Coordinated scheduling method of electric medical waste transfer vehicle and mobile battery exchange vehicle

Abstract

This application provides a collaborative scheduling method for electric medical waste transport vehicles and mobile battery swapping vehicles, relating to the field of scheduling optimization. The method includes: acquiring customer data and initializing algorithm parameters; constructing an initial feasible solution, calculating the total cost and infection risk for each firefly's path, and selecting the firefly path with the minimum objective function value as the current global optimal solution; using a mutation operation to generate neighborhood solutions and calculating the corresponding objective function value, comparing it with the current global optimal solution to update the global optimal solution; updating the position of each firefly in the population according to the firefly algorithm's attraction mechanism, and during the position update process, adjusting and verifying the solution using mutation operations, and iteratively optimizing until termination. This application combines the firefly algorithm with a variable neighborhood search algorithm to achieve efficient path optimization and charging scheduling, achieving a balance between minimizing total cost and minimizing infection risk, thereby improving the transportation efficiency of medical waste and reducing operating costs.

Description

Technical Field

[0001] This application relates to the field of scheduling optimization technology, specifically to a collaborative scheduling method for electric medical waste transport vehicles and mobile battery exchange vehicles. Background Technology

[0002] With the rapid development of the medical industry, the amount of medical waste generated is increasing year by year. Medical waste poses hazards such as infectiousness and toxicity, making its safe and efficient transportation crucial. Traditional fuel-powered transport vehicles suffer from high pollution and cost, making it difficult to meet increasingly stringent environmental requirements and the sophisticated needs of medical waste management. Electric Medical Waste Transport Vehicles (EMWTVs), with their advantages of zero emissions, low noise, and low operating costs, have shown great application potential in the field of medical waste transportation. However, the limited range and long charging time of EMWTVs restrict their large-scale application in medical waste transportation scenarios, especially in complex environments where strict adherence to time windows and infection risk control requirements is necessary.

[0003] Existing research on electric vehicle route optimization and charging scheduling mainly focuses on fields such as express delivery and public transportation, with relatively few studies specifically targeting medical waste transport scenarios. Furthermore, existing research often relies on fixed charging stations for charging strategies. Although Battery Swap Vehicles (BSVs), as an emerging charging method, have been applied in fields such as express delivery and logistics, research on their application in the charging strategies of Electric Medical Waste Transport Vehicles (EMWTVs) remains lacking. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a collaborative scheduling method for electric medical waste transport vehicles and mobile battery swapping vehicles. This method solves the problems of uneven distribution of fixed charging stations, long queuing times, and long charging times in existing electric medical waste transport systems, which lead to a decline in the service quality of electric vehicles and may violate customer service time windows.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] In a first aspect, embodiments of this application provide a method for the coordinated scheduling of an electric medical waste transport vehicle and a mobile battery swapping vehicle. This coordinated scheduling method includes: acquiring customer data and initializing algorithm parameters, and setting a maximum number of iterations t. maxThe initial iteration count t = 1 is set to control the iteration process of the algorithm. Based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV). The total cost F and infection risk I of each firefly in the population are calculated to obtain the comprehensive objective function value f(x). i The firefly path with the smallest objective function value is selected as the current global optimal solution g. best A mutation operation N is selected based on its weight and probability of being selected. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum. Update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm. During the position update process, combine mutation operations to adjust the solution and check its feasibility. Let t = t + 1, and determine if t ≥ t. max If the condition is met, the algorithm terminates and outputs the currently found global optimal solution g. best Otherwise, continue iterative optimization.

[0007] Secondly, embodiments of this application provide a collaborative scheduling system for electric medical waste transport vehicles and mobile battery exchange vehicles. This collaborative scheduling system includes: an acquisition module, a construction module, a calculation and selection module, a first update module, a second update module, and a judgment and optimization module. The acquisition module is used to acquire customer data and initialize algorithm parameters, setting a maximum number of iterations t. max The initial iteration count t = 1 is used to control the iteration process of the algorithm; the construction module is used to construct the initial feasible solution based on the firefly algorithm and the nearest neighbor method to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV); the calculation and selection module is used to calculate the total cost F and infection risk I for the path of each firefly in the population, and obtain the comprehensive objective function value f(x). i The firefly path with the smallest objective function value is selected as the current global optimal solution g. best The first update module is used to select a mutation operation N based on the weight and selection probability of the mutation operation. k The algorithm generates neighborhood solutions. If a solution is feasible, the corresponding objective function value is calculated and compared with the current global optimum to update the global optimum. The second update module updates the position of each firefly in the population according to the attraction mechanism of the firefly algorithm. During the position update process, the solution is adjusted and its feasibility is checked by combining mutation operations. The optimization judgment module is used to determine whether t ≥ t when t = t + 1. max If the condition is met, the algorithm terminates and outputs the currently found global optimal solution g. best Otherwise, continue iterative optimization.

[0008] Thirdly, embodiments of this application provide an electronic device, which includes: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the coordinated scheduling method of the electric medical waste transport vehicle and the mobile battery exchange vehicle described in the first aspect above.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the collaborative scheduling method for the electric medical waste transport vehicle and the mobile battery exchange vehicle described in the first aspect above.

[0010] This application provides a method for the coordinated scheduling of an electric medical waste transport vehicle and a mobile battery swapping vehicle. Compared with the prior art, it has the following advantages:

[0011] This application introduces a mobile battery swapping vehicle (BSV) to provide rapid battery swapping services for electric medical waste transport vehicles (EMWTVs). To improve fleet response speed and service levels, this application constructs an initial feasible solution based on the firefly algorithm and the nearest neighbor method. It then calculates the total cost and infection risk of each firefly's path in the population to determine the objective function value, thereby evaluating the firefly paths and further performing mutation operations to generate neighborhood solutions for processing. This application combines the firefly algorithm with a variable neighborhood search algorithm to achieve efficient path optimization and charging scheduling, achieving a balance between minimizing total cost and minimizing infection risk. Through the dynamic synergy of path optimization and mobile charging, it solves problems such as short driving range, strict time windows, and high infection risk, thereby improving the transportation efficiency of medical waste and reducing operating costs. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a collaborative scheduling method for an electric medical waste transport vehicle and a mobile battery exchange vehicle provided in an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of the structure of a collaborative scheduling system for an electric medical waste transport vehicle and a mobile battery exchange vehicle provided in an embodiment of this application;

[0015] Figure 3This is a flowchart illustrating another method for the coordinated scheduling of an electric medical waste transport vehicle and a mobile battery exchange vehicle provided in this application embodiment;

[0016] Figure 4 This is an exemplary initial route diagram of 10 medical institutions and medical waste treatment centers provided in this application embodiment;

[0017] Figure 5 Yes Figure 4 A diagram illustrating the path reversal operation on the initial path;

[0018] Figure 6 Yes Figure 4 A diagram illustrating the random swapping operation of the initial path in the diagram;

[0019] Figure 7 Yes Figure 4 A diagram illustrating the path segment insertion operation on the initial path;

[0020] Figure 8 Yes Figure 4 A schematic diagram illustrating the path subsequence shifting operation performed on the initial path;

[0021] Figure 9 Yes Figure 4 A schematic diagram illustrating the path jump connection operation performed on the initial path in the diagram;

[0022] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0025] This application provides a collaborative scheduling method for electric medical waste transport vehicles and mobile battery swapping vehicles, which solves the problem that existing electric medical waste transport systems suffer from uneven distribution of fixed charging stations, long queuing times, and long charging times, leading to a decline in electric vehicle service quality and potential violation of customer service time windows.

