Cooperative scheduling method for electric medical waste transfer vehicle and mobile battery exchange vehicle

Abstract

The invention provides a cooperative scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle, and relates to the field of scheduling optimization, and the method comprises the steps: obtaining client data, and initializing algorithm parameters; constructing an initial feasible solution, calculating the total cost and the infection risk of the path of each firefly, and selecting the firefly path with the minimum objective function value as a current global optimal solution; selecting mutation operation to generate a neighborhood solution, calculating a corresponding target function value, and comparing the target function value with the current globally optimal solution to update the globally optimal solution; and updating the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, performing adjustment and feasibility test on the solution in combination with mutation operation in the position updating process, and performing iterative optimization until termination. According to the method, the firefly algorithm and the variable neighborhood search algorithm are combined to realize efficient path optimization and charging scheduling, so that balance between total cost minimization and infection risk minimization is achieved, the medical waste transfer efficiency is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of scheduling optimization, and in particular to a method for collaborative scheduling of an electric medical waste transfer vehicle and a mobile battery exchange vehicle. Background Art

[0002] With the rapid development of the medical industry, the amount of medical waste generated has increased year by year. Medical waste has hazards such as infectiousness and toxicity, and its safe and efficient transportation is of vital importance. Traditional fuel transfer vehicles have problems such as high pollution and high cost, and it is difficult to meet the increasingly stringent environmental protection requirements and the refined needs of medical waste management. Electric Medical Waste Transport Vehicle (EMWTV) has shown great application potential in the field of medical waste transportation due to its zero emissions, low noise, and low operating costs. However, the limited cruising range and long charging time of the electric medical waste transport vehicle EMWTV limit its large-scale application in medical waste transportation scenarios, especially in complex environments where time windows and infection risk control requirements need to be strictly adhered to.

[0003] Existing research on electric vehicle routing optimization and charging scheduling primarily focuses on express delivery, logistics, and public transportation. Relatively little research has focused on medical waste transfer scenarios, and existing charging strategies often rely on fixed charging stations. While mobile battery swap vehicles (BSVs), as an emerging charging method, have been studied in fields like express delivery and logistics, research on their application in charging strategies for electric medical waste transfer vehicles (EMWTVs) remains limited. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application provides a coordinated scheduling method for electric medical waste transfer vehicles and mobile battery exchange vehicles, which solves the problem that the existing electric medical waste transfer system is caused by defects such as uneven distribution of fixed charging stations, long waiting times and long charging time, which leads to a decline in electric vehicle service quality and may violate customer service time window requirements.

[0005] To achieve the above objectives, this application is implemented through the following technical solutions:

[0006] In the first aspect, the embodiment of the present application provides a collaborative scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle. The collaborative scheduling method includes: obtaining customer data and initializing algorithm parameters, setting a maximum number of iterations t maxand the initial number of iterations t = 1 to control the iterative 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 transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV; the total cost F and infection risk I of the path of each firefly in the population are calculated to obtain the comprehensive objective function value f(x i ), and select the firefly path with the smallest objective function value as the current global optimal solution g best ; Select a mutation operation N according to the weight and selection probability of the mutation operation k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution. Update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm. In the process of position update, combine the mutation operation to adjust the solution and test its feasibility. Let t = t + 1, and judge whether t ≥ t max Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g best , otherwise continue iterative optimization.

[0007] In the second aspect, the embodiment of the present application provides a collaborative scheduling system for electric medical waste transfer vehicles and mobile battery exchange vehicles, the collaborative scheduling system includes: an acquisition module, a construction module, a calculation selection module, a first update module, a second update module and a judgment optimization module; wherein the acquisition module is used to obtain customer data and initialize algorithm parameters, set the maximum number of iterations t max and the initial number of iterations t = 1 to control the iterative process of the algorithm; the construction module is used to construct an initial feasible solution based on the firefly algorithm and the nearest neighbor method to characterize the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV; the calculation selection module is used to calculate the total cost F and infection risk I of the path of each firefly in the population, and obtain the comprehensive objective function value f(x i ), and select the firefly path with the smallest objective function value as the current global optimal solution g best The first update module is used to select a mutation operation N according to the weight and selection probability of the mutation operation. k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution. 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 in the process of position update, combine the mutation operation to adjust the solution and test its feasibility. The judgment optimization module is used to let t = t + 1 and judge t ≥ t max Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g best , otherwise continue iterative optimization.

