CN114298428A - Travel route planning optimization method based on value density calculation - Google Patents
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Abstract
The invention discloses a tour route planning optimization method based on value density calculation, which comprises the following steps of S1: collecting information of all tourists before planning a starting moment; step S2: determining parameters required by model calculation; step S3: establishing a mixed integer linear programming model with the maximum total satisfaction of all tourists as an objective function; step S4: and solving the optimization model by using a variable neighborhood search algorithm to finally obtain a tour route planning scheme with the maximum total satisfaction of the tourists. The invention considers the tourism route planning problem of China from the perspective of the agency orientation problem, solves the problem of local congestion which may occur in the traditional tourism, and is beneficial to improving the efficiency of the tourism service system.
Description
Technical Field
The invention relates to the technical field of travel service systems, in particular to a travel route planning optimization method based on value density calculation.
Background
According to the report released by world union of tourist cities in 2021, the total number of tourism in 2020 is reduced to 72.78 hundred million people, and the same ratio is reduced by 40.8%. Under benchmark conditions, the total number of global tourism is estimated to reach 95.45 hundred million people in 2021 years, and the comparably increase is 31.1 percent. Generally, a guest individually selects his favorite sights and determines his/her trip. However, if there are a large number of visitors at certain attractions or attractions with limited reception capacity, this may cause serious congestion problems during certain time periods. Therefore, how the tourist route planner improves the satisfaction of the passengers according to the preferences of the tourists is an important decision problem.
At present, the main domestic categories are as follows. One is to make each guest plan their own route without doing any route planning. Although the method is simple and convenient, if a certain scene is attractive to all tourists strongly, the problem of travel congestion of the scenic spot which is visited by most tourists preferentially can be caused, and the problem is particularly prominent in large holidays. The second method is that travel agencies or travel software plan the journey for each travel group, and although this method performs the most basic route planning, it lacks consideration of personal preference of each visitor, and may result in some visitors not accessing the sights of their own mood. A method for planning a travel route, namely a private customized way, has also emerged in recent years. The method is mainly used for planning the tour route for the tourists by experienced professionals, but the method is high in price, cannot be popularized and is mainly planned by the experience of the professionals, and is not a scientific decision-making method.
The invention solves the problem of travel route planning from the perspective of the service system, is beneficial to improving the operation efficiency of the travel service system, reducing the queuing time of tourists and improving the satisfaction degree of the tourists.
Disclosure of Invention
Aiming at the existing problems, the invention provides a tour route planning optimization method based on value density calculation, which is used for planning a route under the condition of considering limited service capacity of scenic spots, establishing a mixed integer linear planning model by taking the maximum total satisfaction degree of all tourists as an objective function, and solving the model by using a variable neighborhood search algorithm.
In order to solve the problems in the prior art, the technical scheme of the invention is as follows:
a travel route planning optimization method based on value density calculation comprises the following steps:
step S1: collecting information of all tourists before the planning starting time, wherein the collected information of the tourists at least comprises the total available time length of each tourist, and the satisfaction degree of each tourist after receiving the service of each scenic spot;
step S2: determining parameters required by model calculation at the planning starting moment; the parameters comprise the time required by each scenic spot to serve one tourist, the resource constraint of each scenic spot, namely the number of tourists which can be served by each scenic spot simultaneously, and the distance between every two scenic spots;
step S3: establishing a mixed integer linear programming model with the maximum total satisfaction of all tourists as an objective function;
step S4: solving the optimization model by using a variable neighborhood search algorithm to finally obtain a tour route planning scheme with the maximum total satisfaction of tourists;
in S3, the mixed integer linear programming model is further as follows:
step S31: setting a model hypothesis condition: all tourists visit according to the route planned by the system, the phenomenon of queue insertion can not occur, the tourists can go to any other scenic spot from one scenic spot directly without going to other scenic spots, each scenic spot can serve the next tourist immediately after serving one tourist, accidents can not occur in the service process, and the tourists are known during the travel time of different scenic spots and different time periods.
