Dangerous goods vehicle path optimization method considering cargo capacity

Abstract

The invention provides a dangerous goods vehicle path optimization method considering cargo capacity, belongs to the technical field of traffic planning and design, and solves the technical problem that a path optimization model is not perfect enough because the influence of the cargo capacity in dangerous goods transportation on actual transportation is not considered in the transportation process of a dangerous goods vehicle. According to the technical scheme, the method comprises the following steps: S1, assuming a model under the condition of considering the actual transportation condition of a dangerous goods vehicle; s2, calculating a vehicle transportation risk value under the condition of considering the cargo capacity; s3, the fixed cost and the transportation distance cost of the dangerous goods vehicle are calculated; s4, calculating the transportation time cost of the dangerous goods vehicle; and S5, solving the vehicle path optimization model. The method has the beneficial effects that the transportation risk is calculated in combination with the real-time transportation cargo capacity, the transportation condition better fitting the reality is arranged, and the transportation risk of the dangerous goods vehicle is reduced to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic planning and design, and in particular to a method for optimizing the path of a hazardous goods vehicle taking into account the cargo volume. Background Art

[0002] As the industrialization process continues to accelerate, the volume of dangerous goods transportation has shown an upward trend year by year. In stark contrast to ordinary items, dangerous goods are prone to various risk conditions during transportation due to their own characteristics, such as rollover, collision, leakage, combustion, explosion, and poisoning. Once an accident unfortunately occurs, the consequences are disastrous, often leading to serious personal injury and death tragedies, huge property damage and losses, and inestimable pollution and damage to the surrounding environment, posing a direct and huge threat to the lives and safety of residents along the way. In view of this, under the fundamental premise of fully ensuring transportation safety, scientific, reasonable and accurate optimization of the driving path of dangerous goods transportation vehicles has become a vital and urgent key task, which is of great significance for reducing risks, protecting people's lives and property, and maintaining ecological and environmental stability.

[0003] Current and other scholars started to study this kind of problem in the early stage. When they constructed the route selection model, they set the dual goals of minimizing vehicle mileage and reducing the number of people covered by the route to the minimum, striving to achieve dual control of mileage and population coverage risks in route planning. Zografos and others focused on the route selection problem of the three target dimensions of the minimum number of people at risk, the minimum property loss caused by accidents, and the shortest transportation time, and used the hierarchical solution method to sort the goals according to priority to solve the problem, and ensure the orderly optimization of multiple goals through fine hierarchical division. Song Yang and others took the simultaneous minimization of risk and total economic cost as the goal orientation, carefully constructed the objective function, and then used the ant algorithm to solve the objective function, making full use of the unique advantages of the ant algorithm in route optimization to explore the optimal route plan. Kai Yanxia and others made a comprehensive consideration, while taking into account the minimization of transportation costs, focusing on analyzing the losses caused by accidents and the reasonable choice of transportation methods and routes, and exploring the best transportation strategy from the perspective of multi-faceted factors. Up to now, the proposed method ignores the impact of cargo capacity on actual transportation risks. In order to solve the problem that dangerous goods vehicles currently do not consider the impact of cargo capacity on vehicle route selection, the present invention proposes a dangerous goods vehicle route optimization model that takes cargo capacity into consideration on the basis of fully considering the transportation characteristics of dangerous goods vehicles. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a method for optimizing the path of dangerous goods vehicles taking into account the cargo capacity, which solves the technical problem of transportation road conditions that do not take the cargo capacity into consideration during the transportation of dangerous goods.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] The method for optimizing the routing of dangerous goods vehicles considering the cargo volume includes the following steps:

[0007] Step S1, making assumptions about the model considering the actual transportation of dangerous goods vehicles;

[0008] Step S2, calculating the vehicle transportation risk value taking into account the cargo volume;

[0009] Step S3, calculating the fixed cost and transportation distance cost of the dangerous goods vehicle;

[0010] Step S4, calculating the transportation time cost of dangerous goods vehicles;

[0011] Step S5, establish the objective function and use the multi-objective algorithm to solve the vehicle path optimization model.

