A method for vehicle scheduling in a logistics site considering road restrictions

Abstract

The application provides a vehicle scheduling method in a logistics site considering road restrictions, comprising the following steps: abstracting a road network in the logistics site into a topological road network based on a space-time network; constructing a vehicle path planning model in the logistics site based on the space-time network; solving the vehicle path planning model; matching idle vehicles and goods to be transported; sending a request from a loading area in the logistics site, and idle vehicles going to the loading area to load goods after receiving the request; proposing an active real-time scheduling method based on a dynamic relocation strategy for the working process of the vehicles in the loading area; and planning a shortest off-site path for the vehicles to leave the logistics site after completing all loading and unloading work. The application can dynamically adjust the vehicle path planning, optimize the operation process of the vehicles in the unloading area and the loading area, realize efficient matching of the vehicles and the goods, minimize the waiting time and empty running distance of the vehicles, improve the overall operation efficiency of the logistics site, and reduce the logistics transportation cost.

Description

Technical Field

[0001] This invention relates to a vehicle scheduling method within a logistics site that takes into account road restrictions, and belongs to the field of logistics technology. Background Technology

[0002] With the rapid development of e-commerce and the logistics industry, the operational efficiency and management capabilities of logistics facilities have become key factors affecting the responsiveness and cost control of the entire supply chain. Within a logistics facility, vehicle dispatching, as the core link connecting loading, unloading, storage, and transportation of goods, directly impacts the quality of logistics services and customer satisfaction through its efficiency and flexibility. However, in actual operation, logistics facilities often face complex road restrictions, such as one-way traffic, height restrictions, and restricted hours, which significantly increase the difficulty of vehicle dispatching.

[0003] Traditional vehicle dispatching methods are mostly based on static road network information and fixed vehicle route planning, ignoring the dynamic traffic conditions and real-time road constraints within the logistics site. This results in low vehicle dispatching efficiency, high vehicle empty-running rates, and even traffic congestion and delays.

[0004] To address these issues, a novel vehicle scheduling method is needed that comprehensively considers road restrictions, real-time traffic conditions, and vehicle loading / unloading needs within the logistics facility. This method should dynamically adjust vehicle route planning, optimize vehicle operations in unloading and loading areas, and achieve efficient matching of vehicles and goods, minimizing vehicle waiting time and empty driving distance, thereby improving the overall operational efficiency of the logistics facility.

[0005] Therefore, this paper proposes a vehicle scheduling method within logistics sites that considers road constraints. This method constructs a spatiotemporal network-based topological road network model to accurately reflect road limitations and real-time traffic conditions within the logistics site. By building a vehicle path planning model for the unloading area and solving for the optimal solution, it achieves rapid and efficient vehicle operation in the unloading area. Through vehicle-cargo matching and dynamic relocation strategies, it optimizes the vehicle operation process in the loading area. Finally, it plans the shortest departure route for vehicles to ensure smooth logistics operations. This comprehensive scheduling method not only improves the operational efficiency of logistics sites but also reduces operating costs, which is of great significance for promoting the intelligent and efficient development of the logistics industry. Summary of the Invention

[0006] The purpose of this invention is to provide a vehicle scheduling method within a logistics site that takes into account road limitations, thereby addressing the problem of insufficient operational efficiency and management capabilities of logistics sites affecting the response speed and cost control of the entire supply chain, improving overall logistics transportation efficiency, reducing operating costs, and promoting the efficient development of the logistics industry.

[0007] To achieve the above objectives, this invention provides a vehicle scheduling method within a logistics facility that takes into account road limitations, for reducing transportation costs and improving operational efficiency within the logistics facility, mainly comprising the following steps:

[0008] Step 1: Abstract the road network within the logistics site into a topological road network based on spatiotemporal networks;

[0009] Step 2: Construct a vehicle path planning model within the logistics site based on a spatiotemporal network to plan the shortest path for incoming vehicles to unload goods.

[0010] Step 3: Solve the vehicle routing model to obtain the optimal solution that minimizes the time vehicles spend in the unloading area;

[0011] Step 4: Match idle vehicles with goods to be transported;

[0012] Step 5: A request is sent from the loading area within the logistics site. Once the idle vehicle receives the request, it goes to the loading area to load the goods.

[0013] Step 6: Propose an active real-time scheduling method based on dynamic relocation strategy for the working process of vehicles in the loading area;

[0014] Step 7: After completing all loading and unloading work, plan the shortest departure route for the vehicle to leave the logistics site.

[0015] As a further limitation of the present invention, in step 1, the road network within the logistics site is abstracted as a topological network G = (S, L), where L (l ∈ L) is the set of edges in the topological network, and S (s ∈ S) is the set of nodes in the topological network. Nodes include intersection nodes and meeting points, and S... * Meet at the meeting point.

[0016] The attributes of a point in a spatiotemporal network include its corresponding nodes in the topology and its corresponding discrete-time point. The set of points is then... in, Let i be a node in the topology network corresponding to point i. Let i be the discrete time point corresponding to point i, and T represent the set of discrete time points within the study period.

[0017] In a spatiotemporal network, arcs describe vehicle activity and are divided into waiting arcs and traveling arcs. A waiting arc represents the waiting activity of a vehicle at a passing point, and it only occurs at that point. The nodes in the topology corresponding to the start and end points of a waiting arc are the same, and their corresponding discrete time points differ by 1. Where A represents the set of arcs in the spatiotemporal network, (i,j)∈A, A w This represents the set of waiting arcs in a spatiotemporal network. Let j be a node in the topology network corresponding to point j. Let j be the discrete time point.

[0018] A travel arc represents the movement of a vehicle along an edge in a network topology, from the starting node to the ending node. A travel arc is constructed based on each edge in the network topology; the start and end points of the travel arc correspond to the starting and ending nodes of an edge, and the difference between the corresponding discrete time points is the vehicle's travel time along that edge. The set of travel arcs corresponding to the edges in the topological network is: Among them, A τ Let S be the set of travel arcs in the spatiotemporal network, and s(l) and e(l) be the starting / ending nodes of edge l in the topological network, where s(l) and e(l) ∈ S. This represents the time it takes for a vehicle to travel on edge l in the network topology.

[0019] As a further limitation of the present invention, in step 2, binary variables are set based on the constructed spatiotemporal network. A value of 1 indicates that vehicle k traverses arc (i,j) in the spatiotemporal network; otherwise, the value is 0. The model's objective is to minimize the total weighted time of all vehicles in the unloading area of ​​the logistics site, including waiting and travel time. Weights are determined by vehicle priority, ensuring that lower-priority vehicles yield and wait when meeting oncoming traffic. The objective function is then: Where, q k This indicates the priority of vehicle k; the larger the value, the higher the priority.

[0020] The constraints of the model are as follows. Equation (1) indicates that the vehicle enters the field at a given starting point and time; Equation (3) indicates that the vehicle eventually reaches a given destination; Equations (2) and (4) prohibit the vehicle from returning to the starting point and leaving the destination, thereby eliminating sub-loops on the vehicle path; Equation (5) indicates the flow balance constraints of the vehicle at other points.

[0021]

[0022] in, Indicates the entry time of vehicle k, o k ,d k Indicates the starting and ending points of vehicle k, o k ,d k ∈S.

[0023] The vehicle passing and avoidance constraints are shown in Equation (6). For a conflict edge pair (l, l') in the topological network, Equation (6) represents the discrete time point when vehicle k' passes through a driving arc (i', j') corresponding to edge l. Before and after Δt follow Within a time period (i.e., an interval) No other vehicles entered the road section.

[0024]

[0025] Where, Δt pass M represents the time required for two opposing transport vehicles to pass each other at a meeting point, where M represents a sufficiently large positive number.

