Method and system for minimizing restaurant service response time personnel assignment

Abstract

The present application provides a personnel assignment method and system for minimizing restaurant service response time, and relates to the technical field of task scheduling.The present application obtains a restaurant plan and restaurant service parameters; constructs a restaurant environment grid map based on the restaurant plan, and obtains a set of optional grid points for a station based on the restaurant environment grid map; constructs a restaurant service model based on the restaurant service parameters and the set of optional grid points for the station, with the goal of minimizing the total restaurant service response time; and solves the restaurant service model to obtain a restaurant service personnel assignment scheme.The present application solves the technical problem of unstable decision-making effect of existing restaurant service personnel assignment methods, and provides a systematic and theoretically supported optimization method for restaurant service personnel assignment from the perspective of mathematical modeling and intelligent optimization algorithm, so that the decision-making effect is stable, the service response time is reduced, and the customer's consumption experience is improved.

Description

Technical Field

[0001] This invention relates to the field of task scheduling technology, and specifically to a method and system for personnel assignment that minimizes restaurant service response time. Background Technology

[0002] With the improvement of people's living standards, the catering industry has developed rapidly. At the same time, customers' requirements for the service quality of restaurants have expanded from the initial taste to all aspects.

[0003] Service response time is a key indicator of restaurant service quality. Given a fixed restaurant shape and layout, how to assign service staff positions and responsibilities to reduce response time is a concern for every restaurant. Currently, the primary method for assigning restaurant service staff is based on zoning. In this method, decision-makers first divide the service area into several smaller zones based on their subjective experience, and then assign service staff to these zones. Staff assigned to specific zones provide service to all customers within that zone.

[0004] However, the method of assigning restaurant service staff based on regional divisions relies on the decision-maker's subjective experience, lacks systematicity and theoretical support, and the results are greatly affected by the decision-maker's personal ability, resulting in unstable decision-making effects. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for minimizing restaurant service response time, solving the technical problem of unstable decision-making effects in existing restaurant service personnel assignment methods.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a method for personnel assignment that minimizes restaurant service response time, comprising:

[0010] S1. Obtain the restaurant floor plan and restaurant service parameters;

[0011] S2. Construct a restaurant environment raster map based on the restaurant floor plan, and obtain a set of selectable grid points for station locations based on the restaurant environment raster map;

[0012] S3. Based on restaurant service parameters and the set of selectable grid points for station locations, construct a restaurant service model with the goal of minimizing the total restaurant service response time.

[0013] S4. Solve the restaurant service model to obtain the restaurant service staff assignment scheme.

[0014] Preferably, S2 includes:

[0015] Based on the restaurant floor plan, determine the grid precision and construct a restaurant environment grid map;

[0016] Based on the restaurant environment grid map, selectable grid points in the map to construct a set of selectable grid points, which are grid points not occupied by restaurant facilities.

[0017] Preferably, the restaurant service model includes an objective function and constraints, as follows:

[0018]

[0019]

[0020]

[0021] p i ∈P, i=1,2,…,m (4)

[0023] z ij ∈{0,1},i=1,2,…,m,j=1,2,…,n (5)

[0025] Equation (1) is the objective function, which is to minimize the total service response time of the restaurant;

[0026] Equations (2) to (4) are the constraints:

[0027] Formula (2) ensures that each table has exactly one server serving it;

[0028] Formula (3) ensures that the number of customers assigned to each service staff member does not exceed their service capacity;

[0029] Equation (4) represents the restriction on the position of service personnel, whose position can only be selected from the available grid points, and is a decision variable;

[0030] Equation (5) represents the division of labor among service personnel and serves as a decision variable;

[0031] In the formula, m represents the number of service personnel; n represents the number of tables; V represents the set of available grid points for service personnel to stand in; P represents the set of available grid point numbers obtained by numbering each grid point in set V, p i Indicates the number of the selectable grid point occupied by service personnel i; e i c represents the service capacity of service personnel i, i.e., the maximum number of customers they can serve; jThe capacity of table j is represented by f; the frequency of service for a single customer is represented by v; and the speed at which servers move through the restaurant is represented by d. pij Indicates that the number is p i The distance from grid point z to table j; ij For decision variables, when z ij When z = 1, it means that server i serves all customers at table j; otherwise, z = 1. ij =0; t represents the restaurant's total service response time.

