CN-122022308-A - Scheduling method, electronic device, storage medium and program product

CN122022308ACN 122022308 ACN122022308 ACN 122022308ACN-122022308-A

Abstract

The application provides a scheduling method, electronic equipment, a storage medium and a program product, and relates to the technical field of computers. According to the method, the basic information of personnel and the standard information of the shift are comprehensively obtained, accurate data support is provided for shift decision making, and then three shift scenes of minimum, maximum and normal are identified, so that accurate adaptation to different service fluctuations (such as shortage of low-level single quantity, rapid increase of large-level single quantity and stable daily single quantity) is realized, the minimum shift scene can avoid manpower redundancy waste, the maximum shift scene can maximize the work-taking potential to ensure productivity, the normal shift scene balances efficiency and fairness, the problem of poor adaptability of the traditional shift to the service fluctuations is effectively solved, meanwhile, corresponding shift algorithms are executed aiming at different scenes, the rough mode of manually integrating information and making experience decisions is replaced, the shift efficiency and scheme accuracy are greatly improved, and the optimization and upgrading of the integral operation efficiency of storage are realized.

Inventors

JIN GUANG
ZHANG YING
ZHONG DEFU
Wang Senmu

Assignees

上海得物信息集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260126

Claims (13)

1. A method of scheduling, the method comprising: The method comprises the steps of obtaining the shift input data of a target shift day, wherein the shift input data comprises personnel basic information and shift reference information; According to the shift input data, a current shift scene is identified, wherein the shift scene comprises a minimum shift scene, a maximum shift scene and a normal shift scene, the minimum shift scene refers to a scene when the capacity to be discharged on the target shift day is lower than the lowest human configuration capacity, the maximum shift scene refers to a scene when the capacity to be discharged exceeds the full-capacity of the whole member, and the normal shift scene refers to a scene when the capacity to be discharged is between the lowest human configuration capacity and the full-capacity of the whole member; And executing a scheduling algorithm corresponding to the current scheduling scene according to the scheduling input data to generate a corresponding scheduling scheme.
2. The method of claim 1, wherein the executing a scheduling algorithm corresponding to the current scheduling scenario based on the scheduling input data generates a corresponding scheduling scheme, comprising: And if the current scheduling scene is a normal scheduling scene, executing a scheduling algorithm based on a simulated annealing algorithm according to the scheduling input data, and performing iterative optimization with a minimum objective function as a target to generate a corresponding scheduling scheme, wherein the objective function is at least related to total attendance man-hour.
3. The method of claim 2, wherein performing a simulated annealing algorithm based shift algorithm based on the shift input data, performing iterative optimization with a goal of minimizing an objective function, generating a corresponding shift scheme, comprises: setting an objective function of a simulated annealing algorithm according to the shift input data, wherein the objective function is used for minimizing the total attendance man-hour; Generating a scheduling scheme meeting basic constraint through heuristic rules, taking the scheduling scheme as an initial solution of the objective function, and carrying out iterative computation by using a simulated annealing algorithm process; in the iterative process, starting from the current solution, operating the current solution according to a domain operation selection strategy to generate a domain solution; Comparing the objective function value corresponding to the field solution and the current solution, and judging whether to update the current solution and the algorithm optimal solution according to a simulated annealing criterion; And repeating the iterative process until the temperature is reduced to a temperature termination threshold value or the termination condition of the maximum iterative times is met, ending the simulated annealing process, and obtaining an optimal solution corresponding to the minimum objective function value in the simulated annealing process to be used as a scheduling scheme.
4. The method of claim 3, wherein the domain operation selection policy comprises randomly selecting one from a group consisting of personnel interchange, man-hour adjustment, personnel increase and decrease, and cross-shift adjustment.
5. The method of claim 4, wherein the objective function is configured to minimize total attendance man-hours and shift man-hour duty differences, wherein in the iterative process, from a current solution, operating on the current solution according to a domain operation selection policy to produce a domain solution, comprises: in each iteration process, if the current temperature is higher than a preset stage switching temperature threshold value, randomly selecting one from personnel exchange, man-hour adjustment, personnel increase and decrease and span shift adjustment according to a preset probability distribution to generate a domain solution, and setting a weight coefficient of a shift duty ratio difference item in the objective function as a first weight value; And if the current temperature is not higher than a preset phase switching temperature threshold, adjusting a weight coefficient of a shift duty ratio difference item in the objective function to be a second weight value, and increasing the selection probability of the cross-shift adjustment operation in the preset probability distribution, wherein the second weight value is larger than the first weight value.
6. The method of claim 3, wherein generating a scheduling scheme meeting the base constraint by heuristic rules comprises: Determining a month shift attendance staff according to the shift input data; Generating an initial scheduling scheme aiming at the month scheduling attendance staff according to the maximum attendance man-hour principle; adjusting personnel in the initial scheduling scheme to meet the integral attendance constraint and the partner relationship constraint corresponding to each personnel layering label; And under the condition that the integral attendance constraint and the partner relation constraint are met, if the capacity corresponding to the initial scheduling scheme does not meet the capacity requirement, increasing available personnel according to shifts until the capacity requirement is met, and obtaining an adjusted scheduling scheme.
7. The method of claim 6, wherein the personnel layering tags include cores, stability, fluctuation, and new personnel, the overall attendance constraints include an upper attendance limit, an upper attendance man-hour limit, and an upper continuous attendance days limit, and the partner relationship constraints are such that personnel having a partner relationship are prioritized at the time of scheduling.
8. The method of claim 1, wherein the executing a scheduling algorithm corresponding to the current scheduling context based on the scheduling input data generates a corresponding scheduling scheme comprising: If the current scheduling scene is a minimum scheduling scene, determining attendance staff and the capacity to be scheduled on the target scheduling day according to scheduling input data; Generating an initial scheduling scheme for the attendance-available personnel based on a minimum attendance man-hour principle; calculating the difference value between the capacity of the initial scheduling scheme and the capacity to be discharged; removing personnel from the initial scheduling scheme one by one according to a preset personnel removal priority order, and recalculating the difference value after each removal; and when the difference value becomes a negative value, taking the last initial scheduling scheme as the scheduling scheme corresponding to the minimum scheduling scene.
9. The method of claim 1, wherein the executing a scheduling algorithm corresponding to the current scheduling context based on the scheduling input data generates a corresponding scheduling scheme comprising: if the current scheduling scene is a maximum scheduling scene, determining a attendance-available person according to the basic information of the person; And generating a scheduling scheme for the attendance staff based on the maximum attendance man-hour principle, wherein the scheduling scheme is a scheduling scheme corresponding to the maximum scheduling scene.
10. The method of claim 1, wherein the shift reference information includes a capacity to be shifted for the target shift day, and wherein the identifying a current shift scenario based on the shift reference information includes: calculating the maximum productivity after scheduling according to the maximum attendance man-hour according to the personnel basic information; if the maximum productivity is smaller than the productivity to be discharged, determining that the current scheduling scene is a maximum scheduling scene; calculating the minimum productivity according to the basic information of the personnel after scheduling according to the minimum attendance man-hour; if the minimum capacity is larger than the capacity to be discharged, determining that the current scheduling scene is a minimum scheduling scene; and if the maximum capacity is greater than or equal to the capacity to be discharged and/or the minimum capacity is less than or equal to the capacity to be discharged, determining that the current scheduling scene is a normal scheduling scene.
11. An electronic device comprising a processor and a memory storing computer readable instructions that, when executed by the processor, perform the method of any of claims 1-10.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, performs the method according to any of claims 1-10.
13. A computer program product comprising computer program instructions which, when read and executed by a processor, perform the method of any of claims 1-10.

