Intelligent logistics scheduling method and system

Abstract

The invention provides an intelligent logistics scheduling method and system, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining order data of logistics scheduling, the order data comprising a pickup task and a distribution task; pairing the pickup task and the distribution task to determine a pairing task by taking the reduction of the transportation time length from the pickup task to the distribution task as a target; sorting the pairing tasks based on the time windows and the service durations of the pairing tasks; according to a sorting result, executing each pairing task in sequence, and determining an optimal scheduling scheme by taking reduction of total cost and total completion time of logistics scheduling as a target; and according to the optimal scheduling scheme, carrying out logistics scheduling on the sorted paired tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics scheduling, and in particular to an intelligent logistics scheduling method and system. Background Art

[0002] Intelligent logistics scheduling methods utilize artificial intelligence, optimization algorithms, or mathematical modeling to automatically match, sort, and optimize routes between pickup and delivery tasks based on order, vehicle, and cargo data. This approach achieves optimal allocation and scheduling of logistics resources while meeting constraints such as timeliness and capacity. Through dynamic perception, multi-objective decision-making, and real-time adjustments, this method effectively improves the efficiency of logistics systems and reduces operating costs, making it a key technical tool in modern intelligent supply chain management.

[0003] With the development of e-commerce, efficient manufacturing and personalized services, logistics demands have shown the characteristics of diversified tasks, strong timeliness and high resource utilization requirements. Traditional manual scheduling methods are difficult to cope with the complex and changing transportation environment. Developing intelligent logistics scheduling methods and realizing the intelligence, flexibility and sustainable development of logistics systems have become necessary means to enhance the core competitiveness of enterprises and meet market demand.

[0004] However, existing logistics scheduling methods rely too much on manual labor, which can easily lead to a decline in overall transportation efficiency and uneven resource allocation. When performing scheduling tasks, the scheduling objectives considered are too single, ignoring comprehensive indicators such as delivery time, customer satisfaction, and truck utilization. It lacks globality and cannot fully measure the scheduling effect, affecting overall efficiency and customer satisfaction. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the existing technology, the purpose of the embodiments of the present invention is to provide an intelligent logistics scheduling method that can solve the technical problems that the existing logistics scheduling method is too dependent on manual labor, which easily leads to a decline in overall transportation efficiency and uneven resource allocation. When performing scheduling tasks, the scheduling objectives considered are too single, ignoring comprehensive indicators such as delivery time, customer satisfaction, and truck utilization. It lacks globality and cannot comprehensively measure the scheduling effect, affecting overall efficiency and customer satisfaction.

[0006] A first aspect of an embodiment of the present invention provides an intelligent logistics scheduling method, including:

[0007] S1: Obtain order data for logistics scheduling, which includes pickup tasks and delivery tasks;

[0008] S2: with the goal of reducing the transportation time between the pickup task and the delivery task, pair the pickup task with the delivery task to determine the paired task;

[0009] S3: Sort the pairing tasks based on the time window and service duration of the pairing tasks;

[0010] S4: Execute each of the paired tasks in sequence according to the sorting results, with the goal of reducing the total logistics scheduling cost and total completion time, and determine the optimal scheduling plan;

[0011] S5: Perform logistics scheduling on the sorted paired tasks according to the optimal scheduling plan.

[0012] A second aspect of an embodiment of the present invention provides an intelligent logistics scheduling system, including: a processor and a memory;

[0013] The memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the intelligent logistics scheduling method as described in the first aspect are implemented.