[0032] Preferably, before performing step S4, the method further includes:

[0033] The distance matrix is ​​calculated by using the table coordinates and the set of selectable grid points for standing positions to obtain the shortest distance matrix from the selectable grid points to the table.

[0034] Preferably, the shortest distance is calculated using the arrival principle and the A* algorithm;

[0035] The arrival principle refers to the fact that when serving a table, the server can choose any available grid point around the table as the service point.

[0036] Preferably, the step of solving the restaurant service model to obtain the restaurant service staff assignment scheme includes:

[0037] S401. Population initialization: Set the population size, chromosome length, crossover probability, mutation probability, current iteration count t, and maximum iteration count T. The gene combinations of the individuals in the population are (p1, p2, ..., p...). i ,…,p m ), indicating the grid number of the service personnel's station;

[0038] S402. Calculate the fitness value of an individual in the population, wherein the fitness value of the individual is equal to the reciprocal of the total service response time;

[0039] S403. Determine whether t≥T is true. If the termination condition is met, output the positioning and division of labor schemes corresponding to the individual with the largest fitness value, that is, output the restaurant service personnel assignment scheme; otherwise, t=t+1, and proceed to the next step.

[0040] S404. Use roulette wheel selection to select individuals to build a pairing library;

[0041] S405. Based on the preset crossover probability, two individuals are randomly selected from the pairing library. Some genes in the chromosomes of the two selected individuals, namely the service personnel's station grid numbers, are crossed and interchanged to generate a new individual that is different from the parent generation's service personnel stationing scheme.

[0042] S406. Randomly change the values ​​of certain genes of individuals in the population, i.e., the service personnel station grid numbers, with a preset mutation probability, so as to generate more new individuals. The mutation probability is usually very small. After this step, return to S402 to calculate the individual fitness operation.

[0043] Preferably, the calculation of the fitness value of an individual in the population includes:

[0044] First, based on the individual's gene combination, obtain the shortest distance from all station grid points to each table; input the restaurant service model and its related parameters into the Gurobi solver to obtain the minimum total service response time under the current station scheme, and use the reciprocal of the minimum total service response time as the individual's fitness value.

[0045] Secondly, the present invention provides a staff assignment system that minimizes restaurant service response time, comprising:

[0046] The data acquisition module is used to acquire restaurant floor plans and restaurant service parameters;

[0047] The station location selectable grid point set acquisition module is used to construct a restaurant environment raster map based on the restaurant floor plan, and obtain the station location selectable grid point set based on the restaurant environment raster map;

[0048] The model building module is used to build a restaurant service model based on restaurant service parameters and a set of selectable grid points for station locations, with the goal of minimizing the total service response time of the restaurant.

[0049] The model solving module is used to solve the restaurant service model and obtain the restaurant service staff assignment scheme.

[0050] Thirdly, the present invention provides a computer-readable storage medium storing a computer program for minimizing restaurant service response time in personnel assignment, wherein the computer program causes a computer to perform the personnel assignment method for minimizing restaurant service response time as described above.

[0051] Fourthly, the present invention provides an electronic device, comprising:

[0052] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a personnel assignment method for performing the minimization of restaurant service response time as described above.

[0053] (III) Beneficial Effects

[0054] This invention provides a method and system for personnel assignment that minimizes restaurant service response time. Compared with existing technologies, it has the following advantages:

[0055] This invention obtains a restaurant floor plan and service parameters; constructs a restaurant environment grid map based on the floor plan, and obtains a set of selectable grid points for staff positions based on the grid map; constructs a restaurant service model based on the service parameters and the selectable grid point set, with the objective of minimizing the total service response time; and solves the restaurant service model to obtain a restaurant staff assignment scheme. This invention solves the technical problem of unstable decision-making effects in existing restaurant staff assignment methods. From the perspective of mathematical modeling and intelligent optimization algorithms, it provides a systematic and theoretically supported optimization method for restaurant staff assignment, resulting in stable decision-making effects, reduced service response time, and improved customer experience. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a block diagram of a personnel assignment method for minimizing restaurant service response time according to an embodiment of the present invention;

[0058] Figure 2 Diagram showing the location of the dining table;

[0059] Figure 3 This is a schematic diagram illustrating a case study of calculating the distance from the grid point at coordinates (2,7) to the dining table at coordinates [(8,2),(9,3)].

