An emergency material site selection and path optimization method based on whale optimization algorithm

Abstract

The application discloses an emergency material site selection and path optimization method based on a whale optimization algorithm, and specifically as follows: firstly, an emergency material site selection model is established based on urgency and customer satisfaction to select an optimal distribution point as a distribution center; secondly, an emergency material distribution model is established based on transportation cost and time conditions to plan a distribution path for disaster points; finally, parameters of the whale optimization algorithm are adjusted, and paths generated in each iteration are stored to obtain a disaster point path planning route and a global path length. The application realizes path optimization of emergency material distribution, ensures timeliness, realizes the target of reducing cost, and provides effective guarantee for transportation of emergency materials.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of distribution center site selection and path optimization, and particularly relates to an emergency material site selection and path optimization method considering urgency and satisfaction based on a whale optimization algorithm. BACKGROUND

[0002] With the increase of urban population size and the increase of urbanization degree, the frequency of sudden events is increasing. The traditional emergency material site selection and distribution relies on the storage and transportation system of the government or rescue agencies. After the disaster occurs, there are problems such as poor information transmission, traffic congestion, and uneven distribution of materials in the process from demand assessment to material transportation, which leads to low rescue efficiency. Therefore, it is of great theoretical value and practical significance to study how to timely transport materials and improve the distribution efficiency.

[0003] For the site selection of emergency materials, the existing methods mainly include mathematical programming method, geographic information system (GIS) based method, multi-criteria decision analysis (MCDA) such as analytic hierarchy process (AHP), fuzzy set theory, etc. For the path planning of emergency materials, the existing algorithms can be divided into several categories: shortest path algorithms such as Dijkstra algorithm and A algorithm, network flow algorithms such as Ford-Fulkerson method, heuristic search algorithms such as genetic algorithm, ant colony algorithm, and artificial intelligence based methods such as deep learning. However, in practical applications, the above algorithms have limitations such as high time complexity, easy to fall into local optimum, and unable to effectively solve specific problems. At present, there is still a lot of room for development in the research of intelligent optimization algorithms, and many researchers hope to find a more ideal algorithm to be applied to the path optimization of emergency material transportation.

[0004] The present application aims to realize a high-efficiency and high-precision WOA improved algorithm, the main purpose is to improve the algorithm efficiency in order to better apply the direction of emergency material transportation, and secondly to improve the accuracy of the whale optimization algorithm and jump out of the local optimum. The biggest feature of the whale optimization algorithm is to simulate the hunting behavior of humpback whales with random individuals or optimal individuals, and to simulate the bubble net attack mechanism of humpback whales with spiral lines. However, the original whale algorithm will gradually reduce the fluctuation of change with the increase of iteration times, which will lead to local optimum. Therefore, the present application adds a nonlinear convergence factor, an adaptive search control coefficient and a memory library list strategy, thereby forming a new ACSWOA algorithm, which prevents the algorithm from falling into local optimum and speeds up the convergence speed of the algorithm. In this way, not only the path of emergency material distribution is optimized, but also the practicability of the algorithm is enhanced. SUMMARY

[0005] In view of defects of the prior art or improvement needs, the present application provides an emergency material site selection and path optimization method considering urgency and satisfaction based on a whale optimization algorithm, which realizes planning of an emergency material distribution path by combining a site selection and distribution model and the whale optimization algorithm, and improves distribution efficiency.

[0006] In order to achieve the above-mentioned purpose, the technical scheme of the present application is as follows: an emergency material site selection and path optimization method considering urgency and satisfaction based on a whale optimization algorithm, the method comprising the following steps:

[0007] Step 1: establishing an emergency material site selection model based on urgency and customer satisfaction, and selecting optimal distribution points as distribution centers.

[0008] Step 2: establishing an emergency material distribution model based on transportation cost and time conditions, and planning distribution paths for disaster points.

[0009] Step 3: adjusting parameters based on the whale algorithm, and storing paths generated in each iteration.

[0010] Step 4: obtaining disaster point path planning routes and global path lengths.

[0011] Preferably, the emergency material site selection model according to step 1 is sorted according to urgency and customer satisfaction, and then optimal distribution points are selected as distribution centers, and the specific implementation process is as follows:

[0012] Step 1-1: establishing an urgency model. The urgency of emergency material demand for disaster points is evaluated by combining the TOPSIS method and the entropy weight method. The advantage of the TOPSIS method is simplicity and flexibility, and it is easy to calculate, but it has a certain subjectivity in weight determination, while the entropy weight method is an objective weight analysis method, which can reduce the subjectivity of the TOPSIS method, and the specific implementation process is as follows:

[0013] Step 1-1-1: constructing an initial decision matrix. The initial decision matrix composed of index values of each index of n material demand points is standardized.

[0014]

[0015] Wherein, Xij represents the index value of the jth column index of the ith row of the material demand point, X is the initial decision matrix, i=1, 2,..,n, j=1, 2,..,m.

[0016] X is normalized to avoid errors caused by different dimensions between indexes.

[0017]

[0018] Wherein, Cij For decision matrix, max(X j ) is the maximum index value in the jth column index.

[0019] Step 1-1-2: Determine the index weight, and establish the weighted decision matrix R.

[0020]

[0021] Wherein, R is the weighted decision matrix, which is obtained by multiplying the decision matrix and the weight obtained by the entropy weight method.

[0022] Step 1-1-3: Determine the positive and negative ideal solutions.

