A cleaning operation scheduling method based on linear programming

Abstract

The present invention provides a cleaning operation scheduling method based on linear programming, which belongs to the field of cleaning technology; including S1, task information collection; S2, task data processing; S3, establishing a linear programming model; S4, linear programming solution. The present invention adopts a linear programming model for optimization, and can consider multiple factors and constraints to obtain the optimal cleaning operation scheduling plan. Compared with the traditional cleaning operation scheduling method, the beneficial effects of the present invention are: high efficiency: the optimal scheduling plan is automatically solved by a linear programming algorithm, which greatly improves the scheduling efficiency and accuracy; flexibility: it can adapt to cleaning tasks of different scales and complexities, and dynamically adjust according to actual needs; resource optimization: realize the optimal allocation of cleaning resources, reduce resource waste, and reduce operating costs; improve user satisfaction: ensure that various cleaning tasks can be completed on time and with high quality, and improve user satisfaction.

Description

Technical Field

[0001] The present invention relates to the field of cleaning technology, and in particular to a cleaning operation scheduling method based on linear programming. Background Art

[0002] In the cleaning industry, the scheduling of cleaning operations is an important issue. Traditional cleaning operation scheduling methods usually use manual arrangements, which cannot effectively optimize cleaning resources and operation time, resulting in resource waste and low efficiency. Therefore, how to achieve reasonable scheduling of cleaning operations and improve cleaning efficiency and resource utilization is an important issue facing the current cleaning industry.

[0003] Linear programming is a mathematical method used to optimize a specific linear objective function under a set of linear inequality constraints. It can be effectively applied to cleaning job scheduling to determine the optimal resource allocation and time arrangement, thereby improving efficiency and reducing costs. Therefore, this application provides a cleaning job scheduling method based on linear programming to meet the needs. Summary of the invention

[0004] The object of the present invention is to provide a method for providing maintenance on demand according to different states of elevators, so as to solve the problems raised in the above background technology. To solve the above technical problems, the present invention provides the following technical solutions:

[0005] A cleaning operation scheduling method based on linear programming comprises the following steps:

[0006] S1, task information collection;

[0007] S2, task data processing;

[0008] S3. Establish a linear programming model;

[0009] The step S3 of establishing a linear programming model comprises the steps of:

[0010] S31. A linear programming model was constructed with minimization of total manpower and exposure loss costs as the objective function and cleaner working hours, task continuity, and cleaner exclusivity as constraints;

[0011] S32, defining decision parameters;

[0012] The total number of time windows is T;

[0013] The total number of tasks is J;

[0014] The starting time window of task j is s j The end time window is e j ;

[0015] The number of cleaners required for task j is wj ;

[0016] The loss cost of not completing task j is i j ;

[0017] The number of cleaners required for task j in time window t is P it If s j ≤t <e j ), then P it =w j , otherwise, P it = 0

[0018] S33, define decision variables;

[0019] The cleaner is i, and the working status of the cleaner i in the time window t is X it , l means working, 0 means resting;

[0020] Whether task j is completed is y j , the 1 represents work, and the 0 represents rest;

[0021] S34, establishing an objective function;

[0022] The goal is to minimize the loss cost and the number of cleaners. These two objectives can be combined into one objective function by weighted summation: ,in, and is a weight parameter used to balance the importance of the two objectives;

[0023] S35, establishing constraint conditions, wherein the established constraint conditions include cleaner working time constraints, task completion constraints, task continuity constraints and variable value constraints;

[0024] The working hours of the cleaning staff are restricted to no more than 8 hours per day. The restriction time calculation formula is:

[0025] The task completion constraint is that for each task, there must be a sufficient number of cleaners at the task

[0026] Working within the time window, the task completion constraint formula is:

[0027] , ,

[0028] The task continuity constraint is that once a task is started, it cannot be interrupted until it is completed. The task continuity constraint is: ,

[0029] The variable value constraint formula is: ;

[0030] S4. Linear programming solution.

[0031] The step S1 task information collection includes the following steps:

[0032] S11, obtaining the cleaning task location, task content, completion time, operation time, and priority task information;

[0033] S12. Based on the scanning of the project site status, the determination of human flow and other measurements of cleaning defects, a list of cleaning tasks applicable to the project is developed.

[0034] The task list includes the work location, work content, work required completion time, work time, number of workers and priority.