[0060] Figure 4 To verify the restaurant environment raster diagram in Case 1;

[0061] Figure 5 To verify the model optimization output results of Case 1;

[0062] Figure 6 To verify the visualization results of the service personnel assignment scheme in Case 1;

[0063] Figure 7 To verify the restaurant environment raster diagram in Case 2;

[0064] Figure 8 To verify the model optimization output results of Case 2;

[0065] Figure 9 To verify the visualization results of the service personnel assignment scheme in Case 2. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] This application provides a method and system for minimizing restaurant service response time in personnel assignment, solving the technical problem of unstable decision-making effects in existing restaurant service personnel assignment methods. From the perspective of mathematical modeling and intelligent optimization algorithms, it provides a systematic optimization method for restaurant service personnel assignment with sufficient theoretical support, making the decision-making effect stable, reducing service response time, and improving the customer's consumption experience.

[0068] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0069] The restaurant service staff assignment method based on region division has several limitations. First, the region division and service staff allocation process relies on the decision-maker's subjective experience, lacking systematicity and theoretical support. The results are heavily influenced by the decision-maker's individual ability, leading to unstable decision-making outcomes. Second, this method uses regions as the basic allocation unit, with service staff responsible for customers within a certain area. This may result in uneven workloads among service staff, with some staff serving more customers than others. Third, because this method relies excessively on the decision-maker's intellect, its decision-making ability is limited, making it less applicable in large or complex restaurant environments. To overcome these limitations, this invention aims to minimize the total restaurant service response time. It uses a grid-based approach to mathematically model the restaurant service staff assignment problem and designs an optimization scheme based on a genetic algorithm. The goal is to improve restaurant service efficiency by rationally configuring the positions and division of labor among service staff.

[0070] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0071] This invention provides a method for personnel assignment that minimizes restaurant service response time, such as... Figure 1 As shown, it includes:

[0072] S1. Obtain the restaurant floor plan and restaurant service parameters;

[0073] S2. Construct a restaurant environment raster map based on the restaurant floor plan, and obtain a set of selectable grid points for station locations based on the restaurant environment raster map;

[0074] S3. Based on restaurant service parameters and the set of selectable grid points for station locations, construct a restaurant service model with the goal of minimizing the total restaurant service response time.

[0075] S4. Solve the restaurant service model to obtain the restaurant service staff assignment scheme.

[0076] This invention provides a systematic and theoretically supported optimization method for restaurant staff assignment from the perspective of mathematical modeling and intelligent optimization algorithms, which stabilizes decision-making results, reduces service response time, and improves the customer's consumption experience.

[0077] The following is a detailed explanation of each step:

[0078] In step S1, the restaurant floor plan and restaurant service parameters are obtained. The specific implementation process is as follows:

[0079] Restaurant service parameters include: number of service staff (m), and table capacity (c). j Service capabilities of service personnel (i) i (i.e., the maximum number of customers it can serve), the speed at which service staff move in the restaurant (v), and the frequency at which a single customer is served (f).

[0080] In step S2, a restaurant environment raster map is constructed based on the restaurant floor plan, and a set of selectable grid points for station locations is obtained from the restaurant environment raster map. The specific implementation process is as follows:

[0081] Based on the restaurant floor plan, a restaurant environment raster map is constructed using an appropriate level of precision. This raster map displays the coordinates of the tables and the number of tables, n. In this embodiment, the table coordinates are determined using a two-point positioning method, that is, by using two diagonal grid points to locate the tables. Figure 2 As shown in the figure, the dining table is located using the top left and bottom right grid points. The coordinates of this dining table are represented as [(2,2),(3,5)].

[0082] Based on the restaurant environment raster map, select the available grid points in the map to construct a set of available grid points V, and number all feasible grid points. For example, the available grid point with grid coordinates (0,0) is numbered as 1, the available grid point with grid coordinates (0,1) is numbered as 2, and so on, until all available grid points are numbered, thus generating a set of available grid point numbers P.