[0012] In step S1, making assumptions about the model in consideration of the actual transportation of dangerous goods vehicles specifically includes the following steps:

[0013] Step S11, it is known that the dangerous goods vehicle road transportation network system G = (N, A), N = {0, 1, 2, ..., n}, represents the set of dangerous goods vehicle distribution center 0 and all demand nodes i of the distribution center, and A represents the set of all roads connecting the demand nodes and the distribution center.

[0014] Step S12: The distance between each node is d ij (km), the demand quantity and delivery time window requirements of each demand node i are q i (kg) and [ET i , LT i ], the departure time interval of the vehicle is [T a ,T b ].

[0015] Step S13, k delivery vehicles with a capacity of W (kg) depart from the distribution center 0, complete the delivery of the corresponding demand nodes in sequence according to the distribution route, and finally return to the distribution center. x (kg) shall not exceed the maximum load W (kg);

[0016] Step S14: The unit distance travel cost of the dangerous goods vehicle is C n(yuan / km), the fixed cost of using the vehicle is C m (Yuan / vehicle), the penalty cost for early arrival is θ1 (Yuan / h) and the penalty cost for late arrival is θ2 (Yuan / h).

[0017] Step S15, is the number of exposed population from node i to node j when the vehicle is fully loaded; ω ij is the cargo volume on the road section (i, j); t ij is the transportation time required from node i to node j; the probability of an accident occurring on a road section is ρ ij .

[0018] Step S16: Each demand node can only be served by one vehicle, and one delivery vehicle can serve multiple demand nodes at the same time.

[0019] The calculation of the vehicle transportation risk value in S2 considering the cargo volume specifically includes the following steps:

[0020] Step S21: First, the distribution route of each dangerous goods transportation is divided into H sections, and the cargo capacity of the distribution vehicle leaving the demand node i is In the hth (h∈[1,H])th road section, the cargo capacity of the hth road section is The probability of an accident occurring on this road section is The population exposure number of this road section is

[0021] Step S22, it is known that there is a linear relationship between the transport risk consequence and the cargo capacity of the dangerous goods vehicle, so the transport risk value on the road section (i, j) can be obtained accordingly.

[0022]

[0023] in, is the number of exposed population from node i to node j when the vehicle is fully loaded; ω ij is the cargo volume on the road section (i, j); ij is the probability of an accident occurring on the road section; W is the rated load of the vehicle.

[0024] Step S23, based on the continuity of the vehicle on the delivery path, the cargo volume of the vehicle on the hth road section is the sum of the cargo volumes of dangerous goods transported by the nodes passed by the previous h road sections;

[0025] Step S24, in summary, the cargo capacity of the delivery vehicle on the hth delivery route can be calculated using the following formula:

[0026]

[0027] Step S25, in summary, the risk value of a delivery vehicle from demand node i to demand node j considering the cargo volume can be calculated using the following formula:

[0028]

[0029] The calculation of the fixed cost and transportation distance cost of the dangerous goods vehicle in S3 specifically includes the following steps:

[0030] Step S31, firstly, it is necessary to obtain the location of the demand node that the driver needs to go to and the demand volume of each demand node from the dangerous goods vehicle transportation company so as to arrange a reasonable number of vehicles and driving routes. If the dangerous goods vehicle k goes from demand node i to demand node j, then X ijk is recorded as 1, otherwise it is recorded as 0; if the dangerous goods vehicle k serves the demand node i, then Y ik It is recorded as 1, otherwise it is recorded as 0.

[0031] Step S32, obtain the location and demand of each demand node and the total number of vehicles in the distribution center and substitute them into the following formula:

[0032]

[0033] Among them, formula (4) means that the load of dangerous goods cannot exceed the rated load, formula (5) means that the number of dispatched vehicles cannot exceed the number of vehicles owned by the distribution center, formulas (6) and (7) mean that each demand node can only be served by one vehicle and can only be served once, and formula (8) ensures that each customer is visited once and returned to the distribution center.

