CN111091329A - Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances - Google Patents

Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances Download PDF

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CN111091329A
CN111091329A CN201911309655.6A CN201911309655A CN111091329A CN 111091329 A CN111091329 A CN 111091329A CN 201911309655 A CN201911309655 A CN 201911309655A CN 111091329 A CN111091329 A CN 111091329A
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CN111091329B (en
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马红光
李想
周仲鑫
哈明虎
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Beijing University of Chemical Technology
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    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

A semi-open type vehicle path optimization method for multi-vehicle type transportation of hazardous chemical substances comprises the following steps: the characteristics of a semi-open vehicle path for transporting multiple vehicle types are defined; determining risk parameters and cost parameters in the transportation network according to the requirements of a decision maker on reducing risk and cost; establishing a dual-target optimization model, and simultaneously minimizing two conflicting targets; collecting the positions of a warehouse and a client, the requirements of the client and the basic attributes of vehicles of multiple types; designing a hybrid intelligent algorithm for solving a dual-objective optimization model, namely an epsilon-constraint method based on a genetic algorithm; and calculating to obtain a pareto optimal solution and a corresponding optimal path. The method is mainly characterized in that a double-target optimization method is adopted to fully reflect the state that decision preference of a decision maker reaches equilibrium so as to obtain an equilibrium solution under the background of a semi-open type vehicle path problem of dangerous chemical multi-vehicle type transportation. The model provided by the method has effectiveness and optimality in strategy.

Description

Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances
Technical Field
The invention provides an optimization method for processing a semi-open type vehicle path problem of multi-vehicle type transportation of hazardous chemicals, belonging to the technical field of hazardous chemical transportation; particularly, the function of a semi-open vehicle path for multi-vehicle type transportation in hazardous chemical transportation is researched, two goals of minimizing total risk and total cost are considered at the same time, and a dual-objective optimization model is established.
Background
With the continuous development of economy, the demand of hazardous chemicals (hydrocarbons, explosives and other chemical substances) in industries and manufacturing industries is increasing. In 2015, about 11000 dangerous chemical enterprises exist in China, and the enterprises contain 31 ten thousand transport vehicles and 120 ten thousand employees. Meanwhile, the transportation amount of dangerous chemicals is continuously increased every year, the dangerous chemicals can be transported by transportation modes such as roads, pipelines, railways and water paths, and the road transportation is less limited by infrastructure. Therefore, about 2 hundred million tons of hazardous chemicals accounting for 80% of the total amount of hazardous chemicals are transported by road every year in China. However, due to the lower safety of road transportation compared with other transportation methods, and the hazardous chemical substances carried by the road transportation and the hazardous chemical substances, frequent and large-amount transportation of the hazardous chemical substances has higher potential risks. Once an accident happens to the vehicle carrying dangerous chemicals, serious consequences such as economic loss, casualties, environmental pollution and the like can be caused. According to the U.S. department of transportation, 488 severe events occurred in total in 2003, resulting in 15 deaths, 17 major injuries and 18 minor injuries, with a total loss of property amounting to $ 3775 ten thousand. During the period from 2011 to 2015, the chinese national statistics office reported that 1058 was a dangerous chemical incident, causing 1375 deaths. Therefore, transportation of dangerous chemicals has been a hot issue of research at home and abroad for many years, and how to reduce transportation risk to a greater extent is a primary objective of research on the problem.
Road transportation is the main means of dangerous chemical substance transportation, and the path planning researched by the vehicle path problem has important significance for reducing the potential risk of dangerous chemical substance transportation and the cost of the transportation activities of dangerous chemical substance companies. In addition, on the premise of continuously increasing demand of the current dangerous chemicals, the transportation of the dangerous chemicals is no longer simple and only has the characteristics of a single warehouse, a single vehicle type and a closed path. Dangerous chemical company has more storehouses to satisfy customer's demand, and according to actual dangerous chemical type and actual demand, there are multiple type vehicle to transport, because the phenomenon of out of stock or long-distance transport that the transportation process probably exists, cooperation between a plurality of storehouses, the arrangement of carrying out whole transportation has realistic meaning. Therefore, the semi-open type vehicle path problem of dangerous chemical multi-vehicle type transportation is researched, an effective optimization method for reducing transportation risk and cost is provided, path planning is carried out, transportation quality can be improved, and more choices are provided for decision makers.
