CN113988570A - Multi-objective evolutionary algorithm-based tourism bus scheduling optimization method - Google Patents

Multi-objective evolutionary algorithm-based tourism bus scheduling optimization method Download PDF

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CN113988570A
CN113988570A CN202111233508.2A CN202111233508A CN113988570A CN 113988570 A CN113988570 A CN 113988570A CN 202111233508 A CN202111233508 A CN 202111233508A CN 113988570 A CN113988570 A CN 113988570A
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李笠
李广鹏
胡建清
常亮
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Abstract

The invention discloses a multi-objective evolutionary algorithm-based tourism bus dispatching optimization method, which comprises the steps of constructing a tourism bus dispatching optimization model according to the number of tourism buses, the total travel distance, the total workload, the waiting time and the delay time, then solving the tourism bus multi-objective dispatching model by adopting NSGA-II and local search based on R2, and finally meeting the actual demand of tourism bus dispatching. The multi-target evolutionary algorithm based on R2 provided by the invention has simple principle and easy realization, can effectively perform local search in the evolutionary process, ensures the convergence and diversity of the solution, and can rapidly solve the optimal feasible solution; and aiming at the actual demand of the intelligent tourism traffic planning, the invention establishes a perfect tourism bus dispatching optimization model, can solve an optimal feasible solution, and has important significance for improving the satisfaction degree of tourists, reducing the traffic transportation cost and reducing the trip cost of tourists.

