Electric logistics vehicle path optimization method and device, medium and electronic equipment

Abstract

The invention discloses an electric logistics vehicle path optimization method and device, a medium and electronic equipment, and relates to the field of data processing. The distribution path can be optimized, and the cost of electric logistics distribution can be reduced. The electric logistics vehicle path optimization method comprises the steps of obtaining a scheduling demand of an electric logistics vehicle, wherein the scheduling demand comprises a plurality of distribution nodes and a time period of each distribution node; an optimization objective function is determined according to the scheduling demand, the optimization objective function is used for calculating the total distribution cost, the total distribution cost comprises nonlinear time penalty cost, and the time penalty cost is the cost generated in the time period of the distribution node; obtaining constraint conditions for the electric logistics vehicle and the distribution node; and optimizing a distribution path based on the optimization objective function and the constraint condition to obtain a target distribution path of the electric logistics vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of logistics distribution, and in particular to an electric logistics vehicle path optimization method and device, a medium and an electronic device. BACKGROUND

[0002] Reasonable planning and distribution path of electric logistics vehicles is an effective way to reduce costs. The current electric logistics vehicle path optimization mostly uses a traditional time window model. The traditional time window model usually uses a linear penalty function to handle time deviation. The sensitivity of customers to delay is often non-linear, which does not match the characteristics. In particular, the decline in customer satisfaction and the risk of default caused by late arrival often grow exponentially, while the impact of early arrival is relatively limited. The existing linear penalty model cannot accurately depict this reality, resulting in deviations in the allocation of time cost in the path optimization result, and thus affecting the economy and feasibility of the overall scheme. SUMMARY

[0003] The present application provides an electric logistics vehicle path optimization method, device, medium and electronic device, which can make the time window penalty cost allocation more in line with reality, and help improve the applicability of path optimization model cost calculation.

[0004] In a first aspect, the present application provides an electric logistics vehicle path optimization method, comprising: obtaining scheduling requirements of an electric logistics vehicle, wherein the scheduling requirements include a plurality of distribution nodes and a time limit for each distribution node; determining an optimization objective function according to the scheduling requirements, wherein the optimization objective function is used to calculate a total distribution cost, and the total distribution cost includes a non-linear time penalty cost, and the time penalty cost is a cost generated outside the time limit of the distribution node; obtaining constraint conditions for the electric logistics vehicle and the distribution node; optimizing a distribution path based on the optimization objective function and the constraint conditions to obtain a target distribution path of the electric logistics vehicle.

[0005] In the above method of the present embodiment, in the distribution path optimization process, the total distribution cost is calculated by using the optimization objective function, and the time penalty cost in the non-linear form is considered in the total distribution cost, so as to accurately depict the overtime penalty in a non-linear way, avoid overtime risk in the distribution path optimization process, and reduce the cost caused by overtime. At the same time, the constraint conditions of the electric logistics vehicle and the distribution node are considered to ensure on-time delivery while taking into account the feasibility of vehicle operation, thereby enhancing the reliability of the distribution path.

[0006] In a second aspect, the present application provides an electric logistics vehicle path optimization device, comprising: The scheduling data acquisition module is configured to acquire scheduling requirements of the electric logistics vehicle, wherein the scheduling requirements include a plurality of distribution nodes and time limits of each distribution node; The optimization target determination module is configured to determine an optimization target function according to the scheduling requirements, wherein the optimization target function is used to calculate a total distribution cost, and the total distribution cost includes a nonlinear time penalty cost, and the time penalty cost is a cost generated out of the time limit of the distribution node; The constraint determination module is configured to acquire constraint conditions of the electric logistics vehicle and the distribution nodes; The optimization module is configured to optimize a distribution path based on the optimization target function and the constraint conditions to obtain a target distribution path of the electric logistics vehicle.

[0007] In a third aspect, the present application provides an electronic device, which includes a memory and one or more processors. The memory stores one or more computer programs including instructions, which, when executed by the processor, cause the electronic device to perform the electric logistics vehicle path optimization method in the first aspect.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions, when the instructions are run on an electronic device, cause the electronic device to perform the electric logistics vehicle path optimization method in the first aspect.