Step S32: the signs and decision variables of the known parameters in the model are set, which are specifically described as follows: n denotes the number of guests, H denotes the number of attractions other than the start and end points, tliIndicates the upper limit of time available to guest i, stjIndicates the time required for sight j to serve a guest, scjResource restriction, R, representing sight jijIndicates the satisfaction, tt, that the guest i can obtain after receiving the service at the attraction jjkRepresents the time, x, required to arrive at sight k starting from sight jijkIndicating whether the visitor i departs from the sight j to the sight k, if so, it is 1, otherwise, it is 0, and it is a model decision variable, i is 1, 2.
Step S33: according to the travel matrix xijkAnd calculating intermediate variables in the model using the parameters predetermined in step S2; wherein the intermediate variables at least comprise a matrix of resources used by the guest, a matrix of queuing order of the guest, time when the guest begins to receive service at different attractions, and a variable for preventing sub-loops; the step S33 further includes the steps of:
step S331: the calculation formula of the matrix using the resources is:
step S332: the calculation formula of the matrix of the guest queuing sequence is as follows:
step S333: the formula for calculating the time when the tourist starts to receive service at different scenic spots is as follows:
step S334: the formula for calculating the variables for preventing the sub-loop is:
step S34: establishing a tourist route continuity constraint and a tourist time constraint, modeling an objective function, and considering that the total satisfaction of all the tourists is maximum; the step S34 further includes:
step S341: the constraints that all guests need to go from a starting point and to reach an ending point are as follows:
step S342: the constraints that the time when all tourists start to receive service at any attraction and the time when the tourist arrives at the terminal is less than the available time are as follows:
the variable neighborhood searching algorithm of step S4 further includes the following steps:
step S41: generating an initial solution for the local search; the travel route of each tourist is represented by a chain, each position of the chain is a sequence number of an accessed scenic spot, the service sequence of each scenic spot is represented by a chain, each position of the chain is a sequence number of a tourist served, and the sub-cycle is ensured not to be generated in the initialization process. Most of the previous initial solution generation methods adopt traversing all insertable positions and all insertable scenic spots and calculating the ratio of the insertion increase satisfaction degree and the increase time to select the inserted scenic spots and positions. Here, the inserted position only selects the end of each tourist chain and sight chain, and the ratio is calculated by using the sum of the values of all available sights within a certain distance of the selected sight instead of the original inserted added value.
Step S42: the value density of each guest chain is calculated by dividing the satisfaction obtained by each guest by the available time limit of the guest, and all guest chains are divided into two parts according to the value density: good chains and under-planned chains are planned. And selecting the chain with the minimum value density in the underoptimized chains as the selected chain.
Step S43: the neighborhood search uses four neighborhood structures; the step S42 further includes:
step S431: insert sights on the selected chain: the neighborhood structure randomly selects sights that are not visited by the selected guest and inserts them into the last location of the guest link and the last location of the corresponding resource link. It allows the usage time of any under-planned link to exceed its time limit;
step S432: changing the position of the sight on the selected chain: first, the neighborhood structure calculates the ratios of all the sights in the chain using a method similar to the ratio calculation in the initial solution construction algorithm and selects the point with the smallest ratio as the point to be repositioned. The selected attraction is attempted to move to all possible locations and if the move operation reduces the time taken for the selected link to complete the route, the move is accepted, similar to the previous step, which may have the time of use of any under-planned link exceeding its time limit;
step S433: remove sights from selected chains: this neighborhood structure will continually delete the last sight point on the selected link until the guest's age does not exceed the time limit;
step S434: the purpose of this neighborhood structure is to achieve one of the following goals by exchanging a randomly visited sight with an unvisited sight on a randomly selected chain of guests: 1) increasing the overall satisfaction by switching visited attractions to higher satisfaction unvisited attractions, and 2) reducing the total travel time of the corresponding itinerary if the satisfaction of visited and unvisited attractions is the same. If the guest chain has no viable exchanges or finds one, the process ends;