[0008] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, the method for coordinated scheduling of the electric medical waste transfer vehicle and the mobile battery exchange vehicle in the first aspect mentioned above is implemented.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a program or instruction. When the program or instruction is executed by a processor, the method for coordinated scheduling of the electric medical waste transfer vehicle and the mobile battery exchange vehicle in the aforementioned first aspect is implemented.

[0010] This application provides a coordinated dispatching method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle. Compared with the existing technology, it has the following advantages:

[0011] This application introduces a mobile battery swap vehicle (BSV) to provide fast battery swap services for electric medical waste transfer vehicles (EMWTV). To improve the fleet's response speed and service level, this application constructs an initial feasible solution based on the firefly algorithm and the nearest neighbor method. The total cost and infection risk of each firefly's path in the population are calculated to determine the objective function value, and then the firefly path is evaluated, and further mutation operations are performed to generate neighborhood solutions for processing. This application combines the firefly algorithm with the variable neighborhood search algorithm to achieve efficient path optimization and charging scheduling, in order to achieve a balance between minimizing total cost and minimizing infection risk. Through the dynamic coordination of path optimization and mobile charging, it solves problems such as short driving range, strict time windows, and high infection risk, thereby improving the efficiency of medical waste transfer and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 This is a flow chart of a method for collaboratively dispatching an electric medical waste transfer vehicle and a mobile battery exchange vehicle provided in an embodiment of the present application;

[0014] Figure 2 This is a structural diagram of a coordinated dispatching system for an electric medical waste transfer vehicle and a mobile battery exchange vehicle provided in an embodiment of the present application;

[0015] Figure 3This is a flow chart of another method for collaborative scheduling of an electric medical waste transfer vehicle and a mobile battery exchange vehicle provided in an embodiment of the present application;

[0016] Figure 4 This is an exemplary initial path map of 10 medical institutions and medical waste treatment centers provided in an embodiment of the present application;

[0017] Figure 5 Yes Figure 4 Schematic diagram of the path reversal operation on the initial path in;

[0018] Figure 6 Yes Figure 4 Schematic diagram of random exchange operation on the initial path in;

[0019] Figure 7 Yes Figure 4 Schematic diagram of the path segment insertion operation on the initial path in;

[0020] Figure 8 Yes Figure 4 Schematic diagram of the path subsequence shift operation on the initial path in;

[0021] Figure 9 Yes Figure 4 Schematic diagram of path skip connection operation on the initial path in ;

[0022] Figure 10 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive 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, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0025] The embodiments of the present application provide a method for coordinated scheduling of electric medical waste transfer vehicles and mobile battery exchange vehicles, thereby solving the problem that the existing electric medical waste transfer system has defects such as uneven distribution of fixed charging stations, long waiting times, and long charging times, which leads to a decline in the service quality of electric vehicles and may violate customer service time window requirements.

[0026] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] The following first introduces a collaborative scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle provided in an embodiment of the present application.

[0028] The embodiment of the present application provides a flow chart of a coordinated dispatching method of an electric medical waste transfer vehicle and a mobile battery exchange vehicle, as shown in FIG. Figure 1 As shown, the collaborative scheduling method may include the following steps S110-S160.

[0029] S110, obtain customer data and initialize algorithm parameters, set the maximum number of iterations t max and the initial number of iterations t = 1 to control the iterative process of the algorithm;

[0030] S120, based on the firefly algorithm and the nearest neighbor method, construct an initial feasible solution to represent the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV;

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

[0032] S140, select a mutation operation N according to the weight and selection probability of the mutation operation k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution.

[0033] S150, updating the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, and adjusting and feasibility testing the solution in combination with the mutation operation during the position update process;

[0034] S160, let t = t + 1, determine t ≥ t max Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g best , otherwise continue iterative optimization.

[0035] The above is a specific implementation method of the coordinated scheduling method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle provided in the embodiment of this application. It can be understood that this application introduces the mobile battery exchange vehicle BSV to provide a fast battery replacement service for the electric medical waste transfer vehicle EMWTV, overcoming the defects of the existing electric medical waste transfer system such as uneven distribution of fixed charging stations, long waiting time, long charging time and high initial investment cost. These problems lead to a decline in the service quality of electric vehicles and may violate the requirements of the customer service time window.

[0036] Please refer to Figure 1 and Figure 3 In order 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; and calculates the total cost and infection risk of the path of each firefly in the population to determine the objective function value, and 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 the total cost and minimizing the infection risk. Through the dynamic coordination 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, provides a more efficient and flexible solution for medical waste management, improves the level of public health safety and reduces operating costs and environmental impact, while reducing environmental pollution and promoting green and sustainable development.

