Vaccine sorting operation path optimization method

Abstract

The invention belongs to the technical field of logistics path planning, and particularly relates to a vaccine sorting operation path optimization method. According to the improved method, firstly, a target optimization problem including path length, conflict, task priority and smoothness and a fitness function are defined, an improved intelligent multi-factor fusion ant colony algorithm IMFACO is adopted, the volatilization rate is dynamically adjusted according to a path search process, poor-quality paths are screened out at the initial stage, convergence is accelerated, and high-quality path diversity is reserved at the later stage. The method comprises the steps of initializing algorithm parameters, constructing an ant path, selecting nodes according to a state transition probability, calculating fitness and a volatilization rate, updating pheromones and the like, and finally outputting an optimal path. According to the method, the sorting efficiency is effectively improved, conflicts are reduced, the priority is ensured, the smoothness is optimized, and powerful support is provided for safe and efficient distribution of vaccines.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics path planning, and in particular relates to a method for optimizing the path of a vaccine sorting operation. Background Art

[0002] In the modern medical and health system, safe and efficient distribution of vaccines is of vital importance. As a key link in the vaccine distribution process, the efficiency and accuracy of vaccine sorting operations directly affect the timeliness of vaccine supply and quality assurance. Traditional vaccine sorting operation path planning often relies on manual experience or simple rule algorithms, which has many limitations. On the one hand, manual planning is difficult to quickly find the optimal path in a complex sorting environment, resulting in low sorting efficiency. For example, in a large vaccine storage warehouse, there are many sorting points and storage areas. When planning the path manually, it is easy to ignore some potential better path choices, causing the transportation equipment to drive around in the warehouse, increasing unnecessary driving distance and time costs. On the other hand, it is difficult for traditional methods to comprehensively consider the impact of multiple practical factors on the path. For example, in the process of path planning, it is necessary not only to pay attention to the path length to reduce the transportation time, but also to avoid path conflicts, prevent the paths between different transportation equipment or tasks from interfering with each other, and cause congestion or even collision accidents. At the same time, vaccine sorting tasks have different priorities. For example, urgently allocated vaccines should be handled first, but traditional planning methods make it difficult to ensure that high-priority tasks always get priority path arrangements. In addition, the smoothness of the path cannot be ignored. Frequent sharp turns will reduce the operating efficiency of transportation equipment, increase equipment wear and energy consumption, affect the service life of equipment, and thus increase operating costs. Summary of the invention

[0003] In view of the technical problems existing in the above-mentioned background technology, the present invention proposes a vaccine sorting operation path optimization method.

[0004] In order to achieve the above object, the technical solution adopted by the present invention comprises the following steps:

[0005] First, define the target optimization problem, including path length, path conflict, task priority and path smoothness, and give the fitness function;

[0006] The improved intelligent multi-factor fusion ant colony algorithm IMFACO is used to dynamically adjust the volatility rate according to the path search process. In the initial stage, a higher volatility rate is set to screen out inferior paths and accelerate convergence; as the iteration proceeds, the volatility rate is reduced to retain the diversity of high-quality paths and avoid missing the global optimum. The specific implementation is as follows:

[0007] Step 1: Initialize the algorithm parameters first;

[0008] Step 2: Construct an ant path, starting from the starting node for each ant k;

[0009] Step 3: According to the pheromone concentration of the current node And heuristic information, use the state transition probability formula to select the next node to visit, the formula is: ,in is the pheromone concentration between node i and node j at time t, is the heuristic information, Path diversity guidance factor, each ant gradually builds its own complete path according to the state transition until it reaches the target node, and records the path taken by each ant. They are the weight parameter of pheromone concentration, the weight parameter of heuristic factor and the weight parameter of path diversity guiding factor;

[0010] Step 4: Calculate the fitness function value of the path;

[0011] Step 5: Calculate the volatility of this iteration , using a dynamic adjustment strategy, the calculation method is: ,in is the initial value of the pheromone volatilization rate, is the minimum value of pheromone volatilization rate, is the number of current iterations, The maximum number of iterations preset for the algorithm;

[0012] Step 6: Perform global update of pheromones, and volatilize the pheromones on all paths according to the volatilization rate, that is, for all node pairs: , and then let all ants release pheromones according to the fitness of the paths they have walked to enhance the pheromone concentration of high-quality paths: ,in is the amount of pheromone released by ant k on the node pair;

[0013] Step 7: After each iteration, determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to iterate. If the maximum number of iterations has been reached, the algorithm terminates and outputs the current optimal path according to the fitness function.

[0014] Preferably, the fitness function is defined as: ,in They are respectively represented as path length, path conflict penalty, task priority and path order penalty, and path smoothness. The weights of the four influencing factors;

[0015] The path length measures the distance from the starting point to the target: ,in, is the i-th node in the path, Is a node arrive The actual distance;

[0016] The path conflict penalty measures the degree of overlap between paths: ,in Indicates the path and Whether overlap occurs;

[0017] The task priority penalty measures whether the path follows the priority order of the tasks: ,in, is the priority of the task, The target order for the task;

[0018] The path smoothness measures the continuity of the path, reduces sharp turns, and optimizes the coherence of the path: ,in Represents a path node The angle between.

