Intelligent logistics order scheduling method and system

Abstract

The invention discloses an intelligent logistics order scheduling method and system, and the method comprises the steps: obtaining a parameter set, the parameter set comprises solid state parameters and dynamic parameters, the solid state parameters are inherent attribute parameters of each order in a to-be-scheduled order queue, and the dynamic parameters are parameters determined according to the dynamic changes of the order queue and an external environment; obtaining a plurality of weight coefficients, wherein the weight coefficient is an importance degree value preset for each parameter; according to the parameter set and the weight coefficient of each parameter, calculating a scheduling priority weight value corresponding to each order through a preset weighting algorithm; ordering the orders in the order queue to be scheduled according to the calculated scheduling priority weight value; and allocating appropriate deliverymen to the orders according to the sequenced order and generating a scheduling instruction. According to the invention, the problems of single logistics order scheduling strategy and difficulty in adapting to real-time dynamic change scenes in the prior art are solved. The distribution efficiency and the resource utilization rate can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of logistics distribution and supply chain management, and particularly relates to an intelligent logistics order scheduling method and system based on multi-dimensional dynamic weights. BACKGROUND

[0002] With the rapid development of e-commerce, the logistics order volume is showing explosive growth. Traditional order scheduling methods mostly use the simple rule of "first come, first served", or only allocate based on a single factor such as the nearest delivery distance. This method cannot adapt to the complexity of modern logistics scenarios. For example, a common order with a long waiting time but a remote address is different from an order that has just been placed but has a high customer level and fresh goods in terms of urgency.

[0003] In the prior art, it is difficult for fixed rules to take into account various dynamic factors such as order attributes, customer value, real-time traffic, and delivery personnel status, which can easily lead to uneven allocation of delivery resources, delay of high-value orders, and low overall operational efficiency. SUMMARY

[0004] The purpose of the present application is to provide an intelligent logistics order scheduling method and system to solve the technical problems of single scheduling strategy and slow response in the prior art.

[0005] To achieve the above-mentioned purpose, the present application is implemented by the following technical solutions.

[0006] An intelligent logistics order scheduling method, comprising:

[0007] Step one: a first acquisition module (201) acquires a parameter set, the parameter set including static parameters and dynamic parameters, the static parameters being inherent attribute parameters of each order in a to-be-scheduled order queue, and the dynamic parameters being parameters determined according to dynamic changes of the to-be-scheduled order queue and external environment.

[0008] Step two: a second acquisition module (202) acquires a plurality of weight coefficients, the weight coefficients being importance values of each parameter set in advance.

[0009] Step three: according to the parameter set and the weight coefficient of each parameter, a scheduling priority weight value corresponding to each order is calculated by a preset weighting algorithm in a calculation module (203).

[0010] Step four: according to the scheduling priority weight value corresponding to each order, the orders in the to-be-scheduled order queue are sorted by a sorting module (204).

[0011] Step five: according to the sorted order, a scheduling instruction is generated and an appropriate delivery personnel is allocated to the order by a scheduling execution module (205).

[0012] The aforementioned intelligent logistics order scheduling method, the preset weighting algorithm is a first algorithm, the formula is:

[0013] F = C1×P + C2×T + C3×L + C4×W + C5×R

[0014] Wherein, P is order priority, T is cargo type coefficient, L is customer level coefficient, W is order waiting time, R is real-time traffic influence coefficient, C1 to C5 are weight coefficients of corresponding parameters.

[0015] The aforementioned intelligent logistics order scheduling method, before step three, the intelligent logistics order scheduling method further comprises:

[0016] Determine the order priority P according to the payment method of the order, the value of the goods or the customer specified information.

[0017] Determine the cargo type coefficient T according to the perishability, fragility or particularity of the goods contained in the order.

[0018] Determine the customer level coefficient L according to the historical order volume of the ordering customer, the member level.

[0019] Record the parameters P, T, L as the solid parameters of each order.

[0020] The aforementioned intelligent logistics order scheduling method, before step one, the intelligent logistics order scheduling method further comprises:

[0021] Record the timestamp of the order entering the dispatch queue as the submission time.

[0022] Record the current time of the system.

[0023] Calculate the order waiting time W according to the difference between the current time of the system and the submission time.

[0024] The aforementioned intelligent logistics order scheduling method, before step one, the intelligent logistics order scheduling method further comprises:

[0025] Obtain real-time traffic congestion data of the delivery path through a map service interface.

