CN114662884A - International multimodal transport method based on risk assessment model - Google Patents
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Abstract
The invention discloses an international multimodal transport method based on a risk assessment model, which comprises the following steps: and setting appropriate parameters according to the known transportation network information, and establishing a risk assessment model. And calculating the risk control cost according to the risk assessment model, and obtaining a total penalty cost model as a constraint condition by combining the transportation cost and the carbon value cost. Aiming at the original black and wife algorithm, mixed chaos optimization population opening and addition of an iterative speed control factor form a new H-BWO algorithm for optimizing the path planning of international multimodal transport. Finally, a risk avoiding mode of multi-mode intermodal transportation is provided. The invention can realize the control of the total cost in the international multimodal transportation process, particularly the risk cost brought by different paths and different transportation modes, can better ensure the low carbon property of the international multimodal transportation, and has certain promotion effect on the development of green logistics in China.
Description
Technical Field
The invention relates to a joint transportation method, in particular to an international multimodal joint transportation method based on a risk assessment model, and belongs to the technical field of transportation path planning.
Background
The multi-type combined transportation is used as a transportation mode that two or more transportation vehicles are mutually connected and transported to jointly finish the transportation process, can give full play to the advantages of different transportation modes, and avoids the defects of unreachable, uneconomic and poor reliability of the traditional transportation mode. However, the planning of the multi-mode intermodal transportation path is more complicated, and because two or more transportation modes are involved, the transportation time is longer and the transportation risk is higher, and the logistics transit in the connection process of the transportation modes is also considered. Meanwhile, the invention provides a method for calculating the distance from a transport carrier to a path key node with congestion or other high-risk reasons by using a positioning technology aiming at the path key node with congestion or other high-risk reasons, and adopting different risk avoidance modes according to the distance.
At present, the existing multi-type intermodal planning method comprises an ant colony algorithm, a particle swarm algorithm, a difference algorithm and the like, the method is often slow in convergence speed, cannot achieve global optimum, has high requirements on prior knowledge of the environment, needs to occupy large storage space and the like, and once a complex dynamic environment is met, the efficiency of the planning method is greatly reduced. Compared with the method, the black widow algorithm is proposed by inspiring of the unique mating behavior of the black widow spider, and has the advantages of few control parameters, high convergence speed, simple calculation, easy realization and the like.
The black widow algorithm simulates the life cycle of the black widow spider, males distinguish the mating state of females through sex pheromones, and males are not interested in females in a hungry state or malnutrition because females can show cannibalistic behavior. The mechanism of the black and oligogyna algorithm is that decision and optimization are made through unique movement and mating behaviors of a black and oligogyna spider, the method is simple and efficient, and the method is suitable for being applied to the problems of path planning and the like, so that the black and oligogyna algorithm is very suitable for being applied to scheme planning of multi-type intermodal transportation. However, the native black-and-wife algorithm gradually decreases the fluctuation of the change with the increase of the number of iterations, thereby leading to local optimization. .
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an international multimodal transport method based on a risk assessment model, which improves the global search capability and convergence speed, optimizes a planning scheme of multimodal transport, can be closely combined with the actual situation, and enhances the practicability.
The invention provides an international multimodal transport method based on a risk assessment model, which comprises the following steps:
step 1, establishing a risk assessment model; establishing a risk index table and transportation network information according to the actual transportation condition, setting appropriate parameters, and establishing a risk evaluation index model;
step 2, calculating a risk control cost according to the risk assessment index model established in the step 1, and obtaining a total penalty cost model as a constraint condition by combining the comprehensive transportation cost and the carbon value cost;
step 3, expanding and improving a BWO (Black widow algorithm) algorithm; designing a new H-BWO algorithm by mixed chaos optimization population starting and adding an iterative speed control factor;
step 4, generating a global optimal path according to the H-BWO algorithm in the step 3, and setting basic parameters of the H-BWO algorithm: generating a global optimal path by combining a minimum penalty cost function model, and using the global optimal path as a global path planning scheme of the current multimodal transport;
step 5, providing a risk avoiding mode of multi-mode intermodal transportation; aiming at the path key nodes with congestion or other high-risk reasons, different risk avoidance modes are adopted by combining the distance from a transport carrier to the key nodes.
