CN111476490A - Regional multi-line vehicle scheduling algorithm shared by resource pool - Google Patents

Regional multi-line vehicle scheduling algorithm shared by resource pool Download PDF

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CN111476490A
CN111476490A CN202010277794.1A CN202010277794A CN111476490A CN 111476490 A CN111476490 A CN 111476490A CN 202010277794 A CN202010277794 A CN 202010277794A CN 111476490 A CN111476490 A CN 111476490A
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郭建国
郭圆圆
阎磊
赵新潮
孙浩
普秀霞
沈洋
白珂
吕厚发
程行威
林中霞
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Abstract

The invention belongs to the technical field of electric bus scheduling, and particularly relates to a regional multi-line vehicle scheduling algorithm for resource pool sharing aiming at different customer scheduling requirements. Specifically, the method comprises the following steps: and establishing a scheduling model I and constraint conditions, and firstly, having a schedule according with a passenger flow distribution rule on the premise of scheduling vehicles in a resource pool shared region on multiple lines. ) And (4) establishing a second scheduling model and constraint conditions, wherein the optimization goal of scheduling is to minimize the total cost of the crew. The invention aims to combine various different bus line scheduling system data, synthesize a passenger transport information base and a scheduling management system, and is particularly suitable for regional multi-line scheduling to form a resource pool shared multi-line scheduling algorithm scheme framework.

Description

Regional multi-line vehicle scheduling algorithm shared by resource pool
Technical Field
The invention belongs to the technical field of electric bus dispatching, and particularly relates to a multi-line departure interval optimization algorithm aiming at the scheduling requirements of different customers.
Background
Regional Bus Scheduling (RBSP) is a scheduling form of vehicle cross-line operation organized to balance the traffic load between adjacent lines and reduce passenger transfer. Regional bus dispatching is the development trend of future urban public transport, and compared with a single-pick line, the RBSP plans to uniformly and reasonably arrange all vehicles in different yards according to the imbalance of departure frequency of different lines at the same time and meeting a series of constraint conditions, and realizes cross-line dispatching to finish the tasks of fixed schedules of different lines.
Since the regional scheduling algorithm is a typical NP-hard problem, at present, it is only necessary to use linear programming and genetic algorithm to solve the problem, and it can be known according to the current research situation that the public transportation regional scheduling model can be generally categorized into various model structures such as "vehicle transportation model, task allocation model, minimum cost model, set coverage and set segmentation", and then the algorithm for solving the generated model mainly includes "ant colony algorithm, genetic algorithm, emergency search algorithm, heuristic algorithm, branch and bound, linear programming, nonlinear programming and 0-1 integer programming", and finally the algorithm for optimizing the generated solution mainly includes "damage reconstruction disturbance and neighborhood structure search".
The optimization method mainly adopts algorithms such as linear programming, nonlinear programming, 0-1 integer programming and the like, the optimization method has the advantages that a scheduling scheme is relatively ideal but the defect is that long running time is needed, the heuristic idea mainly adopts a genetic algorithm, a simulated annealing algorithm, a tabu search algorithm, an ant colony algorithm and the like, and the heuristic idea has the advantages that the calculation complexity is relatively low, and the defect is that suboptimal solution can be obtained compared with the optimization scheme.
The multi-line vehicle scheduling belongs to a sub-field of regional scheduling. In the text of the 'short-time passenger flow prediction-based bus regional dispatching optimization research', such as the text, the text proposes a short-time passenger flow prediction method for buses based on an RBF neural network model, and the text refers to regional dispatching, mainly relates to a bus dispatching strategy of a transfer time window, and is based on convenience for passenger transfer. Yangli et al also emphatically analyze the regional dispatching model with the optimal transfer service quality in the text of 'urban public transportation regional dispatching method research based on transfer value'. The existing main technical scheme of regional dispatching is around transfer service, but lacks the consideration of the resources of public transportation dispatching. Meanwhile, although the line shift scheduling main technology is rapidly developed in the field of algorithms, research on a multi-line combined shared resource pool is not carried out, and research on the multi-line combined shift scheduling technology is not focused.
