Human resource allocation method based on advanced planning and scheduling

Abstract

The invention provides a human resource allocation method based on advanced planning and scheduling, and the method comprises the following steps: obtaining plan scheduling information which comprises order data, human resource data and traffic data; obtaining a target scheduling mode, and obtaining an adjustment parameter corresponding to the target scheduling mode; obtaining a scheduling scheme data model corresponding to the target scheduling mode and a function relationship between order completion time and required human resources; and substituting the plan scheduling information into the scheduling scheme data model and the function relationship between the order completion time and the required human resources, and solving a target scheduling scheme mathematical model corresponding to the target scheduling mode in combination with the adjustment parameters to obtain a target scheduling scheme. According to the invention, the target scheduling scheme can be flexibly determined according to the plan scheduling information, and the efficiency of human resource allocation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of order processing, and in particular to a human resource allocation method based on advanced planning and scheduling. Background Art

[0002] The order processing method of different industries still adopts the manual arrangement method. For example, in the field of maintenance services, after receiving the order, it is necessary to manually arrange maintenance personnel to go to the designated location to perform maintenance work. The manual arrangement method is not timely enough, the response speed of orders is slow, and the idle time between orders cannot be utilized. In addition, the order changes in different regions are relatively frequent and manual arrangements are not used. The manual arrangement method of processing orders is not conducive to the optimal use of human resources. In addition, the scheduling of staff has higher and higher requirements for fairness and efficiency, and the manual arrangement method is difficult to balance fairness and efficiency.

[0003] The existing solutions generally sort the orders, match the corresponding staff according to the sorted orders, generate a plan schedule, and then generate the order delivery route based on the plan schedule and the default algorithm model. The existing solutions cannot adapt the scheduling method and cannot flexibly and quickly re-formulate the scheduling plan according to the actual situation.

[0004] APS (Advanced Planning and Scheduling) is mainly used to solve production scheduling and production dispatching problems in the manufacturing industry. It has not been used in industries that require the allocation of human resources, such as engineering transportation and delivery, on-site air-conditioning repair and installation, and moving services. Summary of the invention

[0005] The purpose of the present invention is to provide a method for allocating human resources based on advanced planning and scheduling with flexible scheduling scheme.

[0006] In order to achieve the above-mentioned purpose, the present invention provides a human resource allocation method based on advanced planning and scheduling, which includes the following steps: obtaining planning and scheduling information, the planning and scheduling information includes order data, human resource data and traffic data; obtaining a target scheduling mode, and obtaining adjustment parameters corresponding to the target scheduling mode; obtaining a scheduling scheme data model corresponding to the target scheduling mode and a functional relationship between order completion time and required human resources; substituting the planning and scheduling information into the scheduling scheme data model and the functional relationship between order completion time and required human resources, and solving the target scheduling scheme mathematical model corresponding to the target scheduling mode in combination with the adjustment parameters to obtain the target scheduling scheme.

[0007] As can be seen from the above scheme, the present invention determines the corresponding target scheduling mathematical model through the selected target scheduling mode, substitutes the planned scheduling information into the scheduling scheme data model of the target scheduling mathematical model and the functional relationship between the order completion time and the required human resources, thereby solving the target scheduling mathematical model in combination with the selected adjustment parameters to obtain the target scheduling scheme. The present invention can flexibly select the target scheduling mode and adjust the parameters to impose different constraints according to actual needs, thereby obtaining different target scheduling schemes, without manually arranging order processing, ensuring automatic configuration processing of orders, and efficiently allocating human resources.

[0008] A further solution is that the adjustment parameters include a target human resource arrangement module and target condition constraints.

[0009] It can be seen that by selecting the target human resource arrangement module to constrain the available human resource data, and by selecting the target condition restriction to constrain the order data, the target scheduling plan obtained can be more in line with actual needs.

[0010] A further solution is that the target condition is restricted to one of the following: order-task-based priority planning, event-based resource utilization maximization planning, resource-based constraint planning, time-based demand and supply planning, and transportation / traffic time optimization-based planning.

[0011] A further solution is that the target human resource arrangement module is one of the following: a process module, a discrete module, a mixed module of process and discrete, and a project management module.

[0012] A further solution is that the target scheduling mode is one of the following: a rule-based method, a sorting-based method, a heuristic graph search method, and an intelligence-based scheduling method.

