CN115392535A

CN115392535A - Scheduling list optimization method and device for multiple bus lines and related equipment

Info

Publication number: CN115392535A
Application number: CN202210827662.0A
Authority: CN
Inventors: 蚁韩羚; 田贤材; 唐锲; 余晓填; 王孝宇
Original assignee: Shenzhen Intellifusion Technologies Co Ltd
Current assignee: Shenzhen Intellifusion Technologies Co Ltd
Priority date: 2022-07-14
Filing date: 2022-07-14
Publication date: 2022-11-25
Also published as: WO2024011867A1

Abstract

The application belongs to the technical field of intelligent transportation, and provides a scheduling list optimization method and device for a plurality of bus lines, computer equipment and a computer readable storage medium.

Description

Scheduling list optimization method and device for multiple bus lines and related equipment

Technical Field

The application relates to the technical field of intelligent transportation, in particular to a scheduling list optimization method and device for multiple bus routes, computer equipment and a computer readable storage medium.

Background

A public transportation network generally comprises a plurality of public transportation lines, for example, a large-scale urban public transportation network comprises as many as several hundred public transportation lines in the city. The simplest method for optimizing the bus scheduling of the bus network is to independently optimize the bus scheduling schedule of each bus line in the bus network, and then summarize the optimized results of all the bus lines to serve as the solution results of the bus network optimization. However, the bus network optimization method cannot well consider the mutual influence of competing passenger flows among different bus lines, so that the optimization space is compressed.

For the problem of optimizing a plurality of bus lines of a bus line network, if the plurality of bus lines of the bus line network are directly subjected to combined optimization modeling, the solution space is too large and the solution is difficult to solve, so that the time consumption of the solution is increased, and the efficiency of solving the problem of the combined optimization of the plurality of bus lines is reduced.

Disclosure of Invention

The application provides a scheduling list optimization method and device for a plurality of bus lines, computer equipment and a computer readable storage medium, which can solve the technical problem of low efficiency when solving a joint optimization problem of the plurality of bus lines in the traditional technology.

In a first aspect, the present application provides a schedule optimization method for multiple bus routes, including: the method comprises the steps of obtaining a plurality of bus line identifications and obtaining line association degrees corresponding to the two bus line identifications, wherein the bus line identifications are used for distinguishing different bus lines, and the line association degrees describe the association degree between the two bus lines; grouping all the bus route identifications according to the line association degree to obtain a plurality of optimized target bus route groups, wherein the optimized target bus route groups comprise optimized target bus route identifications; according to each optimized target bus route group, obtaining an initial first shift table corresponding to the optimized target bus route identification, obtaining an initial first shift table set corresponding to the optimized target bus route group, optimizing the initial first shift table contained in the initial first shift table set, obtaining a first shift table corresponding to the initial first shift table, and taking the first shift table as a second shift table corresponding to the optimized target bus route identification; obtaining a bus route optimization variable, and judging whether the bus route optimization variable meets a preset bus route optimization termination condition; and if the bus route optimization variable meets the preset bus route optimization termination condition, obtaining respective target scheduling lists of the plurality of bus routes according to the second scheduling lists corresponding to all the optimized target bus route groups.

In a second aspect, the present application further provides a schedule optimization apparatus for multiple bus routes, including: the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a plurality of bus line identifications and acquiring line association degrees corresponding to the two bus line identifications, the bus line identifications are used for distinguishing different bus lines, and the line association degrees describe the association degree between the two bus lines; the first grouping unit is used for grouping all the bus route identifications according to the line association degree to obtain a plurality of optimized target bus route groups, and each optimized target bus route group comprises an optimized target bus route identification; the first optimization unit is used for acquiring an initial first shift table corresponding to the optimization target bus route identification according to each optimization target bus route group, acquiring an initial first shift table set corresponding to the optimization target bus route group, optimizing the initial first shift table contained in the initial first shift table set, acquiring a first shift table corresponding to the initial first shift table, and taking the first shift table as a second shift table corresponding to the optimization target bus route identification; the first judgment unit is used for acquiring a bus route optimization variable and judging whether the bus route optimization variable meets a preset bus route optimization termination condition or not; and the determining unit is used for obtaining the respective target shift lists of the plurality of bus routes according to the second shift list corresponding to all the optimized target bus route groups if the bus route optimization variables meet the preset bus route optimization termination condition.

In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the schedule optimization method for multiple bus routes when executing the computer program.

In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of the schedule optimization method for a plurality of bus routes.

The application provides a scheduling list optimization method and device for a plurality of bus lines, computer equipment and a computer readable storage medium. The processing method comprises the steps of obtaining the line association degree between every two bus lines in the multiple bus lines, grouping all the bus lines according to the line association degree to obtain an optimized target bus line group, and then independently optimizing the bus lines of each group, so that the joint optimization problem of the multiple bus lines is converted into the bus line optimization problem of each group.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a flow chart illustrating a method for optimizing a shift schedule of a plurality of bus routes according to an embodiment of the present application;

fig. 2 is a first sub-flow diagram of a method for optimizing a shift schedule of a plurality of bus routes according to an embodiment of the present application;

fig. 3 is a second sub-flow diagram of the schedule optimization method for multiple bus lines according to the embodiment of the present application;

fig. 4 is an exemplary schematic diagram illustrating grouping of a plurality of bus routes based on graph clustering in the shift schedule optimization method for a plurality of bus routes provided in the embodiment of the present application;

fig. 5 is a third sub-flow diagram of the method for optimizing a shift schedule of a plurality of bus routes according to the embodiment of the present application;

fig. 6 is a fourth sub-flow diagram of the schedule optimization method for multiple bus routes according to the embodiment of the present application;

fig. 7 is a diagram illustrating a schedule optimization concrete example of a schedule optimization method for a plurality of bus routes according to an embodiment of the present application;

fig. 8 is a schematic block diagram of a schedule optimization apparatus for multiple bus routes according to an embodiment of the present disclosure;

fig. 9 is a schematic block diagram of a computer device provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it should be understood that the described embodiments are some, but not all embodiments of the present application. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any inventive effort fall within the scope of protection of the present application.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The embodiment of the application provides a scheduling list optimization method for multiple bus routes, the processing method can be applied to computer equipment such as a desktop or a server, and the scheduling list optimization method for the multiple bus routes provided by the embodiment of the application can be adopted when the problem of joint optimization of the multiple bus routes in a smart bus is solved. For example, when solving the schedule of the bus routes of the city-level large-scale bus line network, the schedule optimization method for the plurality of bus routes provided by the embodiment of the application can be adopted to realize the joint optimization of the departure schedule of the plurality of bus routes included in the city-level bus line network.