According to the method, corresponding penalty cost functions are respectively constructed for the risk control cost, the comprehensive transportation cost and the carbon value cost in the multi-type combined transportation process, a unified minimum penalty cost model is formed to serve as a constraint, and the three aspects are effectively controlled to achieve comprehensive optimization. In addition, in consideration of overhigh and uncertain conditions in the international multimodal transport process, a corresponding risk index table is established to deal with risks brought by different paths and different transport modes, and the problem is solved by modeling which is more practical and more targeted.
Further, in the step 1, the risk index table includes a risk index of a transportation mode, a risk index of a transportation route, and a risk index of a transported item;
the transportation mode at least comprises air transportation, railway transportation, road transportation, pipeline transportation, inland river transportation and sea transportation;
the transportation path comprises key road sections of different levels such as provincial boundary, national boundary and continent boundary;
the transported goods comprise ores, energy sources, agricultural products, high and new technology products, industrial products and medical supplies.
Further, a communication model of each node in the transportation network under each transportation mode is established according to the international logistics transportation network data, and the following model is obtained by considering the condition that whether the node i can reach the node j or not:
i,j∈N；i<j；
wherein,
representing the w-type transportation mode from the point i to the point j; n is represented as a set of all nodes.
Furthermore, a risk evaluation index model of the multimodal transportation is established by combining the risk index table, and the risk evaluation index of the multimodal transportation is related to the types of transported goods, the transportation path and the transportation mode in the transportation process. The following risk assessment index model was therefore established: establishing a risk evaluation model based on the risk evaluation table by combining the actual path data with the parameters of the risk evaluation table:
wherein K represents the overall risk index of the transportation process;
representing the risk index of the w-type transportation mode from the ith point to the jth point; dijRepresenting a risk index of the transport path from the ith point to the jth point; thetaqRepresenting the risk index for transporting class q items.
Further, for the established risk assessment index model, the risk is controlled within an acceptable range through means of insurance, risk management and the like, and therefore, the risk control cost is generated. Thereby incurring risk control costs; classifying and discussing risk control cost, and setting T1And T2Two risk control thresholds and two risk cost control indexes of phi and delta, which are used for classifying and judging different transportation risks under different conditions, wherein the risk control cost is as follows:
wherein, T1And T2Is a risk control threshold, the value can be set by the user according to the actual situation; phi and delta are risk control cost parameters, phi belongs to [1,2 ]]、δ∈[2,3]。
Further, a transportation cost model based on an entropy increase system is established; the multi-type intermodal transportation cost is represented by direct transportation cost related to transportation quantity, time and distance in the transportation process and indirect transportation cost related to transfer and management. The overall process of the multimodal transportation shows the same phenomenon as the law of entropy increase of physics, namely, with the increase of transportation time, transportation distance and transfer nodes, the transportation chaos degree is correspondingly increased, and certain management and control cost must be added to effectively reduce the transportation disorder degree. Therefore, the following transportation cost model is established based on the entropy increase law:
wherein L isijRepresenting the distance transported from the ith point to the jth point; u shapewRepresenting the unit distance cost from the ith point to the jth point by using a w-type transportation mode; g denotes the number or mass or volume of the transported items.
Further, a carbon value cost model is established, and the carbon value cost is related to the transportation volume and distance in the transportation process, so that the following transportation cost model is established:
wherein λ represents a cost parameter for carbon emissions; epsilonwRepresents the unit carbon emission of the w-type transportation.
Furthermore, appropriate weights are taken for the three costs, and a minimum penalty cost model based on a risk assessment model, an efficiency assessment model and a carbon value cost is established, wherein the expression function of the model is as follows:
MinC＝α*C1+β*C2+γ*C3；
α+β+γ＝1，α、β、γ∈[0,1]；
wherein C is the total evaluation value of the multimodal transportation process; c1Is the risk control cost of the multimodal transport process; c2Is the transportation cost of the multimodal transportation process; c3Is the carbon value cost of the multimodal transport process; α, β, γ are weight parameters of the three evaluation values.