[0009] In a fifth aspect, the present application provides a computer program product, when the computer program product is run on an electronic device, cause the electronic device to perform the electric logistics vehicle path optimization method in the first aspect.

[0010] It can be understood that the beneficial effects of the electric logistics vehicle path optimization device, the electronic device, the computer-readable storage medium, and the computer program product provided above can refer to the beneficial effects of the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 A flowchart of the electric logistics vehicle path optimization method provided by the embodiments of the present application is shown; Figure 2 A time penalty cost function diagram in the electric logistics vehicle path optimization method provided by the embodiments of the present application is shown; Figure 3 A structural diagram of the electric logistics vehicle path optimization device provided by the embodiments of the present application is shown; Figure 4 A structural diagram of the electronic device provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0012] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. For example, "first chip" and "second chip" are only used to distinguish different chips and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0013] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or for a period of time after the occurrence of a certain situation. The embodiments of this application do not make specific limitations on this.

[0014] The implementation of this embodiment will now be described in detail with reference to the accompanying drawings.

[0015] This embodiment provides a method for optimizing the route of an electric logistics vehicle. For example, this method can be applied to autonomous electric vehicles, as well as to various electronic devices such as computers (PCs), tablets, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle-mounted systems; this embodiment does not impose any special limitations on it.

[0016] Figure 1 A flowchart illustrating the electric logistics vehicle route optimization method provided in an embodiment of this application is shown.

[0017] like Figure 1 As shown, the electric logistics vehicle route optimization method may include the following steps: Step 101: Obtain the scheduling requirements for electric logistics vehicles, which include multiple delivery nodes and the time limit for each delivery node.

[0018] Step 102: Determine the optimization objective function based on the scheduling requirements. The optimization objective function is used to calculate the total delivery cost. The total delivery cost includes a non-linear time penalty cost, which is the cost incurred outside the time limit of the delivery node.

[0019] Step 103: Obtain the constraints for the electric logistics vehicle and the delivery node.

[0020] Step 104: Optimize the delivery route based on the objective function and the constraints to obtain the target delivery route for the electric logistics vehicle.

[0021] Dispatch requirements refer to information about the items that electric logistics vehicles need to deliver, such as the type and weight of the items, as well as information about the delivery nodes, such as the location and number of delivery nodes, information about the roads between delivery nodes, such as the distance and road conditions, and customer requirements corresponding to the delivery nodes, such as the customer's expected delivery time.

[0022] In this embodiment, the scheduling requirements may also include the following: The location of each delivery node, customer needs, and the initial battery level of each electric logistics vehicle.

[0023] The location of the distribution center is the location of the items to be delivered. The starting point and destination of each electric logistics vehicle's delivery is the distribution center.

[0024] The time limit for each delivery node, i.e., the time window. If the delivery is not completed within the time window, a cost penalty will be imposed according to the time penalty cost.

[0025] Each customer is assigned only one electric logistics vehicle, and each electric logistics vehicle can only be used once per calendar day.

[0026] Based on the scheduling requirements, the optimization objective function can be determined. In this implementation, the optimization objective is to minimize the total delivery cost. Therefore, the optimization objective function can be expressed as: (1) in, For fixed costs, For depreciation expenses, The cost of time penalty.

[0027] The time penalty cost is: (2) in, These represent the start and end times of the delivery node's time limit, respectively. The time when the electric logistics vehicle arrives at the delivery node; For electric logistics vehicles The resulting time penalty cost; For fixed values, This indicates a delivery failure. and same.

[0028] The time penalty cost curve is as follows: Figure 2As shown, for example, the customer specifies a delivery time window of [timeframe]. If the electric vehicle arrives at the specified time There are no penalty costs within the specified service window. If an electric vehicle is delivered earlier than the customer's designated service window, a fixed penalty cost will be incurred. If an electric vehicle arrives late outside the service window, a penalty cost will increase exponentially based on the length of the delay. until the maximum value is reached. The time point corresponding to the time point when the penalty cost exceeds the maximum value. The delivery task was later deemed a failure.