In the face of the scheduling problem of the urban large-scale public traffic network, sometimes dozens or even hundreds of buses needing to be optimized are needed, and the inventor finds that the scheduling problem of a plurality of buses is often difficult to solve due to overlarge solution space if an algorithm is directly used for modeling. Based on this, the inventor proposes a scheduling list optimization method for multiple bus routes in the embodiment of the present application, and the core idea of the embodiment of the present application is as follows: according to the degree of association between the bus lines, divide into groups many bus lines, and optimize alone the bus line of every group, thereby convert many bus line joint optimization problems into the bus line optimization problem of every group, the solution space that carries out the optimization because of the bus line of every group is less than all bus lines and carries out the solution space of optimizing together far away, through reducing the solution space, can improve the efficiency when solving the joint optimization problem of many bus lines scheduling, and, there is the line degree of association between the bus line in every group, can fully synthesize and measure the influence of each other between the different bus lines, improve the validity of bus line scheduling.

Referring to fig. 1, fig. 1 is a schematic flow chart diagram of a shift schedule optimization method for multiple bus routes according to an embodiment of the present application. As shown in fig. 1, the method comprises the following steps S11-S16:

s11, acquiring a plurality of bus line identifications and acquiring line association degrees corresponding to the two bus line identifications, wherein the bus line identifications are used for distinguishing different bus lines, and the line association degrees describe association degrees between the two bus lines.

Specifically, for the bus lines, the bus line identification is generally used for describing the bus line, different bus lines are distinguished by adopting the bus line identification, and when the scheduling of the bus lines is carried out, the corresponding bus lines are often described by the bus line identification.

If one bus line passes through the bus stop A, the other bus line also passes through the bus stop A, the bus stop A is an overlapped stop (namely a common stop) of the two bus lines, and the two bus lines can be called to be overlapped at the bus stop (namely the two bus lines are crossed). For two bus lines with overlapped bus stops, the two bus lines are called to have correlation, particularly when the two bus lines have two overlapped bus stops or even more overlapped bus stops, passengers can select between the two bus lines because of the fact that the two bus lines from one overlapped bus stop to the other overlapped bus stop, the situation that the two bus lines compete for the passengers exists between the overlapped bus stops is existed in the two bus lines, the common passengers between the overlapped bus stops of the two bus lines adopt competitive passenger flow description, and the size of the competitive passenger flow adopts competitive passenger flow description. On the basis, if the number of overlapped bus stops existing between the two bus lines is more, the association degree between the two bus lines is enhanced, the competition passenger flow between the two bus lines is relatively larger, the association degree between the two bus lines is stronger, and conversely, if the number of overlapped bus stops existing between the two bus lines is less, the association degree between the two bus lines is reduced, the competition passenger flow between the two bus lines is relatively smaller, and the association degree between the two bus lines is weaker. The association degree between the two bus lines is described by the line association degree, namely the line association degree is in direct proportion to the crossing degree between the two bus lines, the greater the crossing degree between the two bus lines is, the greater the line association degree is, the smaller the association degree between the two bus lines is, and the smaller the line association degree is. The line association degree is generally a numerical value, the line association degree can be the number of overlapped bus stops between two bus lines, and the line association degree can also be the competitive passenger flow between the two bus lines.

When the scheduling of a plurality of bus lines is optimized in a combined mode, a plurality of bus line identifications are obtained, two line association degrees corresponding to the bus line identifications are obtained, the line association degrees describe the association degree between the two bus lines, the line association degrees can be bus stops where the two bus lines are overlapped, and the line association degrees can also be competitive passenger flow corresponding to passenger flows where the two bus lines are overlapped. The bus lines can be bus lines contained in the bus net, the scheduling of the bus net can be optimized in a combined mode, and particularly when the bus net is a city-level large-scale bus net, the departure scheduling of the bus lines contained in the city-level bus net is optimized in a combined mode.

And S12, grouping all the bus route identifications according to the route association degree to obtain a plurality of optimized target bus route groups, wherein the optimized target bus route groups contain the optimized target bus route identifications.

Specifically, because the line association degrees between the bus lines corresponding to every two different bus line identifications are different, all the bus line identifications are grouped according to whether the different bus lines have associations and the size of the line association degrees, that is, all the bus lines are grouped, for example, all the bus line identifications can be grouped by adopting a preset association degree clustering algorithm, and the preset association degree clustering algorithm can be a grouping algorithm based on graph clustering, so that the bus lines with larger line association degrees are grouped into the same group, the bus lines with smaller line association degrees or without line association degrees are grouped into other groups corresponding to each other, and a plurality of optimized target bus line groups are obtained, the optimized target bus line groups describe the set of the target bus lines to be optimized, the optimized target bus line groups contain the grouped plurality of bus line identifications, the bus line identifications contained in the optimized target bus line groups adopt optimized target bus line identifications, and the optimized target bus lines contained in the optimized target bus line groups are described. Therefore, the multiple bus lines are grouped according to the line association degree between every two bus lines, the joint optimization problem of the multiple bus lines is converted into the single-group bus line optimization problem corresponding to the optimization target bus line group, the solution space of bus line optimization is reduced, and the solution efficiency corresponding to the optimization of the bus lines can be improved.

S13, according to each optimized target bus line group, obtaining an initial first-row schedule corresponding to the optimized target bus line identification, obtaining an initial first-row schedule set corresponding to the optimized target bus line group, optimizing the initial first-row schedule contained in the initial first-row schedule set, obtaining a first-row schedule corresponding to the initial first-row schedule, and taking the first-row schedule as a second-row schedule corresponding to the optimized target bus line identification.

Specifically, after all the bus route identifiers are grouped to obtain different optimization target bus route groups, each optimization target bus route group is optimized independently. And for each optimized target bus route group, acquiring an initial first shift list corresponding to the optimized target bus route identification according to the optimized target bus route identification, namely acquiring the initial first shift list of the bus route, acquiring an initial first shift list set corresponding to the optimized target bus route group, optimizing the initial first shift list contained in the initial first shift list set to obtain a first shift list corresponding to the initial first shift list, and taking the first shift list as a second shift list corresponding to the optimized target bus route identification, so that all the initial first shift lists corresponding to the optimized target bus route group are optimized, different second shift lists of the optimized target bus route group are summarized, and finally, the optimization results corresponding to all the bus routes are obtained. Because the optimization is respectively carried out on each optimization target bus route group, compared with the mode that all bus routes are simultaneously optimized together, the solution space of bus route optimization is reduced, and therefore the solution efficiency corresponding to the optimization of the bus routes can be improved.