Further, in the step 4, according to the minimum penalty cost function model, a specific step of generating a global optimal path through an H-BWO algorithm is as follows:
step 4.1, initializing H-BWO algorithm parameters:
initializing H-BWO algorithm parameters includes: the population scale D, the maximum iteration times Z and the randomly generated parameters m, omega and r are introduced into a chaos mechanism to optimize the population opening of the H-BWO algorithm, so that the initial population individual distribution can fully utilize the whole algorithm space, the information utilization degree of the algorithm space is maximized, and good guidance is provided at the same time, so that the convergence speed of the algorithm is accelerated, and the local optimization is avoided; the characteristics of regularity, randomness, ergodicity and the like of the chaotic sequence are utilized to enable the initial population of the H-BWO algorithm to utilize the information of a search space as much as possible:
wherein a and b are chaotic constants, a belongs to (0,1.4), and b belongs to (0.2, 0.314);
step 4.2, updating the position of the H-BWO algorithm:
the black widow spider moves in a linear and spiral mode in the grid, and the position is updated according to the following formula:
step 4.3, calculating an information element rate value:
pheromones play a very important role in the process of coupling of spiders, while male spiders dislike female spiders with low pheromone content; the pheromone value formula of the black widow spider is as follows:
wherein A isiAn pheromone value representing a black widow spider; f. ofmaxAnd fminThe worst and optimal fitness function values; f. ofiFitness value obtained for the ith black oligowoman;
step 4.4, updating the positions of the black oligomeres with low pheromone values: changing an original BWO position iteration formula, adding an iteration speed control factor to slow down the speed of the BWO algorithm at the initial iteration stage, and avoiding local optimization; meanwhile, as the iteration times gradually rise, the convergence speed is effectively accelerated.
When the pheromone rate value is equal to or less than 0.3, low pheromone level spiders in females represent hungry human-eating spiders; thus, if they are present, the female spiders mentioned above will not be selected, but will be replaced by another; therefore, the position iterative formula is improved, the iterative speed control factor tau is added, and the iterative speed control factor tau has good guiding performance so as to control the convergence speed of the algorithm, the condition of local optimum is avoided in the early stage, the convergence speed of the algorithm is accelerated in the later stage, and the improved formula is as follows:
wherein, Pi(t) is the current black oligogyn position or the black oligogyn position with low pheromone value; pi(t +1) is the updated position of the black oligoniers; pbThe optimal position of the current black oligogynes; pr1(t) and Pr2(t) is the random r1And r2Position of black widow alone, r1And r2Is in [1, D ]]Number within the range, r1≠r2(ii) a Tau is an iteration speed control factor; n is the current iteration number; z is the maximum iteration number of the H-BWO algorithm; η is a random binary number {0, 1 };
step 4.5, re-evaluating the fitness function value, and updating the position and the optimal solution of the optimal black widow;
and 4.6, judging whether the maximum iteration times are met, if the algorithm reaches the maximum iteration times Z, outputting the optimal black and wife positions and the global optimal solution, and if not, returning to the step 4.2 to carry out iterative calculation again.
Aiming at the path key nodes with congestion or other high-risk reasons, the invention provides a method for calculating the distance xi from a transport carrier to the path key nodes with congestion or other high-risk reasons by using a positioning technology, and adopting different risk avoidance modes according to the calculated distance. When e is more than or equal to 100km, updating the logistics information network, removing the key nodes, and re-planning the path through an H-BWO algorithm; when e is more than or equal to 10km and less than 100km, the transport vehicle can stop advancing, and further arrangement is carried out after congestion or other high-risk reduction.
Compared with the prior art, the invention has the beneficial effects that: the invention mixes chaos optimization population opening and adds an iterative speed control factor, thereby forming a new H-BWO algorithm, having better opening and being capable of reducing and maximizing the information utilization degree of the algorithm space. Meanwhile, the method has good guidance so as to control the convergence rate of the algorithm, avoid the situation of local optimum in the early stage and accelerate the convergence rate of the algorithm in the later stage. After a minimum penalty cost model is introduced, a global optimal path is generated through an H-BWO algorithm, the total cost in the international multimodal transportation process, particularly the risk cost caused by different paths and different transportation modes can be controlled, the low carbon property of the international multimodal transportation can be better ensured, and the development of green logistics in China is promoted to a certain extent.