[0083] In step S3, a restaurant service model is constructed based on restaurant service parameters and the available grid point set for station locations, with the objective of minimizing the total restaurant service response time. The specific implementation process is as follows:

[0084] It should be noted that, in this embodiment of the invention, the restaurant service model is constructed based on the following four assumptions.

[0085] 1. All facilities in the restaurant are arranged parallel to the grid points on the grid diagram.

[0086] 2. All facilities occupy their respective grid points.

[0087] 3. All dining tables are rectangular in shape.

[0088] 4. The number of customers at all tables has reached the table capacity.

[0089] The restaurant service model includes an objective function and constraints. Details are as follows:

[0090]

[0091]

[0092]

[0093] p i ∈P, i=1,2,…,m (4)

[0095] z ij ∈{0,1},i=1,2,…,m,j=1,2,…,n (5)

[0097] Equation (1) is the objective function, which is to minimize the total service response time of the restaurant.

[0098] Equations (2) to (4) are constraints.

[0099] Formula (2) ensures that each table has exactly one service staff member.

[0100] Formula (3) ensures that the number of customers assigned to each service staff member does not exceed their service capacity.

[0101] Equation (4) represents the restriction on the position of service personnel, whose position can only be selected from the available grid points, and is a decision variable.

[0102] Equation (5) represents the division of labor among service personnel and serves as a decision variable.

[0103] In the formula, m represents the number of service personnel; n represents the number of tables; V represents the set of available grid points for service personnel positions, obtained from the restaurant environment raster map. P represents the set of available grid point numbers obtained by numbering each grid point in set V, p i Indicates the number of the selectable grid point occupied by service personnel i; e i c represents the service capacity of service personnel i, i.e., the maximum number of customers they can serve; jThe capacity of table j is represented by f; the frequency of service for a single customer is represented by v; and the speed at which servers move through the restaurant is represented by d. pij Indicates that the number is p i The distance from grid point z to table j is calculated using the arrival principle and the A* algorithm in this embodiment of the invention; ij For decision variables, when z ij When z = 1, it means that server i serves all customers at table j; otherwise, z = 1. ij =0; t represents the restaurant's total service response time.

[0104] In the specific implementation process, before solving the model, this method also includes: calculating a distance matrix composed of the shortest distances from the selectable grid points to the table using the table coordinates and the set of selectable grid points for the standing position. Specifically:

[0105] The matrix representation is shown below. In this m×n matrix, each row represents a selectable grid point, and each column represents a table, for example: d 11 This represents the shortest distance from the optional grid point numbered 1 to the table numbered 1. The shortest distance is calculated using the reachability principle and the A* algorithm.

[0106]

[0107] Reaching principle: When serving a table, only reaching the table needs to be considered. That is, the server can choose any grid point near the table (except for grid points that cannot be selected) as the service point.

[0108] A* Algorithm: The A* (A-Star) algorithm is a direct search method that is most efficient in finding the shortest path in a static road network. It can find the shortest distance and path between two points in a relatively short time.

[0109] In the distance calculation of this embodiment of the invention, firstly, all possible service grid points near the table are found according to the arrival principle (excluding grid points outside the restaurant area or those that have been occupied). Then, the A* algorithm is used to calculate the distance from the current grid point to all possible service points. Finally, the shortest distance among these distances is selected as the distance from the current grid point to the table and filled into the distance matrix.

[0110] like Figure 3As shown, the distance from the grid point (2,7) to the table at coordinates [(8,2),(9,3)] is calculated. The gray area in the figure represents obstacles. First, all possible service points for this table are found, excluding points outside the restaurant area and occupied grid points. The possible service grid points are (9,1), (8,1), (7,1), (7,2), (7,3), and (9,4). Then, the A* path planning algorithm is used to calculate the distances from grid point (2,7) to all possible service grid points, which are 9.485, 8.485, 8.071, 7.071, 6.657, and 8.243, respectively. Therefore, the nearest grid point (7,3) is selected as the service provider grid point, and the corresponding distance of 6.657 is filled into the distance matrix as the service distance.

[0111] In step S4, the restaurant service model is solved to obtain the restaurant staff assignment scheme. The specific implementation process is as follows:

[0112] It should be noted that the restaurant service model is solved using a genetic algorithm in this embodiment of the invention. In actual operation, other heuristic algorithms can also be used to solve the model, which will not be elaborated here.

