Method and device for optimizing distribution sequence of intelligent sending trolleys in three-dimensional space

Abstract

The invention relates to a distribution sequence optimization method and device for intelligent sending trolleys in a three-dimensional space, and the method comprises the steps: S1, obtaining the three-dimensional map information of a target distribution region, and generating an expression graph of the target region; s2, obtaining delivery order data, and based on a destination of the delivery order data, in combination with three-dimensional map information of a target delivery area, amplifying virtual nodes; s3, based on the generated virtual nodes, generating a distance completion graph of the target area in combination with the expression graph of the target area; s4, based on the distance complete graph, establishing a mathematical optimization model of a sending service sequence corresponding to the park; s5, performing linearization and simplification processing on the mathematical optimization model; and S6, solving the mathematical optimization model by introducing an improved ant colony algorithm of space-time clustering characteristics to obtain a distribution sequence. Compared with the prior art, the method has the advantages that the height information is combined, the distance complete graph is generated, modeling is carried out based on the distance complete graph, a real three-dimensional distribution scene is more conformed, and the optimization effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics optimization, and in particular to a method and device for optimizing the delivery sequence of an intelligent delivery vehicle in a three-dimensional space. Background Art

[0002] With the rapid development of the logistics industry, the demand for express delivery has increased significantly. However, the rapid growth has also brought many problems, especially in the "last mile" delivery link of logistics. The last mile refers to the final delivery step from the distribution center to the consumer, which is the most complex, costly and inefficient part of the entire logistics supply chain. For customers, since the last mile delivery is usually completed by temporary workers or outsourced personnel, the quality of logistics services is often inconsistent. As services closely related to residents' lives, such as express delivery and food delivery, their service quality will profoundly affect customer satisfaction and put forward new requirements for suppliers.

[0003] Therefore, the logistics system faces the contradiction between supply constraints and personalized needs. To a certain extent, the long-term inefficiency of the last mile logistics has become the bottleneck of the entire logistics system. The emergence of new technologies has provided new solutions to the last mile problem. With the rapid development of artificial intelligence, big data and electrification, driverless technology has become a hot topic of global research. Intelligent delivery robots based on driverless technology are widely believed to completely transform the growing logistics network. These robots can not only reduce transportation costs, but also reduce carbon footprint and improve road safety.

[0004] With the booming development of e-commerce, the logistics and distribution industry is facing unprecedented challenges. The traditional distribution model mainly relies on manual distribution and customer pick-up in stores, but this model has many limitations, such as high labor costs, time costs, and lack of portability. In order to meet these challenges, the logistics industry has begun to explore new distribution models. The expansion of logistics networks and the application of new technologies have made it possible to innovate distribution models. By optimizing the path planning algorithm, the efficiency of distribution vehicles can be significantly improved, and the delivery time and cost can be reduced. On the demand side, consumers have higher and higher requirements for delivery time windows and hope to obtain faster and more accurate delivery services. By optimizing the delivery path, this demand of users can be better met. On the supply side, optimizing the delivery path can not only improve delivery efficiency, but also reduce operating costs. By reducing unnecessary routes and repeated deliveries, fuel consumption and vehicle wear can be reduced, thereby reducing logistics costs. At the same time, by improving operating efficiency, more delivery tasks can be completed in a shorter time, improving the competitiveness of logistics companies.

[0005] In addition, the traditional path planning model only considers the path optimization in two-dimensional space, and does not consider the distance and time optimization of door-to-door delivery. However, with the development of delivery robot technology, robot delivery to door-to-door delivery has become possible. Therefore, it is necessary to perform path optimization in three-dimensional space considering door-to-door delivery. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for optimizing the delivery sequence of an intelligent delivery cart in three-dimensional space, which combines height information, generates a distance completion graph, and models based on the distance completion graph, which is more in line with the real three-dimensional delivery scene, thereby improving the optimization effect.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A method for optimizing the delivery sequence of an intelligent delivery vehicle in three-dimensional space, comprising:

[0009] Step S1: Acquire three-dimensional map information of the target delivery area and generate an expression map of the target area;

[0010] Step S2: acquiring delivery order data, and based on the destination of the delivery order data and the three-dimensional map information of the target delivery area, expanding the virtual node;

[0011] Step S3: Based on the generated virtual nodes and in combination with the expression graph of the target area, a distance completion graph of the target area is generated;

[0012] Step S4: Based on the complete distance graph, a mathematical optimization model of the delivery service sequence corresponding to the park is established;

[0013] Step S5: linearizing and simplifying the mathematical optimization model;

[0014] Step S6: The delivery sequence is obtained by solving the mathematical optimization model by introducing an improved ant colony algorithm with spatiotemporal clustering features.