[0029] Traditional time window penalty costs use a simple linear model, failing to accurately reflect the characteristics of decreased customer satisfaction and economic losses caused by delays in logistics and delivery. This implementation employs a hybrid time window penalty mechanism: early arrival penalties are set at a low, fixed value, while late arrival penalties use an exponential growth model until a preset maximum penalty threshold is reached, at which point delivery is considered a failure. Compared to traditional time window models, the hybrid time window model better reflects the sensitivity of customers to timeliness in actual operations, making the allocation of time window penalty costs more realistic. This helps improve the scientific validity and practical applicability of cost calculations in route optimization models, promoting route optimization and contributing to reducing total logistics transportation costs and improving the economic efficiency of logistics and delivery.

[0030] Fixed costs incurred during delivery This refers to the fixed cost incurred by each electric logistics vehicle for each delivery activity, expressed as: (3) For distribution centers; The cost of a single electric logistics vehicle itself; A collection of electric logistics vehicles; For user collection; This is a collection of battery swapping stations.

[0031] The depreciation costs incurred during delivery are: (4) The depreciation cost per kilometer for a single logistics vehicle; For delivery nodes to delivery node The distance between them; It is a set of delivery nodes, which include battery swapping stations, distribution centers, and users.

[0032] When calculating the objective function, the solution must also satisfy the constraints. Specifically, the constraints include: Energy Constraints for Electric Logistics Vehicles: Electricity Consumption Function Between Every Two Nodes on the Travel Path Including rolling friction resistance and aerodynamic drag : (5) (6) (7) in, Let A be the air drag coefficient, A be the frontal area of ​​the vehicle surface, and a be the vehicle acceleration. air density, Where g is the rolling resistance coefficient, and g is the gravitational coefficient. Let v be the vehicle's weight and v be the constant speed.

[0033] The constraints also include time constraints during the operation of the electric logistics vehicle: (8) in, For electric logistics vehicles Reaching the node Time; For electric logistics vehicles Leave node Time; For electric logistics vehicles from nodes Drive to the node The time constraint refers to the time of electric logistics vehicles. Reaching the node The time is equal to that of electric logistics vehicles Leave node Time and Nodes Drive to the node The sum of the times.

[0034] The constraints also include flow constraints on delivery nodes imposed by electric logistics vehicles: (9) Where V is the set of delivery nodes. This indicates whether the electric logistics vehicle k is traveling on the road between delivery nodes i and j. =1 indicates that the electric logistics vehicle k is traveling on the road between delivery nodes i and j; otherwise... =0. Flow constraint refers to electric logistics vehicles. The number of visits to the same node is the same as the number of departures.

[0035] The constraints also include the starting point constraint for the electric logistics vehicle: (10) O represents the distribution center, and K represents the collection of electric logistics vehicles. Electric logistics vehicles The starting point and the destination are both the distribution center. It starts from the distribution center and finally returns to the distribution center.

[0036] The constraints also include the battery power constraint for electric logistics vehicles: (11) (12) in, This represents the maximum battery capacity for electric logistics vehicles. The battery level at node j. The battery level when leaving node j. The battery level constraint refers to the electric logistics vehicle. Delivery will continue as long as the battery capacity reaches more than 20% of the maximum battery capacity at each node, and the electric logistics vehicle will continue to operate at the node. Before and leaving nodes The battery level remains the same afterward.

[0037] The constraints also include load limits for electric logistics vehicles: (13) in, q i Let Q represent the delivery demand of customer i, and Q represent the vehicle's load capacity.

[0038] Load constraint: This is a crucial constraint in vehicle route optimization to ensure vehicles are not overloaded. The main constraint is that the sum of all customer demands along a single route cannot exceed the vehicle's maximum load capacity. It is typically expressed as a linear superposition constraint of the cumulative demand along the route.

[0039] After determining the objective function and constraints, the minimum value of the objective function that satisfies the constraints is determined, and the solution corresponding to this minimum value is the target delivery path.