S14, obtaining a bus route optimization variable, and judging whether the bus route optimization variable meets a preset bus route optimization termination condition or not;

s15, if the bus route optimization variable meets the preset bus route optimization termination condition, obtaining a target shift list of each of the plurality of bus routes according to the second shift list corresponding to all the optimized target bus route groups;

and S16, if the bus route optimization variable does not meet the preset bus route optimization termination condition, continuously optimizing the initial first schedule until the bus route optimization variable meets the preset bus route optimization termination condition.

Specifically, a bus route optimization variable is preset, where the bus route optimization variable is used to describe an optimization condition of an initial first shift schedule, the bus route optimization variable is used to count the optimization condition of the initial first shift schedule, the bus route optimization variable may be accessed through a variable name, the bus route optimization variable may be described in a key value pair form, a keyword (i.e., a variable name) of the key value pair is used to describe a bus route optimization variable, a value (i.e., a value corresponding to the variable name) of the key value pair is used to describe a quantization value corresponding to optimization of the initial first shift schedule, a value of the key value pair may be accessed through a keyword of the key value pair, the bus route optimization variable may be used to describe an optimization iteration number of the initial first shift schedule, or the bus route optimization variable may be used to describe a quantization preset index of the initial first shift schedule in an optimization process, for example, if a genetic algorithm is used to optimize the bus route, an average population fitness of a shift schedule population of a group may be used as a preset quantization index, and describe an optimization condition of the initial first shift schedule.

Based on the bus route optimization variable, for each time of optimization iteration of the initial first shift schedule, the bus route optimization variable is obtained, and whether the bus route optimization variable meets the preset bus route optimization termination condition or not is judged, the preset bus route optimization termination condition can be the maximum optimization iteration number of the initial first shift schedule, or the preset bus route optimization termination condition can be that the preset quantization index of the initial first shift schedule in the optimization process does not change any more, for example, if the optimization of bus shift scheduling is carried out by adopting a genetic algorithm, the preset bus route optimization termination condition can keep unchanged (S is a preset constant) for continuous S rounds of the average population fitness of the shift schedule population, wherein the average population fitness is the average value of the fitness of all shifts in the shift schedule population.

If the optimized variable of the bus route meets the preset bus route optimization termination condition, obtaining respective target scheduling tables of a plurality of bus routes according to the second scheduling tables corresponding to the optimized target bus route group, summarizing all the second scheduling tables to obtain optimized scheduling tables corresponding to the plurality of bus routes, sequencing all the second scheduling tables according to the route sequence of all the bus routes, and taking the sequenced result as the optimized scheduling table result corresponding to the plurality of bus routes. And if the bus route optimization variable does not meet the preset bus route optimization termination condition, continuously optimizing the initial first schedule until the bus route optimization variable meets the preset bus route optimization termination condition.

The embodiment of the application, through obtaining the line relevance degree between per two bus lines in many bus lines, and according to the size of line relevance degree, divide into groups all bus lines, obtain optimization target bus line group, optimize the bus line of every group alone again, thereby convert many bus line joint optimization problems into the bus line optimization problem of every group, because the solution space that the bus line of every group carries out the optimization is far less than all bus lines and carries out the solution space that optimizes together, through reducing the solution space, can improve the efficiency when solving the joint optimization problem of many bus line scheduling, and, there is the line relevance degree between the bus line in every group, can fully synthesize the influence of each other between the different bus lines of weighing, improve the objectivity of bus line scheduling, therefore, improve the efficiency and the validity of many bus line scheduling that have the relevance.

In an embodiment, please refer to fig. 2, and fig. 2 is a first sub-flow diagram of a schedule optimization method for multiple bus routes according to an embodiment of the present application. As shown in fig. 2, in this embodiment, the obtaining of the line association degrees corresponding to the two bus line identifiers includes:

s21, counting overlapped station pairs between the two bus routes, wherein the overlapped station pairs describe bus station pairs consisting of overlapped departure stations and destination stations in different bus routes;

s22, average passenger flow corresponding to the overlapped station pairs is obtained, the sum of the average passenger flow of all the overlapped station pairs is calculated according to the average passenger flow, competition passenger flow corresponding to competition passenger flow between the two bus lines is obtained, and the competition passenger flow is used as the line association degree between the bus lines corresponding to the two bus line identifications, wherein the competition passenger flow describes the size of the competition passenger flow between the two bus lines.

The average passenger flow is an average value of the number of all passengers in a preset period of time in a unit time at two stations, and the average passenger flow can be a daily average passenger flow.

Specifically, the average passenger flow between any two stops of each bus route in a preset time period may be counted in advance. For example, for the average daily passenger flow, if there is a bus route including a stop a, a stop B, a stop C, and a stop D, the pair of stops included in the bus route includes: each station pair can be a starting station and a destination station of passenger travel, and therefore daily average passenger flow between each station pair can be counted. For example, for the statistics of the average daily passenger flow between the station a and the station B included in one bus route, based on the passenger flow of each bus in the bus route, the image recognition technology can be used to associate the getting-on and getting-off images of the same person at two GPS points or two stations, so as to associate the passenger flow data, i.e., the OD passenger flow, to which the passenger gets on and off the bus, and all the OD passenger flows, i.e., the OD passenger flows between the station a and the station B, are selected from all the OD passenger flows, and the average value of the passenger flows between the station a and the station B in multiple days is counted, i.e., the average daily passenger flow between the station a and the station B is obtained.

For two associated bus lines, particularly for two bus lines with competitive passenger flows, counting all the overlapping station pairs between the two bus lines, then obtaining the average passenger flow corresponding to each overlapping station pair, calculating the sum of the average passenger flows of all the overlapping station pairs according to the average passenger flow, obtaining the number of the competitive passenger flows between the two bus lines, and taking the number as the competitive passenger flow between the two bus lines. For example, for the bus line 1 and the bus line 2, if the overlapping station pair between the bus line 1 and the bus line 2 includes: and a station B to a station D and a station F to a station H, wherein the average passenger flow between the station B and the station D is n1, the average passenger flow between the station F and the station H is n2, and the competing passenger flow corresponding to the competing passenger flow between the bus line 1 and the bus line 2 is the sum of n1 and n 2. The competitive passenger flow describes the size of competitive passenger flow between two bus lines, and the competitive passenger flow is used as the line association degree between the bus lines corresponding to the two bus line identifications, wherein the competitive passenger flow describes the line association degree, the larger the competitive passenger flow is, the higher the line association degree is, the smaller the competitive passenger flow is, and the lower the line association degree is. Because the passenger flow is an important factor of the scheduling of the bus lines, the competitive passenger flow is taken as the line association degree, the mutual influence among different bus lines can be fully and comprehensively measured, the objectivity of the scheduling of a plurality of associated bus lines can be improved, and the effectiveness of the scheduling of the bus lines is further improved.