[0014] According to a third aspect of an embodiment of the present invention, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the intelligent logistics scheduling method as described in the first aspect are implemented.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0016] In an embodiment of the present invention, with the goal of reducing the transportation time between the pickup task and the delivery task, each pickup task and the delivery task can be reasonably matched, which significantly reduces the empty load rate and task waiting time, improves task continuity and truck utilization efficiency, and uses the time window and service time of the paired tasks to sort the paired tasks. It can prioritize the scheduling of tasks with tight time and high delay risks, thereby improving the timeliness and customer satisfaction of the overall scheduling, reducing the overtime rate, and with the goal of reducing the total cost and total completion time of logistics scheduling. Taking into account the constraints of various aspects, it can automatically generate an executable and cost-optimal scheduling plan, improve the efficiency of the overall scheduling, reduce the cost of scheduling, and improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments of the present invention. It is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 This is a flow chart of an intelligent logistics scheduling method provided by an embodiment of the present invention;

[0019] Figure 2 It is a structural diagram of an intelligent logistics scheduling system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the scope of protection of the present invention.

[0021] The intelligent logistics scheduling method provided by the embodiment of the present invention is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0022] Reference Manual Figure 1 , which shows a flow chart of an intelligent logistics scheduling method provided by an embodiment of the present invention.

[0023] An embodiment of the present invention provides an intelligent logistics scheduling method, which may include the following steps:

[0024] S1: Obtain order data for logistics scheduling, which includes pickup tasks and delivery tasks.

[0025] It should be noted that by fully obtaining the order data required for logistics scheduling, it can ensure that the scheduling system can grasp all pickup and delivery needs, and provide an accurate and real-time data basis for subsequent task matching and route optimization, thereby improving the response speed and intelligence of the entire logistics system.

[0026] S2: With the goal of reducing the transportation time between the pickup task and the delivery task, the pickup task and the delivery task are paired and the paired tasks are determined.

[0027] Among them, transportation time refers to the driving time or time required for the truck to travel from the starting point of the pickup task to the starting point of the delivery task.

[0028] It should be noted that by minimizing the transportation time between pickup and delivery tasks and performing intelligent matching, not only can the truck's idle time and energy consumption be effectively reduced, but the overall logistics path's continuity and scheduling efficiency can also be improved, laying the foundation for subsequent task sorting and resource allocation, and significantly improving the system's intelligent level and resource utilization.

[0029] In a possible implementation, S2 specifically includes:

[0030] S201: To reduce the transportation time between the pickup task and the delivery task, establish a pairing objective function:

[0031]

[0032] Among them, f represents the pairing objective function, min represents minimization, T ij represents the transportation time from pickup task i to delivery task j, R P represents the set of pickup tasks, R D Represents the delivery task set, max represents maximization, a j represents the earliest start time of delivery task j, b i represents the latest start time of pickup task i, τ i represents the service time of pickup task i, y ij represents the pairing decision variable between pickup task i and delivery task j, y ij =1 means that the pickup task i is paired with the delivery task j, y ij =0 means that the pickup task i is not paired with the delivery task j.

[0033] Specifically, by establishing a pairing objective function with the goal of minimizing the transportation time between the pickup task and the delivery task, the task pairing process has a quantifiable optimization standard, which can improve pairing efficiency, reduce vehicle driving paths and time, reduce transportation costs, and enhance the overall scheduling intelligence level.

[0034] S202: Setting pairing constraints:

[0035]

[0036] Among them, a i represents the earliest start time of pickup task i, b j The latest start time of delivery task j.

[0037] Specifically, the above pairing constraint means that for each pickup task i∈R P Must and can only be paired with a delivery task j∈R D Each delivery task j∈R D Must and can only be paired with one pickup task i∈R P Ensure that the pairing request is feasible within the time window. Whether the pairing of pickup and delivery tasks is established, y ij =1 means that the pickup task i is paired with the delivery task j, that is, the pickup task i will be executed before the delivery task j. ij =0 means that the pickup task i is not paired with the delivery task j, that is, the pickup task i is not executed before the delivery task j.

[0038] S203: Under the constraints of the pairing constraints, the pairing task is determined with the goal of minimizing the pairing objective function.

[0039] It should be noted that on the basis of strictly following the pairing constraints, with the goal of minimizing indicators such as transportation time, optimizing the matching relationship between pickup and delivery tasks, and determining the pairing tasks can effectively improve the pairing quality and execution efficiency and reduce resource waste.

