Post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency

Abstract

The invention provides a post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, and relates to the technical field of emergency logistics multi-target distribution optimization under the background of urban earthquake disasters, and the method comprises the following specific steps: S1, determining a basic hypothesis: considering the actual scene of disaster rescue operation, and making the basic hypothesis for the emergency material distribution problem; s2, establishing an emergency material distribution fairness model; s3, establishing a disaster point emergency material demand emergency degree evaluation index system; s4, establishing a multi-target path optimization model; and S5, solving the established multi-target model by using an improved mixed multivariate universe optimization algorithm. The mixed multivariate universe algorithm based on differential evolution is provided, higher convergence precision and better robustness and uniformity are achieved, the earthquake disaster emergency logistics distribution path optimization accuracy is improved, material demand urgency meeting and distribution fairness are guaranteed, the distribution cost is reduced, and the distribution efficiency is improved. And the disaster victim satisfaction degree and the demand emergency adaptation degree are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-objective distribution optimization of emergency logistics under the background of urban earthquake disasters, and in particular to a post-earthquake emergency material distribution optimization method that takes distribution fairness and demand urgency into consideration. Background Art

[0002] my country is a country prone to earthquakes, and urban areas have numerous buildings and a large population density. Therefore, after a disaster occurs, in order to ensure that every citizen can obtain the materials they need in a timely manner and prevent subsequent conflicts caused by uneven distribution of materials, the fairness of the distribution of emergency logistics for the disaster area and the urgency of the demand for various materials are crucial. Traditional optimization methods usually only consider time tolerance. As the country matures in emergency disaster prevention and disposal, this demand is gradually met, while similar distribution fairness or demand urgency are often ignored.

[0003] Existing methods for optimizing the distribution paths of emergency materials for natural disasters often focus on the timeliness of distribution or only consider fairness, cost, and economic losses, and lack consideration of factors such as dense populations in urban areas after disasters, numerous material demands, and psychological feelings of disaster victims. Unfair distribution of emergency materials can easily lead to psychological dissatisfaction among disaster victims, leading to contradictions and even conflicts. Moreover, unfair distribution of materials will cause some disaster victims to fail to receive timely rescue, which will increase casualties. Blindly distributing one or several types of materials can indeed solve some problems, but for the wounded and rescuers who need medicines and rescue tools, distributing materials without considering the urgency of demand will only lead to the idleness of useless materials and waste the golden rescue time after the earthquake. Summary of the invention

[0004] In order to solve the shortcomings in the optimization of multi-objective distribution paths of emergency logistics after urban earthquake disasters, the present invention proposes a post-earthquake emergency material distribution optimization method that takes into account distribution fairness and demand urgency, and according to the needs, it is improved on the basis of the multiverse optimization algorithm in order to better solve the established model, improve the accuracy of earthquake disaster emergency logistics distribution path optimization, and make up for the shortcomings of existing research.

[0005] In order to achieve the above effects, the technical solution adopted by the present invention is as follows:

[0006] The optimization method for post-earthquake emergency material distribution considering distribution fairness and demand urgency includes the following steps:

[0007] S1. Determine basic assumptions: Consider the actual scenario of disaster relief operations and make basic assumptions about the emergency material distribution problem;

[0008] S2. Establish a fairness model for emergency material distribution;

[0009] S3. Establish an evaluation index system for the urgency of emergency material needs in disaster-affected areas;

[0010] S4. Establish a multi-objective path optimization model;

[0011] S5. Solving the established multi-objective model using an improved hybrid multiverse optimization algorithm;

[0012] Preferably, the basic assumptions in step S1 are as follows:

[0013] A. The emergency logistics network involved has multiple distribution centers and multiple disaster-affected demand points;

[0014] B. The location coordinates of the distribution center and the disaster site are known;

[0015] C. During the distribution process, each emergency material required by each disaster site is generally delivered by only one vehicle. That is, when the weight and volume of a certain emergency material required by the disaster site are less than the maximum loading capacity of the distribution vehicle, it cannot be distributed by multiple vehicles;