Disclosure of Invention
The invention provides a better vehicle scheduling algorithm aiming at the problems and the current situation in the prior art, aims to combine the data of various different bus line scheduling systems, integrates a passenger transport information base and a scheduling management system, is particularly suitable for regional multi-line scheduling, and forms a multi-line scheduling algorithm scheme framework shared by a resource pool.
The technical scheme is as follows: a regional multi-line vehicle scheduling algorithm shared by a resource pool comprises the following steps.
1) The following scheduling model I and constraint conditions are established, and a schedule according with a passenger flow distribution rule is firstly provided on the premise of scheduling multi-line vehicles in a resource pool shared region.
Figure BDA0002442915250000021
Figure BDA0002442915250000022
And (2) utilizing the first scheduling model in the formula (1) to formulate a departure schedule, wherein the constraint conditions in the formula (2) are that the departure interval in the guaranteed time period should meet the constraint of the given maximum interval and the given minimum interval, and the constraint conditions are that the average full load rate of the line all day should be more than 40%.
The scheduling model-variable definition:
n: an Nth line; k: a Kth time period;
j: station J of a line;
Tk: the time period length of a certain line;
Δtk: departure intervals in a time period of a certain line;
Sj: the total number of passengers getting on the bus in a certain time period;
Qn: the rated number of vehicles on a certain line.
2) And establishing the following scheduling model II and constraint conditions.
The optimization goal for the shift schedule is to minimize the total cost of the crew. Since the operation time of each shift is fixed, any one scheduling scheme includes all shifts, and therefore the shift operation cost may not be considered in the objective function. The magnitude of the drive-in/drive-out yard cost depends on the first and last shift assignments performed by the crew member. For the kth vehicle in yard m, the time cost of entering/exiting the yard is calculated as follows:
Figure BDA0002442915250000031
wherein W1Representing a cost of driving in/out of the yard,
Figure BDA0002442915250000032
indicating that the vehicle is performing task p from yard N1，
Figure BDA0002442915250000033
Indicating that the vehicle has performed task p2 back to yard N.
The stay waiting time is a time difference between a start time of the crew member executing the next shift and an end time of the previous shift. If there is empty driving in the shift, the empty driving time should be subtracted. Then for the kth crew group of yard m, the total cost of the stop wait is expressed as:
Figure BDA0002442915250000034
wherein W2Expressed as a cost of waiting for the driver to stay,
Figure BDA0002442915250000035
indicating whether driver k executed shift j, s immediately after executing shift ii,eiIndicating the start and end times of the shift.
Constructing a vehicle scheduling model (BSP) according to the above requirements is as follows:
min(α*F1+β*F2) (5)
Figure BDA0002442915250000036
and (3) obtaining a minimum scheduling cost initial solution of the regional multi-route vehicle scheduling shared by the resource pool by using a vehicle scheduling model II in the formula (5).
The first constraint condition and the second constraint condition of the formula (6) are used for ensuring that one task can only be completed by one vehicle and one driver, and the driver can only return to the yard or execute the next task after executing one task.
And based on the shift chain provided by the initial solution, improving a vehicle scheduling scheme through local iterative search, comparing the algorithm optimization results, and determining the final release plan scheduling.
Based on the shift chain provided by the initial solution, the vehicle dispatching scheme is improved through local iterative search, and the five strategies are mainly included, so that the cost is reduced through local iteration. The strategy is described as follows.
1) A single shift moves, deletes a shift from a shift chain i and attempts to insert into other appropriate shift chains.
2) A two-shift move deletes two adjacent shifts of a certain shift chain i and attempts to insert into other suitable shift chains j.
3) The shift chains are crossed, the two shift chains are respectively cut off, and whether recombination can be carried out is further tried.
4) And merging the shift chains, merging the two shift chains, and trying to determine whether generation of a new shift chain can be performed.