[0026] Combined with the historical average speed, the real-time traffic influence coefficient R is calculated.

[0027] The aforementioned intelligent logistics order scheduling method, the order is allocated to an appropriate delivery man, and the allocation method comprises:

[0028] Obtain the real-time position, current load and skill label of the available delivery man.

[0029] Calculate the estimated distance and time between the order delivery address and the delivery person's location.

[0030] The optimal delivery personnel are selected and assigned based on a comprehensive assessment of their workload, skill matching, and estimated delivery time.

[0031] The aforementioned intelligent logistics order scheduling method, prior to step four, further includes:

[0032] Associate and map the ID of each order with its calculated scheduling priority weight value.

[0033] The associated data is stored in a priority database for scheduling decision tracing and analysis.

[0034] An intelligent logistics order scheduling system includes:

[0035] The first acquisition module (201) is used to acquire a parameter set, which includes solid parameters and dynamic parameters.

[0036] The second acquisition module (202) is used to acquire multiple pre-set weight coefficients.

[0037] The calculation module (203) is used to calculate the scheduling priority weight value of each order by means of a weighted algorithm based on the parameter set and weight coefficients.

[0038] The sorting module (204) is used to sort the orders according to the scheduling priority weight value.

[0039] The scheduling execution module (206) is used to assign orders to delivery personnel according to the sorting results.

[0040] The output of the first acquisition module (201) is connected to the second acquisition module (202), the other end of the second acquisition module (202) is connected to the input of the calculation module (203), the other end of the calculation module (203) is connected to the input of the sorting module (204), and the other end of the sorting module (204) is connected to the input of the scheduling execution module (205).

[0041] The aforementioned intelligent logistics order scheduling system further includes a data storage module (206) for storing the order ID, scheduling priority weight value, and their corresponding relationships. The output of the data storage module (206) is connected to the first acquisition module (201), the calculation module (203), and the scheduling execution module (205), respectively.

[0042] The beneficial effects of this invention are as follows: This invention provides an intelligent logistics order scheduling method and system, solving the technical problems of single scheduling strategies and slow response in existing technologies. The system of this invention dynamically modifies dynamic parameters by acquiring changes in the external environment and dynamically adjusts the order processing order in conjunction with fixed parameters. By setting different weight coefficients for different parameters, different distribution centers can flexibly formulate scheduling strategies that meet their own business priorities, achieving automated, intelligent, and rapid response in scheduling. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the main process of the intelligent logistics order scheduling method in Embodiment 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of the main structure of the intelligent logistics order scheduling system in Embodiment 1 of the present invention.

[0045] Explanation of reference numerals in the attached figures: 201-First acquisition module; 202-Second acquisition module; 203-Calculation module; 204-Sorting module; 205-Scheduling execution module; 206-Data storage module.

[0046] Specific real-time methods

[0047] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0048] Example 1: As Figure 1 and Figure 2 As shown in the figure, this embodiment provides an intelligent logistics order scheduling method and system.

[0049] The intelligent logistics order scheduling method includes:

[0050] Step 1: The first acquisition module (201) acquires a parameter set, which includes solid parameters and dynamic parameters. The solid parameters are the inherent attribute parameters of each order in the order queue to be scheduled, and the dynamic parameters are the parameters determined according to the dynamic changes of the order queue to be scheduled and the external environment.

[0051] Step 2: The second acquisition module (202) acquires multiple weight coefficients, which are the importance values ​​preset for each parameter.

[0052] Step 3: Based on the parameter set and the weight coefficient of each parameter, calculate the scheduling priority weight value corresponding to each order using the preset weighting algorithm in the calculation module (203).

[0053] Step 4: Sort the orders in the queue of orders to be scheduled by the sorting module (204) according to the scheduling priority weight value corresponding to each order.

[0054] Step 5: In the sorted order, generate scheduling instructions through the scheduling execution module (205) and assign appropriate delivery personnel to the orders.

[0055] The preset weighting algorithm is the first algorithm, and the formula is:

[0056] F = C1×P + C2×T + C3×L + C4×W + C5×R

[0057] Where P is the order priority, T is the cargo type coefficient, L is the customer level coefficient, W is the order waiting time, R is the real-time traffic impact coefficient, and C1 to C5 are the weight coefficients of the corresponding parameters.