[0015] The expression graph of the target region includes nodes and plane distances between nodes.

[0016] The delivery order data includes the order number, the delivery user's house number, the order quantity, the number of large items, the number of small items, the earliest arrival time and the latest arrival time.

[0017] In the step S2, if there are multiple delivery orders with the same node as their destination, the node is expanded to include multiple virtual nodes that are the same number as the number of delivery orders with the node as their destination.

[0018] The distance completion diagram is:

[0019] G=(I,E,W)

[0020] Where G is the distance completion graph, I is the set of nodes after expansion, E is the set of plane distances between nodes after expansion, and W is the set of three-dimensional distances including all nodes after expansion;

[0021] The elements in the three-dimensional distance set represent the three-dimensional distances between nodes;

[0022] If two nodes are in the same building, the three-dimensional distance between the two nodes is:

[0023] w ij =τ×|f i -f j |+D(d i )+D(d j )

[0024] Among them, w ij is the three-dimensional distance between two nodes, τ is the longitudinal distance coefficient, f i is the floor of node i, f j is the floor of node j, d i is the room type corresponding to the house number of node i, d j is the room type corresponding to the house number of node j, D(·) is the distance to the channel exit corresponding to each room type,

[0025] If two nodes are in different buildings, the three-dimensional distance between the two nodes is:

[0026] w ij =τ×(f i +f j )+D(d i )+D(d j )+D ground (e i ,e j )

[0027] Among them, D ground (e i ,e j ) is the plane distance between node i and node j.

[0028] The longitudinal distance coefficient is:

[0029]

[0030] Where: h is the height of a single-storey building;

[0031] The plane distance between node i and node j is the plane distance from the entrance and exit of the unit building of node i to the entrance and exit of the unit building of node j.

[0032] The objective function of the mathematical optimization model is:

[0033]

[0034] Where: p is the sum of vehicle operating cost, vehicle fixed cost and window penalty, is the vehicle distribution parameter. If the vth vehicle passes through the route from node i to node j, the value is 1, otherwise it is 0. V is the vehicle set, C s is the unit time consumption cost of a single vehicle, g ij is the travel time from node i to node j, c f is the fixed cost of each activated vehicle, |·| is the number of elements in the set, and w i is the window penalty for not reaching node i at the target time;

[0035] The constraints of the mathematical optimization model are:

[0036]

[0037]

[0038] Among them, Y i v is the demand execution value. If vehicle v executes the demand of node i, the value is 1, otherwise it is 0. vn is the number of vehicles started, ta j is the actual arrival time at node j, ta i is the actual arrival time at node i, ts i is the time required to serve node i, T max is a large positive number used to relax the condition, β is the penalty coefficient for vehicles arriving later than the latest arrival time, tl i is the amount of time node i is allowed to delay, is the number of goods that have been delivered by node i and the capacity pane of size t before, is the cargo state. If node i needs to receive cargo from the cart, the value is 1, otherwise it is 0. M is a very large positive number. is the number of goods that the capacity pane of node i and previous size t has received, S is the cargo set, n is the number of cargoes of the lower level that can be loaded in the capacity window of the higher level, and F s The number of S-level capacity windows the car has.

[0039] In step S5, the Big M method is used to linearize the model constraints and the objective function.

[0040] The step S6 comprises:

[0041] Step S6-1: clustering based on the earliest arrival time and the latest departure time dimension and the three-dimensional space dimension;

[0042] Step S6-2: Initialize the initial data information of the ant colony algorithm: including setting the initial pheromone on the path, the number of ants in the population, and the maximum number of iterations;

[0043] Step S6-3: Using the objective function of the mathematical optimization model as the fitness function;

[0044] Step S6-4: performing path selection based on the taboo table;

[0045] Step S6-5: By searching with long-time and short-time pheromones, the distribution order is solved.