Disclosure of Invention
In order to solve the problems of multi-type paths and multi-vehicle type vehicles in the field of dangerous chemical transportation at present, the invention provides a semi-open type vehicle path optimization method for solving multi-vehicle type transportation of dangerous chemicals, and the method carries out modeling based on the semi-open type vehicle path problem of multi-vehicle type transportation of the dangerous chemicals; meanwhile, based on the primary target of a decision maker required to be realized by hazardous chemical substance transportation, total transportation risk minimization is provided, the requirement of the decision maker on cost reduction is considered, total cost minimization is provided, the fixed cost and the transportation cost of a vehicle are used, a dual-objective optimization model is established, the model is solved through a hybrid intelligent algorithm, design examples are designed to explain the effectiveness of the model and the algorithm, and finally the optimization method can be compared with the original closed multi-warehouse vehicle path problem of multi-vehicle type transportation and the semi-open vehicle path problem of single-vehicle type transportation, so that the risk and the cost of hazardous chemical substance transportation can be effectively reduced, and the corresponding optimal path can provide a better selection scheme for the decision maker.
The dual-target optimization model provided by the invention mainly aims at minimizing the transportation risk and minimizing the cost, wherein for the minimized risk target, how to measure the risk is the primary problem in the field of transportation of hazardous chemicals. Because the actual dangerous chemical substance vehicle has the characteristic of low probability and high consequence when in accident, the possibility and the consequence of the accident are difficult to be measured by accurate data. The probability of an accident being taken by the present invention is derived from historical data, and the consequences of the accident are determined by the number of people in the area affected by the accident. However, the radius of the accident influence area of a certain road section is related to the actual load of the vehicle on the road section from the angle of the load change of the dangerous chemical substance vehicle after the actual service, the radius of the influence area is different corresponding to the different loads of the vehicle on different road sections, and the influence radius and the actual load of the vehicle are in a nonlinear function relationship.
The invention provides a semi-open type vehicle path optimization method for multi-vehicle type transportation of hazardous chemicals, which comprises the following steps:
step (1) setting constraint conditions: for the semi-open type vehicle path problem of multi-vehicle type transportation of hazardous chemicals, the following constraint condition limits are set: the corresponding cost and risk are generated after the vehicle is selected, the requirement of a client must be met and can only be accessed once by one vehicle, and the vehicle can return to any warehouse after completing the distribution task and can not exceed the limit of the maximum capacity of the vehicle;
step (2) calculating risks on all transport paths: the method comprises the following steps of measuring the consequences of accidents by adopting the actual loads of vehicles of multiple types, wherein the possibility of the accidents has different sizes according to different types of the vehicles;
step (3) total cost solving: considering the sum of fixed cost and transportation cost under the condition that the types of the used vehicles are different according to the requirement of a decision maker on cost reduction;
aiming at the problem of semi-open vehicle paths for multi-vehicle type transportation of hazardous chemicals, establishing a dual-objective optimization model, and simultaneously minimizing the total risk and the total cost of vehicles on all transportation paths;
step (5), solving a dual-objective optimization model: a hybrid intelligent algorithm, namely an epsilon-constraint method based on a genetic algorithm, is adopted to calculate and obtain a pareto solution, and a corresponding optimal path planning scheme is provided.