5) And (4) splitting the shift chain, splitting a certain shift chain, and generating two new shift chains.
The schedule is compiled under the following assumption on the premise of meeting the passenger flow requirement.
1) All day vehicles should be of the same type.
2) Buses should ensure that passengers are not detained at each station during the operation all day long.
3) The operation time period of one day is allowed to be divided into a plurality of time periods, and the departure intervals in the time periods are allowed to be the same.
4) The arrival of passengers at a station within a time period follows a poisson distribution.
The invention aims at a multi-line departure interval optimization algorithm of scheduling requirements of different customers, unifies a deployed resource pool model, and supports two vehicle scheduling modes of line scheduling and mixed scheduling.
According to the invention, the passenger flow-based multi-line departure interval optimization algorithm is designed while the resource pool-shared regional multi-line vehicle scheduling algorithm is designed, so that the accuracy of the schedule is favorably improved, and the probability that the vehicle can be smoothly executed in a given scheduling scheme is greatly improved. And the utilization rate of vehicles can be improved simultaneously by adopting an area scheduling mode that people and vehicles are not fixed, so that the vehicles and driver resources of the public transport enterprise can be comprehensively planned. On one hand, the investment cost of enterprises is saved, and on the other hand, the experience of the masses is facilitated
Compared with the common single-line scheduling, the scheduling plan generated by the regional multi-line gang scheduling method has the advantages that the vehicle is saved, the labor efficiency is improved, and the like. The technical and application advantages of the patent will be further described by comparing the practical application effects with respect to the above features.
The route plan, how many vehicles need to be scheduled to operate, is determined by the peak shift, vehicle turnaround time, and minimum break time of the route. According to the three conditions, the arrangement is carried out in different shifts, the more compact the arrangement is, the smaller the number of vehicles is, and the more vehicles are saved.
The invention increases the number of peak shifts of vehicles by arranging multiple lines in an area, improves the matching proportion in the most compact mode and further saves the operating vehicles on the whole.
The invention saves vehicles in multi-line gang shift compared with single-line shift, reduces vehicles under the condition that the working time of a plurality of lines is a fixed value relatively, increases the number of shifts completed by a single vehicle and the time, and further improves the labor efficiency of drivers relatively.
Drawings
Fig. 1 is a diagram of a regional scheduling operation mode.
FIG. 2 is a block diagram of a bus zone scheduling model scheme of the present invention.
Fig. 3 is a basic flowchart of region scheduling.
Fig. 4 is a system diagram of a single line shift of the line 320.
Fig. 5 is a system display of a single line shift of line 333.
Fig. 6 is a system diagram of the line 320 and the line 333 for large line support for small lines.
Fig. 7 is a system display of the line 9 single line shift.
Fig. 8 is a system display of the line 21 single line shift.
Fig. 9 is a system diagram of line 9 and line 21 for a hybrid shift.
Detailed Description
Compared with single-line scheduling, regional scheduling has great benefit advantage, but the problem of compiling a regional scheduling plan is extremely complex, and the regional scheduling plan belongs to the NP-hard problem. The regional vehicle scheduling problem pertains to the multi-station vehicle scheduling problem, as shown in fig. 1, the multi-station driving planning problem is usually based on a bus network allowing the vehicles to be scheduled across lines. If two lines have the same parking station, cross-line shunting can be realized in the station or by empty driving number. The most difficult part of the vehicle driving planning problem in the public traffic network is to minimize the number of required vehicles on the basis of meeting schedule requirements, and the problem belongs to the problem of minimum cost.