[0013] A further solution is to solve the mathematical model of the target scheduling scheme corresponding to the target scheduling mode in combination with the adjustment parameters, adjust the target scheduling mode, and obtain the target scheduling scheme after obtaining the scheduling plan confirmation information.

[0014] It can be seen that the operator can obtain different scheduling schemes by changing the target scheduling mode and / or adjusting parameters, and then select a target scheduling scheme that meets the requirements.

[0015] A further solution is to solve the mathematical model of the target scheduling scheme corresponding to the target scheduling mode in combination with the adjustment parameters, further process the scheduling mode, and obtain the target scheduling scheme after obtaining the scheduling plan confirmation information.

[0016] It can be seen from this that we can process the order scheduling information by selecting different scheduling modes, or we can process the order scheduling information by combining different scheduling modes to obtain a better target scheduling plan.

[0017] A further solution is to obtain the planning and scheduling information from a data center, which stores human resource data for different time periods.

[0018] It can be seen that the human resources data available in a specific time period is extracted to match the order data.

[0019] A further solution is that after obtaining the target scheduling plan, the processing information is pushed to the employee terminal according to the target scheduling plan, and the employee terminal displays the processing information in a visual manner.

[0020] This shows that the implementation of the target scheduling plan can be facilitated. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a system framework diagram of the human resource allocation system of the present invention.

[0022] Figure 2 It is a flow chart executed by the scheduling center of the present invention.

[0023] Figure 3 It is an example graph when the heuristic graph search method is applied in the present invention.

[0024] The present invention is further described below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0025] The present invention obtains planning and scheduling information, and selects and sets scheduling modes, human resource arrangement modules and conditional restrictions, thereby providing different scheduling schemes and realizing a method for allocating human resources based on advanced planning and scheduling.

[0026] This embodiment is implemented based on advanced planning and scheduling related technologies.

[0027] See also Figure 1 The human resource allocation system implementing this embodiment includes a data center 1, a scheduling center 2 and an employee terminal 3.

[0028] Data transmission between the data center 1 and the scheduling center 2, and between the scheduling center 2 and the employee terminal 3 can be achieved through wired or wireless networks.

[0029] Data center 1 is used to store scheduling information, which includes order data, human resource data and traffic data. Order data is generated based on user orders, including but not limited to the estimated processing time of the order, the order location, the time required for the order to arrive, the order date, etc. Human resource data includes but is not limited to employee working hours, employee leaving time, employee departure location, etc. Traffic data includes road surface information, commuting time between locations, etc.

[0030] The scheduling center 2 is used to obtain the storage plan scheduling information from the data center 1, and allocate human resources to the acquired plan scheduling information based on the selected target scheduling mode, target human resource arrangement module and target condition restrictions, so as to obtain the target scheduling plan. The scheduling center 2 pushes processing information to the employee terminal 3 based on the target scheduling plan, and the processing information includes the specific orders and routes processed. The employee terminal 3 provides a visual graphical user interface to display the processing information, so as to facilitate the employees holding it to process the order through the processing information.

[0031] In different embodiments, the data center 1 and the scheduling center 2 may be integrated.

[0032] Several conditional restrictions, human resource arrangement modules and scheduling modes are pre-maintained in the scheduling center 2. The scheduling mode corresponds to the mathematical model of the scheduling scheme, and can output the scheduling scheme according to the input plan scheduling information, conditional restrictions and human resource arrangement module. The conditional restrictions and human resource arrangement module are used as adjustment parameters to adjust the scheduling scheme output by the scheduling mode. The conditional restrictions implement the constraints on the order data, and the human resource arrangement module implements the constraints on the human resource data. A scheduling mode corresponds to multiple optional conditional restrictions and multiple optional human resource arrangement modules. For different scheduling modes, the multiple optional conditional restrictions and multiple optional human resource arrangement modules can be the same or different.

[0033] The scheduling mathematical model includes the scheduling data model and the functional relationship between the order completion time and the required human resources. For different scheduling mathematical models, their respective scheduling data models and the functional relationship between the order completion time and the required human resources may be the same or different.

