Method, device and electronic equipment for scheduling logistics resources

Abstract

The application discloses a logistics resource scheduling method and device and electronic equipment. The method comprises the following steps: obtaining scheduling task information, wherein the scheduling task information is used to represent a scheduling task of scheduling logistics resources in a preset time period; based on the scheduling task information, a constraint condition and a target function used for executing the scheduling task are constructed, wherein the constraint condition is used to represent a rule required to be met when the scheduling task is executed, and the target function is used to represent a task demand required to be met when the scheduling task is executed; a model solver is called to solve the constraint condition and the target function, and a solution result is obtained, wherein the solution result is used to represent a scheduling strategy of executing the scheduling task, and the scheduling strategy meets the task demand; and the scheduling task is executed according to the scheduling strategy corresponding to the solution result. The application solves the technical problem that logistics resource planning cannot be effectively performed in the related art.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and more specifically, to a method, apparatus, and electronic device for scheduling logistics resources. Background Technology

[0002] In traditional logistics resource management, resource scheduling usually relies on human experience and intuition, and adopts manual or simple rule-based scheduling systems.

[0003] In related technologies, manual scheduling systems typically rely on spreadsheets or similar tools for scheduling operations. Planners need to manually input various data, such as production plans, employee skills, work time constraints, and equipment availability, in an attempt to find a scheduling scheme that meets basic requirements. However, this planning method has many limitations and shortcomings, especially when facing complex and ever-changing logistics demands and increasing business volumes. Its inefficiency and lack of flexibility become increasingly prominent, resulting in technical problems that prevent effective logistics resource planning.

[0004] There is currently no effective solution to the aforementioned technical problems that prevent effective logistics resource planning. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for scheduling logistics resources, in order to at least solve the technical problem in the related art that logistics resource planning cannot be effectively carried out.

[0006] According to one aspect of the embodiments of this application, a method for scheduling logistics resources is provided. The method may include: acquiring scheduling task information, wherein the scheduling task information is used to characterize a scheduling task for scheduling logistics resources within a preset time period; based on the scheduling task information, constructing constraints and an objective function for executing the scheduling task, wherein the constraints characterize the rules to be satisfied when executing the scheduling task, and the objective function characterizes the task requirements to be satisfied when executing the scheduling task; calling a model solver to solve the constraints and objective function to obtain a solution result, wherein the solution result characterizes a scheduling strategy for executing the scheduling task, and the scheduling strategy satisfies the task requirements; and executing the scheduling task according to the scheduling strategy corresponding to the solution result.

[0007] Optionally, based on the scheduling task information, constraints for executing the scheduling task are constructed, including: constructing multiple parameter indicators corresponding to the scheduling task based on the scheduling task information, wherein the parameter indicators are used to characterize the characteristic requirements for executing the scheduling task; and constructing operation duration constraints, equipment capacity constraints, and personnel object quantity constraints for executing the scheduling task based on the multiple parameter indicators, wherein the operation duration constraint is used to characterize that the operation duration for executing the scheduling task is less than or equal to a preset duration, the equipment capacity constraint is used to characterize that during the execution of the scheduling task, the number of personnel objects actually operating the equipment objects is less than or equal to the number of personnel objects required by the equipment objects, and the personnel object quantity constraint is used to characterize that during the execution of the scheduling task, the number of personnel objects in each work shift is less than or equal to a preset quantity, wherein the preset quantity is set by the maximum number of personnel objects required by the work shift.

[0008] Optionally, based on the scheduling task information, a target function for executing the scheduling task is determined, including: determining a demand gap index for executing the scheduling task based on the expected scheduling demand in the scheduling task information and the actual scheduling services that equipment objects and personnel objects can provide, wherein the demand gap index is used to characterize the degree of matching between actual scheduling services and expected scheduling demand; determining a labor cost index for executing the scheduling task based on the shift duration in the scheduling task information and the number of personnel objects corresponding to the shift, wherein the labor cost index is used to characterize the working hours of personnel objects corresponding to the shift; and determining a cross-regional service index for personnel objects based on the job demand information in the scheduling task information, wherein the cross-regional service index is used to characterize the flow of personnel objects between different jobs during the execution of the scheduling task; and determining the target function based on the demand gap index, the labor cost index, and the cross-regional service index.

[0009] Optionally, based on demand gap indicators, labor cost indicators, and cross-regional service indicators, an objective function is determined, including: obtaining the demand gap indicator weighting factor corresponding to the demand gap indicator, the labor cost indicator weighting factor corresponding to the labor cost indicator, and the cross-regional service indicator weighting factor corresponding to the cross-regional service indicator; and weighting and summing the demand gap indicator with its weighting factor, the labor cost indicator with its weighting factor, and the cross-regional service indicator with its weighting factor to determine the objective function.

[0010] Optionally, the model solver is invoked to solve the constraints and objective function to obtain the solution results, including: converting the constraint and objective function according to the data input format of the model solver to obtain the converted constraint and objective function; and inputting the converted constraint and objective function into the model solver for solving to obtain the solution results.

[0011] Optionally, when inputting the transformed constraints and objective function into the model solver for solving, the method further includes: constructing work requirement constraints that satisfy the data input format of the model solver based on the work requirements of the personnel; inputting the work requirement constraints, the transformed constraints, and the objective function together into the model solver for solving, and obtaining the solution result.

[0012] Optionally, the scheduling strategy includes at least: a work plan for the device object that performs the scheduling task, and a work plan for the personnel object that performs the scheduling task.

[0013] According to another aspect of the embodiments of this application, a logistics resource scheduling device is also provided. The device may include: an acquisition unit, configured to acquire scheduling task information, wherein the scheduling task information characterizes a scheduling task for scheduling logistics resources within a preset time period; a construction unit, configured to construct constraints and an objective function for executing the scheduling task based on the scheduling task information, wherein the constraints characterize the rules to be satisfied when executing the scheduling task, and the objective function characterizes the task requirements to be satisfied when executing the scheduling task; an invocation unit, configured to invoke a model solver to solve the constraints and the objective function to obtain a solution result, wherein the solution result characterizes the scheduling strategy for executing the scheduling task, and the scheduling strategy satisfies the task requirements; and an execution unit, configured to execute the scheduling task according to the scheduling strategy corresponding to the solution result.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0019] In this embodiment, scheduling task information is obtained, wherein the scheduling task information is used to characterize the scheduling task of scheduling logistics resources within a preset time period; based on the scheduling task information, constraints and objective functions for executing the scheduling task are determined, wherein the constraints are used to characterize the rules that need to be met to execute the scheduling task, and the objective function is used to characterize the task requirements that need to be met to execute the scheduling task; a model solver is invoked to solve the constraints and objective function to obtain the solution result, wherein the solution result is used to characterize the scheduling strategy for executing the scheduling task, and the scheduling strategy meets the task requirements; the scheduling task is executed according to the scheduling strategy corresponding to the solution result. In other words, in this embodiment, the constraints and objective function for executing the scheduling task can be determined based on the scheduling task information. The constraints characterize the rules that need to be met to execute the scheduling task, and the objective function characterizes the task requirements that need to be met to execute the scheduling task. Based on this, by calling the model solver to solve the constraints and objective function, the scheduling strategy for executing the scheduling task can be obtained. Executing the scheduling task according to this scheduling strategy can effectively plan logistics resources without manually determining the scheduling strategy, greatly improving the efficiency and accuracy of determining the scheduling strategy, and thus solving the technical problem in related technologies of the inability to effectively determine the scheduling strategy for logistics resources. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain this application and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart of a logistics resource scheduling method according to an embodiment of this application;