In an embodiment, please refer to fig. 3 and fig. 4, fig. 3 is a second sub-flow schematic diagram of the shift schedule optimization method for multiple bus lines provided in the embodiment of the present application, and fig. 4 is an exemplary schematic diagram of grouping multiple bus lines based on graph clustering in the shift schedule optimization method for multiple bus lines provided in the embodiment of the present application. As shown in fig. 3, in this embodiment, the grouping all the bus route identifiers according to the size of the route association degree to obtain a plurality of optimized target bus route groups includes:

s31, taking the bus route identifications as nodes of a graph, taking the route association degrees corresponding to the two bus route identifications as edges between the two nodes of the graph, and constructing a bus route graph;

and S32, clustering the nodes of the bus route map by adopting a preset clustering algorithm according to the line association degree, and classifying the bus route identifications corresponding to the nodes of the same class into the same group to obtain a plurality of optimized target bus route groups.

Specifically, when all the bus route identifiers are grouped, a bus route map is constructed in advance, the bus route identifiers are used as nodes of the map, and the route association degrees corresponding to the two bus route identifiers (namely, the route association degrees of the two bus routes) are used as edges between the two nodes of the map, so that the bus route map is constructed. Referring to fig. 4, fig. 4 includes a bus route 1, a bus route 2, a bus route 3, a bus route 4, a bus route 5, a bus route 6, a bus route 7, and a bus route 8, where n is used to describe a route association degree between two bus route identifiers, n may describe a competing passenger flow rate between two bus routes, n1 is used to describe a route association degree between the bus route 1 and the bus route 2, n2 is used to describe a route association degree between the bus route 2 and the bus route 3, n3 is used to describe a route association degree between the bus route 2 and the bus route 4, and others are analogized in sequence, so as to construct a bus route diagram from the bus route 1 to the bus route 8.

Based on the constructed bus route map, clustering nodes of the bus route map by adopting a preset clustering algorithm according to the degree of the line association, for example, an unsupervised clustering algorithm Infomap can be adopted as the preset clustering algorithm, and the bus route identifications corresponding to the nodes in the same class are classified into the same group to obtain a plurality of optimized target bus route groups. Continuing with fig. 4, for example, after graph clustering is performed on the bus route maps of the bus routes 1 to 8, the obtained classes may include: class 1 (including bus lines 1, 2, 3 and 4), class 2 (including bus lines 5, 6 and 7), and class 3 (including bus lines 8), wherein the class formed by clustering is a group, which may also be called a cluster, and each cluster is an optimized target bus line group. Because the clustering algorithm is an unsupervised clustering process, the nodes of the bus route map are clustered by adopting a preset clustering algorithm, automatic classification of the bus routes is realized, and the line association degree between different bus route identifications can be fully embodied between the bus routes contained in each optimized target bus route group, for example, the mutual influence degree of competing passenger flow among the bus routes is embodied, so that the objectivity of scheduling of a plurality of associated bus routes can be improved.

In an embodiment, the optimizing the initial first shift schedule included in the initial first shift schedule set to obtain a first shift schedule corresponding to the initial first shift schedule includes:

and optimizing the initial first-row schedule contained in the initial first-row schedule set based on a preset bus route scheduling joint optimization algorithm to obtain a first-row schedule corresponding to the initial first-row schedule, wherein the preset bus route scheduling joint optimization algorithm describes an algorithm for performing joint optimization on different initial first-row schedules.

Specifically, because every there is the route degree of association between a plurality of bus lines that optimization target bus line group contained, it is different bus scheduling between the bus line influences each other, from this, can with all that initial first row's table set contained initial first row's table carries out joint optimization, thereby obtains every optimize the overall preferred scheduling effect of a plurality of bus lines that optimization target bus line group contained, for example, if one if optimization target bus line group contains bus line A, bus line B and bus line C, will bus line A bus line B with bus line C carries out joint optimization, thereby obtains bus line A bus line B with bus line C overall preferred scheduling effect.

The method and the device for jointly optimizing the scheduling of the bus line adopt a preset bus line scheduling joint optimization algorithm, and the initial first scheduling list set comprises the initial first scheduling list for joint optimization to obtain a first scheduling list corresponding to the initial first scheduling list, wherein the preset bus line scheduling joint optimization algorithm describes a different algorithm for jointly optimizing the initial first scheduling list. The preset bus route scheduling joint optimization algorithm can be an optimization algorithm based on a genetic algorithm and can also be an optimization algorithm based on reinforcement learning, so that different initial first scheduling schedules are used as different factors in the preset bus route scheduling joint optimization algorithm, under the condition that mutual influence among different factors is considered, a feasible solution of each initial first scheduling schedule is obtained, and the overall better effect of all the initial first scheduling schedules is obtained.

For example, referring to fig. 5, fig. 5 is a third sub-flow diagram of the method for optimizing a shift schedule of a plurality of bus routes according to the embodiment of the present application. As shown in fig. 5, in this embodiment, a preset bus route shift scheduling joint optimization algorithm based on a genetic algorithm is adopted, where optimizing the initial first shift schedule included in the initial first shift schedule set to obtain a first shift schedule corresponding to the initial first shift schedule includes:

s131, taking the initial first shift table contained in the initial first shift table set as a node of a gene, and constructing an optimization target shift table gene corresponding to the initial first shift table set according to all the initial first shift tables contained in the initial first shift table set;

s132, optimizing the scheduling list contained in the optimized target scheduling list gene based on a preset bus line scheduling genetic algorithm to obtain a first scheduling list corresponding to the initial first scheduling list.

Specifically, a gene may include a plurality of nodes, and the nodes of the gene may be referred to as gene nodes, and for each gene, a shift table is used as the node of the gene to construct a shift table-based gene. The gene may be described in terms of an array comprising elements, each element may comprise several nodes, such that an array-based gene may be constructed. Because each bus line corresponds to an initial first schedule, and for the optimized target bus line group containing a plurality of bus lines, the optimized target schedule genes corresponding to the optimized target bus line group are constructed according to all the initial first schedules corresponding to the optimized target bus line group (namely, the initial first schedules contained in the initial first schedule set). For example, for an optimized target bus route group including a bus route 1, a bus route 2 and a bus route 3, genes of the bus route 1, the bus route 2 and the bus route 3 are constructed, the genes can be described by an array, and the gene node 1 (the bus route 1), the gene node 2 (the bus route 2) and the gene node 3 (the bus route 3) can be described by a triplet, wherein the triplet is an element contained in the array.