[0023] Positive ideal solution R + :

[0024] R + =(R1 + ,R2 + ,..,R m + ),R j + =max(R ij )

[0025] Negative ideal solution R - :

[0026] R - =(R1 - ,R2 - ,..,R m - ),R j - =min(R ij ) (Formula 4)

[0027] Wherein, R + , R + are the sets of positive and negative ideal solutions, represents the positive ideal solution of the mth column of the matrix, represents the negative ideal solution of the mth column of the matrix, represents the maximum positive ideal solution of the jth column in the weighted decision matrix, represents the minimum negative ideal solution of the jth column in the weighted decision matrix.

[0028] Step 1-1-4: Solve the positive and negative ideal solution distance.

[0029]

[0030] Wherein, C + , C - respectively represent the distance of each scheme to the positive and negative ideal solutions.

[0031] Step 1-1-5: Calculate the relative closeness of the demand point.

[0032]

[0033] Wherein, F1 represents the relative closeness of each disaster point to the ideal solution, the larger F1 represents the higher the demand urgency.

[0034] Step 1-2: Establish a customer satisfaction model. Including hard time window and soft time window, when the emergency materials do not arrive in the hard time window but arrive in the soft time window, the customer satisfaction is calculated according to the degree of deviation from the hard time window. The customer satisfaction calculation formula is as follows:

[0035]

[0036] Wherein, F2 is the customer satisfaction, the hard time window is (ET g ,LT g ), when arriving in the time range, the customer satisfaction is 1; when arriving outside the soft time window (et g ,lt g ), the customer satisfaction is 0; T g represents the time when the service vehicle arrives at the disaster point g;

[0037] Step 1-3: Establish an emergency material model by integrating the urgency and customer satisfaction scores of each disaster point. Normalize the urgency and satisfaction functions and assign weights to get the highest score of the disaster point, select this point as the distribution center to carry out rescue, the specific formula is as follows:

[0038]

[0039] Wherein, F1 and F2 are the urgency and satisfaction values respectively, θ1 and θ2 are the corresponding weights, and F is the comprehensive score.

[0040] Preferably, the emergency material distribution model in step 2 should combine transportation cost, vehicle driving time cost, carbon value cost and government subsidy cost, and obtain less distribution time and low transportation cost as constraint conditions, and the specific implementation process is as follows:

[0041] Step 2-1: Establish a risk management-based transportation cost model. Transportation costs include direct transportation costs related to transportation quantity, time, and distance, as well as indirect transportation costs related to risk management. As transportation time and distance increase, the risk of goods during transportation increases, as more uncertainties such as weather changes and road conditions may be encountered. Similarly, the heavier the goods, the greater the potential loss of unexpected events (such as goods loss risk) during transportation, and the risk management cost will also increase accordingly. Therefore, a risk management-based transportation cost model is established:

[0042]

[0043] where C1 represents transportation cost, d iq represents the distance from point i to point q; U represents the unit distance cost from point i to point q; G represents the quantity or mass or volume of the transported goods; R r is the risk area, R low is the low-risk boundary point, R mid is the medium-risk boundary point; C low is the fixed cost of the low-risk interval; and b mid is the slope and intercept of the medium-risk interval; m high and b high are the slope and intercept of the high-risk interval.

[0044] Step 2-2: Establish a vehicle travel time model. Time cost is used to describe the penalty caused by vehicle allocation time exceeding the expected time due to road congestion. The shortest time a vehicle can travel on a smooth road is considered as the expected time. Based on this, a vehicle travel time model function is established:

[0045]

[0046] where C2 represents time cost, d iq is the distance from i to q; v iq is the speed; and a is the cost coefficient of time delay. To quantitatively describe the driving situation of vehicles under congested road conditions, R is defined as the "road traffic flow evaluation coefficient" by combining the "China Road Capacity Guide" and road traffic characteristics, and the road traffic conditions are divided into five levels: 0.00 < R < 0.30 indicates smooth state; 0.30 < R < 0.60 indicates slight congestion state; 0.60 < R < 0.75 indicates a bit congested state; 0.75 < R < 0.90 indicates relatively congested state; 0.90 < R < 1.00 indicates congested state; and R >= 1.00 indicates severe congestion.

[0047] Step 2-3: Establishing the carbon value cost model. Carbon emission cost is closely related to multiple variables in the transportation process, including the vehicle load, the distance of transportation, and the fuel consumption. In the current global environment of jointly addressing climate change and reducing greenhouse gas emissions, this model aims to achieve the win-win goal of economic benefits and environmental protection.

[0048]

[0049] Wherein, C3 represents the carbon value cost, P represents the material urgency, and C4 represents the government subsidy cost. * P0 represents the fuel consumption per unit of distance when the vehicle is not loaded with goods; Q represents the maximum load of the vehicle; Q represents the current load of the vehicle. * P0 represents the fuel consumption per unit of distance when the vehicle is not loaded with goods; Q represents the maximum load of the vehicle; Q represents the current load of the vehicle. iq P0 represents the fuel consumption per unit of distance when the vehicle is not loaded with goods; Q represents the maximum load of the vehicle; Q represents the current load of the vehicle.

[0050] Step 2-4: Establishing the government subsidy model. In the process of emergency material distribution, the government will give corresponding subsidies according to the urgency. In the formula, the midpoint shift parameter μ is introduced to adjust the specific value of the material urgency when the subsidy amount starts to increase significantly.

[0051]

[0052] Wherein, S(P) represents the government subsidy cost, represented by C4, P is the material urgency, 0≤P≤1; k is the amplification factor of the subsidy, used to adjust the overall level of the subsidy amount; β is the slope parameter, which controls the rate of change of the subsidy amount with P; the larger β, the faster the change; the smaller β, the slower the change; μ is the midpoint shift parameter, used to adjust the position of the center point of the logistic function. When μ = 0.5, the function is symmetric about P = 0.5; when μ deviates from 0.5, the function will shift left or right; b is the base value or minimum value of the subsidy.

