CN117132011A

CN117132011A - Inter-city travel vehicle path determining method, system, electronic equipment and medium

Info

Publication number: CN117132011A
Application number: CN202311209584.9A
Authority: CN
Inventors: 蔡奕侨; 林子帆; 王依婷; 柳培忠; 喻小光
Original assignee: Huaqiao University
Current assignee: Huaqiao University
Priority date: 2023-09-19
Filing date: 2023-09-19
Publication date: 2023-11-28

Abstract

The invention discloses a method, a system, electronic equipment and a medium for determining an inter-city travel vehicle path, which relate to the fields of intelligent optimization algorithms and vehicle scheduling, wherein the method for determining the path comprises the following steps: constructing an inter-city travel vehicle path model by taking the maximized average number of passengers per trip, the minimized number of vehicles, the minimized total travel distance of the vehicles and the minimized total waiting time of the passengers as objective functions and taking the vehicle passenger capacity, the service quality, the time constraint and the safety constraint as constraint conditions; acquiring an inter-city order to be travelled; and determining the travel vehicles corresponding to each travel order according to the inter-city travel vehicle path model. The invention can provide a path planning scheme for meeting a plurality of requirements for the travel of the inter-city network about vehicles.

Description

Inter-city travel vehicle path determining method, system, electronic equipment and medium

Technical Field

The invention relates to the field of intelligent optimization algorithms and vehicle scheduling, in particular to a method, a system, electronic equipment and a medium for determining an inter-city travel vehicle path.

Background

With popularization of smart phones and application programs, more and more companies develop and build riding sharing platforms composed of professional drivers and commercial vehicles, and based on internet technology, the riding sharing platforms reasonably match passengers and drivers by integrating supply and demand information, and travel routes are planned efficiently and safely, so that business activities of non-traditional taxi booking services are realized. This new mobile service has experienced a rapid growth and has grown exponentially to serve most metropolitan areas across 66 countries. Because of these advantages, net-bound vehicles are rapidly spreading in china. The inter-city travel can fully utilize the vehicle vacancy resources to meet the travel demands of the vehicle-free users, avoid crowding and save travel time, meanwhile, the vehicle travel cost of private vehicle owners is reduced, the vehicle utilization rate is improved, traffic jams are relieved, resources are saved, and the environment is protected, so that the inter-city travel has important practical significance.

The current vehicle path problem facing inter-city travel is rarely researched, and most of the current application scenes of the carpool are carpool travel in cities, and the urban travel has four characteristics: high travel demand, high travel frequency, short travel distance and convenience in travel. These features make urban trips generally real-time. The platform needs to respond to the passenger's request in a short time in order to guarantee the quality of service. Unlike urban travel, inter-urban travel has the following characteristics: low travel demand, low travel frequency, long travel distance and less travel selection. These characteristics make inter-urban travel generally highly planned, especially non-self-driving travel. Because the inter-city travel is a long distance travel, most of the travel time is spent on the expressway between two cities. Passengers are not sensitive to small changes in urban travel time. This feature does not allow the platform to operate in real-time. Unlike urban network taxi booking platforms that optimize driver and passenger matching and pricing, the platforms primarily optimize vehicle routes in daily operations. In the real world, passengers need to provide travel information to the platform several hours in advance.

Therefore, since no model in the current research can fully represent the inter-city travel-oriented multi-objective vehicle path problem, the inter-city travel-oriented vehicle path problem is essentially a multi-objective optimization problem, and the multi-objective optimization problem is one of core problems in the optimization problem, many students research the multi-objective optimization problem and propose various meta-heuristic variants for solving the multi-objective optimization problem under different conditions. While for most existing approaches, the problem of optimizing to effectively solve multiple conflicting objectives remains a significant challenge. Meanwhile, in this problem, different targets have different characteristics in physical meaning, scale and difficulty of optimization, and therefore, for some targets that are difficult to optimize with conventional neighborhood search operations, it is necessary to design neighborhood search operations for the targets. Therefore, how to design a high-efficiency algorithm framework aiming at the inter-city travel vehicle path problem has higher theoretical significance and application value.

Disclosure of Invention

The invention aims to provide a method, a system, electronic equipment and a medium for determining an inter-city travel vehicle path, which can provide a path planning scheme for meeting a plurality of requirements for inter-city network travel.

In order to achieve the above object, the present invention provides the following solutions:

an inter-city travel vehicle path determination method, the determination method comprising:

constructing an inter-city travel vehicle path model by taking the maximized average number of passengers per trip, the minimized number of vehicles, the minimized total travel distance of the vehicles and the minimized total waiting time of the passengers as objective functions and taking the vehicle passenger capacity, the service quality, the time constraint and the safety constraint as constraint conditions;

acquiring an inter-city order to be travelled; the inter-city orders to be travelled comprise a plurality of travelling orders; each travel order comprises the number of passengers, departure time, departure place and destination place;

determining travel vehicles corresponding to the travel orders according to the inter-city travel vehicle path model; wherein one travel vehicle and a plurality of travel orders completed by the travel vehicle form a travel solution; a plurality of travel solutions form a travel external archive; and taking the trip external archive as a path planning scheme of the inter-city order to be tripped.

Optionally, the objective function is:

f ₂ ＝|M _k |

wherein the object f ₁ Representing the maximum average number of passengers per trip, M _t 、M _k Respectively represent the number of the travel t and the vehicle k, Q _t Indicating the seating rate of the stroke t, the target f ₂ Representing a minimized number of vehicles, target f ₃ Representing a minimum total travel distance of the vehicle, M _t Indicating the number of strokes t, l _t The total travel distance of the journey t is indicated,for determining whether the journey t is to be routed to the vehicle k, the target f ₄ Meaning minimizing the total waiting time of the passengers.

Optionally, determining, according to the inter-city travel vehicle path model, a travel vehicle corresponding to each travel order, including:

determining a plurality of travel waiting trips corresponding to each travel order according to the constraint conditions and the objective function; wherein each travel to be traveled includes at least one of the travel orders and each travel to be traveled satisfies the constraint condition and the objective function;

determining vehicles corresponding to the travel routes to be traveled according to the constraint conditions and the objective function; wherein one of the vehicles and a plurality of the travel routes to be traveled completed by the vehicle form a solution; a plurality of said solutions constitute an external archive;

Applying a neighborhood operator to each solution to obtain a search result corresponding to each solution;

and applying epsilon-dominant archive update strategies to each solution and the search results corresponding to each solution to obtain the travel external archive.