[0034] Step S33: After the constraints are established, the fixed usage cost C per vehicle is obtained. m Since the fixed cost of using a vehicle is related to the number of hazardous goods vehicles dispatched, the fixed cost of using a vehicle is:

[0035]

[0036] Step S34, obtaining the unit distance transportation cost C of the unit vehicle n , since the transportation distance cost of a vehicle is related to the driving distance, the transportation distance cost of the vehicle is:

[0037]

[0038] The calculation of the transportation time cost of dangerous goods vehicles in S4 specifically includes the following steps:

[0039] Step S41, obtaining the departure time T of vehicle k lk , the time when vehicle k arrives at demand node i The time of leaving demand node i and the service time of vehicle k at demand node i Substituting into the following formula,

[0040]

[0041] This constraint represents the continuity of the travel time of the transport vehicles;

[0042] Step S42, obtaining the delivery time requirement [ET i , L T i ], E.T. i represents the earliest expected arrival time of demand node i, LT i represents the latest expected arrival time of demand node i;

[0043] Step S43, obtain the penalty costs of early arrival and late arrival of dangerous goods vehicles that do not meet the time window, recorded as θ1 and θ2 respectively. i Arriving at demand node i before, incurring premature scheduling loss cost If the dispatch vehicle is in LT i After that, it arrives at demand node i, incurring delayed scheduling costs Otherwise, no time window penalty cost is incurred;

[0044] Step S44, taking user satisfaction as a factor to measure the time cost of dangerous goods transport vehicles, the transportation time cost of dangerous goods vehicles is obtained as follows:

[0045]

[0046] In S5, an objective function is established and a multi-objective algorithm is used to solve the vehicle path optimization model. Specifically, the following steps are included:

[0047] Step S51, through step S25, establish an objective function with the goal of minimizing transportation risk:

[0048]

[0049] Step S52, through steps S33, S34 and S44, establish an objective function with the goal of minimizing the total transportation cost:

[0050] Q2=min(C1+C2+C3) (14)

[0051] C1 represents the fixed cost of using the vehicle, C2 represents the transportation distance cost of the vehicle, and C3 represents the penalty time cost of the vehicle.

[0052] Step S53, putting the established objective function and constraint conditions as well as the relevant information parameters in the dangerous goods vehicle transportation process into MATLAB and using the multi-objective algorithm introduced with the probability model to solve and obtain the corresponding path optimization result.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: the present scheme provides a method for optimizing the path of hazardous goods vehicles taking into account the cargo volume. The hazardous goods vehicle transportation enterprise of the present invention has made improvements on the standard of only using the population exposure number as the wind speed measurement in the past. The present invention, on the basis of fully considering the transportation characteristics of hazardous goods vehicles, proposes a path optimization model for hazardous goods vehicles taking into account the cargo volume. The model is a multi-objective optimization model. When taking into account the cargo volume, two indicators, transportation cost and safety risk, are introduced. The transportation risk is calculated in combination with the real-time transportation cargo volume, so as to make reasonable arrangements that are more in line with the actual transportation situation, so that hazardous goods vehicles can reduce certain transportation risks in transportation tasks and ensure the benefits of the enterprise. The multi-objective model for optimizing the path of hazardous goods vehicles taking into account the cargo volume can provide a more reasonable route arrangement for the transportation unit. The selection of the specific transportation path can be comprehensively decided in combination with factors such as actual road conditions and driver experience. The penalty time cost set on the time cost also indirectly improves the transportation efficiency, thereby increasing customer satisfaction with the transportation company. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0055] Figure 1 A model flow chart of the method for optimizing the path of hazardous goods vehicles taking into account the cargo volume provided by the present invention.

[0056] Figure 2 This is a flow chart of solving the multi-objective algorithm that introduces a probability model in the present invention.

[0057] Figure 3 A solution set of cargo-carrying vehicles is considered for the embodiments of the present invention.

[0058] Figure 4 A delivery route map of a cargo vehicle is considered for an embodiment of the present invention.

[0059] Figure 5 This embodiment of the present invention does not consider the solution set of vehicles with cargo capacity.