[0022] Figure 2 This is a flowchart of another logistics resource scheduling method according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram illustrating the working hour requirements for a certain position in a specific area according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a logistics resource scheduling device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, functional component, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, functional components, or devices.

[0027] According to an embodiment of this application, an embodiment of a method for scheduling logistics resources is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a logistics resource scheduling method according to an embodiment of this application, such as... Figure 1 As shown, the method may include the following steps.

[0029] Step S101: Obtain scheduling task information.

[0030] In the technical solution provided in step S101 of this application, the scheduling task information is used to characterize the scheduling task for scheduling logistics resources within a preset time period. This scheduling task information includes all business data and detailed requirements that the scheduling task for scheduling logistics resources within the preset time period needs to reference. For example, the scheduling task information includes, but is not limited to: production plan data, logistics demand information, and resource base data. For example, production plan data includes, but is not limited to: expected production volume, production line operating cycle time, processing requirements for different products or parts, etc.; logistics demand information includes, but is not limited to: warehouse inbound and outbound volumes, internal logistics transfer requirements, time windows for logistics operations, etc., used to estimate the manpower and equipment required for logistics operations; resource base information includes, but is not limited to: personnel object information, equipment object information and constraint information, and cost parameters. Personnel object information is used to characterize the work capacity, skill certification, and working hours of personnel objects; equipment object information is used to characterize the type, quantity, capacity, maintenance and upkeep requirements of equipment, as well as the equipment's operating efficiency and historical performance records; cost parameters are used to characterize personnel costs and equipment operating costs, used to quantify the differences in economic benefits brought about by different scheduling strategies.

[0031] In this embodiment, scheduling task information can be obtained from a logistics resource system. For example, scheduling task information can be obtained from an Enterprise Resource Planning (ERP) system, or from a Warehouse Management System (WMS).

[0032] Optionally, for information that cannot be automatically obtained, such as special requests from employees or unexpected equipment malfunctions, manual input is required to complete the data. The collected raw data is then cleaned, formatted, and preliminarily analyzed. For example, work hour requirements are converted into comparable units, potential outliers or missing data are identified, and appropriate preprocessing is performed.

[0033] In this step, by obtaining scheduling task information, the system can understand the actual demand for logistics activities within a preset time period, ensuring that scheduling decisions are based on sufficient and accurate information.

[0034] Step S102: Based on the scheduling task information, construct the constraints and objective function for executing the scheduling task.

[0035] In the technical solution provided by step S102 of this application, the above-mentioned constraints are used to characterize the rules that need to be met to execute the scheduling task, and the above-mentioned objective function is used to characterize the task requirements that need to be met to execute the scheduling task.

[0036] In this embodiment, after obtaining the scheduling task information, constraints and objective functions for executing the scheduling task can be further determined based on the scheduling task information. These constraints include, but are not limited to, demand satisfaction constraints, equipment capacity constraints, resource capacity constraints, and personnel skill and job matching constraints. The demand satisfaction constraint ensures that the allocated personnel and equipment are sufficient to cover the predicted operational demand in each shift, at each site, and at each job position, while reserving a buffer to cope with demand fluctuations. The equipment capacity constraint avoids overloading or excessive fatigue based on the maximum operating capacity of equipment (e.g., handling machinery, process vehicles) and the maximum working time of personnel. The personnel skill and job matching constraint ensures that each task is performed by personnel with the corresponding skills to maintain operational safety and efficiency.

[0037] Optionally, as described above, the objective function characterizes the task requirements that need to be met to execute the scheduling task. In other words, the objective function serves as a guide in the model solving process, clarifying the main objective pursued by the scheduling decision. Specifically, the objective function is set around minimizing the utilization efficiency of logistics resources and the overall cost.

[0038] For example, suppose a vehicle manufacturing plant's logistics center needs to adjust its resource scheduling over the next two weeks to cope with an upcoming surge in order deliveries. First, a detailed business dataset is created by collecting various scheduling task information, including projected production volume, employee skill profiles, equipment maintenance records, and shift schedule details. Then, based on this scheduling task information, a series of constraints are defined, such as maximum equipment operating hours, maximum continuous work cycles for employees, and the requirement that certain positions be filled by qualified employees. The objective function is also clearly defined, such as minimizing the additional costs and personnel costs incurred due to unmet demand, while maintaining high equipment utilization and good employee benefits.

[0039] Optionally, after constructing the constraints and objective function, these constraints and objective function can be used as a guide for the scheduling task of logistics resources, laying a solid data foundation for subsequent model solving and scheduling strategy output, and helping to improve the accuracy of scheduling strategy determination.

[0040] Step S103: Call the model solver to solve the constraints and objective function, and obtain the solution results.

[0041] In the technical solution provided by step S103 of this application, the solution result is used to characterize the scheduling strategy for executing the scheduling task, the scheduling strategy meets the task requirements, and the scheduling strategy is used to characterize the rules for executing the scheduling task.

[0042] In this embodiment, after determining the constraints and objective function, the model solver can be invoked to solve the constraints and objective function and obtain the solution result, which is used to characterize the scheduling strategy for executing the scheduling task of logistics resources.

[0043] Optionally, the aforementioned model solver is a software tool specifically designed to solve complex optimization problems. It can perform efficient search and calculation based on constraints and objective functions to find the optimal solution or a set of approximate optimal solutions that satisfy the constraints. The model solver can be an operations research optimization solver (such as OR-TOOLS, SCIP, etc.).

[0044] Optionally, after invoking the model solver, advanced algorithms (such as branch and bound, cutting plane method, etc.) can be used to solve the mathematical model constructed above. This process involves large-scale computation, aiming to find a solution that satisfies all constraints while optimizing the objective function value.