[0034] The scheduling scheme data model includes multiple modeling parameters, including: working time, processing time, idle time, waiting time, transition time, order address, order request time, order date, employee start time, employee end time, employee starting point, estimated traffic time, estimated commuting time between orders, road information, employee capacity limitation, employee real-time location, etc. These modeling parameters can be adopted according to actual conditions, or modeling parameters other than the above can be added according to actual conditions. Based on these modeling parameters, the model results reflecting the scheduling plan can be established in the form of XML modeling configuration files and / or JSON forms and / or transmitted through the user interface (UI) form, and the selected human resource arrangement module can be able to actually be highly consistent with the plan and optimize the human resource arrangement.

[0035] An example but not all data contents of the scheduling plan data model are as shown in Table 1 below:

[0036] Table 1. Example of data content of the scheduling plan data model

[0037]

[0038] The scheduling data model includes the functional relationship between the order completion time and the required human resources. The general functional relationship is as follows: F(X), X = {x i (OrderTime i (Workers in ,Precedence i ) i ,Workers in )∈x|i=1,2,3,……,N},x i Refers to the date and time of each order, OrderTime iIt refers to the estimated time or duration of the order on the day. in means x i At OrderTime i The number of workers who complete the order, n), is x i The independent variable of Workers in x i The number of workers assigned to process orders, the precondition of order i is Precedence i ; N is the total number of orders on the day. The constraints are: C i , i = 1, 2, 3, ... k, where k is the number of constraints. In the general function of F(X), the goal is to find the optimal combination of x so that F(X) has the minimum value, and the constraint C i In addition to considering the general location, time, distance and other factors mentioned above, additional prerequisites are added.

[0039] In the mathematical model of scheduling scheme, cost function, time function, resource allocation function and priority function are generally included. For cost function, in many scheduling problems, the goal is to minimize the total cost. This cost may include production cost, transportation cost, storage cost, etc. The cost function can be expressed as a function, such as C = C (p) + C (T) + C (s), where C (p) is the production cost, C (t) is the transportation cost, and C (s) is the storage cost. For time function, in scheduling problems, we usually need to consider the running time of tasks. The time function can be expressed as T = f (x), where x is the characteristics of the task (for example, size, difficulty, etc.), and f is a function that describes how these characteristics affect time. For resource allocation function, in scheduling problems, we need to allocate resources according to the requirements of the task. The resource allocation function can be expressed as R = g (x), where x is the requirement of the task and g is a function that describes how to allocate resources according to the requirements. For priority function, in some cases, we may need to schedule according to the priority of the task. The priority function can be expressed as P = h(x), where x is the characteristics of the task (e.g., deadline, importance, etc.), and h is a function that describes how these characteristics affect the priority. The above functions can exist independently or affect each other. For example, the time function and resource allocation function may affect the cost function, and at the same time, the priority function may affect the resource allocation function. They need to be set and adjusted according to the specific situation.

[0040] In this embodiment, six conditions are included, namely: priority planning based on order tasks, planning based on maximizing event resource utilization, resource constraint planning, time-based demand and supply planning, and planning based on transportation / traffic time optimization.

[0041] Job-based task priority scheduling uses a one-time order or task (Job ata Time) sequence method to prioritize orders and leave idle buffer time to solve reasonable delivery period feasibility constraints and optimization. This method schedules according to the priority of each order or task. Priority may be determined based on factors such as the urgency of the order, profit, and customer importance. For example, a production department may prioritize orders with the highest profit or the earliest delivery date. Order rules can first sort tasks according to priority, and each assigned task follows these rules.

[0042] The event-based resource utilization maximization plan adopts a one-step or one-operation-at-a-time sequential method, which can handle detailed production scheduling and even split the load of tasks to meet the maximum resource utilization, optimize idle time, minimize the production / process cycle, and respond to the order delivery period as quickly as possible. This method re-evaluates and adjusts the production plan at each key event (such as employee availability or employee arrival) to maximize resource utilization. For example, if a person has extra free time due to early completion or cancellation of an order, the system may immediately adjust or find a task that the employee can start as soon as possible.

[0043] Resource-based (TOC) constraint planning: This method focuses on identifying and managing bottlenecks or constraints in the production process based on the Theory of Constraints (TOC) method. For example, if there are a lot of orders in a certain area and the number of staff remains unchanged, the order processing process will encounter a bottleneck.

[0044] Resource-based planning: This approach considers all resource constraints (including machines, employees, and materials) to create a feasible production plan. For example, if the repair parts inventory is limited, then the plan will ensure that the available inventory is not exceeded; or some large parts require more than one person to handle the repair or delivery.