[0060] Figure 6 This embodiment of the present invention does not consider the delivery route map of the cargo-carrying vehicle. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 to Figure 2 The technical solution provided in this embodiment is a method for optimizing the path of dangerous goods vehicles taking into account the cargo volume, including the following steps:

[0064] Step S1, making assumptions about the model considering the actual transportation of dangerous goods vehicles;

[0065] Step S2, calculating the vehicle transportation risk value taking into account the cargo volume;

[0066] Step S3, calculating the fixed cost and transportation distance cost of the dangerous goods vehicle;

[0067] Step S4, calculating the transportation time cost of dangerous goods vehicles;

[0068] Step S5, establish the objective function and use the multi-objective algorithm to solve the vehicle path optimization model.

[0069] In step S1, making assumptions about the model in consideration of the actual transportation of dangerous goods vehicles specifically includes the following steps:

[0070] Step S11, it is known that the dangerous goods vehicle road transportation network system G = (N, A), N = {0, 1, 2, ..., n}, represents the set of dangerous goods vehicle distribution center 0 and all demand nodes i of the distribution center, and A represents the set of all roads connecting the demand nodes and the distribution center.

[0071] Step S12: The distance between each node is d ij (km), the demand quantity and delivery time window requirements of each demand node i are q i (kg) and [ET i , L T i ], the departure time interval of the vehicle is [T a ,T b ].

[0072] Step S13, k delivery vehicles with a capacity of W (kg) depart from the distribution center 0, complete the delivery of the corresponding demand nodes in sequence according to the distribution route, and finally return to the distribution center. x(kg) shall not exceed the maximum load W (kg);

[0073] Step S14: The unit distance travel cost of the dangerous goods vehicle is C n (yuan / km), the fixed cost of using the vehicle is C m (Yuan / vehicle), the penalty cost for early arrival is θ1 (Yuan / h) and the penalty cost for late arrival is θ2 (Yuan / h).

[0074] Step S15, is the number of exposed population from node i to node j when the vehicle is fully loaded; ω ij is the cargo volume on the road section (i, j); t ij is the transportation time required from node i to node j; the probability of an accident occurring on a road section is ρ ij .

[0075] Step S16: Each demand node can only be served by one vehicle, and one delivery vehicle can serve multiple demand nodes at the same time.

[0076] The calculation of the vehicle transportation risk value in S2 considering the cargo volume specifically includes the following steps:

[0077] Step S21: First, the distribution route of each dangerous goods transportation is divided into H sections, and the cargo capacity of the distribution vehicle leaving the demand node i is In the hth (h∈[1,H])th road section, the cargo capacity of the hth road section is The probability of an accident occurring on this road section is The population exposure number of this road section is

[0078] Step S22, it is known that there is a linear relationship between the transport risk consequence and the cargo capacity of the dangerous goods vehicle, so the transport risk value on the road section (i, j) can be obtained accordingly.

[0079]

[0080] in, is the number of exposed population from node i to node j when the vehicle is fully loaded; ω ij is the cargo volume on the road section (i, j); ij is the probability of an accident occurring on the road section; W is the rated load of the vehicle.

[0081] Step S23, based on the continuity of the vehicle on the delivery path, the cargo volume of the vehicle on the hth road section is the sum of the cargo volumes of dangerous goods transported by the nodes passed by the previous h road sections;

[0082] Step S24, in summary, the cargo capacity of the delivery vehicle on the hth delivery route can be calculated using the following formula:

[0083]

[0084] Step S25, in summary, the risk value of a delivery vehicle from demand node i to demand node j considering the cargo volume can be calculated using the following formula:

[0085]

[0086] The calculation of the fixed cost and transportation distance cost of the dangerous goods vehicle in S3 specifically includes the following steps:

[0087] Step S31, firstly, it is necessary to obtain the location of the demand node that the driver needs to go to and the demand volume of each demand node from the dangerous goods vehicle transportation company so as to arrange a reasonable number of vehicles and driving routes. If the dangerous goods vehicle k goes from demand node i to demand node j, then X ijk is recorded as 1, otherwise it is recorded as 0; if the dangerous goods vehicle k serves the demand node i, then Y ik It is recorded as 1, otherwise it is recorded as 0.