[0045] Optionally, after the model solver outputs the solution results, the results can be verified to ensure that the scheduling strategy represented by the solution satisfies the constraints and objective function, and that the results are obtained within a reasonable computation time. If the verification passes, the scheduling strategy represented by the solution results is determined as the scheduling strategy for executing the logistics resource scheduling task.

[0046] Optionally, the solution result, i.e., the scheduling strategy, is a set of resource allocation decisions obtained by the model solver after comprehensively considering all constraints and the optimization objective function. This solution result not only directly guides how logistics resources are rationally allocated within a preset time period, but also reflects the scientific and intelligent nature of the scheduling decision.

[0047] In this step, the powerful computing capabilities of the model solver are used to transform the scheduling task into a mathematical model and solve for the optimal scheduling strategy, providing guidance for the execution of subsequent logistics resource scheduling tasks.

[0048] Step S104: Execute the scheduling task according to the scheduling strategy corresponding to the solution result.

[0049] In the technical solution provided in step S104 of this application, after obtaining the solution result, the scheduling task can be executed according to the scheduling strategy corresponding to the solution result.

[0050] In this embodiment, the solution results may include, but are not limited to, decision-making data such as personnel configuration rules, equipment configuration rules, and shift scheduling rules. These constitute the scheduling strategy for executing the scheduling task. The personnel configuration rules indicate the manpower requirements for each site, area, and position within a preset time period, and how to select suitable candidates from the existing employee pool to ensure skill matching and workload balance. The equipment configuration rules indicate the equipment usage plan, including which shifts and positions will utilize specific equipment, and how to schedule equipment to reduce idle time and improve equipment utilization. The shift scheduling rules indicate shift adjustment suggestions, including how to balance personnel allocation across shifts and how to meet production and logistics needs while complying with labor regulations. Based on this, after obtaining the solution results, the scheduling task can be executed according to the aforementioned scheduling strategy.

[0051] In this step, the scheduling task is executed according to the solution obtained by the model solver, which can improve the execution efficiency and effect of the scheduling task.

[0052] In steps S101 to S104 of this application, the constraints and objective function for executing the scheduling task can be determined based on the scheduling task information. The constraints represent the rules that need to be met to execute the scheduling task, and the objective function represents the task requirements that need to be met to execute the scheduling task. Based on this, by calling the model solver to solve the constraints and objective function, the scheduling strategy for executing the scheduling task can be obtained. By executing the scheduling task according to the scheduling strategy, logistics resource planning can be effectively carried out without manually determining the scheduling strategy, which greatly improves the efficiency and accuracy of determining the scheduling strategy, thereby solving the technical problem in related technologies that it is impossible to effectively determine the scheduling strategy for logistics resources.

[0053] The method described in this embodiment will be further described below.

[0054] As an optional embodiment, step S102 involves constructing constraints for executing the scheduling task based on the scheduling task information, including: constructing multiple parameter indicators corresponding to the scheduling task based on the scheduling task information, wherein the parameter indicators are used to characterize the characteristic requirements for executing the scheduling task; and constructing work duration constraints, equipment capacity constraints, and personnel object quantity constraints for executing the scheduling task based on the multiple parameter indicators, wherein the work duration constraint characterizes that the work duration for executing the scheduling task is less than or equal to a preset duration, the equipment capacity constraint characterizes that the number of personnel objects actually operating the equipment objects during the execution of the scheduling task is less than or equal to the number of personnel objects required by the equipment objects, and the personnel object quantity constraint characterizes that the number of personnel objects in each work shift during the execution of the scheduling task is less than or equal to a preset quantity, the preset quantity being set by the maximum number of personnel objects required by the work shift.

[0055] In this embodiment, after obtaining the scheduling task information, various parameter indicators corresponding to the scheduling task can be constructed based on the scheduling task information. For example, various physical information in the scheduling task information can be represented in the form of parameter indicators to characterize the characteristic requirements for executing the scheduling task. These various parameter indicators include, but are not limited to: set parameter indicators, feature parameter indicators, and decision variable parameter indicators.

[0056] Optionally, the set parameter index defines a set of various entities involved in the scheduling task. These entities can be sites, regions, job types, planned dates, shifts, personnel groups, etc. Each set represents a unique dimension, and the set parameter index allows for detailed division and description of the scheduling task. Table 1 is a set parameter index table according to an embodiment of this application. As shown in Table 1, it displays the sets of various entities involved in the scheduling task.

[0057] Table 1. Definitions of Symbols and Sets

[0058]

[0059] The system comprises the following components: Site set S includes all sites or warehouses that may be involved in scheduling; Area set A represents multiple work areas that may exist within each site, such as receiving areas, picking areas, and packing areas; Job / task type set O covers various job types or logistics operation types in all scheduled tasks, such as inbound operations, outbound operations, and goods handling; Planned date set D contains all dates within the planned scheduling period; Shift set H covers various shift times, such as morning shift, evening shift, and night shift; and Personnel grouping set Q groups employees based on skills, work time preferences, or any other relevant attributes.

[0060] Optionally, the feature parameter index describes the characteristics or attributes of entities in the set of parameter indices. Table 2 is a feature parameter index table according to an embodiment of this application.

[0061] Table 2 Characteristic Parameter Index Table

[0062]

[0063] Optionally, the decision variable parameter index is used to represent the unknown quantity that needs to be decided. This decision variable parameter index will be determined during the process of solving the constraints and objective function to optimize the scheduling task. Table 3 is a decision variable parameter table according to an embodiment of this application.

[0064] Table 3 Decision Variable Parameter Table

[0065]

[0066] Optionally, after determining the parameter indicators, constraints for executing scheduling tasks can be constructed based on equipment objects and personnel objects. These constraints include, but are not limited to, operation duration constraints, equipment capacity constraints, and personnel object quantity constraints.

[0067] Optionally, the job duration constraint is used to characterize that the execution time of each scheduled task is less than or equal to a preset duration, which characterizes the upper limit of the execution time of the scheduled task. This not only reflects the natural boundaries of production or logistics operations, such as the duration of shifts, but also includes restrictions on the continuous working time of employees and maintenance regulations for the continuous operation cycle of equipment. By setting job duration constraints, it is possible to prevent excessive use of resources and ensure the safety and compliance of operations. The job duration constraint can be expressed by the following formula (1).

[0068] (1)

[0069] in, This can represent the group of people available on date d. This can indicate the available shifts for date d.