[0040] In this embodiment, the delivery route is optimized by combining the Kent mapping and the kingfisher optimization algorithm. The specific optimization process includes: An initial solution, representing a delivery path, is obtained through the Kent map. The fitness of this initial solution, satisfying the constraints, is calculated using the kingfisher optimization algorithm. The initial solution is then optimized based on this fitness until a preset condition is met, yielding the target delivery path. The fitness is calculated using a target optimization function. If the fitness does not meet the preset condition, the initial solution is optimized using a Levy flight strategy, and a global search is performed. The kingfisher optimization algorithm's diving strategy and escape mechanism further optimize and perturb the global search solution, resulting in the initial solution for the next iteration.

[0041] The Kingfisher Optimization Algorithm comprises an initialization phase, an exploration phase, an development phase, and a local escape phase. The initialization phase first generates a set of solutions, i.e., delivery paths. Then, the exploration, development, and local escape phases optimize these solutions to varying degrees. The exploration phase performs a global search to broadly cover the solution space and avoid getting trapped in local optima. The development phase uses a local search to refine the search and improve the accuracy and quality of the current solution. The local escape phase, by simulating inter-organism cooperation, optimizes the search strategy to achieve a balance between global and local search, thereby improving the overall performance of the optimization algorithm. The specific process is as follows: During the initialization phase: The kingfisher initiates the search process by randomly generating a set of initial solutions from the search space. In this embodiment, initialization is performed using the Kent chaotic map. Chaotic maps belong to nonlinear dynamical systems, exhibiting seemingly random and irregular motion within a deterministic framework. This motion displays uncertainty, non-reproducibility, and unpredictability—i.e., chaotic characteristics. The Kent map is a representative and simple discrete chaotic system, possessing better uniform ergodicity than the Logistic map. It can be represented as follows: (14) Control parameters ∈(0,1). To represent the solution of the i-th iteration of the chaotic sequence, This represents the solution for the (i+1)th iteration of the chaotic sequence. The chaotic orbital state values ​​range from (0, 1). The population distribution will be initialized using the Kent chaotic mapping strategy, resulting in a more uniform population that facilitates global traversal of the optimization space by particles, thus enhancing the algorithm's global optimization capability.

[0042] During the exploration phase: In the kingfisher optimization algorithm, the location of the search population is determined based on the kingfisher's foraging activities. The kingfisher's location is updated according to the following formula: (15) This will be the solution for the next iteration. This indicates the current iteration position; t is the iteration number; and N is the total population size.

[0043] Among them, parameters for: (16) Let be a random number from a normal distribution, and D be the dimension of the solution.

[0044] parameter The value is dynamically determined based on the current strategy and can be either "perching" or "hovering" to ensure optimal performance in different operating modes, as shown below: (17) M is the maximum number of iterations, B is the beat factor which is constant at 8, and C is the angle of the kingfisher's crest feathers.

[0045] The parameter T is calculated as follows during hovering: (18) (19) Where F(i) is the fitness of the i-th kingfisher and F(j) is the fitness of the j-th kingfisher. Fitness is calculated by optimizing the objective function.

[0046] In this implementation, during the exploration phase, the position is updated using the Lévy flight strategy, replacing the random step size of the kingfisher optimization algorithm with the Lévy step size. The Lévy flight step size follows a heavy-tailed distribution, exhibiting the characteristics of occasional long jumps and frequent short step sizes. Long jumps help to escape local optima and explore a wider solution space, while short step sizes preserve the ability for fine-grained local search. The Lévy step size is as follows: (20) in, Used to generate long-tailed random step sizes =1.5.

[0047] During the development phase: Update the individual position as shown in the following formula: (twenty one) in, It is a control parameter. and Indicates hunting ability, The formula for calculating the flapping frequency of the kingfisher's wings is as follows: (twenty two) (twenty three) (twenty four) (25) During the partial escape phase: Update the individual position using the following formula: (26) (27) (28) (29) For each individual m drawn from the public; Let n be the number of individuals selected from the population; P be the predation efficiency of the kingfisher. For maximum predation efficiency, a fixed value of 0.5 is used; The minimum predation efficiency is fixed at 0.