Description

Multi-objective evolutionary algorithm-based tourism bus scheduling optimization method
Technical Field
The invention belongs to the technical field of intelligent tourism engineering and intelligent path planning, relates to intelligent allocation of tourism buses, and particularly relates to a tourism bus scheduling optimization method based on a multi-objective evolutionary algorithm.
Background
With the development of internet economy and trade, the scale of the tourism industry is increased day by day, and the influence of the tourism traffic planning on economic activities is more and more obvious. The tourism traffic planning is a large-scale tourism intelligent planning travel system integrating a series of leading-edge technologies such as artificial intelligence, optimized scheduling, intelligent recommendation and the like, wherein the optimized scheduling is of great importance. Tourist bus scheduling is a multi-objective optimization problem relating to transportation costs, passenger demand, passenger satisfaction, customer waiting time and work volume. An optimized dispatching model is established according to actual customer requirements, so that the operation cost of companies such as travel agencies and automobile rentals can be effectively reduced, and the traffic road pressure is reduced. At present, tourism buses mainly rent and package for a long time from domestic and foreign countries, resource waste and traffic pressure are often caused to tourism cities with large passenger flow, and the actual demand of current tourism bus dispatching cannot be effectively met. Therefore, it is necessary to have a method for efficiently dispatching a travel bus.
The evolutionary algorithm is considered to be a simple and universal multi-objective optimization strategy, and by simulating a biological evolution process (such as population intelligence, genetic evolution and the like), a Pareto optimal solution can be found in one operation, and the evolutionary algorithm is successfully used in the field of travel path planning. However, the evolutionary algorithm has various defects, which are mainly expressed as: such as large fluctuation range of the initial solution, slow convergence speed, easy falling into the local optimal solution, etc.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for optimizing the dispatching of a tourism bus based on a multi-objective evolutionary algorithm.
The technical scheme for realizing the purpose of the invention is as follows:
a tourism bus scheduling optimization method based on a multi-objective evolutionary algorithm comprises the following steps:
s1: initialization: randomly generating an initial population with the scale of N, expressing each individual in the population as a bus dispatching scheme, and establishing a bus dispatching optimization model;
s2: parent selection: selecting a parent determined to be bred through the tournament based on the initial population generated at step S1;
s3: and (3) propagation: hybridizing and compiling the parent population to generate an offspring population, and generating a next generation by adopting an SBX crossover operator and a polynomial mutation operator;
s4: normalization: the target values for all individuals were normalized to the following formula:
Figure BDA0003316940960000021
in the formula (1), f'j(xi) Representing an individual xiThe function value on the jth object, M representing the number of objects,
Figure BDA0003316940960000022
and
Figure BDA0003316940960000023
representing ideal and lowest points on the jth target;
s5: non-dominated sorting and based on R2The local search of (2): the filial generation population QtAnd the current population PtMerging to form new population RtAnd sorting the population R according to non-dominated sortingt2N individuals in the group are assigned to non-dominant layers of different levels, while initializing a temporary profile L; then from the first layer F1Initially, traversing the dominating layer to select individuals for entering the next generation population Pt+1And according to R2The index is searched locally, and the searched individuals are put into a temporary file set L until Pt+1Stopping traversing when the population size of the terminal is larger than or equal to N; if Pt+1Is larger than N, the temporary file set L and the finally selected non-dominant layer individual FiMerging, and proceeding to step S6; otherwise, go to step S7;
s6: based on R2Selecting: according to R2The indexes sort the combined temporary file sets L in descending orderAnd N- | P is selectedt+1I Individual entering Pt+1；
S7: updating the external file set E: will Pt+1E, merging, and updating and maintaining an external file set by adopting a non-dominated sorting and crowding distance method;
s8: and repeating the steps S2 to S7 until the termination condition is met, and outputting the optimal solution.
In step S1, the model for optimizing the dispatching of the touring buses is constructed with the objectives of minimum number of the touring buses, minimum total travel distance, maximum total workload, minimum waiting time, and minimum delay time, and the model expression is as follows:
F(x)＝Minimize(f1,f2,-f3,f4,f5) (2)
wherein:
minf1＝|R| (3)
Figure BDA0003316940960000024
Figure BDA0003316940960000025
minf4＝∑k∈KWT(rk) (6)
minf5＝∑k∈KDT(rk) (7)
in the above formula, the objective function minf1Indicating that the number of touring buses is minimized; objective function minf2Represents the total travel distance minimization; objective function maxf3Representing a maximum amount of work; objective function minf4Indicating that the latency is minimized; objective function minf5Indicating that the delay time is minimized; r represents the set of all routes, R ═ R1,r2,…,rk|k∈K}；rkRepresenting the route of the kth bus; q. q.siRepresenting the workload of transporting each customer, N being the number of customers; WT represents the time that the bus waits for the customer; DT stands forDelay time, namely the arrival of the tourism bus after the time window;
the dispatching optimization model of the tourism bus meets the following constraint conditions:
Ck≤Capk,k∈K (8)
∑k∈KCk≤Cap (9)
maxk∈KT(rk)≤LaterTime (10)
wherein, the constraint condition (8) indicates that the load capacity of each tourism bus must be larger than the weight of the customer; constraint (9) indicates that the load capacity of all travel buses must be greater than the total weight of the customer; constraint (10) indicates that the latest vehicle must be greater than the closing time of the business, CkRepresents the total weight of the kth bus customer; capkIndicating the load carrying capacity of the kth bus.
In steps S5 and S6, R is2The index is calculated by the following formula:
Figure BDA0003316940960000031
Figure BDA0003316940960000032
the individual contribution values are as follows:
IR2(a,A,W)＝R2({A},W,z*)-R2({A\a},W,z*) (13)
wherein A is a set of approximate solution sets; w is a set of weight vectors, W ═ W1,w2,…,wm)∈W。
