CN113344378A - Intelligent scheduling algorithm based on driver's on-duty and off-duty site preference - Google Patents
Intelligent scheduling algorithm based on driver's on-duty and off-duty site preference Download PDFInfo
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Abstract
The invention discloses an intelligent scheduling algorithm based on driver commuting place preference, which comprises a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a driving plan compiling model module; the driving plan compiling model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving plan table; the weight analysis module obtains a weight result matrix through analysis, and forms an optimal combination result on the basis of the weight result matrix; then the driving planning model module associates the specific vehicle and the driver to the vehicle number and the driver in the initial driving planning list according to the combined weight result, and finally forms the driving planning list which enables the driver to have the shortest distance to work. The invention can improve the satisfaction degree of the driver on duty and reduce the negative emotion of the driver caused by overlong time on duty.
Description
Technical Field
The invention relates to the technical field of bus operation scheduling management, in particular to an intelligent scheduling algorithm based on driver commuting place preference.
Background
In the intelligent public transportation system, the core is an intelligent public transportation scheduling system, and the core of the public transportation scheduling system is the compilation of driving plans. The management capacity and the operation efficiency of the public transport enterprise are directly influenced by the level of driving planning level and the quality. At present, the difficulty and the shortage of labor are problems in a plurality of industries, the public transport belongs to a special industry, the work of a driver is different from that of workers in other industries, and in recent years, the public transport driver has various problems of difficulty in recruitment, high rate of departure, serious aging and the like. How to improve the working efficiency of the driver to the maximum extent is an urgent problem to be solved.
Moreover, many public transportation enterprises still stop at the stage of compiling the driving plan by using the traditional manual mode, and the public transportation enterprises are not directly connected with data such as passenger flow, road conditions, driver preference and the like mainly according to the experience of workers, and cannot meet the requirements of increasingly developed intelligent public transportation operation even if the public transportation enterprises do not talk about intelligent compilation of the driving plan. Therefore, the intelligent compilation method for deeply researching the driving plan of the public transport vehicle has important significance and economic value.
The individual vehicles in scattered operation are organized through intelligent compilation of a driving plan, planned balanced operation production is realized, and the whole process of line, vehicle and driver operation is guided and organized. Finally, a complete intelligent scheduling chain is formed, the intelligent level of the whole scheduling process is improved, the workload of manual operation is reduced, and the satisfaction degree of drivers is improved.
Disclosure of Invention
The invention aims to provide an intelligent scheduling algorithm based on the driver's preference for the place of going to and from work, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent scheduling algorithm based on driver commuting location preference comprises a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a driving plan compiling model module; the basic data acquisition module comprises a bus route basic data set and data of places where each driver expects to get on or off duty;
the operation data analysis module is used for analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operation data;
the passenger flow data analysis module is used for analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to the historically collected passenger flow data;
the driving plan compiling model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving plan table which mainly considers the vehicle turnover time and the passenger flow demand interval and comprises the required number of vehicles, the required operating mileage, the required number of drivers, the access points and the time of all the shifts;
the weight analysis module is used for analyzing and obtaining a weight result matrix after matching the expected on-duty and off-duty places of all drivers with the on-duty and off-duty places and distances of the drivers needing planning according to an initial driving schedule and information of the expected on-duty and off-duty places of all drivers, forming an optimal combination result on the basis of the weight result matrix and forming an input condition preset by a driving planning model;
the driving planning model module associates the specific vehicle and the driver to the vehicle number and the driver number in the initial driving planning list according to the combined weight result of the weight analysis module, and finally forms the driving planning list which enables the driver to have the shortest distance to go to and from work.
An intelligent scheduling algorithm based on driver commuting site preference is realized by the following steps:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, the up-down length of the line, operation time, stop time of the first station and the last station and the like, and acquiring a home address of a driver from a personnel management information system;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: and the driving plan compiling model module generates an initial driving plan table of the route according to the multi-source data, wherein the initial driving plan table comprises each starting time point, driving time, stop time and each starting interval between adjacent driving times of each shift at the first station and the last station. The initially generated driving schedule mainly considers the turnover time of the vehicle and the passenger flow demand interval to form a train number chain corresponding to each shift, and the vehicle and the driver are not related in the step;
s5: the weight analysis module combines the initial driving schedule of the line and the home address data of the driver, and sets the corresponding weight value to the driver DiDesired shift station LiFollowing the demand driver djMileage on duty/on dutyiMatching is carried out;
s6: and the driving plan compiling model module associates the specific drivers to the driver numbers in the initial driving plan table according to the weight analysis result, and finally forms the driving plan table which can meet the expected requirements of the drivers to the greatest extent.