[0053] Step 2-5: According to the transportation cost, vehicle driving time cost, carbon value cost, and government subsidy, a total cost model is obtained, and the calculation formula is as follows:

[0054] C(cost) = C1 + C2 + C3 - C4 (Formula 5)

[0055] Wherein, C(cost) is the total cost; C1 is the transportation cost; C2 is the vehicle driving time cost; C3 is the carbon value cost; C4 is the government subsidy cost.

[0056] Preferably, a new whale optimization algorithm (ACSWOA) is proposed in step 3. For the original whale (WOA) algorithm, a nonlinear convergence factor is added, an adaptive search control coefficient is introduced, and a memory library list strategy is introduced. The specific process is as follows:

[0057] Step 3-1: improve the parameter a of the original WOA algorithm, add a nonlinear convergence factor to slow down the algorithm from falling into local optimum as the number of iterations increases; at the same time, the convergence speed is effectively accelerated as the number of iterations gradually increases. The changed algorithm parameter a is as follows:

[0058] a = (a start -a end )+(1-t / t max ) / (1-μ1*t / t max )(Formula 1)

[0059] Where a start and a end are the initial value and the termination value of a respectively; μ1 is the adjustment coefficient, take μ1 = 25, M is the maximum number of iterations, and t is the current number of iterations.

[0060] Step 3-2: introduce an adaptive search control coefficient, which dynamically adjusts the size of C according to the number of iterations. In the early stage of the algorithm, the value of C is small, and the moving range of the whale is increased, which helps to improve the global search ability. With the iteration of the algorithm, the moving range of the whale in the search space is reduced, which helps to improve the local search ability. The expression is as follows:

[0061]

[0062] Where C is the adaptive search control coefficient, r is the coefficient that can control the decay rate of C, M is the maximum number of iterations, and t is the current number of iterations. max

[0063] Step 3-3: introduce the memory library list strategy. Add a memory library list to record the optimal solution encountered at each iteration. After reaching the maximum number of iterations, compare the optimal solution obtained in the last iteration with the solutions in the memory library, and select the better solution as the final result. To avoid the memory library capacity being too large, limit the length of the memory library list, and remove the worst solution when the maximum capacity is reached. The expression is as follows:

[0064] S = remove(min(S)) (Formula 3)

[0065] Where each element in the S matrix is the current optimal solution encountered in the iteration process. When the memory library reaches the maximum value, remove the minimum value in the library through remove.

[0066] Preferably, the path planning route of the disaster point obtained in step 4 and the global path length. The specific process is as follows:

[0067] Step 4-1: Set the AC S WOA algorithm parameters: population size N, maximum number of iterations t max ​The current iteration number t and the spiral shape constant b are used to randomly initialize the initial positions of the whale group Xi (i = 1, 2, ..., n);

[0068] Step 4-2: Calculate the fitness of each individual whale using the following formula, sort the fitness values, and find the whale with the lowest fitness value, which is the optimal whale X. * And save it to the list of memory banks. When the bank reaches its maximum capacity, remove the worst solution.

[0069]

[0070] S = remove(min(S))

[0071] (Equation 2)

[0072] Step 4-3: Calculate the parameter 'a' that introduces the nonlinear convergence factor, and update the coefficient vector A;

[0073] a=(a start -a end )+(1-t / t max ) / (1-μ*t / t max )

[0074] (Equation 3)

[0075] Step 4-4: Introduce adaptive search control coefficients and update the coefficient vector C;

[0076]

[0077] Steps 4-5: Utilizing α j =min(X) i,j ),β j =max(X i,j Calculate the dynamic search boundary at this point;

[0078] Steps 4-6: Calculate the value of each individual whale, compare the solution of the reverse population with the solution in the current population, select s whale individuals with high fitness as the next generation population, and locate the position of the whale individual with the lowest fitness as the prey.

[0079] Step 4-7: If t≤t max Then update parameters a, A, C, l, and p;

[0080] Steps 4-8: When p < 0.5, if |A| < 1, update the spatial position of the current whale individual according to equation (5); when |A| >= 1, randomly select the position X of the whale individual. rand And update the spatial position of the current whale individual according to formula (6);

[0081] D = |C·X* (t)-X(t),X(t+1)=X(t)-A·D (Formula 5)

[0082] D = |C·X rand (t)-X|,X(t+1)=X rand -A·D (Equation 6)

[0083] Where X*(t) is the position of the current best solution, X(t) is the current whale position, X(t+1) is the updated whale position, and D is the distance between the individual whale and the current best solution.

[0084] Steps 4-9: When p>=0.5, update the spatial position of the current whale individual according to formula (7);

[0085]

[0086] Where D' is the distance between the current search individual and the current optimal solution, l is a random number with a uniform distribution in the range [-1,1], and b is the spiral shape parameter.

[0087] Steps 4-10: Based on the constraints, determine the new location. If the new location is feasible, the location of the individual whale will be updated; otherwise, the location of the individual whale will not change.

[0088] Step 4-11: Determine the algorithm termination condition: If t = t max If yes, proceed to step 4-10; otherwise, repeat steps 4-7 to 4-11.

[0089] Step 4-12: Output the spatial location X of the optimal individual whale. * Its adaptability, that is, outputting the emergency supplies delivery route and calculating the route length.

[0090] Preferably, an emergency supplies location and route optimization system based on the whale optimization algorithm, considering both urgency and satisfaction, includes:

[0091] The site selection and sorting module is used to sort the needs and satisfaction levels of different disaster-stricken locations and select the disaster-stricken location with the highest score as the distribution center.