[0026] Considering the road restrictions within the logistics area, additional constraints are added for road width and turning radius. Equation (7) represents the road width constraint. In practical applications, since vehicles typically do not occupy the entire road width when driving, this means that the maximum lateral offset of the vehicle (relative to the road centerline) will not exceed the actual road width. Equation (8) represents the turning radius constraint, ensuring that vehicles will not scrape or collide due to an excessively small turning radius when turning.

[0027] num×Offset_max≤W(7)

[0028]

[0029] Where num is the number of lanes in the site, Offset_max represents the maximum lateral offset of a vehicle relative to the road centerline during its movement, and W is the road width. R min This indicates the vehicle's minimum turning radius.

[0030] WB represents the minimum turning radius required for the road, ξ is the vehicle's wheelbase, M is the vehicle's maximum steering angle, and M is an additional safety margin distance added to ensure safety.

[0031] As a further limitation of this invention, in step 3, when multiple vehicles gather in a certain area of ​​the road network, congestion may occur. At this time, if the subsequent impact is not considered when adjusting the vehicle's travel path, waiting at the previous node of the conflicting road segment or path readjustment may lead to secondary conflicts in the vehicle's trajectory. To solve the above-mentioned problems of secondary vehicle conflicts and oncoming conflicts between multiple vehicles on the same road segment, a conflict resolution method based on a spatiotemporal network is proposed. Assume that it is necessary to plan the path of transport vehicle C to D at time t after passing through n time scales. If C does not exist in the spatiotemporal network... t To D t+n The path (where arcs and points are occupied by other transport vehicles) is blocked, so C needs to reach D in time. t+n Waiting at the meeting point t Until the congestion is cleared, the arrival time of transport vehicle C is updated to the network node.

[0032] The main flow of the algorithm is as follows:

[0033] Step 3.1: The first vehicle enters the road network, numbered i=1. Dijkstra's algorithm is used to calculate the shortest path for vehicle 1, and the set of shortest road segments for vehicle 1 is recorded {[S p ,S q ]}, the time period through which the road segment was collected in Starting from point S o To S p The distance is v, and the speed is v.

[0034] Step 3.2: For each new vehicle entering the road network, record i = i + 1. If i > i, proceed to step 3.5; otherwise, initialize the K short-circuit set P. i Candidate set X i Determine the starting point s i , endpoint t i The relaxation coefficient δ (the ratio of the length of the K shortest path to the length of the shortest path) is used to calculate the shortest path traveled by vehicle i using Dijkstra's algorithm. Put into set P i In the middle, calculation length make Let k = 1, record Road segment collection and road segment time period set in

[0035] Step 3.3: If k > K, proceed to step 3.2; otherwise, proceed from... From the point closest to t to point s i Point r swings to all possible connected points m, and rm satisfies that it is not in the candidate set X. i The conditions are as follows. Using Dijkstra's method, a search is performed to find the point from m to t. i The shortest path, denoted as p m , put p ksr rm and p m The deviated paths formed are placed into the candidate set X i In, where p ksr express From s i The subpath to r.

[0036] Step 3.4, if candidate set X i If the value is empty, proceed to step 3.2; if it is not empty, calculate all candidate paths f(p) in ascending order, and denote the path with the minimum value as... Only keep the first K paths, if Proceed to step 3.5, otherwise... Move into P iIn this case, k = k + 1, proceed to step 3.3.

[0037] Step 3.5: Compare the queue of road segments traversed by all vehicles in the venue with the queue of the occupied time of each traversed road segment pairwise. If there are two road segments where two vehicles travel in opposite directions and their occupied times conflict, extract the road segment that occurred first [S]. i ,S j Simultaneously, the time each vehicle occupied when passing through the aforementioned road section was recorded. If it is determined that the two vehicles will collide while traveling on the track, proceed to step 3.6 to compare the priorities P of the two vehicles. m P n If there are no road segments with overlapping occupancy times [S] i ,S j If the vehicle travels according to the initial shortest path spatiotemporal information, then the vehicle will travel according to the initial shortest path spatiotemporal information.

[0038] Step 3.6: Compare the priority attribute values ​​of the vehicles on the field. Let the vehicle with higher priority be m1 and the vehicle with lower priority be m2. Guide the two vehicles to pass each other according to the vehicle passing and avoidance criteria. First, traverse the K-short-circuit set of vehicle m2 to determine if there is a secondary short-circuit that is not occupied by a vehicle with higher priority. Calculate the total travel time t on this path. k If it exists, then instruct vehicle m2 to travel along the shortest path and update the road segment {[S] traversed by vehicle m2. p ,S q ]} and time period set If it doesn't exist, then iterate through the intersections between the entrance and the conflict section for vehicles with lower priority. If a meeting point exists, have the vehicle wait at the meeting point and update the road segment {[S] that vehicle m2 has traversed. p ,S q ]} and time period set If no meeting point exists, vehicle m2 will wait at the vehicle entrance for a specified time. And recalculate the total travel time t of the shortest path the vehicle travels at this point, where Update the avoidance vehicle path information and return to step 3.5.

[0039] Step 3.7: Terminate the process by comparing the queues of all vehicles passing through the road segments and the queues of the occupied time of the passed road segments with the five overlapping parts.

[0040] As a further limitation of the present invention, step 4 employs a cluster-based vehicle-cargo matching method. Assume there are n vehicles to be matched within the logistics site, denoted as vehicle set K = {k1, k2, ..., k...}. n The maximum load capacity of each truck is [missing information]. There are c goods to be matched, denoted as the goods set G = {G1, G2, ..., G}.b ,...G c Let d be the distance between the destinations of goods b and c. bc Vehicle k n A matching relationship between b and goods b is

[0041] From all matching relationships The resulting n x c matrix represents a matching scheme for the vehicle-cargo matching problem, i.e., a solution to the problem, denoted as [matrix name missing]. The nth row vector of Y represents vehicle k n The matching scheme is given by the c-th column vector, which represents the matching scheme for cargo c.

[0042] In the process of matching vehicles and goods, to concentrate the goods in the matching scheme as much as possible, the transportation points of the goods can be clustered. DBSCAN clustering can be used. After clustering, the original set of goods G can be represented as g={g1,g2,...,g c}. Where g c This represents a cluster after clustering. According to the DBSCDN clustering rules, each noise point in the output is considered as a cluster. A cluster may contain multiple transport points or only one transport point. The coordinates of the cluster center containing multiple transport points are calculated. For a given vehicle, its load factor Here, full load is defined as 1, and its calculation formula is as follows: in, Indicate whether cargo b is transported by vehicle k n Delivery, k n ∈K, b∈G; W b This indicates the weight of cargo b. Indicates maximum load capacity; V b This represents the volume of cargo b. This indicates the maximum volume.

[0043] For a given matching scheme Y, the load factor (SZR) is the ratio of the sum of the load factors of all trucks to the sum of the matching relationships, and its calculation formula is as follows:

[0044] For a given transport vehicle, because the goods it transports are highly concentrated, the impact of loading and unloading times on the vehicle's total transport time can be ignored. Therefore, the delivery time problem can be simplified to considering only the vehicle's travel time. Thus, the travel time tk of a given vehicle is... n The formula is The travel time for a certain route is Stk. n Its formula is in, Indicates the distance the vehicle has traveled; This indicates the vehicle's speed.

[0045] With the objectives of minimizing the number of vehicle departures, minimizing vehicle travel time, and maximizing vehicle loading rate, and assuming there are a total of u selectable solutions, the optimization objectives are to minimize the number of vehicle departures Z1, minimize the delivery time Z2, and maximize the loading rate Z3. The objective function is as follows:

[0046] Z1 = min{Y1,Y2,...,Y} u}(9)

[0047] Z2 = max{SZR1,SZR2,...,SZR} u}(10)

[0048] Z3=min{Stk n1 ,Stk n2 ,...,Stk nu}(11)

[0049] Decision variables are set as k n ∈K, b∈G.