[0045] Time-based (historical, present, future) demand and supply planning: This method uses historical data and current and future demand forecasts to create production plans. For example, if inspection orders are concentrated in a certain period, such as the number of inspection orders at the end of the year, then the plan will ensure that there are enough people to handle the orders in the next cycle.

[0046] Planning based on transport / traffic time optimization: This method optimizes production planning by taking transport or traffic time into consideration. For example, order locations that are relatively close are planned based on the first departure point selected by the personnel.

[0047] In addition to priority sorting, other possible sorting conditions include early completion (i.e., early release) and minimization of idle time. From a mathematical perspective, not all rules are optimizations, and each rule represents the focus of different strategies and plans. Among them, the task priority plan based on order tasks is committed to completing the most important tasks as quickly as possible.

[0048] After sorting out the variables based on the above conditions, we can emphasize that the plan based on order task priority is aimed at completing the most important tasks as soon as possible. This means that in the process of task scheduling and execution, tasks with higher importance will be given priority to ensure their completion as early as possible. This prioritization strategy helps to ensure the timely completion of critical tasks, thereby improving overall production efficiency and meeting important needs.

[0049] In this embodiment, four types of human resource arrangement modules are included, namely, a flow program module, a discrete module, a mixed module of flow and discrete, and a project management module.

[0050] The flow program module mainly solves the problem of sequential optimization. For example, employees need to take maintenance parts according to a specific route before arriving at the maintenance location to work. If there are any changes (for example, an employee is sick or a machine is broken), the entire process may need to be rearranged.

[0051] Discrete module, which mainly solves the problem of optimal scheduling of multiple orders and multiple resources. For example, the equipment to be repaired includes mechanical, electronic and software, which requires different maintenance personnel with different skills. In this case, human resource scheduling will need to consider how to most effectively allocate personnel and corresponding maintenance orders, and take into account the multiple capabilities possessed by employees.

[0052] A hybrid module of process and discrete, which mainly solves the optimization problems of sequence and scheduling at the same time. For example, a device has multiple problems that need to be repaired, and they need to be inspected and repaired in a specified order. In this case, scheduling will need to consider how to most effectively allocate employees and corresponding repair orders. In this case, human resource scheduling will need to consider how to most effectively allocate workers and parts to meet all order requirements at the same time.

[0053] Project management module, which mainly solves the problem of resource constraints and cost-time minimization. For example, a maintenance order needs to be completed before a specified date or time, and there may be other resource constraints (such as workers, parts, etc.). The scheduling of human resources will need to consider how to minimize cost and time while meeting all constraints.

[0054] In this embodiment, four scheduling modes are included, namely, a rule-based method, a sorting-based method, a heuristic graph search method, and an intelligence-based scheduling method.

[0055] The rule-based approach uses scheduling rules because of their simple rules, easy implementation, and low computational complexity. They have been put into practical use in this plan, such as reducing waiting time, improving order processing data, and achieving more scientific and effective scheduling results under a package of constraints.

[0056] Rule-based approach: This approach relies on predefined rules to determine the order in which tasks are to be performed. These rules may be based on priorities, due dates, required resources, etc. Applicable scenarios: This approach is effective when the order of work can be determined by clear, fixed rules. The specific process is generally as follows: (1) Identify all tasks: List all tasks that need to be completed. For example, the maintenance process includes: A takes maintenance tools, B takes maintenance parts, checks, and performs maintenance. (2) Set rules: Determine the rules for scheduling. These rules may be based on priorities, due dates, required resources, etc. For example, the rule may be: complete the work that requires special equipment first, and then complete the work with the closest due date. (3) Apply rules: Sort the tasks according to the set rules. For example, if work A requires special equipment to be repaired, it can only be used in the morning. (4) Allocate resources: Based on the results of the sorting, allocate resources (e.g., employees, equipment, etc.) to each task. For example, people with mechanical knowledge are assigned to mechanical maintenance work. (5) Execute and monitor: Start executing the schedule and continue to monitor the progress. If any problems arise (e.g., maintenance tools or equipment failure, employee sick leave, etc.), the schedule may need to be adjusted. (6) Evaluate and adjust: After completing the schedule, evaluate the results and adjust the rules as needed. For example, if you find that a certain type of order is often delayed, you may need to change the rules or adjust the time or resources required.

[0057] The sorting-based method is to first determine the feasible order processing sequence, then determine the start time of each order, and optimize the sequence, which includes technologies such as local search and genetic algorithms.