[0070] Optionally, the equipment capacity constraint ensures that when executing a scheduling task, the number of personnel actually operating the equipment is less than or equal to the number of personnel required by the equipment. For example, if a batch of equipment requires 10 personnel, then the number of personnel actually operating that batch of equipment must be less than or equal to 10 to prevent equipment overload. This equipment capacity constraint can be expressed by the following formula (2).

[0071] (2)

[0072] Optionally, the personnel quantity constraint is used to characterize that the number of personnel in each work shift is less than or equal to a preset number during the execution of scheduling tasks. That is, the personnel quantity constraint focuses on the quantity control of personnel configuration within each work shift to ensure that the number of personnel assigned to a specific position in any shift will not exceed the preset maximum value. The setting of the personnel quantity constraint needs to comprehensively consider the employee's work skills, work intensity, shift arrangement, and possible overtime to achieve a balance between personnel utilization and cost control. The personnel quantity constraint can be expressed by the following formula (3).

[0073] (3)

[0074] Optionally, cross-regional / job volatility is defined for each group q, with respect to its allowed range. and all orderly train schedules It satisfies the following formula (4).

[0075] (4)

[0076] Alternatively, since all permutations are listed, the above formula simultaneously restricts positive and negative differences, thus yielding the following formula (5).

[0077] (5)

[0078] Optionally, if or Then forced .

[0079] In this step, by reasonably setting constraints on operation duration, equipment capacity, and number of personnel, the model solver can be guided to search for a better scheduling strategy, thereby optimizing resource allocation, reducing operating costs, and improving the efficiency and stability of overall logistics operations while meeting the needs.

[0080] As an optional embodiment, step S102, based on the scheduling task information, determines the objective function for executing the scheduling task, including: determining a demand gap index for executing the scheduling task based on the expected scheduling demand in the scheduling task information and the actual scheduling services that equipment objects and personnel objects can provide, wherein the demand gap index is used to characterize the degree of matching between actual scheduling services and expected scheduling demand; determining a labor cost index for executing the scheduling task based on the shift duration in the scheduling task information and the number of personnel objects corresponding to the shift, wherein the labor cost index is used to characterize the working hours of personnel objects corresponding to the shift; and determining a cross-regional service index for personnel objects based on the job demand information in the scheduling task information, wherein the cross-regional service index is used to characterize the flow of personnel objects between different jobs during the execution of the scheduling task; and determining the objective function based on the demand gap index, the labor cost index, and the cross-regional service index.

[0081] In this embodiment, the aforementioned demand gap indicator is a quantitative metric that measures the difference between actual scheduling services and expected scheduling demand, reflecting the system's inadequacy in meeting production or logistics operation needs. This indicator is typically calculated by comparing the total expected working hours to be completed with the actual available working hours, emphasizing the urgency and importance of scheduling task requirements and ensuring that any scheduling decision prioritizes complete demand coverage.

[0082] Optionally, when determining the demand gap index, the expected scheduling demand for each task can be calculated first based on the production plan and standard timetable of the process in the scheduling task requirements. Then, this expected scheduling demand is compared with the actual scheduling services that the equipment and personnel can provide to calculate the demand gap index. Both the expected scheduling demand and the actual scheduling services can be reflected in the number of working hours. That is, the demand gap index is determined by comparing the expected total working hours with the actual working hours that the equipment and personnel can provide. This demand gap index can be obtained from the aforementioned decision variable table. To express.

[0083] Optionally, the labor cost indicator focuses on the actual working hours of personnel corresponding to a work shift, which is directly related to human resource expenditures. The purpose of setting this labor cost indicator is to minimize the total personnel usage cost while ensuring that production needs are met.

[0084] Optionally, when determining labor cost indicators, the duration of each work shift (e.g., the duration of each shift) and the number of personnel corresponding to each shift can be determined first to establish the labor cost indicator. For example, this labor cost indicator can be determined through... To indicate, among which, The working hours for shift h in Table 2 above. The number of people in the personnel group q in Table 3 above who are assigned to venue s, area a, and post o in shift h.

[0085] Optionally, cross-regional service indicators can be determined based on job requirement information in the scheduling task information. This quantifies the fluctuation in the actual number of employees served across different shifts and positions, reducing efficiency losses and additional costs caused by job changes, while improving employee job satisfaction and avoiding frequent training and adaptation processes. For example, this cross-regional service indicator can be derived from the indicators in Table 3 above. This is expressed as a measure of the maximum fluctuation in the number of personnel served across venues / regions / positions.

[0086] Optionally, after determining the demand gap indicator, labor cost indicator, and cross-regional service indicator, an objective function can be further determined based on the demand gap indicator, labor cost indicator, and cross-regional service indicator.

[0087] In this step, by defining demand gap indicators, labor cost indicators, and cross-regional service indicators, and incorporating them into the construction of the objective function, a clear optimization direction is provided for subsequent model solving, ensuring the scientific nature and effectiveness of logistics resource scheduling decisions.

[0088] The process of determining the objective function based on demand gap indicators, labor cost indicators, and cross-regional service indicators will be further introduced next.

[0089] As an optional implementation method, the objective function is determined based on the demand gap indicator, the labor cost indicator, and the cross-regional service indicator, including: obtaining the demand gap indicator weight factor corresponding to the demand gap indicator, the labor cost indicator weight factor corresponding to the labor cost indicator, and the cross-regional service indicator weight factor corresponding to the cross-regional service indicator; and respectively weighting and summing the demand gap indicator with the demand gap indicator weight factor, the labor cost indicator with the labor cost indicator weight factor, and the cross-regional service indicator with the cross-regional service indicator weight factor to determine the objective function.

[0090] In this embodiment, after determining the demand gap indicator, labor cost indicator, and cross-regional service indicator, the weighting factors corresponding to the demand gap indicator, labor cost indicator, and cross-regional service indicator can be further obtained. The demand gap indicator weighting factor characterizes the importance of the demand gap indicator in the objective function. This indicator reflects the cost impact of failing to meet production demand, i.e., the degree of negative impact caused by the demand gap. A higher demand gap weight means that in the scheduling strategy determination process, greater emphasis is placed on fully meeting production demand to avoid additional costs caused by stockouts or delays. The labor cost indicator weighting factor quantifies the cost of human resource utilization, including wages, overtime pay, and training costs. A high weight indicates that in the scheduling strategy determination process, labor costs must be strictly controlled, and personnel allocation and shift arrangements optimized to achieve cost savings. The cross-regional service indicator weighting factor measures the efficiency loss and cost increase of personnel moving and serving between different regions or positions. A high weight means that in the scheduling strategy determination process, there is a greater tendency to reduce the cross-use of personnel to reduce training needs, improve operational proficiency, and increase employee satisfaction.