[0040] In a possible implementation manner, after S2, the method further includes:

[0041] Set the time window and service duration for the pairing task.

[0042] Among them, the time window (a k ,b k ) refers to the executable time range of the paired task k, where a k Indicates the earliest start time, b k Indicates the latest allowed start time, which is an important parameter to ensure timeliness in scheduling. Service duration (τ k ) refers to the total service time required to complete the matching task k, which usually includes the service time of the pickup task and the delivery task (such as loading and unloading, processing time, etc.). It is a key indicator for evaluating the feasibility of scheduling.

[0043] Specifically, by setting the time window and service duration of the paired tasks, each task can be reasonably executed within the feasible time range and the required time can be accurately estimated, which can ensure that the subsequent sorting and scheduling arrangements meet the time constraints and improve the feasibility, accuracy and timeliness of the overall scheduling.

[0044] The time windows are as follows:

[0045] a k =min{max(a i ,a j -T ij -τ i ),b i}

[0046] b k =min{b i ,b j -T ij -τ i}

[0047] Among them, a k represents the earliest start time of pairing task k, b k represents the latest start time of pairing task k.

[0048] The service duration is as follows:

[0049] τk =max(a j -b i ,T ij +τ i )+τ j

[0050] Among them, τ k represents the service time of pairing task k, τ j represents the service time of delivery task j.

[0051] S3: Sort the pairing tasks based on their time windows and service durations.

[0052] It should be noted that by comprehensively analyzing the time windows and service durations of paired tasks and prioritizing the tasks, not only can conflicts and delays caused by time overlap between tasks be avoided, but also the overall resource scheduling efficiency and the rationality of the execution sequence can be improved, thereby maximizing the guarantee of on-time completion of tasks and the stability of system operation.

[0053] In a possible implementation, S3 specifically includes:

[0054] S301: Calculate the task urgency based on the time window and service duration.

[0055] Among them, task urgency is an indicator that measures the "urgency" of a task in terms of time and resources, reflecting the priority of task execution, and is usually related to the remaining available time, waiting time and delay risk.

[0056] Specifically, by combining factors such as time window width, service time, waiting time and delay risk, we can scientifically evaluate the urgency of the task, reasonably determine the execution priority of the task, and improve the accuracy of scheduling and the real-time response capability of the system.

[0057] S302: Calculate the sorting priority according to the task urgency.

[0058] S303: Sort the pairing tasks according to the sorting priority.

[0059] The specific urgency of the tasks is:

[0060]

[0061] Among them, U k Indicates the urgency of pairing task k, Wait k DelayRisk represents the waiting time of pairing task k, k represents the delay time of pairing task k, γ1 represents the time window weight, γ2 represents the waiting time weight, and γ3 represents the delay time weight.

[0062] The sorting priorities are as follows:

[0063] P k =α1·a k +α2·τ k +α3·U k +α4·(a k +τ k )

[0064] Among them, P k represents the sorting priority of pairing task k, and α1, α2, α3 and α4 are weight coefficients.

[0065] S4: Execute each matching task in sequence according to the sorting results, with the goal of reducing the total logistics scheduling cost and total completion time, and determine the optimal scheduling plan.

[0066] It should be noted that by executing the pairing tasks in sequence according to the sorting results, the orderliness of task execution and the optimal allocation of time can be ensured, with the goal of reducing the total cost and total completion time of logistics scheduling. Taking cost and timeliness into comprehensive consideration, it is possible to maximize resource utilization efficiency while ensuring that tasks are completed on time, improve the response speed and operating efficiency of the overall scheduling system, and ultimately improve the accuracy and economy of logistics management.

[0067] In a possible implementation, S4 specifically includes:

[0068] S401: Calculate the total logistics scheduling cost.

[0069] S402: Establish a scheduling objective function with the goal of reducing the total logistics scheduling cost and total completion time.