Randomly initializing a shift table contained in the optimization target shift table gene based on the constructed optimization target shift table gene, obtaining a corresponding initialized shift table gene individual for the shift table contained in the optimized target shift table gene every time of random initialization, thus, the optimized target shift schedule gene is initialized randomly for a plurality of times to obtain a plurality of initialized shift schedule gene individuals, all the initialized shift schedule gene individuals form a group to obtain an initial generation group, namely an initial population, then evaluating the fitness of each initialized shift table gene individual in the initial generation population, according to the fitness, reserving gene individuals with high fitness in the initial generation population, eliminating gene individuals with low fitness, realizing natural selection of the gene individuals in the initial generation population to obtain a generation population, selecting a plurality of gene individuals in the generation population for genetic variation to generate a plurality of new scheduling table gene individuals, and adding all the new scheduling table gene individuals to the generation population, updating the first generation population, then naturally selecting all gene individuals in the updated first generation population to obtain a second generation population, iterating the process of gene variation and natural selection of the gene individuals until the iteration of the population meets the iteration termination condition of a preset population, thereby realizing the genetic algorithm for carrying out the gene variation and natural selection on the schedule table contained in the optimized target schedule table gene based on the preset bus route schedule, and performing iterative optimization on the shift schedule contained in the optimization target shift schedule gene, and finally obtaining a first shift schedule corresponding to the initial first shift schedule.

For example, if a preset bus route shift scheduling joint optimization algorithm based on reinforcement learning is adopted, the preset bus route shift scheduling joint optimization algorithm comprises the following constituent elements: 1) The agent is the initial first shift schedule contained in the initial first shift schedule set, if the initial first shift schedule set contains a plurality of initial first shift schedules, the agents are a plurality of agents, and the agents can influence each other, wherein the agent is an enhanced learning body and is used as a learner or a decision maker; 2) The environment is everything except the reinforcement learning agent and mainly consists of a state set; 3) The states are data representing the environment, and the state set is all possible states in the environment; 4) The action is an action that the agent can make, and is an action for updating the shift schedule, for example, the shift schedule a is updated to an action 1 corresponding to the shift schedule B, the shift schedule a is updated to an action 2 corresponding to the shift schedule C, the actions 1 and 2 are different actions, and the action set is all actions that the agent can make; 5) The reward is a positive/negative feedback signal obtained after the intelligent agent executes an action, and the reward set is all feedback information which can be obtained by the intelligent agent; 6) And (4) strategy. Reinforcement learning is mapping learning from an environmental state to an action, and this mapping relationship is called a policy. In the embodiment of the application, the strategy is a thinking process of how the agent updates the shift table according to the environment, namely, a thinking and selecting process of updating the shift table A to the shift table B or other shift tables according to the environment determination. 7) And (4) a target. The agent automatically finds the optimal scheduling strategy in a continuous time sequence, and the optimal scheduling strategy refers to maximizing the long-term cumulative rewards. The preset bus route scheduling joint optimization algorithm based on reinforcement learning mainly comprises an intelligent agent and an environment, and because the interaction mode of the intelligent agent and the environment is similar to the interaction mode of organisms and the environment, a scheduling table described for the intelligent agent is a decision process for automatically updating the scheduling table into optimal scheduling as far as possible in the process of interacting with the environment. And the scheduling list corresponding to the intelligent agent interacts with the environment through the state, the action and the reward, so that the scheduling is updated into a better scheduling.

Further, please refer to fig. 6 to 7, fig. 6 is a fourth sub-flow schematic diagram of the schedule optimization method for multiple bus routes according to the embodiment of the present application, and fig. 7 is a specific example diagram of the schedule optimization method for multiple bus routes according to the embodiment of the present application. As shown in fig. 6 and 7, in this embodiment, the optimizing the initial first shift schedule included in the initial first shift schedule set based on a preset bus route shift scheduling joint optimization algorithm to obtain a first shift schedule corresponding to the initial first shift schedule includes:

s61, obtaining a sequencing sequence of all initial first-row shift tables contained in the initial first-row shift table set to obtain an optimized target shift table sequence;

s62, selecting one scheduling table from the scheduling tables of the optimization target scheduling table sequence as a primary selection scheduling table, and fixing other scheduling tables contained in the optimization target scheduling table sequence;

s63, optimizing the primary selection shift schedule to obtain a first shift schedule corresponding to the primary selection shift schedule;

and S64, circularly selecting the other scheduling tables as other initial selection scheduling tables, and optimizing the other initial selection scheduling tables until each scheduling table contained in the optimization target scheduling table sequence is optimized to obtain a first scheduling table corresponding to each initial first scheduling table.

Specifically, all the initial first-row shift tables included in the initial first-row shift table set are sorted to obtain a plurality of sorting sequence sets, and the sorting sequence sets are sampled, or all the initial first-row shift tables included in the initial first-row shift table set are randomly sorted to obtain a sorting sequence of all the initial first-row shift tables, which is an optimization target shift table sequence.

And then selecting one scheduling table from the scheduling tables of the optimized target scheduling table sequence as a primary scheduling table, fixing other scheduling tables contained in the optimized target scheduling table sequence, optimizing the primary scheduling table to obtain a first scheduling table corresponding to the primary scheduling table, so as to convert the combined scheduling optimization problem of multiple public transportation lines into the scheduling optimization problem of a single public transportation line, circularly selecting the other scheduling tables as the other primary scheduling tables, and sequentially optimizing the other primary scheduling tables according to the mode that the scheduling tables are selected to be fixed with the other scheduling tables until each scheduling table contained in the optimized target scheduling table sequence is optimized to obtain each first scheduling table corresponding to the initial first scheduling table, so that each initial first scheduling table contained in the initial first scheduling table set is optimized one time, and all the initial first scheduling tables corresponding to the optimized target public transportation line group are optimized one time.

According to the embodiment of the application, based on the preset bus line scheduling joint optimization algorithm, when multiple bus lines with relevance are subjected to joint optimization, due to the fact that the optimization mode that one scheduling table is selected and other scheduling tables are fixed is adopted, the joint scheduling optimization problem of the multiple bus lines is converted into the scheduling optimization problem of a single bus line, compared with the mode that the multiple bus lines are simultaneously optimized based on the preset bus line scheduling joint optimization algorithm, the complexity of the scheduling table optimization is further greatly reduced, the solution space of the multiple bus line optimization is reduced, the relevance influence among different bus lines can be fully reflected, and the efficiency and the effectiveness of feasible solutions when the multiple bus lines are optimized based on the preset bus line scheduling joint optimization algorithm are improved.