[0058] Ranking-based approach: This approach relies on ranking tasks to determine the order in which they should be performed. Ranking can be based on various factors, such as order level or due date. For example, in a call center, incoming calls may be ranked based on their urgency or customer importance. Ranking-based scheduling ensures that the most important calls are handled first. This approach is effective when tasks can be ranked based on some comparable attributes, such as priority or due date. The specific process is generally as follows: (1) Identify all tasks: List all tasks that need to be completed. For example, customers are divided into high-level, medium-level, low-level, etc. (2) Set ranking criteria: Determine the criteria for ranking tasks. These criteria may be based on priority, due date, time required, etc. For example, the ranking criteria may be: process the highest priority order first, then the next highest priority order. (3) Perform ranking: Sort the tasks according to the set criteria. For example, if order A has the highest priority, then order A may be ranked first. (4) Allocate resources: Based on the results of the ranking, allocate resources (e.g., customer service representatives) to each task. For example, designate maintenance personnel or maintenance personnel with more experience to handle high-level customers. (5) Execute and monitor: Begin processing incoming calls and continue to monitor progress. If any problems arise (e.g., a maintenance worker is out sick or an order requires additional staff), the sequencing may need to be adjusted. (6) Evaluate and adjust: Once scheduling is complete, evaluate the results and adjust the sequencing criteria as needed. For example, if you find that the highest priority orders often have to wait a long time, you may need to adjust the sequencing criteria to ensure that these orders can be processed more quickly.

[0059] The heuristic graph search method is to first construct a feasible solution, and then use the implicit enumeration search method to continuously improve the suboptimality of the solution, including the beam search method to identify constraints and perform scheduling; as the amount of data increases (because it is not fully applied in the market, but there is a possibility of it), the filter beam search method is used to solve the machine problem. The optimization sorting of the priority tree search method based on A* includes but is not limited to two method improvements: a). Only limited nodes are expanded during the search process; b). Use a weighted balance function. Its function calculates the priority of each node: f(n) = g(n) + h(n), f(n) is the comprehensive priority of the node, and according to the parameter adjustment set by the module, the comprehensive node value is selected as the lowest; g(n) is the node, indicating the starting point of the employee's departure location. h(n) is the total estimated cost of the node from the employee's last order, where h(n) and f(n) can be adjusted according to the above modeling parameters.

[0060] Heuristic graph search (A* algorithm search) builds the shortest path by continuously selecting the node with the smallest estimated distance H and according to the required parameter module of f(n). Figure 3 , Heuristics represents the total time units of each order and labor, and each order and employee representative appears once in this graph, and this graph will be repeated as the amount of data increases. To simplify the graph, Figure 3 This is a summary of only one of the methods, not all node methods.

[0061] Heuristic graph search method: This method uses a graph search algorithm to find the best order for executing tasks. Heuristic graph search usually uses a technique called heuristic to guide the search, which can greatly improve efficiency. Example: In transportation, determine the best driving route and the best order processing route. Applicable scenarios: This method is effective when the order of tasks needs to consider multiple factors and the optimal solution needs to be found. The specific process is generally as follows: (1) Establish a graph model: Model all tasks and their relationships as a graph. Each task is a node, and the relationship between tasks (for example, dependency or cost) is an edge. For example, in logistics and transportation, the delivery or maintenance location is a node, and the distance or time between two points can be an edge. (2) Define a heuristic function: Set a heuristic function to estimate the cost from a node (task) to the target node. The heuristic function can be set according to the actual situation. For example, the straight-line distance, expected traffic conditions, etc. can be used to estimate the cost. (3) Perform a graph search: Use a graph search algorithm (for example, A* search) to find the path with the lowest cost. This path is the optimal order for executing tasks. (4) Allocate resources: Based on the search results, assign resources (e.g., maintenance personnel) to the various tasks. For example, deliveries can be scheduled in the order determined by the search results. (5) Execute and monitor: Start executing the schedule and continue to monitor the progress. If any problems arise (e.g., traffic jams or vehicle breakdowns), you may need to re-run the graph search to adjust the schedule. (6) Evaluate and adjust: After completing the schedule, evaluate the results and adjust the graph model or heuristic function as needed. For example, if you find that actual delivery times often exceed expectations, you may need to adjust the heuristic function to more accurately estimate costs.