[0070] In a possible implementation, the scheduling objective function is specifically:

[0071] Z=min[λ(A+B+C)+(1-λ)Q max ]

[0072] Among them, Z represents the dispatch objective function, A represents the penalty cost, B represents the truck usage cost, C represents the mixed loading cost, and Q max represents the total completion time, and λ represents the weight parameter.

[0073] S403: Setting scheduling constraints.

[0074] S404: Under the constraints of the scheduling constraints, the optimal scheduling solution is determined with the goal of minimizing the scheduling objective function.

[0075] Specifically, by calculating the total cost of logistics scheduling and constructing a scheduling objective function, with minimizing the total cost and total completion time as the optimization goal, we ensure that the scheduling plan not only has low operating costs but also ensures timely delivery. After setting reasonable scheduling constraints, by solving the optimal scheduling plan, the system can complete tasks efficiently and economically under resource constraints, significantly improving logistics operation efficiency, saving costs and increasing customer satisfaction.

[0076] In a possible implementation, S404 specifically includes:

[0077] Under the constraints of scheduling conditions, the whale optimization algorithm is used to determine the optimal scheduling plan with the goal of minimizing the scheduling objective function.

[0078] Among them, the Whale Optimization Algorithm (WOA) is an optimization algorithm based on the foraging behavior of whales in nature. It performs global optimization search by simulating the predation behavior exhibited by whales in the process of searching for food. It is suitable for solving complex multi-objective optimization problems.

[0079] It's important to note that the Whale Optimization algorithm, combined with scheduling constraints and the objective function, effectively explores the solution space and finds the optimal scheduling solution. The Whale Optimization algorithm boasts powerful global search capabilities and the ability to avoid local optima. It can accurately optimize under complex constraints, ensuring that logistics scheduling solutions achieve the optimal balance between cost and completion time, thereby improving scheduling efficiency, reducing operating costs, and meeting customer timeliness requirements.

[0080] In one possible implementation, the whale optimization algorithm is used to determine the optimal scheduling solution, specifically including:

[0081] Initialize the population and determine the initial population, wherein the initial population includes multiple whale individuals, and each whale individual represents a scheduling scheme.

[0082] Set a fitness function and calculate the fitness value of the individual whales in the initial population, where the fitness function is specifically the inverse of the scheduling objective function.

[0083] The position of the whale individual with the largest fitness value is taken as the current position of the whale individual.

[0084] Update the control factor:

[0085] J=2a·r1-a

[0086] H=2r2

[0087]

[0088] Among them, J, u and H are control factors, r1 and r2 are random numbers between [0,1], t′ is the current number of iterations, T′ max represents the maximum number of iterations, and π represents the ratio of circumference to pi.

[0089] Determine whether the probability of the predation mechanism is less than 0.5. If so, proceed to the next step. Otherwise, enter the hunting phase.

[0090] Specifically, the hunting phase is as follows:

[0091]

[0092] Where D represents the distance between the current whale individual position and the optimal whale individual position in the hunting phase, X * (t′) represents the position of the optimal whale individual at the t′th iteration, X(t′) represents the position of the current whale individual at the t′th iteration, l represents a random number between [0, 1], X(t′+1) represents the position of the whale individual at the t′+1th iteration, v represents the logarithmic spiral shape constant, and ω represents the adaptive inertia weight.

[0093] Determine whether the absolute value of the control factor A is less than 1. If so, enter the bracketing phase. Otherwise, enter the search phase.

[0094] Specifically, the encirclement phase is as follows:

[0095]

[0096] Where D′ represents the distance between the position of the current whale individual and the position of the optimal whale individual in the encirclement stage.

[0097] Optionally, the calculation formula of the search phase is specifically:

[0098]

[0099] Where D″ represents the distance between the current whale individual position and the position of the randomly selected whale individual in the search phase, and X rand (t′) represents the position of the randomly selected whale individual in the t′th iteration, p1 represents a random number in the range [0, 1], and F represents a constant with a value of 0.4.