Further, the step (2) is specifically as follows:
defining a symbol system:
i, the number of warehouses;
j, the number of clients;
v, summing points;
e, total arc number;
k, total number of vehicles;
s, vehicle type;
Figure BDA0002324162960000031
the likelihood of an accident with an s-type vehicle on arc (i, j);
Figure BDA0002324162960000032
the number of persons affected by the accident of the s-type vehicle k on the arc (i, j);
Figure BDA0002324162960000033
radius of influence caused by accidents of s-type vehicles k on arc (i, j);
τijpopulation density on arc (i, j);
Figure BDA0002324162960000034
the cost per distance of transportation of s-type vehicles on arc (i, j);
lijthe distance of the arc (i, j);
fsfixed cost for s-type vehicles;
a subset of U set J;
the number of the elements of the set U is | U |;
djthe requirements of customer j;
qscapacity of an s-type vehicle;
Figure BDA0002324162960000035
risk of transportation of s-type vehicle k on arc (i, j);
Figure BDA0002324162960000036
total cost of s-type vehicle k on arc (i, j);
Figure BDA0002324162960000037
a decision variable, taking 1 if s-type vehicle k runs on arc (i, j); otherwise, 0 is selected;
Figure BDA0002324162960000038
decision variables, the actual load of s-type vehicle k on arc (i, j);
zska decision variable, taking 1 if s-type vehicle k is used; otherwise, 0 is taken.
The expression of the conventional risk model is as follows:
Figure BDA0002324162960000039
the probability of an accident being caused is determined by historical data, the number of people affected by the accident is determined by the area of the area affected by the accident and the population density of the area:
Figure BDA0002324162960000041
the influence radius is considered to be influenced by the actual load of the multi-vehicle type vehicle and the type of dangerous chemicals, and the influence radius and the load are in a nonlinear function relation:
Figure BDA0002324162960000042
wherein α is a constant number, and depends on different hazardous chemical types;
the risk model on the final arc (i, j) is:
Figure BDA0002324162960000043
the goal is to minimize the risk on all transport paths:
Figure BDA0002324162960000044
further, the step (3) aims at minimizing the total cost, the fixed cost of the multi-vehicle type vehicle and the transportation cost per unit distance are different, so the total cost on the arcs (i, j) is as follows:
Figure BDA0002324162960000045
the goal is to minimize the cost on all transport paths:
Figure BDA0002324162960000046
further, the step (4) is specifically as follows:
aiming at the problem of a semi-open type vehicle path for dangerous chemical multi-vehicle type transportation, under the constraints of maximum capacity of a vehicle, customer requirements, meeting the semi-open type path and the like, a dual-objective optimization model for minimizing total risk and total cost at the same time is established, and is specifically represented as follows:
Figure BDA0002324162960000051
Figure BDA0002324162960000052
Figure BDA0002324162960000053
Figure BDA0002324162960000054
Figure BDA0002324162960000055
Figure BDA0002324162960000056
Figure BDA0002324162960000057
Figure BDA0002324162960000058
Figure BDA0002324162960000059
Figure BDA00023241629600000510
Figure BDA00023241629600000511
further, the specific steps of solving the dual target model in the step (5) are as follows:
step 5.1, dividing the dual-target optimization problem into a single-target cost problem and a single-target risk problem under the rule of an epsilon-constraint method through a hybrid intelligent algorithm, solving the two single-target problems through a genetic algorithm, and respectively calculating the single-target cost problem under the constraint of a risk optimal value and the single-target risk problem under the constraint of a cost optimal value after solving an optimal solution;
step 5.2, determining the range of the risk function value;
step 5.3, setting an initial epsilon value;
step 5.4, under the upper bound condition that the initial epsilon value is taken as a risk value, calculating the risk by a genetic algorithm to be taken as a constrained minimum cost function;
step 5.5, according to the rule of the epsilon-constraint method, respectively substituting the optimal solution obtained in the previous step into a single-target cost model and a single-target risk model to obtain optimal values of risk and cost;
step 5.6, under different epsilon values, repeatedly solving the problem of single target cost with risk as constraint through a genetic algorithm until the epsilon value is smaller than the minimum risk value, and returning to the step 5.4 if the epsilon value is not smaller than the minimum risk value;
and 5.7, obtaining the pareto optimal solution and the corresponding optimal path, and providing a plurality of selected schemes for decision makers with different preferences.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the research on the vehicle path problem of dangerous chemical transportation in the prior art, the method considers the more practical transportation situation, combines the semi-open type, multi-vehicle type and multi-warehouse type vehicle path problem of dangerous chemical, and has practical significance.
