Volume-constrained dust suppression vehicle path optimization method considering heterogeneous services

Abstract

The invention relates to a capacity-constrained dust suppression vehicle path optimization method considering heterogeneous services, and belongs to the field of traffic engineering. According to the method, firstly, a road network is selected, road sections with dust suppression cleaning requirements are determined, and then a dust suppression utility function, a dust suppression revenue function and a comprehensive profit maximization function are constructed. Thirdly, describing a path planning model by adopting an integer nonlinear planning model, and solving the model by applying an improved genetic algorithm and a variable neighborhood search algorithm; according to the method, on the premise that the dust suppression requirement is met, the path of the dust suppression vehicle is optimized, empty driving and redundant driving of the vehicle are reduced, the efficiency of cleaning operation is improved, the operation time is shortened, and therefore the number of used vehicles and fuel consumption are reduced, and the operation cost is reduced. Meanwhile, the method ensures the cleaning and dust suppression effects of urban roads through accurate path planning, improves the atmospheric microenvironment of the roads, and reduces the air pollution exposure of travelers.

Description

Technical Field

[0001] The invention belongs to the field of traffic engineering and relates to a dust suppression vehicle path optimization method with capacity constraints considering heterogeneous services. Background Art

[0002] Urban roads are the main places for residents to travel on a daily basis. Dust pollution on roads and their surrounding areas poses a significant threat to the health of travelers. Road dust and the increase in particulate matter (PM) concentration in the road atmospheric microenvironment caused by dust will have a significant negative impact on human health.

[0003] Although dust suppression and cleaning work on urban roads has received a certain degree of attention, and professional multifunctional dust suppression and cleaning vehicles with flushing, watering, and fog cannon services have been put into existing road cleaning operations, there are still limitations in the management methods of their vehicle operation and use. On the one hand, the daily operating costs of these vehicles, including purchase, maintenance, fuel, and labor costs, are high, limiting the number of vehicles that can be put into use in a specific area. On the other hand, the current vehicle route planning and the operation methods adopted for road sections are mostly based on experience, lacking scientificity and systematicity, resulting in inefficient resource utilization. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a capacity-constrained dust suppression vehicle path optimization method considering heterogeneous services, so as to optimize resource allocation, improve cleaning efficiency, and reduce dust concentration in the road atmospheric microenvironment, thereby improving the air quality of urban roads and protecting the health of travelers.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A dust suppression vehicle routing optimization method with capacity constraints considering heterogeneous services, the method comprising the following steps:

[0007] Step 1: Select the road network and determine the road sections that require dust suppression and cleaning;

[0008] Step 2: For a demand section, construct the dust suppression utility function within a given time range [0, T]:

[0009]

[0010] Among them, t service is the service start time; O is the service mode set, O = {1, 2, 3}, 1 represents fog cannon operation, 2 represents watering operation, and 3 represents flushing operation; a o ,b o ,c o It is the model parameter of the dust suppression characteristic curve when the service mode o is used for service;

[0011] Step 3: For a section e ∈ E1 that needs dust suppression and cleaning, construct a dust suppression revenue function:

[0012]

[0013] where E is the set of sections, E = {e1, e2, ···, e M}, and at the same time, the set E can also be represented in the form of arcs, that is, E = {(v i , v j ): v i , v j ∈ V, i < j}; E1 is the set of demand sections; E2 is the complement of E1 in E, E = E1 ∪ E2; t e is the service start time when edge e is served; R e is the average sojourn time of pedestrians on edge e; L e is the administrative level parameter of section e, and the larger the parameter value, the higher its service priority; [0, T] is the dust suppression utility settlement time; P e (t) is the road pedestrian flow function at time t; U e is the dust suppression utility function of section e at time t;

[0014] Step 4: For a section e ∈ E1 that needs dust suppression and cleaning, construct a comprehensive profit maximization function:

[0015]

[0016] where is the service cost incurred when the vehicle serves on edge e in service mode o; tc e is the running cost of the vehicle on section e; α, β, γ are coefficients for converting the function values of each part into monetary units (yuan);