[0026] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0027] The following section first introduces a method for the coordinated scheduling of an electric medical waste transport vehicle and a mobile battery exchange vehicle, as provided in the embodiments of this application.

[0028] This application provides a flowchart illustrating a collaborative scheduling method for an electric medical waste transport vehicle and a mobile battery swapping vehicle, as shown in the embodiments below. Figure 1 As shown, the collaborative scheduling method may include the following steps S110-S160.

[0029] S110. Obtain customer data and initialize algorithm parameters, setting the maximum number of iterations t. max The initial iteration count t = 1 is used to control the iteration process of the algorithm;

[0030] S120. Based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV).

[0031] S130. Calculate the total cost F and infection risk I for each firefly in the population, and obtain the comprehensive objective function value f(x). i The firefly path with the smallest objective function value is selected as the current global optimal solution g. best ;

[0032] S140. Select a mutation operation N based on the weights and selection probabilities of the mutation operations. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum.

[0033] S150. Update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, and adjust the solution and check its feasibility in combination with the mutation operation during the position update process.

[0034] S160. Let t = t + 1, determine if t ≥ t. max If the condition is met, the algorithm terminates and outputs the currently found global optimal solution g. best Otherwise, continue iterative optimization.

[0035] The above are specific implementation methods of the collaborative scheduling method between electric medical waste transport vehicles and mobile battery swapping vehicles provided in this application. It can be understood that this application introduces mobile battery swapping vehicles (BSVs) to provide fast battery swapping services for electric medical waste transport vehicles (EMWTVs), overcoming the shortcomings of existing electric medical waste transport systems such as uneven distribution of fixed charging stations, long queuing times, long charging times, and high initial investment costs. These problems lead to a decline in the service quality of electric vehicles and may violate the requirements of customer service time windows.

[0036] Please refer to the above as well. Figure 1 and Figure 3 To improve the response speed and service level of the fleet, this application constructs an initial feasible solution based on the firefly algorithm and the nearest neighbor method; calculates the total cost and infection risk of each firefly in the population to determine the objective function value, then evaluates the firefly path, and further performs mutation operations to generate neighborhood solutions for processing.

[0037] Based on this, this application combines the firefly algorithm and the variable neighborhood search algorithm to achieve efficient path optimization and charging scheduling, so as to achieve a balance between minimizing total cost and minimizing infection risk. Through the dynamic synergy of path optimization and mobile charging, it solves problems such as short driving range, strict time window and high infection risk, improves the transportation efficiency of medical waste, reduces operating costs, and ensures medical safety. It provides a more efficient and flexible solution for medical waste management, improves the level of public health safety, reduces operating costs and environmental impact, and at the same time reduces environmental pollution and promotes green and sustainable development.

[0038] Furthermore, this application addresses the shortcomings of existing charging strategies, which rely solely on fixed charging stations and suffer from insufficient algorithm optimization. It introduces a novel charging mode—a mobile battery swapping vehicle—and an improved FA-VNS hybrid algorithm to the electric medical waste transport vehicle problem. The FA-VNS hybrid algorithm combines the Firefly Algorithm (FA) with the Variable Neighborhood Search (VNS) algorithm. Although still employing a single battery swapping method, it innovatively achieves a shift from fixed charging to mobile battery swapping, significantly shortening energy replenishment time and solving the core problems of limited fixed charging station deployment and low charging efficiency. Combined with improvements in global search and convergence speed of the FA-VNS algorithm, this application enhances the operational efficiency and scheduling flexibility of the transport vehicle to a certain extent, providing a new approach for optimizing electric medical waste transportation systems. Future research and inventions can explore extended solutions such as hybrid charging networks based on this foundation.

[0039] In one example, customer data includes the number of medical institutions N, and the location information (x) of each medical institution. i y i Service demand D i Service time window (e i ,l i ) and exposed population value E ei ; where e i and l i These are the earliest service start time and the latest service end time for client node i, respectively.

[0040] Algorithm parameters include: the maximum load capacity C of the electric medical waste transport vehicle (EMWTV), the maximum battery capacity Q, the lower limit coefficient ρ of the battery capacity, and the maximum number of batteries U of the mobile battery swapping vehicle (BSV). n Fixed cost parameter C of the electric medical waste transport vehicle (EMWTV) K Fixed cost parameter C of mobile battery swapping vehicle (BSV) B Variable cost parameter C of the electric medical waste transport vehicle (EMWTV) k Variable cost parameter C of mobile battery swapping vehicle (BSV) b Battery replacement cost parameter C s Weighting factor α used to balance cost and infection risk, and disease infection probability I. ij The driving speed v of the electric medical waste transport vehicle EMWTV k The travel speed v of the mobile battery swapping vehicle (BSV) b .

[0041] In some embodiments, based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV). Specifically, the aforementioned S120 may include the following steps:

[0042] S210. For each firefly z (z = 1, 2, ..., popsize) in the population, initialize the path list P of the electric medical waste transport vehicle EMWTV. z Set the current location of EMWTV to point 0 to represent the medical waste treatment center, set the load w=0, and the power level... Construct a candidate set S = {1, 2, ..., N}, where N is the number of medical institutions excluding medical waste treatment centers; popsize represents the population size;

[0043] S220. Based on the optimized nearest neighbor method, nodes in the candidate set S are screened to simultaneously satisfy the load capacity condition, power consumption condition, and time window condition; if the load capacity condition, power consumption condition, and time window condition are all satisfied, node j is added to the initial path P of the electric medical waste transport vehicle EMWTV corresponding to firefly z. z Meanwhile, information related to the electric medical waste transport vehicle EMWTV will be updated.

[0044] S230, Under the condition that the load-bearing condition is not met, i.e., w+D j If >C, then construction of the current path will stop; if D > j The service demand for node j;

[0045] If the power requirements are not met, that is... At the same time, calculate and compare the cost of dispatching the mobile battery swapping vehicle (BSV) and the cost of dispatching the new electric medical waste transport vehicle (EMWTV). If dispatching the BSV is cheaper, send a battery swapping request to the BSV in advance, and update the battery level information after accepting the battery swapping service. Set the charge level of the electric medical waste transport vehicle (EMWTV) when it leaves node j, remove node j from the candidate set S, and continue path construction; otherwise, stop the current path construction.