[0050] Assume that the final delivery will require a total number of trips, where y is the trip number and y∈total. y∈total, b∈G.

[0051] To meet the above requirements and the actual situation of vehicle-cargo matching in distribution centers, the model's constraints are mainly based on the following factors:

[0052] Constraint 1: When vehicles are loaded into clusters, the weight of the cargo within each cluster cannot exceed the vehicle's rated load capacity, as shown below.

[0053] Constraint 2: When vehicles are loaded into clusters, the volume of cargo within each cluster cannot exceed the vehicle's rated volume, as shown below.

[0054] Constraint 3: After the vehicle is fully loaded, the weight of the loaded cargo must not exceed the vehicle's rated load capacity, as indicated by...

[0055] Constraint 4: After the vehicle is fully loaded, the volume of the loaded cargo must not exceed the vehicle's rated volume, expressed as follows:

[0056] Constraint 5: All goods within the site must be fully loaded, as indicated by...

[0057] Constraint 6: Vehicles included in the vehicle-cargo matching must be within the dispatchable range of the logistics site, as indicated by... Where D represents the schedulable range of the logistics site; This indicates the distance between the vehicle and the center of the logistics site.

[0058] Constraint 7: Each item can only be loaded into one vehicle, as shown below.

[0059] Next, we will calculate the satisfaction of both parties with the truck-cargo matching result. Assume the set of cargo owners includes CO... γ This represents the γth cargo owner and vehicle owner group, VO. λ S represents the λth car owner. sn E represents the nth indicator of the cargo owner. sn This represents the nth indicator for the car owner; (Note: The original text contains some formatting errors and inconsistencies. A CO for cargo owners γ For car owner VO λ Regarding indicator S sn satisfaction For car owners VO λ For cargo owner VO λ Regarding indicator E sn Satisfaction level.

[0060] The first factor is the satisfaction of the cargo owners.

[0061] (1) For the demand date S 1 satisfaction

[0062] Cargo owner CO γ Give the required date S 1 interval Car owner VO λ Give its working date E 1 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0063]

[0064] Where, 0 < σ γ <1, where M represents a sufficiently large positive number.

[0065] (2) For the transportation quotation S 2 satisfaction

[0066] Cargo owner CO γ Provide a transportation quote S 2 Specific value Car owner VO λ Give its delivery pricing E 2 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0067]

[0068] in, 0 < θ γ ≤1.

[0069] (3) For model S 3 satisfaction

[0070] Cargo owner CO γ Give the required vehicle model S 3 Specific value Car owner VO λ Give its model E 3 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0071]

[0072] in,

[0073] Next is the owner's satisfaction.

[0074] (1) For delivery pricing E 2 satisfaction

[0075] Car owner VO λ Give its delivery pricing E 2 Specific value Cargo owner CO γ Provide a transportation quote S 2 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0076]

[0077] in, 0 < θ λ ≤1.

[0078] (2) For deliverable location E 4 satisfaction

[0079] Car owner VO λ Give its deliverable address E 4 Preference order Cargo owner CO γ Give the delivery address S 4 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0080]

[0081] Among them, rankγλ Indicates the owner's CO γ The actual value of the given indicator In the car owner VO λ The ranking in the preference order, 0 < τ λ ≤1, 0<ξ λ <1.

[0082] The coefficient of variation method was used to determine the index weights. The calculation process is as follows:

[0083] First calculate in, μ represents the coefficient of variation of the κ-th index; κ x represents the standard deviation of the κ-th index; κ This represents the average of the κ-th indicator.

[0084] Let the weight of each indicator be ω. κ Its calculation formula is

[0085] Based on the weighting method described above, the weight values ​​of each indicator participating in the satisfaction measurement are determined. Attribute indicators are not included in the satisfaction aggregation and their weights are set to 0. Therefore, the overall satisfaction level of cargo owners and vehicle owners with potential matching partners is obtained:

[0086] Where, α γλ This indicates the overall satisfaction of cargo owners with vehicle owners; β γλ This indicates the overall satisfaction level of vehicle owners with cargo owners; This indicates that the cargo owner has a positive view of indicator S. sn The weights; This indicates that the car owner is interested in the E indicator. sn The weights;

[0087]

[0088] When the overall satisfaction of either party is too low, the vehicle-cargo matching scheme needs to be redesigned.

[0089] As a further limitation of the present invention, the dynamic relocation strategy in step 6 aims to proactively guide idle vehicles into areas where future service requests are likely to be concentrated, thereby reducing the response time to requests. The dynamic relocation strategy is only executed for idle vehicles.

[0090] Dynamic relocation strategies aim to take proactive action based on information from future requests, primarily focusing on the arrival time of virtual requests in typical transportation plans. A dynamic relocation strategy consists of three parts: a triggering mechanism, an implementation mechanism, and a remedial mechanism.

[0091] The dynamic relocation strategy is triggered by idle vehicles located in parking area p∈P at time t. The trigger time for these vehicles is any time t within the parking area.

[0092] For those located in the parking area (a represents the current partition of vehicle k) Idle vehicles The vehicle must meet the following conditions to trigger the implementation mechanism:

[0093] In the parking area There should be at least one vehicle other than vehicle k. Vehicles k and k' stop simultaneously. Furthermore, there is no scheduling service request for vehicle k', and vehicle k' is not in the set of vehicles for which a relocation strategy can be implemented. In the middle, or It is the set of vehicles traveling at time t, meaning there exists at least one vehicle. Their destination is the parking area. The above conditions ensure that at least one vehicle is parked in the current parking area within time t, so as to quickly respond to new requests arriving in the partition at any time.

[0094] RT sea It refers to the search time range, within [t+T]. lead ,t+T lead +RT sea During this period, the number of virtual requests for vehicles arriving in typical transportation planning zones should be less than the number currently in the parking area. The number of vehicles in the partition. This condition ensures that after vehicle k is relocated, the remaining vehicles in the partition can quickly respond to new requests that may arrive densely in a short period of time.

[0095] RT int It is the minimum time interval between two consecutive repositionings. When vehicle k arrives at the parking area... There should be a minimum time interval RT between the current time t and the moment t. int This is to prevent the vehicle from being repositioned repeatedly within a short period of time.

[0096] The destination after vehicle relocation should be the area with the highest requested arrival rate in the specified future time period. Vehicle migration should be implemented before the specified latest migration time τ. The specific steps are as follows:

[0097] Step 6.1, if If the current time t≤τ, then proceed to step 6.2; otherwise, proceed to step 6.7.

[0098] Step 6.2, RT sta This represents the time interval for calculating the arrival of goods. A typical freight transport plan is modeled, and the arrival time in [t+T] is calculated. lead,t+T lead +RT sta The number of goods arriving in each partition a∈A during the period is Q. a If the highest value collected from a partition satisfies the minimum repositioning criterion Q for the vehicle... min ,Right now, If the partitions in the set that do not meet the minimum relocation criterion are removed, proceed to step 6.3; otherwise, proceed to step 6.7.

[0099] Step 6.3: Update set A, including the arrival quantity Q. a The highest-ranking partition is defined as a partition. If partitions with the same number of arrivals exist, partitions are selected in order of their partition numbers, and the set is then... The vehicles currently located in this zone's parking area Remove.

[0100] Step 6.4, Update the set if If it flashes, proceed to step 6.5; otherwise, jump to step 6.7.

[0101] Step 6.5: Determine the current location and move to partition a. * Check if the number of vehicles exceeds the capacity limit. If not, proceed to step 6.6; otherwise, partition a. * Remove from set A and return to step 6.3.