[0058] Before step three, the intelligent logistics order scheduling method further includes: determining the order priority P based on the order's payment method, product value, or customer-specified information; determining the goods type coefficient T based on the perishability, fragility, or special characteristics of the goods included in the order; determining the customer level coefficient L based on the ordering customer's historical order volume and membership level; and recording parameters P, T, and L as fixed parameters for each order.

[0059] Before step one, the intelligent logistics order scheduling method further includes: recording the timestamp of the order entering the scheduling queue as the submission time; recording the current system time; and calculating the order waiting time W based on the difference between the current system time and the submission time.

[0060] Prior to step one, the intelligent logistics order scheduling method further includes: obtaining real-time traffic congestion data of the delivery route through a map service interface; and calculating the real-time traffic impact coefficient R by combining historical average vehicle speeds.

[0061] The aforementioned intelligent logistics order scheduling method, wherein the allocation of a suitable delivery person to an order includes: obtaining the real-time location, current load, and skill tags of available delivery persons; calculating the estimated distance and time between the order delivery address and the delivery person's location; and selecting the optimal delivery person for allocation by comprehensively considering the delivery person's load, skill matching degree, and estimated time.

[0062] Before step four, the intelligent logistics order scheduling method further includes: associating and mapping the ID of each order with its calculated scheduling priority weight value; and storing the associated data in a priority database for scheduling decision tracing and analysis.

[0063] The intelligent logistics order scheduling system includes:

[0064] The first acquisition module (201) is used to acquire a parameter set, which includes solid parameters and dynamic parameters.

[0065] The second acquisition module (202) is used to acquire multiple pre-set weight coefficients.

[0066] The calculation module (203) is used to calculate the scheduling priority weight value of each order by means of a weighted algorithm based on the parameter set and weight coefficients.

[0067] The sorting module (204) is used to sort the orders according to the scheduling priority weight value.

[0068] The scheduling execution module (206) is used to assign orders to delivery personnel according to the sorting results.

[0069] The output of the first acquisition module (201) is connected to the second acquisition module (202), the other end of the second acquisition module (202) is connected to the input of the calculation module (203), the other end of the calculation module (203) is connected to the input of the sorting module (204), and the other end of the sorting module (204) is connected to the input of the scheduling execution module (205).

[0070] The system also includes a data storage module (206) for storing the order ID, scheduling priority weight value and their corresponding relationship; the output end of the data storage module (206) is connected to the first acquisition module (201), the calculation module (203) and the scheduling execution module (205) respectively.

[0071] Example 2: Based on the intelligent logistics order scheduling method and system provided in Example 1, this example provides a preferred embodiment process, including:

[0072] Step S1: Obtain the parameter set. Solid-state parameters.

[0073] For example, the system reads order priority P (e.g., divided into 1-5 levels, with 5 being the highest), goods type coefficient T (e.g., 1 for ordinary goods, 2 for fresh produce, and 3 for fragile items) and customer level coefficient L (e.g., 1 for ordinary users, 2 for silver card users, and 3 for gold card users) from the order database. Dynamic parameters include: calculating order waiting time W (current time - order creation time, in minutes); and obtaining real-time traffic conditions by calling the Gaode Map API and normalizing them into a traffic impact coefficient R between 0 and 1 (1 representing extreme congestion).

[0074] Step S2: Obtain the weight coefficients.

[0075] The system reads the preset weight coefficients C1, C2, C3, C4, and C5 for this region from the system configuration library. For example, a business district that emphasizes timeliness may set higher C4 (waiting time weight) and C5 (traffic weight), such as C1=1.2, C2=0.8, C3=1.0, C4=0.1, and C5=0.15.

[0076] Step S3: Calculate the scheduling priority weight value.

[0077] For each order, substituting into the first algorithm: F = 1.2×P + 0.8×T + 1.0×L + 0.1×W + 0.15×R. Assuming an order: P=5, T=2, L=3, W=30 (minutes), R=0.8, then its weight value F = 1.2×5 + 0.8×2 + 1.0×3 + 0.1×30 + 0.15×0.8 = 6 + 1.6 + 3 + 3 + 0.12 = 13.72.

[0078] Step S4: Sort.

[0079] After calculating the F-values ​​of all orders in the queue, they are sorted from highest to lowest F-value. A higher F-value indicates a higher scheduling priority.

[0080] Step S5: Allocation and Scheduling.