Further, the obtaining a ranking sequence of all the initial first shift lists included in the initial first shift list set to obtain an optimized target shift list sequence includes:

and randomly sequencing all the initial first-row shift tables contained in the initial first-row shift table set to obtain a sequencing sequence of all the initial first-row shift tables, and taking the sequencing sequence as an optimization target shift sequencing sequence.

Specifically, all the initial first shift lists included in the initial first shift list set are randomly ordered to obtain an ordering sequence of all the initial first shift lists, and the ordering sequence is used as an optimization target shift ordering sequence. Due to the randomness of random sequencing, the randomness of scheduling optimization based on a preset bus line scheduling joint optimization algorithm can be reflected, and the objectivity of mutual influence among different bus lines can be fully and comprehensively measured, so that the objectivity of scheduling of a plurality of related bus lines is improved.

For example, please refer to fig. 7, if the optimized target bus route group includes a bus route a, a bus route B, and a bus route C that are associated with each other, the scheduling of the bus route a, the bus route B, and the bus route C needs to be optimized based on a preset bus route scheduling joint optimization algorithm, no matter the joint scheduling optimization of multiple bus routes is performed based on a genetic algorithm, or the joint scheduling optimization of multiple bus routes is performed based on other algorithms such as reinforcement learning, in the conventional joint optimization algorithm, the scheduling of the bus route a, the bus route B, and the bus route C is optimized simultaneously, so as to optimize the scheduling of the bus route a, the bus route B, and the bus route C. In the embodiment of the application, when joint optimization of a schedule is performed on a bus line a, a bus line B and a bus line C based on a preset bus line scheduling joint optimization algorithm, the bus line a, the bus line B and the bus line C are randomly ordered at first, and a bus line ordering sequence obtained is assumed to be [ the bus line a, the bus line B and the bus line C ], and then a single bus line optimization solution can be sequentially performed according to the following modes: 1) Fixing the scheduling of the bus lines B and C, and optimizing the scheduling of the bus line A; 2) Fixing respective scheduling of the bus routes A and C, and optimizing the scheduling of the bus route B; 3) The scheduling of each bus line A and B is fixed, the scheduling of the bus line C is optimized, after one round of optimization, the scheduling of each bus line is optimized once, and the competing passenger flow influence of other bus lines is considered when each bus line is optimized, for example, if the competing passenger flow of the bus lines A, B and C is 5000 people, when the bus lines A, B and C are scheduled, the mutual influence among the bus lines A, B and C is considered, the passenger flow of 5000 people is estimated and distributed to the bus lines A, B and C, the scheduling of the bus lines A, B and C is planned, the scheduling of other bus lines is considered while the fixed scheduling of other bus lines is adopted, the scheduling of one bus line is optimized, the scheduling of the optimized bus lines is converted into the scheduling of other bus lines, and the objective and combined optimization of the bus lines is achieved.

In an embodiment, the optimizing the initial first shift schedule included in the initial first shift schedule set includes:

and performing parallel optimization on different initial first-ranking shift table sets.

Specifically, all the bus route identifications are grouped, the obtained optimized target bus route group is generally a plurality of, every optimized target bus route group corresponds to one initial first-row schedule set, because every optimized target bus route group corresponds to an initial first-row schedule set which is optimized independently and different, the initial first-row schedule sets do not influence each other, and when every optimized target bus route group corresponds to an initial first-row schedule set, the initial first-row schedule sets can be performed in parallel.

It should be noted that, in the method for optimizing the schedule of multiple bus routes described in each of the above embodiments, the technical features included in different embodiments may be recombined as needed to obtain a combined implementation, but all of the embodiments are within the protection scope claimed in the present application.

Referring to fig. 8, fig. 8 is a schematic block diagram of a shift schedule optimization apparatus for multiple bus routes according to an embodiment of the present disclosure. Corresponding to the scheduling list optimization method for the plurality of bus lines, the embodiment of the application further provides a scheduling list optimization device for the plurality of bus lines. As shown in fig. 8, the schedule optimization apparatus for multiple bus lines includes a unit for executing the schedule optimization method for multiple bus lines, and may be configured in a computer device. Specifically, referring to fig. 8, the schedule optimization apparatus 80 for multiple bus routes includes a first obtaining unit 81, a first grouping unit 82, a first optimizing unit 83, a first judging unit 84, and a determining unit 85.

The first obtaining unit 81 is configured to obtain a plurality of bus route identifiers and obtain route association degrees corresponding to two bus route identifiers, where the bus route identifiers are used to distinguish different bus routes, and the route association degrees describe association degrees between the two bus routes;

the first grouping unit 82 is configured to group all the bus route identifiers according to the line association degree to obtain a plurality of optimized target bus route groups, where each optimized target bus route group includes an optimized target bus route identifier;

a first optimization unit 83, configured to obtain, according to each optimized target bus route group, an initial first-row schedule corresponding to the optimized target bus route identifier, obtain an initial first-row schedule set corresponding to the optimized target bus route group, optimize the initial first-row schedule included in the initial first-row schedule set, obtain a first-row schedule corresponding to the initial first-row schedule, and use the first-row schedule as a second-row schedule corresponding to the optimized target bus route identifier;

the first judging unit 84 is configured to obtain a bus route optimization variable, and judge whether the bus route optimization variable meets a preset bus route optimization termination condition;

and the determining unit 85 is used for obtaining the respective target scheduling list of the plurality of bus lines according to the second scheduling list corresponding to all the optimized target bus line groups if the bus line optimization variables meet the preset bus line optimization termination condition.

In an embodiment, the first obtaining unit 81 includes:

the first counting subunit is used for counting overlapped stop point pairs between the two bus routes, wherein the overlapped stop point pairs describe bus stop point pairs consisting of overlapped departure stop points and destination stop points in different bus routes;

the first calculating subunit is configured to obtain an average passenger flow volume corresponding to the overlapping station pair, calculate a sum of the average passenger flow volumes of all the overlapping station pairs according to the average passenger flow volume, obtain a competitive passenger flow volume corresponding to a competitive passenger flow volume between two bus lines, and use the competitive passenger flow volume as a line association degree between the bus lines corresponding to two bus line identifiers, where the competitive passenger flow volume describes a size of the competitive passenger flow volume between the two bus lines.