[0102] Step 6.6, in the set The vehicle closest to the parking area within the designated zone will be relocated. The relocation destination will be the parking area within the aforementioned zone, and the vehicle will be removed from the set. Remove from the list and return to step 6.4.

[0103] Step 6.7: End the process.

[0104] For vehicles that failed to be repositioned (For example, if a vehicle cannot be relocated due to a limit on the maximum number of vehicles that can be accommodated in a zone), a vehicle located in a parking zone will remain parked in the current parking zone until the conditions for dynamic relocation are met or until it receives a service reservation request.

[0105] The beneficial effects of this invention are: it can dynamically adjust vehicle route planning, optimize the operation process of vehicles in unloading and loading areas, and achieve efficient matching of vehicles and goods, minimize vehicle waiting time and empty driving distance, improve the overall operating efficiency of logistics sites, and reduce logistics transportation costs. Attached Figure Description

[0106] Figure 1 This is a flowchart of the present invention.

[0107] Figure 2 This refers to the path planning based on spatiotemporal networks in this invention.

[0108] Figure 3 This is used to compare vehicle trajectories with and without the dynamic relocation strategy. Detailed Implementation

[0109] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0110] like Figure 1 As shown, the present invention provides a vehicle scheduling method within a logistics site that takes into account road restrictions, comprising the following steps:

[0111] Step 1: Abstract the road network within the logistics site into a topological road network based on a spatiotemporal network. The specific process is as follows:

[0112] The road network within the logistics site is abstracted as a topological network G = (S, L), where L (l ∈ L) is the set of edges in the topological network, and S (s ∈ S) is the set of nodes in the topological network. Nodes include intersection nodes and meeting points. * Meet at the meeting point.

[0113] The attributes of a point in a spatiotemporal network include its corresponding nodes in the topology and its corresponding discrete-time point. The set of points is then... in, Let i be a node in the topology network corresponding to point i. Let i be the discrete time point corresponding to point i, and T represent the set of discrete time points within the study period.

[0114] In a spatiotemporal network, arcs describe vehicle activity and are divided into waiting arcs and traveling arcs. A waiting arc represents the waiting activity of a vehicle at a passing point, and it only occurs at that point. The nodes in the topology corresponding to the start and end points of a waiting arc are the same, and their corresponding discrete time points differ by 1. Where A represents the set of arcs in the spatiotemporal network, (i,j)∈A, A w This represents the set of waiting arcs in a spatiotemporal network. Let j be a node in the topology network corresponding to point j. Let j be the discrete time point.

[0115] A travel arc represents the movement of a vehicle along an edge in a network topology, from the starting node to the ending node. A travel arc is constructed based on each edge in the network topology; the start and end points of the travel arc correspond to the starting and ending nodes of an edge, and the difference between the corresponding discrete time points is the vehicle's travel time along that edge. The set of travel arcs corresponding to the edges in the topological network is: Among them, A τ Let S be the set of travel arcs in the spatiotemporal network, and s(l) and e(l) be the starting / ending nodes of edge l in the topological network, where s(l) and e(l) ∈ S. This represents the time it takes for a vehicle to travel on edge l in the network topology.

[0116] Step 2: Construct a vehicle routing model within the logistics site based on a spatiotemporal network to plan the shortest path for incoming vehicles to unload goods. The specific process is as follows:

[0117] Based on the constructed spatiotemporal network, binary variables are set. A value of 1 indicates that vehicle k traverses arc (i,j) in the spatiotemporal network; otherwise, the value is 0. The model's objective is to minimize the total weighted time of all vehicles in the unloading area of ​​the logistics site, including waiting and travel time. Weights are determined by vehicle priority, ensuring that lower-priority vehicles yield and wait when meeting oncoming traffic. The objective function is then: Where, q k This indicates the priority of vehicle k; the larger the value, the higher the priority.

[0118] The constraints of the model are as follows. Equation (1) indicates that the vehicle enters the field at a given starting point and time; Equation (3) indicates that the vehicle eventually reaches a given destination; Equations (2) and (4) prohibit the vehicle from returning to the starting point and leaving the destination, thereby eliminating sub-loops on the vehicle path; Equation (5) indicates the flow balance constraints of the vehicle at other points.

[0119]

[0120] in, Indicates the entry time of vehicle k, o k ,d k Indicates the starting and ending points of vehicle k, o k ,d k ∈S.

[0121] The vehicle passing and avoidance constraints are shown in Equation (6). For a conflict edge pair (l, l') in the topological network, Equation (6) represents the discrete time point when vehicle k' passes through a driving arc (i', j') corresponding to edge l. Before and after Δt follow Within a time period (i.e., an interval) No other vehicles entered the road section.

[0122]

[0123] Where, Δt pass M represents the time required for two opposing transport vehicles to pass each other at a meeting point, where M represents a sufficiently large positive number.

[0124] Considering the road restrictions within the logistics area, additional constraints are added for road width and turning radius. Equation (7) represents the road width constraint. In practical applications, since vehicles typically do not occupy the entire road width when driving, this means that the maximum lateral offset of the vehicle (relative to the road centerline) will not exceed the actual road width. Equation (8) represents the turning radius constraint, ensuring that vehicles will not scrape or collide due to an excessively small turning radius when turning.

[0125] num×Offset_max≤W(7)

[0126]

[0127] Where num is the number of lanes in the site, Offset_max represents the maximum lateral offset of a vehicle relative to the road centerline during its movement, and W is the road width. R min This indicates the vehicle's minimum turning radius. WB represents the minimum turning radius required for the road, ξ is the vehicle's wheelbase, M is the vehicle's maximum steering angle, and M is an additional safety margin distance added to ensure safety.

[0128] Step 3: Solve the vehicle routing model to obtain the optimal solution that minimizes the vehicle's dwell time in the unloading area. The specific process is as follows:

[0129] When multiple vehicles converge in a certain area of ​​a road network, congestion may occur. If the subsequent impact is not considered when adjusting vehicle routes, waiting at the preceding node or route readjustment may lead to secondary vehicle collisions. To address the aforementioned secondary vehicle collisions and the problem of multiple vehicles encountering each other on the same road segment, a conflict resolution method based on spatiotemporal networks is proposed.

[0130] like Figure 2 As shown, suppose we need to plan the path for transport vehicle C to reach D via n time points at time t. If C does not exist in the spatiotemporal network... t To D t+n The path (where arcs and points are occupied by other transport vehicles) is blocked, so C needs to reach D in time. t+n Waiting at the meeting point tUntil the congestion is cleared, the arrival time of transport vehicle C is updated to the network node.

[0131] The main flow of the algorithm is as follows:

[0132] Step 3.1: The first vehicle enters the road network, numbered i=1. Dijkstra's algorithm is used to calculate the shortest path for vehicle 1, and the set of shortest road segments for vehicle 1 is recorded {[S p ,S q ]}, the time period through which the road segment was collected in Starting from point S o To S p The distance is v, and the speed is v.

[0133] Step 3.2: For each new vehicle entering the road network, record i = i + 1. If i > i, proceed to step 3.5; otherwise, initialize the K short-circuit set P. i Candidate set X i Determine the starting point s i , endpoint t i The relaxation coefficient δ (the ratio of the length of the K shortest path to the length of the shortest path) is used to calculate the shortest path traveled by vehicle i using Dijkstra's algorithm. Put into set P i In the middle, calculation length make Let k = 1, record Road segment set {[S p ,S q ]} and road segment time period set in

[0134] Step 3.3: If k > K, proceed to step 3.2; otherwise, proceed from... From the point closest to t to point s i Point r swings to all possible connected points m, and rm satisfies that it is not in the candidate set X. i The conditions are as follows. Using Dijkstra's method, a search is performed to find the point from m to t. i The shortest path, denoted as p m , put p ksr rm and p m The deviated paths formed are placed into the candidate set X i In, where p ksr express From s i The subpath to r.