[0062] Based on intelligent scheduling methods, the current application of ISIS (Intelligent Scheduling and Information System) research in the manufacturing industry, artificial intelligence has just been applied to scheduling problems, and knowledge-based scheduling methods use expert systems to automatically generate schedules or human-assisted scheduling. The common characteristics are: when the resource conditions supporting the activity are specific (decision-making touch method), the rules to be adopted are determined based on the data in the system. Since the employee labor variables are completely different from those in the manufacturing industry, the employee labor variables are set here. For details, see the setting of modeling parameters.

[0063] Intelligence-based scheduling method: This method uses artificial intelligence (such as machine learning or optimization algorithms) to determine the best order for executing tasks. For example, based on historical order records, intelligent scheduling can learn and predict future personnel and parts requirements and resource allocation. Applicable scenarios: This method is effective when the order of tasks needs to consider a large amount of data and variables and needs to be continuously adjusted and optimized.

[0064] The above scheduling methods are not mutually exclusive. In many cases, multiple scheduling methods can be combined to achieve the best schedule. For example, a company may use rule-based scheduling to determine the basic work sequence, and then use intelligent scheduling to fine-tune and optimize.

[0065] See also Figure 2 This embodiment is implemented by the scheduling center 2 executing a computer program, which specifically includes the following steps:

[0066] S1: Set up the mathematical model of the scheduling scheme.

[0067] Among them, different scheduling scheme mathematical models are set according to different scheduling schemes, and several optional conditional restrictions and human resource arrangement modules are set for each scheduling scheme mathematical model. The scheduling scheme mathematical model, conditional restrictions and human resource arrangement module can be found in the introduction of the above scheduling center and will not be repeated here.

[0068] S2: Get planning and scheduling information.

[0069] Among them, the planning and scheduling information of a set time period (day, week, month, etc.) can be obtained, so as to obtain the order data, available human resource data and traffic data within the time period.

[0070] S3: Obtain a target scheduling mode and a target human resource arrangement module and target condition constraints corresponding to the target scheduling mode.

[0071] The operator of the scheduling center selects a target scheduling plan from a plurality of scheduling plans on a visual graphical user interface provided by the scheduling center, and selects a target human resource arrangement module and target condition restrictions of the target scheduling plan.

[0072] S4: Obtain a scheduling plan data model corresponding to the target scheduling mode and a functional relationship between order completion time and required human resources.

[0073] S5: Substitute the planned scheduling information into the scheduling plan data model and the functional relationship between the order completion time and the required human resources, combine the target human resource arrangement module and the target condition constraints to solve the target scheduling plan mathematical model corresponding to the target scheduling mode, and obtain a preliminary scheduling plan.

[0074] S6: Determine whether scheduling plan confirmation information is obtained.

[0075] After browsing the preliminary scheduling plan, the operator of the scheduling center determines whether to adopt the preliminary scheduling plan. When it is determined to adopt the preliminary scheduling plan, the operator inputs the scheduling plan confirmation information, and then continues to execute step S7. When the preliminary scheduling plan is not adopted, it returns to step S3 and adjusts the preliminary scheduling plan by changing the target scheduling mode, target human resource arrangement module or target condition restriction until the requirements are met.

[0076] S7: Output the preliminary schedule as the target schedule.

[0077] The target plan scheduling scheme can be displayed through visualization technology, for example, on a graphical user interface, combined with relevant charts such as Gantt charts. Therefore, the planning and scheduling information is scheduled using the target plan scheduling scheme, specifically including pushing the processing information corresponding to different employees in the target plan scheduling scheme to the employee terminal, so that the employee terminal displays the processing information in a visual manner, which is convenient for employees to complete the corresponding orders according to the processing information.

[0078] This embodiment can be applied to industries that need to allocate human resources to process orders, including but not limited to the maintenance industry, building inspection industry, home education industry (such as home tutoring), postal affairs (such as letter delivery), emergency services (such as ambulance or fire fighting services), takeaway order processing (based on known manpower and known order time), delivery services of large online shopping platforms, and other logistics, construction sites or construction industries that require multiple orders or workflows to be processed simultaneously and provide services outside.

