logistics Park Number Logistics Park Latitude Coordinates Logistics Park Longitude Coordinates 1 32.0309 118.7911

[0041] As shown in Table 2, this is the location information of 10 candidate distribution centers obtained in the embodiment of the present invention.

[0042] Table 2. Location information of candidate distribution centers

[0043] Candidate distribution center number Latitude coordinates of candidate distribution centers Longitude coordinates of candidate distribution centers 1 32.0364 118.7676 2 32.0309 118.7827 3 32.0365 118.7875 4 32.0400 118.7648 5 32.0384 118.7925 6 32.0523 118.7784 7 32.0532 118.7821 8 32.0519 118.7874 9 32.0397 118.7807 10 32.0568 118.7664

[0044] As shown in Table 3, this is the location information of 100 potential customers obtained in the embodiment of the present invention.

[0045] Table 3. Location information of potential customers

[0046]

[0047]

[0048] S13. Obtain historical data on freight demand of potential customers from logistics companies.

[0049] As shown in Table 4, this is the historical data of freight demand of 100 potential customers obtained in the embodiment of the present invention.

[0050] Table 4. Freight demand of potential customers

[0051] Customer Number Freight demand Customer Number Freight demand Customer Number Freight demand 1 12 34 26 70 31 2 26 35 15 71 31 3 19 36 15 72 39 4 3 37 7 73 16 5 9 38 31 74 1 6 17 39 21 75 38 7 27 40 34 76 28 8 38 41 22 77 34 9 8 42 3 78 12 … … … … … … 25 33 61 38 92 14 26 5 62 39 93 7 27 4 63 31 94 9 28 39 64 9 95 29 29 20 65 12 96 37 30 34 66 25 97 23 31 6 67 36 98 11 32 8 68 31 99 30 33 36 69 31 100 17

[0052] S14. Obtain the vehicle's rated load and fuel consumption level data from the vehicle manufacturer.

[0053] As shown in Table 5, the rated load and fuel consumption level data of common vehicle types (including 3.8-meter van, 6.8-meter van, 9.6-meter high-sided truck, and 13-meter semi-trailer) obtained in this embodiment.

[0054] Table 5. Rated load and fuel consumption levels of common vehicle models

[0055] Common models Rated load (tons) Fuel consumption level (liters / 100 kilometers) 3.8m van 1.5 11.5 6.8m van 9.0 19.2 9.6m high-rail truck 18.5 28.4 13m semi-trailer 32.2 33.8

[0056] S15. Obtain average vehicle speed data of vehicles passing through each road section in the road network from the traffic control department.

[0057] As shown in Table 6, this is the average vehicle speed data of vehicles passing through each road section in the road network obtained in this embodiment.

[0058] Table 6. Average speed of vehicles passing through each section of the road network

[0059] Section number Average speed (km / h) Section number Average speed (km / h) Section number Average speed (km / h) 0 32 202 35 403 52 1 59 203 42 404 49 2 58 204 51 405 49 3 38 205 30 406 50 4 49 206 49 407 36 5 54 207 54 408 49 6 54 208 42 409 37 7 40 209 40 410 39 8 36 210 31 411 43 9 51 211 39 412 32 … … … … … … 193 43 394 57 595 39 194 30 395 36 596 51 195 41 396 41 597 48 196 35 397 50 598 42 197 57 398 30 599 50 198 57 399 43 600 34 199 40 400 41 601 39 200 52 401 43 602 49 201 47 402 30 603 36

[0060] S16. Obtain information related to freight traffic management policies from the traffic management department, that is, the names of the sections in the road network where traffic restrictions are imposed.

[0061] As shown in Table 7, the names of the road sections where the traffic restriction measures are taken are obtained in this embodiment.