[0092] The material distribution module is used to plan material distribution routes, and obtain low-cost and fast delivery routes based on transportation costs, vehicle travel time costs, carbon cost and government subsidies.

[0093] The algorithm optimization module is used for parameter adjustment and data storage based on the whale optimization algorithm. It applies the improved algorithm to path planning to obtain the planned route and the global path length.

[0094] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0095] 1. This emergency supplies location and route optimization method, based on the whale optimization algorithm and considering both urgency and satisfaction, selects the optimal disaster-stricken location as a distribution center by combining the urgency of supplies with user satisfaction. This ensures that supplies can be delivered to disaster-stricken areas quickly and efficiently, reducing losses for disaster victims. It takes into account the varying shortages of supplies at different disaster-stricken locations, avoiding oversupply or undersupply. Furthermore, considering the satisfaction of residents in disaster-stricken areas with emergency relief efforts helps the emergency management bureau better serve the people.

[0096] 2. This emergency supplies location and route optimization method based on the whale optimization algorithm, which considers urgency and satisfaction, combines transportation costs, vehicle travel time costs, carbon cost, and government subsidy costs to achieve timely and low-cost delivery, thereby improving delivery efficiency.

[0097] 3. This paper proposes an emergency material location and route optimization method based on the whale optimization algorithm, considering both urgency and satisfaction. Addressing the issue of the original whale algorithm easily getting trapped in local optima, it introduces a nonlinear convergence factor and an adaptive search control coefficient to balance exploration and development, avoiding premature immersion in local optima. A memory list strategy is also employed to avoid redundant calculations and accelerate the convergence process. Applying the ACSWOA algorithm to an emergency material transportation model generates globally optimal routes, enabling control over total cost and time during transportation and better ensuring low-carbon practices, thus contributing to the development of green logistics in my country. Attached Figure Description

[0098] Figure 1 This is a flowchart of an emergency supplies location and route optimization method based on the whale optimization algorithm, taking into account urgency and satisfaction, provided by an embodiment of the present invention.

[0099] Figure 2 This is a module partitioning diagram of an emergency material location and route optimization system based on the whale optimization algorithm, which considers urgency and satisfaction, provided in an embodiment of the present invention.

[0100] Figure 3 This is a flowchart of delivery route planning based on the whale optimization algorithm. Specific implementation methods

[0101] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0102] Example 1: As Figure 1 and Figure 3 The diagram shows a flowchart of an emergency supplies location and route optimization method based on the whale optimization algorithm, considering urgency and satisfaction, according to an example of the present invention. The method includes the following steps:

[0103] Step 1: Establish an emergency supplies location model based on urgency and customer satisfaction, and select the optimal distribution point as the distribution center.

[0104] Step 1-1: Establish an urgency model. The TOPSIS method combined with the entropy weight method is used to evaluate the urgency of emergency supplies needs at disaster sites. The TOPSIS method is advantageous due to its simplicity, flexibility, and ease of calculation, but it has a degree of subjectivity in determining the weights. The entropy weight method, on the other hand, is an objective weight analysis method that can reduce the subjectivity of the TOPSIS method. The specific implementation process is as follows:

[0105] Step 1-1-1: Construct the initial decision matrix. Standardize it, and the matrix composed of the index values ​​of n material demand points is called the initial decision matrix.

[0106]

[0107] Where Xij represents the index value of the index in the i-th row and j-th column of the material demand point, and X is the initial decision matrix, i = 1, 2, ..., n, j = 1, 2, ..., m.

[0108] X is normalized to avoid errors caused by different dimensions between indicators.

[0109]

[0110] Among them, C ij Let X be the decision matrix, and max(X) j ) represents the largest index value in the j-th column.

[0111] Step 1-1-2: Determine the indicator weights and establish a weighted decision matrix R.

[0112]

[0113] Where R is the weighted decision matrix, which is obtained by multiplying the decision matrix by the weights obtained by the entropy weight method.

[0114] Step 1-1-3: Determine the positive and negative ideal solutions.

[0115] The ideal solution R + :

[0116] R + =(R1) + R2 + ,..,R m + ),R j + =max(R) ij )

[0117] Negative ideal solution R - :

[0118] R - =(R1) - R2 - ,..,R m - ),R j - =min(R) ij (Equation 4)

[0119] Among them, R + R + It is the set of positive and negative ideal solutions. This represents the positive ideal solution of the m-th column of the matrix. This represents the negative ideal solution of the m-th column of the matrix. This represents the largest positive ideal solution in the j-th column of the weighted decision matrix. This represents the smallest negative ideal solution in the j-th column of the weighted decision matrix.

[0120] Step 1-1-4: Solve for the distance between the positive and negative ideal solutions.

[0121]

[0122] Among them, C + C - These represent the distances from each solution to the positive and negative ideal solutions, respectively.

[0123] Step 1-1-5: Calculate the relative proximity of the demand points.

[0124]

[0125] F1 represents the relative proximity of each disaster point to the ideal solution; the larger the F1 value, the higher the urgency of the need.