The invention provides a multi-objective evolutionary algorithm-based tourism bus dispatching optimization method, which constructs a tourism bus dispatching optimization model by using the number, total travel distance, total workload, waiting time and delay time of tourism buses, adopts NSGA-II and is based on R2The local search solves the multi-target dispatching model of the tourism bus, and finally the requirement of the tourism bus can be metActual requirements of the schedule. In early research, methods such as integer programming, linear programming and dynamic programming are mainly adopted for optimal scheduling. However, the tourism bus has various dispatching requirements, a large number of targets and complex constraints, and a practical and effective result cannot be obtained. The multi-target evolutionary algorithm based on R2 provided by the invention has simple principle and easy realization, can effectively perform local search in the evolutionary process, ensures the convergence and diversity of the solution, and can rapidly solve the optimal feasible solution; and aiming at the actual demand of the intelligent tourism traffic planning, the invention establishes a perfect tourism bus dispatching optimization model, can solve an optimal feasible solution, and has important significance for improving the satisfaction degree of tourists, reducing the traffic transportation cost and reducing the trip cost of tourists.
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FIG. 1 is a directed graph of a tourism bus dispatching optimization model;
FIG. 2 is a flow chart of solving the dispatching of the tourism bus by adopting a multi-objective evolutionary algorithm based on R2;
fig. 3 is a detailed process diagram of parent cross-evolution in multi-objective evolution.
Detailed Description
The invention will be further elucidated with reference to the drawings and examples, without however being limited thereto.
Example (b):
as shown in fig. 1, the tourism bus scheduling optimization model is defined as that a directed graph G is (V, E), and {0} is a tourism bus station, from which the tourism bus must start and return within a specified time; each edge containing a travel distance dijAnd a travel time tijTwo attributes, i and j are nodes; each node Vi(i ≠ 0) all have an associated time window [ b ]i,ei]In the embodiment, soft constraints are used, the bus for traveling needs to visit the node in the interval as much as possible, resource waste caused by waiting for customers is avoided, and the satisfaction degree of the customers is increased.
A multi-objective evolutionary algorithm-based tourism bus dispatching optimization method constructs a model by bus number, total travel distance, total work amount, waiting time and delay time, can effectively reduce the transport distance in the dispatching process of tourism buses, further remarkably reduces the transport cost of enterprises, increases the profits of the enterprises, improves the satisfaction degree of customers, and effectively relieves traffic pressure, and as shown in figure 2, the method comprises the following steps:
s1: initialization: randomly generating an initial population with the scale of N, wherein each individual in the population is represented as a bus tourism scheduling scheme; meanwhile, a tourism bus dispatching optimization model is established according to the number of buses, the total travel distance, the total workload, the waiting time and the delay time as follows:
F(x)＝Minimize(f1,f2,-f3,f4,f5) (2)
wherein:
minf1＝|R| (3)
Figure BDA0003316940960000041
Figure BDA0003316940960000042
minf4＝∑k∈KWT(rk) (6)
minf5＝∑k∈KDT(rk) (7)
in the above formula, the objective function minf1Indicating that the number of touring buses is minimized; objective function minf2Represents the total travel distance minimization; objective function maxf3Representing a maximum amount of work; objective function minf4Indicating that the latency is minimized; objective function minf5Indicating that the delay time is minimized; r represents the set of all routes, R ═ R1,r2,…,rk|k∈K}；rkShowing the route of the kth travel bus, as shown in fig. 1: the figure includes a set of routes for two travel buses, vehicle 1: 0-1-2-3-0, vehicle 2: 0-4-5-6-0, wherein 0 is a station, and the bus must start from 0 to end from 0; q. q.siRepresenting the workload of transporting each customer, N being the number of customers; WT represents the time that the bus waits for the customer; DT represents the delay time, i.e. the arrival of the touring bus after the time window;
the tourism bus dispatching optimization model meets the following constraint conditions:
Ck≤Capk,k∈K (8)
∑k∈KCk≤Cap (9)
maxk∈KT(rk)≤LaterTime (10)
wherein, the constraint condition (8) indicates that the load capacity of each tourism bus must be larger than the weight of the customer; constraint (9) indicates that the load capacity of all travel buses must be greater than the total weight of the customer; constraint (10) indicates that the latest vehicle must be greater than the closing time of the business, CkRepresents the total weight of the kth bus customer; capkIndicating the load carrying capacity of the kth bus.
S2: parent selection: the parent determined to be bred is selected by the tournament based on the initial population generated at step S1.
S3: and (3) propagation: the parent population is crossed and compiled by adopting a traditional evolutionary algorithm to generate an offspring population, wherein an SBX crossing operator and a polynomial mutation operator are adopted, and the crossing process of parent individuals is described in detail in the attached figure 3.
S4: normalization: individual targets were normalized as follows:
Figure BDA0003316940960000051
wherein, f'j(xi) Representing an individual xiThe function value on the jth object, M representing the number of objects,
Figure BDA0003316940960000052
and
Figure BDA0003316940960000053
means for representing j-th targetA notional point and a nadir;
s5: non-dominated sorting and based on R2The local search of (2): the filial generation population QtAnd the current population PtMerging to form new population RtAnd sorting the population R according to non-dominated sortingt2N individuals are assigned to different levels of non-dominant layers while initializing temporary set L. Then from the first layer F1Initially, traversing the dominating layer to select individuals for entering the next generation population Pt+1And according to R2The index is searched locally and added to the temporary set L until Pt+1Stopping traversal when the population size of (1) is greater than or equal to N. If Pt+1Is larger than N, the temporary file set L and the finally selected non-dominant layer individual FiMerging, and entering the next step; otherwise, go to S7;
s6: based on R2Selecting: according to R2Sorting the merged file set L in descending order, and selecting N- | Pt+1I Individual entering Pt+1，R2The calculation is as follows:
Figure BDA0003316940960000061
Figure BDA0003316940960000062
the individual contribution values are as follows:
IR2(a,A,W)＝R2({A},W,z*)-R2({A\a},W,z*) (14)
wherein A is a set of approximate solution sets; w is a set of weight vectors, W ═ W1,w2,…,wm)∈W。
S7: updating the external file set E: will Pt+1E, merging, and updating and maintaining an external file set by adopting a non-dominated sorting and crowding distance method;
s8: and repeating the steps S2 to S7 until the termination condition is met, and outputting the optimal solution.