As a further scheme of the invention: the driving schedule initially generated in step S4 mainly takes into account the vehicle turnaround time and the passenger flow demand interval to form a train number chain corresponding to each shift, and there is no vehicle and driver associated in this step.
As a further scheme of the invention: the driving schedule in step S4 can be regarded as a matrix of m × n, where m is the maximum number of required shifts, n is the maximum required single number, F (i, j), i is 1,2, …, m; j is 1,2, …, n, which refers to the data set of time point, travel time and stop time when each departure time of the first and last stations of each shift;
wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, F (i, j) is null, it indicates that the operation is stopped, and all the data in the data set are 0;
according to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of each shift can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n.
As a further scheme of the invention: all drivers D are denoted by ω (i, j) in the step S5iDesired shift station LiFollowing the demand driver djMileage on duty/on dutyiThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of matched drivers, n is the number of required shifts, and m is more than or equal to n; if all drivers DiThe expected trip mileage standard value of MiUpper limit of
For any matching result ω (i, j), the corresponding weight value is defined as:
each weight value in the match result weight value matrix can be obtained.
Compared with the prior art, the invention analyzes the on-duty mileage weight values according to the expected on-duty and off-duty places of all drivers, and establishes the multi-objective optimization model by combining the constraint conditions of the number of line vehicles, the stop and rest time of the drivers, the departure intervals of different peak sections, the vehicle running time of different peak sections and the like, thereby providing a feasible running schedule meeting the expected requirements of the drivers to the maximum extent, improving the satisfaction degree of the drivers on-duty and off-duty and reducing the negative emotion of the drivers caused by overlong on-duty and off-duty time.
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Fig. 1 is a schematic structural diagram of an intelligent shift scheduling algorithm based on driver's preferences of places on and off duty.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Referring to fig. 1, an intelligent scheduling system based on driver's preferences of on-duty and off-duty locations includes a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module, and a driving planning model module;
the basic data acquisition module comprises a bus route basic data set and data of places where each driver expects to get on or off duty;
the operation data analysis module is used for analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operation data;
the passenger flow data analysis module is used for analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to the historically collected passenger flow data;
the driving plan compiling model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving plan table which mainly considers the vehicle turnover time and the passenger flow demand interval and comprises the required number of vehicles, the required operating mileage, the required number of drivers, the access points and the time of all the shifts;
the weight analysis module is used for analyzing and obtaining a weight result matrix after matching the expected on-duty and off-duty places of all drivers with the on-duty and off-duty places and distances of the drivers needing planning according to an initial driving schedule and information of the expected on-duty and off-duty places of all drivers, forming an optimal combination result on the basis of the weight result matrix and forming an input condition preset by a driving planning model;
and the driving planning model module associates the specific vehicle and the driver to the vehicle number and the driver number in the initial driving planning table according to the combined weight result, and finally forms the driving planning table which enables the driver to have the shortest distance to work.
An intelligent shift scheduling algorithm based on driver commute site preferences, comprising:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, the up-down length of the line, operation time, stop time of the first station and the last station and the like, and acquiring a home address of a driver from a personnel management information system;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: the initial driving schedule of the route is generated by the driving schedule compiling model module according to the multi-source data and comprises each departure time point, the driving time, the stop time and the departure interval between each adjacent number of the buses of each shift at the first and last stations. The initially generated driving schedule mainly considers the vehicle turnover time and the passenger flow demand interval to form a train number chain corresponding to each shift, and the vehicle and the driver are not related in the step.
The initial schedule is shown in table 1:
TABLE 1
The driving schedule can be seen as a matrix of m × n, where m is the maximum number of required shift, n is the maximum required single number, F (i, j), i is 1,2, …, m; j is 1,2, …, n, which refers to the data set of time of departure, time of travel, and time of stop at each departure time at the first and last stations of each shift.
Wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, and F (i, j) is null, it indicates that the operation is stopped, and all the data in the data set are 0.
According to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of each shift can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n.
S5: the weight analysis module combines the initial driving schedule of the line and the home address data of the driver, and sets the corresponding weight value to the driver DiDesired shift station LiFollowing the demand driver djMileage on duty/on dutyiAnd (6) matching.