step S44: the purpose of the shaking process is to jump the solution out of the possible local optimum by randomly changing the solution. There are two different shocks in the shaking procedure: local oscillations and global oscillations. For local concussions, if the selected chain cannot become a well-planned chain, the local concussion process is used to jump the selected chain out of the current sight selection. If no better solution can be found after a certain number of iterations, a global oscillation is initiated. The step S44 further includes the steps of:
step S441: local oscillation. First, the time limit of the selected chain is swapped with the time limit of another under-planned chain, which is small (the swap does not violate the time limit, meaning the swap does not cause the visitor's age to exceed its time limit). If the selected chain still cannot be a well-planned chain after the exchange, the process randomly deletes a plurality of sights, and the number of the sights deleted is calculated by the following formula:
nd＝γτ·n
where n is the number of sights on the selected chain, and γ and τ are two random numbers evenly distributed over (0, 1.) in this process, the tabu list to prevent repeated insertions into the same sight will be eliminated, however, if the selected chain has entered the local concussion program more than Ω times, it can be considered as a well-planned chain regardless of its value density;
step S442: and (5) overall oscillation. Similar to the local concussion program, this program randomly deletes each visitor-linked sight, the number of sights deleted being equal to half of the local concussion. After deletion, randomly inserting the unaccessed scenic spots into each tourist chain;
step S45: when a better solution than the current optimal solution is obtained, the optimal solution is updated.
Compared with the prior art, the invention has the following beneficial effects:
the invention solves the problem of tour route planning in China from the perspective of a service system, and by adopting the technical scheme of the invention, the problem of tour route planning can be solved, thereby being beneficial to improving the operation efficiency of the tour service system, reducing the queuing time of tourists and improving the satisfaction degree of the tourists.
Drawings
FIG. 1 is a schematic view of a travel route planning problem;
FIG. 2 is a flow chart of a method for travel route planning based on value density calculation in accordance with the present invention;
FIG. 3 is a detailed flowchart of the variable neighborhood search of step S4 of the travel route planning method based on value density calculation according to the present invention;
the following detailed description of embodiments of the invention refers to the accompanying drawings.
Referring to fig. 1, a flow chart of a method for planning a travel route based on value density calculation is shown, which includes the following steps:
step S1: collecting information of all tourists before the planning starting time, wherein the collected information of the tourists at least comprises the total available time length of each tourist, and the satisfaction degree of each tourist after receiving the service of each scenic spot;
step S2: determining parameters required by model calculation at the planning starting moment; the parameters comprise the time required by each scenic spot to serve one tourist, the resource constraint of each scenic spot, namely the number of tourists which can be served by each scenic spot simultaneously, and the distance between every two scenic spots;
step S3: establishing a mixed integer linear programming model with the maximum total satisfaction of all tourists as an objective function;
step S4: solving the optimization model by using a variable neighborhood search algorithm to finally obtain a tour route planning scheme with the maximum total satisfaction of tourists;
in S3, the mixed integer linear programming model is further as follows:
step S31: setting a model hypothesis condition: all tourists visit according to the route planned by the system, the phenomenon of queue insertion can not occur, the tourists can go to any other scenic spot from one scenic spot directly without going to other scenic spots, each scenic spot can serve the next tourist immediately after serving one tourist, accidents can not occur in the service process, and the tourists are known during the travel time of different scenic spots and different time periods.
Step S32: the signs and decision variables of the known parameters in the model are set, which are specifically described as follows: n denotes the number of guests, H denotes the number of attractions other than the start and end points, tliIndicates the upper limit of time available to guest i, stjIndicates the time required for sight j to serve a guest, scjResource restriction, R, representing sight jijIndicates the satisfaction, tt, that the guest i can obtain after receiving the service at the attraction jjkRepresents the time, x, required to arrive at sight k starting from sight jijkIndicating whether the visitor i departs from the sight j to the sight k, if so, it is 1, otherwise, it is 0, and it is a model decision variable, i is 1, 2.