[0046] A device for optimizing the delivery sequence of an intelligent delivery vehicle in three-dimensional space, comprising a memory, a processor, and a program stored in the memory, wherein the processor implements the above-mentioned method when executing the program.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. Combined with height information, a distance completion map is generated, and modeling is performed based on the distance completion map, which is more in line with the real three-dimensional distribution scenario, thereby improving the optimization effect.

[0049] 2. The model is built based on the capacity characteristics of the intelligent delivery vehicle, and the multi-level capacity panes and soft time window characteristics of the vehicle are considered. An improved ant colony search algorithm is proposed, and the solution is innovatively performed in three-dimensional space to achieve adaptation to real three-dimensional delivery scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a schematic flow chart of the main steps of the method of the present invention. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0052] A method for optimizing the delivery sequence of intelligent delivery vehicles in three-dimensional space. Figure 1 As shown, including:

[0053] Step S1: Acquire three-dimensional map information of the target delivery area and generate an expression map of the target area;

[0054] The expression graph of the target region includes nodes and planar distances between nodes.

[0055] Step S2: acquiring delivery order data, and based on the destination of the delivery order data and the three-dimensional map information of the target delivery area, expanding the virtual node;

[0056] Delivery order data includes order number, delivery user's house number, order quantity, number of large items, number of small items, earliest arrival time and latest arrival time.

[0057] And if there are multiple delivery orders with the same node as their destination, the node is expanded to include multiple virtual nodes that match the number of delivery orders with the node as their destination.

[0058] Step S3: Based on the generated virtual nodes and in combination with the expression graph of the target area, a distance completion graph of the target area is generated;

[0059] The distance to completion is as follows:

[0060] G=(I,E,W)

[0061] Where G is the distance completion graph, I is the set of nodes after expansion, E is the set of plane distances between nodes after expansion, and W is the set of three-dimensional distances including all nodes after expansion;

[0062] The elements in the three-dimensional distance set represent the three-dimensional distances between nodes;

[0063] If two nodes are in the same building, the three-dimensional distance between the two nodes is:

[0064] w ij =τ×|f i -f j |+D(d i )+D(d j )

[0065] Among them, w ij is the three-dimensional distance between two nodes, τ is the longitudinal distance coefficient, f i is the floor of node i, f j is the floor of node j, d i is the room type corresponding to the house number of node i, d j is the room type corresponding to the house number of node j, D(·) is the distance to the channel exit corresponding to each room type,

[0066] If two nodes are in different buildings, the three-dimensional distance between the two nodes is:

[0067] w ij =τ×(f i +f j )+D(d i )+D(d j )+Dground (e i ,e j )

[0068] Among them, D ground (e i ,e j ) is the plane distance between node i and node j.

[0069] The longitudinal distance coefficient is:

[0070]

[0071] Where: h is the height of a single-storey building;

[0072] The plane distance between node i and node j is the plane distance from the entrance and exit of the unit building of node i to the entrance and exit of the unit building of node j.

[0073] Step S4: Based on the complete distance graph, a mathematical optimization model of the delivery service sequence corresponding to the park is established;

[0074] The establishment of mathematical optimization model is based on the following assumptions:

[0075] 1. There is only one distribution center, and the delivery vehicles must start from here and return here.

[0076] 2. The delivery trolley models are consistent, and the maximum load capacity is known and the same.

[0077] 3. The vehicle transportation and stair climbing speeds are both uniform and known.

[0078] 4. The capacity of the large pane allows the storage of goods from multiple small panes.

