CN104794551A - Automatic optimization system and method of itinerary with time window - Google Patents
Automatic optimization system and method of itinerary with time window Download PDFInfo
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Abstract
The invention discloses an automatic optimization system and method of an itinerary with a time window. The system comprises a database module, a matrix calculation module, an itinerary generation module with a time window and a global itinerary check module, wherein the database module is used for storing and maintaining the opening/closing time of POIs and traffic data between the POIs; the matrix calculation module is used for inquiring the traffic data between every two POIs to obtain a matrix; the itinerary generation module is used for generating an itinerary approximately satisfying the time window by utilizing a heuristic algorithm; the global itinerary check module is used for checking whether the POIs in the itinerary satisfy the constraint of the opening/closing time one by one, and re-generating a new itinerary with all POIs satisfying the constraint or achieving the iterations if the POIs do not satisfy the constraint. The automatic optimization system has the benefits that the work of looking up data by a user is omitted, the user can be helped to rapidly arrange reasonable itineraries, and the problems that the scenic spot sequence is not reasonable during the itinerary arrangement and arranged scenic spots are not opened or already closed at the arrival time can be effectively solved.
Description
Technical field
The present invention relates to one route optimization system and method every day, be specifically related to a kind of route optimization system and method every day with time window.
Background technology
Tourist industry is current popular industry, and is the focus of future investments.Along with the raising of people's living standard, the generation frequency of people's tourism is more and more higher, and changes free walker by traditional into group's trip gradually.Always need before people go on a tour to spend a lot of time read tourism attack strategy and formulate stroke plan, in order to avoid be at a loss after arriving destination.And traditional travel agency still needs for visitor provides study of tourism itinerary production.Therefore, individual visitor and travel agency all need to make itinerary, but making itinerary is a difficult process, current most of individual visitor and travel agency are when formulating itinerary, the main still manual type of the method that arrangement sight spot uses in order, namely first collects the travel information of a large amount of destination, then utilizes map tool from various channel, analyze according to the position of sight spot on map, the one that is finally ranked is played sequentially.This mode very wastes time and energy, inefficiency.
Along with the development of mobile Internet, in recent years occurred many website and application providing Tourism Information Resources, user generally only needs two steps can arrange the order of playing at sight spot:
The first step: generate an itinerary at application interface;
Second step: use the built-in optimization tool of application to rearrange sight spot order.
The website of this class and application greatly facilitate people's layout sight spot order, for manual type brings facility, but it also exists following shortcoming: have ignored switch gate time at sight spot when optimizing sight spot order, may cause missing the switch gate time at sight spot and cannot playing during the actual tourism of user.
Summary of the invention
For solving the deficiencies in the prior art, the object of the present invention is to provide a kind of itinerary Automatic Optimal system and method with time window, this route Automatic Optimal system can remove the work of people's inspection information from, help the rational stroke route of people's quickly arranging, and effectively can solve the problems such as the sight spot order possibility when layout stroke route is unreasonable, may also not open the door when arriving or close the door in the sight spot of arrangement.
In order to realize above-mentioned target, the present invention adopts following technical scheme:
With an itinerary Automatic Optimal system for time window, it is characterized in that, comprising: the Route Generation module of database module, matrix computations module, band time window and overall route inspection module,
Database module: the traffic data between switch gate time of storage and maintenance POI, POI, aforementioned traffic data comprises: the distance between two POI needed for traffic, time and expense;
Matrix computations module: the traffic data in data query library module between two between POI, obtains the matrix M of a N × N, and aforementioned N is the number needing the POI played in a day;
Route Generation module with time window: the switch gate time of the POI stored in the matrix M calculated according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window, aforementionedly approximate meet time window and refer to: in this itinerary, ensure that each POI meets time window as far as possible, but there is the probability that indivedual POI can not meet this requirement;
Overall situation route inspection module: whether the POI in the route of the Route Generation module generation of verification band time window one by one meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
Utilize the method for aforesaid itinerary Automatic Optimal system Automatic Optimal itinerary, it is characterized in that, comprise the following steps:
Step one: store the traffic data between the switch gate time of POI, POI in database module, aforementioned traffic data comprises: the distance between two POI needed for traffic, time and expense;
Step 2: the traffic data in matrix computations module polls database module between two between POI, obtains the matrix M of a N × N, aforementioned N is the number needing the POI played in a day;
Step 3: the switch gate time of the POI stored in the matrix M that the Route Generation module of band time window calculates according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window, aforementionedly approximate meet time window and refer to: in this itinerary, ensure that each POI meets time window as far as possible, but there is the probability that indivedual POI can not meet this requirement;
Step 4: whether the POI in the route that the Route Generation module that overall route inspection module verifies band time window one by one produces meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
The method of aforesaid Automatic Optimal itinerary, is characterized in that, in step 3, the approximate method meeting the itinerary of time window of Route Generation CMOS macro cell one of band time window is:
Step1: the itinerary to be optimized such as to represent with oriented weighted graph G (V, E), wherein, POI is regarded as the summit V of figure G, the traffic distance/traffic time/transport cost between POI is regarded as the limit E in figure G between summit;
Step2: find a unduplicated shortest path L from oriented weighted graph G (V, E), aforementioned shortest path L is from the summit V that user specifies
sset out, to the summit V specified
eterminate, by all summit V of figure G (V, E), and each summit is only by once, requires that the moment arriving each summit is in the time window on this summit simultaneously.