Step S33: according to the travel matrix xijkAnd calculating intermediate variables in the model using the parameters predetermined in step S2; wherein the intermediate variables at least comprise a matrix of resources used by the guest, a matrix of queuing order of the guest, time when the guest begins to receive service at different attractions, and a variable for preventing sub-loops; the step S33 further includes the steps of:
step S331: the calculation formula of the matrix using the resources is:
step S332: the calculation formula of the matrix of the guest queuing sequence is as follows:
step S333: the formula for calculating the time when the tourist starts to receive service at different scenic spots is as follows:
step S334: the formula for calculating the variables for preventing the sub-loop is:
step S34: establishing a tourist route continuity constraint and a tourist time constraint, modeling an objective function, and considering that the total satisfaction of all the tourists is maximum; the step S34 further includes:
step S341: the constraints that all guests need to go from a starting point and to reach an ending point are as follows:
step S342: the constraints that the time when all tourists start to receive service at any attraction and the time when the tourist arrives at the terminal is less than the available time are as follows:
the variable neighborhood searching algorithm included in step S4 described with reference to fig. 2 further includes the following steps:
step S41: generating an initial solution for the local search; the travel route of each tourist is represented by a chain, each position of the chain is a sequence number of an accessed scenic spot, the service sequence of each scenic spot is represented by a chain, each position of the chain is a sequence number of a tourist served, and the sub-cycle is ensured not to be generated in the initialization process. Most of the previous initial solution generation methods adopt traversing all insertable positions and all insertable scenic spots and calculating the ratio of the insertion increase satisfaction degree and the increase time to select the inserted scenic spots and positions. Here, the inserted position only selects the end of each tourist chain and sight chain, and the ratio is calculated by using the sum of the values of all available sights within a certain distance of the selected sight instead of the original inserted added value.
Step S42: the value density of each guest chain is calculated by dividing the satisfaction obtained by each guest by the available time limit of the guest, and all guest chains are divided into two parts according to the value density: good chains and under-planned chains are planned. And selecting the chain with the minimum value density in the underoptimized chains as the selected chain.
Step S43: the neighborhood search uses four neighborhood structures; the step S42 further includes:
step S431: insert sights on the selected chain: the neighborhood structure randomly selects sights that are not visited by the selected guest and inserts them into the last location of the guest link and the last location of the corresponding resource link. It allows the usage time of any under-planned link to exceed its time limit;
step S432: changing the position of the sight on the selected chain: first, the neighborhood structure calculates the ratios of all the sights in the chain using a method similar to the ratio calculation in the initial solution construction algorithm and selects the point with the smallest ratio as the point to be repositioned. The selected attraction is attempted to move to all possible locations and if the move operation reduces the time taken for the selected link to complete the route, the move is accepted, similar to the previous step, which may have the time of use of any under-planned link exceeding its time limit;
step S433: remove sights from selected chains: this neighborhood structure will continually delete the last sight point on the selected link until the guest's age does not exceed the time limit;
step S434: the purpose of this neighborhood structure is to achieve one of the following goals by exchanging a randomly visited sight with an unvisited sight on a randomly selected chain of guests: 1) increasing the overall satisfaction by switching visited attractions to higher satisfaction unvisited attractions, and 2) reducing the total travel time of the corresponding itinerary if the satisfaction of visited and unvisited attractions is the same. If the guest chain has no viable exchanges or finds one, the process ends;
step S44: the purpose of the shaking process is to jump the solution out of the possible local optimum by randomly changing the solution. There are two different shocks in the shaking procedure: local oscillations and global oscillations. For local concussions, if the selected chain cannot become a well-planned chain, the local concussion process is used to jump the selected chain out of the current sight selection. If no better solution can be found after a certain number of iterations, a global oscillation is initiated. The step S44 further includes the steps of:
step S441: local oscillation. First, the time limit of the selected chain is swapped with the time limit of another under-planned chain, which is small (the swap does not violate the time limit, meaning the swap does not cause the visitor's age to exceed its time limit). If the selected chain still cannot be a well-planned chain after the exchange, the process randomly deletes a plurality of sights, and the number of the sights deleted is calculated by the following formula:
nd＝γτ·n
where n is the number of sights on the selected chain, and γ and τ are two random numbers evenly distributed over (0, 1.) in this process, the tabu list to prevent repeated insertions into the same sight will be eliminated, however, if the selected chain has entered the local concussion program more than Ω times, it can be considered as a well-planned chain regardless of its value density;
step S442: and (5) overall oscillation. Similar to the local concussion program, this program randomly deletes each visitor-linked sight, the number of sights deleted being equal to half of the local concussion. After deletion, randomly inserting the unaccessed scenic spots into each tourist chain;
step S45: when a better solution than the current optimal solution is obtained, the optimal solution is updated.