[0035] The step S2 task data processing comprises the following steps:

[0036] S21, encode the tasks and modularize the task contents;

[0037] S22. Merge the tasks in the task list that have the same completion time requirement and task location, and encode them.

[0038] The modular task content includes the operation start time, operation completion time, the number of people required for the operation and the exposure loss caused by non-operation.

[0039] The task merging includes merging the lobby, elevator, and ground floor unit entrances and exits of the building into one task, which is convenient for solving the linear programming model;

[0040] The job start time is calculated from the job completion time requirement and the job duration;

[0041] The number of people required for the job is given in the task list;

[0042] The loss of unworked exposure is calculated by priority and number of defect exposures.

[0043] The linear programming solution in step S4 includes using the integer linear programming Pulp solution model based on Python to obtain the optimal cleaning staff allocation and task scheduling plan.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects:

[0045] In the above scheme, by adopting the linear programming model for optimization, multiple factors and constraints can be considered to obtain the optimal cleaning operation scheduling scheme. Compared with the traditional cleaning operation scheduling method, the beneficial effects of the present invention are:

[0046] Efficiency: The optimal scheduling solution is automatically solved through the linear programming algorithm, which greatly improves the scheduling efficiency and accuracy;

[0047] Flexibility: Able to adapt to cleaning tasks of different scales and complexities, and dynamically adjust according to actual needs;

[0048] Resource optimization: optimize the allocation of cleaning resources, reduce resource waste, and lower operating costs;

[0049] Improve user satisfaction: Ensure that all cleaning tasks can be completed on time and with high quality to improve user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable those skilled in the relevant art to make and use the present disclosure.

[0051] Figure 1 This is a schematic diagram of the steps of the cleaning job scheduling method based on linear programming.

[0052] As shown in the figure, in order to clearly implement the structure of the embodiment of the present invention, specific structures and devices are marked in the figure, but this is only for illustrative purposes and is not intended to limit the present invention to the specific structure, device and environment. According to specific needs, ordinary technicians in this field can adjust or modify these devices and environments, and the adjustments or modifications made are still included in the scope of the attached claims. DETAILED DESCRIPTION

[0053] The following is a detailed description of a cleaning operation scheduling method based on linear programming provided by the present invention in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternative methods to implement some known technologies; and the accompanying drawings are only for a more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0054] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0055] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0056] It will be understood that the meaning of “on,” “over,” and “above” in this disclosure should be interpreted in the broadest manner, so that “on” means not only “directly on” something, but also includes the meaning of being “on” something with intervening features or layers therebetween, and “on” or “over” means not only “on” or “above” something, but also includes the meaning of being “on” or “above” something with no intervening features or layers therebetween.

[0057] Additionally, spatially relative terms such as "under," "beneath," "lower," "above," "upper," and the like may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as shown in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein may be similarly interpreted accordingly.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a cleaning operation scheduling method based on linear programming, comprising the following steps:

[0059] S1, task information collection;

[0060] S2, task data processing;

[0061] S3. Establish a linear programming model;

[0062] The step S3 of establishing a linear programming model comprises the steps of:

[0063] S31. A linear programming model was constructed with minimization of total manpower and exposure loss costs as the objective function and cleaner working hours, task continuity, and cleaner exclusivity as constraints;

[0064] S32, defining decision parameters;

[0065] The total number of time windows is T;

[0066] The total number of tasks is J;

[0067] The starting time window of task j is s j The end time window is e j ;

[0068] The number of cleaners required for task j is w j ;

[0069] The loss cost of not completing task j is i j ;

[0070] The number of cleaners required for task j in time window t is P it If s j ≤t <e j ), then P it =w j , otherwise, P it = 0;

[0071] S33, define decision variables;

[0072] The cleaner is i, and the working status of the cleaner i in the time window t is X it , l means working, 0 means resting;

[0073] Whether task j is completed is y j , the 1 represents work, and the 0 represents rest;

[0074] S34, establishing an objective function;

[0075] The goal is to minimize the loss cost and the number of cleaners. These two objectives can be combined into one objective function by weighted summation: ,in, and is a weight parameter used to balance the importance of the two objectives;

[0076] S35, establishing constraint conditions, wherein the established constraint conditions include cleaner working time constraints, task completion constraints, task continuity constraints and variable value constraints;

[0077] The working hours of the cleaning staff are restricted to no more than 8 hours per day. The restriction time calculation formula is:

[0078] The task completion constraint is that for each task, there must be a sufficient number of cleaners at the task

[0079] Working within the time window, the task completion constraint formula is:

[0080] , ,

[0081] The task continuity constraint is that once a task is started, it cannot be interrupted until it is completed. The task continuity constraint is: ,

[0082] The variable value constraint formula is: ;

[0083] S4. Linear programming solution.