[0062] Table 7. Names of road sections where traffic restrictions are imposed

[0063]

[0064] 2. Data processing; specifically:

[0065] S21. Based on Python and ArcMap, process the urban road network information, logistics park location information, candidate distribution center location information, potential customer location information and restricted road section information, including:

[0066] Processing of urban road network information: Based on the Python development environment, the graph_from_bbox function in the third-party open source package OSMnx is called. The function parameter settings include inputting the longitude range (118.7633, 118.7961) and latitude range (32.0302, 32.0587), selecting "drive" as the road type, and running the Python program to obtain a Shapefile folder containing urban road network information (containing geometric shape attribute data dbf files, geometric position index shx files, vector graphics shp files, etc.); open the map making software ArcMap, and connect the Shapefile folder through the "Folder Connection" command button. The edges.shp and nodes.shp files will be automatically generated in the Shapefile folder. Drag the edges.shp and nodes.shp files into the data view window to obtain the urban road network map. Right-click to open the content list-layer-nodes and edges files in the Shapefile folder to obtain the node feature attribute table and the road section feature attribute table respectively.

[0067] Processing of location information of logistics parks, candidate distribution centers and potential customers: Based on the ArcMap visualization environment, the location information of logistics parks, candidate distribution centers and potential customers is added to the node feature attribute table and marked with different legends in the urban road network map.

[0068] Processing of restricted road section information: Based on the ArcMap visualization environment, the corresponding road section is retrieved from the road section feature attribute table according to the name of the road section where the restricted measures are taken, hidden in the road section feature attribute table, and marked with different legends in the urban road network map.

[0069] The node feature attribute table and the road segment feature attribute table are stored in csv format.

[0070] like Figure 3 As shown, it is an urban road network map obtained in an embodiment of the present invention, which includes information on the urban road network, logistics park locations, candidate distribution center locations, potential customer locations, and restricted road sections.

[0071] As shown in Table 8, it is a node element attribute table obtained in an embodiment of the present invention, which includes information on the urban road network, the location of the logistics park, the location of the candidate distribution center, and the location of the potential customer.

[0072] Table 8. Node feature attribute table

[0073] Node Number Latitude coordinates Longitude coordinates Node Number Latitude coordinates Longitude coordinates Node Number Latitude coordinates Longitude coordinates 1 32.0309 118.7911 132 32.0322 118.7860 263 32.0318 118.7681 2 32.0364 118.7676 133 32.0346 118.7869 264 32.0306 118.7672 3 32.0309 118.7827 134 32.0313 118.7897 265 32.0356 118.7668 4 32.0365 118.7875 135 32.0306 118.7924 266 32.0342 118.7675 5 32.0400 118.7648 136 32.0328 118.7941 267 32.0322 118.7773 6 32.0384 118.7925 137 32.0400 118.7958 268 32.0303 118.7766 7 32.0523 118.7784 138 32.0404 118.7938 269 32.0373 118.7656 8 32.0532 118.7821 139 32.0402 118.7866 270 32.0328 118.7753 9 32.0519 118.7874 140 32.0422 118.7875 271 32.0335 118.7691 … … … … … … … … … 123 32.0395 118.7745 254 32.0364 118.7717 383 32.0473 118.7939 124 32.0403 118.7751 255 32.0473 118.7768 384 32.0569 118.7950 125 32.0402 118.7753 256 32.0422 118.7938 385 32.0491 118.7958 126 32.0413 118.7762 257 32.0355 118.7744 386 32.0496 118.7937 127 32.0434 118.7675 258 32.0416 118.7702 387 32.0500 118.7915 128 32.0445 118.7676 259 32.0413 118.7706 388 32.0503 118.7925 129 32.0459 118.7669 260 32.0306 118.7856 389 32.0498 118.7924 130 32.0446 118.7656 261 32.0311 118.7812 390 32.0498 118.7692 131 32.0447 118.7645 262 32.0302 118.7881 391 32.0482 118.7690

[0074] As shown in Table 9, it is a road section element attribute table including restricted road section information obtained in an embodiment of the present invention.

[0075] Table 9. Road segment feature attribute table

[0076]

[0077]

[0078] S22. Processing of customer demand information: Find the maximum value 51 and the minimum value 1 of freight demand from the historical data of potential customers' freight demand, divide the freight demand of potential customers into 10 groups: [1,6], (6,11], (11,16], ..., (46,51], and count the frequency of freight demand in each group. Generate a histogram in Excel with freight demand as the horizontal axis and frequency as the vertical axis (e.g. Figure 4 As shown in the figure, based on the shape of the histogram, it is concluded that the freight demand of potential customers obeys the normal distribution, so the normal distribution function is used as the probability distribution fit for the freight demand of potential customers, and the parameters of the normal distribution function are calculated based on the historical data of the freight demand of potential customers, with a mean of μ=26 and a variance of σ=10.