[0046] Calculate the time window penalty cost when the time window condition is not met. The cost of dispatching the new electric medical waste transport vehicle (EMWTV) is compared with that of the two; among which, p j The time window penalty cost coefficient. As decision variables, This indicates that the electric medical waste transport vehicle k did not violate the time window of node j; otherwise... If the penalty cost within the time window is low, continue building the path while recording the penalty cost; otherwise, stop building the current path.

[0047] S240. If node j is added to the initial path P of the electric medical waste transport vehicle (EMWTV) z In the middle, update the time B when the electric medical waste transport vehicle EMWTV leaves node j. j B j ≥max{e j +s j ,e j +s j +x′ ij t s A j +s j +x′ ij t s}, s j and t s These represent the service time of the electric medical waste transport vehicle (EMWTV) at node j and the service time required to perform one battery swap service, respectively.

[0048] In one example, the S240 process involves three scenarios:

[0049] 1. The electric medical waste transport vehicle (EMWTV) arrives at node j earlier than the earliest service start time, i.e., A. j <e j No need to accept battery swapping service or accepted battery swapping service but A j ≤e j -t s If the mobile battery swapping vehicle (BSV) performs a battery swap for the electric medical waste transport vehicle (EMWTV) while it is waiting for the medical waste loading service to begin, then the time when the electric medical waste transport vehicle (EMWTV) leaves node j is B. j =e j +s j .

[0050] 2. The electric medical waste transport vehicle (EMWTV) arrives at node j, i.e., A, exactly at the earliest service start time. j =e j If a battery swapping service is performed, the time it takes for the electric medical waste transport vehicle (EMWTV) to leave node j is B. j =e j +s j +t s If battery replacement service is not required, the time for the electric medical waste transport vehicle EMWTV to leave node j is B. j =e j +s j .

[0051] 3. The electric medical waste transport vehicle (EMWTV) arrives at node j later than the earliest service start time, i.e., A. j>e j If a battery swapping service is performed, the time it takes for the electric medical waste transport vehicle (EMWTV) to leave node j is B. j =A j +s j +t s If battery replacement service is not required, the time for the electric medical waste transport vehicle EMWTV to leave node j is B. j =A j +s j Then, it continues to determine whether the next node can be added to the initial path of the Electric Medical Waste Transport Vehicle (EMWTV).

[0052] S250. When there are no available nodes in the candidate set S, add the starting point 0 to the initial path P. z Filter the set of paths, remove invalid paths that only contain the starting point, and keep paths that satisfy |P z The path with |>2 is determined to be the initial feasible solution of EMWTV constructed by firefly z;

[0053] S260. Based on the received battery swapping request and the constructed initial feasible solution of EMWTV, determine the node whose battery power is below the threshold and needs battery replacement as the service node of the mobile battery swapping vehicle (BSV), and ensure that the mobile battery swapping vehicle (BSV) meets the time conditions.

[0054] Specifically, based on the received battery swapping requests and the constructed initial feasible solution of EMWTV, this application determines the nodes whose battery power is below a threshold and require battery replacement as BSV serving nodes; at the same time, it ensures that the BSV meets the time condition, i.e. The time A′ for BSV to reach node j j The time should be earlier than the time when EMWTV arrives at node j, so that it can wait for the battery replacement service for EMWTV; M is a sufficiently large positive number, x ij and x′ ij All are decision variables, x ij =1 indicates that an electric medical waste transport vehicle (EMWTV) exists and travels from node i to node j; otherwise, x = 1. ij =0; x′ ij =1 indicates that a mobile battery swapping vehicle (BSV) exists and travels from node i to node j; otherwise, x′ ij =0.

[0055] Upon receiving a battery swap request, the number of customers C that meet the time requirements and have already been served is... n Then add node j to the initial path P′ of the BSV and update the number of batteries loaded at node j. ​Increment the number of customers C served by 1; calculate the travel time from the last served node i to node j. Update the time when BSV arrives at node j v b and B′ i U represents the travel speed of the BSV and the time it takes for the BSV to leave node i, respectively. n The maximum number of batteries that the BSV load can use for battery swapping services; if one of these conditions is not met, the construction of the current path is stopped.

[0056] S270. If node j is added to the initial path P′ of the mobile battery swapping vehicle (BSV), determine the start time of the BSV battery swapping service. And update the time when the mobile battery swapping vehicle (BSV) leaves node j. Then, it continues to determine whether the next node can be added to the initial path P′ of the mobile battery swapping vehicle (BSV).

[0057] S280. If there are no available nodes in the candidate set S, add the starting point 0 to P′, filter the path set, remove invalid paths that only contain the starting point, and keep the paths that satisfy |P′|>2 to obtain the initial feasible solution of BSV.

[0058] In the embodiments of this application, it can be understood that, for each firefly z in the population, this application constructs an initial solution for the Electric Medical Waste Transport Vehicle (EMWTV) by combining the nearest neighbor method and considering vehicle capacity and time window constraints; for the path construction of the Mobile Battery Exchange Vehicle (BSV), this application constructs the requirements and time window information for relevant nodes based on the battery exchange events in the EMWTV path, and then adds nodes until a complete and constrained initial driving route is formed.

[0059] In one example, in the aforementioned S270, there are three scenarios regarding the time when the mobile battery swapping vehicle starts the battery swapping service at node i:

[0060] Scenario 1: The electric medical waste transport vehicle (EMWTV) arrives at node j (A) earlier than the earliest service start time. j <e j And A j ≤e j -t s If the time the electric medical waste transport vehicle (EMWTV) waits for the medical waste loading service to begin is sufficient for the mobile battery swapping vehicle (BSV) to provide battery swapping services, then the start time of the battery swapping service by the BSV at node j is the time when the electric medical waste transport vehicle (EMWTV) arrives at node j.

[0061] Scenario 2: When the arrival time of the electric medical waste transport vehicle (EMWTV) at node j plus the battery swapping service time is later than the earliest service start time of the medical institution, and the arrival time of the electric medical waste transport vehicle is earlier than the earliest service time of the medical institution, i.e., e j -t s j ≤e j Therefore, the start time of the battery swapping service at node j for the mobile battery swapping vehicle (BSV) is the earliest service start time of the medical institution plus the medical waste loading service time.

[0062] Scenario 3: The electric medical waste transport vehicle (EMWTV) arrives at node j (A) later than the earliest service start time. j >e j Therefore, the start time of the battery swapping service at the node for the mobile battery swapping vehicle (BSV) is when the electric medical waste transport vehicle (EMWTV) finishes its medical waste loading service, i.e.

