A risk assessment model for international multimodal transport

Abstract

The present invention discloses an international multimodal transport method based on a risk assessment model, comprising the following steps: according to known transport network information, appropriate parameters are set to establish a risk assessment model. The risk control cost is calculated according to the risk assessment model, and the total penalty cost model is obtained as a constraint condition by combining the transport cost and the carbon value cost. For the original Black Widow algorithm, a new H‑BWO algorithm is formed by mixing chaos optimization population start and adding an iteration speed control factor to optimize the path planning of international multimodal transport. Finally, a risk avoidance method for multimodal transport is proposed. The present invention can realize the control of the total cost in the process of international multimodal transport, especially the risk cost brought by different paths and different modes of transport, and can better ensure the low carbon nature of international multimodal transport, which has a certain driving effect on the development of green logistics in my country.

Description

Technical Field

[0001] The invention relates to a combined transport method, in particular to an international multimodal transport method based on a risk assessment model, and belongs to the technical field of transport route planning. Background Art

[0002] As a mode of transportation in which two or more modes of transportation are connected and transferred to complete the transportation process, multimodal transport can give full play to the advantages of different modes of transportation and avoid the defects of traditional modes of transportation, such as inaccessibility, uneconomical and poor reliability. However, the route planning of multimodal transport is also more complicated. Since it involves two or more modes of transportation, it usually takes longer transportation time and greater transportation risks, and the logistics transit during the connection of transportation modes must also be considered. At the same time, the present invention proposes to use positioning technology to calculate the distance from the transport vehicle to the key nodes of the path where congestion or other high-risk reasons have occurred, and adopt different risk avoidance methods according to the size of the distance.

[0003] At present, the existing multimodal transport planning methods include ant colony algorithm, particle swarm algorithm, differential algorithm, etc. These methods often have problems such as slow convergence, failure to achieve global optimality, high requirements for prior knowledge of the environment, and need to occupy a large storage space. Once a complex dynamic environment is encountered, the efficiency of these planning methods will drop significantly. In comparison, the black widow algorithm is inspired by the unique mating behavior of the black widow spider. It has the advantages of fewer control parameters, fast convergence, simple calculation, and easy implementation.

[0004] The Black Widow Algorithm simulates the life cycle of the black widow spider. Males use sex pheromones to identify the mating status of females. Because females exhibit cannibalism, males are not interested in hungry or malnourished females. The mechanism of the Black Widow Algorithm is to make decisions and optimize through the unique movement and mating behavior of the black widow spider. It is simple and efficient, and is suitable for application in path planning and other problems. This makes the Black Widow Algorithm very suitable for application in multimodal transport plan planning. However, the original Black Widow Algorithm will gradually reduce the fluctuation of changes as the number of iterations increases, which will lead to local optimality. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention proposes an international multimodal transport method based on a risk assessment model, which improves the global search capability and increases the convergence speed. It not only optimizes the planning scheme of multimodal transport, but also can be closely integrated with the actual situation to enhance practicality.

[0006] The present invention proposes an international multimodal transport method based on a risk assessment model, comprising the following steps:

[0007] Step 1: Establish a risk assessment model; establish a risk index table and transportation network information based on actual transportation conditions, set appropriate parameters, and establish a risk assessment index model;

[0008] Step 2: Calculate the risk control cost based on the risk assessment index model established in step 1, combine the comprehensive transportation cost and carbon value cost, and derive the total penalty cost model as a constraint condition;

[0009] Step 3: Expand and improve the Black Widow algorithm BWO; design a new H-BWO algorithm by optimizing the population start with hybrid chaos and adding an iteration speed control factor;

[0010] Step 4: Generate a global optimal path according to the H-BWO algorithm in step 3, and set the basic parameters of the H-BWO algorithm: Combined with the minimum penalty cost function model, generate a global optimal path as the global path planning solution for the current multimodal transport;

[0011] Step 5: Propose a risk avoidance method for multimodal transport; for key nodes on the path that have already experienced congestion or other high-risk reasons, adopt different risk avoidance methods based on the distance from the transport vehicle to the key nodes.