2. Based on the semi-open vehicle path problem of hazardous chemical substance multi-vehicle type transportation, a dual-objective optimization model is provided, and the minimum risk and the minimum cost are considered from the perspective of a decision maker. Although the two proposed targets conflict with each other, an equilibrium solution for both targets can be found.
3. Based on a dual-objective optimization model, a hybrid intelligent algorithm is designed, under the framework of a traditional epsilon-constraint method for solving multiple objectives, a heuristic genetic algorithm is used for solving single objective functions appearing in specific epsilon-constraint methods such as nonlinear single objective functions, the dual-objective optimization problem is well solved, and subjectivity caused by using a common weighting method is avoided.
4. The effectiveness of the model and the algorithm is proved through an example, balanced solutions about risks and cost are obtained, choices are provided for decision makers with different preferences, and the corresponding optimal path planning can solve the problems. Finally, the optimization method provided by the invention can effectively reduce the risk and cost of hazardous chemical substance transportation by solving the original closed multi-warehouse vehicle path problem of multi-vehicle type transportation and the semi-open vehicle path problem of single-vehicle type transportation, and the corresponding optimized path can provide a better decision scheme for decision makers.
Drawings
FIG. 1 is a schematic diagram of three types of vehicle paths;
FIG. 2 is a schematic view of a semi-open vehicle path problem for multi-vehicle transport;
FIG. 3 is a flow chart of a hybrid intelligence algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person skilled in the art based on the embodiments of the present invention belong to the protection scope of the present invention without creative efforts.
The invention provides a semi-open type vehicle path optimization method for multi-vehicle type transportation of hazardous chemicals, which comprises the following steps:
(1) the invention aims at solving the problem of semi-open vehicle path for multi-vehicle type transportation of hazardous chemicals, and aims to find an optimal path planning scheme under a risk and cost balance state for decision makers with different decision preferences. Secondly, considering the problem, the constraint conditions that the vehicle generates corresponding cost and risk after being selected, the requirement of a client must be met and can only be visited once by one vehicle, and the vehicle can return to any warehouse after completing the distribution task and cannot exceed the maximum capacity of the vehicle are met;
(2) aiming at the requirement of a decision maker on reducing the risk, the total risk minimization is provided, the improvement is carried out on the basis of a traditional risk model, the different accident occurrence probabilities of vehicles of multiple types are considered, and the influence of the number of people influenced by the accident on the actual load of the vehicles and the types of dangerous chemicals transported is considered;
(3) aiming at the requirement of a decision maker on cost reduction, the total cost minimization is provided, and the sum of fixed cost and transportation cost under the condition of using different types of vehicles is considered;
(4) aiming at the problem of semi-open vehicle paths for multi-vehicle type transportation of hazardous chemical substances, a dual-objective optimization model is established, and the total risk and the total cost of vehicles on all transportation paths are minimized;
(5) in order to solve the dual-objective optimization model, a hybrid intelligent algorithm is designed, namely an epsilon-constraint method based on a genetic algorithm is used for calculating to obtain a pareto solution, and a corresponding optimal path planning scheme is provided for decision makers with different decision criteria.
The method comprises the following specific steps:
(1) defining a symbol system:
i, the number of warehouses;
j, the number of clients;
v, summing points;
e, total arc number;
k, total number of vehicles;
s, vehicle type;
Figure BDA0002324162960000071
the likelihood of an accident with an s-type vehicle on arc (i, j);
Figure BDA0002324162960000072
the number of persons affected by the accident of the s-type vehicle k on the arc (i, j);
Figure BDA0002324162960000073
radius of influence caused by accidents of s-type vehicles k on arc (i, j);
τijpopulation density on arc (i, j);
Figure BDA0002324162960000074
the cost per distance of transportation of s-type vehicles on arc (i, j);
lijthe distance of the arc (i, j);
fsfixed cost for s-type vehicles;
a subset of U set J;
the number of the elements of the set U is | U |;
djthe requirements of customer j;
qscapacity of an s-type vehicle;
Figure BDA0002324162960000081
risk of transportation of s-type vehicle k on arc (i, j);
Figure BDA0002324162960000082
total cost of s-type vehicle k on arc (i, j);
Figure BDA0002324162960000083
a decision variable, taking 1 if s-type vehicle k runs on arc (i, j); otherwise, 0 is selected;
Figure BDA0002324162960000084
decision variables, the actual load of s-type vehicle k on arc (i, j);
zska decision variable, taking 1 if s-type vehicle k is used; otherwise, 0 is taken.