[0063] In some embodiments, the aforementioned total cost F includes vehicle fixed costs, transportation costs, battery replacement costs, and time window penalty costs. The total cost F is the total cost calculated for the path of each firefly in the population; the infection risk I is determined by the exposed population value E. i At that time, the medical waste load capacity was w i and the probability of infection E eij Joint decision;

[0064] The load condition satisfies the expression: w i +D j ≤C, where w i The load capacity of the electric medical waste transport vehicle EMWTV when it leaves node i, D j Let C be the service demand of node j, and C be the maximum load capacity of the electric medical waste transport vehicle EMWTV.

[0065] The power condition satisfies the expression: ρ represents the lower limit of the battery capacity of the electric medical waste transport vehicle (EMWTV). The charge level when it reaches node j;

[0066] The time window condition must satisfy the expression: the arrival time must be within the customer service time window, i.e., e j ≤A j j ≤l j e j For the earliest start time of the service, l j The latest end time of service; A j and B j These represent the arrival and departure times of the electric medical waste transport vehicle EMWTV at node j, respectively.​​

[0067] In some embodiments, updating EMWTV-related information for electric medical waste transport vehicles includes: updating load information, updating battery level information, and updating time information; updating load information includes: updating vehicle load w j =w i +D j D j The service demand for node j;

[0068] Updated power information includes: based on the load-dependent energy consumption formula E ij ≈(h+ωw i )d ij Calculate the energy consumption E from the current node i to node j. ij This will update the battery level. Where h and ω represent the energy consumption per unit distance of the EMWTV electric medical waste transport vehicle when it is unloaded and the additional energy consumption per unit distance per unit load, respectively; w i d represents the vehicle load of the electric medical waste transport vehicle EMWTV when it leaves node i. ij Let be the distance between node i and node j; and These represent the battery levels of the electric medical waste transport vehicle EMWTV when it leaves node i and arrives at node j, respectively.

[0069] Update time information includes: calculating the travel time of the electric medical waste transport vehicle (EMWTV) from the current client node i to the next client node j. Among them, v k Update the arrival time of the electric medical waste transport vehicle EMWTV at node j to reflect its travel speed.

[0070] In some embodiments, the aforementioned calculation of the total cost F and infection risk I for each firefly in the population yields a comprehensive objective function value f(x). i The firefly path with the smallest objective function value is selected as the current global optimal solution g. best Specifically, the aforementioned S130 may include the following steps:

[0071] S310. Calculate the total cost F of each firefly's path:

[0072] F = DC + FC + BC + PC

[0073] Where DC is the variable cost of vehicle transportation and satisfies the following expression:

[0074]

[0075] FC is the fixed cost of the vehicle and satisfies the expression:

[0076]

[0077] In the formula, x 0j This indicates that the electric medical waste transport vehicle (EMWTV) moves from the medical waste treatment center to node j, x′. 0j This indicates that the mobile battery swapping vehicle (BSV) moves from the medical waste treatment center to node j.

[0078] BC represents the battery replacement cost and satisfies the expression:

[0079]

[0080] In the formula, x′ ij This indicates that the mobile battery swapping vehicle (BSV) moves from node i to node j.

[0081] PC is the time window penalty cost and satisfies the expression:

[0082]

[0083] In the formula, p i It is the time window penalty cost coefficient. It is a decision variable; if the electric medical waste transport vehicle (EMWTV) violates the time window (e i ,l i The constraint has a value of 1 if it is set to 1 otherwise it is set to 0.

[0084] S320. Calculate the infection risk I for each firefly's path:

[0085]

[0086] In the formula, E eij Let be the number of exposed people traveling from node i to node j, i.e., the number of exposed people on arc (i, j).

[0087] S330. Calculate the value of the comprehensive objective function:

[0088] f(x i )=α×F+(1-α)×I

[0089] S340. Determine the current global optimal solution: Traverse all firefly paths, for each path P k The corresponding comprehensive objective function value f k If f k <f best Then update the objective function value f of the global optimal solution. best =f k And update the globally optimal path P. best =Pk .

[0090] In some embodiments, a mutation operation N is selected based on the weight of the mutation operation and its selection probability. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum. Specifically, S140 may include the following steps:

[0091] S410. Regarding the current globally optimal solution g best Perform a variable neighborhood search (VNS).

[0092] S420. A local search is performed in the solution space using five preset neighborhood operations to find a better path scheme. The five neighborhood operations include: path reversal operation, random swap operation, path segment insertion operation, path subsequence shift operation, and path jump connection operation.

[0093] S430. After each neighborhood search, recalculate the objective function value. If a better solution is found, update the global optimal solution g. best .

[0094] S440. The weights of different mutation operations are dynamically adjusted according to the quality of the mutation results. When the mutation results are excellent, the weight of the mutation operation is increased; when the mutation results are poor, the weight of the mutation operation is decreased.

[0095] In the embodiments of this application, it can be understood that the path reversal operation is used to reverse the order of a segment of nodes in the vehicle's driving path, the random swap operation is used to randomly select two nodes in the path and swap their positions, the path segment insertion operation is used to insert a segment of a path into another position in the path, the path subsequence shift operation is used to move a subsequence of a path to a different position in another path, and the path jump connection operation is used to create new connections between different paths, skipping some intermediate nodes.

[0096] In one example, the specific operation of the aforementioned S140 can be as follows:

[0097] S401, Define the neighborhood structure N k k = 1, 2, 3, 4, 5, corresponding to different neighborhood operations; initialize k = 1, and set the maximum number of iterations t′. max Let the iteration number t′=0.

[0098] S402, when t′ <t′ max If the condition is met, proceed with S403; otherwise, end the search.

[0099] S403. Based on the neighborhood index k and the weights of each neighborhood operation, select a neighborhood operation to generate a new solution P. new :

[0100] Initialize the weights of the five neighborhood operations, setting them to the same value at the beginning, and then select one neighborhood operation for mutation processing based on the weight;

[0101] After performing the mutation operation, check whether the new path contains all nodes and has no duplicates. If it does not meet the requirements, select a new neighborhood to generate a new solution; if it does meet the requirements, continue to the next step.

[0102] S404, Check the newly generated solution P new Check if the constraints such as capacity and time window are met; if they are met, proceed to S405; if not, return to S403 to generate a new solution.

[0103] S405, if P new The constraints are satisfied, according to the formula:

[0104] f = α × F new +(1-α)×I new

[0105] Calculate its objective function value f new F new For the total cost of the new solution, I new The newly identified infection risk;

[0106] S406. Change the objective function value f of the new solution. new The objective function value f of the current global optimal solution best Comparison:

[0107] If f new <f best Then update the global optimal solution, let P best =P new f best =f new And set the neighborhood index k = 1, and then proceed to step S407;

[0108] If f new ≥f best If k > 5, then let k = k + 1; if k > 5, then let k = 1, and then proceed to step S407.