Drawings
FIG. 1 is a block flow diagram of the present invention.
FIG. 2 is a flow chart of the H-BWO algorithm of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The international multimodal transport method based on the risk assessment model provided by the embodiment comprises the following specific steps:
1. modeling a minimum penalty cost function
Assuming the input related logistics information: the transported goods are agricultural products; the origin of the shipment is in the state of iowa in the united states and the destination is in wuhan in china. The method comprises the following steps of combining related data of three tables of risk index evaluation tables of different transportation modes, risk index evaluation tables of key road sections of transportation paths and risk index evaluation tables of different transported goods, wherein the values of basic parameters are as follows: phi 1.5, delta 2.5, T1＝10、T2＝20、θiλ is 0.5 and 3. Forming a comprehensive minimum penalty cost model such as:
MinC＝0.5*C1+0.3*C2+0.2*C3
the constraints are as follows:
i,j∈N；i<j；
wherein the risk index evaluation table is as follows:
according to the actual transportation situation, a corresponding risk index table is established for different main transportation modes such as air transportation, railway transportation, road transportation, pipeline transportation, inland river transportation and sea transportation. The table is as follows:
TABLE 1 Risk index evaluation table for different transportation modes
Meanwhile, according to the actual situation, a risk index table for the key road sections of the transportation path is constructed, and risk index evaluation is performed for the key road sections of different levels such as provincial boundaries, national boundaries, continent boundaries and the like, particularly for the transportation path passing through the world-level transportation hub and the key road. The table is as follows:
TABLE 2 Risk index evaluation chart for key road sections of transportation route
And according to the actual situation, a risk index table aiming at different transported goods is constructed, and risk index evaluation is carried out aiming at ores, energy, agricultural products, high and new technology products and the like, particularly the evaluation aiming at industrial products and fossil energy.
TABLE 3 Risk index evaluation chart for different transported goods
2. Improving the algorithm and solving
In the planning process of the distribution path of international multimodal transport, the invention introduces chaos and changes the original BWO position iterative formula, and improves the BWO algorithm in multiple aspects such as population starting optimization, thereby forming a new H-BWO algorithm and solving the model with the minimum penalty cost. The H-BWO algorithm finds an optimal path among many paths by calculation.
1) Initializing H-BWO algorithm parameters
Initializing H-BWO algorithm parameters comprises; the population size D, the maximum iteration times Z, and randomly generated parameters m, omega and r. Wherein a chaos mechanism is introduced to carry out population opening optimization. The characteristics of regularity, randomness, ergodicity and the like of the chaotic sequence are utilized to enable the initial population of the H-BWO algorithm to utilize the information of a search space as much as possible:
2) H-BWO algorithm location update
The black widow spider moves in a linear and spiral mode in the grid, and the position is updated according to the following formula:
3) calculating pheromone rate values
Pheromones play a very important role in the process of idol seeking in spiders, while male spiders dislike female spiders with low pheromone content. The pheromone value formula of the black widow spider is as follows:
4) updating black oligonier locations with low pheromone values
When the pheromone rate value is equal to or less than 0.3, low pheromone level spiders within females represent hungry human-eating spiders. Thus, if they are present, the female spiders mentioned above will not be selected, but will be replaced by another. The invention improves the position iteration formula, adds an iteration speed control factor tau, and ensures that the iteration speed control factor tau has good guidance so as to control the convergence speed of the algorithm, thereby avoiding the situation of local optimum in the early stage and accelerating the convergence speed of the algorithm in the later stage, wherein the improved formula is as follows:
5) and re-evaluating the fitness function value, and updating the position and the optimal solution of the optimal black widow.
6) Algorithm termination
And (4) if the algorithm reaches the maximum iteration times Z, outputting the optimal black and wife positions and the global optimal solution, and otherwise, returning to the step 2 to carry out iterative calculation again.