[0135] Step 3.4, if candidate set Xi If the value is empty, proceed to step 3.2; if it is not empty, calculate all candidate paths f(p) in ascending order, and denote the path with the minimum value as... Only keep the first K paths, if Proceed to step 3.5, otherwise... Move into P i In this case, k = k + 1, proceed to step 3.3.

[0136] Step 3.5: Compare the queue of road segments traversed by all vehicles in the venue with the queue of the occupied time of each traversed road segment pairwise. If there are two road segments where two vehicles travel in opposite directions and their occupied times conflict, extract the road segment that occurred first [S]. i ,S j Simultaneously, the time each vehicle occupied when passing through the aforementioned road section was recorded. If it is determined that the two vehicles will collide while traveling on the track, proceed to step 3.6 to compare the priorities P of the two vehicles. m P n If there are no road segments with overlapping occupancy times [S] i ,S j If the vehicle travels according to the initial shortest path spatiotemporal information, then the vehicle will travel according to the initial shortest path spatiotemporal information.

[0137] Step 3.6: Compare the priority attribute values ​​of the vehicles on the field. Let the vehicle with higher priority be m1 and the vehicle with lower priority be m2. Guide the two vehicles to pass each other according to the vehicle passing and avoidance criteria. First, traverse the K-short-circuit set of vehicle m2 to determine if there is a secondary short-circuit that is not occupied by a vehicle with higher priority. Calculate the total travel time t on this path. k If it exists, then instruct vehicle m2 to travel along the shortest path and update the road segment {[S] traversed by vehicle m2. p ,S q ]} and time period set If it doesn't exist, then iterate through the intersections between the entrance and the conflict section for vehicles with lower priority. If a meeting point exists, have the vehicle wait at the meeting point and update the road segment {[S] that vehicle m2 has traversed. p ,S q ]} and time period set If no meeting point exists, vehicle m2 will wait at the vehicle entrance for a specified time. And recalculate the total travel time t of the shortest path the vehicle travels at this point, where Update the avoidance vehicle path information and return to step 3.5.

[0138] Step 3.7: Terminate the process by comparing the queues of all vehicles passing through the road segments and the queues of the occupied time of the passed road segments with the five overlapping parts.

[0139] Step 4: Match idle vehicles with goods to be transported. The specific process is as follows:

[0140] A cluster-based vehicle-cargo matching method is adopted. Assume there are n vehicles to be matched in the logistics area, denoted as vehicle set K = {k1, k2, ..., k}. n The maximum load capacity of each truck is [missing information]. There are c goods to be matched, denoted as the goods set G = {G1, G2, ..., G}. b ,...G c Let d be the distance between the destinations of goods b and c. bc Vehicle k n A matching relationship between b and goods b is

[0141] From all matching relationships The resulting n x c matrix represents a matching scheme for the vehicle-cargo matching problem, i.e., a solution to the problem, denoted as [matrix name missing]. The nth row vector of Y represents vehicle k n The matching scheme is given by the c-th column vector, which represents the matching scheme for cargo c.

[0142] In the process of matching vehicles and goods, to concentrate the goods in the matching scheme as much as possible, the transportation points of the goods can be clustered. DBSCAN clustering can be used. After clustering, the original set of goods G can be represented as g={g1,g2,...,g c}. Where g c This represents a cluster after clustering. According to the DBSCDN clustering rules, each noise point in the output is considered as a cluster. A cluster may contain multiple transport points or only one transport point. The coordinates of the cluster center containing multiple transport points are calculated.

[0143] For a given vehicle, its load factor Here, full load is defined as 1, and its calculation formula is as follows: in, Indicate whether cargo b is transported by vehicle k n Delivery, k n ∈K, b∈G; W b This indicates the weight of cargo b. Indicates maximum load capacity; V b This represents the volume of cargo b. This indicates the maximum volume.

[0144] For a given matching scheme Y, the load factor (SZR) is the ratio of the sum of the load factors of all trucks to the sum of the matching relationships, and its calculation formula is as follows:

[0145] For a given transport vehicle, because the goods it transports are highly concentrated, the impact of loading and unloading times on the vehicle's total transport time can be ignored. Therefore, the delivery time problem can be simplified to considering only the vehicle's travel time. Thus, the travel time tk of a given vehicle is... n The formula is The travel time for a certain route is Stk. n Its formula is in, Indicates the distance the vehicle has traveled; This indicates the vehicle's speed.

[0146] With the objectives of minimizing the number of vehicle departures, minimizing vehicle travel time, and maximizing vehicle loading rate, and assuming there are a total of u selectable solutions, the optimization objectives are to minimize the number of vehicle departures Z1, minimize the delivery time Z2, and maximize the loading rate Z3. The objective function is as follows:

[0147] Z1 = min{Y1,Y2,...,Y} u}(9)

[0148] Z2 = max{SZR1,SZR2,...,SZR} u}(10)

[0149] Z3=min{Stk n1 ,Stk n2 ,...,Stk nu}(11)

[0150] Decision variables are set as k n ∈K, b∈G.

[0151] Assume that the final delivery will require a total number of trips, where y is the trip number and y∈total. y∈total, b∈G.

[0152] To meet the above requirements and the actual situation of vehicle-cargo matching in distribution centers, the model's constraints are mainly based on the following factors:

[0153] Constraint 1: When vehicles are loaded into clusters, the weight of the cargo within each cluster cannot exceed the vehicle's rated load capacity, as shown below.

[0154] Constraint 2: When vehicles are loaded into clusters, the volume of cargo within each cluster cannot exceed the vehicle's rated volume, as shown below.

[0155] Constraint 3: After the vehicle is fully loaded, the weight of the loaded cargo must not exceed the vehicle's rated load capacity, as indicated by...

[0156] Constraint 4: After the vehicle is fully loaded, the volume of the loaded cargo must not exceed the vehicle's rated volume, expressed as follows:

[0157] Constraint 5: All goods within the site must be fully loaded, as indicated by...

[0158] Constraint 6: Vehicles included in the vehicle-cargo matching must be within the dispatchable range of the logistics site, as indicated by... Where D represents the schedulable range of the logistics site; This indicates the distance between the vehicle and the center of the logistics site.

[0159] Constraint 7: Each item can only be loaded into one vehicle, as shown below.

[0160] Next, we will calculate the satisfaction of both parties with the truck-cargo matching result. Assume the set of cargo owners includes CO... γ This represents the γth cargo owner and vehicle owner group, VO. λ S represents the λth car owner. sn E represents the nth indicator of the cargo owner. sn This represents the nth indicator for the car owner; (Note: The original text contains some formatting errors and inconsistencies. A CO for cargo owners γ For car owner VO λ Regarding indicator S sn satisfaction For car owners VO λ For cargo owner VO λ Regarding indicator E sn Satisfaction level.

[0161] The first factor is the satisfaction of the cargo owners.

[0162] (1) For the demand date S 1 satisfaction

[0163] Cargo owner CO γ Give the required date S 1 interval Car owner VO λ Give its working date E 1 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0164]

[0165] Where, 0 < σ γ <1, where M represents a sufficiently large positive number.

[0166] (2) For the transportation quotation S2 satisfaction

[0167] Cargo owner CO γ Provide a transportation quote S 2 Specific value Car owner VO λ Give its delivery pricing E 2 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0168]

[0169] in,

[0170] (3) For model S 3 satisfaction

[0171] Cargo owner CO γ Give the required vehicle model S 3 Specific value Car owner VO λ Give its model E 3 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0172]

[0173] in,

[0174] Next is the owner's satisfaction.

[0175] (1) For delivery pricing E 2 satisfaction

[0176] Car owner VO λ Give its delivery pricing E 2 Specific value Cargo owner CO γ Provide a transportation quote S 2 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0177]

[0178] in, 0 < θ λ ≤1.