[0016] D. All transport vehicles in the model only load materials at the distribution center and only unload materials at the demand point;

[0017] E. The transportation conditions from the distribution center to the disaster site are ideal, and the situation of being unable to deliver due to the earthquake is not considered;

[0018] Preferably, the fairness model of emergency material distribution established in step S2 is considered from two aspects: psychological satisfaction and material demand satisfaction rate. The expression of psychological perception satisfaction of distribution fairness is given as follows:

[0019]

[0020] Among them, λ is the time decay factor, 0<λ<1. When λ is closer to 1, it means that the loss or damage at the disaster site changes slowly; when λ is close to 0, it means that the change is fast. By adding the time decay factor, the changes in the psychological activities of the victims at the disaster site over time can be better reflected.

[0021] θ is a parameter that balances the unequal effects of disadvantage and advantage.

[0022] a j and β j are weights used to adjust for inequality of disadvantage and advantage.

[0023] w l It is the weight assigned to the disaster site I, which can be adjusted according to the importance of the disaster site or other factors.

[0024] It represents the type i emergency supplies actually obtained by disaster site j at time t. It represents the amount of emergency supplies of type i required by disaster site j at time t. It is a maximum value function used to compare the gaps between disaster-affected points. The purpose of this function is to quantify the gap between disaster-affected points and ensure that the value is positive, with the aim of focusing on the advantages between disaster-affected points and ignoring the losses between them.

[0025] is the weighted sum, which means calculating the weighted difference between all disaster-affected points.

[0026] At the same time, proportional fairness is introduced to consider the minimum satisfaction of emergency supplies at each disaster site. The minimum satisfaction rate is as follows:

[0027]

[0028]

[0029] Among them, φ i (s,t) is the minimum demand satisfaction rate of the i-th type of materials at the disaster site j at time t in stage s; δ is the proportional fairness coefficient, δ∈[0,1], which can be determined by expert judgment.

[0030] Preferably, the specific method of step S3 is as follows:

[0031] A system of evaluation indicators for the urgency of emergency material needs at disaster-affected sites has been established, as shown in Table 1:

[0032]

[0033]

[0034] Table 1

[0035] At the same time, the data in the index layer is normalized to avoid inconsistent measurement units, and out-of-range values ​​are processed as follows:

[0036] C * =min(max(X,X min ),X max )

[0037] Among them, C * is the standardized value of each index of each disaster point index layer, X is the original data of each indicator of each disaster point index layer, X min is the minimum value of the original data, X max is the maximum value of the original data.

[0038] Secondly, the urgency of the needs of various emergency supplies at each disaster site is set as follows:

[0039]

[0040] Among them, ω r is the weight of the indicator r of each indicator layer to the urgency of the demand, r∈{1,2,...,k}. k is the total number of indicators. C jir It is the standardized value of the relationship between the disaster site j and the material i. Experts score the weight of each indicator at the indicator level, collect and process the data, and finally list the judgment matrix for data processing using SPSS.

[0041] Finally, calculate the relative urgency of the disaster site to the material ji , and taking the disaster site with the least urgency of demand as the benchmark, we have:

[0042]

[0043] Preferably, the specific contents of step S4 are as follows:

[0044] Table 2 gives the definitions of the symbols involved:

[0045]

[0046]

[0047] Table 2

[0048] The model constructed by the present invention takes into account the limited capacity resources of emergency logistics carriers and the satisfaction of each disaster-affected point. The vehicle utilization rate introduces the load utilization rate and volume utilization rate into the model, and the established multi-objective optimization model also takes into account the distribution cost.