[0079] S23. Processing of vehicle information: The rated load and fuel consumption level of the vehicle are stored in csv format according to the vehicle type; the length of each road section in the road section element attribute table is divided by the average speed of the road section to obtain the average time for the vehicle to pass through each road section in the road network.

[0080] As shown in Table 10, it is the average time for vehicles to pass through each section in the road network obtained in this embodiment.

[0081] Table 10. Average time for vehicles to pass through each section of the road network

[0082]

[0083]

[0084] S3. Operation environment simulation: The operation environment of urban logistics is divided into three types: deterministic, uncertain customer demand, and uncertain vehicle travel time. Specifically:

[0085] S31. Simulation for deterministic operation environment: Based on Python development environment, use import command to load node feature attribute table and road feature attribute table csv files, and use csv.reader function to read data, call shortest_path_length function in third-party open source package NetworkX to obtain distance matrix between logistics park and candidate distribution center, candidate distribution center and potential customers, and potential customers and potential customers; use import command to load csv file containing vehicle rated load and fuel consumption level, and use csv.reader function to read data; based on the probability distribution of potential customer freight demand function, calling the normal distribution sampling function numpy.random.normal in the numpy library to generate freight demand data for potential customers; using the random function multiple times to randomly select 75 customers from 100 potential customers as customers who generate freight demand, by loading the distance matrix between the logistics park and the candidate distribution center, the candidate distribution center and the 75 selected customers, and the distance matrix between the selected customers, the vehicle rated load and fuel consumption level data, and the freight demand data of the selected customers, the simulation of the deterministic operating environment is realized, and multiple groups of simulation examples of the deterministic urban logistics operating environment are generated (different selected customer sets represent different examples).

[0086] As shown in Table 11, it is the distance matrix between the logistics park and the candidate distribution center, the candidate distribution center and the 75 selected customers, and the selected customers and the selected customers obtained in the embodiment of the present invention.

[0087] Table 11. Distance matrix between logistics parks, candidate distribution centers, and customers (unit: kilometers)

[0088]

[0089]

[0090] The vehicle rated load and fuel consumption level data are shown in Table 5.

[0091] As shown in Table 12, this is the freight demand of 75 selected customers obtained in the embodiment of the present invention.

[0092] Table 12. Freight demand of selected customers

[0093]

[0094] S32. Simulation of an operating environment with uncertain customer demand: divide potential customers into two, set the freight demand of one part of customers to be known, and the freight demand of the remaining customers to be unknown. The key to the simulation is to generate a random matrix that generates freight demand to simulate the uncertainty of the freight demand of the remaining customers. Specifically: based on the Python development environment, use the import command to load the node feature attribute table and the road segment feature attribute table csv files, and use the csv.reader function to read the data, call the shortest_path function in the third-party open source package NetworkX, and find the shortest path between the logistics park and the candidate distribution center, the candidate distribution center and the potential customer, and the potential customer and the potential customer. Use the import command to load the average time t for vehicles to pass through each section in the road network. i csv file, and use the csv.reader function to read the data to obtain the travel time matrix between the logistics park and the candidate distribution center, the candidate distribution center and the potential customer, and the potential customer and the potential customer; use the import command to load the csv file containing the vehicle rated load and fuel consumption level, and use the csv.reader function to read the data; based on the probability distribution function of the potential customer's freight demand, call the normal distribution sampling function numpy.random.normal in the numpy library to generate the freight demand data of the potential customer; use the random function to randomly select 50 customers from the potential customers as customers with known freight demand (that is, the freight demand generation time t = 0), set the time range for the freight demand of the remaining 50 potential customers to [1,480], with minutes as the time unit, and the probability P of the remaining potential customer j generating freight demand at time t (1≤t<480) is j t follows a binomial distribution, and its service time window is [e j ,l j ](1 <e j <l j ≤480), and repeatedly call the numpy.random.binomial function in the numpy library to sample the probability of all remaining potential customers' freight demands at each moment in [1,480], and obtain multiple groups of random matrices of freight demands generated by all remaining potential customers in [1,480]. By loading the travel time matrix between the logistics park and the candidate distribution center, the candidate distribution center and the customers to be served (including customers with known freight demands and customers with random freight demands), and the customers to be served and the customers to be served, the vehicle rated load and fuel consumption level data, and the known and random freight demand data of the customers to be served, the simulation of the uncertain customer demand operation environment is realized, and multiple groups of simulation examples of the uncertain customer demand urban logistics operation environment are generated (different random matrices that generate freight demands represent different examples).