[0179] (2) For deliverable location E 4 satisfaction

[0180] Car owner VO λ Give its deliverable address E 4 Preference order Cargo owner CO γ Give the delivery address S 4 Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows:

[0181]

[0182] Among them, rank γλ Indicates the owner's CO γ The actual value of the given indicator In the car owner VO λ The ranking in the preference order, 0 < τ λ ≤1, 0<ξ λ <1.

[0183] The coefficient of variation method was used to determine the index weights. The calculation process is as follows:

[0184] First calculate in, μ represents the coefficient of variation of the κ-th index; κ x represents the standard deviation of the κ-th index; κ This represents the average of the κ-th indicator.

[0185] Let the weight of each indicator be ω. κ Its calculation formula is

[0186] Based on the weighting method described above, the weight values ​​of each indicator participating in the satisfaction measurement are determined. Attribute indicators are not included in the satisfaction aggregation and their weights are set to 0. Therefore, the overall satisfaction level of cargo owners and vehicle owners with potential matching partners is obtained:

[0187] Where, α γλ This indicates the overall satisfaction of cargo owners with vehicle owners; β γλ This indicates the overall satisfaction level of vehicle owners with cargo owners; This indicates that the cargo owner has a positive view of indicator S. sn The weights; This indicates that the car owner is interested in the E indicator. sn The weights;

[0188]

[0189] When the overall satisfaction of either party is too low, the vehicle-cargo matching scheme needs to be redesigned.

[0190] Step 5: A request is sent from the loading area within the logistics site. Once the idle vehicle receives the request, it goes to the loading area to load the goods.

[0191] Step 6: A proactive real-time scheduling method based on a dynamic relocation strategy is proposed for the vehicle's operation in the loading area. The specific process is as follows:

[0192] like Figure 3 The image shows a comparison of vehicle trajectories with and without the dynamic relocation strategy.

[0193] The dynamic relocation strategy aims to proactively guide idle vehicles into areas where future service requests are likely to be concentrated, thereby reducing response time. The dynamic relocation strategy is only applied to idle vehicles.

[0194] Dynamic relocation strategies aim to take proactive action based on information from future requests, primarily focusing on the arrival time of virtual requests in typical transportation plans. A dynamic relocation strategy consists of three parts: a triggering mechanism, an implementation mechanism, and a remedial mechanism.

[0195] The dynamic relocation strategy is triggered by idle vehicles located in parking area p∈P at time t. The trigger time for these vehicles is any time t within the parking area.

[0196] For those located in the parking area (a represents the current partition of vehicle k) Idle vehicles The vehicle must meet the following conditions to trigger the implementation mechanism:

[0197] In the parking area There should be at least one vehicle other than vehicle k. Vehicles k and k' stop simultaneously. Furthermore, there is no scheduling service request for vehicle k', and vehicle k' is not in the set of vehicles for which a relocation strategy can be implemented. In the middle, or It is the set of vehicles traveling at time t, meaning there exists at least one vehicle. Their destination is the parking area. The above conditions ensure that at least one vehicle is parked in the current parking area within time t, so as to quickly respond to new requests arriving in the partition at any time.

[0198] RT sea It refers to the search time range, within During this period, the number of virtual requests for vehicles arriving in typical transportation planning zones should be less than the number currently in the parking area. The number of vehicles in the partition. This condition ensures that after vehicle k is relocated, the remaining vehicles in the partition can quickly respond to new requests that may arrive densely in a short period of time.

[0199] RT intIt is the minimum time interval between two consecutive repositionings. When vehicle k arrives at the parking area... There should be a minimum time interval RT between the current time t and the moment t. int This is to prevent the vehicle from being repositioned repeatedly within a short period of time.

[0200] The destination after vehicle relocation should be the area with the highest requested arrival rate in the specified future time period. Vehicle migration should be implemented before the specified latest migration time τ. The specific steps are as follows:

[0201] Step 6.1, if If the current time t≤τ, then proceed to step 6.2; otherwise, proceed to step 6.7.

[0202] Step 6.2, RT sta This represents the time interval for calculating the arrival of goods. A typical freight transport plan is modeled, and the arrival time in [t+T] is calculated. lead ,t+T lead +RT sta The number of goods arriving in each partition a∈A during the period is Q. a If the highest value collected from a partition satisfies the minimum repositioning criterion Q for the vehicle... min ,Right now, If the partitions in the set that do not meet the minimum relocation criterion are removed, proceed to step 6.3; otherwise, proceed to step 6.7.

[0203] Step 6.3: Update set A, including the arrival quantity Q. a The highest-ranking partition is defined as a partition. If partitions with the same number of arrivals exist, partitions are selected in order of their partition numbers, and the set is then... The vehicles currently located in this zone's parking area Remove.

[0204] Step 6.4, Update the set if If it flashes, proceed to step 6.5; otherwise, jump to step 6.7.

[0205] Step 6.5: Determine the current location and move to partition a. * Check if the number of vehicles exceeds the capacity limit. If not, proceed to step 6.6; otherwise, partition a. * Remove from set A and return to step 6.3.

[0206] Step 6.6, in the set The vehicle closest to the parking area within the designated zone will be relocated. The relocation destination will be the parking area within the aforementioned zone, and the vehicle will be removed from the set. Remove from the list and return to step 6.4.

[0207] Step 6.7: End the process.

[0208] For vehicles that failed to be repositioned (For example, if a vehicle cannot be relocated due to a limit on the maximum number of vehicles that can be accommodated in a zone), a vehicle located in a parking zone will remain parked in the current parking zone until the conditions for dynamic relocation are met or until it receives a service reservation request.

[0209] Step 7: After completing all loading and unloading work, plan the shortest departure route for the vehicle to leave the logistics site.

[0210] The present invention has been described in detail above. Those skilled in the art should understand that appropriate modifications and variations can be made without departing from the scope of the invention.

Claims

1. A method for vehicle scheduling in a logistics site considering road restrictions, characterized in that, Includes the following steps: Step 1: Abstract the road network within the logistics site into a topological road network based on spatiotemporal networks; Step 2: Construct a vehicle path planning model within the logistics site based on a spatiotemporal network to plan the shortest path for incoming vehicles to unload goods. Step 3: Solve the vehicle routing model to obtain the optimal solution that minimizes the time vehicles spend in the unloading area; Step 4: Match idle vehicles with goods to be transported; Step 5: A request is sent from the loading area within the logistics site. Once the idle vehicle receives the request, it goes to the loading area to load the goods. Step 6: Propose an active real-time scheduling method based on dynamic relocation strategy for the working process of vehicles in the loading area; Step 7: After completing all loading and unloading work, plan the shortest departure route for the vehicle to leave the logistics site; Step 6 includes the following: The dynamic relocation strategy aims to proactively guide idle vehicles into areas where future service requests are likely to be concentrated, thereby reducing the response time to requests; the dynamic relocation strategy is only executed for idle vehicles. The dynamic relocation strategy aims to take proactive action based on information from future requests, focusing on the arrival time of virtual requests in typical transportation plans; the dynamic relocation strategy consists of three parts: triggering mechanism, implementation mechanism, and remediation mechanism; The dynamic relocation strategy is triggered by time. Located in the parking area Idle vehicles These vehicles can be triggered at any time while they are within the parking area. ; For idle vehicles located in parking areas The vehicle must meet the following conditions to trigger the enforcement mechanism:​ In the parking area There should be something besides vehicles At least one vehicle other than , , and Parking simultaneously; furthermore, there is no targeting of the vehicle. The dispatch service request, and the vehicle The set of vehicles that are not subject to the implementation mechanism for relocation strategies. In the middle, or It is in time The set of vehicles traveling in a given location, i.e., there exists at least one vehicle. , Their destination was the parking area. The above conditions ensure that within time... At least one vehicle must be parked in the current parking area to ensure a quick response to any new requests arriving in the partition at any time; It refers to the search time range, within During this period, the number of virtual requests for vehicles arriving in typical transportation planning zones should be less than the number currently in the parking area. The number of vehicles in the system; this condition ensures that the number of vehicles is within the system. After relocation, the remaining vehicles in the partition are able to quickly respond to new requests that may arrive in a dense manner within a short period of time. It is the minimum time interval between two consecutive repositionings; in the vehicle Arrive at the parking area The moment and the current time There should be a minimum time interval between them. This is to prevent the vehicle from being repositioned repeatedly within a short period of time. The destination of the vehicle after relocation should be the area with the highest request arrival rate in the specified future period; the implementation time of vehicle migration should be earlier than the specified latest migration time The specific steps are as follows: Step 6.1, if And the current time If yes, proceed to step 6.2; otherwise, proceed to step 6.