The method of aforesaid Automatic Optimal itinerary, it is characterized in that, in Step2, the algorithm finding a unduplicated shortest path L use from oriented weighted graph G (V, E) is ant group algorithm, random greedy algorithm, the method for exhaustion or branch and bound method.
Usefulness of the present invention is:
(1) the itinerary Automatic Optimal system of band time window of the present invention, it can be optimized adjustment to existing stroke route, not only ensure the authenticity of traffic data and starting and terminal point, and when ensure that user arrives sight spot, this sight spot is in open state;
(2) the itinerary Automatic Optimal system of band time window of the present invention, it can save the time of consulting map for user, easily arranges route of reasonably playing;
(3) the itinerary Automatic Optimal system of band time window of the present invention, that takes into account the switch gate time at each sight spot, greatly improves the rationality optimizing route;
(4) the itinerary automatic optimization method of band time window of the present invention, it is when the process switch gate time, the soft-constraint condition of switch gate time window as algorithm is used, first search an approximate solution, again by successive ignition attempt find out the solution satisfied condition completely, effectively prevent time window is caused as hard constraint condition solve difficulty.
Accompanying drawing explanation
Fig. 1 is the composition schematic diagram of the itinerary Automatic Optimal system of band time window of the present invention;
Fig. 2 is the itinerary before optimizing;
Fig. 3 is the itinerary after optimizing.
Embodiment
Terminological interpretation:
(1) POI-point of interest, refers in particular to sight spot, hotel, dining room etc. in travel folder;
(2) the switch gate time of time window-POI, only within this time period, this POI that just can play is arrived;
(3) method of figure is described, the abstract distance/expense/time of point-to-point transmission in each element corresponding diagram of matrix in matrix-graph theory.
First, the itinerary Automatic Optimal system of band time window of the present invention is introduced.
The itinerary Automatic Optimal system of band time window of the present invention, it can be made stroke route every day and optimizing and revising, consider the impact of POI time window, rearrange the order of POI in stroke route every day, make every day stroke route by way of traffic distance the shortest, or transport cost is economized most, or traffic time is minimum, and above three kinds of optimisation strategy can be chosen any one kind of them according to actual needs.
Below in conjunction with the drawings and specific embodiments, concrete introduction is done to system of the present invention.
With reference to Fig. 1, the itinerary Automatic Optimal system of band time window of the present invention, it mainly comprises following four functional modules: the Route Generation module of database module, matrix computations module, band time window and overall route inspection module.Introduce each functional module respectively below.
(1) database module
Database module is used for the traffic data between switch gate time of storage and maintenance POI, POI, and this traffic data comprises: the information such as the distance between two POI needed for traffic, time and expense, and these data are the necessary data of route optimization.
Database has huge effect to the robustness of route optimization system and improved efficiency.
(2) matrix computations module
Matrix computations module is used for the traffic data in data query library module between two between POI, and obtain the matrix M of a N × N, wherein, N is the number needing the POI played in a day.
Matrix M is used in the Route Generation module of band time window.
(3) the Route Generation module of time window is with
The switch gate time of the POI stored in the matrix M that the Route Generation module with time window calculates according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window.
Here, have illustrate at 2 for so-called " be similar to and meet time window ":
(1) meet time window: in this itinerary, the moment arriving some POI just in time drops in the opening time of this POI;
(2) approximately time window is met: in this itinerary, ensure that each POI meets time window as far as possible, but there is the probability that indivedual POI can not meet this requirement.
(4) overall route inspection module
Whether the POI in the route that the Route Generation module that overall situation route inspection module is used for verifying band time window one by one produces meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
Visible, itinerary Automatic Optimal system of the present invention can be optimized adjustment to existing stroke route, can not only guarantee the authenticity of traffic data and starting and terminal point, and when can guarantee that user arrives sight spot, this sight spot is in open state.
In addition, because itinerary Automatic Optimal system of the present invention considers the switch gate time at each sight spot, so greatly improve the rationality optimizing route.