[0109] S407. Let the iteration count t′ = t′ + 1, then return to S402 to continue iterating and judging until the maximum iteration count t′ is reached. max .

[0110] For example, to optimize the scheduling scheme of electric medical waste transport vehicles (EMWTVs) and mobile battery swapping vehicles (BSVs), this application comprehensively considers scheduling costs and potential infection risks during medical waste transportation as objective functions. The problem is described as follows: Given the medical waste transportation needs of N medical institutions, it is necessary to rationally schedule electric medical waste transport vehicles (EMWTVs) and mobile battery swapping vehicles (BSVs) to complete the service. Each medical institution has specific geographical coordinates, daily medical waste generation volume, service time window requirements, and the exposed population value of its area. The electric medical waste transport vehicle (EMWTV) is responsible for the collection and transportation of medical waste. Its operation is constrained by both battery capacity and load capacity limitations. When the battery level is below a threshold, the mobile battery swapping vehicle (BSV) needs to provide battery swapping services. The mobile battery swapping vehicle (BSV) dynamically adjusts its service route based on the battery status of the electric medical waste transport vehicle (EMWTV) to ensure the continuity of the transportation task. The problem assumptions are as follows:

[0111] (1) All electric medical waste transport vehicles are identical in specifications and maintain a constant speed.

[0112] (2) All mobile battery swapping vehicles are identical in specifications and can carry up to 5 batteries for battery swapping services on a single trip. They maintain a constant speed during the trip and do not require energy replenishment.

[0113] (3) Electric medical waste transport vehicles have a maximum waste handling capacity. If the capacity is exceeded, the vehicle must be taken to a medical waste treatment center.

[0114] (4) The total capacity of the medical waste treatment center must be sufficient to handle all collected waste.

[0115] (5) The energy consumption of electric medical waste transport vehicles is directly proportional to the weight of the waste they carry.

[0116] (6) All vehicles depart from and eventually return to the medical waste treatment center.

[0117] This example analyzes the collaborative scheduling problem between Electric Medical Waste Transport Vehicles (EMWTVs) and Mobile Battery Swapping Vehicles (BSVs). Input information from 10 medical institutions includes their geographical coordinates, daily medical waste generation, service time windows, and the exposed population in their respective areas.

[0118] After algorithmic calculation, the optimal solution obtained is as follows:

[0119] The driving path of EMWTV-1 is: [0,1,3,4,6,7,0];

[0120] The driving route of EMWTV-2 is: [0,8,5,10,9,2,0];

[0121] The service path for BSV-1 is: [0,2,0];

[0122] In this system, 0 represents the medical waste treatment center, numbers 1-10 represent the codes of medical institutions, EMWTV1 and EMWTV2 represent two electric medical waste transport vehicles, and BSV-1 represents one mobile battery swapping vehicle. The service path of BSV-1 is [0,2,0], indicating that the mobile battery swapping vehicle (BSV) moves from the medical waste treatment center to medical institution 2 to perform a battery swap for EMWTV2 and then returns to the medical waste treatment center. EMWTV1 has sufficient power throughout the process and does not require battery swapping. This optimal solution achieves comprehensive optimization of scheduling costs and potential infection risks during medical waste transportation under conditions such as vehicle load limits, power limits, and time window constraints, providing a valuable reference for actual medical waste transportation scheduling.

[0123] Please refer to the above as well. Figures 4-9 It should be noted that `reverse` represents a path reversal operation; `random_swap` represents a random swap operation; `Or_opt_mutation` represents a path segment insertion operation; `route_subsequence_shift` represents a path subsequence shift operation; and `route_jump_connection` represents a path jump connection operation. The neighborhood structure N is defined in the aforementioned S401. k The process is as follows:

[0124] N1: Path reversal operation; please refer to... Figure 5 Randomly select a path. When the number of nodes on the path is greater than 3, randomly select two positions i and j from the non-starting and non-ending parts of the path. Reverse the order of the sub-paths [i,j] between these two positions to obtain a new solution P. new For example, if you select the subpath [4,6,7] in the path [0,2,3,4,6,7,0], it will become [0,2,3,7,6,4,0] after reversal.

[0125] N2: Random swap operation; please refer to... Figure 6 Randomly select a path. When the number of nodes on the path is greater than 3, randomly select two positions i and j from the non-starting and non-ending parts of the path, and swap the nodes at these two positions to obtain a new solution P. new For example, swapping the positions of nodes 3 and 6 in the path [0,2,3,4,6,7,0] results in [0,2,6,4,3,7,0].

[0126] N3: Or-opt operation for path segments; please refer to... Figure 7Randomly select a path. When the number of nodes on the path is greater than 3, randomly select two positions i and j from the non-starting and non-ending parts of the path. Cut off the sub-path [i,j] between these two positions and randomly insert it into other positions on the path to obtain a new solution P. new For example, inserting [8,5,10] from the path [0,8,5,10,9,1,0] between nodes 9 and 1 results in the new path [0,9,8,5,10,1,0].

[0127] N4: Path subsequence shifting operation; please refer to... Figure 8 When P best When the number of paths is greater than 1, randomly select two paths. From the first path, if the number of nodes in that path is greater than 3, randomly select two positions i and j from the non-starting and non-ending parts, extract the subsequence [i,j] between these two positions, and move it to different positions on the second path to obtain a new solution P. new For example, in path 2: [0,8,5,10,9,1,0], a subsequence [8] is selected and moved to path 1: [0,2,3,4,6,7,0] to obtain new paths: [0,2,8,3,4,6,7,0] and [0,5,10,9,1,0].

[0128] N5: Path jump connection operation; please refer to... Figure 9 When P best When the number of paths is greater than 1, randomly select two paths. When the number of nodes on both paths is greater than 3, randomly select position i from the non-starting and non-ending part of the first path, and randomly select position j from the non-starting and non-ending part of the second path. Connect the part before i on the first path with the part after j on the second path to form a new path, replace the first path and delete the second path to obtain a new solution P. new For example, [0,8,5,10,9,1,0] selected from path 2: [0,8,5,10,9,1,0] and [0,2] selected from path 1: [0,2,3,4,6,7,0] are jumped and connected to form a new path [0,8,5,10,9,2,0]. The remaining path segments are also jumped and connected to form a new path [0,1,3,4,6,7,0].

[0129] When k=1, the path reversal operation is selected, and the operation is performed according to the N1 rule;

[0130] When k=2, the random swap operation is selected and the operation is performed according to the N2 rule;

[0131] When k=3, the Or-opt operation is selected for the path segment and operated according to the N3 rule;

[0132] When k=4, the path subsequence shift operation is selected and operated according to the N4 rule;

[0133] When k=5, the path jump connection operation is selected and operated according to the N5 rule.