[0100] Update the current whale's position.

[0101] Calculate the fitness value of the updated whale individual.

[0102] Determine whether the fitness value of the updated whale individual is not less than the fitness value of the previous whale individual. If so, update the position of the current whale individual. Otherwise, keep the position of the current whale individual unchanged.

[0103] Determine whether the maximum number of iterations has been reached. If so, output the optimal scheduling solution. Otherwise, reinitialize the population.

[0104] In a possible implementation, the total logistics scheduling cost specifically includes: penalty cost, truck usage cost, and cargo cost.

[0105] The specific delay penalty cost is:

[0106]

[0107] Among them, A represents the penalty cost, ε st represents the unit early penalty coefficient of the outgoing truck s at time t, E st represents the early completion time of the outbound truck s at time t, β st represents the unit delay penalty coefficient of the outgoing truck s at time t, L st It represents the delay time of outgoing truck s at time t, s=1,2,…,S, S represents the total number of outgoing trucks, t=1,2,…,T, T represents the total scheduling time.

[0108] The specific costs of using a truck are:

[0109]

[0110] Among them, B represents the cost of using the truck, C b represents the fixed cost required to start the truck, r=1,2,…,R, R represents the total number of inbound trucks, WR rt represents the entry decision variable between the inbound truck r and time t. If the inbound truck r is enabled at time t, then WR rt =1, otherwise WR rt =0,WS st represents the outbound decision variable. If outbound truck s is enabled at time t, then WS st =1, otherwise WS st =0.

[0111] The specific cargo costs are:

[0112]

[0113] Among them, C represents the mixed loading cost, C c represents the cost of unused truck capacity, C d Indicates the mixed loading cost caused by the new product type, YR rtYR represents the number of product types loaded by the truck r entering the station at time t. st CapR represents the number of product types loaded by the truck s leaving the station at time t. rt Denotes the unused capacity ratio of truck r entering the station at time t, CapR st represents the unused capacity ratio of truck s entering the station at time t.

[0114] In a possible implementation, the constraints specifically include: material balance constraints, product loading constraints, truck activation constraints, truck capacity constraints, and total completion time constraints.

[0115] The material balance constraints are as follows:

[0116]

[0117]

[0118] Among them, I ht represents the actual inventory of product h in the distribution center at time t, V ht represents the customer demand for product h at time t, m1 represents the defect rate of product h, m2 represents the proportion of qualified products among returned products, m3 represents the repair proportion of qualified products, NoS sh(t-1) represents the number of qualified products h returned from supplier v at time t-1.

[0119] The specific product loading constraints are:

[0120]

[0121] Among them, NoR rht NoS represents the number of products h loaded onto the inbound truck r at time t. sht represents the quantity of product h loaded onto the outbound truck s at time t, U ht represents the number of defects of product k at time t.

[0122] The specific constraints for truck activation are:

[0123]

[0124] Where M represents the control coefficient, h = 1, 2,…, N, and N represents the total number of product types.

[0125] M is a large constant used to control the logical binding between "enable-load" to ensure that the model is feasible without losing accuracy.

[0126] The truck capacity constraints are as follows:

[0127]

[0128] in, represents the minimum load of the incoming truck r, represents the maximum load of the incoming truck r, represents the minimum acceptable load of outbound truck s, Indicates the maximum acceptable load of outbound truck s.

[0129] The total completion time constraints are as follows:

[0130] Q max ≥Q s

[0131] Q s =G s +GP s

[0132] Among them, Q s represents the time it takes for the outbound truck s to complete its transportation, G s Indicates the time when the outbound truck s leaves the shipping area, GP s It represents the transportation time of outbound truck s from the shipping area to the customer.

[0133] S5: Perform logistics scheduling for the sorted paired tasks according to the optimal scheduling plan.