Claims (3)

1. A tourism bus scheduling optimization method based on a multi-objective evolutionary algorithm is characterized by comprising the following steps:
s1: initialization: randomly generating an initial population with the scale of N, expressing each individual in the population as a bus dispatching scheme, and establishing a bus dispatching optimization model;
s2: parent selection: selecting a parent determined to be bred through the tournament based on the initial population generated at step S1;
s3: and (3) propagation: hybridizing and compiling the parent population to generate an offspring population, and generating a next generation by adopting an SBX crossover operator and a polynomial mutation operator;
s4: normalization: the target values for all individuals were normalized to the following formula:
Figure FDA0003316940950000011
in the formula (1), fj′(xi) Representing an individual xiThe function value on the jth object, M representing the number of objects,
Figure FDA0003316940950000012
and
Figure FDA0003316940950000013
representing ideal and lowest points on the jth target;
s5: non-dominated sorting and based on R2The local search of (2): the filial generation population QtAnd the current population PtMerging to form new population RtAnd sorting the population R according to non-dominated sortingt2N individuals in the group are assigned to non-dominant layers of different levels, while initializing a temporary profile L; then from the first layer F1Initially, traversing the dominating layer to select individuals for entering the next generation population Pt+1And according to R2The index is searched locally, and the searched individuals are put into a temporary file set L until Pt+1Is greater than or equal to NStopping traversing; if Pt+1Is larger than N, the temporary file set L and the finally selected non-dominant layer individual FiMerging, and proceeding to step S6; otherwise, go to step S7;
s6: based on R2Selecting: according to R2The index sorts the combined temporary file set L in descending order and selects N- | Pt+1I Individual entering Pt+1；
S7: updating the external file set E: will Pt+1E, merging, and updating and maintaining an external file set by adopting a non-dominated sorting and crowding distance method;
s8: and repeating the steps S2 to S7 until the termination condition is met, and outputting the optimal solution.
2. The method as claimed in claim 1, wherein in step S1, the model for optimizing bus dispatching is constructed with the objectives of minimum number of buses, minimum total distance traveled, maximum total workload, minimum waiting time and minimum delay time, and the model expression is as follows:
F(x)＝Minimize(f1,f2,-f3,f4,f5) (2)
wherein:
minf1＝|R| (3)
Figure FDA0003316940950000021
Figure FDA0003316940950000022
minf4＝∑k∈KWT(rk) (6)
minf5＝∑k∈KDT(rk) (7)
in the above formula, the objective function minf1Indicating that the number of touring buses is minimized; objective function minf2Represents the total travel distance minimization; objective function maxf3Representing a maximum amount of work; objective function minf4Indicating that the latency is minimized; objective function minf5Indicating that the delay time is minimized; r represents the set of all routes, R ═ R1,r2,…,rk|k∈K}；rkRepresenting the route of the kth bus; q. q.siRepresenting the workload of transporting each customer, N being the number of customers; WT represents the time that the bus waits for the customer; DT represents the delay time, i.e. the arrival of the touring bus after the time window;
the dispatching optimization model of the tourism bus meets the following constraint conditions:
Ck≤Capk,k∈K (8)
∑k∈KCk≤Cap (9)
maxk∈KT(rk)≤LaterTime (10)
wherein, the constraint condition (8) indicates that the load capacity of each tourism bus must be larger than the weight of the customer; constraint (9) indicates that the load capacity of all travel buses must be greater than the total weight of the customer; constraint (10) indicates that the latest vehicle must be greater than the closing time of the business, CkRepresents the total weight of the kth bus customer; capkIndicating the load carrying capacity of the kth bus.
3. The method as claimed in claim 1, wherein the method for optimizing the dispatching of the touring bus based on the multi-objective evolutionary algorithm comprises steps S5 and S62The index is calculated by the following formula:
Figure FDA0003316940950000023
Figure FDA0003316940950000024
the individual contribution values are as follows:
IR2(a,A,W)＝R2({A},W,z*)-R2({A\a},W,z*) (13)
wherein A is a set of approximate solution sets; w is a set of weight vectors, W ═ W1,w2,…,wm)∈W。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115130787A (en) * 2022-08-29 2022-09-30 深圳市城市公共安全技术研究院有限公司 Configuration method, system, terminal equipment and medium of emergency resource scheduling scheme

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115130787A (en) * 2022-08-29 2022-09-30 深圳市城市公共安全技术研究院有限公司 Configuration method, system, terminal equipment and medium of emergency resource scheduling scheme

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