[0084] The step S1 task information collection includes the following steps:

[0085] S11, obtaining the cleaning task location, task content, completion time, operation time, and priority task information;

[0086] S12. Based on the scanning of the project site status, the determination of human flow and other measurements of cleaning defects, a list of cleaning tasks applicable to the project is developed.

[0087] The task list includes the work location, work content, work required completion time, work time, number of workers and priority.

[0088] The step S2 task data processing comprises the following steps:

[0089] S21, encode the tasks and modularize the task contents;

[0090] S22. Merge the tasks in the task list that have the same completion time requirement and task location, and encode them.

[0091] The modular task content includes the operation start time, operation completion time, the number of people required for the operation and the exposure loss caused by non-operation.

[0092] The task merging includes merging the lobby, elevator, and ground floor unit entrances and exits of the building into one task, which is convenient for solving the linear programming model;

[0093] The job start time is calculated from the job completion time requirement and the job duration;

[0094] The number of people required for the job is given in the task list;

[0095] The loss of unworked exposure is calculated by priority and number of defect exposures.

[0096] The linear programming solution in step S4 includes using the integer linear programming Pulp solution model based on Python to obtain the optimal cleaning staff allocation and task scheduling plan.

[0097] The following is a detailed explanation of the above constraints:

[0098] Cleaning staff working hours

[0099] This constraint ensures that each cleaner does not work more than 8 hours a day. This is based on labor law regulations or company policies to ensure that the cleaner's working hours and rest time are balanced.

[0100] Specifically, each cleaner (i) has a working status (X it ) cannot exceed 8h.

[0101] Task completion constraints

[0102] This constraint ensures that each task has a sufficient number of cleaners to complete within the specified time window. If a task (j) requires (P it ) cleaners, then at least this many cleaners need to be on duty within this time window.

[0103] In other words, for each task (j), from the start time window (s j To the end time window j ), the number of cleaners working in each time window (t) must be greater than or equal to the number of cleaners required for the task within this time window (P it ).

[0104] Task continuity constraints

[0105] This constraint ensures that once a task is started, it cannot be interrupted and must be completed continuously. If a task (j) is not completed (i.e. (y j = 0)), then in each time window from the start to the end of the task, there must be a sufficient number of cleaners working to ensure the continuity of the task.

[0106] Specifically, if a task is not completed (y j = 0)), the cumulative number of cleaners from the start time window to the end time window is insufficient Must be less than or equal to the total duration of the task multiplied by the number of cleaners required for the task (((e j -s j ) j )).

[0107] Variable value constraints

[0108] This constraint specifies the range of values ​​of the decision variable. Here, (X it ) and (y j) are binary variables and can only take the values ​​0 or 1. (X it = 1) indicates that cleaner (i) works in time window (t), (X it = 0) means rest; (y j =1) indicates that task (j) is completed, (y j = 0) indicates not completed.

[0109] The cleaning operation scheduling method based on linear programming of the present invention can consider multiple factors and constraints to obtain the optimal cleaning operation scheduling scheme by adopting a linear programming model for optimization. Compared with the traditional cleaning operation scheduling method, it has the following advantages:

[0110] Efficiency: The optimal scheduling solution is automatically solved through the linear programming algorithm, which greatly improves the scheduling efficiency and accuracy.

[0111] Flexibility: Able to adapt to cleaning tasks of different scales and complexities, and dynamically adjust according to actual needs.

[0112] Resource optimization: optimize the allocation of cleaning resources, reduce resource waste, and lower operating costs.

[0113] Improve user satisfaction: Ensure that all cleaning tasks can be completed on time and with high quality to improve user satisfaction.

[0114] Take the cleaning task scheduling of a community as an example

[0115] 1. Task information collection: obtain cleaning task information (including task location, task content, completion time, operation time, and priority).

[0116] As shown in the task list in Table 1, this community has a total of 190 tasks per day, requiring a total of 3430 minutes (57.17 hours) of work.

[0117]

[0118] Table 1 Task list (partial)

[0119] 2. Task data processing: Encode the tasks and modularize the task content: job start time, job completion time, number of people required for the job, and loss caused by non-operation.