7. Step 6.2 This represents the time interval for calculating cargo arrival times; it models a typical cargo transportation plan and calculates the time intervals between arrivals. During each partition Number of goods arriving If the highest arrival point collected from a partition satisfies the minimum relocation criterion for the vehicle. ,Right now, If the condition is met, remove the partitions in the set that do not meet the minimum relocation criterion and proceed to step 6.3; otherwise, proceed to step 6.

7. Step 6.3, Update the set The amount to be reached The highest-ranking partition is defined as a partition. If partitions with the same number of arrivals exist, partitions are selected in order of their partition numbers, and the set is then... The vehicles currently located in this zone parking area Remove; Step 6.

4. Update the set If Flashing, go to step 6.5; otherwise, jump to step 6.7; Step 6.5: Determine the current location and move to the designated partition. Check if the number of vehicles exceeds the capacity limit. If not, proceed to step 6.6; otherwise, partition the system. From the set Remove from the list and return to step 6.3; Step 6.6, in the set Find the vehicle closest to the parking area within the designated zone; this vehicle will be relocated. The relocation destination will be the parking area within the aforementioned zone. Simultaneously, the vehicle will be removed from the set... Remove from the list and return to step 6.4; Step 6.7: End the process; For vehicles that fail to relocate vehicles located in a parking area will continue to park in the current parking area until the dynamic relocation condition is met or until they receive a service appointment request.

2. The vehicle dispatching method within a logistics site considering road restrictions according to claim 1, characterized in that, Step 1 includes the following: Abstracting the road network within the logistics site into a topological network ,in, It is the set of edges in a topological network. ; It is the set of nodes in a topological network. Nodes include intersections and meeting points. Meet at the meeting point; The attributes of a point in a spatiotemporal network include its corresponding nodes in the topology and its corresponding discrete-time point. The set of points is then... ,in, For point The corresponding nodes in the topology network For point The corresponding discrete time points, Represents the set of discrete time points within the research period; In a spatiotemporal network, arcs describe vehicle activity and are divided into waiting arcs and traveling arcs. A waiting arc represents the waiting activity of a vehicle at a passing point, and it only occurs at that point. The nodes in the topology corresponding to the start and end points of a waiting arc are the same, and their corresponding discrete time points differ by 1. ,in, Represents the set of arcs in a spatiotemporal network. , This represents the set of waiting arcs in a spatiotemporal network. For point The corresponding nodes in the topology network For point The corresponding discrete time points; A travel arc represents the movement of a vehicle along an edge in a network topology, from the starting node to the ending node. The travel arc is constructed based on each edge in the network topology; the start and end points of the travel arc correspond to the starting and ending nodes of an edge, and the difference between the corresponding discrete time points is the vehicle's travel time along the edge. The set of travel arcs corresponding to the edges in the topological network is: ;in, For the set of travel arcs in the spatiotemporal network, , For edges in the topology network The start / end point nodes, , This indicates that the vehicle is an edge in the topology network. The runtime on the device.

3. The method of scheduling a vehicle within a logistics site taking into account road restrictions according to claim 2, characterized in that, Step 2 includes the following: Based on the constructed spatiotemporal network, binary variables are set. When its value is 1, it indicates that the vehicle... Arc in the spatiotemporal network Conversely, the value is 0. The model's objective is to minimize the total weighted time of all vehicles in the unloading area of ​​the logistics site, including waiting and travel time. Weights are determined by vehicle priority, ensuring that lower-priority vehicles yield and wait when meeting oncoming traffic. The objective function is then: ,in, Indicates vehicle The higher the value, the higher the priority. The constraints of the model are as follows: Equation (1) indicates that the vehicle enters the field at a given starting point and time; Equation (3) indicates that the vehicle eventually reaches a given destination; Equations (2) and (4) prohibit the vehicle from returning to the starting point and leaving the destination, thereby eliminating sub-loops on the vehicle path; Equation (5) indicates the flow balance constraints of the vehicle at other points. （1） （2） （3） （4） （5） wherein, representing the entry time of the vehicle , representing the start and end points of the vehicle , ; The vehicle passing and avoidance constraints are shown in equation (6); for a conflict edge pair in the topology network For example, equation (6) means that when the vehicle Passing by A corresponding travel arc At time, discrete time point front and back Within a time period, i.e., an interval No other vehicles entered the section of road. ; ， （6） wherein denotes the time taken by two oncoming transport vehicles to complete a meeting at a meeting point, denotes a sufficiently large positive number; Considering the road restrictions in the logistics site, additional constraints on road width and turning radius are added; Equation (7) represents the road width constraint. In practical applications, since vehicles usually do not occupy the entire road width when driving, it means that the maximum lateral offset of the vehicle will not exceed the actual road width; Equation (8) represents the turning radius constraint, ensuring that the vehicle will not scrape or collide due to the small turning radius when turning. （7） （8） in, This refers to the number of lanes within the venue. This indicates the maximum lateral deviation of the vehicle relative to the centerline of the road during its movement. Road width; This indicates the vehicle's minimum turning radius. Indicates the minimum turning radius required for the road. It refers to the vehicle's wheelbase. It is the vehicle's maximum steering angle. This is an extra safety margin distance added to ensure safety.