[0079] The objective function of the mathematical optimization model is:

[0080]

[0081] Where: p is the sum of vehicle operating cost, vehicle fixed cost and window penalty, is the vehicle distribution parameter. If the vth vehicle passes through the route from node i to node j, the value is 1, otherwise it is 0. V is the vehicle set, C s is the unit time consumption cost of a single vehicle, g ij is the travel time from node i to node j, c f is the fixed cost of each activated vehicle, |·| is the number of elements in the set, and w i is the window penalty for not reaching node i at the target time;

[0082] The constraints of the mathematical optimization model are:

[0083]

[0084]

[0085] Among them, Y i v is the demand execution value. If vehicle v executes the demand of node i, the value is 1, otherwise it is 0. vn is the number of vehicles started, ta j is the actual arrival time at node j, ta i is the actual arrival time at node i, ts i is the time required to serve node i, T max is a large positive number used to relax the condition, β is the penalty coefficient for vehicles arriving later than the latest arrival time, tl i is the amount of time node i is allowed to delay, is the number of goods that have been delivered by node i and the capacity pane of size t before, is the cargo state. If node i needs to receive cargo from the cart, the value is 1, otherwise it is 0. M is a very large positive number. is the number of goods that the capacity pane of node i and previous size t has received, S is the set of goods, n is the number of k-level goods that can be accommodated by the k+1-level capacity pane, n s-t is the st power of n, F s The number of S-level capacity windows the car has.

[0086] Constraints (2) and (3) define the domain of the decision variables. Constraint (4) ensures that all vehicles start from a fixed depot. Constraint (5) requires that all vehicles return to the depot. Constraint (6) states that vehicles cannot go to the same location. Constraint (7) calculates the number of starting vehicles. Constraints (8) and (9) require that a customer can only be served once by a vehicle at a time. Constraint (10) ensures that vehicles must leave a node after visiting the same node. Constraint (11) is used to limit the time to reach a node. Constraint (12) specifies the penalty incurred when a vehicle fails to meet the time window required by the customer. Constraints (13) and (14) calculate the number of goods that need to be received and the number of goods that need to be shipped before the node. Constraint (15) determines whether the capacity constraints of different sizes are met by calculating the number of goods that still need to be shipped and the number of goods received previously, and adding them together to obtain the current load.

[0087] Step S5: linearizing and simplifying the mathematical optimization model;

[0088] Specifically, the Big M method is used to linearize the model constraints and objective function. First, the model is linearized and simplified to obtain the following constraints:

[0089]

[0090] Constraint (17) is replaced by constraints (19)-(24) to achieve model linearization.

[0091]

[0092] in is the binary variable involved in the linearization of the time penalty function.

[0093] First, we divide the constraint (16) into two sub-problems. and When , constraint (21) is simplified to Constraint (22) is redundant. Combined with constraint (19), we can derive The constraint (20) is simplified to t ei ≤t ai This means that the vehicle arrives at node i later than the earliest arrival time, and the penalty for early arrival is equal to 0. and When , constraint (22) becomes Constraint (21) becomes redundant. Combining constraints (19) and (20), we obtain and t ei ≥t ai It means that the vehicle arrives at node i earlier than the earliest time, and the penalty for early arrival is equal to α(t ei -t ai ).when and Sometimes, there is and This means that the vehicle arrives just at the earliest allowed arrival time. For constraint (18), by the same linearization method, constraint (25)-(30) can be used to replace constraint (18).

[0094]

[0095] Step S6: Solving the mathematical optimization model by introducing an improved ant colony algorithm with spatiotemporal clustering features to obtain the delivery sequence, including:

[0096] Step S6-1: clustering based on the earliest arrival time and the latest departure time dimension and the three-dimensional space dimension;

[0097] Step 1: Clustering in the time dimension based on the earliest arrival time and the latest departure time. Focusing on the time characteristics of user demand, the time distance matrix is ​​calculated as the clustering index by sending the Euclidean distance of the corresponding demand time between users. The specific formula is:

[0098]

[0099] Among them, T ij Represents the distance in demand time between two users.

[0100] Step 2: Clustering in three-dimensional space, clustering based on three-dimensional space and category features, unit, floor and house number information, H ij Represents the distance between two nodes as a clustering indicator, specifically:

[0101]

[0102] Wherein, e represents the unit, which can be T5A, T5B, ..., T5D in this example. f represents the floor, which can be 1, 2, ..., 6 in this example. d represents the house number, which can be 1, 2, ..., 10 in this example. α and β are 0.5 and 0.4 respectively.