Next, the method utilizing itinerary Automatic Optimal system Automatic Optimal itinerary of the present invention is introduced.
Step one: store the traffic data between the switch gate time of POI and POI
Database module stores the traffic data between switch gate time of POI and POI, and this traffic data comprises: the information such as the distance between two POI needed for traffic, time and expense.
Step 2: inquire about the traffic data between two between POI
According to route every day of input, can know the POI that this day needs to play, so inquire about the traffic data between two between POI in matrix computations module to database module, obtain the matrix M of a N × N, wherein, N is the number needing the POI played in a day.
Matrix M is used in the Route Generation module of band time window.
Step 3: generate an approximate itinerary meeting time window
The switch gate time of the POI stored in the matrix M that the Route Generation module with time window calculates according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window.
When Route Generation CMOS macro cell with time window one is similar to and meets the itinerary of time window, have the optimisation strategy that three kinds different, namely traffic distance is the shortest, transport cost is economized most, traffic time is minimum.
Be introduced respectively for these three kinds different optimisation strategy below.
The first: traffic distance is the shortest
Mathematically, optimization route every day belongs to a branch of OVRP (Open Vehicle RoutingProblem) problem, and OVRP is a class specific question of TSP (Travelling SalesmanProblem) problem, this is the NP-hard problem that a class solves Combinatorial Optimization, is difficult to by calculating optimum solution when the large order of magnitude.
Solve this kind of problem, often need first path planning problem to be converted into graph theory model, therefore the 1st step optimizing route every day is with oriented weighted graph G (V, E) route every day that expression etc. are to be optimized, wherein, POI is regarded as the summit V of figure G, the traffic distance between POI is regarded as the limit E in figure G between summit.So the matrix M that matrix computations module provides schemes the expression matrix form of G (V, E) exactly.
So the 2nd step optimizing route every day is exactly find one article of unduplicated shortest path L from oriented weighted graph G (V, E), and this shortest path L is from the summit V that user specifies
sset out, to the summit V specified
eterminate, by all summit V of figure G (V, E), and each summit is only by once, requires that the moment arriving each summit is in the time window on this summit simultaneously.
The method solving this mathematical model has many, mainly heuritic approach, is divided into exact algorithm and approximate data two class.Traditional exact algorithm such as the method for exhaustion, branch and bound method etc. can calculate optimum solution to a certain extent, but their time complexity is very large, and along with the increase of POI quantity, these methods were all difficult to obtain result within the limited time.And approximate data pursues the feasible solution of suboptimum, these algorithms are as random greedy algorithm, simulated annealing, genetic algorithm and Tabu search algorithm etc., preferably feasible solution can be obtained within a certain period of time, but once the quantity of POI increases, also be difficult to obtain good result in finite time.
One is also had to propose ant group algorithm (Ant ColonyOptimization) for TSP problem specially, much smaller than other several heuritic approach on time complexity, and precision is also high than other algorithm, the TSP problem of up to ten thousand POI can be solved in finite time, be therefore used widely.
For the itinerary Automatic Optimal system of band time window, the POI quantity of every day is very limited, generally can not more than 8, therefore no matter adopts the method for exhaustion or ant group algorithm can obtain good effect.For the requirement to system module performance, ant group algorithm remains best selection.When specific implementation ant group algorithm, need the constraint considering time window, the summit of time window constraint can not be met, should get rid of as far as possible outside the list of next step search.
The second: transport cost is economized most
The optimization method the shortest with traffic distance is substantially identical, only the transport cost between POI is seen the limit E between summit in mapping G.Concrete:
First, with oriented weighted graph G (V, E) route every day that expression etc. are to be optimized, wherein, POI is regarded as the summit V of figure G, transport cost between POI is seen the limit E in mapping G between summit, then the matrix M of the figure G (V, E) now obtained just characterizes the transport cost relation between each POI.
Then, from oriented weighted graph G (V, E), find a unduplicated shortest path L, this shortest path L is from the summit V that user specifies
sset out, to the summit V specified
eterminate, by all summit V of figure G (V, E), and each summit is only passed through once.
After solving, the circuit that transport cost is economized most can be obtained.
The third: traffic time is minimum
The optimization method the shortest with traffic distance is substantially identical, only the traffic time between POI is seen the limit E between summit in mapping G.Concrete:
First, with oriented weighted graph G (V, E) route every day that expression etc. are to be optimized, wherein, POI is regarded as the summit V of figure G, traffic time between POI is seen the limit E in mapping G between summit, then the matrix M of the figure G (V, E) now obtained just characterizes the traffic time relation between each POI.
Then, from oriented weighted graph G (V, E), find a unduplicated shortest path L, this shortest path L is from the summit V that user specifies
sset out, to the summit V specified
eterminate, by all summit V of figure G (V, E), and each summit is only passed through once.