Optionally, determining a plurality of travel routes to be traveled corresponding to each travel order according to the constraint condition and the objective function specifically includes:

taking the travel order with the earliest departure time in the preprocessing order set as a reference order of an initial journey; the initial journey at least comprises one travel order; the preprocessing order set comprises a plurality of travel orders with the same departure place;

selecting a preset pre-processing order from the to-be-processed order set, and adding the preset pre-processing order into the initial journey to obtain an updated initial journey and an order queue; the to-be-processed order set is the rest order after the reference order is removed from the pre-processing order set; the order queue is the rest order after one preset pre-processing order is removed from the to-be-processed order set;

judging whether the updated initial travel meets the constraint condition or not;

when the updated initial journey does not meet the constraint condition, deleting the preset pre-processing order from the updated initial journey, and returning to the step of selecting one preset pre-processing order from the to-be-processed order set to be added into the initial journey to obtain an updated initial journey and an order queue, and continuing to execute;

When the updated initial journey meets the constraint condition, judging whether the number of passengers in the updated initial journey reaches the passenger capacity of the vehicle;

when the number of passengers in the updated initial journey reaches the passenger capacity of the vehicle, the updated initial journey is used as the final initial journey, and travel orders in the final initial journey are deleted from the preprocessing order set, so that an updated preprocessing order set is obtained;

when the number of passengers in the updated initial journey does not reach the passenger capacity of the vehicle, judging whether the number of travel orders in the order queue is 0;

when the number of the travel orders in the order queue is not 0, the order queue is used as an order set to be processed, and a step of selecting a preset pre-processing order from the order set to be processed and adding the preset pre-processing order into the initial journey is returned to obtain an updated initial journey and the order queue;

when the number of the travel orders in the order queue is 0, returning to the step of taking the updated initial journey as the final initial journey, and deleting the travel orders in the final initial journey from the preprocessing order set to obtain an updated preprocessing order set;

When the number of the travel orders in the updated pretreatment order set is not 0, the updated pretreatment order set is used as a pretreatment order set, and the step of continuously executing the travel order with the earliest departure time in the pretreatment order set as a reference order of an initial journey is returned;

and when the number of the updated pre-processing order sets for the travel orders is 0, determining the final initial travel as a plurality of travel waiting travels corresponding to the travel orders.

Optionally, determining the vehicle corresponding to each travel to be performed according to the constraint condition and the objective function specifically includes:

taking the travel route to be traveled with the earliest departure time in the preprocessing route set as a reference route of the vehicle; the vehicle at least comprises one travel route to be traveled; the pretreatment travel set comprises a plurality of travel to be performed travel routes with the same departure place;

selecting a preset pretreatment travel from the travel set to be treated, adding the preset pretreatment travel into the initial travel, and obtaining updated vehicles and travel queues; the stroke set to be processed is the rest stroke of the preprocessing stroke set after the reference stroke is removed; the stroke queue is the residual stroke after one preset pretreatment stroke is removed from the to-be-treated stroke set;

Judging whether the updated vehicle meets the constraint condition or not;

when the updated vehicle does not meet the constraint condition, deleting the preset pretreatment travel from the updated vehicle, and returning to the step of selecting one preset pretreatment travel from the to-be-treated travel set to be added into the vehicle to obtain an updated vehicle and a travel queue, and continuously executing;

when the updated vehicle meets the constraint condition, the updated vehicle is used as the final vehicle, and the travel journey to be performed in the final vehicle is deleted from the pretreatment journey set, so that an updated pretreatment journey set is obtained;

judging whether the number of travel strokes to be performed in the stroke queue is 0;

when the number of travel routes to be traveled in the route queue is not 0, the route queue is used as a route set to be processed, and a step of selecting a preset pretreatment route from the route set to be processed and adding the preset pretreatment route into the vehicle is returned to obtain an updated vehicle and route queue;

when the number of travel routes to be traveled in the route queue is 0, returning to the step of selecting a preset pretreatment route from the travel route set to be treated and adding the preset pretreatment route into the initial route to obtain an updated vehicle and route queue;

Judging whether the number of travel routes to be traveled in the updated pretreatment route set is 0;

when the number of travel routes to be traveled in the updated pretreatment route set is not 0, the updated pretreatment route set is used as a pretreatment route set, and the step of continuously executing the travel route to be traveled with the earliest departure time in the pretreatment route set as a reference route of the vehicle is returned;

and when the number of the travel routes to be traveled in the updated pretreatment route set is 0, determining that the final vehicle is a plurality of travel routes to be traveled corresponding to each travel route to be traveled.

Optionally, applying a neighborhood operator to each solution to obtain a search result corresponding to each solution, which specifically includes:

searching each solution by applying a plurality of neighborhood operators to obtain a plurality of initial search results corresponding to each solution;

calculating the lifting degree corresponding to each solution according to a plurality of initial search results;

when the searching times are smaller than the preset times, returning to the step of applying a neighborhood operator to the preset solutions in the solutions to obtain initial searching results, and continuing to execute the steps;

when the searching times are greater than or equal to preset times, calculating an improved crowding distance of each solution according to each solution and the initial searching result corresponding to each solution;

Normalizing the improved crowding distance of each solution to obtain the solution selection probability of each solution;

determining the operation selection probability of each neighborhood operator according to the lifting degree corresponding to each solution;

selecting a solution to be processed from a plurality of solutions according to the solution selection probability;

determining target neighborhood operation from a plurality of neighborhood operation operators according to the operation selection probability;

applying the target neighborhood operation to perform local search on the solution to be processed to obtain a final search result corresponding to the solution to be processed;

and updating the initial search result according to the final search result to obtain the search result corresponding to each solution.

An inter-city travel vehicle path determining system, to which the above-mentioned inter-city travel vehicle path determining method is applied, the determining system includes:

the construction module is used for constructing an inter-city travel vehicle path model by taking the maximized average number of passengers per trip, the minimized number of vehicles, the minimized total running distance of the vehicles and the minimized total waiting time of the passengers as objective functions and taking the vehicle passenger capacity, the service quality, the time constraint and the safety constraint as constraint conditions;

the acquisition module is used for acquiring an inter-city order to be travelled; the inter-city orders to be travelled comprise a plurality of travelling orders; each travel order comprises the number of passengers, departure time, departure place and destination place;

The scheme determining module is used for determining the travel vehicles corresponding to the travel orders according to the inter-city travel vehicle path model; wherein one travel vehicle and a plurality of travel orders completed by the travel vehicle form a travel solution; a plurality of travel solutions form a travel external archive; and taking the trip external archive as a path planning scheme of the inter-city order to be tripped.

An electronic device comprising a memory for storing a computer program and a processor running the computer program to cause the electronic device to perform the inter-urban travel vehicle path determination method described above.