[0113] S401. Population initialization: Set the population size, chromosome length, crossover probability, mutation probability, current iteration count t, and maximum iteration count T. Specifically, this includes:

[0114] Initialize parameters such as population size, chromosome length, crossover probability, mutation probability, and maximum number of iterations. In this embodiment of the invention, the variable optimized by the genetic algorithm is the grid number of the service personnel's station, with a total of m variables, denoted as (p1, p2, ..., p...). i ,…,p m Therefore, each chromosome has m genes.

[0115] S402. Calculate the fitness value of an individual in the population, wherein the fitness value of an individual is equal to the reciprocal of the total service response time, specifically including:

[0116] In this embodiment of the invention, the fitness value of an individual is calculated using an objective function, and the individual fitness value is equal to the reciprocal of the total service response time. Given a fixed service personnel location, the total service response time is closely related to the division of labor among the service personnel. Therefore, to calculate the fitness of an individual in this model, it is necessary to solve the sub-problem of service personnel division of labor. This embodiment of the invention uses the following steps to solve this problem: First, based on the individual's gene combination (p1, p2, ..., p... i ,…,p m By indexing all corresponding station grid points in the distance matrix to each table, the decision variable of the restaurant service model becomes only the division of labor variable z of the service personnel. ijThen, the above restaurant service model and its related parameters are input into the Gurobi solver for solving, thereby obtaining the minimum total service response time under the current station location scheme, that is, the fitness value of the individual.

[0117] S403. Determine whether t≥T is true. If the termination condition is met, output the positioning scheme and division of labor scheme corresponding to the individual with the largest fitness value, that is, output the restaurant service personnel assignment scheme; otherwise, t=t+1, and proceed to the next step.

[0118] S404. Use roulette wheel selection to select individuals to establish a pairing pool. Following the principle that higher fitness corresponds to a higher selection probability, select superior individuals from the population and place them into the pairing pool.

[0119] S405. Based on the preset crossover probability, two individuals are randomly selected from the pairing library. Some genes in the chromosomes of the two selected individuals, namely the service personnel's station grid numbers, are crossed over and interchanged to generate a new individual that is different from the parent generation's service personnel stationing scheme. The crossover position is randomly determined.

[0120] S406. Randomly change the values ​​of certain genes of individuals in the population, i.e., the service personnel station grid numbers, with a preset mutation probability, so as to generate more new individuals. The mutation probability is usually very small. After this step, return to S402 to calculate the individual fitness operation.

[0121] The feasibility of the embodiments of the present invention will be verified below.

[0122] Verification Case 1:

[0123] The entire verification process was completed using Python v3.9. The restaurant environment raster image for this verification case is shown below. Figure 4 As shown in the diagram, black dots represent dining tables, and gray dots represent other obstacles in the dining area (such as walls, restrooms, etc.).

[0124] Based on this raster image, the set of optional grid points is obtained: V = [(0,0),(0,1),…,(24,23),(24,24)]. The feasible grid points are then numbered to construct the set of optional grid point numbers P = (1,2,…,487,488).

[0125] In this example, there are 18 tables and 3 service staff. The specific parameters of the service staff and tables are shown in Table 1 and Table 2. The service frequency for a single diner is set to 1.

[0126] Table 1 Service Personnel Parameter Table

[0127] Service personnel number Service capabilities movement speed 1 28 1 2 28 1 3 28 1

[0128] Table 2. Table of Dining Table Related Parameters

[0129]

[0130]

[0131] The distance matrix is ​​calculated based on the arrival principle and the A* algorithm. Partial information of the matrix is ​​as follows:

[0132]

[0133] Next, the model was optimized by setting the basic parameters of the genetic algorithm as follows: population size = 50, chromosome length = 3, crossover probability = 0.8, mutation probability = 0.2, number of iterations = 100. The output results are as follows. Figure 5 As shown.

[0134] Total service response time: 280.024.

[0135] The service personnel assignment scheme is shown in Table 3:

[0136] Table 3 Service Personnel Assignment Plan

[0137] Service personnel number Station grid number Station position corresponding to grid coordinates Service table number 1 401 (20,7) 9,12,14,15,16 2 296 (14,17) 4,8,10,11,13,17,18 3 137 (6,9) 1,2,3,5,6,7

[0138] Results visualization as follows Figure 6 As shown, the triangles represent service personnel.