[0038] Furthermore, this application addresses the problems of the existing single charging strategy, reliance on fixed charging stations, and insufficient algorithm optimization. It introduces a new charging mode of mobile battery swap vehicles and an improved FA-VNS hybrid algorithm in the problem of electric medical waste transfer vehicles. The FA-VNS hybrid algorithm is a combination of the firefly algorithm FA and the variable neighborhood search algorithm VNS. Although a single battery swap method is still used, it innovatively realizes the transition from fixed charging to mobile battery swapping, greatly shortens the energy replenishment time, and solves the core problems of limited layout of fixed charging stations and low charging efficiency. Combined with the improvements in the global search and convergence speed of the FA-VNS algorithm, this application has improved the operational efficiency and scheduling flexibility of the transfer vehicle to a certain extent, and provided new ideas for the optimization of the electric medical waste transportation system; subsequent research and inventions can explore expansion solutions such as hybrid charging networks on this basis.

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

[0040] The algorithm parameters include: the maximum load C of the electric medical waste transfer vehicle EMWTV, the maximum battery capacity Q, the lower limit coefficient ρ, the maximum number of batteries U loaded by the mobile battery swap vehicle BSV n , Fixed cost parameter C of electric medical waste transfer vehicle EMWTV K 、Fixed cost parameter C of mobile battery swap vehicle BSV B , variable cost parameter C of electric medical waste transfer vehicle EMWTV k Variable cost parameter C of mobile battery swap vehicle BSV b , Battery replacement cost parameter C s , weighting factor α used to balance cost and infection risk, disease infection probability I ij , the driving speed v of the electric medical waste transfer vehicle EMWTV k and the driving speed v of the mobile battery swap vehicle BSV b .

[0041] In some embodiments, based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to represent the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV. That is, the aforementioned S120 may specifically 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 transfer vehicle EMWTV z , set the current position of EMWTV to 0 to represent the medical waste treatment center, set the load w = 0, and the power 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, the nodes in the candidate set S are screened to simultaneously meet the load condition, the power condition and the time window condition; when the load condition, the power condition and the time window condition are all met, the node j is added to the initial path P of the electric medical waste transfer vehicle EMWTV corresponding to the firefly z z Meanwhile, the relevant information of Electric Medical Waste Transfer Vehicle EMWTV will be updated;

[0044] S230, when the load condition is not met, that is, w+D j >C, then stop building the current path, D j is the service demand of node j;

[0045] If the power condition is not met, When , the cost of dispatching the mobile battery swap vehicle BSV and the cost of dispatching the new electric medical waste transfer vehicle EMWTV are calculated and compared; if the cost of dispatching the mobile battery swap vehicle BSV is lower, a battery replacement request is sent to the mobile battery swap vehicle BSV in advance, and the power information is updated after the battery replacement service is accepted. is the power of the electric medical waste transfer vehicle EMWTV when it leaves node j, and node j is removed from the candidate set S and the path construction continues; otherwise, the current path construction is stopped;

[0046] Calculate the time window penalty cost when the time window conditions are not met and the cost of dispatching a new electric medical waste transport vehicle EMWTV, comparing the two; where p j is the time window penalty cost coefficient, is the decision variable, Indicates that the electric medical waste transfer vehicle k does not violate the time window of node j, otherwise If the time window penalty cost is low, continue to build the path and record the penalty cost; otherwise, stop the current path construction;

[0047] S240, if node j joins the initial path P of the electric medical waste transfer vehicle EMWTV z Then update the time B when the electric medical waste transfer 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 They represent the service time of the electric medical waste transfer vehicle EMWTV at node j and the service time required for a battery replacement service.

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

[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 , do not need to accept the battery replacement service or accept the battery replacement service but A j ≤e j -t s , that is, the mobile battery swap vehicle BSV provides battery replacement service for the electric medical waste transfer vehicle EMWTV while the electric medical waste transfer vehicle EMWTV is waiting for the medical waste loading service to start. Then the time when the electric medical waste transfer 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 at the earliest service start time, i.e. A j =e j If the battery replacement service is carried out, the time when the electric medical waste transfer vehicle EMWTV leaves node j is B j =e j +s j +t s If the battery replacement service is not required, the time when the electric medical waste transfer vehicle EMWTV leaves 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 the battery replacement service is carried out, the time when the electric medical waste transfer vehicle EMWTV leaves node j is B j =A j +s j +t s If the battery replacement service is not required, the time when the electric medical waste transfer vehicle EMWTV leaves node j is B j =A j +s j ; Then continue to determine whether the next node can be added to the initial path of the electric medical waste transfer vehicle EMWTV.