A computer readable storage medium storing a computer program which when executed by a processor implements the inter-urban travel vehicle path determination method described above.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention defines the problem of optimizing the vehicle-passing path of the inter-city network as a multi-objective problem comprising four objectives, takes the maximum average number of passengers per trip, the minimum number of vehicles, the minimum total running distance of the vehicles and the minimum total waiting time of the passengers as objective functions, and takes the passenger capacity, the service quality, the time constraint and the safety constraint of the vehicles as constraint conditions to construct an inter-city travel vehicle path model; solving an inter-city travel vehicle path model according to acquired inter-city orders to be traveled, and determining travel vehicles corresponding to the travel orders; the invention can reflect the essence of the inter-city travel problem more comprehensively and truly, and provides a path planning scheme for meeting a plurality of requirements for the inter-city network vehicle travel.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for determining the route of an intercity travel vehicle according to the present invention;

FIG. 2 is a flow chart of the adaptive local search method for solving the multi-objective inter-urban travel vehicle path problem of the present invention.

Fig. 3 is a flowchart of the time-series-based initialization construction method of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The invention has specificity in the aspects of selecting an optimization target, driving safety, fatigue driving limitation and the like, so that various constraints of intercity travel in practical application are to be analyzed, and a path optimization model with certain universality is constructed by combining the intercity travel characteristics. Aiming at the multi-objective characteristics and the practical application of the inter-city travel vehicle path problem, a general multi-objective path optimization model which is comprehensively considered and is fit with the reality is established, and a test case of the inter-city travel case is established based on the real world data. And the neighborhood searching operation aiming at each target is designed, and finally the problem of the inter-city travel vehicle path is solved by combining with the self-adaptive selection framework based on learning, so that the travel path of the inter-city network about vehicle is planned finally, efficiently and intelligently, comfortable travel experience is brought to a traveling customer, the transportation cost can be saved, and more benefits are brought to companies and drivers. In the inter-city network taxi-offer industry, planning of travel path schemes is one of the key problems in the network taxi-offer service.

In the present invention, it is intended to accomplish two-stage allocation of an order to a trip, and a trip to a vehicle (trip task). Minimizing the total travel distance of the vehicle, minimizing the total waiting time of passengers, maximizing the travel occupancy, and minimizing the number of vehicles by optimizing the vehicle route. Therefore, an initialization construction method based on time series is proposed to generate an initialization planning scheme. In the method, based on random selection of time sequence, the planning scheme can be ensured to be obtained simultaneously without premature convergence, and the solution quality is relatively better and meanwhile, the method has diversity. Note also that since the research model of the present invention is an inter-city travel scenario, there are two types of order sets (i.e., order set Ord from city a to city b ₁ And order set Ord from city b to city a ₂ The travel set Trips obtained on the basis ₁ /Trips ₂ And vehicle set Car ₁ /Car ₂ Also of two types).

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

Example 1

As shown in fig. 1, the present invention provides a method for determining an inter-city travel vehicle path, the method comprising:

Step S1: and constructing an inter-city travel vehicle path model by taking the maximum average number of passengers per trip, the minimum number of vehicles, the minimum total running distance of the vehicles and the minimum total waiting time of the passengers as objective functions and taking the vehicle passenger capacity, the service quality, the time constraint and the safety constraint as constraint conditions.

Specifically, the objective function is:

The path planning problem for interurban network taxi service may be defined as a platform that a private company has developed to provide Interurban Ride Share (IRS) service for passengers between two cities. Passengers between two cities need to provide their travel information to the platform several hours in advance. The concrete description is as follows: a set of orders (denoted c= {1,2, …, n }) each with one passenger number, i.e. demand q, are served by companies with the same size of network vehicle seats _i And a departure time window [ b ] _i ,e _i ]And can only be serviced by one vehicle, and each belongs to the city where the vehicle origin and destination are located. The vehicle plans a delivery route according to the getting-on and getting-off points of each order, and sequentially delivers passengers of each order to the corresponding getting-off points. And requires that any vehicle K e K require that all passengers on each trip get off before the next new passenger. The passenger orders the invention in consideration of the inter-city travel characteristicsThe individual time information is a soft time window, i.e. the difference after the latest departure time is defined as the waiting time of the passenger. At the same time, in order to ensure driving safety, each driver must take a minimum of 20 minutes of rest within the national regulatory continuous driving time of 4 hours. At the same time, the number of passengers per order cannot exceed the maximum passenger capacity of the vehicle and the total number of passengers for all orders allocated for the same trip cannot exceed the maximum passenger capacity of the vehicle.

The model of the inter-city network vehicle-approaching path planning problem comprises a plurality of constraint conditions, and is specifically defined as follows:

1) Capacity constraint: the number of passengers served by all vehicles does not exceed the maximum load, i.e. the number of passengers per order cannot exceed the maximum load of the vehicle and the total number of passengers for all orders allocated for the same journey cannot exceed the maximum load of the vehicle. Namely, the following conditions are satisfied:

Wherein,representing the number of passengers for the p-th order of trip t, M _t Indicating the number of vehicle strokes t.

2) Service constraint one: to guarantee the quality of service of the network about, any vehicle K e K needs to get all passengers of this path off before the next new passenger batch. Namely, the following conditions are satisfied:

wherein,indicating the current number of passengers after the vehicle k has completed service j, +.>Is 0-1 changeQuantity for judging whether the pick-up point i is arranged to the vehicle k, < >>Is a 0-1 variable for determining whether the journey t is arranged to the vehicle k,/or not>Is a 0-1 variable, and determines whether the departure point i is the last delivery point of the trip t. It is known that this formula holds if and only if +.>That is, the pick-up j of the vehicle k service is the last pick-up point of the journey, so in this case +.>Indicating that the number of passengers is 0 after the vehicle k has served the last pick-up point j.

3) Service constraint two: each order is serviced by a vehicle.

Wherein,is a 0-1 variable for determining whether order p is to be placed on vehicle k.

4) Service constraints three: in inter-city travel, the pick-up point for any one order will only appear in one and the same path, and the pick-up point for an order at the departure city must appear before the pick-up point for the destination city.

Wherein,is a 0-1 variable for determining whether pick-up points i, j are to be placed to order p.Is a 0-1 variable for determining whether order p is to be placed on vehicle k.The time when the vehicle k reaches the pick-up point i is indicated.

5) Service constraint four: the number of times a spot is serviced in a trip of all vehicles is not more than one.

Wherein,is a 0-1 variable for determining whether the pick-up point i is scheduled to the trip t.

6) Service constraint five: indicating that all vehicles must serve the passenger's destination if they serve the passenger's departure point.

Wherein,is a 0-1 variable for determining whether pick-up points i, j are to be placed to order p.Is a 0-1 variable for determining whether the order p is to be placed on the vehicle k,/or not>Is a 0-1 variable for determining whether the pick-up point i is arranged to the vehicle k.

7) Time constraint: in order to guarantee the quality of service of the inter-city network about vehicles, the start service time must meet the service time window. Namely, the following conditions are satisfied:

wherein,is a 0-1 variable for determining whether order p is to be placed on vehicle k. e, e _P Represents the latest start-up service time of order p, < >>The time when the vehicle k reaches the pick-up point i is indicated. b _P Representing the earliest start of service time for order p.