The problem of bus scheduling and driver scheduling is a huge challenge. Firstly, considering factors such as spatial distribution of bus stations and facilities, vehicle types and the like, the vehicle scheduling problem and the driver scheduling problem are both mathematical problems with high calculation complexity. Secondly, the difficulty in vehicle dispatch, driver scheduling, compared to other transportation problems is the tidal nature of the passenger ride. Most public transport lines have double peak characteristics, and few lines have multi-peak or flat peak characteristics, so that the demands of vehicles and drivers are greatly unbalanced in one day, and the difficulty of vehicle scheduling and driver scheduling is further increased. Thirdly, a people-vehicle-state operation mode is adopted, bus dispatching and driver scheduling need to be considered comprehensively, and related algorithms and software tools are lacked at present. Software systems and related algorithms developed by international bus software providers cannot be used for operation planning in a manned and nationally determined mode. Fourth, cross-line scheduling has significant benefits, but new management schemes present challenges, particularly with respect to job planning, which can be extremely complex.
In this embodiment, a bus region scheduling policy, that is, a resource pool shared region multi-line vehicle scheduling policy is mainly introduced, and in brief, the resource pool shared region multi-line vehicle scheduling policy may be understood as combining and recombining the two lines and setting a constraint condition for performing combined scheduling.
First we assume that:
1) the number of daily shifts of all the bus lines, the initial station of each shift and the arrival and departure time are determined. The shifts are arranged in ascending order according to the time series of the departure time of the starting station;
2) the distances between all the starting stations and the parking lot are determined;
3) the check-in and check-out yards of the crew members can be different yards, the crew members are separated from the vehicles, and the vehicles are allowed to be replaced in the midway, the crew member 1 starts from the yard 1 to execute a series of shifts and finally returns to the yard 1, and the executed tasks are effective tasks; crew member 2 starts a train of shifts from yard 2 and returns to yard 1, where the tasks being performed are still valid tasks.
And (4) an algorithm of bus dispatching and driver scheduling. The algorithm principle is shown in fig. 2.
Making a departure schedule:
on the premise of scheduling vehicles in a regional multi-line shared resource pool, a schedule according with a passenger flow distribution rule is firstly required, and the schedule is compiled on the premise of meeting the passenger flow requirement, so that unnecessary investment is reduced as much as possible, and the following assumptions are made:
1) all-day vehicles are of the same type;
2) the public transport should ensure that passengers do not stay at each station during the whole day operation period;
3) the method comprises the steps that a one-day operation time period is allowed to be divided into a plurality of time periods, and departure intervals in the time periods are allowed to be the same;
4) the arrival of passengers at a station within a time period follows a poisson distribution.
The method mainly comprises two targets, wherein one target is the minimum scheduling cost, the other target is the minimum passenger waiting time, the two targets are both the minimum, the scheduling frequency is less if the scheduling cost is the minimum, and the more the passenger waiting time is required to be the minimum, the better the scheduling frequency is required to be, so the method is a multi-target function.
Model variable definition:
n: an Nth line;
k: a Kth time period;
j: station J of a line;
Tk: the time period length of a certain line;
Δtk: departure intervals in a time period of a certain line;
Sj: the total number of passengers getting on the bus in a certain time period;
Qn: the rated number of vehicles on a certain line.
Figure BDA0002442915250000081
Figure BDA0002442915250000082
In the model, the constraint condition is to ensure that the departure interval in the time period should meet the constraint of the given maximum interval and the given minimum interval, and the constraint condition is to ensure that the average full load rate of the line all day should be more than 40%.
Scheduling of vehicles in a resource pool shared region in multiple lines:
the size of the inbound/outbound yard cost depends on the first and last shift mission performed by the crew, and for the kth vehicle in yard m, its inbound/outbound yard time cost is calculated as follows:
Figure BDA0002442915250000083
wherein W1Representing a cost of driving in/out of the yard,
Figure BDA0002442915250000084
indicating that the vehicle is performing task p from yard N1，
Figure BDA0002442915250000085
Indicating that the vehicle has performed task p2 back to yard N.
If there is an empty drive between the shifts, the empty drive time should be subtracted.
Figure BDA0002442915250000086
Wherein W2Expressed as a cost of waiting for the driver to stay,
Figure BDA0002442915250000087
indicating whether driver k executed shift j, s immediately after executing shift ii,eiIndicating the start and end times of the shift.