[0139] The following verifies the applicability of the method of this invention in scenarios with complex restaurant shapes and layouts. The entire verification process is still completed using Python v3.9.

[0140] Verification Case 2:

[0141] Restaurant environment grid diagram Figure 7 As shown.

[0142] The set of selectable grid points for the station can be obtained from the raster map, V = [(0,7),(0,8),…,(29,28),(29,29)], and the set of selectable grid point numbers P = (1,2,…,440,441) can be constructed.

[0143] In this case, there are 4 service staff and 35 tables, including 10 two-person tables, 20 four-person tables and 5 eight-person tables. The service staff and table-related parameters are shown in Tables 4 and 5 below. The frequency of service for a single customer is 1.

[0144] Table 4 Summary Table of Service Personnel Parameters

[0145] Service personnel number Service capabilities movement speed 1 38 1 2 38 1 3 38 1 4 38 1

[0146] Table 5 Summary of Dining Table Parameters

[0147]

[0148]

[0149] Based on the above information, the distance matrix is ​​calculated. Partial information of the distance matrix is ​​as follows:

[0150]

[0151] Next, a genetic algorithm was used to optimize the model. The basic parameters of the genetic algorithm were set as follows: population size = 50, chromosome length = 4, crossover probability = 0.8, mutation probability = 0.2, and number of iterations = 100. The optimization results are as follows: Figure 8 As shown.

[0152] Optimized total service response time: 580.735.

[0153] The service personnel assignment plan is shown in Table 6:

[0154] Table 6 Service Personnel Assignment Scheme

[0155]

[0156]

[0157] The results are visualized as follows: Figure 9 As shown.

[0158] Through the two verification processes mentioned above, it can be seen that the personnel assignment method of the present invention can effectively solve the problem of restaurant service personnel assignment. While ensuring the superiority of the results, it also improves the solution efficiency and reduces the optimization cost. Moreover, the method still has good applicability in the case of complex restaurant environments.

[0159] This invention also provides a personnel assignment system for minimizing restaurant service response time, comprising:

[0160] The data acquisition module is used to acquire restaurant floor plans and restaurant service parameters;

[0161] The station location selectable grid point set acquisition module is used to construct a restaurant environment raster map based on the restaurant floor plan, and obtain the station location selectable grid point set based on the restaurant environment raster map;

[0162] The model building module is used to build a restaurant service model based on restaurant service parameters and a set of selectable grid points for station locations, with the goal of minimizing the total service response time of the restaurant.

[0163] The model solving module is used to solve the restaurant service model and obtain the restaurant service staff assignment scheme.

[0164] It is understood that the personnel assignment system for minimizing restaurant service response time provided in this embodiment of the invention corresponds to the personnel assignment method for minimizing restaurant service response time described above. The explanations, examples, and beneficial effects of the relevant content can be found in the corresponding content of the carbon emission prediction method for multiple urban scenarios, and will not be repeated here.

[0165] This invention also provides a computer-readable storage medium storing a computer program for minimizing restaurant service response time in personnel assignment, wherein the computer program causes a computer to execute the personnel assignment method for minimizing restaurant service response time as described above.

[0166] This invention also provides an electronic device, comprising:

[0167] One or more processors;

[0168] Memory; and

[0169] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a personnel assignment method for performing the restaurant service response time as described above.

[0170] In summary, compared with existing technologies, it has the following beneficial effects:

[0171] 1. The embodiments of the present invention provide a systematic and theoretically supported optimization method for the assignment of restaurant service personnel from the perspective of mathematical modeling and intelligent optimization algorithms, so as to stabilize the decision-making effect, reduce service response time, and improve the customer's consumption experience.

[0172] 2. This invention combines a grid-based method to construct a mathematical model, reducing model complexity while ensuring the practicality of the results. The restaurant service model includes the constraint that "service personnel cannot overlap with tables or obstacles." Traditional mathematical modeling methods would construct a complex non-convex space optimization problem. However, combining grid-based modeling transforms the problem into a discrete programming problem, reducing model complexity. Furthermore, in real-world restaurant staff assignments, decision-makers typically do not require highly precise staff positioning coordinates; therefore, the optimization results of this method also have good practicality.