[0019] As a preference, the path conflict penalty The specific definition of is: , when two paths overlap, the path conflict penalty is 1, otherwise it is 0.

[0020] Preferably, the path node The angle between The specific implementation is: , the smaller the angle value, the smoother the path.

[0021] As a preference, The weight values ​​of the four influencing factors are determined by genetic algorithm, and .

[0022] As a preference, the path diversity guiding factor in step 3 The specific implementation method is: ,in, It represents the number of times the ant chooses the path from node i to node j before time t.

[0023] As a preference, the heuristic information in step 3 Take path length, conflict, task priority, and smoothness into account.

[0024] Preferably, the pheromone amount in step 6 is Calculated as: .

[0025] Compared with the prior art, the advantages and positive effects of the present invention are that, by improving the intelligent multi-factor fusion ant colony algorithm, the volatility rate is dynamically adjusted, the inferior paths are screened out in the early stage to accelerate convergence, and the diversity of high-quality paths is retained in the later stage to avoid missing the global optimum. This makes the path planning more scientific and reasonable, effectively improves the sorting efficiency, reduces the transportation time; reduces the risk of path conflict and improves the safety of operations; ensures the priority of tasks and guarantees the emergency vaccine distribution; optimizes the path smoothness, reduces the operating cost, and provides strong support for vaccine distribution. DETAILED DESCRIPTION

[0026] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0028] Example: In the vaccine distribution center, a large number of vaccine sorting tasks of different types and batches need to be processed every day to meet the vaccination needs of various places. The traditional manual path planning method exposes many problems when faced with complex warehouse layouts, numerous sorting points and diverse task requirements. It is difficult to quickly calculate the optimal path manually, resulting in long driving time and low efficiency of transport vehicles in the warehouse. At the same time, it is impossible to effectively balance multiple factors such as path length, path conflict, task priority and path smoothness, which may cause adverse consequences such as vaccine distribution delays and increased equipment loss.

[0029] First, define the target optimization problem, including path length, path conflict, task priority and path smoothness, and give the fitness function. Explicitly include path length, path conflict, task priority and path smoothness into the target optimization problem. Path length directly affects the transportation time, which is measured by accurately calculating the distance from the starting point to the target. Path conflict is penalized by judging whether different paths overlap. Once overlapped, a certain penalty value is given to avoid interference between transportation equipment. Task priority sets different levels according to the urgency of the vaccine task, and penalizes path selection that does not meet the priority order. Path smoothness focuses on the angle between path nodes, reduces sharp turns, and optimizes path continuity. The fitness function combines these factors and calculates a fitness value for each path as a criterion for evaluating the quality of the path. The fitness function is defined as: ,in They are respectively represented as path length, path conflict penalty, task priority and path order penalty, and path smoothness. The weights of the four influencing factors are determined by genetic algorithm and ; where the path length measures the distance from the starting point to the goal: ,in, is the i-th node in the path, Is a node arrive The actual distance between the paths is: ,in Indicates the path and Whether overlap occurs , when two paths overlap, the path conflict penalty is 1, otherwise it is 0; the task priority penalty measures whether the path follows the priority order of the task: ,in, is the priority of the task, is the target order of the task; path smoothness measures the continuity of the path, reduces sharp turns, and optimizes the coherence of the path: ,in Represents a path node The angle between , the smaller the angle value, the smoother the path. In this way, the path quality can be comprehensively and systematically evaluated to avoid a single factor dominating the path selection, making the final path planning more reasonable and scientific.