In one embodiment, the first grouping unit 82 includes:

the first construction subunit is used for constructing a bus route map by taking the bus route identifications as nodes of the map and taking the route association degrees corresponding to the two bus route identifications as edges between the two nodes of the map;

and the clustering subunit is used for clustering the nodes of the bus route map by adopting a preset clustering algorithm according to the line association degree, classifying the bus route identifications corresponding to the nodes of the same class into the same group, and obtaining a plurality of optimized target bus route groups.

In an embodiment, the first optimizing unit 83 is configured to optimize the initial first shift list included in the initial first shift list set based on a preset bus route shift scheduling joint optimization algorithm to obtain a first shift list corresponding to the initial first shift list, where the preset bus route shift scheduling joint optimization algorithm describes an algorithm for performing joint optimization on different initial first shift lists.

In an embodiment, the first optimization unit 83 includes:

the first obtaining subunit is configured to obtain a ranking sequence of all the initial first shift lists included in the initial first shift list set, and obtain an optimized target shift list sequence;

the selecting subunit is used for selecting one scheduling table from the scheduling tables of the optimization target scheduling table sequence as a primary selection scheduling table and fixing other scheduling tables contained in the optimization target scheduling table sequence;

the second optimization subunit is used for optimizing the primary selection shift schedule to obtain a first shift schedule corresponding to the primary selection shift schedule;

and the circulation subunit is used for circularly selecting the other scheduling tables as other primarily selected scheduling tables, and optimizing the other primarily selected scheduling tables until each scheduling table included in the optimization target scheduling table sequence is optimized, so as to obtain a first scheduling table corresponding to each initial first scheduling table.

In an embodiment, the first obtaining subunit is configured to randomly sort all the initial first shift lists included in the initial first shift list set, obtain a sorting sequence of all the initial first shift lists, and use the sorting sequence as an optimization target shift sequence.

In an embodiment, the first optimization unit 83 is configured to perform parallel optimization on different initial first scheduling table sets.

It should be noted that, as can be clearly understood by those skilled in the art, the detailed implementation process of the schedule optimization apparatus and each unit of the multiple bus routes may refer to the corresponding description in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.

Meanwhile, the division and connection modes of the units in the schedule optimization device for the plurality of bus lines are only used for illustration, in other embodiments, the schedule optimization device for the plurality of bus lines can be divided into different units as required, and different connection sequences and modes can be adopted for the units in the schedule optimization device for the plurality of bus lines, so that all or part of functions of the schedule optimization device for the plurality of bus lines can be completed.

The schedule optimization apparatus for multiple bus lines described above may be implemented in the form of a computer program that is executable on a computer device such as that shown in fig. 9.

Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a computer device such as a desktop computer or a server, or may be a component or part of another device.

Referring to fig. 9, the computer device 500 includes a processor 502, a memory, which may include a non-volatile storage medium 503 and an internal memory 504, which may also be a volatile storage medium, and a network interface 505 connected by a system bus 501.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a method for schedule optimization of a plurality of bus routes as described above.

The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the computer program 5032 in the non-volatile storage medium 503 to run, and when the computer program 5032 is executed by the processor 502, the processor 502 can execute a schedule optimization method for the plurality of bus lines.

The network interface 505 is used for network communication with other devices. It will be appreciated by those skilled in the art that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the scope of the present application as such may be used in a computer device 500, and that a particular computer device 500 may include more or fewer components than those shown, or some of the components may be combined, or have a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 9, and are not described herein again.

Wherein the processor 502 is configured to execute the computer program 5032 stored in the memory to perform the steps of: the method comprises the steps of obtaining a plurality of bus line identifications and obtaining line association degrees corresponding to the two bus line identifications, wherein the bus line identifications are used for distinguishing different bus lines, and the line association degrees describe the association degree between the two bus lines; grouping all the bus route identifications according to the line association degree to obtain a plurality of optimized target bus route groups, wherein the optimized target bus route groups contain the optimized target bus route identifications; according to each optimized target bus route group, obtaining an initial first-row schedule corresponding to the optimized target bus route identification, obtaining an initial first-row schedule set corresponding to the optimized target bus route group, optimizing the initial first-row schedule contained in the initial first-row schedule set, obtaining a first-row schedule corresponding to the initial first-row schedule, and taking the first-row schedule as a second-row schedule corresponding to the optimized target bus route identification; obtaining a bus route optimization variable, and judging whether the bus route optimization variable meets a preset bus route optimization termination condition; and if the bus route optimization variable meets the preset bus route optimization termination condition, obtaining respective target scheduling tables of the plurality of bus routes according to the second scheduling tables corresponding to all the optimized target bus route groups.

In an embodiment, when the processor 502 obtains the route association degrees corresponding to the two bus route identifiers, the following steps are specifically implemented:

counting overlapped station pairs between the two bus routes, wherein the overlapped station pairs describe bus station pairs consisting of overlapped departure station and destination station in different bus routes;

and obtaining the average passenger flow corresponding to the overlapped station pairs, calculating the sum of the average passenger flows of all the overlapped station pairs according to the average passenger flow, obtaining the competitive passenger flow corresponding to the competitive passenger flow between the two bus lines, and taking the competitive passenger flow as the line association degree between the bus lines corresponding to the two bus line identifications, wherein the competitive passenger flow describes the size of the competitive passenger flow between the two bus lines.

In an embodiment, when the processor 502 implements the grouping of all the bus line identifiers according to the line association degree to obtain a plurality of optimized target bus line groups, the following steps are specifically implemented:

taking the bus route identifications as nodes of a graph, and taking the route association degrees corresponding to the two bus route identifications as edges between the two nodes of the graph to construct a bus route graph;

and according to the line association degree, clustering the nodes of the bus line graph by adopting a preset clustering algorithm, classifying the bus line identifications corresponding to the nodes of the same class into the same group, and obtaining a plurality of optimized target bus line groups.

In an embodiment, when the processor 502 optimizes the initial first shift schedule included in the initial first shift schedule set to obtain a first shift schedule corresponding to the initial first shift schedule, the following steps are specifically implemented:

and optimizing the initial first shift schedule contained in the initial first shift schedule set based on a preset bus route shift scheduling joint optimization algorithm to obtain a first shift schedule corresponding to the initial first shift schedule, wherein the preset bus route shift scheduling joint optimization algorithm describes an algorithm for performing joint optimization on the initial first shift schedule, wherein the algorithm is different.