[0095] As shown in Table 13, it is the travel time matrix between the logistics park and the candidate distribution center, the candidate distribution center and the customer to be served, and the customer to be served and the customer to be served obtained in the embodiment of the present invention.

[0096] Table 13. Travel time matrix between logistics parks, candidate distribution centers, and customers (unit: seconds)

[0097]

[0098]

[0099] The vehicle rated load and fuel consumption level data are shown in Table 5.

[0100] As shown in Table 14, the freight demand of customers with known demand is obtained in the embodiment of the present invention.

[0101] Table 14. Freight demand of customers with known demand

[0102]

[0103] As shown in Table 15, it is a random matrix of freight demand generated by all remaining potential customers within the [1,480] time range obtained in the embodiment of the present invention.

[0104] Table 15. Random matrix of freight demand generated by all remaining potential customers in the time range [1,480]

[0105]

[0106] S33, simulation of the uncertain operation environment of vehicle travel time: the time for a vehicle to pass a certain section of road is set to consist of two parts, one of which is a constant t i , and the other part is the variable δ i The constant is expressed by the average time it takes for vehicles to pass through the road section. The key to the simulation is in [-αt i ,αt i Randomly generate variable δ in the range of ](0<α<1) i , use t i +δ i To simulate the uncertainty of the time it takes for a vehicle to pass through a road section; specifically: based on the Python development environment, use the import command to load the node feature attribute table and the road section feature attribute table csv file, and use the csv.reader function to read the data; use the import command to load the average time t of each road section in the road network for vehicles to pass through i csv file, and use the csv.reader function to read the data, and use the random function to generate a random value δ for each road segment i(-αt i ≤δ i ≤αt i , α is 0.8), and T i =t i +δ i Characterize the random travel time of each road section; use the import command to load the csv file containing the vehicle's rated load and fuel consumption level, and use the csv.reader function to read the data; based on the probability distribution function of potential customers' freight demand, call the normal distribution sampling function numpy.random.normal in the numpy library to generate potential customers' freight demand data; use the random function multiple times to randomly select 75 customers from potential customers as customers who generate freight demand, and load the random travel time of each section between the logistics park and the candidate distribution center, the candidate distribution center and the 75 selected customers, and the selected customers and the selected customers, the vehicle's rated load and fuel consumption level data, and the freight demand data of the selected customers, to achieve the simulation of the uncertain travel time operation environment, and generate multiple sets of simulation examples of the uncertain travel time urban logistics operation environment (different selected customer sets represent different examples).

[0107] As shown in Table 16, the random travel time of each section between the logistics park, the candidate distribution center, and the selected customer obtained in the embodiment of the present invention.