[0079] This embodiment focuses on scheduling logic constraints and resource capacity constraints, close connection between human resources and order processes, various optimization rules, calculation of the earliest possible start time and the latest possible start time, human resource reallocation and substitutability, enterprise resource reallocation and substitutability, planning and scheduling taking into account the possibility of buffering, cost constraints, considering non-deterministic processes and statistical probability theory, shortest paths and various mathematical methods to integrate planning, and considering comparative analysis of multiple optimization schemes. The purpose and characteristics of planning modeling are to be as simple as possible and to express reality in detail as needed. In other words, it is simple without ignoring important constraints in reality. Predictive models are used to predict future conditions and explain the relationship between inputs and outputs of complex systems.

[0080] In addition, scheduling schemes often have several criteria at the same time, and there are conflicts between the objectives. There is no clear optimal solution for each scheduling scheme. For example, the customer waiting time is the shortest, the employee attendance time is the shortest, but at the same time, the employee idle time is as long as possible. In this case, there is no optimal solution (that is, the three objectives cannot be optimized at the same time). The method to deal with multiple objectives is to set one objective within the minimum or maximum acceptable range, and then optimize the other objective.

[0081] The human resource allocation method based on advanced planning and scheduling of this embodiment can be implemented based on a computer device, which may include but is not limited to a processor and a memory. Those skilled in the art can understand that the computer device may include more or fewer components, or a combination of certain components, or different components, for example, the computer device may also include input and output devices, network access devices, buses, etc.

[0082] For example, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microcontroller or any conventional processor, etc. The processor is the control center of a computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0083] The memory can be used to store computer programs and / or modules. The controller realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. For example, the memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound receiving function, a sound conversion to text function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0084] If the module integrated in the above-mentioned computer device is implemented in the form of 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, all or part of the process of the embodiment of the human resource allocation method based on advanced planning and scheduling can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the controller, it can implement the steps of the embodiment of the human resource allocation method based on advanced planning and scheduling. Among them, the computer program includes a computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. The storage medium can include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, CD, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in computer-readable media can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0085] The above-mentioned embodiment of the method for allocating human resources based on advanced planning and scheduling can also be implemented by a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes each step of the above-mentioned method for allocating human resources based on advanced planning and scheduling.

Claims

1. A human resource allocation method based on advanced planning and scheduling, characterized in that: The following steps are involved: Acquiring planning and scheduling information, wherein the planning and scheduling information includes order data, human resources data, and traffic data; Obtaining a target scheduling mode, and obtaining adjustment parameters corresponding to the target scheduling mode; Obtaining a scheduling scheme data model corresponding to the target scheduling mode and a functional relationship between order completion time and required human resources; Substitute the planned scheduling information into the scheduling scheme data model and the functional relationship between the order completion time and the required human resources, and combine the adjustment parameters to solve the target scheduling scheme mathematical model corresponding to the target scheduling mode to obtain the target scheduling scheme.

2. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 1, characterized in that: The adjustment parameters include a target human resource arrangement module and target condition restrictions.

3. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 2, characterized in that: The target condition is limited to one of the following: order task-based priority planning, event-based resource utilization maximization planning, resource-based constraint planning, time-based demand and supply planning, and transportation / traffic time optimization-based planning.

4. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 2, characterized in that: The target human resource arrangement module is one of the following: a flow program module, a discrete module, a mixed module of flow and discrete, and a project management module.

5. The method for allocating human resources based on advanced planning and scheduling according to any one of claims 1 to 4, characterized in that: The target scheduling mode is one of the following: a rule-based method, a sorting-based method, a heuristic graph search method, and an intelligence-based scheduling method.

6. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 5, characterized in that: After solving the mathematical model of the target scheduling scheme corresponding to the target scheduling mode in combination with the adjustment parameters, the target scheduling mode is adjusted, and after obtaining the scheduling plan confirmation information, the target scheduling scheme is obtained.

7. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 5, characterized in that: After solving the mathematical model of the target scheduling scheme corresponding to the target scheduling mode in combination with the adjustment parameters, the target scheduling scheme is obtained by further processing by adjusting the scheduling mode and after obtaining the scheduling plan confirmation information.

8. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 7, characterized in that: The planning and scheduling information is obtained from a data center, where the data center stores the human resource data of different time periods.

9. The method for allocating human resources based on advanced planning and scheduling as claimed in claim 8, characterized in that: After the target scheduling scheme is obtained, processing information is pushed to the employee terminal according to the target scheduling scheme, and the employee terminal displays the processing information in a visual manner.

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