[0091] Optionally, the weighting factors for the different indicators mentioned above are not fixed but can be dynamically adjusted based on changes in market trends, corporate strategies, or operating costs. This flexibility ensures that the objective function always closely aligns with current optimization needs, whether in cost control, demand response, or employee care.

[0092] Optionally, after determining the weighting factors of the demand gap indicator, the labor cost indicator, and the cross-regional service indicator, respectively, the objective function can be determined by weighting and summing the weighting factors of the demand gap indicator, the labor cost indicator, and the cross-regional service indicator. This objective function minZ can be expressed by the following formula (6).

[0093] (6)

[0094] in, It can be used to represent the weighting factor of the demand gap indicator. Demand gap indicator Weighting factors for labor cost indicators Human resource cost indicators Weighting factors for cross-regional service indicators Cross-regional service indicators.

[0095] In this step, by combining demand gap indicators, labor cost indicators, and cross-regional service indicators with their respective weighting factors and performing a weighted sum, a comprehensive optimization objective function that reflects both current business priorities and long-term strategic goals can be constructed. This provides a clear optimization direction for the model solver, enabling it to find a logistics resource scheduling strategy that minimizes demand gap, controls labor costs, and reduces cross-regional service costs under various constraints.

[0096] As an optional embodiment, step S103 involves calling the model solver to solve the constraints and objective function to obtain the solution results. This includes: converting the constraint conditions and objective function according to the data input format of the model solver to obtain the converted constraint conditions and objective function; and inputting the converted constraint conditions and objective function into the model solver for solving to obtain the solution results.

[0097] In this embodiment, by calling a specific model solver, the constructed constraints and objective function are solved to obtain the solution result, which is used to characterize the scheduling strategy for executing the scheduling task.

[0098] Optionally, when calling the model solver to solve for the constraints and objective function, the constraints and objective function can first be formatted according to the input format of the model solver to match the data input standard of the model solver. This model solver can be an operations research optimization solver (such as OR-Tools, SCIP, etc.).

[0099] Optionally, after data format conversion, the converted constraints and objective function can be input into the model solver for solving. During the model solver's process, the status and progress of the model solver can be monitored in real time. By adjusting the solving parameters, such as increasing computation time or changing the algorithm strategy, the solving efficiency and solution quality can be improved.

[0100] Optionally, after the solution is completed, the model solver will produce the solution result, namely the scheduling strategy of the scheduling task. The solution result can be verified to ensure that the solution result meets the constraints and that the function value of the objective function reaches the expected level.

[0101] Optionally, the scheduling strategy in the solution result may include the staff schedule and the equipment usage plan.

[0102] In this step, the constraints and objective function of the scheduling task are solved using a model solver. Leveraging the powerful computational capabilities of the model solver, traditional manual operations are transformed into an intelligent decision-making process, significantly improving the efficiency and accuracy of scheduling strategy determination. Furthermore, in the face of fluctuations in production demand, the model solver can quickly adjust the scheduling strategy, reducing response time and ensuring production continuity and efficiency.

[0103] As an optional implementation, when inputting the transformed constraints and objective function into the model solver for solving, the method further includes: constructing work requirement constraints that meet the data input format of the model solver based on the work requirements of the personnel; inputting the work requirement constraints, the transformed constraints, and the objective function together into the model solver for solving, and obtaining the solution result.

[0104] In this embodiment, in order to effectively incorporate the work requirements of personnel into the model solving process, work requirement constraints that conform to the data input format of the model solver can be constructed. These constraints may include quantitative descriptions of working hours, shift arrangements, skill matching, job alternation frequency, etc., to ensure that the scheduling strategy of the scheduling task meets both production needs and the work preferences of personnel.

[0105] For example, work requirement constraints can be constructed based on the work preferences of the personnel, and these work requirement constraints, along with the transformed constraints and the objective function, can be input into the model solver for solving to obtain the solution results.

[0106] In this step, by taking into account the work needs and preferences of the target audience, the solution obtained is more in line with their usage needs, thereby improving their satisfaction.

[0107] As an optional implementation, the scheduling strategy includes at least: the work plan for the device object that performs the scheduling task, and the work plan for the personnel object that performs the scheduling task.

[0108] In this embodiment, the scheduling strategy determined by solving the constraints and objective function includes at least the work plan of the device object that performs the scheduling task and the work plan of the personnel object that performs the scheduling task.

[0109] Optionally, the work planning for equipment focuses on how to optimally utilize existing logistics equipment (such as handling machines, process vehicles, etc.) to adapt to constantly changing logistics operation needs. The planning content may include: equipment usage schedules and equipment maintenance and upkeep plans. The equipment usage schedule specifies when and where each piece of equipment will be put into use, as well as the duration of continuous operation. The equipment maintenance and upkeep plan includes the necessary maintenance and upkeep time for each piece of equipment to ensure that the equipment is in good condition at critical times and to reduce production interruptions caused by equipment failure.

[0110] Optionally, personnel-specific work planning focuses on optimizing human resource allocation to ensure an appropriate workload for each employee, while also considering individual skills, preferences, and work rules. This primarily includes: employee shift and job allocation, skills and tasks matching, and personnel mobility and replacement plans. Employee shift and job allocation clarifies the position and working hours for each employee in one or more shifts. Skills and tasks matching ensures that each employee's tasks are aligned with their skills. For example, employees with specialized equipment operation skills should be placed in positions requiring such skills to improve operational efficiency and safety. Personnel mobility and replacement plans consider the movement of personnel between different positions or shifts, as well as personnel replacement mechanisms in emergency situations. This helps reduce efficiency losses caused by personnel transfers and ensures that logistics operations can quickly return to normal in the face of emergencies.

[0111] In the above embodiments of this application, constraints and objective functions for executing the scheduling task are determined based on the scheduling task information. The constraints characterize the rules that need to be met to execute the scheduling task, and the objective function characterizes the task requirements that need to be met to execute the scheduling task. Based on this, by calling the model solver to solve the constraints and objective function, a scheduling strategy for executing the scheduling task can be obtained. Executing the scheduling task according to this scheduling strategy can effectively plan logistics resources without the need for manual determination of the scheduling strategy, greatly improving the efficiency and accuracy of determining the scheduling strategy, and thus solving the technical problem in related technologies of the inability to effectively determine the scheduling strategy for logistics resources.

[0112] The preferred embodiments of the present application will now be described in conjunction with the above-mentioned logistics resource scheduling method.