[0049] The vehicle utilization rate mainly considers two aspects: load utilization rate and volume utilization rate. Here we choose the larger one as the evaluation standard. The specific method is as follows:

[0050]

[0051] This formula calculates the vehicle stowage utilization rate, where: max,n is the maximum transport weight of vehicle a in group n, V max,n is the maximum transport volume of vehicle a of group n. The calculation method is:

[0052] The weight of emergency supplies transported to disaster site j by vehicle a:

[0053]

[0054] The volume of emergency supplies transported to disaster site j by vehicle a:

[0055]

[0056] The following formula indicates whether vehicle a with model number n delivers type i emergency supplies required by disaster site j, which is a 0-1 variable:

[0057]

[0058] Finally, calculate the total stowage utilization of all delivery vehicles:

[0059]

[0060] Secondly, calculate the vehicle transportation cost. The vehicle transportation cost calculation formula is as follows:

[0061]

[0062]

[0063] The above expression is a 0-1 variable, indicating whether vehicle a directly transports the emergency supplies needed by the disaster site from the emergency supplies distribution center 0 to the disaster site j.

[0064]

[0065] The above expression is a 0-1 variable indicating whether vehicle a directly transports the emergency supplies needed by disaster site k from disaster site j to disaster site k.

[0066] The mathematical model constructed by the present invention is as follows:

[0067] Objective function:

[0068]

[0069]

[0070] (1) It includes the minimum vehicle transportation cost, the maximum vehicle space utilization, the emergency demand value of emergency supplies delivered before the latest time required by the disaster site, and the fairness based on psychological satisfaction and material distribution satisfaction rate. Among them, ∑ a∈A F a Indicates that the vehicle transportation cost is minimal;

[0071] ∑ j∈J ∑ i∈I γ ji ×ε ji It means maximizing the urgency value of emergency material delivery before the latest delivery time required by the disaster site, indicating that the higher the urgency of the demand, the higher the priority of the allocation; φ i (s, t) represents the psychological satisfaction of each disaster-affected point at time t; Indicates the material demand satisfaction rate of each disaster-stricken site. The larger the values ​​of these two parameters are, the fairer the corresponding emergency material distribution will be.

[0072] (2) represents the maximization of psychological satisfaction and material demand satisfaction, reflecting fair distribution.

[0073] Constraints:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] (3) Indicates the actual time it takes for vehicles to reach the disaster site.

[0084] (4) All kinds of materials required for each disaster-stricken area should be included.

[0085] (5) is a 0-1 variable indicating whether the emergency supplies were delivered before the latest time required by the disaster site.

[0086] (6) Indicates the maximum loading weight limit of the vehicle.

[0087] (7) Indicates the maximum loading volume limit of the vehicle.

[0088] (8) indicates that a vehicle can deliver to a maximum of 10 disaster-stricken sites at a time.

[0089] (9) Indicates the maximum number of vehicles in each group.

[0090] (10) is a 0-1 variable indicating whether a vehicle in a certain group has reached the disaster site.

[0091] (11) is a 0-1 variable indicating the assignment of tasks to vehicle a in group n.

[0092] To sum up, weights are given to the fairness of emergency material distribution and the urgency of demand at disaster sites respectively, and the above multi-objective model is transformed into a single-objective model. The objective function expression of the problem is as follows: maxf=m1f1+m2f2, where m1 and m2 are weight coefficients.

[0093] Preferably, the improved hybrid multiverse algorithm in step S5 is a hybrid multiverse algorithm based on differential evolution, which uses the differential evolution algorithm to adjust the population diversity, and introduces the universe difference operator and chaotic tent mapping in the search mechanism to improve its solution performance. The specific process is as follows:

[0094] Table 3 is the following symbol definitions:

[0095]

[0096] Table 3

[0097] S5.1: Parameter initialization. The parameters include universe dimension D, number of universes binary number (binary number), number of iterations G, crossover probability (binary number), difference coefficient (binary number), search space upper limit (binary number) xmax, search space lower limit (binary number) xmin, vehicle speed (binary number), unit time freight (binary number) Q.

[0098] S5.2: Multiverse initialization. Initialize the loop according to the total number of orders N0, and generate a random integer between 1 and binary operation (binary operation) v for each order. After the loop is completed, calculate the orders owned by each vehicle and determine all routes for delivering orders. Calculate the delivery path with the shortest time and determine whether the path meets the constraints. If it meets the conditions, add it to the multiverse. If not, repeat this step until the multiverse has a binary number that meets the constraints.