8) Safety constraints: considering that the travel time of one trip of the inter-city travel is longer than that of the inter-city travel, in order to ensure the safety of passengers and drivers when the inter-city network is about to travel and to ensure the travel efficiency of the drivers, all vehicles are required to have a minimum rest time of national regulations after reaching the maximum continuous travel time of the national regulations of the drivers. Namely, the following conditions are satisfied:

Wherein,indicating the time when the vehicle k reaches the pick-up point i, t _i,j G represents the travel time of the vehicle from pickup i to pickup j _i Representing service time, T _R Indicating that the country specifies a minimum rest time exceeding the continuous driving time,/->Is a 0-1 decision variable for determining whether or not the continuous travel time when the vehicle k reaches the pick-up point i exceeds a prescribed maximumContinuous driving time, in china, the maximum specified continuous driving time is 4 hours.

Step S2: acquiring an inter-city order to be travelled; the inter-city orders to be travelled comprise a plurality of travelling orders; each travel order includes a number of passengers, a departure time, a departure location, and a destination location.

Step S3: determining travel vehicles corresponding to the travel orders according to the inter-city travel vehicle path model; wherein one travel vehicle and a plurality of travel orders completed by the travel vehicle form a travel solution; a plurality of travel solutions form a travel external archive; and taking the trip external archive as a path planning scheme of the inter-city order to be tripped.

S3 specifically comprises:

step S31: determining a plurality of travel waiting trips corresponding to each travel order according to the constraint conditions and the objective function; wherein each travel to be traveled includes at least one of the travel orders and each travel to be traveled satisfies the constraint condition and the objective function.

S31 specifically includes:

step S3100: taking the travel order with the earliest departure time in the preprocessing order set as a reference order of an initial journey; the initial journey at least comprises one travel order; the preprocessing order set comprises a plurality of travel orders with the same departure place.

Step S3101: selecting a preset pre-processing order from the to-be-processed order set, and adding the preset pre-processing order into the initial journey to obtain an updated initial journey and an order queue; the to-be-processed order set is the rest order after the reference order is removed from the pre-processing order set; the order queue is the rest order after one preset pre-processing order is removed from the to-be-processed order set.

Step S3102: and judging whether the updated initial travel meets the constraint condition.

Step S3103: and deleting the preset pre-processing order from the updated initial journey when the updated initial journey does not meet the constraint condition, and returning to the step of selecting one preset pre-processing order from the to-be-processed order set to be added into the initial journey to obtain the updated initial journey and the order queue for continuous execution.

Step S3104: and when the updated initial journey meets the constraint condition, judging whether the number of passengers in the updated initial journey reaches the passenger capacity of the vehicle.

Step S3105: when the number of passengers in the updated initial journey reaches the passenger capacity of the vehicle, the updated initial journey is used as the final initial journey, and the travel orders in the final initial journey are deleted from the preprocessing order set, so that an updated preprocessing order set is obtained.

Step S3106: and when the number of passengers in the updated initial journey does not reach the passenger capacity of the vehicle, judging whether the number of travel orders in the order queue is 0.

Step S3107: when the number of the travel orders in the order queue is not 0, the order queue is used as a to-be-processed order set, and the step of selecting a preset pre-processed order from the to-be-processed order set to be added into the initial journey is returned to obtain an updated initial journey and the order queue.

Step S3108: and when the number of the travel orders in the order queue is 0, returning to the step of taking the updated initial journey as the final initial journey, and deleting the travel orders in the final initial journey from the preprocessing order set to obtain an updated preprocessing order set.

Step S3109: when the number of the travel orders in the updated pretreatment order set is not 0, the updated pretreatment order set is used as a pretreatment order set, and the step of continuously executing the travel order with the earliest departure time in the pretreatment order set as a reference order of an initial journey is returned.

Step S3110: and when the number of the updated pre-processing order sets for the travel orders is 0, determining the final initial travel as a plurality of travel waiting travels corresponding to the travel orders.

In practical application, as shown in fig. 3, determining a plurality of travel routes to be traveled corresponding to each travel order is an initialization construction method based on a time sequence, which includes the following seven steps:

step one, ord is collected for two orders ₁ /Ord ₂ Sorting according to the ascending order of the earliest departure time of orders, and selecting an order set Ord to be processed ₁ /Ord ₂ Is the first order o of (2) ₁ A trip node vector t is generated for the reference. Order o ₁ From a set of pending orders Ord ₁ /Ord ₂ And (5) removing. To-be-processed order set Ord ₁ /Ord ₂ Adds the orders in (a) to the order queue S and sets the status of all orders as "unprocessed".

In practical application, a path planning scheme X is a set o= { O1 of k routes, oi., ok } wherein Is a path consisting of an access sequence comprising Ni orders, 2Ni pick-up points,/I>The jth client point of the ith path is represented. Since each order contains the point of entry of the customer in the departure city and the point of exit of the destination city, each order is represented as two pick-up points in each path, i.e., the point of entry of the order in the departure city and the point of exit of the destination city. In one path planning scheme, two pick-up points for any one order will only appear in one and the same path.

Step two, from the current to-be-processed order set Ord ₁ /Ord ₂ Order o is randomly selected to join t, and the state of order o is set as processed. And checking whether the stroke t added by the order o meets the model constraint, if so, entering a step III, and if not, removing the order o from the stroke t and returning to the step II.

Step three, checking whether the sum of the number of passengers in the order currently added to the journey t reaches the passenger capacity of the vehicle, if so, entering step four, and if not, entering step six.

Step four, adding t to the travel set Trips ₁ /Trips ₂ And order of journey t is collected from pending orders Ord ₁ /Ord ₂ And removing and simultaneously emptying t.

Step five, judging an order set Ord to be processed ₁ /Ord ₂ If the air is empty, the step seven is entered if the air is satisfied, and if the air is not satisfied, the step one is returned.

Step six, judging whether the order state in the order queue S is 'unprocessed', if yes, returning to the step two, and if not, returning to the step four.

Step seven, initializing a travel set Trips ₁ /Trips ₂ With the success of the process,

step S32: determining vehicles corresponding to the travel routes to be traveled according to the constraint conditions and the objective function; wherein one of the vehicles and a plurality of the travel routes to be traveled completed by the vehicle form a solution; a plurality of the solutions constitute an external archive.

S32 specifically includes:

step S3201: taking the travel route to be traveled with the earliest departure time in the preprocessing route set as a reference route of the vehicle; the vehicle at least comprises one travel route to be traveled; the pretreatment travel set comprises a plurality of travel to be traveled travels with the same departure place.