[0081] For the top-ranked order, the system retrieves a list of available delivery personnel. After comparing factors such as delivery person A (nearby but fully booked) and delivery person B (slightly farther away but lightly booked and with matching skills), delivery person B is selected for assignment, and a dispatch instruction is generated and pushed to their terminal device.

Claims

1. A smart logistics order scheduling method, characterized in that, include: Step 1: The first acquisition module (201) acquires a parameter set, which includes solid parameters and dynamic parameters. The solid parameters are the inherent attribute parameters of each order in the order queue to be scheduled, and the dynamic parameters are the parameters determined according to the dynamic changes of the order queue to be scheduled and the external environment. Step 2: The second acquisition module (202) acquires multiple weight coefficients, wherein the weight coefficients are pre-set importance values ​​for each parameter; Step 3: Based on the parameter set and the weight coefficient of each parameter, calculate the scheduling priority weight value corresponding to each order using the preset weighting algorithm in the calculation module (203); Step 4: Sort the orders in the queue of orders to be scheduled by the sorting module (204) according to the scheduling priority weight value corresponding to each order; Step 5: In the sorted order, generate scheduling instructions through the scheduling execution module (205) and assign appropriate delivery personnel to the orders.

2. The intelligent logistics order scheduling method according to claim 1, characterized in that, The preset weighting algorithm is the first algorithm, and the formula is: F = C1×P + C2×T + C3×L + C4×W + C5×R Where P is the order priority, T is the cargo type coefficient, L is the customer level coefficient, W is the order waiting time, R is the real-time traffic impact coefficient, and C1 to C5 are the weight coefficients of the corresponding parameters.

3. The intelligent logistics order scheduling method according to claim 2, characterized in that, Before performing step three, the method further includes the following steps performed by the first acquisition module: Order priority P is determined based on the payment method, product value, or customer-specified information. The cargo type coefficient T is determined based on the perishability, fragility, or special characteristics of the goods included in the order; The customer level coefficient L is determined based on the customer's historical order volume and membership level. Record parameters P, T, and L as fixed parameters for each order.

4. The intelligent logistics order scheduling method according to claim 2, characterized in that, Before performing step one, the method further includes the following steps performed by the first acquisition module: Record the timestamp of the order entering the scheduling queue as the submission time; Record the system's current time; The order waiting time W is calculated based on the difference between the current system time and the submission time.

5. The method according to claim 2, characterized in that, Before performing step one, the method further includes the following steps performed by the first acquisition module: Obtain real-time traffic congestion data for delivery routes through map service interfaces; The real-time traffic impact coefficient R is calculated by combining historical average vehicle speeds.

6. The intelligent logistics order scheduling method according to claim 1, characterized in that, The process of assigning suitable delivery personnel to orders includes: obtaining the real-time location, current load, and skill tags of available delivery personnel; calculating the estimated distance and time between the order delivery address and the delivery personnel's location; and selecting the optimal delivery personnel based on a comprehensive consideration of delivery personnel load, skill matching, and estimated time.

7. The intelligent logistics order scheduling method according to claim 1, characterized in that, After step three and before step four, the method further includes the following steps performed by the data storage module: Associate and map the ID of each order with its calculated scheduling priority weight value; The associated data is stored in a priority database for scheduling decision tracing and analysis.

8. An intelligent logistics order scheduling system, characterized in that, include: The first acquisition module (201) is used to acquire a parameter set, the parameter set including solid parameters and dynamic parameters; The second acquisition module (202) is used to acquire multiple pre-set weight coefficients; The calculation module (203) is used to calculate the scheduling priority weight value of each order by means of a weighted algorithm based on the parameter set and weight coefficients. The sorting module (204) is used to sort the orders according to the scheduling priority weight value; The scheduling execution module (206) is used to assign orders to delivery personnel according to the sorting results; The output of the first acquisition module (201) is connected to the second acquisition module (202), the other end of the second acquisition module (202) is connected to the input of the calculation module (203), the other end of the calculation module (203) is connected to the input of the sorting module (204), and the other end of the sorting module (204) is connected to the input of the scheduling execution module (205).

9. The intelligent logistics order scheduling system according to claim 8, characterized in that, The system also includes a data storage module (206) for storing the order ID, scheduling priority weight value and their corresponding relationship; the output end of the data storage module (206) is connected to the first acquisition module (201), the calculation module (203) and the scheduling execution module (205) respectively.

Images