[0048] Specifically, the optimization algorithm in this embodiment is as follows: 1) The initial population is generated using Kent chaotic mapping, replacing the traditional random initialization method, thus enhancing population diversity and spatial distribution uniformity. The population consists of multiple individuals, each representing a delivery path.

[0049] 2) Calculate the optimization objective function value for each individual (path solution), taking into account fixed costs, depreciation expenses and penalty costs of mixed timestamps, and verify the satisfaction of constraints such as power consumption and load capacity.

[0050] The system determines whether the obtained objective function value meets preset conditions. If it does, the current path solution is the target delivery path; otherwise, it proceeds to the exploration phase. The preset conditions are reaching the maximum number of iterations or achieving a preset convergence accuracy.

[0051] 3) In the exploration phase, the Levy flight strategy is introduced to conduct large-scale exploration and update the population. By combining long jumps with short step sizes in the search mode, the algorithm's ability to escape local optima is enhanced.

[0052] 4) Then, the population is updated again during the development phase. During this phase, the diving behavior of kingfishers is simulated for a refined search, combining the current optimal solution with individual historical optimal information to improve the accuracy and convergence speed of the solution.

[0053] 5) When a decline in population diversity or a local optimum is detected, an escape mechanism is triggered to maintain population vitality through random perturbation.

[0054] In this embodiment, the kingfisher optimization algorithm is improved by combining the Kent mapping and the Levy flight strategy. The Kent mapping can enhance population diversity, and the Levy flight strategy can improve global exploration ability, thereby reducing the problem that high-dimensional multi-constraint path optimization is prone to getting trapped in local optima.

[0055] Furthermore, this embodiment also provides an electric logistics vehicle route optimization device, which can be used to execute the above-described electric logistics vehicle route optimization method. For example... Figure 3 As shown, the electric logistics vehicle route optimization device 300 specifically includes: a scheduling data acquisition module 301, used to acquire the scheduling requirements of the electric logistics vehicle, the scheduling requirements including multiple delivery nodes and the time limit of each delivery node; an optimization objective determination module 302, used to determine an optimization objective function based on the scheduling requirements, the optimization objective function being used to calculate the total delivery cost, the total delivery cost including a non-linear time penalty cost, the time penalty cost being the cost incurred outside the time limit of the delivery node; a constraint determination module 303, used to acquire the constraint conditions for the electric logistics vehicle and the delivery nodes; and an optimization module 304, used to optimize the delivery path based on the optimization objective function and the constraint conditions to obtain the target delivery path of the electric logistics vehicle.

[0056] The specific details of each module or unit in the above-mentioned electric logistics vehicle route optimization device have been described in detail in the corresponding electric logistics vehicle route optimization method, so they will not be repeated here.

[0057] This application also provides an electronic device. Figure 4 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 4 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0058] like Figure 4 As shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0059] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0060] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined in the embodiments of this application.

[0061] For example, when the computer program is executed by the central processing unit (CPU) 601, it can perform the following: obtain the scheduling requirements of the electric logistics vehicle, the scheduling requirements including multiple delivery nodes and the time limit of each delivery node; determine an optimization objective function based on the scheduling requirements, the optimization objective function being used to calculate the total delivery cost, the total delivery cost including a non-linear time penalty cost, the time penalty cost being the cost incurred outside the time limit of the delivery node; obtain the constraints for the electric logistics vehicle and the delivery nodes; optimize the delivery path based on the optimization objective function and the constraints to obtain the target delivery path for the electric logistics vehicle.

[0062] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0063] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0064] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0065] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0066] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the route of an electric logistics vehicle, characterized in that, include: Obtain the scheduling requirements of electric logistics vehicles, which include multiple delivery nodes and the time limit for each delivery node; Based on the scheduling requirements, an optimization objective function is determined. The optimization objective function is used to calculate the total delivery cost. The total delivery cost includes a non-linear time penalty cost, which is the cost incurred outside the time limit of the delivery node. Obtain the constraints for the electric logistics vehicle and the delivery node; The delivery route is optimized based on the objective function and the constraints to obtain the target delivery route for the electric logistics vehicle.