[0012] The present invention constructs corresponding penalty cost functions for the risk control cost, comprehensive transportation cost and carbon value cost in the multimodal transport process, and forms a unified minimum penalty cost model as a constraint, so as to effectively control the above three aspects to achieve comprehensive optimization. In addition, considering the high uncertainty in the international multimodal transport process, a corresponding risk index table is established to deal with the risks brought by different routes and different modes of transportation, so as to solve the problem with more practical and targeted modeling.

[0013] Furthermore, in step 1, the risk index table includes the risk index of the mode of transportation, the risk index of the transportation route, and the risk index of the transported item;

[0014] The transportation modes include at least air transportation, railway transportation, road transportation, pipeline transportation, inland waterway transportation and sea transportation;

[0015] The transport route includes key sections at different levels such as provincial boundaries, national boundaries and continental boundaries;

[0016] The transported items include minerals, energy, agricultural products, high-tech products, industrial supplies and medical supplies.

[0017] Furthermore, based on the international logistics transportation network data, a connectivity model of each node in the transportation network under each transportation mode is established, considering whether node i can reach node j, and the following model is obtained:

[0018]

[0019] i,j∈N;i <j;

[0020] in, It means that w types of transportation are used from point i to point j; N represents the set of all nodes.

[0021] Furthermore, a risk assessment index model for multimodal transport is established in combination with the risk index table. The risk assessment index of multimodal transport is related to the type of transported goods, transport route, and mode of transport during the transportation process. Therefore, the following risk assessment index model is established: Using the actual route data combined with the parameters of the risk assessment table, a risk assessment model based on the risk assessment table is established:

[0022]

[0023] Among them, K represents the total risk index of the transportation process; The risk index of transport from point i to point j by using type w transport mode; D ij represents the risk index of the transportation path from point i to point j; θ q Represents the risk index of transporting category q items.

[0024] Furthermore, for the established risk assessment index model, the risk is controlled within an acceptable range through insurance, risk management and other means, which generates risk control costs. Risk control costs are generated; the risk control costs are classified and discussed, and two risk control thresholds T1 and T2, as well as two risk cost control indexes Φ and δ, are set to classify and judge different transportation risks under different circumstances. The risk control costs are as follows:

[0025]

[0026] Among them, T1 and T2 are risk control thresholds, and the values ​​can be set by the user according to actual conditions; Φ and δ are risk control cost parameters, Φ∈[1,2], δ∈[2,3].

[0027] Furthermore, a transportation cost model based on the entropy increase system is established; the multimodal transport cost is reflected in the direct transportation cost related to the transportation quantity, time, and distance during the transportation process, as well as the indirect transportation cost related to transit and management. Since the overall process of multimodal transport reflects the same phenomenon as the law of entropy increase in physics, that is, with the increase of transportation time, transportation distance, and transit nodes, the disorder of transportation will increase accordingly, and a certain amount of management and control costs must be added to effectively reduce the disorder of transportation. Therefore, the following transportation cost model is established based on the law of entropy increase:

[0028]

[0029] Among them, L ij Indicates the distance from point i to point j; U w It represents the unit distance cost from point i to point j using type w of transportation; G represents the quantity, quality or volume of the transported items.

[0030] Furthermore, a carbon cost model is established. The carbon cost is related to the transportation volume and distance during transportation. Therefore, the following transportation cost model is established:

[0031]

[0032] Among them, λ represents the cost parameter of carbon emission; ε w Represents the unit carbon emission of type w transportation mode.