[0108] Table 16. Random travel time of each road section between logistics park, candidate distribution center and selected customers

[0109] Section number Travel time(s) Section number Travel time(s) Section number Travel time(s) 0 <![CDATA[17+δ0]]> 202 <![CDATA[4+δ 202 ]]> 403 <![CDATA[16+δ 403 ]]> 1 <![CDATA[5+δ1]]> 203 <![CDATA[15+δ 203 ]]> 404 <![CDATA[9+δ 404 ]]> 2 <![CDATA[7+δ2]]> 204 <![CDATA[30+δ 204 ]]> 405 <![CDATA[7+δ 405 ]]> 3 <![CDATA[13+δ3]]> 205 <![CDATA[20+δ 205 ]]> 406 <![CDATA[15+δ 406 ]]> 4 <![CDATA[15+δ4]]> 206 <![CDATA[11+δ 206 ]]> 407 <![CDATA[30+δ 407 ]]> 5 <![CDATA[6+δ5]]> 207 <![CDATA[17+δ 207 ]]> 408 <![CDATA[6+δ 408 ]]> … … … … … … 197 <![CDATA[6+δ 197 ]]> 398 <![CDATA[6+δ 398 ]]> 599 <![CDATA[10+δ 599 ]]> 198 <![CDATA[4+δ 198 ]]> 399 <![CDATA[12+δ 399 ]]> 600 <![CDATA[17+δ 600 ]]> 199 <![CDATA[8+δ 199 ]]> 400 <![CDATA[9+δ 400 ]]> 601 <![CDATA[14+δ 601 ]]> 200 <![CDATA[2+δ 200 ]]> 401 <![CDATA[20+δ 401 ]]> 602 <![CDATA[8+δ 602 ]]> 201 <![CDATA[10+δ 201 ]]> 402 <![CDATA[21+δ 402 ]]> 603 <![CDATA[18+δ 603 ]]>

[0110] The vehicle rated load and fuel consumption level data are shown in Table 5.

[0111] As shown in Table 17, this is the freight demand data of 75 selected customers obtained in the embodiment of the present invention.

[0112] Table 17. Freight demand data of 75 selected customers

[0113] Customer Number Latitude coordinates Longitude coordinates Freight demand Customer Number Latitude coordinates Longitude coordinates Freight demand 1 32.0583 118.7936 19 39 32.0320 118.7646 7 2 32.0567 118.7688 3 40 32.0346 118.7645 19 3 32.0522 118.7719 8 41 32.0335 118.7791 19 4 32.0486 118.7719 19 42 32.0565 118.7705 13 5 32.0454 118.7887 8 43 32.0577 118.7705 6 … … … … … … … … 34 32.0472 118.7939 8 71 32.0520 118.7822 27 35 32.0490 118.7958 13 72 32.0378 118.7887 30 36 32.0497 118.7692 29 73 32.0414 118.7903 1 37 32.0485 118.7752 29 74 32.0364 118.7642 6 38 32.0354 118.7743 31 75 32.0356 118.7668 1

[0114] The simulation implementation system of the urban logistics operation environment of the present invention includes a data acquisition module, a data processing module and an operation environment simulation module, wherein the data acquisition module is used to collect geographical related information, customer demand related information, vehicle related information and freight traffic management policy related information, the data processing module processes the data information collected by the data acquisition module, and the operation environment simulation module simulates the data processed by the data processing module for a deterministic operation environment, a customer demand uncertain operation environment, and a vehicle travel time uncertain operation environment to obtain different calculation examples. This embodiment has the same beneficial effects as the simulation implementation method of the urban logistics operation environment of the above embodiment.

[0115] A storage medium having a computer program stored thereon, wherein the computer program, when executed by at least one processor, implements the steps of the above-mentioned method for simulating the urban logistics operation environment and achieves the same technical effect as the above-mentioned method.