(2) The invention is improved based on a traditional risk measurement model for dangerous chemical transportation, wherein the expression of the traditional risk model is as follows:
Figure BDA0002324162960000085
the probability of an accident is determined by historical data, and the number of people affected by the accident can be determined by the area affected by the accident and the population density of the area, and is as follows:
Figure BDA0002324162960000086
the invention considers the influence of different types of the actual load and the dangerous chemicals of the multi-vehicle type vehicles on the influence radius, and the influence radius and the load are in a nonlinear function relation:
Figure BDA0002324162960000087
wherein α is a constant number, and depends on different hazardous chemical types;
the risk model on the final arc (i, j) is:
Figure BDA0002324162960000088
the goal is to minimize the risk on all transport paths:
Figure BDA0002324162960000089
(3) for the total cost minimization objective, the fixed costs for multi-vehicle use and the cost per unit distance of transportation are different, the cost on arc (i, j) is:
Figure BDA00023241629600000810
the goal is to minimize the cost on all transport paths:
Figure BDA0002324162960000091
(4) aiming at the problem of a semi-open type vehicle path for dangerous chemical multi-vehicle type transportation, under the constraints of maximum capacity of a vehicle, customer requirements, meeting the semi-open type path and the like, a dual-objective optimization model for minimizing total risk and total cost at the same time is established, and is specifically represented as follows:
Figure BDA0002324162960000092
Figure BDA0002324162960000093
Figure BDA0002324162960000094
Figure BDA0002324162960000095
Figure BDA0002324162960000096
Figure BDA0002324162960000097
Figure BDA0002324162960000098
Figure BDA0002324162960000099
Figure BDA00023241629600000910
Figure BDA00023241629600000911
Figure BDA00023241629600000912
(5) in order to solve a dual-objective optimization model, a hybrid intelligent algorithm is designed, namely an epsilon-constraint method based on a genetic algorithm is finally solved to obtain a plurality of equilibrium solutions similar to pareto frontier, and corresponding optimal path planning is provided for decision makers with different preferences, and the specific steps are as follows:
step 1, dividing a dual-objective optimization problem into a single-objective cost problem and a single-objective risk problem under the rule of an epsilon-constraint method through a hybrid intelligent algorithm, solving the two single-objective problems through a genetic algorithm, and respectively calculating the single-objective cost problem under the constraint of a risk optimal value and the single-objective risk problem under the constraint of a cost optimal value after solving an optimal solution;
step 2, determining the range of the risk function value;
step 3, setting an initial epsilon value;
step 4, under the upper bound condition that the initial epsilon value is taken as a risk value, calculating the risk by a genetic algorithm to be taken as a constrained minimum cost function;
step 5, according to the rule of the epsilon-constraint method, respectively substituting the optimal solution obtained in the previous step into a single-target cost model and a single-target risk model to obtain optimal values of risk and cost;
step 6, under different epsilon values, repeatedly solving the problem of minimized cost with risk less than epsilon as one of constraint conditions through a genetic algorithm until the epsilon value is less than the lowest risk value, and returning to the step 4 if the epsilon value is not less than the lowest risk value;
and 7, obtaining the pareto optimal solution and the corresponding optimal path, and providing a plurality of selected schemes for decision makers with different preferences.
The present invention is further analyzed in detail, taking 3 warehouses, 10 customers and 3 types of vehicle routing questions as examples. First, information about risk and cost in the transportation graph is gathered such as: the location of the warehouse and customer and the customer's needs, the probability of an accident, the population density of the affected area, the attributes of different types of vehicles, etc.