[0017] Step 5: Use an integer nonlinear programming model to describe the proposed path planning model as follows:

[0018]

[0019] where K is the set of vehicles, K = {1, 2, ···, k}; A is the set of arcs, A = {(v i , v j ): v i , v j ∈ V, i ≠ j}, (v i , v j ) and (v j , v i ) are two different directions; V is the set of nodes, V = {v0, v1, ···, vn}, where v0 represents the starting point of the vehicle; is the decision variable, Represents arc (v i ,v j ) is served by vehicle k in operation mode o, otherwise, is the decision variable, To represent the arc (v i ,v j ) is driven by vehicle k along node v i To node v j The number of times the direction was visited;

[0020] The constraints of the model are as follows:

[0021]

[0022]

[0023] in, is the decision variable, Represents arc (v i ,v j ) is served by vehicle k; otherwise, is the vehicle on the side (v i ,v j ) is the water consumption when the vehicle is serviced in service mode o; Q is the maximum water capacity of the vehicle; It represents the average service workload of each vehicle when all road sections are served by the basic operation with the least cost, fog cannon operation; ε is a hyperparameter used to limit the range of fluctuation; — auxiliary variables, It is used to eliminate the independent closed sub-loops that may appear in the vehicle k route, where That is, S is a non-empty subset of the set consisting of all other nodes that do not contain the starting point;

[0024] Step 6: Use the hybrid genetic algorithm (HGE) and variable neighborhood search algorithm (VNS) to solve the model.

[0025] Further, in step six, the improved genetic algorithm HGE includes the following steps:

[0026] Initialize the population: randomly generate a certain number of chromosomes, each chromosome represents a possible vehicle path planning solution;

[0027] Fitness evaluation: Evaluate the fitness of each chromosome according to the objective function value;

[0028] Selection operation: select some chromosomes for crossover and mutation operations according to fitness;

[0029] Crossover operation: exchange some genes of the selected chromosome to generate new chromosomes;

[0030] Mutation operation: randomly change some genes of chromosomes to increase the diversity of the population;

[0031] Iteration: Repeat the selection, crossover, and mutation operations until the termination condition is met, including reaching the maximum number of iterations or the fitness no longer improves.

[0032] Further, in step 6, the variable neighborhood search algorithm VNS includes the following steps:

[0033] Initialization solution: randomly generate an initial solution, i.e. a vehicle path planning solution;

[0034] Neighborhood search: Search for a better solution in the neighborhood of the current solution by exchanging segments in the path or changing the service mode;

[0035] Solution update: If a better solution is found, update the current solution;

[0036] Neighborhood structure update: Regularly update the neighborhood structure and change the scope or method of neighborhood search;

[0037] Iteration: Repeat the neighborhood search and solution update operations until the termination conditions are met, including reaching the maximum number of iterations or the solution no longer improves.

[0038] The beneficial effects of the present invention are: by optimizing the path of dust suppression vehicles, the empty driving and redundant driving of vehicles are reduced, the efficiency of cleaning operations is improved, and the operation time is shortened. The optimization method can reduce the number of vehicles used and fuel consumption, and reduce operating costs while meeting the dust suppression needs. In addition, through precise path planning, the cleanliness and dust suppression effects of urban roads are ensured, the road atmospheric microenvironment is improved, and the air pollution exposure of travelers is reduced. The method provides a basis for scientific decision-making for urban management departments and helps to formulate more reasonable cleaning operation plans and resource allocation plans.

[0039] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 The schematic diagram of the road network in Pidong; (a) is the actual map; (b) is the dust suppression service road network structure;

[0042] Figure 2 Compare the calculation results of different schemes. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0044] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0045] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0046] In order to better describe the established model, a certain area operation road network is selected for illustration. The road network includes 78 operation sections and 51 nodes. The network structure of a certain area operation road network is as follows: Figure 1 As shown in the figure, (a) is the actual map, and (b) is the road network structure diagram of the roads that the dust suppression vehicles need to serve. A total of 6 multifunctional dust suppression vehicles are responsible for dust suppression and cleaning operations in the area, and Depot is the starting point of the vehicle.