[0088] Step S32, obtain the location and demand of each demand node and the total number of vehicles in the distribution center and satisfy the following constraints:

[0089] (1) The cargo volume of dangerous goods cannot exceed the rated load;

[0090] (2) The number of vehicles dispatched cannot exceed the number of vehicles owned by the distribution center;

[0091] (3) Each demand node can only be served by one vehicle and can only be served once;

[0092] (4) Ensure that each customer is visited once and returned to the distribution center.

[0093] Step S33: After the constraints are established, the fixed usage cost C per vehicle is obtained. m The fixed cost of using a vehicle is equal to the number of hazardous goods vehicles dispatched multiplied by the fixed cost of using the vehicle C m ;

[0094] Step S34, obtaining the unit distance transportation cost C of the unit vehicle n Since the transportation distance cost of a vehicle is equal to the travel distance multiplied by the unit distance transportation cost C of a unit vehicle, n ;

[0095] The calculation of the transportation time cost of dangerous goods vehicles in S4 specifically includes the following steps:

[0096] Step S41, obtaining the departure time T of vehicle k lk , the time when vehicle k arrives at demand node i The time of leaving demand node i and the service time of vehicle k at demand node i And ensure the continuity of transportation vehicle driving time;

[0097] Step S42, obtaining the delivery time requirement [ET i , LT i ], E.T. i represents the earliest expected arrival time of demand node i, LT i represents the latest expected arrival time of demand node i;

[0098] Step S43, obtain the penalty costs of early arrival and late arrival of dangerous goods vehicles that do not meet the time window, recorded as θ1 and θ2 respectively. i Arriving at demand node i before, incurring premature scheduling loss cost If the dispatch vehicle is in LT i After that, it arrives at demand node i, incurring delayed scheduling costs Otherwise, no time window penalty cost is incurred;

[0099] Step S44, taking user satisfaction as a consideration for the time cost of the dangerous goods transport vehicle, the transportation time cost of the dangerous goods transport vehicle is obtained as the sum of the penalty time cost for early arrival and the penalty time cost for late arrival.

[0100] In S5, an objective function is established and a multi-objective algorithm is used to solve the vehicle path optimization model. Specifically, the following steps are included:

[0101] Step S51, through step S25, establish an objective function with the goal of minimizing transportation risk:

[0102]

[0103] Step S52, through steps S33, S34 and S44, establish an objective function with the goal of minimizing the total transportation cost:

[0104] Q2=min(C1+C2+C3) (5)

[0105] C1 represents the fixed cost of using the vehicle, C2 represents the transportation distance cost of the vehicle, and C3 represents the penalty time cost of the vehicle.

[0106] Step S53, putting the established objective function and constraint conditions as well as the relevant information parameters in the dangerous goods vehicle transportation process into MATLAB and using the multi-objective algorithm with the introduction of the probability model to solve and obtain the corresponding path optimization result.

[0107] Example 2

[0108] See also Figures 3 to 6 Based on the same inventive concept as the method for optimizing the path of dangerous goods vehicles taking into account the cargo volume in the above embodiment, a circle is drawn with a dangerous goods distribution center in Qidong City as the center and a radius of 40 km. The road network within the circle is the simulation area, and 19 customer factories are randomly selected as distribution points in the simulation area. The following is a specific example:

[0109] 1. Data collection and processing: A rectangular coordinate system is established with the distribution center as the origin. The number, demand, horizontal and vertical coordinates, and time window of each demand node are shown in Table 1. Since vehicles cannot strictly maintain a straight line in actual distribution, the detour coefficient is introduced to calculate the actual distance between two points.