Claims

1. A simulation method for an urban logistics operation environment, characterized in that: The following steps are involved: S1. Basic data collection; Basic data includes geographical information, customer demand information, vehicle information, and freight transportation management policy information; S2, data processing: processing the basic data collected in step S1; S3, operating environment simulation: according to the data of step S1 and step S2, respectively simulate the operating environments of deterministic type, uncertain customer demand type, and uncertain vehicle travel time type, and generate a number of calculation examples; The simulation of the uncertain operation environment of customer demand is as follows: loading the node element attribute table and the road segment element attribute table, finding the shortest path between the logistics park and the candidate distribution center, the candidate distribution center and the potential customer, and the potential customer and the potential customer; loading the average time t of each road segment in the road network for vehicles to pass through; i The data file containing the vehicle's rated load and fuel consumption level is loaded; the travel time matrix between the logistics park and the candidate distribution center, the candidate distribution center and the potential customer, and the potential customer and the potential customer is calculated; the data file containing the vehicle's rated load and fuel consumption level is loaded; Generate the freight demand data of potential customers based on the probability distribution function of the freight demand volume of potential customers; randomly select some customers from potential customers as customers with known freight demand, set the time range for the generation of freight demand of the remaining potential customers as [1, H], with minutes as the time unit, and the probability P of the remaining potential customer j generating freight demand at time t j t follows a binomial distribution, where 1 ≤ t < H, and its service time window is [e j , l j , where 1 < e j < l j ≤ H. Sample the generation probability of freight demand of all the remaining potential customers at each moment within [1, H] to obtain the random matrix of freight demand generation of all the remaining potential customers within [1, H]; by respectively loading the travel time matrix, vehicle rated load and fuel consumption level data, and the known and random freight demand data of the customers to be served between the logistics park and the candidate distribution centers, between the candidate distribution centers and the customers to be served, and between the customers to be served and the customers to be served, realize the simulation of the operation environment with uncertain customer demand, where the customers to be served include the customers with known freight demand and the customers generating random freight demand.

2. The method for simulating the urban logistics operation environment according to claim 1, characterized in that: The geographical related information in step S1 includes urban road network information, location information of logistics parks, location information of candidate distribution centers and location information of potential customers. The urban road network information is obtained through map vendors, and the location information of logistics parks, candidate distribution centers and potential customers is obtained from logistics companies; customer demand related information mainly refers to historical data on potential customers' freight demand, and customer demand related information is obtained from logistics companies; vehicle related information includes the vehicle's rated load, fuel consumption level, and the average speed of the vehicle passing through each section in the road network. The vehicle's rated load and fuel consumption level are obtained from logistics companies or automobile manufacturers, and the average speed of the vehicle passing through each section in the road network is obtained from the traffic management department; freight traffic management policy related information mainly refers to the name of the section where restricted measures are taken for freight vehicles, and freight traffic management policy related information is obtained from the traffic management department.

3. The method for simulating the urban logistics operation environment according to claim 1, characterized in that: Step S2 includes the following steps: S21, processing urban road network information, logistics park location information, candidate distribution center location information, potential customer location information, and restricted road section information; S22. Processing of customer demand information: fitting the probability distribution of the potential customer's freight demand based on the historical data of the potential customer's freight demand, and calculating the parameter value of the probability distribution function; S23, vehicle information processing: the rated load and fuel consumption level data of the vehicle are stored separately according to the vehicle type; according to the average speed V of the vehicle passing through the road section in the road network i and the segment length L in the segment feature attribute table i , calculate the average time t for vehicles to pass through a section in the road network i , where t i =L i / V i , i is any road section.

4. The method for simulating the urban logistics operation environment according to claim 3, characterized in that: Step S21 specifically includes: Processing of urban road network information: Based on the longitude, latitude and road type of the selected urban area, a set of data files containing urban road network information is calculated, and then the urban road network map is obtained, and then the node element attribute table and the road section element attribute table are obtained; Processing of location information of logistics parks, candidate distribution centers and potential customers: adding the location information of logistics parks, candidate distribution centers and potential customers to the node feature attribute table and marking them with different legends in the urban road network map; Processing of restricted road section information: Retrieve the corresponding road section from the road section feature attribute table according to the name of the road section where the restricted measures are taken, hide it in the road section feature attribute table, and mark it with different legends in the urban road network map; The node feature attribute table and the road segment feature attribute table are stored separately.

5. The method for simulating the urban logistics operation environment according to claim 3, characterized in that: Step S22 is specifically: finding the maximum value q of freight demand from the historical data of freight demand of potential customers max and the minimum value q min , divide the potential customer freight demand into [q min ,q min +(q max -q min ) / N]、(q min ,q min +2(q max -q min ) / N],…,(q max -(q max -q min ) / N,q max ]N groups, and count the frequency of each group of freight demands respectively; generate a histogram with freight demand as the horizontal axis and frequency as the vertical axis, and judge the probability distribution of potential customers' freight demands according to the shape of the histogram, and use this probability distribution function as the probability distribution fit of potential customers' freight demands, and calculate the parameter value of the probability distribution function according to the historical data of potential customers' freight demands.