[0113] In the logistics field, logistics resource scheduling is a complex and crucial operational link that directly affects efficiency, cost, employee satisfaction, and safety. Currently, logistics resource scheduling relies more on planners' manual planning, with resource calculations and scheduling based on manual experience. However, due to fluctuations in production plans and logistics demands, which are frequently affected by factors such as order volume and season (e.g., e-commerce promotions, holidays), the static and fixed scheduling mode manually output by humans is difficult to adapt to the fluctuations in production demands, resulting in either insufficient staffing, leading to problems such as low efficiency and employee overwork, or excessive staffing, leading to problems such as waste of labor costs and employee idleness and dissatisfaction.

[0114] Furthermore, planners' manual planning requires manually collecting information from all parties (employee leave, skills, availability), and then repeatedly modifying and adjusting in complex Excel spreadsheets, which is extremely inefficient and time-consuming. Moreover, during the planning process, a large number of constraints need to be considered simultaneously, such as: maximum working hours, mandatory rest times, overtime limits, night shift regulations, employee skill qualifications, leave applications, etc. Manual operations are difficult to handle complex constraints and are prone to errors.

[0115] Furthermore, when sudden situations occur (e.g., a sudden increase in orders, equipment failures, employees' temporary leave), it is very difficult to manually adjust the scheduling strategy, which is likely to cause disruptions in on-site operations and response delays, directly affecting the order fulfillment timeliness and customer satisfaction.

[0116] To address the technical problem that manual planning cannot effectively determine the scheduling strategy for logistics resources, this application provides a method for scheduling logistics resources. The method includes the following steps: obtaining scheduling task information, wherein the scheduling task information characterizes a scheduling task for scheduling logistics resources within a preset time period; based on the scheduling task information, determining constraints and an objective function for executing the scheduling task, wherein the constraints characterize the rules to be satisfied when executing the scheduling task, and the objective function characterizes the task requirements to be satisfied when executing the scheduling task; calling a model solver to solve the constraints and objective function to obtain a solution result, wherein the solution result characterizes the scheduling strategy for executing the scheduling task, the scheduling strategy satisfies the task requirements, and the scheduling strategy characterizes the rules for executing the scheduling task; and executing the scheduling task according to the scheduling strategy corresponding to the solution result. In other words, in this embodiment, the constraints and objective function for executing the scheduling task can be determined based on the scheduling task information. The constraints characterize the rules that need to be met to execute the scheduling task, and the objective function characterizes the task requirements that need to be met to execute the scheduling task. Based on this, by calling the model solver to solve the constraints and objective function, the scheduling strategy for executing the scheduling task can be obtained. Executing the scheduling task according to this scheduling strategy can effectively plan logistics resources without manually determining the scheduling strategy, greatly improving the efficiency and accuracy of determining the scheduling strategy, and thus solving the technical problem in related technologies of the inability to effectively determine the scheduling strategy for logistics resources.

[0117] Figure 2 This is a flowchart of another logistics resource scheduling method according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps.

[0118] Step S201: Input resource scheduling data.

[0119] In this embodiment, resource scheduling data includes, but is not limited to: scheduling demand information, basic process information, time management information, process requirement information, algorithm configuration information, and production resource information. The scheduling demand information includes location, vehicle type, and cycle time. Location indicates the specific physical location of the logistics operation, which can be a warehouse, a specific area within a factory, or a production line. Vehicle type indicates the various types of vehicles used in logistics operations, such as forklifts and automated guided vehicles (AGVs). Cycle time indicates the average time interval for completing a specific task and is an important indicator for measuring the rhythm of the production line.

[0120] Optionally, the basic process information includes area, work group, process, step, transport material unit (TMU), frequency, and allocation time. Area indicates the functional divisions within a factory or warehouse, such as storage area, picking area, and packaging area. Process indicates the specific steps in the logistics operation flow, such as receiving, shipping, picking, and packaging. Action classification indicates the categorization of employee actions in logistics operations, such as handling, scanning, and sorting. Step supports the subdivision of operations within a process. Frequency indicates the repetition frequency of a specific process or step. Table 4 is a basic process table according to an embodiment of this application.

[0121] Table 4 Basic Process Table

[0122]

[0123] Optionally, Table 5 is a time management table according to an embodiment of this application. As shown in Table 5, the time corresponding to each process is displayed.

[0124] Table 5 Work Hour Management Table

[0125]

[0126] Optionally, the process requirements include job titles and equipment requirements. The job titles include the names of specific work positions, such as material handlers, pickers, and quality inspectors. Equipment requirements include the types and quantities of equipment needed to complete a specific process, which helps in calculating equipment utilization and maintenance needs. Table 6 is a process requirement table according to an embodiment of this application.

[0127] Table 6 Process Requirements

[0128]

[0129] Optionally, the algorithm configuration includes scheduling dates, upper / lower load limits, shift times, and rest times. The scheduling date refers to the specified scheduling cycle, such as the start and end dates of a week or a month. The upper / lower load limits refer to the maximum / minimum workload set to ensure employee health and work efficiency. The shift times indicate the working hours of each shift and the interval between shifts. The rest times indicate the rest time and rest rules for employees.

[0130] Optionally, Table 7 is a production resource table according to an embodiment of this application. As shown in Table 7, it illustrates the mapping relationship between job information, shifts, and quantities of equipment and personnel.

[0131] Table 7 Production Resources Table

[0132]

[0133] Step S202: Generate the demand.

[0134] In this embodiment, the demand generation aims to accurately calculate the specific daily demand for each region and each position based on the different types of tasks in each region, so as to ensure that resource scheduling decisions are based on reliable data.

[0135] Optionally, Figure 3 This is a schematic diagram illustrating the working hour requirements for a specific job in a certain area according to an embodiment of this application, such as... Figure 3 As shown, this displays the job hour requirements for each position from the 1st to the 14th day of January 2024.

[0136] Step S203: Construct a resource scheduling model.

[0137] In this embodiment, the construction of the resource scheduling model is the core of the entire resource scheduling algorithm. By defining a set of mathematical equations and constraints, the complex logistics resource scheduling problem is transformed into an optimization problem that can be solved by a computer.

[0138] Optionally, constraints may include, but are not limited to: resource demand constraints, service duration constraints, shift duration constraints, constraints on the product of service personnel and shift duration, and resource and equipment capacity constraints. Among these, resource demand constraints ensure that the resource (including manpower and equipment) needs of each position are met at every moment within each shift. In other words, the model must guarantee that at any time and in any position, there are sufficient employees and equipment present to complete the predetermined logistics operations.

[0139] Optionally, shift duration constraints are used to set the upper and lower limits of the total service hours that each personnel group can undertake throughout the entire scheduling cycle. This includes limits on overtime hours to prevent employees from becoming overworked, while ensuring that the service hours of employees in each shift are sufficient to complete the required tasks.