The 3 types of vehicle paths in fig. 1 are closed, open and semi-open paths, respectively. The invention mainly considers the application of the semi-open path in the transportation of dangerous chemicals. Fig. 2 shows the problem of the invention to be optimized, in particular the semi-open path mode of transportation for multi-vehicle vehicles. FIG. 3 shows the hybrid intelligent algorithm designed by the present invention to achieve the desired result.
And designing a representation structure of a genetic algorithm, wherein each chromosome comprises a start-stop warehouse and all clients needing service, and obtaining an optimal solution of a single objective function in the steps of the hybrid intelligent algorithm through selection, intersection and variation. Setting 20 different epsilon values based on the risk difference value, and setting the maximum iteration number of the genetic algorithm to be 1000. And obtaining the pareto optimal solution by continuously changing the constraint value epsilon. The optimal path and risk and cost values corresponding to an epsilon constraint value and the optimal path and risk and cost values of the closed multi-warehouse vehicle path problem of multi-vehicle type transportation and the semi-open vehicle path problem of single-vehicle type transportation which are obtained by using the hybrid algorithm are shown in a table 1, and the effectiveness of the model and algorithm provided by the invention is illustrated.
TABLE 1
Figure BDA0002324162960000101
Note 1,2, … …,10 represents the customer; 11,12,13 represent warehouses.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, but various changes may be apparent to those skilled in the art, and it is intended that all inventive concepts utilizing the inventive concepts set forth herein be protected without departing from the spirit and scope of the present invention as defined and limited by the appended claims.

Claims (5)

1. A semi-open type vehicle path optimization method for multi-vehicle type transportation of hazardous chemicals is characterized by comprising the following steps: the method comprises the following steps:
step (1) setting constraint conditions: for the semi-open type vehicle path problem of multi-vehicle type transportation of hazardous chemicals, the following constraint condition limits are set: the corresponding cost and risk are generated after the vehicle is selected, the requirement of a client must be met and can only be accessed once by one vehicle, and the vehicle can return to any warehouse after completing the distribution task and can not exceed the limit of the maximum capacity of the vehicle;
step (2) calculating risks on all transport paths: the method comprises the following steps of measuring the consequences of accidents by adopting the actual loads of vehicles of multiple types, wherein the possibility of accidents is different according to different types of vehicles;
step (3) total cost solving: considering the sum of fixed cost and transportation cost under the condition that the types of the used vehicles are different according to the requirement of a decision maker on cost reduction;
aiming at the problem of semi-open vehicle paths for multi-vehicle type transportation of hazardous chemicals, establishing a dual-objective optimization model, and simultaneously minimizing the total risk and the total cost of vehicles on all transportation paths;
step (5), solving a dual-objective optimization model: a hybrid intelligent algorithm, namely an epsilon-constraint method based on a genetic algorithm, is adopted to calculate and obtain a pareto solution, and a corresponding optimal path planning scheme is provided.
2. The method for optimizing the path of the semi-open type vehicle for multi-vehicle transportation of the hazardous chemical substances according to claim 1, wherein the method comprises the following steps: the step (2) is specifically as follows:
defining a symbol system:
i, the number of warehouses;
j, the number of clients;
v, summing points;
e, total arc number;
k, total number of vehicles;
s, vehicle type;
Figure FDA0002324162950000011
the likelihood of an accident with an s-type vehicle on arc (i, j);
Figure FDA0002324162950000012
the number of persons affected by the accident of the s-type vehicle k on the arc (i, j);
Figure FDA0002324162950000013
radius of influence caused by accidents of s-type vehicles k on arc (i, j);
τijpopulation density on arc (i, j);
Figure FDA0002324162950000021
the cost per distance of transportation of s-type vehicles on arc (i, j);
lijthe distance of the arc (i, j);
fsfixed cost for s-type vehicles;
a subset of U set J;
the number of the elements of the set U is | U |;
djthe requirements of customer j;
qsmaximum capacity of s-type vehicle;
Figure FDA0002324162950000022
risk of transportation of s-type vehicle k on arc (i, j);
Figure FDA0002324162950000023
total cost of s-type vehicle k on arc (i, j);
Figure FDA0002324162950000024
a decision variable, taking 1 if s-type vehicle k runs on arc (i, j); otherwise, 0 is selected;
Figure FDA0002324162950000025
decision variables, the actual load of s-type vehicle k on arc (i, j);
zska decision variable, taking 1 if s-type vehicle k is used; otherwise, 0 is taken.