Calculating the result by an H-BWO algorithm: from the state of Iowa, transported to the harbor of los Angeles by railway, transferred to the harbor of Shanghai, China, and then transported along the Yangtze river to Wuhan by inland river shipping.
From the results of the calculations it can be seen that: the H-BWO algorithm formed by mixing chaos and adding an iterative speed control factor tau can effectively improve the convergence speed and the global optimization capability of the BWO algorithm and avoid falling into local optimization. Aiming at the complex situation of international multimodal transport, the H-BWO algorithm is combined with a minimum penalty cost model to generate a whole local optimal path, so that the risk in the international multimodal transport process can be effectively controlled, the carbon emission can be further reduced, and the development of the whole green logistics industry in China is promoted.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims.
Claims (9)
1. An international multimodal transport method based on a risk assessment model is characterized in that: the method comprises the following steps:
step 1, establishing a risk assessment model; establishing a risk index table and transportation network information according to the actual transportation condition, setting appropriate parameters, and establishing a risk evaluation index model;
step 2, calculating a risk control cost according to the risk assessment index model established in the step 1, and obtaining a total penalty cost model as a constraint condition by combining the comprehensive transportation cost and the carbon value cost;
step 3, expanding and improving a BWO (Black widow algorithm) algorithm; designing a new H-BWO algorithm by mixed chaos optimization population starting and adding an iterative speed control factor;
step 4, generating a global optimal path according to the H-BWO algorithm in the step 3, and setting basic parameters of the H-BWO algorithm: generating a global optimal path by combining a minimum penalty cost function model, and using the global optimal path as a global path planning scheme of the current multimodal transport;
step 5, providing a risk avoiding mode of multi-mode intermodal transportation; aiming at the key nodes of the path with congestion or other high-risk reasons, different risk avoiding modes are adopted by combining the distance from a transport carrier to the key nodes.
2. The risk assessment model-based international multimodal transport method according to claim 1, characterized in that: in the step 1, the risk index table comprises risk indexes of transportation modes, risk indexes of transportation paths and risk indexes of transported goods;
the transportation mode at least comprises air transportation, railway transportation, road transportation, pipeline transportation, inland river transportation and sea transportation;
the transportation path comprises key road sections of different levels such as provincial boundary, national boundary and continent boundary;
the transported goods comprise ores, energy sources, agricultural products, high and new technology products, industrial products and medical supplies.
3. An international multimodal transport method based on a risk assessment model according to claim 1, characterized in that: establishing a communication model of each node in the transportation network under each transportation mode according to international logistics transportation network data, and considering whether a slave node i can reach a node j, obtaining the following model:
wherein,
representing w types of transportation modes from the point i to the point j; n is represented as a set of all nodes.
4. An international multimodal transport method based on a risk assessment model according to claim 2, characterized in that: and establishing a risk evaluation index model of the multimodal transport by combining the risk indexes in the risk index table: establishing a risk evaluation model based on the risk evaluation table by using the actual path data and combining the parameters of the risk evaluation table:
wherein K represents the overall risk index of the transportation process;
representing the risk index of w-type transportation modes from the ith point to the jth point; dijRepresenting a risk index of the transport path from the ith point to the jth point; theta.theta.qRepresenting the risk index for transporting class q items.
5. An international multimodal transport method based on a risk assessment model according to claim 4, characterized in that: and controlling the risk range by at least means of insurance and risk management aiming at the established risk assessment index model, thereby generating risk control cost.
Classifying and discussing risk control cost, and setting T1And T2Two risk control thresholds and two risk cost control indexes of phi and delta, which are used for classifying and judging different transportation risks under different conditions, wherein the risk control cost is as follows:
wherein, T1And T2Is a risk control threshold, the value can be set by the user according to the actual situation; phi and delta are risk control cost parameters, phi belongs to [1,2 ]]、δ∈[2,3]。
6. An international multimodal transport method based on a risk assessment model according to claim 1, characterized in that: in the step 2, a transportation cost model is established based on an entropy increase system; the multi-type intermodal transportation cost is embodied in direct transportation cost related to transportation quantity, time and distance in the transportation process and indirect transportation cost related to transfer and management; the overall process of the multimodal transportation shows the same phenomenon as the law of entropy increase of physics, namely, the transportation chaos degree is correspondingly increased along with the increase of transportation time, transportation distance and transfer nodes, and the transportation disorder degree can be effectively reduced only by adding certain management and control cost; therefore, the following transportation cost model is established based on the entropy increase law:
wherein L isijRepresenting the distance transported from the ith point to the jth point; u shapewRepresenting the unit distance cost from the ith point to the jth point by using a w-type transportation mode; g denotes the number or mass or volume of the transported items.