Next, a verification experiment is carried out, the experiment uses an example of a small-scale problem to compare and change CPLEX to solve an accurate solution, a neighborhood search algorithm is adopted, and relevant parameters of algorithm design are as follows: and (3) variable neighborhood searching algorithm: the algorithm terminates condition 20 and steps into condition 50 of the global search, initially planning a good chain ratio of 0.75.
The results of the numerical experiments are shown in table 1. From the results, it can be known that the variable neighborhood search algorithm can find a better route and the solving time is far less than that of CPLEX.
TABLE 1
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (2)
1. A travel route planning optimization method based on value density calculation is characterized by comprising the following steps:
step S1: collecting information of all tourists before the planning starting time, wherein the collected information of the tourists at least comprises the total available time length of each tourist, and the satisfaction degree of each tourist after receiving the service of each scenic spot;
step S2: determining parameters required by model calculation at the planning starting moment; the parameters comprise the time required by each scenic spot to serve one tourist, the resource constraint of each scenic spot, namely the number of tourists which can be served by each scenic spot simultaneously, and the distance between every two scenic spots;
step S3: establishing a mixed integer linear programming model with the maximum total satisfaction of all tourists as an objective function;
step S4: solving the optimization model by using a variable neighborhood search algorithm to finally obtain a tour route planning scheme with the maximum total satisfaction of tourists;
in S3, the mixed integer linear programming model is further as follows:
step S31: setting a model hypothesis condition: all tourists visit according to the route planned by the system, no queue-jumping phenomenon occurs, the tourists can go from one scenic spot to any other scenic spot directly without going to other scenic spots, each scenic spot can serve the next tourist immediately after serving one tourist, no accident occurs in the service process, and the tourists are known in the travel time of different time periods among different scenic spots;
step S32: the signs and decision variables of the known parameters in the model are set, which are specifically described as follows: n denotes the number of guests, H denotes the number of attractions other than the start and end points, tliIndicates the upper limit of time available to guest i, stjIndicates the time required for sight j to serve a guest, scjResource restriction, R, representing sight jijIndicates the satisfaction, tt, that the guest i can obtain after receiving the service at the attraction jjkRepresents the time, x, required to arrive at sight k starting from sight jijkIndicates whether the visitor i isStarting from the sight j to the sight k, if 1, otherwise 0, is the model main decision variable, i is 1,2ijIs an auxiliary variable for preventing loop back; z is a radical ofijmWhen the tourist i accesses the scenic spot j, the occupied resource n is 1, otherwise the occupied resource n is 0, and the resource n is an auxiliary decision variable; q. q.siljmIndicating that for resource m at sight point j, guest i is 1 when served before guest j, otherwise 0, which is an auxiliary decision variable;
step S33: according to the travel matrix xijkAnd calculating intermediate variables in the model using the parameters predetermined in step S2; the intermediate variables at least comprise a matrix of resources used by the tourists, a matrix of queuing sequences of the tourists, the time when the tourists start to receive services at different scenic spots and a variable for preventing sub-circulation; the step S33 further includes the steps of:
step S331: the calculation formula of the matrix using the resources is:
step S332: the calculation formula of the matrix of the guest queuing sequence is as follows:
step S333: the formula for calculating the time when the tourist starts to receive service at different scenic spots is as follows:
step S334: the formula for calculating the variables for preventing the sub-loop is:
step S34: establishing a tourist route continuity constraint and a tourist time constraint, modeling an objective function, and considering that the total satisfaction of all the tourists is maximum; the step S34 further includes:
step S341: the constraints that all guests need to go from a starting point and to reach an ending point are as follows:
step S342: the constraints that the time when all tourists start to receive service at any attraction and the time when the tourist arrives at the terminal is less than the available time are as follows:
2. the method for optimizing a tour route plan based on value density calculation as claimed in claim 1, wherein said step S4 further comprises the steps of:
step S41: generating an initial solution for the local search; the travel route of each tourist is represented by a chain, each position of the chain is a serial number of an accessed scenic spot, the service sequence of each scenic spot is represented by a chain, each position of the chain is a serial number of a tourist served, and a sub-cycle is ensured not to be generated in the initialization process; most of the previous initial solution generation methods adopt traversing all insertable positions and all insertable scenic spots and calculating the ratio of the insertion increasing satisfaction degree and the increasing time to select the inserted scenic spots and positions; the inserted position only selects the end of each tourist chain and the sight spot chain, and the value sum of all available sight spots within a certain distance of the selected sight spots is used for replacing the original inserted added value to calculate the ratio;
step S42: the value density of each guest chain is calculated by dividing the satisfaction obtained by each guest by the available time limit of the guest, and all guest chains are divided into two parts according to the value density: planning good chains and under-planned chains; selecting the chain with the minimum value density in the underoptimized chains as the selected chain;
step S43: the neighborhood search uses four neighborhood structures; the step S43 further includes:
step S431: insert sights on the selected chain: the neighborhood structure randomly selects the scenic spots which are not visited by the selected visitor and inserts the scenic spots into the last position of the visitor link and the last position of the corresponding resource link; it allows the usage time of any under-planned link to exceed its time limit;
step S432: changing the position of the sight on the selected chain: firstly, the neighborhood structure calculates the ratios of all scenic spots on a chain by adopting a method similar to the ratio calculation in an initial solution construction algorithm and selects a point with the minimum ratio as a point to be changed in position; the selected attraction is attempted to move to all possible locations and if the move operation reduces the time taken for the selected link to complete the route, the move is accepted, similar to the previous step, which may have the time of use of any under-planned link exceeding its time limit;
step S433: remove sights from selected chains: this neighborhood structure will continually delete the last sight point on the selected link until the guest's age does not exceed the time limit;
step S434: the purpose of this neighborhood structure is to achieve one of the following goals by exchanging a randomly visited sight with an unvisited sight on a randomly selected chain of guests: 1) increasing overall satisfaction by exchanging visited attractions for unvisited attractions with higher satisfaction, 2) reducing the total travel time of the corresponding route if the satisfaction of the visited and unvisited attractions is the same; if the guest chain has no viable exchanges or finds one, the process ends;
step S44: in the oscillation process, the solution is randomly changed to jump out possible local optimum; there are two different shocks in the shaking procedure: local oscillation and global oscillation; for local oscillation, if the selected chain can not become a well-planned chain, the selected chain is made to jump out of the current scenic spot selection by using a local oscillation process; if no better solution can be found after a certain number of iterations, starting the whole oscillation;
the step S44 further includes the steps of:
step S441: local oscillation; firstly, exchanging the time limit of a selected chain with the time limit of another under-planned chain; if the selected chain still cannot be a well-planned chain after the exchange, the process randomly deletes a plurality of sights, and the number of the sights deleted is calculated by the following formula:
nd＝γτ·n
where n is the number of sights on the selected chain, and γ and τ are two random numbers evenly distributed over (0, 1), in which the tabu list to prevent repeated insertions into the same sight will be eliminated, but if the selected chain has entered the local oscillation procedure more than Ω times, it can be considered as a well-planned chain regardless of its value density;
step S442: carrying out overall oscillation; randomly deleting the scenic spots linked by each visitor, wherein the number of the deleted scenic spots is equal to half of the local oscillation; after deletion, randomly inserting the unaccessed scenic spots into each tourist chain;
step S45: when a better solution than the current optimal solution is obtained, the optimal solution is updated.
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