[0052] S250: When there is no available node in the candidate set S, add the starting point 0 to the initial path P. z , filter the path set, remove invalid paths that only contain starting points, and retain those that satisfy |P z |>2, the obtained path set is determined to be the initial feasible solution of EMWTV constructed by Firefly z;

[0053] S260: Based on the received battery swap request and the constructed EMWTV initial feasible solution, determine that the node whose battery power is lower than the threshold and requires battery replacement is the service node of the mobile battery swap vehicle BSV, and ensure that the mobile battery swap vehicle BSV meets the time condition.

[0054] Specifically, this application determines that the node whose battery power is lower than the threshold and needs battery replacement is the BSV service node based on the received battery replacement request and the constructed EMWTV initial feasible solution; at the same time, it is necessary to ensure that BSV meets the time condition, that is, The time A′ when BSV arrives at node j j It 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 are decision variables, x ij =1 means there is an electric medical waste transfer vehicle EMWTV passing through node i to node j, otherwise x ij =0; x′ ij =1 means there is a mobile battery swap vehicle BSV passing through node i to node j, otherwise x′ ij =0.

[0055] When receiving a battery replacement request, the number of customers who meet the time conditions and have been served C n , then add node j to the initial path P′ of BSV and update the number of batteries loaded at node j ​The number of customers C served is increased by 1; the travel time from the last served node i to node j is calculated Update the time when BSV arrives at node j v b and B′ i are the speed of BSV and the time when BSV leaves node i, U n The maximum number of batteries that the BSV load can use for the battery swap service. Once one of the conditions is not met, the construction of the current path is stopped.

[0056] S270: When node j joins the initial path P′ of the mobile battery swap vehicle BSV, determine the start time of the mobile battery swap vehicle BSV battery swap service. And update the time when the mobile battery swap car BSV leaves node j Then continue to determine whether the next node can be added to the initial path P′ of the mobile battery swap vehicle BSV.

[0057] S280. When 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, retain the paths that satisfy |P′|>2, and obtain the BSV initial feasible solution.

[0058] In the embodiments of the present application, it can be understood that for each firefly z in the population, the present application constructs an initial solution of the electric medical waste transfer vehicle EMWTV by combining the nearest neighbor method and a strategy that considers vehicle capacity and time window constraints; for the path construction of the mobile battery swap vehicle BSV, the present application constructs demand and time window information for related nodes based on the battery swap events in the path of the electric medical waste transfer vehicle EMWTV, 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 situations when the mobile battery exchange vehicle starts the battery exchange service at node i:

[0060] Case 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 , and A j ≤e j -t s , that is, the time that the electric medical waste transfer vehicle EMWTV waits for the start of the medical waste loading service is enough for the mobile battery swap vehicle BSV to provide it with a battery replacement service. Then the start time of the mobile battery swap vehicle BSV's battery replacement service at the node is the time when the electric medical waste transfer vehicle EMWTV arrives at the node j, that is

[0061] Case 2: When the arrival time of the electric medical waste transfer vehicle EMWTV at node j plus the battery replacement service time is later than the earliest service start time of the medical institution and the arrival time of the electric medical waste transfer vehicle is earlier than the earliest service time of the medical institution, that is, e j -t s j ≤e j , then the start time of the battery replacement service of the mobile battery swap vehicle BSV at node j is the earliest service start time of the medical institution plus the medical waste loading service time

[0062] Case 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 , then the start time of the battery replacement service of the mobile battery swap vehicle BSV at the node is when the electric medical waste transfer vehicle EMWTV finishes the medical waste loading service, that is

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

[0064] The load condition satisfies the expression: w i +D j ≤C, where w i is the load of the electric medical waste transfer vehicle EMWTV when it leaves node i, D j is the service demand of node j, C is the maximum load of the electric medical waste transfer vehicle EMWTV;

[0065] The power condition satisfies the expression: ρ is the lower limit of the battery power of the electric medical waste transfer vehicle EMWTV, is the amount of electricity when it reaches node j;

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

[0067] In some embodiments, updating the relevant information of the electric medical waste transfer vehicle EMWTV includes: updating the load information, updating the power information and updating the time information; updating the load information includes: updating the vehicle load w j =w i +D j , D j is the service demand of node j;

[0068] Update power information includes: according to 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 , and then update the power Where h and ω are the energy consumption per unit distance of the electric medical waste transfer vehicle EMWTV when it is unloaded and the additional energy consumption per unit distance per unit load, respectively; w i is the vehicle load of the electric medical waste transfer vehicle EMWTV when it leaves node i, d ij is the distance between node i and node j; and They represent the power of the electric medical waste transfer vehicle EMWTV when it leaves node i and arrives at node j;

[0069] Update time information includes: Calculate the travel time of the electric medical waste transfer vehicle EMWTV from the current customer node i to the next customer node j Among them, v k is the driving speed of the electric medical waste transfer vehicle EMWTV, and updates the time when the electric medical waste transfer vehicle EMWTV arrives at node j.