[0047] Dust suppression characteristic parameter a of actual road network o 、b o 、c o The basic information of the road, including the parameters m and k of the passenger flow function P(t) and the road function level L, is shown in Tables 1 and 2. The calculation method of the average stay time R of the road section and the conversion coefficients α, β, and γ of the monetization unit are the same as those in the virtual network.

[0048] Table 1 Dust suppression characteristic parameters of actual operation road network

[0049]

[0050]

[0051]

[0052] Table 2 Basic parameters of the actual operation road network

[0053]

[0054]

[0055]

[0056] The current path planning scheme in a certain region is used to calculate the objective function value of the model, and the results obtained will be compared with the calculation results of the algorithm proposed in this paper. The current path planning scheme is shown in Table 3.

[0057] Table 3 Vehicle service path table of current actual scheme

[0058] Vehicle number Vehicle service route 1 1,2,4,5,10,6,7,18,19,30,25,17,22,16,9 2 3,8,15,21,27,36,37,28,23,24,29 3 14,26,35,42,50,51,52,53,43,44,45,38,39,20 4 56,58,63,68,69,70,71,72,73,74,75,76,77,78 5 48,49,54,55,57,67,62,66,61,47,34 6 11,12,13,32,31,59,64,65,41,46,60,40,33

[0059] At present, in actual operation, the multifunctional dust suppression vehicle uses a single fog cannon operation mode to serve the road section. In order to compare the effectiveness of the single operation mode and the combined operation mode, the single operation service mode is stipulated as follows: the vehicle uses a single operation mode and a circular operation mode to repeatedly serve the road section until all the water is consumed. In addition, due to the omissions in the service of the road section in actual work, the calculation is performed after adjusting it to a complete plan based on the actual vehicle trajectory.

[0060] When the multifunctional dust suppression vehicle adopts the above single fog cannon operation, the objective function values ​​calculated by the cyclic operation are accumulated, and the total comprehensive profit is 1489.84 yuan.

[0061] When the multifunctional dust suppression vehicle operates according to the current operation route using the combined operation method proposed in this article, the comprehensive profits calculated under various combination methods are shown in Table 4.

[0062] Table 4 Comprehensive profit statement of the current actual scheme under combined operation

[0063] Combined operation mode Comprehensive profit (yuan) Mist cannon, sprinkler 2375.26 Mist cannon, flushing 2588.02 Sprinkling water, flushing 2665.34 Mist cannon, watering, flushing 2814.47

[0064] From the results in Table 4, it can be seen that among all the combinations, the combination of fog cannon, water sprinkling and flushing has the largest comprehensive profit of 2814.57 yuan. The other combinations also greatly increase the comprehensive profit, and the comprehensive profit increases with the increase of the average water consumption of a single service. This shows that when there is sufficient water, flushing should be used first to serve the road section, followed by water sprinkling, and finally fog cannon operation.

[0065] The optimal path planning scheme for vehicles in the actual road network is solved by using the improved genetic algorithm as shown in Table 5, and the service mode of vehicles for road sections is shown in Table 6. The parameters of the genetic algorithm are set as follows: population size 60; genetic generation gap 0.9; population crossover probability 0.75, population mutation probability 0.25; maximum number of iterations 200 generations (converged). The setting of the parameters is adjusted according to the results of multiple calculations. The optimal iterative objective function value is 2865.03.

[0066] Table 5 Optimal vehicle service path table of HGA algorithm

[0067] Vehicle number Vehicle service route 1 1,2,4,9,10,6,7,18,19,30,25,17,22,5,16 2 3,8,15,21,27,36,37,28,23,29,38,35,26,14,42,50,51,52,53,43,44,45,24 3 20,39,56,58,63,68,70,69,71,72,73,74,75,76 4 66,67,57,54,62,77,78,49,55 5 34,41,61,31,12,11,13,32,48 6 47,46,40,60,64,65,59,33