[0126] Steps 1-2: Establish a customer satisfaction model. This includes hard and soft time windows. If emergency supplies arrive at the disaster site within the soft time window but not within the hard time window, customer satisfaction is calculated based on the degree of deviation from the hard time window. The formula for calculating customer satisfaction is as follows:

[0127]

[0128] Where F2 represents customer satisfaction, and the hard time window is (ET) g ,LT g When this timeframe is reached, customer satisfaction is 1; within the soft time window (et... g ,lt g Customer satisfaction was 0% upon arrival outside the local area; g The time it takes for the service vehicle to arrive at the disaster site g;

[0129] Steps 1-3: Establish an emergency supplies model by combining the urgency and customer satisfaction scores of each disaster-stricken area. Normalize the urgency and satisfaction functions, and assign weights to obtain the disaster-stricken area with the highest score. Select this point as the distribution center for rescue operations. The specific formula is as follows:

[0130]

[0131] Where F1 and F2 are the urgency and satisfaction values, respectively, θ1 and θ2 are the corresponding weights, and F is the overall score.

[0132] Step 2: Establish an emergency supplies distribution model based on transportation costs and time, and plan the distribution routes to disaster-stricken areas.

[0133] The emergency supplies delivery model should incorporate transportation costs, vehicle travel time costs, carbon emission costs, and government subsidy costs to determine constraints such as shorter delivery times and lower transportation costs. The specific steps are as follows:

[0134] Step 2-1: Establish a transportation cost model based on risk management. Transportation costs are reflected in the direct transportation costs related to the quantity, time, and distance of goods transported, as well as the indirect transportation costs related to risk management. As transportation time and distance increase, the risks faced by goods during transportation increase because more uncertainties may arise, such as weather changes and road conditions. Similarly, the greater the weight of the goods, the greater the potential losses from unforeseen events during transportation (such as the risk of cargo loss), and the higher the risk management costs will be. Therefore, a transportation cost model based on risk management is established:

[0135]

[0136] Where C1 represents transportation cost, d iqDenote the distance of transportation from the i-th point to the q-th point; U represents the unit distance cost from the i-th point to the q-th point; G represents the quantity, quality or volume of the transported goods; R r is the risk area, R low is the low-risk demarcation point, R mid is the medium-risk demarcation point; C low is the fixed cost in the low-risk interval; and b mid is the slope and intercept in the medium-risk interval; m high and b high are the slope and intercept in the high-risk interval;

[0137] Step 2-2: Establish a vehicle travel time model. The time cost is used to describe the penalty caused by the vehicle allocation time exceeding the expected time due to road congestion. The shortest time for the vehicle to travel under unobstructed road conditions is taken as the expected time. Based on this, the research established a vehicle travel time model function:

[0138]

[0139] where C2 represents the time cost, d iq is the distance from i to q; v iq is the speed; α is the cost coefficient of time delay; in order to quantitatively describe the driving situation of vehicles under congested road conditions, combining the "China Road Capacity Guidelines" and the low characteristics of road traffic, R is defined as the "road traffic flow evaluation coefficient", and the road traffic conditions are divided into five levels: 0.00 < R < 0.30 represents the unobstructed state; 0.30 < R < 0.60 represents the slightly congested state; 0.60 < R < 0.75 represents the somewhat congested state; 0.75 < R < 0.90 represents the relatively congested state; 0.90 < R < 1.00 represents the congested state; R ≥ 1.00 represents the severely congested state.

[0140] Step 2-3: Establish a carbon value cost model. The carbon emission cost is closely related to multiple variables in the transportation process, including the vehicle load capacity, transportation distance, fuel quantity, etc. In today's global environment where all countries are committed to addressing climate change and reducing greenhouse gas emissions, this model aims to achieve a win-win goal of economic benefits and environmental protection.

[0141]

[0142] where C3 represents the carbon value cost, P * is the fuel consumption per unit mileage; P0 is the fuel consumption per unit mileage of the vehicle when it is not loaded; Q * is the maximum load capacity of the vehicle; Q iq is the current vehicle load capacity.

[0143] Steps 2-4: Establish a government subsidy model. During the distribution of emergency supplies, the government will provide corresponding subsidies based on the urgency of the situation. Introducing a midpoint offset parameter μ into the formula can adjust the specific value of the urgency of the supplies when the subsidy amount begins to increase significantly.

[0144]

[0145] Where S(P) represents the cost of government subsidies, denoted by C4, P is the urgency of the supplies, 0≤P≤1; k is the subsidy amplification factor, used to adjust the overall level of the subsidy amount; β is the slope parameter, controlling the rate at which the subsidy amount changes with P; the larger β is, the faster the change; the smaller β is, the smoother the change; μ is the midpoint offset parameter, used to adjust the position of the center point of the logistic function. When μ=0.5, the function is symmetric about P=0.5; when μ deviates from 0.5, the function will shift to the left or right; b is the base value or minimum value of the subsidy.

[0146] Steps 2-5: Based on transportation costs, vehicle travel time costs, carbon emission costs, and government subsidies, a total cost model is derived. The calculation formula is shown below:

[0147] C(cost) = C1 + C2 + C3 - C4 (Equation 5)

[0148] Where C(cost) is the total cost; C1 is the transportation cost; C2 is the vehicle travel time cost; C3 is the carbon cost; and C4 is the government subsidy cost.

[0149] Step 3: Adjust the parameters based on the whale algorithm and store the paths generated in each iteration.

[0150] The original Whale Algorithm (WOA) is modified by adding a nonlinear convergence factor, introducing adaptive search control coefficients, and adopting a memory list strategy. The specific steps are as follows:

[0151] Step 3-1: Improve the parameter 'a' in the original WOA algorithm by adding a non-linear convergence factor to slow down the algorithm from getting stuck in local optima as the number of iterations increases; simultaneously, the convergence speed is effectively accelerated as the number of iterations gradually increases. The modified algorithm parameter 'a' is as follows: a = (a start -a end )+(1-t / t max ) / (1-μ1*t / t max (Equation 1)

[0152] Among them, a start and a end Here, are the initial and final values ​​of a, respectively; μ1 is the adjustment coefficient, taken as μ1 = 25; M is the maximum number of iterations; and t is the current number of iterations.