After solving, the circuit that traffic time is economized most can be obtained.
Step 4: export optimised after route
Whether the POI in the route that the Route Generation module that overall situation route inspection module verifies band time window one by one produces meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
Fig. 2 is itinerary every day before optimizing, and drawing pin A is the starting point of stroke, and the background of Fig. 2 is map (omitting), and each point represents a POI.When route before the optimization provided according to Fig. 2 is played, visitor is when arrival the 4th POI, and closing the door in this sight spot, cannot play.
Adopt ant group algorithm to itinerary optimize after, optimization the results are shown in Figure 3.When route after the optimization provided according to Fig. 3 is played, visitor is when arrival the 3rd POI, and not closing the door in this sight spot, still can play.
Contrast from Fig. 2 and Fig. 3: if visitor not have in advance map with reference to or the open hour at sight spot are not understood time the itinerary formulated; or be the route tentatively generated by machine; usually the intersection of circuit is there will be in the route of these " tentatively "; or the sight spot time arranged not within the switch gate time at this sight spot, thus cannot play when visitor can be allowed to walk the road of repetition or arrive this sight spot.After Automatic Optimal system optimization of the present invention, the intersecting routes originally occurred is eliminated, and travelling route becomes clear, and visitor can not go a long way unnecessarily again, and one enters surely to play when arriving sight spot.
As can be seen here, the automatic optimization method of itinerary of the present invention, it is when the process switch gate time, the soft-constraint condition of switch gate time window as algorithm is used, first search an approximate solution, again by successive ignition attempt find out the solution satisfied condition completely, effectively prevent time window is caused as hard constraint condition solve difficulty.
It should be noted that, above-described embodiment does not limit the present invention in any form, the technical scheme that the mode that all employings are equal to replacement or equivalent transformation obtains, and all drops in protection scope of the present invention.
Claims (4)
1. the itinerary Automatic Optimal system with time window, is characterized in that, comprising: the Route Generation module of database module, matrix computations module, band time window and overall route inspection module,
Database module: the traffic data between switch gate time of storage and maintenance POI, POI, described traffic data comprises: the distance between two POI needed for traffic, time and expense;
Matrix computations module: the traffic data in data query library module between two between POI, obtains the matrix M of a N × N, and described N is the number needing the POI played in a day;
Route Generation module with time window: the switch gate time of the POI stored in the matrix M calculated according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window, describedly approximate meet time window and refer to: in this itinerary, ensure that each POI meets time window as far as possible, but there is the probability that indivedual POI can not meet this requirement;
Overall situation route inspection module: whether the POI in the route of the Route Generation module generation of verification band time window one by one meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
2. utilize the method for the itinerary Automatic Optimal system Automatic Optimal itinerary described in claim 1, it is characterized in that, comprise the following steps:
Step one: store the traffic data between the switch gate time of POI, POI in database module, described traffic data comprises: the distance between two POI needed for traffic, time and expense;
Step 2: the traffic data in matrix computations module polls database module between two between POI, obtains the matrix M of a N × N, described N is the number needing the POI played in a day;
Step 3: the switch gate time of the POI stored in the matrix M that the Route Generation module of band time window calculates according to matrix computations module and database, heuritic approach is utilized to generate an approximate itinerary meeting time window, describedly approximate meet time window and refer to: in this itinerary, ensure that each POI meets time window as far as possible, but there is the probability that indivedual POI can not meet this requirement;
Step 4: whether the POI in the route that the Route Generation module that overall route inspection module verifies band time window one by one produces meets the constraint of switch gate time, if there is POI not meet the constraint of switch gate time, then regenerate a new route, until all POI all meet constraint or reach iterations, finally export optimised after route.
3. the method for Automatic Optimal itinerary according to claim 2, is characterized in that, in step 3, the approximate method meeting the itinerary of time window of Route Generation CMOS macro cell one of band time window is:
Step1: the itinerary to be optimized such as to represent with oriented weighted graph G (V, E), wherein, POI is regarded as the summit V of figure G, the traffic distance/traffic time/transport cost between POI is regarded as the limit E in figure G between summit;
Step2: find a unduplicated shortest path L from oriented weighted graph G (V, E), described shortest path L is from the summit V that user specifies
sset out, to the summit V specified
eterminate, by all summit V of figure G (V, E), and each summit is only by once, requires that the moment arriving each summit is in the time window on this summit simultaneously.
4. the method for Automatic Optimal itinerary according to claim 3, it is characterized in that, in Step2, the algorithm finding a unduplicated shortest path L use from oriented weighted graph G (V, E) is ant group algorithm, random greedy algorithm, the method for exhaustion or branch and bound method.
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