[0033] Furthermore, appropriate weights are given to the above three costs to establish a minimum penalty cost model based on the risk assessment model, efficiency assessment model and carbon value cost, and its expression function is:

[0034] MinC=α*C1+β*C2+γ*C3;

[0035] α+β+γ=1, α, β, γ∈[0,1];

[0036] Among them, C is the total evaluation value of the multimodal transport process; C1 is the risk control cost of the multimodal transport process; C2 is the transportation cost of the multimodal transport process; C3 is the carbon value cost of the multimodal transport process; α, β, and γ are the weight parameters of the three evaluation values.

[0037] Furthermore, in step 4, according to the minimum penalty cost function model, the specific steps of generating a global optimal path through the H-BWO algorithm are as follows:

[0038] Step 4.1, initialize H-BWO algorithm parameters:

[0039] Initialization parameters of H-BWO algorithm include: population size D, maximum number of iterations Z, randomly generated parameters m, Ω and r. Chaos mechanism is introduced to optimize the population start of H-BWO algorithm, so that the individual distribution of initial population can make full use of the entire algorithm space, maximize the information utilization of algorithm space, and also have good guidance, so as to speed up the convergence of algorithm and avoid local optimum. The regularity, randomness and ergodicity of chaotic sequence are used to make the initial population of H-BWO algorithm make full use of the information of search space as much as possible:

[0040]

[0041] Among them, a, b are chaotic constants, a∈(0,1.4), b∈(0.2,0.314];

[0042] Step 4.2, H-BWO algorithm position update:

[0043] The black widow spider moves in a linear and spiral manner within the grid, and its position is updated as follows:

[0044]

[0045] Step 4.3, calculate the pheromone rate value:

[0046] Pheromones play a very important role in the courtship process of spiders, and male spiders do not like female spiders with low pheromone content; the pheromone rate value formula of black widow spiders is as follows:

[0047]

[0048] Among them, A i represents the pheromone rate value of the black widow spider; f max and f min is the worst and best fitness function value; f i The fitness value obtained for the i-th black widow;

[0049] Step 4.4, update the black widow position with low pheromone rate value: change the original BWO position iteration formula, add an iteration speed control factor to slow down the speed of the BWO algorithm in the early iteration to avoid local optimality; at the same time, as the number of iterations gradually increases, the convergence speed is effectively accelerated.

[0050] When the pheromone rate value is equal to or less than 0.3, the female spider with low pheromone level in the body represents a hungry man-eating spider; therefore, if they are present, the above female spider will not be selected, but will be replaced by another one; therefore, the position iteration formula is improved, and the iteration speed control factor τ is added to make it have good guidance, so as to control the convergence speed of the algorithm, avoid the local optimal situation in the early stage, and speed up the convergence speed of the algorithm in the later stage. The improved formula is as follows:

[0051]

[0052]

[0053] Among them, P i (t) is the current black widow position or the black widow position with low pheromone rate value; P i (t+1) is the updated black widow position; P b is the optimal position of the current Black Widow; P r1 (t) and Pr2 (t) is the position of the random r1th and r2th black widows, r1 and r2 are numbers in the range [1, D], r1≠r2; τ is the 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};

[0054] Step 4.5, re-evaluate the fitness function value, and update the position of the optimal black widow and the optimal solution;

[0055] Step 4.6: Determine whether the maximum number of iterations is met. If the algorithm reaches the maximum number of iterations Z, output the optimal black widow position and the global optimal solution. Otherwise, return to step 4.2 and iterate again.

[0056] The present invention proposes to use positioning technology to calculate the distance ξ between the transport vehicle and the key node of the path where congestion or other high-risk reasons have occurred, and adopt different risk avoidance methods according to the size of the calculated distance. When e≥100km, update the logistics information network and remove the key node, and then re-plan the path through the H-BWO algorithm; when 10km≤e<100km, the transport vehicle can stop moving forward and wait for congestion or other high risks to be reduced before making further arrangements.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses a hybrid chaos optimization population start and adds an iteration speed control factor, thereby forming a new H-BWO algorithm with a better start and maximizing the information utilization of the algorithm space. At the same time, it also has good guidance, so as to control the convergence speed of the algorithm, avoid the local optimal situation in the early stage, and accelerate the convergence speed of the algorithm in the later stage. After introducing the minimum penalty cost model, the global optimal path is generated by the H-BWO algorithm, which can realize the control of the total cost in the international multimodal transport process, especially the risk cost brought by different paths and different modes of transportation, and can better ensure the low carbon nature of international multimodal transport, which has a certain driving effect on the development of green logistics in my country. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the present invention.