4. The vehicle dispatching method within a logistics site considering road restrictions according to claim 3, characterized in that, Step 3 includes the following: When multiple vehicles converge in a certain area of ​​a road network, congestion may occur. If subsequent impacts are not considered when adjusting vehicle routes, waiting at the preceding node or route readjustment in the conflict segment may lead to secondary vehicle collisions. To address these secondary vehicle collisions and the problem of multiple vehicles meeting on the same road segment, a conflict resolution method based on a spatiotemporal network is proposed. This assumes that transportation vehicles need to be planned. exist Time passes The time scale has reached If the path does not exist in the spatiotemporal network arrive The path, i.e., the arcs and points in the path, is occupied by other transport vehicles. Need to arrive Waiting at the meeting point Until the congestion is cleared, the transport vehicles Arrival time network node update ; The algorithm flow is as follows: Step 3.1: The first vehicle enters the road network and is numbered. Dijkstra's algorithm is used to calculate the shortest path for vehicle 1, and the set of shortest road segments for vehicle 1 is recorded. Gathering during the time period of the road section ,in , , From the starting point arrive distance, It's the vehicle speed; Step 3.2: Record each subsequent new vehicle entering the road network. ,like Proceed to step 3.5, otherwise initialize. Short circuit set Candidate set Determine the starting point ,end relaxation coefficient , it is The ratio of the shortest path length to the shortest path length is used to calculate the vehicle's distance using Dijkstra's algorithm. Shortest path , Add to collection In the middle, calculation length ,make ,make ,Record Road segment collection and road segment time period set ,in , ; Step 3.3, if Proceed to step 3.2, otherwise from Closest to From point to point ,point Swing to all possible points of connection superior, Satisfaction not in the candidate set conditions; The point was found using Dijkstra's method. arrive The shortest path, denoted as ,Bundle , and The deviated paths formed are added to the candidate set. Among them express From arrive Subpaths; Step 3.4, if the candidate set If empty, proceed to step 3.2; if not empty, calculate the paths in all candidate sets. Sort in ascending order, the path to the minimum value is denoted as Only keep the first one a path, if Proceed to step 3.5, otherwise... move in Proceed to step 3.3; Step 3.5: Compare the queue of road segments traversed by all vehicles in the venue with the queue of the occupied time of each road segment. If there are two road segments with conflicting travel directions and occupied times, extract the road segment that occurred first. Simultaneously, the time each vehicle occupied when passing through the aforementioned road section was recorded. If it is determined that the two vehicles will collide while driving on the track, then proceed to step 3.6 to compare the priorities of the two vehicles. , If there are no road sections with overlapping time occupancy... Then the vehicle travels according to the initial shortest path spatiotemporal information; Step 3.6: Compare the priority attribute values ​​of the vehicles on the field, and assign the vehicle with the higher priority as... Vehicles with low priority are ; Guide the two vehicles to pass each other according to the aforementioned vehicle passing and avoidance rules, first traversing the vehicles. of The short-circuit set is used to determine if there are any secondary short circuits not occupied by vehicles with higher priority, and the total travel time for these paths is calculated. ; If it exists, then let the vehicle... Update the vehicle according to this short route. Passing section and time period set If it doesn't exist, then iterate through the intersections between the entrance and the conflict section for vehicles with lower priority. If a meeting point exists, have the vehicle wait at the meeting point and update the vehicle list. Passing section and time period set If there is no meeting point, then the vehicle... Wait at the vehicle entrance, waiting time And recalculate the total travel time of the shortest path the vehicle has traveled at this point. ,in Update the avoidance vehicle path information and return to step 3.5; Step 3.7: Terminate the process by comparing the queues of all vehicles passing through the road segments and the queues of the occupied time of the passed road segments with the five overlapping parts.

5. The method of scheduling a vehicle within a logistics site taking into account road restrictions according to claim 4, characterized in that, Step 4 includes the following: A cluster-based vehicle-cargo matching method is adopted, assuming that there are [missing information] within the logistics site. There are 10 vehicles to be matched, and the vehicle set is recorded. The maximum load capacity of each truck is ;have Let there be a set of goods to be matched. Record the goods With goods The distance between delivery destinations is Vehicle records and goods A matching relationship between them is ; From all matching relationships Composition OK The column matrix represents a matching scheme for the vehicle-cargo matching problem, i.e., a solution to the problem, denoted as . ; The Row vectors represent vehicles The matching scheme, the first Column vectors represent goods Matching scheme; During the vehicle-cargo matching process, to concentrate the goods in the matching scheme as much as possible, the transport points of the goods are clustered using DBSCAN clustering. Based on the clustering, the original set of goods is... Then it is represented as ;in This represents a cluster after clustering. According to the DBSCDN clustering rules, each noise point in the output is considered as a cluster. A cluster may contain multiple transport points or only one transport point. The coordinates of the cluster center containing multiple transport points are calculated. ; For a given vehicle, its load factor Here, full load is defined as 1, and its calculation formula is: ;in, Indicates goods Whether by vehicle Delivery, ; Indicates goods The weight, Indicates the maximum load capacity; Indicates goods volume, Indicates the maximum volume; For a given matching scheme Y, the loading rate The ratio of the sum of the loading rates of all trucks to the sum of their matching relationships is calculated using the following formula: ; For a given transport vehicle, because the goods it transports are highly concentrated, the impact of loading and unloading times on the vehicle's transport time can be ignored. Therefore, the delivery time problem can be simplified to considering only the vehicle's travel time; thus, the travel time of a given vehicle is... The formula is The travel time for a certain route is Its formula is ;in, Indicates the distance the vehicle has traveled; Indicates the vehicle's speed; With the objectives of minimizing the number of departures from the site, minimizing vehicle travel time, and maximizing vehicle load factor, let the total number of selectable solutions be: The optimization objective is to minimize the number of trains. Shortest delivery time and maximize load rate The objective function is as follows: （9） （10） （11） The decision variable is set to ; Assuming that the last time the vehicle needs to be used The distribution of the trip is completed, The trip number is numbered, , ; To meet the above requirements and the actual situation of vehicle-cargo matching in distribution centers, the model's constraints are mainly based on the following factors: Constraint 1: When vehicles are loaded according to the clustering, the weight of the goods in the cluster cannot exceed the rated load of the vehicle, expressed as ; Constraint 2: When vehicles are loaded by cluster, the volume of goods in the cluster cannot exceed the rated volume of the vehicle, expressed as ; Constraint 3: After the vehicle is fully loaded, the weight of the loaded goods cannot exceed the rated load of the vehicle, denoted as ; Constraint 4: After the vehicle is loaded, the volume of the loaded cargo cannot exceed the rated volume of the vehicle, denoted as ; Constraint 5: All cargo in the yard must be loaded, denoted as ; Constraint 6: Vehicles included in the vehicle-cargo matching must be within the dispatchable range of the logistics site, as indicated by... ;in, Indicates the available range of logistics facilities; Indicates the distance of the vehicle from the center of the logistics site; Constraint 7: Each item can be loaded into only one vehicle, denoted as ; Next, we will calculate the satisfaction of both parties with the vehicle-cargo matching result; assuming the cargo owner set includes... Indicates the first Freighters and truck owners gather. Indicates the first One car owner, Indicates the cargo owner's One indicator, Indicates the car owner's number Individual indicators; record For cargo owners For car owners Regarding indicators satisfaction For car owners For cargo owners Regarding indicators Satisfaction level; First is the satisfaction of the cargo owner; (1) Satisfaction of the demand date ​ cargo owner Please provide the required date. interval Car owner Give its working dates Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows: （12） wherein , represents a sufficiently large positive number; (2) Satisfaction with the shipping quote ​ cargo owner Provide a shipping quote Specific value Car owner Provide its delivery pricing Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows: （13） in, , , ; (3) For vehicle models satisfaction cargo owner Provide the required vehicle model Specific value Car owner Give its model Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows: （14） in, ; Next is the owner's satisfaction; (1) Regarding delivery pricing satisfaction Car owner Provide its delivery pricing Specific value cargo owner Provide a shipping quote Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows: （15） in, , , ; (2) For deliverable locations satisfaction Car owner Give its deliverable address Preference order cargo owner Give delivery address Specific value The formula for calculating cargo owner satisfaction under this indicator is as follows: （16） in, Indicates cargo owner The actual value of the given indicator In the car owner Rank in preference order , ; The coefficient of variation method was used to determine the index weights. The calculation process is as follows: First calculate ,in, Indicates the first The coefficient of variation of the indicator; Indicates the first The standard deviation of the indicators; Indicates the first The average of the indicators; Let the weights of each indicator be... Its calculation formula is ; Based on the weighting method described above, the weight values ​​of each indicator participating in the satisfaction measurement are determined. Attribute indicators are not included in the satisfaction aggregation and their weights are set to 0. Therefore, the overall satisfaction of cargo owners and vehicle owners with potential matching partners is obtained. ; in, This indicates the overall satisfaction level of cargo owners with vehicle owners; This indicates the overall satisfaction level of vehicle owners with cargo owners; This indicates the cargo owner's preference for the quota. The weights; This indicates the car owner's opinion on the quota. The weights; ; ; When the overall satisfaction of either party is too low, the vehicle-cargo matching scheme needs to be redesigned.

Images