All drivers D are denoted by ω (i, j)iDesired shift station LiFollowing the demand driver djMileage on duty/on dutyiThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of matched drivers, n is the number of required shifts, and m is more than or equal to n; the matching result weight value matrix is shown in table 2:
TABLE 2
Suppose all drivers DiThe expected trip mileage standard value of MiUpper limit of
For any matching result ω (i, j), the corresponding weight value is defined as:
each weight value in the match result weight value matrix may be obtained. Then m equipped drivers and n required drivers are arranged and combined, each combination obtains a combination weight result value, and the combination weight result value can be obtained from the combination weight result value
And searching out an optimal combination result from the combination results, and finding out an optimal matching scheme meeting the work attendance requirements of the driver according to the combination result.
S6: and the driving plan compiling model module associates the specific drivers to the driver numbers in the initial driving plan table according to the weight analysis result, and finally forms the driving plan table which can meet the expected requirements of the drivers to the greatest extent.
Although the preferred embodiments of the present patent have been described in detail, the present patent is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present patent within the knowledge of those skilled in the art.
Claims (5)
1. An intelligent scheduling algorithm based on driver commuting site preference is characterized by comprising a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a driving plan compiling model module;
the basic data acquisition module comprises a bus route basic data set and data of places where each driver expects to get on or off duty;
the operation data analysis module is used for analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operation data;
the passenger flow data analysis module is used for analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to the historically collected passenger flow data;
the driving plan compiling model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving plan table which mainly considers the vehicle turnover time and the passenger flow demand interval and comprises the required number of vehicles, the required operating mileage, the required number of drivers, the access points and the time of all the shifts;
the weight analysis module is used for analyzing and obtaining a weight result matrix after matching the expected on-duty and off-duty places of all drivers with the on-duty and off-duty places and distances of the drivers needing planning according to an initial driving schedule and information of the expected on-duty and off-duty places of all drivers, forming an optimal combination result on the basis of the weight result matrix and forming an input condition preset by a driving planning model;
the driving planning model module associates the specific vehicle and the driver to the vehicle number and the driver number in the initial driving planning list according to the combined weight result of the weight analysis module, and finally forms the driving planning list which enables the driver to have the shortest distance to go to and from work.
2. The intelligent shift scheduling algorithm based on the driver's on-off duty site preference as claimed in claim 1 is implemented by the following steps:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, the up-down length of the line, operation time, stop time of the first station and the last station and the like, and acquiring a home address of a driver from a personnel management information system;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: the driving plan compiling model module generates an initial driving plan table of the route according to the multi-source data, wherein the initial driving plan table comprises each starting time point, driving time, stop time and each starting interval between every two adjacent driving times of each shift at the first and last stations;
s5: the weight analysis module is used for matching an expected on-duty site of the driver with the required on-duty mileage of the driver by setting a corresponding weight value according to a driving schedule of the initial line and the home address data of the driver;
s6: and the driving plan compiling model module associates the specific drivers to the driver numbers in the initial driving plan table according to the weight analysis result, and finally forms the driving plan table which can meet the expected requirements of the drivers to the greatest extent.
3. The intelligent driver-commuting location preference based scheduling algorithm as claimed in claim 2, wherein the initially generated driving schedule in step S4 is formed into a train number chain corresponding to each shift by mainly considering vehicle turnaround time and passenger flow demand interval, and there is no association between the vehicle and the driver in this step.
4. The intelligent driver shift scheduling algorithm based on driver' S commuting site preference as claimed in claim 2, wherein the driving schedule in step S4 can form a matrix of m × n, m is the maximum number of required shifts, n is the maximum required single sign, F (i, j), i ═ 1,2, …, m; j is 1,2, …, n, which refers to the data set of time point, travel time and stop time when each departure time of the first and last stations of each shift;
wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, F (i, j) is null, it indicates that the operation is stopped, and all the data in the data set are 0;
according to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of each shift can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n.
5. The driver-based commute as in claim 2Intelligent spot-preference scheduling algorithm, characterized in that all drivers D are represented by ω (i, j) in step S5iDesired shift station LiFollowing the demand driver djMileage on duty/on dutyiThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of matched drivers, n is the number of required shifts, and m is more than or equal to n; if all drivers DiThe expected trip mileage standard value of MiUpper limit of
For any matching result ω (i, j), the corresponding weight value is defined as:
each weight value in the match result weight value matrix can be obtained.
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