6. The method for simulating the urban logistics operation environment according to claim 1, characterized in that: The simulation for the deterministic operation environment in step S3 is specifically as follows: loading the node element attribute table and the road segment element attribute table, calculating the distance matrix between the logistics park and the candidate distribution center, the candidate distribution center and the potential customer, and the potential customer and the potential customer; loading the data file containing the vehicle rated load and fuel consumption level; Generate the freight demand data of potential customers based on the probability distribution function of the freight demand volume of potential customers; randomly select some customers from potential customers as the customers generating freight demand; realize the simulation of the deterministic operation environment by respectively loading the distance matrices between the logistics park and candidate distribution centers, between candidate distribution centers and selected customers, and between selected customers and selected customers, the vehicle rated load and fuel consumption level data, and the freight demand data of selected customers.

7. The method for simulating the urban logistics operation environment according to claim 1, characterized in that: The simulation of the vehicle travel time uncertain operation environment in step S3 is specifically as follows: loading the node element attribute table and the road segment element attribute table, loading the average time t of vehicles passing through each road segment in the road network i A random quantity δ is generated for each road segment. i , where -αt i ≤δ i ≤αt i , α is the proportionality coefficient, and T i =t i +δ i Characterize the random travel time of each road section; load the data file containing the rated load and fuel consumption level of the vehicle; generate the freight demand data of potential customers based on the probability distribution function of the freight demand of potential customers; randomly select some customers from the potential customers as customers who generate freight demand; by loading the random travel time of each section between the logistics park and the candidate distribution center, the candidate distribution center and the selected customers, and the selected customers and the selected customers, the vehicle rated load and fuel consumption level data, and the freight demand data of the selected customers, the simulation of the operation environment with uncertain travel time is realized.

8. The simulation implementation system of the urban logistics operation environment is characterized by: It includes a data acquisition module, a data processing module, and an operation environment simulation module. Among them, the data acquisition module is used to acquire geographical information, customer demand information, vehicle information, and freight traffic management policy information. The data processing module processes the data information acquired by the data acquisition module. The operation environment simulation module simulates the data processed by the data processing module for the deterministic operation environment, the customer demand uncertain operation environment, and the vehicle passing time uncertain operation environment respectively to obtain different examples. The simulation for the customer demand uncertain operation environment is specifically as follows: load the node element attribute table and the section element attribute table, and calculate the shortest paths between the logistics park and candidate distribution centers, between candidate distribution centers and potential customers, and between potential customers and potential customers; load the data file containing the average time ti for the vehicle to pass through each section in the road network, and calculate the passing time matrix between the logistics park and candidate distribution centers, between candidate distribution centers and potential customers, and between potential customers and potential customers; load the data file containing the vehicle rated load and fuel consumption level; generate the freight demand data of potential customers based on the probability distribution function of the freight demand volume of potential customers; randomly select some customers from potential customers as the customers with known freight demand, set the time range for the remaining potential customers to generate freight demand as [1, H], with minutes as the time unit. The probability Pt j that the remaining potential customer j generates freight demand at time t follows a binomial distribution, where 1 ≤ t < H, and its service time window is [ej, lj], where 1 < ej < lj ≤ H. Sample the generation probabilities of freight demands of all remaining potential customers at each moment within [1, H] to obtain the random matrix of freight demands generated by all remaining potential customers within [1, H]; realize the simulation of the customer demand uncertain operation environment by respectively loading the passing time matrix between the logistics park and candidate distribution centers, between candidate distribution centers and customers to be served, and between customers to be served and customers to be served, the vehicle rated load and fuel consumption level data, and the known and random freight demand data of customers to be served, where the customers to be served include customers with known freight demand and customers generating random freight demands.

9. A storage medium, characterized in that: A computer program is stored on the storage medium, and when the computer program is executed by at least one processor, the steps of the method for simulating the urban logistics operation environment according to any one of claims 1 - 7 are implemented.

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