The vehicle scheduling model (BSP) can be constructed according to the above requirements as follows:
min(α*F1+β*F2)
Figure BDA0002442915250000091
the first constraint condition and the second constraint condition in the model are used for ensuring that one task can only be completed by one vehicle and one driver, and the driver can only return to the yard or execute the next task after executing one task.
Based on the shift chain provided by the initial solution, the vehicle dispatching scheme is improved through local iterative search, and the five strategies are mainly included, so that the cost is reduced through local iteration. The strategy is described as follows:
1) moving a single shift, deleting a certain shift of a certain shift chain i, and trying to insert into other suitable shift chains;
2) moving in double shifts, deleting two adjacent shifts of a certain shift chain i, and trying to insert into other proper shift chains j;
3) performing run chain crossing, respectively cutting off two run chains, and further trying whether recombination can be performed;
4) combining the shift chains, combining the two shift chains, and trying to determine whether a new shift chain can be generated;
5) and (4) splitting the shift chain, splitting a certain shift chain, and generating two new shift chains.
The basic flow of the algorithm is shown in fig. 3.
In the embodiment, the passenger flow-based multi-line departure interval optimization algorithm is designed while the resource pool shared regional multi-line vehicle scheduling algorithm is designed, so that the accuracy of the schedule is improved, and the probability that the vehicle can be smoothly executed in a given scheduling scheme is greatly improved. And the utilization rate of vehicles can be improved simultaneously by adopting an area scheduling mode that people and vehicles are not fixed, so that the vehicles and driver resources of the public transport enterprise can be comprehensively planned. On one hand, the investment cost of enterprises is saved, and on the other hand, the experience of the masses is facilitated
Compared with the common single-line scheduling, the scheduling plan generated by the regional multi-line linkage is greatly improved in the aspects of saving vehicles, improving the labor efficiency and the like. The technical and application advantages of the patent will be further described by comparing the practical application effects with respect to the above features.
The route plan, how many vehicles need to be scheduled to operate, is determined by the peak shift, vehicle turnaround time, and minimum break time of the route. The arrangement of the shifts is carried out according to the three conditions, the more compact the arrangement is, the smaller the number of the vehicles is, and the more the vehicles are saved.
The regional multiline gang arrangement increases the number of peak shifts of vehicles, improves the proportion of matching in the most compact manner, and further saves operating vehicles as a whole.
The labor efficiency is improved, and the calculation formula of the labor efficiency is as follows: the labor efficiency is the time/working time at the vehicle. Because the regional multiwire is arranged in line and is arranged the gear and save the vehicle than the single line gear, under the condition that many circuit operating time are fixed value relatively, the vehicle reduces, and the shift that the bicycle was accomplished is more, just also more at the time of the bus, and then driver's labor efficiency also can promote relatively.
According to different application scenes, the regional multi-line gang arrangement can be divided into two application types of large line support small line scheduling and mixed scheduling, and the operation of different lines is compared as follows.
1. The large line supports the small line shift by taking the lines 320 and 333 as an example, and compared with the effect of the large line supporting the small line shift and the single line shift, the advantages of the large line supporting the small line can be seen more intuitively, such as the single line shift-line 320 shown in fig. 4, the single line shift-line 333 shown in fig. 5, and the large line supporting the small line shift- lines 320 and 333 shown in fig. 6.
The following table shows specific duty cycles comparisons:
Figure BDA0002442915250000101
Figure BDA0002442915250000111
2. mixed shift
Taking the line 9 and the line 21 as an example, a better scheduling effect of the hybrid scheduling can be more intuitively shown compared with the single-line scheduling and the hybrid scheduling effects, such as the single-line scheduling-line 9 shown in fig. 7, the single-line scheduling-line 21 shown in fig. 8, and the hybrid scheduling-21-way and 9-way shown in fig. 9.