[0110] Table 1. Customer point information

[0111]

[0112]

[0113] 2. Model parameter setting:

[0114] The multi-objective genetic algorithm and model parameters are shown in Tables 2 and 3

[0115] Table 2. NSGA-Ⅱ algorithm parameters

[0116]

[0117] Table 3. Vehicle model parameters

[0118]

[0119] 4. Simulation results and analysis:

[0120] A genetic algorithm was designed using MATLAB 2022a to solve the example consisting of 19 customer factories in Table 1. In order to show the calculation results more clearly, the algorithm was run 10 times. The convergence process of the optimal result is shown in the following figure. Figure 3 , 5 shown.

[0121] Table 4. Algorithm solution results considering cargo volume

[0122]

[0123] Table 5. Algorithm solution results without considering cargo volume

[0124]

[0125] By analyzing the corresponding data in Table 4 and Table 5, and comparing the impact of considering and not considering the cargo volume on the vehicle distribution path and the corresponding transportation indicators, it can be seen that: the transportation risk under the condition of considering the cargo volume is reduced by 8% compared with the transportation risk without considering the cargo volume, and the transportation cost is reduced by 7.3%. It can be concluded that the transportation risk and transportation cost of dangerous goods transport vehicles are reduced when the cargo volume is considered, which verifies that the dangerous goods vehicle path optimization method considering the cargo volume proposed in the present invention can make certain contributions to the planning of dangerous goods vehicle paths for relevant departments.

[0126] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the routing of dangerous goods vehicles taking into account the cargo volume, characterized in that: The following steps are involved: Step S1, making assumptions about the model considering the actual transportation of dangerous goods vehicles; Step S2, calculating the vehicle transportation risk value taking into account the cargo volume; Step S3, calculating the fixed cost and transportation distance cost of the dangerous goods vehicle; Step S4, calculating the transportation time cost of dangerous goods vehicles; Step S5, establish the objective function and use the multi-objective algorithm to solve the vehicle path optimization model.

2. The method for optimizing the path of dangerous goods vehicles considering the cargo volume according to claim 1 is characterized in that: In step S1, making assumptions about the model in consideration of the actual transportation of dangerous goods vehicles specifically includes the following steps: Step S11, it is known that the dangerous goods vehicle road transportation network system G = (N, A), N = {0, 1, 2, ..., n}, represents the set of dangerous goods vehicle distribution center 0 and all demand nodes i of the distribution center, and A represents the set of all roads connecting the demand nodes and the distribution center; Step S12: The distance between each node is d ij (km), the demand quantity and delivery time window requirements of each demand node i are q i (kg) and [ET i , LT i ], the departure time interval of the vehicle is [T a ,T b ]; Step S13, k delivery vehicles with a capacity of W (kg) depart from the distribution center 0, complete the delivery of the corresponding demand nodes in sequence according to the distribution route, and finally return to the distribution center; and during the transportation of dangerous goods by vehicles, the weight of the dangerous goods transported by vehicles is W x (kg) shall not exceed the maximum load W (kg); Step S14: The unit distance travel cost of the dangerous goods vehicle is C n (yuan / km), the fixed cost of using the vehicle is C m (yuan / vehicle), the penalty cost for early arrival is θ1 (yuan / h) and the penalty cost for late arrival is θ2 (yuan / h); Step S15, t ij is the transportation time required from node i to node j; Step S16: Each demand node can only be served by one vehicle, and one delivery vehicle can serve multiple demand nodes at the same time.

3. The method for optimizing the path of dangerous goods vehicles considering the cargo volume according to claim 2 is characterized in that: In step S2, calculating the vehicle transportation risk value in consideration of the cargo volume specifically includes the following steps: Step S21: First, the distribution route of each dangerous goods transportation is divided into H sections, and the cargo capacity of the distribution vehicle leaving the demand node i is In the hth (h∈[1,H])th road section, the cargo capacity of the hth road section is The probability of an accident occurring on this road section is The population exposure number of this road section is Step S22, it is known that there is a linear relationship between the transport risk consequence and the cargo volume of the dangerous goods vehicle, so the transport risk value on the road section (i, j) can be obtained accordingly; in, is the number of exposed population from demand node i to demand node j when the vehicle is fully loaded; ω ij is the cargo volume on the road section (i, j); ij is the probability of an accident occurring on the road section; W is the rated load of the vehicle; Step S23, based on the continuity of the vehicle on the distribution path, the cargo volume of the vehicle on the hth road section is the sum of the cargo volumes of dangerous goods transported by the demand nodes passed by the previous h road sections; Step S24, in summary, the cargo capacity of the delivery vehicle on the hth delivery route can be calculated using the following formula: Step S25, in summary, the risk value of a delivery vehicle from demand node i to demand node j considering the cargo volume can be calculated using the following formula:

4. The method for optimizing the path of dangerous goods vehicles considering the cargo volume according to claim 3 is characterized in that: In step S3, calculating the fixed cost and transportation distance cost of the dangerous goods vehicle specifically includes the following steps: Step S31, firstly, it is necessary to obtain the location of the demand node that the driver needs to go to and the demand volume of each demand node from the dangerous goods vehicle transportation company so as to arrange a reasonable number of vehicles and driving routes. If the dangerous goods vehicle k goes from demand node i to demand node j, then X ijk is recorded as 1, otherwise it is recorded as 0; if the dangerous goods vehicle k serves the demand node i, then Y ik It is recorded as 1, otherwise it is recorded as 0; Step S32, obtain the location and demand of each demand node and the total number of vehicles in the distribution center and satisfy the following constraints: (1) The cargo volume of dangerous goods cannot exceed the rated load; (2) The number of vehicles dispatched cannot exceed the number of vehicles owned by the distribution center; (3) Each demand node can only be served by one vehicle and can only be served once; (4) Ensure that each customer is visited once and returned to the distribution center; Step S33: After the constraints are established, the fixed usage cost C per vehicle is obtained. m The fixed cost of using a vehicle is equal to the number of hazardous goods vehicles dispatched multiplied by the fixed cost of using the vehicle C m ; Step S34, obtaining the unit distance transportation cost C of the unit vehicle n Since the transportation distance cost of a vehicle is equal to the travel distance multiplied by the unit distance transportation cost C of a unit vehicle, n .

5. The method for optimizing the path of dangerous goods vehicles considering the cargo volume according to claim 4 is characterized in that: In step S4, calculating the transportation time cost of dangerous goods vehicles specifically includes the following steps: Step S41, obtaining the departure time T of vehicle k lk , the time when vehicle k arrives at demand node i The time of leaving demand node i and the service time of vehicle k at demand node i And ensure the continuity of transportation vehicle driving time; Step S42, obtaining the delivery time requirement [ET i , LT i ], E.T. i represents the earliest expected arrival time of demand node i, LT i represents the latest expected arrival time of demand node i; Step S43, obtain the penalty costs of early arrival and late arrival of dangerous goods vehicles that do not meet the time window, recorded as θ1 and θ2 respectively. i Arriving at demand node i before, incurring premature scheduling loss cost If the dispatch vehicle is in LT i After that, it arrives at demand node i, incurring delayed scheduling costs Otherwise, no time window penalty cost is incurred; Step S44, taking user satisfaction as a consideration for the time cost of the dangerous goods transport vehicle, the transportation time cost of the dangerous goods transport vehicle is obtained as the sum of the penalty time cost for early arrival and the penalty time cost for late arrival.

6. The method for optimizing the path of dangerous goods vehicles considering the cargo volume according to claim 5 is characterized in that: In step S5, an objective function is established and a multi-objective algorithm is used to solve the vehicle path optimization model, which specifically includes the following steps: Step S51, through step S25, establish an objective function with the goal of minimizing transportation risk: Step S52, through steps S33, S34 and S44, establish an objective function with the goal of minimizing the total transportation cost: Q2=min(C1+C2+C3) (5) C1 represents the fixed cost of using the vehicle, C2 represents the transportation distance cost of the vehicle, and C3 represents the penalty time cost of the vehicle; Step S53, putting the established objective function and constraint conditions as well as the relevant information parameters in the dangerous goods vehicle transportation process into MATLAB and using the multi-objective algorithm with the introduction of the probability model to solve and obtain the corresponding path optimization result.

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