[0099] S5.3: Calculate and normalize the expansion of the universe. Calculate the expansion of each universe based on the objective function value (fitness function value). In this model, the universe with the largest expansion represents the optimal solution, so the universe that does not meet the constraints is assigned a value of negative infinity. Use the normalization formula to normalize the expansion of the universe:

[0100]

[0101] S5.4: Update the global extremum and save the universe with the largest expansion as S5.5: The universe exchanges matter through black holes and white holes, Generate a random number between 0 and 1 if The universe that produces the white hole is selected according to the roulette method The universe with the black hole sends matter to the universe that produces the white hole. The process is as follows:

[0102]

[0103] S5.6: Cosmic difference. If Then a random number between 0 and 1 is generated in the entire universe like Generate a random number The optimal universe sends matter to the universe through wormholes as follows:

[0104]

[0105] WEP represents the probability of the existence of a wormhole.

[0106] S5.7: Random search based on chaotic tent mapping. To perform a cosmic chaos search, first, generate random numbers The interval range [(x max ,x min ),2(x max ,x min )], calculate the minimum search step length l0:

[0107]

[0108] Then, generate a random number The interval range is [-0.5, 0.5], and the chaos search strategy is as follows:

[0109]

[0110] Next, update the search step size:

[0111]

[0112] Finally, the rate at which the universe is expanding is calculated and updated individually:

[0113]

[0114] S5.8: If the maximum number of iterations is reached, exit the optimization and output the results, otherwise return to step 3.

[0115] Compared with the prior art, the present invention has the following beneficial effects:

[0116] The present invention optimizes the emergency logistics distribution link after an urban earthquake disaster by considering the fairness of emergency logistics distribution and the urgency of material demand. By constructing a multi-objective distribution mathematical model based on fairness and demand urgency and improving the hybrid multiverse algorithm, the differential evolution algorithm is used to adjust the population diversity, and the universe difference operator and chaotic tent mapping are introduced into the search mechanism, so that the algorithm has high convergence accuracy, good uniformity, and good robustness, which greatly improves the solution performance. Compared with other biological heuristic algorithms, it has significant advantages, ensures the satisfaction of material demand urgency and fairness of distribution, ensures the priority of material delivery, and greatly reduces the distribution cost, improves the satisfaction of disaster victims and the urgency of demand adaptation. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 It is a flow chart of the present invention.

[0118] Figure 2 It is a flow chart of the hybrid multiverse algorithm based on differential evolution described in the present invention.

[0119] Figure 3 It is a visualization diagram of task and vehicle distribution in the multi-objective distribution model constructed by the present invention. DETAILED DESCRIPTION

[0120] The technical solutions in the implementation of the present invention are described clearly and completely below in conjunction with the accompanying drawings and embodiments of the present invention.

[0121] like Figure 1 As shown, the specific process of the present invention is as follows:

[0122] The first step is to establish hypothetical conditions based on the actual situation in the urban earthquake-stricken areas.

[0123] The second step is to establish a fairness model for emergency material distribution during urban earthquakes: Based on the existing fairness model, a corresponding mathematical model is constructed by comprehensively considering the psychological satisfaction of disaster victims and the satisfaction rate of material needs.

[0124] The third step is to build an emergency material demand urgency model for disaster-stricken areas: a disaster-stricken area emergency material demand urgency evaluation index system suitable for urban earthquake disasters was established. At the same time, the index layer data was normalized to avoid inconsistent measurement units, and out-of-range value processing was added.

[0125] The fourth step is to build a multi-objective optimization model. Based on the first three steps, a multi-objective path optimization model that comprehensively considers fairness and demand urgency is established.

[0126] The fifth step is to use the improved hybrid multiverse algorithm to solve the model machine, adjust the population diversity through the differential evolution algorithm, and introduce the universe difference operator and chaotic tent mapping into the search mechanism to improve its solving performance.