The following table shows specific duty cycles comparisons:
Figure BDA0002442915250000112
compared with the above cases, the regional multi-line arrangement has different application characteristics in different application scenes, and mainly comprises two aspects of optimization range of the scheduling plan and performability of the scheduling plan.
Optimizing amplitude
The large line supports the small line shift and pays attention to the local optimization of the line shift result, and the optimization amplitude is relatively small. Optimizing the shift schedule of the supported line also takes into account the superiority of the shift schedule of the supporting line, which limits the space for optimization to some extent.
In the optimization process of the hybrid shift, the advantages of other lines are not considered, and the hybrid shift is integrally rearranged, so that a larger space is provided in the optimization range.
Performability
The shift result of the large line supporting the small line shift is easier to be executed on the ground than the shift result of the mixed shift. This is determined by the computational emphasis of the algorithm.
The large line supports the small line, and the vehicle is adjusted from the large line to supplement the small line shift under the condition of ensuring the shift result of the small line not to be changed. The original scheduling plan of the original line is not changed integrally, so that the performability is relatively strong.
The mixed scheduling is to regard the original line as one line and mix all the shifts together for rescheduling, which breaks through the original scheduling habit of the original line and further increases the difficulty of plan execution, so the performability is relatively weak.

Claims (3)

1. A resource pool shared regional multi-line vehicle scheduling algorithm is characterized by comprising the following steps:
1) the following scheduling model one and constraint conditions are established,
Figure FDA0002442915240000011
Figure FDA0002442915240000012
a dispatching model I in a formula (1) is utilized to formulate a dispatching schedule, wherein the constraint condition of the formula (2) is to ensure that dispatching intervals in a time period meet the constraint of a given maximum interval and a given minimum interval, and the constraint condition is to ensure that the average full load rate of a line all day is more than 40%;
the scheduling model-variable definition:
n: an Nth line; k: a Kth time period;
j: station J of a line;
Tk: time period length of a certain line
Δtk: departure interval in time period of certain line
Sj: total number of persons getting on bus in time period of certain line
Qn: rated number of vehicles on certain line
2) The following scheduling model two and constraint conditions are established,
for the kth vehicle in yard m, the time cost of entering/exiting the yard is calculated as follows:
Figure FDA0002442915240000021
wherein W1Representing a cost of driving in/out of the yard,
Figure FDA0002442915240000022
indicating that the vehicle is performing task p from yard N1，tN,p2Indicating that the vehicle has performed task p2And returning to the parking lot N.
The stay waiting time is a time difference between a start time of the crew member executing the next shift and an end time of the previous shift. If there is empty driving in the shift, the empty driving time should be subtracted. Then for the kth crew group of yard m, the total cost of the stop wait is expressed as:
Figure FDA0002442915240000023
wherein W2Expressed as a cost of waiting for the driver to stay,
Figure FDA0002442915240000024
indicating whether driver k executed shift j, s immediately after executing shift ii,eiIndicating the start and end times of the shift.
Constructing a vehicle scheduling model (BSP) according to the above requirements is as follows:
min(α*F1+β*F2) (5)
Figure FDA0002442915240000025
obtaining a minimum scheduling cost initial solution of the regional multi-line vehicle scheduling shared by the resource pool by using a vehicle scheduling model II in the formula (5);
the first constraint condition and the second constraint condition of the formula (6) are used for ensuring that one task can only be completed by one vehicle and one driver, and the driver can only return to the yard or execute the next task after executing one task.
And based on the shift chain provided by the initial solution, improving a vehicle scheduling scheme through local iterative search, comparing the algorithm optimization results, and determining the final release plan scheduling.