[0068] Table 6 Optimal vehicle service mode of HGA algorithm

[0069] Vehicle number Vehicle service mode (1 represents fog cannon, 2 represents water spraying, 3 represents flushing) 1 3,1,2,1,2,3,3,3,3,2,2,1,1,1,1 2 2,3,1,1,1,3,3,2,1,2,2,3,2,2,1,1,2,2,2,2,1,1,1 3 3,2,1,1,3,3,2,3,2,1,1,1,2,3 4 3,3,3,2,2,1,1,1,1 5 3,2,3,3,2,2,2,2,1 6 2,3,2,3,3,1,1,1

[0070] The optimal path planning scheme for vehicles solved in the actual road network using the variable neighborhood search algorithm is shown in Table 7, and the service mode of the vehicle to the path is shown in Figure 8. The parameter W of the permutation transformation and the parameter w2 of the card-cutting transformation in the variable neighborhood search algorithm are taken as 5 and 2 respectively. The maximum number of iterations of the variable neighborhood search algorithm is set to 200 generations (converged), and the iterative optimal objective function value obtained is 2912.69.

[0071] Table 7 Optimal vehicle service path table of VNS algorithm

[0072] Vehicle number Vehicle service route 1 1,2,4,10,7,6,18,19,45,30,25,17,22,16,9,3 2 8,20,35,36,37,38,39,29,23,21,15,26,27,42,50,51,52,53,43,44,5 3 14,28,24,56,58,63,68,70,72,74,77,73,69,71 4 66,67,75,78,76,55,57,54,49,62 5 11,13,61,47,31,34,12,32,48 6 64,59,40,46,65,60,41,33

[0073] Table 8 Optimal vehicle service mode of VNS algorithm

[0074] Vehicle number Vehicle service mode (1 represents fog cannon, 2 represents water spraying, 3 represents flushing) 1 3,2,3,1,3,3,3,2,2,2,2,1,1,1,1,1 2 3,3,3,3,3,3,2,1,1,1,1,1,1,1,2,2,2,2,1,1,1 3 2,2,2,3,2,3,3,3,1,1,1,1,3,2 4 3,2,2,2,3,1,3,2,1,1 5 2,2,3,1,3,3,2,2,1 6 3,1,2,3,1,3,3,1

[0075] The calculation results of the above different schemes are sorted out, as follows: Figure 2 As shown. Figure 2It can be seen from the results that the comprehensive profit of the optimal objective function value obtained by the current single fog cannon operation mode is the smallest, which is 1489.84 yuan. When the combined operation mode is used to execute the current path planning scheme, the comprehensive profit is 2814.58 yuan, which increases the comprehensive profit of the current actual scheme by 88.9%, indicating that the combined operation mode considering heterogeneous services proposed in this paper can greatly improve the actual effectiveness of dust suppression and cleaning work. Under the combined operation mode, the improved genetic algorithm (HGA algorithm) and the variable neighborhood search algorithm (VNS algorithm) are used to adjust the existing path planning scheme. The calculation results show that the two algorithms further increase the comprehensive profit of the current actual scheme under the combined operation by 1.8% and 3.5%, respectively, and the improvement over the current actual scheme reaches 92.3% and 95.4%, respectively.

[0076] Through the comparison of the calculation results of different schemes, the following conclusions can be drawn: (1) The combined operation method can greatly improve the effectiveness of dust suppression and cleaning operations. In actual work, the combined operation method should be adopted to improve the quality and efficiency of operations. (2) Both algorithms proposed in this paper can effectively improve the existing path planning schemes.