[0059] Figure 2 It is the flow chart of H-BWO algorithm in the present invention. DETAILED DESCRIPTION

[0060] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0061] The international multimodal transport method based on the risk assessment model proposed in this embodiment includes the following specific steps:

[0062] 1. Minimum Penalty Cost Function Modeling

[0063] Assume that the relevant logistics information input is: the transported goods are agricultural products; the transport origin is Iowa, USA, and the destination is Wuhan, China. Combined with the relevant data of the three tables of risk index assessment table for different transportation modes, risk index assessment table for key sections of the transportation route, and risk index assessment table for different transported goods, the basic parameter values ​​are: Φ=1.5, δ=2.5, T1=10, T2=20, θ i =3, λ= 0.5. A comprehensive minimum penalty cost model is formed as follows:

[0064] MinC=0.5*C1+0.3*C2+0.2*C3

[0065]

[0066]

[0067]

[0068]

[0069] The constraints are as follows:

[0070]

[0071] i,j∈N;i <j;

[0072] Among them, the risk index assessment table is as follows:

[0073] According to the actual transportation situation, corresponding risk index tables are established for different major modes of transportation such as air transportation, railway transportation, road transportation, pipeline transportation, inland waterway transportation, and sea transportation. The table is as follows:

[0074]

[0075] Table 1. Risk index assessment table for different modes of transportation

[0076] At the same time, based on the actual situation, a risk index table for key sections of transportation routes was constructed, and risk index assessments were conducted for key sections at different levels such as provincial boundaries, national boundaries, and continental boundaries, especially for transportation routes passing through world-class transportation hubs and major roads. The table is as follows:

[0077]

[0078] Table 2. Risk index assessment table for key sections of transportation routes

[0079] In addition, based on actual conditions, risk index tables for different transport items have been constructed, and risk index assessments have been conducted for minerals, energy, agricultural products, high-tech products, etc., especially for industrial supplies and fossil energy.

[0080]

[0081] Table 3. Risk index assessment table for different transport items

[0082] 2. Improve the algorithm and solve it

[0083] In the process of international multimodal transport distribution route planning, the present invention introduces chaos, changes the original BWO position iteration formula, and improves the BWO algorithm in many aspects such as population start optimization, thereby forming a new H-BWO algorithm to solve the minimum penalty cost model. The H-BWO algorithm will find an optimal path among many paths through calculation.

[0084] 1) Initialize H-BWO algorithm parameters

[0085] Initialization parameters of H-BWO algorithm include population size D, maximum number of iterations Z, randomly generated parameters m, Ω and r. Chaos mechanism is introduced to optimize the population start. The regularity, randomness and ergodicity of chaotic sequence are used to make the initial population of H-BWO algorithm make full use of the information of search space:

[0086]

[0087] 2) H-BWO algorithm position update

[0088] The black widow spider moves in a linear and spiral manner within the grid, and its position is updated as follows:

[0089]

[0090] 3) Calculate the pheromone rate value

[0091] Pheromones play a very important role in the courtship process of spiders, and male spiders do not like female spiders with low pheromone content. The formula for the pheromone rate value of black widow spiders is as follows:

[0092]

[0093] 4) Update the location of black widows with low pheromone rate values

[0094] When the pheromone rate value is equal to or less than 0.3, the female spider with low pheromone level in the body represents a hungry man-eating spider. Therefore, if they are present, the above female spider will not be selected, but will be replaced by another one. Here, the present invention improves the position iteration formula and adds an iteration speed control factor τ to make it have good guidance, so as to control the convergence speed of the algorithm, avoid the local optimum in the early stage, and speed up the convergence speed of the algorithm in the later stage. The improved formula is as follows:

[0095]

[0096]

[0097] 5) Re-evaluate the fitness function value and update the optimal black widow position and optimal solution.