2. The resource pool shared regional multiline vehicle scheduling algorithm of claim 1 wherein the vehicle scheduling scheme is improved by local iterative search based on the shift chain provided by the initial solution, comprising mainly five strategies, with local iterations to reduce cost. The strategy is described as follows:
1. moving a single shift, deleting a certain shift of a certain shift chain i, and trying to insert into other suitable shift chains;
2. moving in double shifts, deleting two adjacent shifts of a certain shift chain i, and trying to insert into other proper shift chains j;
3. performing run chain crossing, respectively cutting off two run chains, and further trying whether recombination can be performed;
4. combining the shift chains, combining the two shift chains, and trying to determine whether a new shift chain can be generated;
5. and (4) splitting the shift chain, splitting a certain shift chain, and generating two new shift chains.
3. The resource pool-shared regional multiline vehicle scheduling algorithm of claim 1, wherein the schedule is compiled under the following assumption that the traffic demand is satisfied:
1) all-day vehicles are of the same type;
2) the public transport should ensure that passengers do not stay at each station during the whole day operation period;
3) the method comprises the steps that a one-day operation time period is allowed to be divided into a plurality of time periods, and departure intervals in the time periods are allowed to be the same;
4) the arrival of passengers at a station within a time period follows a poisson distribution.
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CN113205239A (en) * 2021-03-17 2021-08-03 郑州天迈科技股份有限公司 Bus scheduling method and system with priority in task amount configuration
CN114036837A (en) * 2021-11-08 2022-02-11 中国人民解放军国防科技大学 Equipment combination method, system, equipment and storage medium based on co-construction sharing
CN115952985A (en) * 2022-12-21 2023-04-11 大连理工大学 Mixed scheduling method of module vehicle and bus in multi-line multi-shift traffic system

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108960634A (en) * 2018-07-06 2018-12-07 郑州天迈科技股份有限公司 A kind of vehicle based on people's vehicle binding pattern is arranged an order according to class and grade algorithm

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108960634A (en) * 2018-07-06 2018-12-07 郑州天迈科技股份有限公司 A kind of vehicle based on people's vehicle binding pattern is arranged an order according to class and grade algorithm

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
侯彦娥: "公交司机排班问题的混合元启发算法研究" *
陈明明: "多车场公交乘务排班问题优化" *
魏 明: "求解多目标区域公交车辆调度问题的遗传算法" *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112365050A (en) * 2020-11-10 2021-02-12 郑州天迈科技股份有限公司 Wire net canceling, shortening and splitting method based on partial passenger flow proportion evaluation indexes
CN112365050B (en) * 2020-11-10 2023-04-07 郑州天迈科技股份有限公司 Wire net canceling, shortening and splitting method based on partial passenger flow proportion evaluation indexes
CN112686458A (en) * 2021-01-05 2021-04-20 昆明理工大学 Optimized scheduling method for multi-vehicle fleet cargo delivery process
CN112686458B (en) * 2021-01-05 2023-03-07 昆明理工大学 Optimized dispatching method for multi-vehicle fleet delivery process
CN113205239A (en) * 2021-03-17 2021-08-03 郑州天迈科技股份有限公司 Bus scheduling method and system with priority in task amount configuration
CN113205239B (en) * 2021-03-17 2024-03-15 郑州天迈科技股份有限公司 Bus dispatching method and system with priority of task allocation
CN113053119A (en) * 2021-03-18 2021-06-29 重庆交通开投科技发展有限公司 Round time prediction method based on public transport operation historical data
CN112990754A (en) * 2021-04-13 2021-06-18 北京顺达同行科技有限公司 Scheduling adjustment method, device and storage medium
CN114036837A (en) * 2021-11-08 2022-02-11 中国人民解放军国防科技大学 Equipment combination method, system, equipment and storage medium based on co-construction sharing
CN114036837B (en) * 2021-11-08 2024-06-04 中国人民解放军国防科技大学 Equipment combination method, system, equipment and storage medium based on co-construction sharing
CN115952985A (en) * 2022-12-21 2023-04-11 大连理工大学 Mixed scheduling method of module vehicle and bus in multi-line multi-shift traffic system
CN115952985B (en) * 2022-12-21 2023-08-18 大连理工大学 Mixed scheduling method of module vehicle and bus in multi-line multi-shift traffic system

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