[0140] Optionally, the shift duration constraint indicates that each shift has a fixed start and end time. The model must ensure that all personnel are assigned within the specified shift time and are not allowed to exceed the shift duration. In addition, the connection between shifts is also considered to avoid scheduling conflicts.

[0141] Optionally, the constraint of the product of the number of service personnel and the shift duration is used to characterize that the product of the number of service personnel and the shift duration is not less than the service duration of the area of ​​the post.

[0142] Optionally, resource and equipment constraints are used to characterize the equipment capacity limit for each position, that is, only a fixed number of devices can be used at the same time, in order to avoid increased wear and tear caused by overuse of equipment or waste of resources caused by idle equipment.

[0143] Optionally, through the above constraints, the resource scheduling model ensures that resource allocation meets production needs, follows best practices in human resource management, and takes into account the effective utilization and maintenance of equipment. This model construction method, through mathematical programming, can find a balance among multiple objectives, achieving automation and optimization of resource scheduling, and providing strong support for resource management in large or complex logistics environments.

[0144] Step S204: Solve the resource scheduling model.

[0145] In this embodiment, after constructing the resource scheduling model through the above step S203, the resource scheduling model can be solved.

[0146] For example, the solution can be obtained by calling an operations research and optimization solver, while simultaneously designing operations research and optimization algorithms to improve the solution efficiency and computational accuracy requirements.

[0147] Step S205: Output the resource scheduling results.

[0148] In this embodiment, after calling the operations research solver to solve the resource scheduling optimization model, resource scheduling results can be output. These results include a scheduling overview, a scheduling detail table, and a scheduling table. The scheduling overview includes the total number of employees per shift, the number of employees per position, the number of devices, and the average load factor. The total number of employees per shift includes all shifts and positions. This helps managers clearly understand overall personnel needs and assess the gap with the current staff size. The number of employees per position indicates the number of employees in each job position. The number of devices lists the total number of various devices recommended for use throughout the scheduling cycle. The average load factor refers to the average workload ratio of personnel or equipment, typically the ratio of actual working hours to maximum available time. The average load factor reflects the degree of balanced resource utilization.

[0149] Optionally, the shift schedule includes job title, area, process, working hours, shift duration, actual load factor, and required equipment. The job title clearly identifies the specific logistics operation position, such as warehouse clerk, picker, and quality inspector. The area displays the physical area or logistics node where each position is located, facilitating spatial resource location. The process lists the logistics operation procedures responsible for each position, such as receiving goods, sorting goods, packing and shipping. Working hours refer to the duration of work for an employee in a given shift, used for individual work scheduling and attendance management. Shift duration is the total working hours for the shift. Actual load factor reflects the specific load factor for each position, comparing actual workload with the maximum load of the position, helping to assess individual stress and work efficiency. Required equipment indicates the type and quantity of equipment needed for each position during the shift, facilitating advance equipment preparation and maintenance.

[0150] Optionally, the schedule shows the allocation status of employees or equipment in different shifts and time periods. Table 8 is a schedule according to an embodiment of this application.

[0151] Table 8 Shift Schedule

[0152]

[0153]

[0154] In steps S201 to S205 above, starting from data input, the entire process of optimizing scheduling decisions is gradually refined and realized through steps such as demand generation, model building, model solving, and result output. Each link is closely connected and interdependent, jointly ensuring that the algorithm can quickly and accurately respond to changes in demand and optimize resource allocation in a complex and ever-changing logistics environment, thereby achieving the technical effects of improving operational efficiency and reducing costs.

[0155] According to an embodiment of this application, a logistics resource scheduling device is also provided. It should be noted that this logistics resource scheduling device can be used to execute the logistics resource scheduling method described in the embodiments.

[0156] Figure 4 This is a schematic diagram of a logistics resource scheduling device according to an embodiment of this application. Figure 4 As shown, the logistics resource scheduling device 400 may include: an acquisition unit 401, a construction unit 402, a calling unit 403, and an execution unit 404.

[0157] The acquisition unit 401 is used to acquire scheduling task information, wherein the scheduling task information is used to characterize the scheduling tasks for scheduling logistics resources within a preset time period.

[0158] The construction unit 402 is used to construct constraints and objective functions for executing the scheduling task based on the scheduling task information. The constraints are used to characterize the rules that need to be met to execute the scheduling task, and the objective function is used to characterize the task requirements that need to be met to execute the scheduling task.

[0159] Calling unit 403 is used to call the model solver to solve the constraints and objective function and obtain the solution results. The solution results are used to characterize the scheduling strategy for executing the scheduling task, and the scheduling strategy meets the task requirements.

[0160] Execution unit 404 is used to execute scheduling tasks according to the scheduling strategy corresponding to the solution results.

[0161] Optionally, the construction unit 402 is further configured to: construct multiple parameter indicators corresponding to the scheduling task based on the scheduling task information, wherein the parameter indicators are used to characterize the characteristic requirements for executing the scheduling task; and construct operation duration constraints, equipment capacity constraints, and personnel object quantity constraints for executing the scheduling task based on the multiple parameter indicators, wherein the operation duration constraint is used to characterize that the operation duration for executing the scheduling task is less than or equal to a preset duration, the equipment capacity constraint is used to characterize that during the execution of the scheduling task, the number of personnel objects actually operating the equipment objects is less than or equal to the number of personnel objects required by the equipment objects, and the personnel object quantity constraint is used to characterize that during the execution of the scheduling task, the number of personnel objects in each work shift is less than or equal to a preset quantity, wherein the preset quantity is set by the maximum number of personnel objects required by the work shift.

[0162] Optionally, the construction unit 402 is further configured to: determine a demand gap index for executing the scheduling task based on the expected scheduling demand in the scheduling task information and the actual scheduling services that equipment objects and personnel objects can provide, wherein the demand gap index is used to characterize the degree of matching between actual scheduling services and expected scheduling demand; and determine a labor cost index for executing the scheduling task based on the shift duration in the scheduling task information and the number of personnel objects corresponding to the shift, wherein the labor cost index is used to characterize the working hours of personnel objects corresponding to the shift; and determine a cross-regional service index for personnel objects based on the job requirement information in the scheduling task information, wherein the cross-regional service index is used to characterize the flow of personnel objects between different jobs during the execution of the scheduling task; and determine an objective function based on the demand gap index, the labor cost index, and the cross-regional service index.