Step S3202: selecting a preset pretreatment travel from the travel set to be treated, adding the preset pretreatment travel into the initial travel, and obtaining updated vehicles and travel queues; the stroke set to be processed is the rest stroke of the preprocessing stroke set after the reference stroke is removed; the travel queue is the remaining travel after one preset pretreatment travel is removed from the to-be-treated travel set.

Step S3203: and judging whether the updated vehicle meets the constraint condition or not.

Step S3204: and deleting the preset pretreatment journey from the updated vehicle when the updated vehicle does not meet the constraint condition, and returning to the step of selecting one preset pretreatment journey from the to-be-treated journey set to be added into the vehicle to obtain the updated vehicle and journey queue, and continuously executing.

Step S3205: and when the updated vehicle meets the constraint condition, the updated vehicle is used as the final vehicle, and the travel journey to be performed in the final vehicle is deleted from the preprocessing journey set, so that an updated preprocessing journey set is obtained.

Step S3206: and judging whether the number of travel strokes to be performed in the stroke queue is 0.

Step S3207: when the number of travel routes to be traveled in the route queues is not 0, the route queues are used as a route set to be processed, and the step of selecting a preset pretreatment route from the route set to be processed and adding the preset pretreatment route into the vehicle is returned to obtain updated vehicles and route queues.

Step S3208: and when the number of travel routes to be traveled in the route queue is 0, returning to the step of selecting a preset pretreatment route from the travel route set to be treated and adding the preset pretreatment route into the initial route to obtain an updated vehicle and route queue.

Step S3209: and judging whether the number of travel strokes to be performed in the updated pretreatment stroke set is 0.

Step S3210: when the number of travel routes to be traveled in the updated pretreatment route set is not 0, the updated pretreatment route set is used as a pretreatment route set, and the step of continuously executing the travel route to be traveled with the earliest departure time in the pretreatment route set as a reference route of the vehicle is returned.

Step S3211: and when the number of the travel routes to be traveled in the updated pretreatment route set is 0, determining that the final vehicle is a plurality of travel routes to be traveled corresponding to each travel route to be traveled.

In practical application, as shown in fig. 3, the process of determining the vehicle corresponding to each travel waiting course specifically includes:

step one, and for twoSeed travel set Trips ₁ /Trips ₂ Ordered in ascending order of the earliest start time of the journey.

Step two, selecting a to-be-processed travel set Trips ₁ /Trips ₂ Is the first stroke t of (2) ₁ A vehicle node vector c is generated for the reference. Will travel t ₁ From the pending trip set Trips ₁ /Trips ₂ And (5) removing. To-be-processed journey set Trips ₁ /Trips ₂ Adds to the travel queue T and sets the status of all travel as "unprocessed".

Step three, from the current journey set Trips to be processed ₁ /Trips ₂ Randomly selecting a travel t and adding the travel t to the vehicle c, setting the travel t state as processed, checking whether the vehicle c added with the travel t meets model constraint, if so, entering a step four, and if not, removing the travel t from the vehicle c, and returning to the step three.

And step four, judging whether the stroke state in the stroke queue T is 'unprocessed', if yes, returning to the step three, and if not, entering the step five.

Step five, adding c to the vehicle set Car ₁ /Car ₂ And the journey of the vehicle c is from the journey set to be processed Trips ₁ /Trips ₂ Remove while clearing c. Judging a to-be-processed journey set Trips ₁ /Trips ₂ If the air is empty, the step six is entered if the air is satisfied, and if the air is not satisfied, the step two is returned.

Step six, initializing a vehicle set Car ₁ /Car ₂ Success (all vehicles of both vehicle sets constitute one solution).

Step S33: and applying a neighborhood operator to each solution to obtain a search result corresponding to each solution.

Specifically, the neighborhood operators provided by the invention are eight neighborhood operators, which are respectively:

neighborhood operation one, delete reinsertion operation for a single client point. Neighborhood operation two, switching operation for single client point. Neighborhood operation three, delete reinsertion operation for multiple client points. Neighborhood operation four, delete reinsertion operation for multiple exchanges. Neighborhood operation five, delete reinsertion operation for a single run. Neighborhood operation six, swap operation for single trip. Neighborhood operation seven, delete reinsertion operation for multiple runs. Neighborhood operation eight, swap operation for multiple runs.

Furthermore, eight neighborhood operators designed by the invention aiming at the characteristics of inter-city travel problems are used for carrying out local search optimization on solutions. Whereas the inter-city travel-oriented vehicle path problem is essentially a multi-objective optimization problem, the multi-objective optimization problem is one of the core problems in the optimization problem, in which different objectives have different characteristics in terms of physical meaning, scale and optimization difficulty, and therefore, for some objectives that are difficult to optimize with conventional neighborhood search operations, it is necessary to design neighborhood search operations for inter-city travel characteristics. The specific definition is as follows:

neighborhood operation one: deleting a client point, reinserting the client point to the optimal position, randomly selecting a client point on a path for deletion, and reinserting the client point to the optimal position. The method for sequentially applying polar angle sequencing to the path departure points is specifically as follows: and rearranging the getting-off sequence of the client points according to the polar angle of the getting-off points after sequencing (the getting-off route of the client points is readjusted after the operation of the next neighborhood, and the adopted method is consistent with the method).

Neighborhood operation two: exchanging individual client points, firstly, randomly selecting one client point on one path to prepare for exchanging, and secondly, sequentially trying the client point to exchange with the client points on other paths. The delta value is recorded as the value of the target after optimization. And thirdly, comparing to obtain a min_delta value, and exchanging the client point with the client point under the condition that the target minimum value is obtained at the moment.

Neighborhood operation three: deleting a plurality of client points to reinsert the optimal position, randomly selecting a plurality of client points on a path to delete, and reinserting the client points to the optimal position.

Neighborhood operation four: exchanging a plurality of client points (two-opt operation), firstly, randomly selecting one client point on one path as a starting point to prepare for exchanging, secondly, alternately selecting one client point on other paths as a starting point of a second exchanging, and exchanging the path with the client point on the exchanging path as the starting point. The delta value is recorded as the value of the target after optimization. And thirdly, comparing to obtain a min_delta value, and exchanging the path taking the client point as a starting point with the client point under the condition that the target minimum value is obtained at the moment.

Neighborhood operation five: deleting one path and inserting the client point on the path into the other path (reinsertion for a single trip deletion), in a first step, randomly selecting one path (assuming path trip_ind). Second, each client point on the path tries to insert into other paths, 1: if all the client points on the path can not be deleted and inserted successfully, the restore path returns false,2: if all the client points on the path are deleted and inserted successfully, the path trip_ind is an empty path. This time division into two cases continues to be considered:

2-1: the path trip_ind is the last path of the vehicle, and the deletion of the path trip_ind can be performed directly. And returning true.