2. The electric logistics vehicle route optimization method according to claim 1, characterized in that, The optimization of the delivery route based on the constraints and the objective function includes: An initial solution is obtained through the Kent map, and the initial solution represents a delivery route; The fitness of the initial solution that satisfies the constraints is calculated using the kingfisher optimization algorithm, and the initial solution is optimized based on the fitness until the fitness meets the preset conditions to obtain the target delivery route. The fitness is calculated using a target optimization function.

3. The electric logistics vehicle route optimization method according to claim 2, characterized in that, The optimization of the initial solution based on the fitness includes: When the fitness does not meet the preset conditions, the initial solution is optimized using the Levy flight strategy, and a global search for a solution is performed. The global search solution is optimized and perturbed by the diving strategy and escape mechanism of the kingfisher optimization algorithm to obtain the initial solution for the next iteration.

4. The electric logistics vehicle route optimization method according to claim 1, characterized in that, The total delivery cost also includes fixed costs and depreciation expenses; The optimization objective function is: in, For fixed costs, For depreciation expenses, The cost of time penalty.

5. The electric logistics vehicle route optimization method according to claim 4, characterized in that, The time penalty cost is: in, These represent the start and end times of the delivery node's time limit, respectively. The time when the electric logistics vehicle arrives at the delivery node; For electric logistics vehicles The resulting time penalty cost; For fixed values, This indicates a delivery failure.

6. The electric logistics vehicle route optimization method according to claim 1, characterized in that, The constraints include: Energy consumption constraints of electric logistics vehicles: in, This is a power consumption function. For rolling friction resistance, For aerodynamic drag, Let A be the air drag coefficient, A be the frontal area of ​​the vehicle surface, and a be the vehicle acceleration. air density, Where g is the rolling resistance coefficient, and g is the gravitational coefficient. Let v be the vehicle's weight and v be the constant speed. Time constraints during the operation of electric logistics vehicles: in, For electric logistics vehicles Reaching the node Time; For electric logistics vehicles Leave node Time; For electric logistics vehicles from nodes Drive to the node Time; Electric logistics vehicles impose flow constraints on delivery nodes: Where V is the set of delivery nodes. This indicates whether the electric logistics vehicle k is traveling on the road between delivery nodes i and j. =1 indicates that the electric logistics vehicle k is traveling on the road between delivery nodes i and j; otherwise... =0; Starting point constraints for electric logistics vehicles: O represents a distribution center, and K represents a collection of electric logistics vehicles; Electricity constraints of electric logistics vehicles: in, This represents the maximum battery capacity for electric logistics vehicles. The battery level at node j. Battery level when leaving node j; Load constraints for electric logistics vehicles: in, q i Let Q represent the delivery demand of customer i, and Q represent the vehicle's load capacity.

7. The electric logistics vehicle route optimization method according to claim 4, characterized in that, The depreciation expense is: The depreciation cost per kilometer for a single logistics vehicle; For delivery nodes to delivery node The distance between them; This is a set of delivery nodes.

8. An electric logistics vehicle route optimization device, characterized in that, include: The scheduling data acquisition module is used to acquire the scheduling requirements of electric logistics vehicles, which include multiple delivery nodes and the time limit for each delivery node. The optimization objective determination module is used to determine the optimization objective function based on the scheduling requirements. The optimization objective function is used to calculate the total delivery cost, which includes a non-linear time penalty cost, which is the cost incurred outside the time limit of the delivery node. The constraint determination module is used to obtain the constraint conditions for the electric logistics vehicle and the delivery node; The optimization module is used to optimize the delivery route based on the optimization objective function and the constraints to obtain the target delivery route for the electric logistics vehicle.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the electric logistics vehicle route optimization method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the electric logistics vehicle route optimization method according to any one of claims 1-7.

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