[0134] It should be noted that by executing the sorted paired tasks according to the optimal scheduling plan, ensuring that each task is carried out in the optimal order, maximizing resource utilization and reducing unnecessary idle time, logistics tasks can be efficiently scheduled, delays can be reduced, and the overall system operation efficiency can be improved.

[0135] Reference Manual Figure 2 , which shows a structural diagram of an intelligent logistics scheduling system provided by an embodiment of the present invention.

[0136] The embodiment of the present invention provides an intelligent logistics scheduling system 20, comprising: a processor 201 and a memory 202;

[0137] The memory 202 stores programs or instructions that can be run on the processor 201. When the programs or instructions are executed by the processor 201, the steps of the above-mentioned intelligent logistics scheduling method are implemented and the same technical effect can be achieved. To avoid repetition, the present invention will not be described in detail.

[0138] It should be understood that the processor 201 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0139] It should also be understood that the memory 202 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0140] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0141] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0143] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] If the 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] An embodiment of the present invention provides a readable storage medium including: a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the steps of the above-mentioned intelligent logistics scheduling method are implemented and the same technical effect can be achieved. To avoid repetition, the present invention will not be described in detail.

[0149] Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. An intelligent logistics scheduling method, characterized in that: include: S1: Obtain order data for logistics scheduling, which includes multiple pickup tasks and multiple delivery tasks; S2: with the goal of reducing the transportation time between each of the pickup tasks and each of the delivery tasks, pairing each of the pickup tasks with each of the delivery tasks to determine a plurality of paired tasks; S3: sorting the pairing tasks based on their time windows and service durations; S4: Execute each of the paired tasks in sequence according to the sorting results, with the goal of reducing the total logistics scheduling cost and total completion time, and determine the optimal scheduling plan; S5: Perform logistics scheduling on the sorted paired tasks according to the optimal scheduling plan.

2. The intelligent logistics scheduling method according to claim 1, characterized in that: The S2 specifically includes: S201: Establishing a pairing objective function with the goal of reducing the transportation time between each pickup task and each delivery task; S202: Setting pairing constraints; S203: Under the constraints of the pairing constraints, with the goal of minimizing the pairing objective function, determine the plurality of pairing tasks.

3. The intelligent logistics scheduling method according to claim 1, characterized in that: After S2, the method further includes: Set the time window and service duration of the pairing task.

4. The intelligent logistics scheduling method according to claim 1, characterized in that: The S3 specifically includes: S301: Calculating the task urgency of each delivery task according to the time window and the service duration; S302: Calculate the sorting priority according to the task urgency; S303: Sort the pairing tasks according to the sorting priority.

5. The intelligent logistics scheduling method according to claim 1, characterized in that: The S4 specifically includes: S401: Execute each of the pairing tasks in sequence according to the sorting results, and calculate the total logistics scheduling cost; S402: Establishing a scheduling objective function with the goal of reducing the total logistics scheduling cost and the total completion time; S403: Setting scheduling constraints; S404: Under the constraints of the scheduling constraints, the optimal scheduling solution is determined with the goal of minimizing the scheduling objective function.

6. The intelligent logistics scheduling method according to claim 5, characterized in that: The total logistics scheduling cost specifically includes: penalty cost, truck usage cost and cargo cost.

7. The intelligent logistics scheduling method according to claim 5, characterized in that: The constraints specifically include: material balance constraints, product loading constraints, truck activation constraints, truck capacity constraints, and total completion time constraints.

8. The intelligent logistics scheduling method according to claim 5, characterized in that: The S404 is specifically as follows: Under the constraints of the scheduling constraints, the optimal scheduling solution is determined by adopting the whale optimization algorithm with the goal of minimizing the scheduling objective function.

9. An intelligent logistics scheduling system, characterized in that: include: processor and memory; The memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the intelligent logistics scheduling method as described in any one of claims 1 to 8 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the intelligent logistics scheduling method as described in any one of claims 1 to 8 are implemented.

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