[0153] Step 3-2: Introduce an adaptive search control coefficient, dynamically adjusting the value of C based on the number of iterations. In the early stages of the algorithm, a smaller value of C increases the whale's movement range, which helps improve global search capabilities. As the algorithm iterates, the whale's movement range in the search space decreases, which helps improve local search capabilities. The expression is as follows:

[0154]

[0155] Where C is the adaptive search control coefficient, r is a coefficient that controls the decay rate of C, and t max t represents the maximum number of iterations, and t represents the current number of iterations.

[0156] Step 3-3: Introduce a memory list strategy. Add a memory list to record the optimal solution encountered in each iteration. After reaching the maximum number of iterations, compare the optimal solution obtained in the last iteration with the solutions in the memory list, and select the better solution as the final result. To avoid the memory capacity becoming too large, limit the length of the memory list; when the maximum capacity is reached, remove the worst solution. The expression is as follows:

[0157] S=remove(min(S)) (Formula 3)

[0158] In this matrix S, each element represents the current optimal solution encountered during the iteration process. When the memory reaches its maximum value, the minimum value in the memory is removed by using the remove function.

[0159] Step 4: Obtain the planned route and global path length for the disaster-affected area.

[0160] Step 4-1: Set ACSWOA algorithm parameters: population size N, maximum number of iterations t max The current iteration number t and the spiral shape constant b are used to randomly initialize the initial positions of the whale group Xi (i = 1, 2, ..., n);

[0161] Step 4-2: Calculate the fitness of each individual whale using the following formula, sort the fitness values, and find the whale with the lowest fitness value, which is the optimal whale X. * And save it to the list of memory banks. When the bank reaches its maximum capacity, remove the worst solution.

[0162]

[0163] S = remove(min(S))

[0164] (Equation 2)

[0165] Step 4-3: Calculate the parameter 'a' that introduces the nonlinear convergence factor, and update the coefficient vector A;

[0166] a=(a start -a end )+(1-t / t max ) / (1-μ*t / t max )

[0167] (Equation 3)

[0168] Step 4-4: Introduce adaptive search control coefficients and update the coefficient vector C;

[0169]

[0170] Steps 4-5: Utilizing α j =min(X) i,j ),β j =max(X i,j Calculate the dynamic search boundary at this point;

[0171] Steps 4-6: Calculate the value of each individual whale, compare the solution of the reverse population with the solution in the current population, select s whale individuals with high fitness as the next generation population, and locate the position of the whale individual with the lowest fitness as the prey.

[0172] Step 4-7: If t≤t max Then update parameters a, A, C, l, and p;

[0173] Steps 4-8: When p < 0.5, if |A| < 1, update the spatial position of the current whale individual according to equation (5); when |A| >= 1, randomly select the position X of the whale individual. rand And update the spatial position of the current whale individual according to formula (6);

[0174] D = |C·X * (t)-X(t),X(t+1)=X(t)-A·D (Formula 5)

[0175] D = |C·X rand (t)-X|,X(t+1)=X rand -A·D (Equation 6)

[0176] Where X*(t) is the position of the current best solution, X(t) is the current whale position, X(t+1) is the updated whale position, and D is the distance between the individual whale and the current best solution.

[0177] Steps 4-9: When p>=0.5, update the spatial position of the current whale individual according to formula (7);

[0178]

[0179] Where D' is the distance between the current search individual and the current optimal solution, l is a random number with a uniform distribution in the range [-1,1], and b is the spiral shape parameter.

[0180] Steps 4-10: Based on the constraints, determine the new location. If the new location is feasible, the location of the individual whale will be updated; otherwise, the location of the individual whale will not change.

[0181] Step 4-11: Determine the algorithm termination condition: If t = t max If yes, proceed to step 4-10; otherwise, repeat steps 4-7 to 4-11.

[0182] Step 4-12: Output the spatial location X of the optimal individual whale. * Its adaptability, that is, outputting the emergency supplies delivery route and calculating the route length.

[0183] Example 2: As Figure 2 As shown in the figure, this invention provides an emergency supplies location and route optimization system based on the whale optimization algorithm, considering both urgency and satisfaction. The system includes the following modules:

[0184] The site selection and sorting module is used to sort the needs and satisfaction levels of different disaster-stricken locations and select the disaster-stricken location with the highest score as the distribution center.

[0185] The material distribution module is used to plan material distribution routes, and obtain low-cost and fast delivery routes based on transportation costs, vehicle travel time costs, carbon cost and government subsidies.

[0186] The algorithm optimization module is used for parameter adjustment and data storage based on the whale optimization algorithm. It applies the improved algorithm to path planning to obtain the planned route and the global path length. The specific implementation methods of each module can be found in the description of the above method embodiments; these will not be repeated in this embodiment.