[0098] 6) Algorithm termination

[0099] If the algorithm reaches the maximum number of iterations Z, the optimal black widow position and the global optimal solution are output, otherwise return to step 2 and iterate again.

[0100] Calculation results using the H-BWO algorithm show that the cargo is transported from Iowa by rail to the Port of Los Angeles, then transported by sea to the Port of Shanghai, China, and then transported by inland waterway along the Yangtze River to Wuhan.

[0101] From the calculation results, it can be seen that the H-BWO algorithm formed by mixing chaos and adding an iteration speed control factor τ can effectively improve the convergence speed and global optimization ability of the BWO algorithm and avoid falling into the local optimum. In view of the complex situation of international multimodal transport, the H-BWO algorithm combines the minimum penalty cost model to generate the global optimal path, which can effectively control the risks in the international multimodal transport process and further reduce carbon emissions, which has a role in promoting the development of my country's entire green logistics industry.

[0102] 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

1. An international multimodal transport method based on a risk assessment model, Features: The steps include: Step 1, establish a risk assessment model; establish a risk index table and transportation network information according to the actual transportation situation, set parameters, and establish a risk assessment index model; the risk index table includes the risk index of the mode of transportation, the risk index of the transportation route, and the risk index of the transported goods; the mode of transportation includes at least air transportation, railway transportation, road transportation, pipeline transportation, inland waterway transportation, and sea transportation; the transportation route includes key sections at different levels of provincial boundaries, national boundaries, and continental boundaries; the transported goods include ores, energy, agricultural products, high-tech products, industrial products, and medical supplies; Based on the data of international logistics transportation network, the connectivity model of each node in the transportation network under each transportation mode is established, considering whether node i can reach node j, and the following model is obtained: in, It means that w types of transportation are used from point i to point j; N is the set of all nodes; Combine the risk index table to establish a multimodal transport risk assessment index model: Use the actual route data combined with the parameters of the risk assessment table to establish a risk assessment model based on the risk assessment table: Among them, K represents the total risk index of the transportation process; The risk index of transport from point i to point j by using type w transport mode; D ij represents the risk index of the transportation path from point i to point j; θ q Indicates the risk index for transporting category q items; Step 2: Calculate the risk control cost based on the risk assessment index model established in step 1, combine the comprehensive transportation cost and carbon value cost, and derive the total penalty cost model as a constraint condition; The transportation cost model is established based on the entropy increase system; the multimodal transport cost is reflected in the direct transportation cost related to the transportation quantity, time, and distance during the transportation process, as well as the indirect transportation cost related to transit and management; since the overall process of multimodal transport reflects the same phenomenon as the law of entropy increase in physics, that is, with the increase of transportation time, transportation distance and transit nodes, the disorder of transportation will increase accordingly, and a certain amount of management and control costs must be added to effectively reduce the disorder of transportation; therefore, the following transportation cost model is established based on the law of entropy increase: Among them, L ij Indicates the distance from point i to point j; U w represents the unit distance cost from point i to point j using type w of transportation; G represents the quantity, quality or volume of the transported goods; The carbon cost is related to the transportation volume and distance during transportation, so the following transportation cost model is established: Among them, λ represents the cost parameter of carbon emission; ε w represents the unit carbon emission of type w transport mode; Step 3: Expand and improve the Black Widow algorithm BWO; design a new H-BWO algorithm by optimizing the population start with