[0127] like Figure 2 As shown in the figure, the process steps of the hybrid multiverse algorithm based on differential evolution are as follows:

[0128] The first step is to initialize the parameters. The parameters include universe dimension D, number of universes (binary number), number of iterations G, crossover probability (binary number), difference coefficient (binary number), search space upper limit (binary number) xmax, search space lower limit (binary number) xmin, vehicle speed (binary number), and unit time freight (binary number) Q.

[0129] Step 2: Initialize the multiverse. Initialize the loop based on the total number of orders N0, and generate a random integer between 1 and binary operation (binary operation) v for each order. After the loop is completed, calculate the orders owned by each vehicle and determine all routes for delivering orders. Calculate the delivery path with the shortest time and determine whether the path meets the constraints. If it meets the conditions, add it to the multiverse. If not, repeat this step until the multiverse has a binary number that meets the constraints.

[0130] The third step is to calculate and normalize the expansion of the universe. The expansion of each universe is calculated based on the objective function value (fitness function value). In this model, the universe with the largest expansion represents the optimal solution, so the universe that does not meet the constraints is assigned a value of negative infinity. The expansion of the universe is normalized using the normalization formula:

[0131]

[0132] The fourth step is to update the global extreme value and save the universe with the largest expansion as

[0133] The fifth step is that the universe exchanges matter through black holes and white holes, creating a new universe for each Generate a random number between 0 and 1 if The universe that produces the white hole is selected according to the roulette method The universe with the black hole sends matter to the universe that produces the white hole. The process is as follows:

[0134]

[0135] Step 6, cosmic difference. If Then a random number between 0 and 1 is generated in the entire universe like Generate a random number The optimal universe sends matter to the universe through wormholes as follows:

[0136]

[0137] WEP represents the probability of the existence of a wormhole.

[0138] Step 7: Random search based on chaotic tent mapping. To perform a cosmic chaos search, first, generate random numbers The interval range [(x max ,x min ),2(x max ,x min )], calculate the minimum search step length l0:

[0139]

[0140] Then, generate a random number The interval range is [-0.5, 0.5], and the chaos search strategy is as follows:

[0141]

[0142] Next, update the search step size:

[0143]

[0144] Finally, the rate at which the universe is expanding is calculated and updated individually:

[0145]

[0146] Step 8. If the maximum number of iterations is reached, exit the optimization and output the results; otherwise, return to step 3.

[0147] like Figure 3 As shown in the figure, the specific steps of task and vehicle distribution in the multi-objective distribution model are as follows:

[0148] The first step is to establish the demand for different materials at different disaster-stricken sites. For a batch of orders, a random integer between 1 and the total number of vehicles is generated for each order, indicating the order of the number of transport vehicles.

[0149] The second step is to set the demand for a certain type of materials at the disaster site to be transported by only one vehicle, and use the vehicle as a variable for optimization.

[0150] The third step is to determine the distribution layer, that is, the order tasks carried by each vehicle.

[0151] The fourth step, the optimal layer, determines the optimal task allocation order for each vehicle.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, characterized in that: The following steps are involved: S1. Determine basic assumptions: Consider the actual scenario of disaster relief operations and make basic assumptions about the emergency material distribution problem; S2. Establish a fairness model for emergency material distribution; S3. Establish an evaluation index system for the urgency of emergency material needs in disaster-affected areas; S4. Establish a multi-objective path optimization model; S5. Use the improved hybrid multiverse optimization algorithm to solve the established multi-objective model.

2. The post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency according to claim 1 is characterized in that: The basic assumptions in step S1 are as follows: A. The emergency logistics network involved has multiple distribution centers and multiple disaster-affected demand points; B. The location coordinates of the distribution center and the disaster site are known; C. During the distribution process, each emergency material required by each disaster site is generally delivered by only one vehicle. That is, when the weight and volume of a certain emergency material required by the disaster site are less than the maximum loading capacity of the delivery vehicle, it cannot be distributed by multiple vehicles; D. All transport vehicles in the model only load materials at the distribution center and only unload materials at the demand point; E. The transportation conditions required from the distribution center to the disaster site are ideal, and situations such as failure to deliver due to earthquakes are not considered.