[0173] 3. This embodiment of the invention uses the arrival principle and the A* path planning algorithm to measure service distance, which is more in line with real-world service scenarios. The arrival principle reflects the flexibility of services in real-world scenarios, while the use of the A* algorithm takes into account the movement obstacles in real restaurants; both increase the realism of the model. At the same time, the combined use of the grid method and the A* algorithm greatly enriches the applicable scenarios of the method, enabling it to be applied to service personnel assignment problems in scenarios with complex restaurant shapes and layouts.

[0174] 4. The embodiments of the present invention design a solution scheme for the model based on genetic algorithm, which improves the solution efficiency and reduces the optimization cost while ensuring the superiority of the results.

[0175] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0176] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for personnel assignment that minimizes restaurant service response time, characterized in that, include: S1. Obtain the restaurant floor plan and restaurant service parameters; S2. Construct a restaurant environment raster map based on the restaurant floor plan, and obtain a set of selectable grid points for station locations based on the restaurant environment raster map; S3. Based on restaurant service parameters and the set of selectable grid points for station locations, construct a restaurant service model with the goal of minimizing the total restaurant service response time. S4. Solve the restaurant service model to obtain the restaurant service staff assignment scheme; The restaurant service model includes an objective function and constraints, as detailed below: （1） （2） （3） （4） （5） Equation (1) is the objective function, which is to minimize the total service response time of the restaurant; Equations (2) to (4) are the constraints: Formula (2) ensures that each table has exactly one service staff member providing service; Formula (3) ensures that the number of customers assigned to each service staff member does not exceed their service capacity; Equation (4) represents the restriction on the position of service personnel, whose position can only be selected from the available grid points, and is a decision variable; Equation (5) represents the division of labor among service personnel and serves as a decision variable; In the formula, Indicates the number of service personnel; Indicates the number of tables; P represents the set of available grid points for service personnel positions; P represents the set of grid points. The set of optional grid point numbers obtained by numbering each grid point in the array. Indicates service personnel The number of the available grid points; Indicates service personnel Its service capacity, i.e., the maximum number of customers it can serve; Represents dining table The capacity; This indicates the frequency of service received by a single customer. This indicates the speed at which service staff move within the restaurant; Indicates the number is Grids to the dining table The distance; As a decision variable, when When =1, it indicates that it is handled by service personnel. For the dining table All customer services; otherwise, =0; This indicates the restaurant's total service response time; Solving the restaurant service model to obtain the restaurant service staff assignment scheme includes: S401. Population initialization: Set the population size, chromosome length, crossover probability, mutation probability, current iteration count t, and maximum iteration count T. The gene combinations of the individuals in the population are as follows: This indicates the grid number of the service personnel's station; S402. Calculate the fitness value of an individual in the population, wherein the fitness value of the individual is equal to the reciprocal of the total service response time; S403. Determine if t≥T is true. If the termination condition is met, output the positioning and division of labor schemes corresponding to the individual with the largest fitness value, i.e., output the restaurant service staff assignment scheme; otherwise, t=t+1 and proceed to the next step. S404. Use roulette wheel selection to select individuals to build a pairing library; S405. Based on the preset crossover probability, two individuals are randomly selected from the pairing library. Some genes in the chromosomes of the two selected individuals, namely the service personnel's station grid numbers, are crossed and interchanged to generate a new individual that is different from the parent generation's service personnel stationing scheme. S406. Randomly change the values ​​of certain genes of individuals in the population, i.e., the service personnel station grid number, with a preset mutation probability, so as to generate more new individuals. The mutation probability is usually very small. After this step, return to S402 to calculate the individual fitness operation. The calculation of the fitness value of individuals in the population includes: First, based on the individual's gene combination, obtain the shortest distance from all station grid points to each table; input the restaurant service model and its related parameters into the Gurobi solver to obtain the minimum total service response time under the current station scheme, and use the reciprocal of the minimum total service response time as the individual's fitness value.

2. The personnel assignment method for minimizing restaurant service response time as described in claim 1, characterized in that, S2 includes: Based on the restaurant floor plan, determine the grid precision and construct a restaurant environment grid map; Based on the restaurant environment grid map, selectable grid points in the map to construct a set of selectable grid points, which are grid points not occupied by restaurant facilities.