hybrid chaos and adding an iteration speed control factor; Step 4: Generate a global optimal path according to the H-BWO algorithm in step 3, and set the basic parameters of the H-BWO algorithm: Combined with the minimum penalty cost function model, generate a global optimal path as the global path planning solution for the current multimodal transport; According to the minimum penalty cost function model, the specific steps of generating a global optimal path through the H-BWO algorithm are as follows: Step 4.1, initialize H-BWO algorithm parameters: Initialization parameters of the H-BWO algorithm include: population size D, maximum number of iterations Z, randomly generated parameters m, Ω and r, among which a chaotic mechanism is introduced to optimize the population start; the regularity, randomness and ergodicity of the chaotic sequence are used to make the initial population of the H-BWO algorithm make full use of the information of the search space: Among them, a and b are chaotic constants, a∈(0, 1.4), b∈(0.2, 0.314]; Step 4.2, H-BWO algorithm position update: The black widow spider moves in a linear and spiral manner within the grid, and its position is updated as follows: Step 4.3, calculate the pheromone rate value: Pheromones play a very important role in the courtship process of spiders, and male spiders do not like female spiders with low pheromone content; the pheromone rate value formula of black widow spiders is as follows: Among them, A i represents the pheromone rate value of the black widow spider; f max and f min is the worst and best fitness function value; f i The fitness value obtained for the i-th black widow; Step 4.4, update the black widow position with low pheromone rate value: When the pheromone rate value is equal to or less than 0.3, the female spider with low pheromone level in the body represents a hungry man-eating spider; therefore, if they are present, the above female spider will not be selected, but will be replaced by another one; therefore, the position iteration formula is improved, and the iteration speed control factor τ is added to make it have good guidance, so as to control the convergence speed of the algorithm, avoid the local optimal situation in the early stage, and speed up the convergence speed of the algorithm in the later stage. The improved formula is as follows: Among them, P i (t) is the current black widow position or the black widow position with low pheromone rate value; P i (t+1) is the updated black widow position; P b is the optimal position of the current Black Widow; P r1 (t) and P r2 (t) is the random rth 1 and r 2 Only the location of Black Widow, r 1 and r 2 is a number in the range [1, D], r 1 ≠r 2 ; τ is the 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-evaluate the fitness function value, and update the position of the optimal black widow and the optimal solution; Step 4.6: Determine whether the maximum number of iterations is met. If the algorithm reaches the maximum number of iterations Z, output the optimal black widow position and the global optimal solution. Otherwise, return to step 4.2 and iterate again. Step 5: Propose a risk avoidance method for multimodal transport; for key nodes on the path that have already experienced congestion or other high-risk reasons, adopt different risk avoidance methods based on the distance from the transport vehicle to the key nodes.

2. The international multimodal transport method based on the risk assessment model according to claim 1, Features: For the established risk assessment index model, at least the risk scope is controlled through insurance and risk management, thereby generating risk control costs; the risk control costs are discussed by category and T is set. 1 and T 2 Two risk control thresholds and two risk cost control indexes, Φ and δ, are used to classify and judge different transportation risks under different circumstances. The risk control costs are as follows: Among them, T 1 and T 2 is the risk control threshold, and the value can be set by the user according to the actual situation; Φ and δ are risk control cost parameters, Φ∈[1,2], δ∈[2,3].

3. The international multimodal transport method based on the risk assessment model according to claim 1, Features: A minimum penalty cost model based on risk assessment model, efficiency assessment model and carbon value cost is established, and its expression function is: Min C=α*C 1 +β*C 2 +γ*C 3 ; α+β+γ=1, α, β, γ∈[0, 1]; Where C is the total evaluation value of the multimodal transport process; C 1 is the risk control cost of the multimodal transport process; C 2 is the transportation cost of the multimodal transport process; C 3 is the carbon cost of the multimodal transport process; α, β, and γ are the weight parameters of the three evaluation values.

Images