3. The post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency according to claim 1 is characterized in that: The emergency material distribution fairness model established in step S2 is as follows: Fairness psychological perception satisfaction expression: Among them, λ is the time decay factor, 0<λ<1. When λ is closer to 1, the loss or damage of the disaster site changes slowly; when λ is close to 0, it means that the change is fast. θ is the parameter that balances the unequal impact of disadvantages and advantages. j and β j is the weight used to adjust the inequality of disadvantages and advantages, w l is the weight assigned to the disaster site I, which can be adjusted according to the importance of the disaster site or other factors; represents the type i emergency supplies actually obtained by the disaster site j at time t, represents the amount of emergency supplies of type i required by disaster site j at time t, It is a maximum value function used to compare the gaps between disaster-affected points; This function quantifies the gap between disaster-affected points and ensures that the value is positive; is the weighted sum, which means calculating the weighted difference between all disaster-affected points; The minimum demand satisfaction rate model for emergency supplies at each disaster site is constructed as follows: Among them, φ i (s,t) is the minimum demand satisfaction rate of the i-th type of materials at the disaster site j at time t in stage s; δ is the proportional fairness coefficient, δ∈[0,1].

4. The post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency according to claim 1 is characterized in that: The method of establishing the emergency material demand urgency evaluation index system of the disaster-affected site in step S3 is as follows: Firstly, based on the "Emergency Evaluation Index System for the Wounded" established by Cheraghi and Hosseini-Motlagh (2020), the emergency evaluation index system for emergency supplies demand at disaster sites of the present invention was established, as shown in Table 1: Table 1 At the same time, the data in the index layer is normalized to avoid inconsistent measurement units. Here, we add processing for values ​​out of range as follows: C * =min(max(X,X min ),X max ) Among them, C * is the standardized value of each index of each disaster point index layer, X is the original data of each indicator of each disaster point index layer, X min is the minimum value of the original data, X max is the maximum value of the original data; Secondly, the urgency of the needs of various emergency supplies at each disaster site is set as follows: Among them, ω r is the weight of the indicator r of each indicator layer to the urgency of the demand, r∈{1,2,...,k}, k is the total number of indicators, C jir It is the standardized value of the relationship between disaster site j and material i. Experts score the weight of each indicator at the indicator level, collect and process the data, and finally list the judgment matrix for data processing using SPSS; Finally, calculate the relative urgency of the disaster site to the material ji , and taking the disaster site with the least urgency of demand as the benchmark, we have:

5. The post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency according to claim 1 is characterized in that: The specific contents of step S4 are as follows: Table 2 gives the definitions of the symbols involved: Table 2 The vehicle utilization rate of the present invention mainly considers two aspects: load utilization rate and volume utilization rate. Here, the larger one of the two is selected as the evaluation standard. The specific method is as follows: The calculation formula for vehicle stowage utilization is as follows: Where: w max,n is the maximum transport weight of vehicle a in group n, V max,n is the maximum transport volume of vehicle a in group n, and is calculated as follows: the weight of emergency supplies transported to disaster site j by vehicle a: The volume of emergency supplies transported to disaster site j by vehicle a: Whether vehicle a of model n delivers type i emergency supplies required by disaster site j is a 0-1 variable, and its formula is as follows: Finally, calculate the total stowage utilization of all delivery vehicles: Secondly, the vehicle transportation cost is calculated, which is a 0-1 variable. The calculation formula is as follows, indicating whether vehicle a directly transports the emergency supplies needed by the disaster site from the emergency supplies distribution center 0 to the disaster site j: Whether vehicle a directly transports the emergency supplies needed by disaster site k from disaster site j to disaster site k is a 0-1 variable, and its formula is as follows: The mathematical model constructed by the present invention is as follows: Objective function: (1) The vehicle transportation cost is minimized, the vehicle space utilization rate is maximized, the emergency demand value of emergency supplies delivered before the latest time required by the disaster site is the highest, and the fairness based on psychological satisfaction and material distribution satisfaction rate is the highest, where ∑ a∈ A F a Indicates that the vehicle transportation cost is minimal; ∑ j∈J ∑ i∈I γ ji ×ε ji It means maximizing the urgency value of emergency material delivery before the latest delivery time required by the disaster site, indicating that the higher the urgency of the demand, the higher the priority of the allocation; φ i (s, t) represents the psychological satisfaction of each disaster-affected point at time t; It indicates the satisfaction rate of material demand at each disaster site. The larger the values ​​of these two parameters are, the fairer the corresponding distribution of emergency materials will be. (2) represents the maximization of psychological satisfaction and material demand satisfaction, reflecting fair distribution; Constraints: (3) indicates the actual time of delivery of vehicles to the disaster site; (4) Indicate the various materials needed for each disaster site, without omission; (5) is a 0-1 variable indicating whether the emergency supplies were delivered before the latest time required at the disaster site; (6) Indicates the maximum loading weight limit of the vehicle; (7) Indicates the maximum loading volume limit of the vehicle; (8) indicates that a vehicle can deliver to a maximum of 10 disaster-affected sites at one time; (9) Indicates the maximum number of vehicles in each group; (10) is a 0-1 variable indicating whether a vehicle in a certain group has reached the disaster site; (11) is a 0-1 variable, indicating the assignment of tasks to vehicle a in group n; Weights are given to the fairness of emergency material distribution and the urgency of demand at the disaster site, and the above multi-objective model is transformed into a single-objective model. The objective function expression of the problem is as follows: maxf=m1f1+m2f2 Among them, m1 and m2 are weight coefficients.

6. The post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency according to claim 5 is characterized in that: The improved hybrid multiverse algorithm in step S5 is a hybrid multiverse algorithm based on differential evolution, which uses the differential evolution algorithm to adjust the population diversity and introduces the universe difference operator and chaotic tent mapping in the search mechanism to improve its solution performance. The specific process is as follows: Table 3 is the following symbol definitions: Table 3 S5.1: Parameter initialization, including universe dimension D, number of universes binary number (binary number), number of iterations G, crossover probability (binary number), difference coefficient (binary number), search space upper limit (binary number) xmax, search space lower limit (binary number) xmin, vehicle speed (binary number), unit time freight (binary number) Q; S5.2: Multiverse initialization, initialize the loop according to the total number of orders N0, and generate a random integer between 1 and binary operation (binary operation) v for each order. After the loop is completed, calculate the orders owned by each vehicle and determine all routes for delivering orders, calculate the delivery path with the shortest time, and determine whether the path meets the constraints. If it meets the conditions, add it to the multiverse; if not, repeat this step until the multiverse has a binary number that meets the constraints; S5.3: Calculate and normalize the expansion of the universe. Calculate the expansion of each universe based on the objective function value (fitness function value). In this model, the universe with the largest expansion represents the optimal solution, so the universe that does not meet the constraints is assigned a value of negative infinity. The expansion of the universe is normalized using the normalization formula: S5.4: Update the global extremum and save the universe with the largest expansion as S5.5: The universe exchanges matter through black holes and white holes, and each universe Generate a random number between 0 and 1 if The universe that produces the white hole is selected according to the roulette method The universe with the black hole sends matter to the universe that produces the white hole. The process is as follows: S5.6: Cosmic difference, if Then a random number between 0 and 1 is generated in the entire universe like Generate a random number The optimal universe sends matter to the universe through wormholes as follows: WEP represents the probability of the existence of a wormhole; S5.7: Random search based on chaotic tent mapping, when To perform a cosmic chaos search, first, generate random numbers The range of interval [(x max ,x min ),2(x max ,x min )], calculate the minimum search step length l0: Then, generate a random number The interval range is [-0.5, 0.5], and the chaos search strategy is as follows: Next, update the search step size: Finally, the rate at which the universe is expanding is calculated and updated individually: S5.8: If the maximum number of iterations is reached, exit the optimization and output the results, otherwise return to S5.3.

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