[0187] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for emergency material location selection and route optimization based on the whale optimization algorithm, characterized in that, Includes the following steps: Step 1: Establish an emergency supplies location model based on urgency and customer satisfaction, and select the optimal distribution point as the distribution center; Step 2: Establish an emergency supplies distribution model based on transportation costs and time, and plan the distribution routes to disaster-stricken areas; Step 3: Adjust the parameters based on the whale algorithm and store the paths generated in each iteration; Step 4: Obtain the planned route and global path length for the disaster-affected area; In step 2, the specific implementation process of establishing the emergency supplies distribution model is as follows: Step 2-1: Establish a transportation cost model based on risk management; transportation costs are reflected in the direct transportation costs related to the quantity, time, and distance of the transported goods, as well as the indirect transportation costs related to risk management. As transportation time and distance increase, the risks faced by goods during transportation also increase; the greater the weight of the goods, the greater the potential losses from unforeseen events during transportation, and the higher the risk management costs will be; therefore, a transportation cost model based on risk management should be established: Where C1 represents transportation cost, d iq U represents the distance transported from point i to point q; U represents the unit distance cost from point i to point q; G represents the quantity, quality, or volume of the transported goods; R represents the distance transported from point i to point q. r It is a high-risk area, R low It is the low-risk dividing line, R mid It is the dividing point between medium and high risk; C low It represents the fixed costs in the low-risk range; m mid and b mid These are the slope and intercept of the medium-risk range; m high and b high These are the slope and intercept of the high-risk interval; Step 2-2: Establish a vehicle travel time model; time cost is used to describe the penalty caused by road congestion resulting in vehicle allocation time exceeding the expected time; the shortest travel time for a vehicle under unobstructed road conditions is taken as the expected time; based on this, a vehicle travel time model function is established: Among them, C2 represents the time cost, d iq is the distance from point i to point q; v iq is the speed; α is the cost coefficient of time delay; in order to quantitatively describe the driving conditions of vehicles under congested road conditions, R is defined as the "road traffic flow evaluation coefficient", and the road traffic conditions are divided into five levels: 0.00 < R < 0.30 indicates a smooth state; 0.30 < R < 0.60 indicates a slightly congested state; 0.60 < R < 0.75 indicates a moderately congested state; 0.75 < R < 0.90 indicates a relatively congested state; 0.90 < R < 1.00 indicates a congested state; R ≥ 1.00 indicates a severely congested state; Steps 2-3: Establish a carbon cost model; carbon emission costs are closely related to multiple variables in the transportation process, including the vehicle's load capacity, the transportation distance, and the amount of fuel used. Where C3 represents carbon cost, P * P0 represents the fuel consumption per unit distance traveled when the vehicle is not carrying any cargo; Q represents the fuel consumption per unit distance traveled. * Q represents the vehicle's maximum load capacity. iq This represents the current vehicle load capacity. Steps 2-4: Establish a government subsidy model; during the distribution of emergency supplies, the government will provide corresponding subsidies based on the urgency of the situation; the midpoint offset parameter μ is introduced into the formula to adjust the specific value of the urgency of the supplies when the subsidy amount begins to increase significantly; Where S(P) represents the cost of government subsidies, denoted by C4, P is the urgency of the supplies, 0≤P≤1; k is the subsidy amplification factor, used to adjust the overall level of the subsidy amount; β is the slope parameter, controlling the rate at which the subsidy amount changes with P; the larger β is, the faster the change; the smaller β is, the smoother the change; μ is the midpoint offset parameter, used to adjust the position of the center point of the logistic function; when μ=0.5, the function is symmetric about P=0.5; when μ deviates from 0.5, the function will shift to the left or right; b is the base value or minimum value of the subsidy. Steps 2-5: Based on transportation costs, vehicle travel time costs, carbon emission costs, and government subsidies, a total cost model is derived. The calculation formula is shown below: C(cost) = C1 + C2 + C3 - C4 Where C(cost) is the total cost; C1 is the transportation cost; C2 is the vehicle travel time cost; C3 is the carbon cost; and C4 is the government subsidy cost. In step 3, the specific implementation process of an improved whale optimization algorithm is as follows: Step 3-1: Improve the parameter 'a' in the original Whale WOA algorithm by adding a non-linear convergence factor to slow down the algorithm from getting stuck in local optima as the number of iterations increases; at the same time, the convergence speed is effectively accelerated as the number of iterations gradually increases; the modified algorithm parameter 'a' is as follows: a=(a start -a end )+(1-t / t max ) / (1-μ1*t / t max ) Among them, a start and a end These are the initial and final values ​​of a, respectively; μ1 is the adjustment coefficient, taken as μ1 = 25, t max The maximum number of iterations is t, and the current iteration number is t. Step 3-2: Introduce an adaptive search control coefficient, dynamically adjusting the value of C based on the number of iterations. In the early stages of the algorithm, a smaller value of C increases the whale's movement range, which helps improve global search capabilities. As the algorithm iterates, the whale's movement range in the search space decreases, which helps improve local search capabilities. The expression is as follows: Where C is the adaptive search control coefficient, r is a coefficient that controls the decay rate of C, and t max The maximum number of iterations is t, and the current iteration number is t. Step 3-3: Introduce a memory list strategy; add a memory list to record the optimal solution encountered in each iteration; after reaching the maximum number of iterations, compare the optimal solution obtained in the last iteration with the solutions in the memory list, and select the better solution as the final result; to avoid the memory capacity becoming too large, limit the length of the memory list, and remove the worst solution when the maximum capacity is reached; the expression is as follows: S = remove(min(S)) In this matrix S, each element represents the current optimal solution encountered during the iteration process. When the memory reaches its maximum value, the minimum value in the memory is removed by using the remove function.