2-2: if the path trip_ind is not the last path of the vehicle, checking whether the next path trip_next of the path trip_ind can also become an empty path according to the same method, namely, each client point on the path trip_next tries to insert other paths, if the client points on the path trip_next cannot be deleted and inserted successfully, the path trip_ind and trip_next are restored, and false is returned. If all the client points on the path trip_next are successfully deleted and inserted, the path trip_next is an empty path. Then the path trip_ind and path trip_next may be deleted and true returned.

Neighborhood operation six: a single trip is exchanged by first randomly selecting two different paths on two vehicles to be exchanged and second exchanging the two paths. The delta value is recorded as the value of the target after optimization. And thirdly, if the delta value is smaller than the original target value, exchanging the two paths.

Neighborhood operation seven: deleting one vehicle and inserting the return of its vehicle into the other vehicle (deleting and reinserting for a plurality of trips), in a first step, selecting the one vehicle car_ind with the least number of current paths. Secondly, taking the route on the vehicle as a return unit or a special single journey (the journey does not have the next journey) to try to traverse and insert other vehicles, (1) returning the restored vehicle to false if the route on the vehicle cannot be completely deleted and inserted successfully, and (2) taking the vehicle car_ind as an empty route if the route on the vehicle is completely deleted and inserted successfully. The vehicle car ind may be deleted and true returned.

Neighborhood operation eight: a single backhaul (exchanging multiple trips) is exchanged, first, two different backhauls on two vehicles are randomly selected to be exchanged, and second, the two backhauls are exchanged. The delta value is recorded as the value of the target after optimization. And thirdly, if the delta value is smaller than the original target value, exchanging the two backhauls.

In the present invention, regarding the application of local search, the following strategies are adopted: probability + probability strategy: and selecting a solution from the external archive solution set EP according to the potential values of different solutions each time, and selecting a neighborhood operation mode to perform one-time deep local search according to the contribution degree of each neighborhood operation. Wherein regarding the contribution of the neighborhood operation, it should be more relevant to its contribution to convergence, i.e. the improvement of the respective target value. The selection probability for each neighborhood operation can therefore be defined as the contribution to the convergence of the external archive within the L window (near L searches), where L is defined as the respective number of selected rounds for each neighborhood operation, while a matrix is set to maintain the probability list. Regarding the potential values of the externally archived solutions, since all of the solutions are non-dominant, their potential values reflect the potential for diversity, the probability of selection of each solution can be defined as the crowding distance. I.e. the invention adopts an adaptive local search strategy for generating a new path planning scheme. The specific definition is as follows:

1. Potential value definition of solution: regarding the potential values of the externally archived solutions, since all of the solutions are non-dominant, the potential values reflect the potential for diversity, so the potential value of each solution can be defined as the crowding distance. However, since the most adopted calculation method is the congestion distance calculation method in NSGA-II, the method has the characteristic of simplicity and rapidity, but the final solution set remained is uniformly distributed only on one objective function, and the conditions of non-uniformity exist on other functions due to the fact that the congestion distances are calculated according to the order of the objective function. Therefore, on the basis of the method, an improved congestion distance calculation method is selected, and the calculation formula is as follows:

wherein:representing the mth dimensional objective function value of the next particle of the ith example after sorting from small to large in the mth dimensional objective function value;The mth-dimensional objective function value of the particle preceding the ith particle after the order of the mth-dimensional objective function values from small to large is shown. The improved crowding distance calculation method still only considers the front particle and the rear particle, and comprehensively considers the objective function of each dimension, thereby being more reasonable. It is also noted that when i=1 or i=n, dis _i If the two extremum solutions are considered, the probability of being selected of the solutions in the following calculation formula can be regarded as 100% (necessary), and since the two extremum solutions can be regarded as elite solutions in practical sense, the two extremum solutions are directly put into external archive storage and do not participate in the next solution selection.

Thereby at dis _i On the basis of (a) to obtain PV _i (probability of being selected for the ith solution in EP):

where n represents the number of solutions in the current EP and epsilon is the minimum probability of being selected that is set. Ensuring that each solution has an opportunity to be used throughout the local search process.

2. Contribution definition of neighborhood operations: regarding the contribution of the neighborhood operation search, it should be more relevant to its contribution to convergence, so the probability of selection of the kth neighborhood operation can be defined as the contribution to the convergence of the external archive within the L window, i.e., the lifting of the DOI for each target value _k (Degree of improvement):

Wherein Obj' _i An ith target value, obj, representing a solution after normalization of the neighborhood operation _i Representing the ith target value of the solution before the normalized neighborhood operation. num represents the number of targets in the model, here 4. Note that the calculation formula is used if and only if the solution after neighborhood operation optimization enters the external archive solution set EP update, the rest of the cases DOI _k Is 0. Wherein the normalization processing of the target value adopts a dispersion normalization formula, which is linear transformation of the original data, so that the result falls to [0,1 ]]The interval, transfer function is as follows:

wherein f _i An ith target value, max, representing the solution before the neighborhood operation _i And min _i The maximum value in the ith target in all solutions in the EP and the minimum value in the ith target in all solutions in the EP are respectively represented.

Thus at DOI _k LS is obtained on the basis _k (probability of selection of kth neighborhood operation):

where gen represents the current number of times the neighborhood operation is selected, ε is the minimum probability of being selected that is set. Ensuring that each neighborhood operator has an opportunity to be used throughout the local search process.

S33 specifically comprises:

step S3301: and searching each solution by applying a plurality of neighborhood operators to obtain a plurality of initial search results corresponding to each solution.

Step S3302: and calculating the lifting degree corresponding to each solution according to a plurality of initial search results.

Step S3303: and when the searching times are smaller than the preset times, returning to the step of applying a neighborhood operator to the preset solutions in the solutions to obtain initial searching results, and continuing to execute.

Step S3304: and when the searching times are greater than or equal to the preset times, calculating the improved crowding distance of each solution according to each solution and the initial searching result corresponding to each solution.

Step S3305: and normalizing the improved crowding distance of each solution to obtain the solution selection probability of each solution.

Step S3306: and determining the operation selection probability of each neighborhood operator according to the lifting degree corresponding to each solution.

Step S3307: and selecting one solution to be processed from a plurality of solutions according to the solution selection probability.

Step S3308: and determining target neighborhood operation from a plurality of neighborhood operation operators according to the operation selection probability.

Step S3309: and carrying out local search on the solution to be processed by applying the target neighborhood operation to obtain a final search result corresponding to the solution to be processed.

Step S3310: and updating the initial search result according to the final search result to obtain the search result corresponding to each solution.

In practical application, as shown in fig. 2, a specific process of obtaining the search result corresponding to each solution is as follows:

step one, randomly selecting a solution in the external archive EP, randomly selecting a neighborhood according to the average (1/8) probabilityOperator LS _n (n=1..8) local search is performed while updating the external archive solution set EP with epsilon dominance.