[0077] By comparing the objective function values ​​of different schemes, it is shown that using a combined operation method and improving the path plan using the proposed algorithm can greatly improve the comprehensive benefits of the current single fog cannon operation plan, and the scheme design meets the requirements of actual operations and can provide scientific guidance for the daily route arrangement of operating vehicles.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A dust suppression vehicle routing optimization method with capacity constraints considering heterogeneous services, characterized by: The method comprises the following steps: Step 1: Select the road network and determine the road sections that require dust suppression and cleaning; Step 2: For a demand section, construct the dust suppression utility function within a given time range [0, T]: Among them, t service is the service start time; O is the service mode set, O = {1, 2, 3}, 1 represents fog cannon operation, 2 represents watering operation, and 3 represents flushing operation; a o ,b o ,c o It is the model parameter of the dust suppression characteristic curve when the service mode o is used for service; Step 3: For a road section e∈E1 that needs dust suppression and cleaning, construct a dust suppression benefit function: Among them, E is the set of road segments, E = {e1, e2, ···, e M}, and at the same time, the set E can also be expressed in the form of arcs, that is, E = {(v i , v j ): v i , v j ∈ V, i < j}; E1 is the set of demand road segments; E2 is the complement of E1 in E, E = E1 ∪ E2; t e is the service start time when the edge e is served; R e is the average sojourn time of pedestrians on the edge e; L e is the administrative level parameter of the road segment e, and the larger the parameter value, the higher its service priority; [0, T] is the dust suppression utility settlement time; P e (t) is the road pedestrian flow function at time t; U e is the dust suppression utility function of the road segment e at time t; Step 4: For a road section e∈E1 that needs dust suppression and cleaning, construct a comprehensive profit maximization function: in, is the service cost incurred by the vehicle when providing service in service mode o on edge e; tc e is the cost of the vehicle traveling on the road section e; α, β, γ are the coefficients for converting the function values ​​of each part into monetary units (yuan); Step 5: Use the integer nonlinear programming model to describe the proposed path planning model as follows: Where K is the vehicle set, K = {1, 2, ···, k}; A is the arc set, A = {(v i ,v j ):v i ,v j ∈V,i≠j),(v i ,v j ) and (v j ,v i ) are two different directions; V is a set of nodes, V = {v0,v1,···,v n }, where v0 represents the starting point of the vehicle; is the decision variable, Represents arc (v i ,v j ) is served by vehicle k in operation mode o, otherwise, is the decision variable, To represent the arc (v i ,v j ) is driven by vehicle k along node v i To node v j The number of times the direction was visited; The constraints of the model are as follows: in, is the decision variable, Represents arc (v i ,v j ) is served by vehicle k; otherwise, is the vehicle on the side (v i ,v j ) is the water consumption when the vehicle is serviced in service mode o; Q is the maximum water capacity of the vehicle; It represents the average service workload of each vehicle when all road sections are served by the basic operation with the least cost, fog cannon operation; ε is a hyperparameter used to limit the range of fluctuation; — auxiliary variables, It is used to eliminate the independent closed sub-loops that may appear in the vehicle k route, where That is, S is a non-empty subset of the set consisting of all other nodes that do not contain the starting point; Step 6: Use the hybrid genetic algorithm (HGE) and variable neighborhood search algorithm (VNS) to solve the model.

2. According to claim 1, a dust suppression vehicle path optimization method with capacity constraints considering heterogeneous services is characterized by: In step 6, the improved genetic algorithm HGE includes the following steps: Initialize the population: randomly generate a certain number of chromosomes, each chromosome represents a possible vehicle path planning solution; Fitness evaluation: Evaluate the fitness of each chromosome according to the objective function value; Selection operation: select some chromosomes for crossover and mutation operations according to fitness; Crossover operation: exchange some genes of the selected chromosome to generate new chromosomes; Mutation operation: randomly change some genes of chromosomes to increase the diversity of the population; Iteration: Repeat the selection, crossover, and mutation operations until the termination condition is met, including reaching the maximum number of iterations or the fitness no longer improves.

3. According to claim 1, a dust suppression vehicle path optimization method with capacity constraints considering heterogeneous services is characterized by: In step 6, the variable neighborhood search algorithm VNS includes the following steps: Initialization solution: randomly generate an initial solution, i.e. a vehicle path planning solution; Neighborhood search: Search for a better solution in the neighborhood of the current solution by exchanging segments in the path or changing the service mode; Solution update: If a better solution is found, update the current solution; Neighborhood structure update: Regularly update the neighborhood structure and change the scope or method of neighborhood search; Iteration: Repeat the neighborhood search and solution update operations until the termination conditions are met, including reaching the maximum number of iterations or the solution no longer improves.

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