[0103] Step S6-2: Initialize the initial data information of the ant colony algorithm: including setting the initial pheromone on the path, the number of ants in the population, and the maximum number of iterations, specifically including:

[0104] In the first step, the pheromone on all paths is set to the same initial value, such as 1, to ensure that each path has the same attractiveness at the beginning of the algorithm.

[0105] Step 2: Determine the ant population size, which can be 100 in this case. This determines how many ants will construct the solution in each iteration.

[0106] Step 3: Set the maximum number of iterations, which can be 50 in this example, as one of the conditions for stopping the algorithm to control the running time of the algorithm or the termination of the search process.

[0107] Step S6-3: Using the objective function of the mathematical optimization model as the fitness function;

[0108] Step S6-4: Path selection is performed based on the taboo table, which is specifically divided into three steps:

[0109] Step 1: Introduce the search method of the taboo table: Use the roulette wheel selection method to determine the next node. Check whether the node is in the taboo table. If the node is not in the taboo table, continue and initialize a value on the corresponding edge to record the number of iterations before the node is released from the taboo table. If the node is in the taboo table, the relevant value will be reduced by 1. Then use the roulette wheel selection method to determine the next node.

[0110] Step 2: Determine the candidate subset. During the path generation process, a node is randomly selected from multiple clusters. Then nodes within the same cluster are selected until all nodes in the cluster are selected. This process is called intra-cluster node selection. After that, return to the global node selection to determine the next node from a different cluster.

[0111] Step 3: Path cutoff mechanism. To solve the problem of capacity waste caused by discrete capacity containers, the mechanism introduces a look-ahead function. When the large-capacity container of the delivery robot cannot be fully utilized, the robot usually returns to the warehouse, resulting in insufficient use of the small-capacity container. The look-ahead mechanism allows multiple steps to be considered when the capacity limit is not met, thereby improving the overall utilization of the robot container.

[0112] Step S6-5: By searching with long-time and short-time pheromones, the distribution order is solved.

[0113] Update pheromones. The pheromone update mechanism of the ant colony algorithm includes the volatilization and increase of pheromones. The volatilization of pheromones is to avoid over-reliance on previous paths and maintain the exploratory nature of the algorithm. Usually, a volatilization coefficient is set to reduce pheromones. The increase of pheromones is to accumulate new pheromones through the paths taken by ants. The shorter or better solutions will receive more pheromones, attracting subsequent ants to choose them first. The two work together to enable the algorithm to explore new paths and utilize existing excellent paths.

[0114] Through the search of long-term and short-term pheromones, the formula is as follows:

[0115]

[0116] The calculation formula of the time pheromone from node i to node j is as follows:

[0117]

[0118] Part 1: Capturing nodes that are penalized in the current time window is called short-time pheromone. The calculation formula is as follows:

[0119]

[0120] It represents the time for the lth ant to go from node to node.

[0121] Part 2: Introduce the knowledge from previous iterations to capture the long-term time penalty relationship, which is called global time pheromone. In order to increase the flexibility of vehicle arrival time and improve the ability to jump out of the local optimal solution, the probability formula of vehicle arrival is as follows:

[0122]

[0123] After deriving the arrival density function, from te j To tl j Points, as follows:

[0124]

[0125] This formula can be used to calculate the probability of a vehicle arriving on time, denoted as Δω j Time pheromone also has an iterative update process, specifically:

[0126]

[0127] Through this mechanism, global temporal pheromones undergo a dynamic process of memory and forgetting.

[0128] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

Claims

1. A method for optimizing the delivery sequence of an intelligent delivery vehicle in three-dimensional space, characterized in that: include: Step S1: Acquire three-dimensional map information of the target delivery area and generate an expression map of the target area; Step S2: acquiring delivery order data, and based on the destination of the delivery order data and the three-dimensional map information of the target delivery area, expanding the virtual node; Step S3: Based on the generated virtual nodes and in combination with the expression graph of the target area, a distance completion graph of the target area is generated; Step S4: Based on the complete distance graph, a mathematical optimization model of the delivery service sequence corresponding to the park is established; Step S5: linearizing and simplifying the mathematical optimization model; Step S6: The delivery sequence is obtained by solving the mathematical optimization model through the improved ant colony algorithm that introduces spatiotemporal clustering features.