Description

Scheduling method, electronic device, storage medium and program product Technical Field The present application relates to the field of computer technology, and in particular, to a scheduling method, an electronic device, a storage medium, and a program product. Background With the high-speed development of the e-commerce industry, the performance capability of the tidal current e-commerce platform becomes one of the core competitiveness, and the warehouse serves as a key hub for commodity circulation, so that the operation efficiency directly determines the user receiving experience and the platform operation cost. In the business scene of the electric motor, the single quantity fluctuation has obvious burstiness and periodicity, for example, the single quantity can be several times of daily activities during the period of large promotion, and the single quantity can obviously fall back in the period of off-season or night, in addition, the conditions of new products on the market, stock allocation, burst orders and the like can also cause dynamic changes of daily work load. This requires a shift schedule that can adapt to business fluctuations quickly, avoiding human waste while ensuring sufficient capacity. However, most warehouse enterprises in the current industry still rely on the traditional manual scheduling mode, and the operation requirements of modern warehouse are difficult to meet. Disclosure of Invention The embodiment of the application aims to provide a scheduling method, electronic equipment, a storage medium and a program product, which are used for solving the problem that the existing manual scheduling mode cannot meet the modern warehouse operation requirements. In a first aspect, an embodiment of the present application provides a scheduling method, where the method includes: The method comprises the steps of obtaining the shift input data of a target shift day, wherein the shift input data comprises personnel basic information and shift reference information; According to the shift input data, a current shift scene is identified, wherein the shift scene comprises a minimum shift scene, a maximum shift scene and a normal shift scene, the minimum shift scene refers to a scene when the capacity to be discharged on the target shift day is lower than the lowest human configuration capacity, the maximum shift scene refers to a scene when the capacity to be discharged exceeds the full-capacity of the whole member, and the normal shift scene refers to a scene when the capacity to be discharged is between the lowest human configuration capacity and the full-capacity of the whole member; And executing a scheduling algorithm corresponding to the current scheduling scene according to the scheduling input data to generate a corresponding scheduling scheme. In the implementation process, the accurate data support is provided for the scheduling decision by comprehensively acquiring the basic information of personnel and the scheduling reference information, then the accurate adaptation to different service fluctuations (such as insufficient in off-season single quantity, rapid increase in large promotion single quantity and stable daily single quantity) is realized by identifying three scheduling scenes with extremely small size, extremely large size and normal size, the extremely small scheduling scene can avoid manpower redundancy waste, the extremely large scheduling scene can maximize the excavation attendance potential to ensure the productivity, the normal scheduling scene balances efficiency and fairness, the problem of poor adaptability of the traditional scheduling to the service fluctuations is effectively solved, meanwhile, the corresponding scheduling algorithm is executed aiming at different scenes, the manual information integration is replaced, the accuracy of the scheme is greatly improved, and the optimization upgrading of the integral operation efficiency of storage is realized. Optionally, executing a scheduling algorithm corresponding to the current scheduling scene according to the scheduling input data, and generating a corresponding scheduling scheme, including: And if the current scheduling scene is a normal scheduling scene, executing a scheduling algorithm based on a simulated annealing algorithm according to the scheduling input data, and performing iterative optimization with a minimum objective function as a target to generate a corresponding scheduling scheme, wherein the objective function is at least related to total attendance man-hour. In the implementation process, a simulated annealing algorithm is adopted under a normal scheduling scene, iteration optimization is carried out by taking the minimized total attendance man-hour as a core target, the situation that a local optimal solution is trapped is avoided by means of the global searching capability of the algorithm, a scheduling scheme considering cost and efficiency is accurately found, and the labor cost for