2. The emergency material location and route optimization method based on the whale optimization algorithm according to claim 1, characterized in that, In step 1, the specific implementation process of establishing the emergency supplies location model is as follows: Step 1-1: Establish an urgency model; use a combination of the TOPSIS method and the entropy weight method to evaluate the urgency of emergency supplies needs at disaster sites; the specific implementation process is as follows: Step 1-1-1: Construct the initial decision matrix and standardize it. The matrix composed of the index values ​​of n material demand points is called the initial decision matrix. Where Xij represents the index value of the index in the j-th column of the i-th row of the material demand point, and X is the initial decision matrix, i = 1, 2, ..., n, j = 1, 2, ..., m; X is normalized to avoid errors caused by different dimensions between indicators; Among them, C ij Let X be the decision matrix, and max(X) j ) represents the largest index value in the j-th column; Step 1-1-2: Determine the indicator weights and establish the weighted decision matrix R; Where R is the weighted decision matrix, which is obtained by multiplying the decision matrix by the weights obtained by the entropy weight method; Step 1-1-3: Determine the positive and negative ideal solutions; The ideal solution R + : R + =(R1 + ,R2 + ,..,R m + ),R j + =max(R ij ) Negative ideal solution R - : R - =(R1 - ,R2 - ,..,R m - ),R j - =min(R ij ) Among them, R + R + It is the set of positive and negative ideal solutions. This represents the positive ideal solution of the m-th column of the matrix. This represents the negative ideal solution of the m-th column of the matrix. This represents the largest positive ideal solution in the j-th column of the weighted decision matrix. This represents the smallest negative ideal solution in the j-th column of the weighted decision matrix; Step 1-1-4: Solve for the distance between the positive and negative ideal solutions; Among them, C + C - These represent the distances from each solution to the positive and negative ideal solutions, respectively. Step 1-1-5: Calculate the relative proximity of the demand points; Wherein, F1 represents the relative proximity of each disaster point to the ideal solution, i.e., the urgency; the larger the F1, the higher the urgency of the need. Steps 1-2: Establish a customer satisfaction model, including hard and soft time windows. If emergency supplies do not arrive within the hard time window but do arrive at the disaster site within the soft time window, customer satisfaction is calculated based on the degree of deviation from the hard time window. The formula for calculating customer satisfaction is as follows: Where F2 represents customer satisfaction, and the hard time window is (ET) g ,LT g When this timeframe is reached, customer satisfaction is 1; within the soft time window (et... g ,lt g Customer satisfaction was 0 upon arrival outside the local area; g The time it takes for the service vehicle to arrive at the disaster site g; Steps 1-3: Establish an emergency supplies model by combining the urgency and customer satisfaction scores of each disaster-stricken area; normalize the urgency and satisfaction functions, and assign weights to obtain the disaster-stricken area with the highest score, selecting this point as the distribution center for rescue operations. The specific formula is as follows: Where F1 and F2 are the urgency and satisfaction values, respectively, θ1 and θ2 are the corresponding weights, and F is the overall score.

3. The emergency material location and route optimization method based on the whale optimization algorithm according to claim 2, characterized in that, In step 4, the steps for obtaining the disaster-affected point path planning route and global path length based on the improved whale optimization algorithm are described in detail below: Step 4-1: Set the parameters of the whale-optimized ACSWOA algorithm: population size N, maximum number of iterations t max The current iteration number t and the spiral shape constant b are used to randomly initialize the initial position Xi of the whale group, where i = 1, 2, ..., n; Step 4-2: Calculate the fitness of each individual whale using the following formula, sort the fitness values, and find the whale with the lowest fitness value, which is the optimal whale X. * And save it to the list of memory banks. When the bank reaches its maximum capacity, remove the worst solution. S = remove(min(S)) Step 4-3: Calculate the parameter 'a' that introduces the nonlinear convergence factor, and update the coefficient vector A; a=(a start -a end )+(1-t / t max ) / (1-μ*t / t max ) Step 4-4: Introduce adaptive search control coefficients and update the coefficient vector C; Steps 4-5: Utilizing α j =min(X) i,j ),β j =max(X i,j Calculate the dynamic search boundary at this point; Steps 4-6: Calculate the value of each individual whale, compare the solution of the reverse population with the solution in the current population, select s whale individuals with high fitness as the next generation population, and locate the position of the whale individual with the lowest fitness as the prey. Step 4-7: If t≤t max Then update parameters a, A, C, l, and p; Steps 4-8: When p < 0.5; If |A|<1, update the spatial position of the current whale individual according to the following formula; D=|C·X * (t)-X(t)|,X(t+1)=X(t)-A·D If |A|>=1, then randomly select the location X of an individual whale. rand And update the spatial position of the current whale individual according to the following formula; D=|C·X rand (t)-X|,X(t+1)=X rand -A·D Where X*(t) is the position of the current best solution, X(t) is the current whale position, X(t+1) is the updated whale position, and D is the distance between the individual whale and the current best solution; Steps 4-9: When p>=0.5, update the spatial position of the current whale individual according to the following formula; Where D' is the distance between the current search individual and the current optimal solution, l is a random number with a uniform distribution in the range [-1,1], and b is the spiral shape parameter; Steps 4-10: Based on the constraints, determine the new location. If the new location is feasible, the location of the individual whale will be updated; otherwise, the location of the individual whale will not change. Step 4-11: Determine the algorithm termination condition: If t = t max If yes, proceed to step 4-10; otherwise, repeat steps 4-7 to 4-11. Step 4-12: Output the spatial location X of the optimal individual whale. * Its adaptability, that is, outputting the emergency supplies delivery route and calculating the route length.

4. The emergency material location and route optimization method based on the whale optimization algorithm according to claim 1, characterized in that, The method is based on an emergency supplies location and route optimization system, which includes: The site selection and sorting module is used to sort the needs and satisfaction levels of different disaster-stricken locations and select the disaster-stricken location with the highest score as the distribution center. The material distribution module is used to plan material distribution routes, and obtain low-cost and fast delivery routes based on transportation costs, vehicle travel time costs, carbon cost and government subsidies. The algorithm optimization module is used for parameter adjustment and data storage based on the whale optimization algorithm. It applies the improved algorithm to path planning to obtain the planned route and the global path length.

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