2. According to claim 1, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space is characterized in that: The expression graph of the target region includes nodes and plane distances between nodes.

3. According to claim 1, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space is characterized in that: The delivery order data includes the order number, the delivery user's house number, the order quantity, the number of large items, the number of small items, the earliest arrival time and the latest arrival time.

4. According to claim 1, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space is characterized in that: In the step S2, if there are multiple delivery orders with the same node as their destination, the node is expanded to include multiple virtual nodes that are the same number as the number of delivery orders with the node as their destination.

5. According to claim 4, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space is characterized in that: The distance completion diagram is: G=(I,E,W) Where G is the distance completion graph, I is the set of nodes after expansion, E is the set of plane distances between nodes after expansion, and W is the set of three-dimensional distances including all nodes after expansion; The elements in the three-dimensional distance set represent the three-dimensional distances between nodes; If two nodes are in the same building, the three-dimensional distance between the two nodes is: w ij =τ×|f i -f j |+D(d i )+D(d j ) Among them, w ij is the three-dimensional distance between two nodes, τ is the longitudinal distance coefficient, f i is the floor of node i, f j is the floor of node j, d i is the room type corresponding to the house number of node i, d j is the room type corresponding to the house number of node j, D(·) is the distance to the channel exit corresponding to each room type, If two nodes are in different buildings, the three-dimensional distance between the two nodes is: w ij =τ×(f i +f j )+D(d i )+D(d j )+D ground (e i ,e j ) Among them, D ground (e i ,e j ) is the plane distance between node i and node j.

6. According to claim 5, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space is characterized in that: The longitudinal distance coefficient is: Where: h is the height of a single-storey building; The plane distance between node i and node j is the plane distance from the entrance and exit of the unit building of node i to the entrance and exit of the unit building of node j.

7. According to claim 6, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space, characterized in that: The objective function of the mathematical optimization model is: Where: p is the sum of vehicle operating cost, vehicle fixed cost and window penalty, is the vehicle distribution parameter. If the vth vehicle passes through the route from node i to node j, the value is 1, otherwise it is 0. V is the vehicle set, C s is the unit time consumption cost of a single vehicle, g ij is the travel time from node i to node j, c f is the fixed cost of each activated vehicle, |·| is the number of elements in the set, and w i is the window penalty for not reaching node i at the target time; The constraints of the mathematical optimization model are: Among them, Y i v is the demand execution value. If vehicle v executes the demand of node i, the value is 1, otherwise it is 0. vn is the number of vehicles started, ta j is the actual arrival time at node j, ta i is the actual arrival time at node i, ts i is the time required to serve node i, T max is a large positive number used to relax the condition, β is the penalty coefficient for vehicles arriving later than the latest arrival time, tl i is the amount of time node i is allowed to delay, is the number of goods that have been delivered by node i and the capacity pane of size t before, is the cargo state. If node i needs to receive cargo from the cart, the value is 1, otherwise it is 0. M is a very large positive number. is the number of goods that the capacity pane of node i and previous size t has received, S is the cargo set, n is the number of cargoes of the lower level that can be loaded in the higher level capacity window, and F s The number of S-level capacity windows the car has.

8. According to claim 6, a method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space, characterized in that: In step S5, the Big M method is used to linearize the model constraints and the objective function.

9. The method for optimizing the delivery sequence of a smart delivery vehicle in three-dimensional space according to claim 6, characterized in that: The step S6 comprises: Step S6-1: clustering based on the earliest arrival time and the latest departure time dimension and the three-dimensional space dimension; Step S6-2: Initialize the initial data information of the ant colony algorithm: including setting the initial pheromone on the path, the number of ants in the population, and the maximum number of iterations; Step S6-3: Using the objective function of the mathematical optimization model as the fitness function; Step S6-4: Path selection based on the taboo table; Step S6-5: By searching with long-time and short-time pheromones, the distribution order is solved.

10. A device for optimizing the delivery sequence of an intelligent delivery vehicle in a three-dimensional space, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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