[0134] In some embodiments, the position of each firefly in the population is updated according to the attraction mechanism of the firefly algorithm, and during the position update process, the solution is adjusted and its feasibility is checked by combining mutation operations. That is, the aforementioned S150 may specifically include the following steps:

[0135] S510. For each firefly j in the population, calculate the objective function value f corresponding to firefly j. j As the corresponding overall score;

[0136] S520. Iterate through the other fireflies j′ (j≠j′) and calculate the overall score f of the other fireflies j′. j′ If f j′ <f j If so, then firefly j′ is brighter than firefly j;

[0137] S530. After determining firefly j′, assume that the partial path constructed by the current firefly is... The pathways of other fireflies in the population are as follows Using the defined path difference metric function The path difference r between fireflies j and j′ is approximated by the sum of path lengths. jj′ The attraction β of the current firefly to other fireflies is calculated based on the preset attraction formula.

[0138] In one example, the attraction β satisfies the expression:

[0139] β=β0exp(-γr jj′ )

[0140] Wherein, β0 is the initial attraction, which is used to determine the degree of attraction between fireflies at a distance of 0, and γ is the light intensity absorption coefficient, which is used to characterize the rate at which the attraction decreases with increasing distance.

[0141] S540. For the EMWTV path solutions corresponding to fireflies j and j′, traverse their sub-paths. For each node position i in the sub-path, generate a random number r∈[0,1]. If r<β, select a node from the sub-path of firefly j and add it to the new path. If r≥β, select a node from the sub-path of firefly j′ and add it to the new path to generate a new EMWTV path.

[0142] S550. Perform a mutation operation on the updated path, based on the selection probability P of the mutation operation. k Select mutation type N k k = 1, 2, 3, 4, 5, further adjust the path;

[0143] S560. Perform a feasibility test on the new EMWTV path and BSV path, checking whether the path meets the vehicle load limit, power limit, and time window constraints; if it meets the constraints, use it as the updated position of Firefly j; if it does not meet the constraints, try to re-mutate or retain the original position.

[0144] In this embodiment, it is understood that the position of each firefly in the population is updated according to the attraction mechanism of the firefly algorithm. Dimmer fireflies will move towards brighter fireflies, with the movement distance determined by the attraction intensity. During the position update process, this application incorporates mutation operations to adjust the solution. After the position update, the newly generated solution undergoes a rigorous feasibility check, comprehensively verifying whether it meets all predetermined conditions such as vehicle load limits, battery limits, and time window constraints to ensure the validity of the solution.

[0145] In some embodiments, this application provides a collaborative scheduling system 600 for electric medical waste transport vehicles and mobile battery swapping vehicles, such as... Figure 2 As shown, the collaborative scheduling system 600 may include the following modules:

[0146] Module 610 is used to acquire customer data and initialize algorithm parameters, setting the maximum number of iterations t. max The initial iteration count t = 1 is used to control the iteration process of the algorithm;

[0147] Module 620 is used to construct initial feasible solutions based on the firefly algorithm and the nearest neighbor method to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV).

[0148] The calculation selection module 630 is used to calculate the total cost F and infection risk I for each firefly in the population, and obtain the comprehensive objective function value f(x). i The firefly path with the smallest objective function value is selected as the current global optimal solution g. best ;

[0149] The first update module 640 is used to select a mutation operation N based on the weight and selection probability of the mutation operation. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum.

[0150] The second update module 650 is used to update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, and to adjust the solution and verify its feasibility in combination with mutation operation during the position update process.

[0151] Optimization module 660 is used to determine whether t ≥ t when t = t + 1.max If the condition is met, the algorithm terminates and outputs the currently found global optimal solution g. best Otherwise, continue iterative optimization.

[0152] According to embodiments of this application, any and multiple modules among the acquisition module 610, construction module 620, calculation and selection module 630, first update module 640, second update module 650, and judgment and optimization module 660 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

[0153] Figure 2 Each module in the system shown has the function of implementing each step in the aforementioned collaborative scheduling method of electric medical waste transport vehicle and mobile battery exchange vehicle, and can achieve the corresponding technical effect. For the sake of brevity, it will not be elaborated here.

[0154] In some embodiments, this application provides an electronic device, the structural schematic of which is shown below. Figure 10 As shown.

[0155] The electronic device may include a processor 710 and a memory 720 storing computer program instructions.

[0156] Specifically, the processor 710 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0157] Memory 720 may include mass storage for data or instructions. For example, and not limitingly, memory 720 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 720 may include removable or non-removable (or fixed) media. Where appropriate, memory 720 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 720 is non-volatile solid-state memory.

[0158] The memory 720 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, the memory 720 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the above embodiments of the coordinated scheduling method for electric medical waste transport vehicles and mobile battery swapping vehicles.

[0159] The processor 710 reads and executes computer program instructions stored in the memory 720 to implement any of the collaborative scheduling methods for electric medical waste transport vehicles and mobile battery exchange vehicles in the above embodiments.

[0160] In one example, the electronic device may also include a communication interface 730 and a bus 700. For example, Figure 10 As shown, the processor 710, memory 720, and communication interface 730 are connected via bus 700 and communicate with each other.

[0161] The communication interface 730 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0162] Bus 700 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 700 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0163] Furthermore, in conjunction with the collaborative scheduling method of electric medical waste transport vehicles and mobile battery exchange vehicles in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the collaborative scheduling methods of electric medical waste transport vehicles and mobile battery exchange vehicles in the above embodiments.

[0164] In summary, compared with the prior art, this application has the following beneficial effects:

[0165] 1. In the research on the problems of electric medical waste transport vehicles, this application introduces a novel strategy of mobile battery swapping vehicle charging, providing a rapid battery swapping service for the electric medical waste transport vehicle (EMWTV). This strategy effectively improves the flexibility and efficiency of vehicle power replenishment during transport, reduces transport delays caused by insufficient power, helps medical waste reach the treatment location more promptly, provides a new solution for medical waste transport, and improves transport efficiency to a certain extent.

[0166] 2. This application fully considers multiple objectives for optimization during the transportation process, incorporating cost, infection risk, and load-dependent energy consumption into a unified consideration. Through precise cost accounting and control measures, operating costs during transportation are reduced; scientific route planning and scheduling methods are used to avoid densely populated areas as much as possible, reducing infection risk; and based on research on load-dependent energy consumption, vehicle routes and load allocation are rationally planned to reduce energy consumption, moving towards a balance between economic, safety, and environmental benefits.