3. The personnel assignment method for minimizing restaurant service response time as described in any one of claims 1 to 2, characterized in that, Before performing step S4, the method further includes: The distance matrix is ​​calculated by using the table coordinates and the set of selectable grid points for standing positions to obtain the shortest distance matrix from the selectable grid points to the table.

4. The personnel assignment method for minimizing restaurant service response time as described in claim 3, characterized in that, The shortest distance is calculated using the arrival principle and the A* algorithm; The arrival principle refers to the fact that when serving a table, the server can choose any available grid point around the table as the service point.

5. A personnel assignment system that minimizes restaurant service response time, characterized in that, include: The data acquisition module is used to acquire restaurant floor plans and restaurant service parameters; The station location selectable grid point set acquisition module is used to construct a restaurant environment raster map based on the restaurant floor plan, and obtain the station location selectable grid point set based on the restaurant environment raster map; The model building module is used to build a restaurant service model based on restaurant service parameters and a set of selectable grid points for station locations, with the goal of minimizing the total service response time of the restaurant. The model solving module is used to solve the restaurant service model and obtain the restaurant service staff assignment scheme; The restaurant service model includes an objective function and constraints, as detailed below: （1） （2） （3） （4） （5） Equation (1) is the objective function, which is to minimize the total service response time of the restaurant; Equations (2) to (4) are the constraints: Formula (2) ensures that each table has exactly one service staff member providing service; Formula (3) ensures that the number of customers assigned to each service staff member does not exceed their service capacity; Equation (4) represents the restriction on the position of service personnel, whose position can only be selected from the available grid points, and is a decision variable; Equation (5) represents the division of labor among service personnel and serves as a decision variable; In the formula, Indicates the number of service personnel; Indicates the number of tables; P represents the set of available grid points for service personnel positions; P represents the set of grid points. The set of optional grid point numbers obtained by numbering each grid point in the array. Indicates service personnel The number of the available grid points; Indicates service personnel Its service capacity, i.e., the maximum number of customers it can serve; Represents dining table The capacity; This indicates the frequency of service received by a single customer. This indicates the speed at which service staff move within the restaurant; Indicates the number is Grids to the dining table The distance; As a decision variable, when When =1, it indicates that it is handled by service personnel. For the dining table All customer services; otherwise, =0; This indicates the restaurant's total service response time; Solving the restaurant service model to obtain the restaurant service staff assignment scheme includes: S401. Population initialization: Set the population size, chromosome length, crossover probability, mutation probability, current iteration count t, and maximum iteration count T. The gene combinations of the individuals in the population are as follows: This indicates the grid number of the service personnel's station; S402. Calculate the fitness value of an individual in the population, wherein the fitness value of the individual is equal to the reciprocal of the total service response time; S403. Determine if t≥T is true. If the termination condition is met, output the positioning and division of labor schemes corresponding to the individual with the largest fitness value, i.e., output the restaurant service staff assignment scheme; otherwise, t=t+1 and proceed to the next step. S404. Use roulette wheel selection to select individuals to build a pairing library; S405. Based on the preset crossover probability, two individuals are randomly selected from the pairing library. Some genes in the chromosomes of the two selected individuals, namely the service personnel's station grid numbers, are crossed and interchanged to generate a new individual that is different from the parent generation's service personnel stationing scheme. S406. Randomly change the values ​​of certain genes of individuals in the population, i.e., the service personnel station grid number, with a preset mutation probability, so as to generate more new individuals. The mutation probability is usually very small. After this step, return to S402 to calculate the individual fitness operation. The calculation of the fitness value of individuals in the population includes: First, based on the individual's gene combination, obtain the shortest distance from all station grid points to each table; input the restaurant service model and its related parameters into the Gurobi solver to obtain the minimum total service response time under the current station scheme, and use the reciprocal of the minimum total service response time as the individual's fitness value.

6. A computer-readable storage medium, characterized in that, It stores a computer program for minimizing restaurant service response time in personnel assignment, wherein the computer program causes a computer to perform the personnel assignment method for minimizing restaurant service response time as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a personnel assignment method for minimizing restaurant service response time as described in any one of claims 1 to 4.

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