In an embodiment, when the processor 502 implements the joint optimization algorithm for scheduling based on a preset bus route, the following steps are specifically implemented when the initial first schedule included in the initial first schedule set is optimized to obtain a first schedule corresponding to the initial first schedule:

obtaining a sequencing sequence of all the initial first shift lists contained in the initial first shift list set to obtain an optimized target shift list sequence;

selecting one scheduling table from the scheduling tables of the optimization target scheduling table sequence as a primary selection scheduling table, and fixing other scheduling tables contained in the optimization target scheduling table sequence;

optimizing the primary selection shift schedule to obtain a first shift schedule corresponding to the primary selection shift schedule;

and circularly selecting the other scheduling tables as other primarily selected scheduling tables, and optimizing the other primarily selected scheduling tables until each scheduling table contained in the optimization target scheduling table sequence is optimized to obtain a first scheduling table corresponding to each initial first scheduling table.

In an embodiment, when the processor 502 obtains the sorting sequence of all the initial first shift lists included in the initial first shift list set to obtain an optimized target shift list sequence, the following steps are specifically implemented:

In an embodiment, when the processor 502 optimizes the initial first shift schedule included in the initial first shift schedule set, the following steps are specifically implemented:

and performing parallel optimization on different initial first-line shift table sets.

It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will be understood by those skilled in the art that all or part of the processes of the method for implementing the above embodiments may be implemented by a computer program, and the computer program may be stored in a computer readable storage medium. The computer program is executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.

Accordingly, the present application also provides a computer-readable storage medium. The computer readable storage medium may be a non-volatile computer readable storage medium or a volatile computer readable storage medium, the computer readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of:

a computer program product which, when run on a computer, causes the computer to perform the steps of the schedule optimization method for a plurality of bus lines described in the embodiments above.

The computer readable storage medium may be an internal storage unit of the aforementioned device, such as a hard disk or a memory of the device. The computer readable storage medium may also be an external storage device of the device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the device. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the apparatus.

It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described devices, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The storage medium is a physical, non-transitory storage medium, and may be various physical storage media capable of storing computer programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the various embodiments described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the various embodiments have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated in another system or certain features may be omitted, or not implemented.

The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solutions of the present application may substantially or partially contribute to the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be a personal computer, a terminal, or a network device) to perform all or part of the steps of the methods described in the embodiments of the present application.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A scheduling list optimization method for a plurality of bus lines is characterized by comprising the following steps:

the method comprises the steps of obtaining a plurality of bus line identifications and obtaining line association degrees corresponding to the two bus line identifications, wherein the bus line identifications are used for distinguishing different bus lines, and the line association degrees describe the association degree between the two bus lines;

grouping all the bus line identifications according to the line association degree to obtain a plurality of optimized target bus line groups, wherein the optimized target bus line groups comprise the optimized target bus line identifications;

according to each optimized target bus route group, obtaining an initial first-row schedule corresponding to the optimized target bus route identification, obtaining an initial first-row schedule set corresponding to the optimized target bus route group, optimizing the initial first-row schedule contained in the initial first-row schedule set, obtaining a first-row schedule corresponding to the initial first-row schedule, and taking the first-row schedule as a second-row schedule corresponding to the optimized target bus route identification;

obtaining a bus route optimization variable, and judging whether the bus route optimization variable meets a preset bus route optimization termination condition;

and if the bus route optimization variable meets the preset bus route optimization termination condition, obtaining respective target scheduling tables of the plurality of bus routes according to the second scheduling tables corresponding to all the optimized target bus route groups.

2. The method for optimizing the shift schedule of the plurality of bus routes according to claim 1, wherein the obtaining of the route association degrees corresponding to the two bus route identifiers comprises:

counting overlapped stop point pairs between the two bus routes, wherein the overlapped stop point pairs describe bus stop point pairs consisting of overlapped departure stop points and destination stop points in different bus routes;

3. The method for optimizing the shift schedule of a plurality of bus routes according to claim 1, wherein the step of grouping all the bus route identifications according to the degree of association of the routes to obtain a plurality of optimized target bus route groups comprises the steps of:

4. The method for optimizing the shift schedule of the plurality of bus lines according to claim 1, wherein the step of optimizing the initial first shift schedule included in the initial first shift schedule set to obtain a first shift schedule corresponding to the initial first shift schedule comprises:

5. The method for optimizing the shift schedule of the plurality of bus routes according to claim 4, wherein the step of optimizing the initial first shift schedule contained in the initial first shift schedule set based on a preset bus route shift scheduling joint optimization algorithm to obtain a first shift schedule corresponding to the initial first shift schedule comprises:

obtaining a sequencing sequence of all initial first-row shift tables contained in the initial first-row shift table set to obtain an optimized target shift table sequence;

optimizing the preliminary selection schedule to obtain a first schedule corresponding to the preliminary selection schedule;

and circularly selecting the other scheduling tables as other primarily selected scheduling tables, and optimizing the other primarily selected scheduling tables until each scheduling table contained in the optimization target scheduling table sequence is optimized to obtain the first scheduling table corresponding to each initial first scheduling table.

6. The method according to claim 5, wherein the obtaining of the sequence of all the initial first shift lists contained in the initial first shift list set to obtain the sequence of the optimized target shift list comprises:

randomly sequencing all the initial first shift lists contained in the initial first shift list set to obtain a sequencing sequence of all the initial first shift lists, and taking the sequencing sequence as an optimization target shift sequence.

7. The method for optimizing the shift schedule of the plurality of bus lines according to claim 1, wherein the optimizing the initial first shift schedule included in the initial first shift schedule set comprises:

8. The utility model provides a scheduling table optimizing device of many bus lines which characterized in that includes:

the bus route identification system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a plurality of bus route identifications and acquiring route association degrees corresponding to two bus route identifications, the bus route identifications are used for distinguishing different bus routes, and the route association degrees describe association degrees between the two bus routes;

the first grouping unit is used for grouping all the bus line identifications according to the line association degree to obtain a plurality of optimized target bus line groups, and each optimized target bus line group comprises an optimized target bus line identification;

the first optimization unit is used for acquiring an initial first shift table corresponding to the optimization target bus route identification according to each optimization target bus route group, acquiring an initial first shift table set corresponding to the optimization target bus route group, optimizing the initial first shift table contained in the initial first shift table set, acquiring a first shift table corresponding to the initial first shift table, and taking the first shift table as a second shift table corresponding to the optimization target bus route identification;

the first judgment unit is used for acquiring the optimized variable of the bus route and judging whether the optimized variable of the bus route meets the preset bus route optimization termination condition or not;

and the determining unit is used for obtaining respective target scheduling lists of the plurality of bus lines according to the second scheduling lists corresponding to all the optimized target bus line groups if the bus line optimization variables meet the preset bus line optimization termination conditions.

9. A computer device, comprising a memory and a processor coupled to the memory; the memory is used for storing a computer program; the processor is adapted to run the computer program to perform the steps of the method according to any of claims 1-7.

10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.