[0163] Optionally, the construction unit 402 is further configured to: obtain the demand gap indicator weight factor corresponding to the demand gap indicator, the human cost indicator weight factor corresponding to the human cost indicator, and the cross-regional service indicator weight factor corresponding to the cross-regional service indicator; and respectively perform weighted summation of the demand gap indicator with the demand gap indicator weight factor, the human cost indicator with the human cost indicator weight factor, and the cross-regional service indicator with the cross-regional service indicator weight factor to determine the objective function.

[0164] Optionally, the building unit 403 is also used to: convert the constraint conditions and objective function according to the data input format of the model solver to obtain the converted constraint conditions and objective function; input the converted constraint conditions and objective function into the model solver for solving to obtain the solution result.

[0165] Optionally, the device 400 is also used to: construct work requirement constraints that satisfy the data input format of the model solver based on the work requirements of the personnel; input the work requirement constraints, the transformed constraints, and the objective function together into the model solver for solving, and obtain the solution result.

[0166] In the logistics resource scheduling device described above in this application, the constraints and objective function for executing the scheduling task can be determined based on the scheduling task information. The constraints characterize the rules that need to be met to execute the scheduling task, and the objective function characterizes the task requirements that need to be met to execute the scheduling task. Based on this, by calling the model solver to solve the constraints and objective function, the scheduling strategy for executing the scheduling task can be obtained. By executing the scheduling task according to the scheduling strategy, logistics resource planning can be effectively carried out without manually determining the scheduling strategy, which greatly improves the efficiency and accuracy of determining the scheduling strategy and solves the technical problem in related technologies that it is impossible to effectively determine the scheduling strategy for logistics resources.

[0167] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the logistics resource scheduling method in various embodiments of the present invention during runtime.

[0168] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to execute the logistics resource scheduling method of various embodiments of the present invention.

[0169] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the logistics resource scheduling method in various embodiments of the present invention.

[0170] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the logistics resource scheduling method in various embodiments of the present invention.

[0171] The embodiments of this application also provide a computer program that, when executed by a processor, implements the logistics resource scheduling method in the various embodiments of the present invention described above.

[0172] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0173] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0175] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0178] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for scheduling logistics resources, characterized in that, include: Obtain scheduling task information, wherein the scheduling task information is used to characterize the scheduling tasks for scheduling logistics resources within a preset time period; Based on the scheduling task information, constraints and objective functions are constructed for executing the scheduling task, wherein the constraints are used to characterize the rules that need to be satisfied to execute the scheduling task, and the objective function is used to characterize the task requirements that need to be satisfied to execute the scheduling task. The model solver is invoked to solve the constraints and the objective function to obtain the solution results, wherein the solution results are used to characterize the scheduling strategy for executing the scheduling task, and the scheduling strategy satisfies the task requirements; The scheduling task is executed according to the scheduling strategy corresponding to the solution result.

2. The method according to claim 1, characterized in that, Based on the scheduling task information, constraints for executing the scheduling task are constructed, including: Based on the scheduling task information, multiple parameter indicators are constructed for the scheduling task, wherein the parameter indicators are used to characterize the characteristic requirements for executing the scheduling task. Based on the aforementioned multiple parameter indicators, constraints on job duration, equipment capacity, and personnel quantity are constructed for executing the scheduling task. The job duration constraint indicates that the job duration for executing the scheduling task is less than or equal to a preset duration. The equipment capacity constraint indicates that during the execution of the scheduling task, the number of personnel actually operating the equipment is less than or equal to the number of personnel required by the equipment. The personnel quantity constraint indicates that during the execution of the scheduling task, the number of personnel in each work shift is less than or equal to a preset number, which is set by the maximum number of personnel required by the work shift.

3. The method according to claim 2, characterized in that, Based on the scheduling task information, a target function for executing the scheduling task is determined, including: Based on the anticipated scheduling requirements in the scheduling task information, and the actual scheduling services that the equipment and personnel can provide, a demand gap index is determined for executing the scheduling task, wherein the demand gap index characterizes the degree of matching between the actual scheduling services and the anticipated scheduling requirements; and, Based on the shift duration in the scheduling task information and the number of personnel corresponding to the shift, a labor cost indicator for executing the scheduling task is determined, wherein the labor cost indicator characterizes the working hours of the personnel corresponding to the shift; and, Based on the job requirement information in the scheduling task information, the cross-regional service index of the personnel is determined, wherein the cross-regional service index is used to characterize the movement of the personnel between different jobs during the execution of the scheduling task. The objective function is determined based on the demand gap indicator, the labor cost indicator, and the cross-regional service indicator.

4. The method according to claim 3, characterized in that, Based on the demand gap indicator, the labor cost indicator, and the cross-regional service indicator, the objective function is determined, including: Obtain the demand gap indicator weight factor corresponding to the demand gap indicator, the human cost indicator weight factor corresponding to the human cost indicator, and the cross-regional service indicator weight factor corresponding to the cross-regional service indicator. The objective function is determined by weighting and summing the demand gap indicator with its weighting factor, the labor cost indicator with its weighting factor, and the cross-regional service indicator with its weighting factor.

5. The method according to claim 4, characterized in that, The model solver is invoked to solve the constraints and the objective function, and the solution results are obtained, including: Based on the data input format of the model solver, the constraints and the objective function are converted to obtain the converted constraints and the objective function. The transformed constraints and the objective function are input into the model solver for solving, and the solution result is obtained.

6. The method according to claim 5, characterized in that, When the transformed constraints and the objective function are input into the model solver for solving, the method further includes: Based on the work requirements of the personnel, construct work requirement constraints that satisfy the data input format of the model solver; The work requirement constraints, the transformed constraints, and the objective function are input together into the model solver for solving, and the solution result is obtained.

7. The method according to any one of claims 1 to 6, characterized in that, The scheduling strategy includes at least: a work plan for the device object that performs the scheduling task, and a work plan for the personnel object that performs the scheduling task.

8. A scheduling device for logistics resources, characterized in that, include: The acquisition unit is used to acquire scheduling task information, wherein the scheduling task information is used to characterize the scheduling tasks for scheduling logistics resources within a preset time period. The construction unit is used to construct constraints and objective functions for executing the scheduling task based on the scheduling task information, wherein the constraints are used to characterize the rules that need to be satisfied to execute the scheduling task, and the objective function is used to characterize the task requirements that need to be satisfied to execute the scheduling task; The calling unit is used to call the model solver to solve the constraints and the objective function to obtain the solution result, wherein the solution result is used to characterize the scheduling strategy for executing the scheduling task, and the scheduling strategy satisfies the task requirements; An execution unit is used to execute the scheduling task according to the scheduling strategy corresponding to the solution result.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

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