The expression of the conventional risk model is as follows:
Figure FDA0002324162950000026
the probability of an accident being caused is determined by historical data, the number of people affected by the accident is determined by the area of the area affected by the accident and the population density of the area:
Figure FDA0002324162950000027
the influence radius is considered to be influenced by the actual load of the multi-vehicle type vehicle and the type of dangerous chemicals, and the influence radius and the load are in a nonlinear function relation:
Figure FDA0002324162950000028
wherein α is a constant number, and depends on different hazardous chemical types;
the risk model on the final arc (i, j) is:
Figure FDA0002324162950000029
the goal is to minimize the risk on all transport paths:
Figure FDA00023241629500000210
3. the method for optimizing the path of the semi-open type vehicle for multi-vehicle transportation of the hazardous chemical substances according to claim 2, wherein the method comprises the following steps: the step (3) is used for minimizing the total cost target, wherein the fixed cost and the unit distance transportation cost of the vehicles of multiple vehicle types are different, so the total cost on the arcs (i, j) is as follows:
Figure FDA0002324162950000031
the goal is to minimize the cost on all transport paths:
Figure FDA0002324162950000032
4. the method for optimizing the path of the semi-open type vehicle for multi-vehicle transportation of the hazardous chemical substances according to claim 2, wherein the method comprises the following steps: the step (4) is specifically as follows:
aiming at the problem of a semi-open type vehicle path for dangerous chemical multi-vehicle type transportation, under the constraints of maximum capacity of a vehicle, customer requirements, meeting the semi-open type path and the like, a dual-objective optimization model for minimizing total risk and total cost at the same time is established, and is specifically represented as follows:
Figure FDA0002324162950000033
Figure FDA0002324162950000034
Figure FDA0002324162950000035
Figure FDA0002324162950000036
Figure FDA0002324162950000037
Figure FDA0002324162950000038
Figure FDA0002324162950000039
Figure FDA00023241629500000310
Figure FDA00023241629500000311
Figure FDA00023241629500000312
Figure FDA00023241629500000313
5. the method for optimizing the path of the semi-open type vehicle for multi-vehicle transportation of the hazardous chemical substances according to claim 2, wherein the method comprises the following steps: the specific steps of solving the dual-target model in the step (5) are as follows:
step 5.1, dividing the dual-target optimization problem into a single-target cost problem and a single-target risk problem under the rule of an epsilon-constraint method through a hybrid intelligent algorithm, solving the two single-target problems through a genetic algorithm, and respectively calculating the single-target cost problem under the constraint of a risk optimal value and the single-target risk problem under the constraint of a cost optimal value after solving an optimal solution;
step 5.2, determining the range of the risk function value;
step 5.3, setting an initial epsilon value;
step 5.4, under the upper bound condition that the initial epsilon value is taken as a risk value, calculating the risk by a genetic algorithm to be taken as a constrained minimum cost function;
step 5.5, according to the rule of the epsilon-constraint method, respectively substituting the optimal solution obtained in the previous step into a single-target cost model and a single-target risk model to obtain optimal values of risk and cost;
step 5.6, under different epsilon values, repeatedly solving the problem of single target cost with risk as constraint through a genetic algorithm until the epsilon value is smaller than the minimum risk value, and returning to the step 5.4 if the epsilon value is not smaller than the minimum risk value;
and 5.7, obtaining the pareto optimal solution and the corresponding optimal path, and providing a plurality of selected schemes for decision makers with different preferences.
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