7. The risk assessment model-based international multimodal transport method according to claim 1, characterized in that: in the step 2, the carbon value cost is related to the transportation volume and distance in the transportation process, so that the following transportation cost model is established:
wherein λ represents a cost parameter for carbon emissions; epsilonwRepresents the unit carbon emission of the w-type transportation.
8. An international multimodal transport method based on a risk assessment model according to claim 5 or 6 or 7, characterized by: establishing a minimum penalty cost model based on a risk assessment model, an efficiency assessment model and a carbon value cost, wherein an expression function of the model is as follows:
MinC＝α*C1+β*C2+γ*C3；
α+β+γ＝1，α、β、γ∈[0,1]；
wherein C is the total evaluation value of the multimodal transportation process; c1Is the risk control cost of the multimodal transport process; c2Is the transportation cost of the multimodal transportation process; c3Is the carbon value cost of the multimodal transport process; α, β, γ are weight parameters of the three evaluation values.
9. The risk assessment model-based international multimodal transport method according to claim 1, characterized in that: in the step 4, according to the minimum penalty cost function model, a specific step of generating a global optimal path through an H-BWO algorithm is as follows:
step 4.1, initializing parameters of an H-BWO algorithm:
initializing H-BWO algorithm parameters includes: the method comprises the following steps of (1) carrying out population scale D, maximum iteration times Z and randomly generated parameters m, omega and r, wherein a chaotic mechanism is introduced to carry out population opening optimization; the characteristics of regularity, randomness, ergodicity and the like of the chaotic sequence are utilized to enable the initial population of the H-BWO algorithm to utilize the information of a search space as much as possible:
wherein a and b are chaotic constants, a belongs to (0,1.4), and b belongs to (0.2, 0.314);
step 4.2, updating the position of the H-BWO algorithm:
the black widow spider moves in a linear and spiral mode in the grid, and the position is updated according to the following formula:
step 4.3, calculating an information element rate value:
pheromones play a very important role in the process of coupling of spiders, while male spiders dislike female spiders with low pheromone content; the pheromone value of the black widow spider is formulated as follows:
wherein A isiAn pheromone value representing a black widow spider; vmaxAnd VminThe worst and optimal fitness function values; f. ofiFitness value obtained for the ith black oligowoman;
step 4.4, updating the positions of the black oligomeres with low pheromone values:
when the pheromone rate value is equal to or less than 0.3, low pheromone level spiders in females represent hungry human-eating spiders; thus, if they are present, the female spiders mentioned above will not be selected, but will be replaced by another; therefore, the position iterative formula is improved, and the iterative speed control factor tau is added to ensure that the iterative speed control factor tau has good guidance so as to control the convergence speed of the algorithm, so that the condition of local optimum is avoided in the early stage, the convergence speed of the algorithm is accelerated in the later stage, and the improved formula is as follows:
wherein, Pi(t) is the current black oligogyn position or the black oligogyn position with low pheromone value; p isi(t +1) is the updated position of the black oligoniers; pbThe optimal position of the current black oligogynes; pr1(t) and Pr2(t) is a random r1And r2Position of black widow alone, r1And r2Is in [1, D ]]Number within the range, r1≠r2(ii) a Tau is an iteration speed control factor; n is the current iteration number; z is the maximum iteration number of the H-BWO algorithm; η is a random binary number {0, 1 };
step 4.5, re-evaluating the fitness function value, and updating the position and the optimal solution of the optimal black widow;
and 4.6, judging whether the maximum iteration times are met, if the algorithm reaches the maximum iteration times Z, outputting the optimal black and wife positions and the global optimal solution, and if not, returning to the step 4.2 to carry out iterative calculation again.
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