[0030] The improved intelligent multi-factor fusion ant colony algorithm IMFACO is used to dynamically adjust the volatility rate according to the path search process. In the initial stage, a higher volatility rate is set to screen out poor quality paths and accelerate convergence; as the iteration progresses, the volatility rate is lowered to retain the diversity of high-quality paths and avoid missing the global optimum. First, the algorithm parameters are initialized. According to the actual scale and task characteristics of the vaccine distribution center, the appropriate number of ants, initial pheromone concentration, initial value and minimum value of pheromone volatility rate, maximum number of iterations and other parameters are set. Construct the ant path. For each ant k, start from the starting node; according to the pheromone concentration of the current node, And heuristic information, use the state transition probability formula to select the next node to visit, the formula is: ,in is the pheromone concentration between node i and node j at time t, is the heuristic information, Path diversity guidance factor, each ant gradually builds its own complete path according to the state transition until it reaches the target node, and records the path taken by each ant. are the weight parameters of pheromone concentration, heuristic factor and path diversity guiding factor respectively; The specific implementation method is: ,in, Indicates the number of times the ant chooses the path from node i to node j before time t. Heuristic information Comprehensively consider the path length, conflict, task priority and smoothness. After the ants build the path, the fitness value of each path is calculated according to the defined fitness function. Accurate fitness value calculation provides a basis for subsequent path screening and pheromone update, which can effectively distinguish the advantages and disadvantages of different paths. Then calculate the volatility of this iteration , using a dynamic adjustment strategy, the calculation method is: ,in is the initial value of the pheromone volatilization rate, is the minimum value of pheromone volatilization rate, is the number of current iterations, is the maximum number of iterations preset by the algorithm; perform global update of pheromones, and volatilize pheromones on all paths according to the volatilization rate, that is, for all node pairs: , and then let all ants release pheromones according to the fitness of the paths they have walked to enhance the pheromone concentration of high-quality paths: ,in is the amount of pheromone released by ant k on the node pair, ; Finally, after each iteration, it is determined whether the current number of iterations has reached the maximum number of iterations. If not, the iteration continues. If the maximum number of iterations has been reached, the algorithm terminates and the current optimal path is output according to the fitness function. The vaccine sorting operation path optimization method of the present invention has shown significant advantages in practical applications, effectively solving many problems in traditional vaccine sorting operation path planning, and providing strong support for the safe and efficient distribution of vaccines.

[0031] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A vaccine sorting operation path optimization method, characterized in that: The following steps are involved: First, define the target optimization problem, including path length, path conflict, task priority and path smoothness, and give the fitness function; The improved intelligent multi-factor fusion ant colony algorithm IMFACO is used to dynamically adjust the volatility rate according to the path search process. In the initial stage, a higher volatility rate is set to screen out inferior paths and accelerate convergence; as the iteration proceeds, the volatility rate is reduced to retain the diversity of high-quality paths and avoid missing the global optimum. The specific implementation is as follows: Step 1: Initialize the algorithm parameters first; Step 2: Construct an ant path, starting from the starting node for each ant k; Step 3: According to the pheromone concentration of the current node And heuristic information, use the state transition probability formula to select the next node to visit, the formula is: ,in is the pheromone concentration between node i and node j at time t, is the heuristic information, Path diversity guidance factor, each ant gradually builds its own complete path according to the state transition until it reaches the target node, and records the path taken by each ant. They are the weight parameter of pheromone concentration, the weight parameter of heuristic factor and the weight parameter of path diversity guiding factor; Step 4: Calculate the fitness function value of the path; Step 5: Calculate the volatility of this iteration , using a dynamic adjustment strategy, the calculation method is: ,in is the initial value of the pheromone volatilization rate, is the minimum value of pheromone volatilization rate, is the number of current iterations, The maximum number of iterations preset for the algorithm; Step 6: Perform global update of pheromones, and volatilize the pheromones on all paths according to the volatilization rate, that is, for all node pairs: , and then let all ants release pheromones according to the fitness of the paths they have walked to enhance the pheromone concentration of high-quality paths: ,in is the amount of pheromone released by ant k on the node pair; Step 7: After each iteration, determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to iterate. If the maximum number of iterations has been reached, the algorithm terminates and outputs the current optimal path according to the fitness function.

2. A vaccine sorting operation path optimization method according to claim 1, characterized in that: The fitness function is defined as: ,in They are respectively represented as path length, path conflict penalty, task priority and path order penalty, and path smoothness. The weights of the four influencing factors; The path length measures the distance from the starting point to the target: ,in, is the i-th node in the path, Is a node arrive The actual distance; The path conflict penalty measures the degree of overlap between paths: ,in Indicates the path and Whether overlap occurs; The task priority penalty measures whether the path follows the priority order of the tasks: ,in, is the priority of the task, The target order for the task; The path smoothness measures the continuity of the path, reduces sharp turns, and optimizes the coherence of the path: ,in Represents a path node The angle between.

3. A vaccine sorting operation path optimization method according to claim 2, characterized in that: The path conflict penalty The specific definition of is: , when two paths overlap, the path conflict penalty is 1, otherwise it is 0.

4. A vaccine sorting operation path optimization method according to claim 2, characterized in that: The path node The angle between The specific implementation is: , the smaller the angle value, the smoother the path.

5. A vaccine sorting operation path optimization method according to claim 2, characterized in that: The weights of the four influencing factors are determined by genetic algorithm, and .

6. A vaccine sorting operation path optimization method according to claim 1, characterized in that: The path diversity guiding factor in step 3 The specific implementation method is: ,in, It represents the number of times the ant chooses the path from node i to node j before time t.

7. A vaccine sorting operation path optimization method according to claim 1, characterized in that: The heuristic information in step 3 Take path length, conflict, task priority, and smoothness into account.

8. A vaccine sorting operation path optimization method according to claim 1, characterized in that: The amount of pheromone in step 6 Calculated as: .

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