Step two, neighborhood operation LS _n Search times m _n And adding one, calculating the lifting degree of the neighborhood operation, and storing the result into a learning matrix.

Step three, judging whether the searching times m of the neighborhood operation exist or not _n If the number of times is less than m, returning to the first step, if the number of times is not satisfied, and entering the fourth step. In practical application, m is a preset value; alternatively, the value of m may be determined empirically or experimentally beforehand.

Step four, carrying out improved crowding distance calculation on each solution of the external archive solution set EP, carrying out normalization processing to obtain the selection probability of each solution, and simultaneously carrying out LS based on a learning matrix _k The calculation formula of (2) obtains the selection probability of each neighborhood operation.

And fifthly, selecting a solution from the external archive solution set EP according to the solution selection probability, and selecting a neighborhood operation according to the operation probability to perform deep local search.

And step six, updating the external archive solution set EP by using epsilon dominance and updating the learning matrix. Returning to the fourth step.

Specifically, the deep local search operation process comprises the following steps:

step one, the searched times m=0 are set, and the maximum search depth is D. Wherein D is a preset value; alternatively, the value of D may be determined empirically or experimentally beforehand.

And step two, carrying out neighborhood operation on the selected solution, judging whether the solution after neighborhood searching operation can enter the external archive EP, if so, returning to the step one, and if not, entering the step three.

And thirdly, adding one operation to the searched times m, judging whether the searched times m are equal to D, if yes, ending, and if not, returning to the second step.

Step S34: and applying epsilon-dominant archive update strategies to each solution and the search results corresponding to each solution to obtain the travel external archive.

In practical application, the epsilon-dominant archive update strategy comprises the following steps:

if Archive is empty, then path planning scheme X will be generated _new Added to Archive. Where Archive is a defined word in the theory of multi-objective optimization algorithms, meaning Archive, i.e. for storing the excellent individuals generated for each generation, i.e. non-dominant solutions, during the algorithm optimization process.

If Archive is not empty, then path planning scheme X will be generated _new Performing epsilon dominance comparison with the existing path planning scheme; if existing scheme takes over X _new Or with X _new Identical, then X _new Discarding; if X _new The existing path planning schemes are preempted, all the preempted schemes are deleted, and X is determined _new Adding into an Archive; if X _new Not occupying all path planning schemes, X is calculated _new Added to Archive.

Where ε is dominant for archive updates. Specifically, each non-dominant solution in the archive is assigned an association vector b= { B ₁ ,B ₂ ,…,B ₄ And 4 represents 4 optimization targets,wherein the parameter variable epsilon is used to control the size of the archive.

Archiving update policies: in the multi-objective inter-city network taxi-offer path planning problem, the comparison between path planning schemes is made by multi-objective dominant relationships. The definition of the dominant relationship related to the invention is as follows: for path planning schemes X and Y, if

1) For all target values f _j (X)≤f _j (Y)，j＝1,2。

2) At least one j is present such that f _j (X)<f _j (Y)。

When the two conditions are met, the X is called as the dominant Y; otherwise, it is called that X and Y are not dominant to each other, X and Y are non-dominant solutions.

In addition, in the base of multi-objective dominanceOn the basis, an epsilon dominance strategy is added for limiting the size of the external archives during the search. In the epsilon-dominant archive, each non-dominant solution in the archive corresponds to an associated vector b= { B ₁ ,B ₂ ,B ₃ ,B ₄ }, wherein B is _i ＝log(f _i +1)/log (1+ε), one non-dominant solution is stored in each hypercube. Thus, the epsilon-based archives not only evenly distribute the non-dominant solutions, but also define the size of the external archives during the search.

According to the definition of the dominant relationship, the archiving and updating strategy of the multi-objective inter-city network about vehicle path planning problem is as follows: if Archive is empty, then path planning scheme X will be generated _new Added to Archive.

When the invention is actually applied, after the inter-city order to be taken is obtained, the following operations are executed:

(1) Ordering the two order sets according to the ascending order of the earliest departure time of the order, generating a journey meeting the constraint according to the method, finishing the initialization of the two journey sets, ordering the journey sets according to the ascending order of the earliest departure time of the journey, generating vehicles meeting the constraint according to the method (trip tasks, the same applies below), finishing the distribution of the two vehicle sets, generating an initial feasible planning scheme, and updating the scheme into an external archive solution EP.

(2) Judging whether the initialization times n reach the set maximum cycle times n _max If so, entering the step (3); otherwise, returning to the step (1). Wherein the maximum number of cycles n _max Is a preset value; alternatively, the maximum number of cycles n _max May be determined based on empirical values or pre-experiments.

(3) And for a path planning scheme in the external archiving solution set EP, performing self-adaptive local search strategy updating EP based on eight neighborhood operators designed based on inter-city travel characteristics.

(4) Judging whether the running time h reaches the set maximum running time h _max If so, the step (5) is carried out, otherwise, the step (3) is returned.

(5) A path planning scheme in the external archive solution set EP is selected according to the requirements of a decision maker (according to the actual needs of four targets), and each path sequence in the scheme is allocated to a vehicle (driver) corresponding to the path sequence.

There are two drawbacks to the use of the current widely used examples for the inter-city travel path optimization problem: first, the travel distance and travel time between client points in a dataset is over-idealized, which relies on Euclidean distance and assumes that the triangle inequality relationship is satisfied, but in the real world is often not in a proportional relationship and does not necessarily satisfy the triangle inequality. Second, in this example, the targets exhibit weak correlation between them, and thus cannot be applied to the study of multi-target optimization. Due to the lack of a vehicle path problem test example suitable for intercity travel of the study, the effectiveness of the model and algorithm provided by the study is further verified. Therefore, the invention designs a vehicle path problem test calculation example suitable for intercity travel-oriented study: testing is performed based on real order data of a certain intercity network vehicle-restraining platform. The test examples are specifically defined as follows:

Based on the data of the actual scene of the vehicle-restraining platform of a certain network of the mansion, the actual order data between the mansion and the Zhangzhou is selected to design a test example, the coordinates of the client point and the garage are generated based on the actual position, meanwhile, the running time and the running distance between any two points are calculated based on the actual road network, the actual road network can be a hundred-degree map, and the data which more accord with the actual travel rule can be obtained. The distance is accurate to meters and the time is accurate to minutes. Meanwhile, in order to ensure the authenticity and randomness of the data as much as possible, the departure time of the order should meet the actual situation of the peak in the morning and evening, so the random departure time in the corresponding time window is proportionally generated, and the number of passengers of the order is generated in a purely random mode. In addition, by consulting documents and investigation, three types of scale scenes are defined by changing the number of orders, the travel demand proportion and the time window types, each type of scale is divided into five numbers of client points due to the difference of the travel demand proportion, and meanwhile, the scenes are further combined according to the difference of the three time window types, so that 45 test cases can be obtained. The maximum number of the client points can reach 600, so that the method is a large-scale test example.