[0120] Assuming that every 10 minutes is a time window, 6:00-6:10 is time window 1, 6:10-6:20 is time window 2, and so on. For example, if the start time window of task LD1-01-01 is 1 and the end time window is 3, it means that the working time of this task should be 6:00-6:20.

[0121] If no one is working on the mission that day, the total exposure loss is 3357.

[0122]

[0123] Table 2 Task data processing (part)

[0124] 3. Establish a linear programming model: Take minimizing the total cost (including labor cost and exposure loss) as the objective function, and the cleaner’s working hours, task continuity, and cleaner’s exclusivity as constraints to construct a linear programming model.

[0125] The weight parameter for the objective function and ,set up , To meet the needs of community projects to minimize exposure losses while investing as few operators as possible.

[0126] 4. Linear programming solution: Use the Python-based integer linear programming Pulp solution model to obtain the optimal cleaning staff allocation and task scheduling plan.

[0127]

[0128] Table 3 Linear programming calculation results (partial)

[0129] After solving the linear programming problem, the optimal schedule for the project is shown in Table 3. A total of 6 operators are required, and the optimal exposure loss is 477.

[0130] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0131] A person skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as RoM / RAM, a disk, an optical disk, etc.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A cleaning operation scheduling method based on linear programming, characterized in that: The following steps are involved: S1, task information collection; S2, task data processing; S3. Establish a linear programming model; The step S3 of establishing a linear programming model comprises the steps of: S31. A linear programming model was constructed with minimization of total manpower and exposure loss costs as the objective function and cleaner working hours, task continuity, and cleaner exclusivity as constraints; S32, defining decision parameters; The total number of time windows is T; The total number of tasks is J; The starting time window of task j is s j The end time window is e j ; The number of cleaners required for task j is w j ; The loss cost of not completing task j is i j ; The number of cleaners required for task j in time window t is P it If s j ≤t < e j ), then P it = w j , otherwise, P it = 0; S33, define decision variables; The cleaner is i, and the working status of the cleaner i in the time window t is X it , l means working, 0 means resting; Whether task j is completed is y j , the 1 represents work, and the 0 represents rest; S34, establishing an objective function; The goal is to minimize the loss cost and the number of cleaners. These two objectives can be combined into one objective function by weighted summation: ,in, and is a weight parameter used to balance the importance of the two objectives; S35, establishing constraint conditions, wherein the established constraint conditions include cleaner working time constraints, task completion constraints, task continuity constraints and variable value constraints; The working hours of the cleaning staff are restricted to no more than 8 hours per day. The restriction time calculation formula is: The task completion constraint is that for each task, there must be a sufficient number of cleaners at the task Working within the time window, the task completion constraint formula is: ， ， The task continuity constraint is that once a task is started, it cannot be interrupted until it is completed. The task continuity constraint is: , The variable value constraint formula is: ; S4. Linear programming solution.

2. The cleaning operation scheduling method based on linear programming according to claim 1 is characterized in that: The step S1 task information collection includes the following steps: S11, obtaining the cleaning task location, task content, completion time, operation time, and priority task information; S12. Based on the scanning of the project site status, the determination of human flow and other measurements of cleaning defects, a list of cleaning tasks applicable to the project is developed.

3. The cleaning operation scheduling method based on linear programming according to claim 2 is characterized in that: The task list includes the work location, work content, work required completion time, work time, number of workers and priority.

4. The cleaning operation scheduling method based on linear programming according to claim 1 is characterized in that: The step S2 task data processing comprises the following steps: S21, encode the tasks and modularize the task contents; S22. Merge the tasks in the task list that have the same completion time requirement and task location, and encode them.

5. The cleaning operation scheduling method based on linear programming according to claim 4 is characterized in that: The modular task content includes the operation start time, operation completion time, the number of people required for the operation and the exposure loss caused by non-operation.

6. The cleaning operation scheduling method based on linear programming according to claim 4 is characterized in that: The task merging includes merging the lobby, elevator, and ground floor unit entrances and exits of the building into one task, which is convenient for solving the linear programming model; The job start time is calculated from the job completion time requirement and the job duration; The number of people required for the job is given in the task list; The loss of unworked exposure is calculated by priority and number of defect exposures.

7. The cleaning operation scheduling method based on linear programming according to claim 1 is characterized in that: The linear programming solution in step S4 includes using the integer linear programming Pulp solution model based on Python to obtain the optimal cleaning staff allocation and task scheduling plan.

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