[0167] 3. This application combines the Firefly Algorithm (FA) with the Variable Neighborhood Search (VNS) algorithm and applies it to the problem scenario of electric medical waste transport vehicles. This algorithm, relying on its unique search mechanism, achieves a balance between minimizing total cost and minimizing infection risk, and can quickly generate high-quality path planning and scheduling schemes under complex transport conditions. Through in-depth optimization of the algorithm and in conjunction with actual transport needs, and through the dynamic synergy of path optimization and mobile charging, it solves problems such as short driving range, strict time windows, and high infection risk, further reducing transport costs and making the transport scheme more scientific and reasonable.

Claims

1. A method for coordinated scheduling of electric medical waste transport vehicles and mobile battery swapping vehicles, characterized in that, include: Acquire customer data and initialize algorithm parameters, setting the maximum number of iterations. The initial iteration count t=1 is used to control the iteration process of the algorithm; Based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV). Calculate the total cost F and infection risk I for each firefly in the population, and obtain the comprehensive objective function value. The firefly path with the smallest objective function value is selected as the current global optimal solution. ; A mutation operation N is selected based on its weight and selection probability. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum. The position of each firefly in the population is updated according to the attraction mechanism of the firefly algorithm, and the solution is adjusted and its feasibility is checked by combining mutation operation during the position update process. Let t = t + 1, determine if t ≥ If the condition is met, the algorithm terminates and outputs the currently found globally optimal solution. Otherwise, continue iterative optimization; The process of updating the position of each firefly in the population based on the attraction mechanism of the firefly algorithm, and adjusting and verifying the solution by incorporating mutation operations during the position update process, includes: For each firefly j in the population, calculate the objective function value of the solution corresponding to firefly j. As the corresponding overall score; Explore other fireflies (j) ), calculate other fireflies Overall score ;like Then it can be determined that the firefly is the firefly. Brighter than a firefly; In identifying fireflies Then, assuming the current firefly constructs a partial path as follows: The pathways of other fireflies in the population are as follows: ; Using the defined path difference metric function The path length sum is used to approximate the calculation of firefly j and . Corresponding path differences The attraction level of a firefly to other fireflies is calculated based on a preset attraction formula. ; For fireflies j and For the corresponding EMWTV path solution, traverse their sub-paths, and for each node position i in the sub-path, generate a random number. ;like If so, then select a node from the subpath of firefly j and add it to the new path; if Then from fireflies Select nodes from the sub-paths and add them to the new path to generate a new EMWTV path; Perform a mutation operation on the updated path, based on the probability of the mutation operation being selected. Select mutation type Further adjustments to the route were made; among them, This indicates a path reversal operation; This indicates a random swap operation; This indicates the Or-opt operation for the path segment; This indicates a path subsequence shift operation; This indicates a path jump join operation; Feasibility tests are performed on the new EMWTV and BSV paths to check if they meet vehicle load limits, battery limits, and time window constraints. If they do, they are used as the updated positions for Firefly j; otherwise, re-mutation is attempted or the original positions are retained. attraction Satisfying the expression: = ; in, This is the initial attraction level, used to determine the degree of attraction between fireflies at a distance of 0. It is the light intensity absorption coefficient and is used to characterize the rate at which attraction decreases with increasing distance.

2. The method of coordinated scheduling of an electrically powered medical waste transporter with a mobile battery exchange vehicle of claim 1, wherein, The customer data includes the number of medical institutions N, and the location information of each medical institution ( , ), service demand Service Hour Window ( ) and exposed population value ; The algorithm parameters include: the maximum load capacity C of the electric medical waste transport vehicle (EMWTV), the maximum battery capacity Q, and the lower limit coefficient of battery power. Maximum number of batteries in a mobile battery swapping vehicle (BSV) load Fixed cost parameters of the Electric Medical Waste Transport Vehicle (EMWTV) Fixed cost parameters of mobile battery swapping vehicle (BSV) Variable cost parameters of the Electric Medical Waste Transport Vehicle (EMWTV) Variable cost parameters of mobile battery swapping vehicle (BSV) Battery replacement cost parameters Weighting factor α used to balance cost and infection risk, and the probability of disease infection. The driving speed of the electric medical waste transport vehicle EMWTV The speed of the mobile battery swapping vehicle (BSV) .

3. The method of coordinating the scheduling of an electrically powered medical waste transporter and a mobile battery exchange vehicle of claim 1, wherein, The initial feasible solution constructed based on the firefly algorithm and the nearest neighbor method to characterize the initial path of the electric medical waste transport vehicle (EMWTV) and the initial path of the mobile battery swapping vehicle (BSV) includes: For each firefly z (z=1,2,...,popsize) in the population, initialize the path list of the Electric Medical Waste Transport Vehicle (EMWTV). Set the current location of EMWTV to 0 to represent the medical waste treatment center, set the load w=0, and the power level... Construct a candidate set S={1,2,...,N}, where N is the number of medical institutions excluding medical waste treatment centers; popsize represents the population size; Based on the optimized nearest neighbor method, nodes in the candidate set S are screened to simultaneously satisfy load conditions, power conditions, and time window conditions. If all three conditions are met, node j is added to the initial path of the electric medical waste transport vehicle (EMWTV) corresponding to firefly z. Meanwhile, information related to the electric medical waste transport vehicle EMWTV will be updated. If the aforementioned load-bearing conditions are not met, i.e. If this happens, then construction of the current path will be stopped. The service demand for node j; If the aforementioned power condition is not met, i.e. At the same time, calculate and compare the cost of dispatching the mobile battery swapping vehicle (BSV) and the cost of dispatching the new electric medical waste transport vehicle (EMWTV). If dispatching the BSV is cheaper, send a battery swapping request to the BSV in advance, and update the battery level information after accepting the battery swapping service. , Set the charge level of the electric medical waste transport vehicle (EMWTV) when it leaves node j, remove node j from the candidate set S, and continue path construction; otherwise, stop the current path construction. Calculate the time window penalty cost if the aforementioned time window conditions are not met. The cost of dispatching the new electric medical waste transport vehicle (EMWTV) is compared between the two; among them, The time window penalty cost coefficient. As decision variables, =1 indicates that the electric medical waste transport vehicle k does not violate the time window of node j; otherwise... =0; If the time window penalty cost is low, continue building the path and record the penalty cost; otherwise, stop building the current path. If node j is added to the initial path of the Electric Medical Waste Transport Vehicle (EMWTV) Then update the time when the electric medical waste transport vehicle EMWTV leaves node j. , , and These represent the service time of the electric medical waste transport vehicle (EMWTV) at node j and the service time required to perform one battery swap service, respectively. When there are no available nodes in the candidate set S, add the starting point 0 to the initial path. Filter the set of paths, remove invalid paths that only contain the starting point, and keep those that meet the requirements. The path with a value greater than 2 is determined to be the initial feasible solution of EMWTV constructed by firefly z. Based on the received battery swapping requests and the constructed initial feasible solution of EMWTV, the nodes whose battery power is below the threshold and require battery replacement are identified as the service nodes of the mobile battery swapping vehicle (BSV), and the mobile battery swapping vehicle (BSV) is ensured to meet the time conditions. The initial path of the mobile battery swapping vehicle (BSV) was added at node j. In the case of [the situation], determine the start time of the BSV (Battery Swapping Vehicle) battery swapping service. And update the time when the mobile battery swapping vehicle (BSV) leaves node j. Then, it continues to determine whether the next node can be added to the initial path of the mobile battery swapping vehicle (BSV). middle; If there are no available nodes in the candidate set S, add the starting point 0. Filter the set of paths, remove invalid paths that only contain the starting point, and keep those that meet the requirements. The path >2 yields the initial feasible solution for BSV.