The self-adaptive local search method for solving the path problem of the multi-objective inter-city traveling vehicle provided by the invention has the advantages that the self-adaptive local search method is obviously reduced in 4 objective aspects and is also obviously improved in the aspect of seating rate and passenger evaluation feedback. Meanwhile, all solutions of the calculation example are taken out, the obtained target values are subjected to pairwise comparison of balance graphs (namely four targets and pairwise comparison, for example, the target f1 is taken as an x axis, the target f2 is taken as a y axis), and the relation among the targets is approximately an inverse proportion function from the view of the result graph, so that negative correlation exists among 4 targets selected by the model, and the accuracy of target selection of the multi-target inter-city travel vehicle path problem model established by the invention is verified. In addition, in order to further verify the effectiveness of the proposed method, the proposed method is statistically analyzed with the comparative experimental results of the LSMOVRPTW algorithm, the MOEA/D algorithm, and the NSGA2 algorithm. And two indexes widely used by a multi-objective optimization algorithm are adopted, namely: IGD and HV. They are used to demonstrate the convergence and diversity of the algorithm from different angles, where IGD and HV are univariate indicators, and the results indicate that the method of the invention is significantly better than the LSMOVRPTW algorithm in both IGD and HV indicators. In particular, the method of the present invention is significantly better than the LSMOVRPTW algorithm in 40 cases and worse than the LSMOVRPTW algorithm in 1 case, in terms of IGD metrics, based on the single problem analysis of the Wilcoxon test. In terms of HV index, the method of the invention is remarkable in 37 calculation examples Better than the LSMOVRPTW algorithm, and worse in 3 cases. Based on the multi-problem analysis of Wilcoxon test, the method of the present invention obtains R+ values higher than R-in both IGD and HV indices. The method of the present invention is generally superior to the comparative algorithm in all rwMOVRPTW examples. At the same time, in order to intuitively demonstrate the convergence and diversity of the method and LSMOVRPTW algorithm of the present invention, the non-dominant solution obtained in several representative examples is found in the second and third targets (f ₂ -f ₃ ) A second target and a fourth target (f ₂ -f ₄ ) And mapping on. The non-dominant solution distribution of the method of the present invention is broader and also more closely approximates the pareto front than the comparative algorithm. Meanwhile, the eight neighborhood operations provided by the invention are subjected to module comparison test, each local search operator is respectively omitted, and the algorithm is operated for 30 times. The average target per instance is reduced after omitting one neighborhood operator. It can be observed that the performance of the method of the invention becomes worse in terms of goals due to the lack of any neighborhood operators, especially as the number of nodes of an instance increases. This means that all neighborhood operators are very important for the algorithm proposed by the present invention. Using IGD, HV, wilcoxon and Fredman tests, it can be observed that each neighborhood operator that is missing is inferior to the result before the missing, with the effect of neighborhood operators L4, L5, L6 and L8 being more pronounced. In conclusion, the method provided by the invention can effectively and intelligently solve the problem of path planning of the inter-city network vehicle travel.

The invention defines the inter-city network vehicle-closing path optimization problem as a multi-objective problem comprising four objectives, and reflects the essence of inter-city travel problems more comprehensively and truly; constructing an initial non-dominant vehicle path scheme by an initial construction method based on a time sequence; and then eight neighborhood operation operators designed based on inter-city travel characteristics utilize convergence of local search and diversity characteristics of solution, a selection strategy of a solution and a local search selection strategy are prepared, the path planning scheme is subjected to iterative optimization by utilizing a self-adaptive local search strategy, effective coordination by utilizing potential synergistic effects among a plurality of neighborhood operations is realized, and meanwhile, the quality of a multi-objective solution is further improved. The effective combination of the mechanisms can not only efficiently plan the travel path of the inter-city network taxi-offer, but also provide a high-quality planning scheme set with different requirements for the inter-city network taxi-offer service by utilizing a multi-objective optimization method.

Example two

In order to perform a corresponding method of the above embodiment to achieve the corresponding functions and technical effects, an inter-city travel vehicle path determining system is provided below, the determining system includes:

The construction module is used for constructing an inter-city travel vehicle path model by taking the maximum average number of passengers going out per trip, the minimum number of vehicles, the minimum total running distance of the vehicles and the minimum total waiting time of the passengers as objective functions and taking the vehicle passenger capacity, the service quality, the time constraint and the safety constraint as constraint conditions.

The acquisition module is used for acquiring an inter-city order to be travelled; the inter-city orders to be travelled comprise a plurality of travelling orders; each travel order includes a number of passengers, a departure time, a departure location, and a destination location.

Example III

The embodiment of the invention provides electronic equipment, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the inter-city travel vehicle path determining method in the first embodiment.

Alternatively, the electronic device may be a server.

In addition, the embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the inter-city travel vehicle path determining method of the first embodiment when being executed by a processor.

In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.

The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims

1. An inter-city travel vehicle path determining method, comprising:

2. The inter-city travel vehicle path determining method of claim 1, wherein the objective function is:

f ₂ ＝|M _k |

3. The inter-city travel vehicle path determining method according to claim 1, wherein determining a travel vehicle corresponding to each travel order according to the inter-city travel vehicle path model specifically comprises:

4. The inter-city travel vehicle path determining method according to claim 3, wherein determining a plurality of travel routes to be traveled corresponding to each of the travel orders according to the constraint condition and the objective function, specifically comprises:

when the number of passengers in the updated initial journey reaches the passenger capacity of the vehicle, the updated initial journey is used as a final initial journey, and travel orders in the final initial journey are deleted from the preprocessing order set, so that an updated preprocessing order set is obtained;

5. The inter-city travel vehicle path determining method according to claim 3, wherein determining vehicles corresponding to each of the travel routes to be traveled according to the constraint condition and the objective function, specifically comprises:

selecting a preset pretreatment travel from the travel set to be treated, and adding the preset pretreatment travel set to the vehicle to obtain an updated vehicle and travel queue; the stroke set to be processed is the rest stroke of the preprocessing stroke set after the reference stroke is removed; the stroke queue is the residual stroke after one preset pretreatment stroke is removed from the to-be-treated stroke set;

Judging whether the updated vehicle meets the constraint condition or not;

when the updated vehicle meets the constraint condition, the updated vehicle is used as a final vehicle, and the travel route to be traveled in the final vehicle is deleted from the pretreatment route set, so that an updated pretreatment route set is obtained;

6. The method for determining an inter-city travel vehicle path according to claim 1, wherein applying a neighborhood operator to each solution to obtain a search result corresponding to each solution, comprises:

7. An intercity travel vehicle path determination system, the determination system comprising:

8. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the inter-urban travel vehicle path determination method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the inter-urban travel vehicle path determination method according to any one of claims 1 to 6.