4. The method of coordinated scheduling of an electrically powered medical waste transporter with a mobile battery exchange vehicle of claim 3, wherein, The total cost F includes vehicle fixed costs, transportation costs, battery replacement costs, and time window penalty costs. The infection risk I is determined by the exposed population value. At that time, the load capacity of medical waste and infection probability Joint decision; The load condition satisfies the expression: ,in The load capacity of the electric medical waste transport vehicle EMWTV when it leaves node i. Let C be the service demand of node j, and C be the maximum load capacity of the electric medical waste transport vehicle EMWTV. The energy condition satisfies the expression: , This refers to the minimum battery capacity of the EMWTV (Electric Medical Waste Transport Vehicle). The charge level when it reaches node j; The time window condition satisfies the expression: the arrival time must be within the customer service time window, i.e. , For the earliest start time of service, The latest end time for service; and These represent the arrival and departure times of the electric medical waste transport vehicle EMWTV at node j, respectively.

5. The method of coordinating scheduling of an electrically powered medical waste transporter with a mobile battery exchange vehicle of claim 4, wherein, The updated EMWTV-related information for the electric medical waste transport vehicle includes: updated load information, updated battery level information, and updated time information; the updated load information includes: updated vehicle load. , The service demand for node j; The updated power information includes: based on the load-dependent energy consumption formula. From the current node i to the node This will update the battery level. ;in, ω and ω represent the energy consumption per unit distance of the electric medical waste transport vehicle EMWTV when it is unloaded and the additional energy consumption per unit distance per unit load, respectively. The load of the electric medical waste transport vehicle (EMWTV) when it leaves node i. Let be the distance between node i and node j; and These represent the battery levels of the electric medical waste transport vehicle EMWTV when it leaves node i and arrives at node j, respectively. The update time information includes: calculating the electric medical waste transport vehicle (EMWTV) from the current client node i to the next client node. travel time ,in, Update the arrival time of the electric medical waste transport vehicle EMWTV at node j to reflect its travel speed. .

6. The method of coordinated scheduling of an electrically powered medical waste transporter with a mobile battery exchange vehicle of claim 1, wherein, The mutation operation N is selected based on its weight and selection probability. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum, including: a current global optimum solution performing variable neighborhood search VNS; A better path solution is found by performing a local search in the solution space through five preset neighborhood operations; the five neighborhood operations include: path reversal operation, random swap operation, path segment insertion operation, path subsequence shift operation, and path jump connection operation. After each neighborhood search, the objective function value is recalculated, and if a better solution is found, the global optimum is updated. ; The weights of different mutation operations are dynamically adjusted based on the quality of the mutation results. When the mutation results are excellent, the weight of the mutation operation is increased; when the mutation results are poor, the weight of the mutation operation is decreased.

7. A collaborative scheduling system for an electric medical waste transport vehicle and a mobile battery swapping vehicle, characterized in that, include: The acquisition module is used to acquire customer data, initialize algorithm parameters, and set the maximum number of iterations. The initial iteration count t=1 is used to control the iteration process of the algorithm; The module is used to construct initial feasible solutions based on the firefly algorithm and the nearest neighbor method to characterize the initial paths of the electric medical waste transport vehicle (EMWTV) and the mobile battery swapping vehicle (BSV). The selection module calculates the total cost F and infection risk I for each firefly in the population, yielding the comprehensive objective function value. The firefly path with the smallest objective function value is selected as the current global optimal solution. ; The first update module is used to select a mutation operation N based on the weight and selection probability of the mutation operation. k Generate neighborhood solutions. If a solution is feasible, calculate the corresponding objective function value and compare it with the current global optimum to update the global optimum. The second update module is used to update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, and to adjust the solution and check its feasibility in combination with mutation operation during the position update process. The optimization module is used to determine whether t ≥ 1 when t = t + 1. If the condition is met, the algorithm terminates and outputs the currently found globally optimal solution. Otherwise, continue iterative optimization; The process of updating the position of each firefly in the population based on the attraction mechanism of the firefly algorithm, and adjusting and verifying the solution by incorporating mutation operations during the position update process, includes: for each firefly j in the population, calculate the objective function value of the solution corresponding to firefly j as a corresponding aggregate score; Explore other fireflies (j) ), calculate other fireflies Overall score ;like Then it can be determined that the firefly is the firefly. Brighter than a firefly; In identifying fireflies Then, assuming the current firefly constructs a partial path as follows: The pathways of other fireflies in the population are as follows: ; Using the defined path difference metric function The path length sum is used to approximate the calculation of firefly j and . Corresponding path differences The attraction level of a firefly to other fireflies is calculated based on a preset attraction formula. ; For fireflies j and For the corresponding EMWTV path solution, traverse their sub-paths, and for each node position i in the sub-path, generate a random number. ;like If so, then select a node from the subpath of firefly j and add it to the new path; if Then from fireflies Select nodes from the sub-paths and add them to the new path to generate a new EMWTV path; Perform a mutation operation on the updated path, based on the probability of the mutation operation being selected. Select mutation type Further adjustments to the route were made; among them, This indicates a path reversal operation; This indicates a random swap operation; This indicates the Or-opt operation for the path segment; This indicates a path subsequence shift operation; This indicates a path jump join operation; Feasibility tests are performed on the new EMWTV and BSV paths to check if they meet vehicle load limits, battery limits, and time window constraints. If they do, they are used as the updated positions for Firefly j; otherwise, re-mutation is attempted or the original positions are retained. Attractiveness satisfies the expression: = ; in, This is the initial attraction level, used to determine the degree of attraction between fireflies at a distance of 0. It is the light intensity absorption coefficient and is used to characterize the rate at which attraction decreases with increasing distance.

8. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the coordinated scheduling method of the electric medical waste transport vehicle and the mobile battery exchange vehicle as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the coordinated scheduling method for electric medical waste transport vehicles and mobile battery exchange vehicles as described in any one of claims 1 to 6.

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