[0070] In some embodiments, the total cost F and infection risk I of each firefly path in the population are calculated to obtain the comprehensive objective function value f(x i ), and select the firefly path with the smallest objective function value as the current global optimal solution g best , that is, the aforementioned S130 may specifically include the following steps:

[0071] S310, calculate the total cost F of each firefly path:

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

[0073] Where DC is the vehicle transportation (variable) cost and satisfies the expression:

[0074]

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

[0076]

[0077] Where x 0j Indicates that the electric medical waste transport vehicle EMWTV moves from the medical waste treatment center to node j, x′ 0j Indicates that the mobile battery swap vehicle BSV moves from the medical waste treatment center to node j.

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

[0079]

[0080] Where x′ ij Indicates that the mobile battery swap vehicle BSV moves from node i to node j.

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

[0082]

[0083] Where p i is the time window penalty cost coefficient, is a decision variable. If the electric medical waste transfer vehicle EMWTV violates the time window (e i ,l i ) constraint, its value is 1, otherwise it is 0.

[0084] S320. Calculate the infection risk I of each firefly path:

[0085]

[0086] Where, E eij is the number of exposed people traveling from node i to node j, that is, the number of exposed people on arc (i, j).

[0087] S330, calculate the comprehensive objective function value:

[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 global optimal path P best =Pk .

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

[0091] S410, the current global optimal solution g best Perform variable neighborhood search (VNS).

[0092] S420. Perform a local search in the solution space through five preset neighborhood operations to find a better path solution; the five neighborhood operations include: path reversal operation, random exchange 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, and update the global optimal solution g if a better solution is found best .

[0094] S440. Dynamically adjust the weights of different mutation operations according to the quality of the mutation results. When the mutation result is excellent, increase the weight of the mutation operation; when the mutation result is poor, reduce the weight of the mutation operation.

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

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

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

[0098] S402, when t′ <t′ max When , proceed to S403, otherwise end the search;

[0099] S403, according to the neighborhood index k and the weight of each neighborhood operation, select a neighborhood operation to generate a new solution P new :

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

[0101] After performing the mutation operation, check whether the new path contains all nodes without duplication. If not, reselect the neighborhood operation to generate a new solution; if satisfied, proceed to the next step;

[0102] S404, check the newly generated solution P new Whether the constraints such as capacity and time window are met; if so, proceed to S405; if not, return to S403 and regenerate a new solution;

[0103] S405, if P new Satisfy the constraints, according to the formula:

[0104] f=α×F new +(1-α)×I new

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

[0106] S406, the new objective function value f new and the objective function value f of the current global optimal solution best For 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 go to step S407;

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

[0109] S407: Set the number of iterations t′=t′+1, then return to S402 to continue iterating until the maximum number of iterations t′ is reached. max .

[0110] For example, in order to optimize the scheduling scheme of electric medical waste transfer vehicles EMWTV and mobile battery exchange vehicles BSV, this application takes the scheduling cost and the potential infection risk during medical waste transfer as objective functions for comprehensive consideration. The problem is described as follows: Given the medical waste transfer needs of N medical institutions, it is necessary to reasonably schedule electric medical waste transfer vehicles EMWTV and mobile battery exchange vehicles BSV to complete the service. Each medical institution has specific geographical coordinates, daily medical waste generation, service time window requirements, and exposed population values ​​in the area. The electric medical waste transfer vehicle EMWTV is responsible for the collection and transportation of medical waste. Its operation is subject to the dual constraints of battery capacity and load limit. When the power is lower than the threshold, the mobile battery exchange vehicle BSV is required to provide battery replacement service. The mobile battery exchange vehicle BSV dynamically adjusts the service route according to the power status of the electric medical waste transfer vehicle EMWTV to ensure the continuity of the transfer task. The problem assumptions are as follows:

[0111] (1) All electric medical waste transfer vehicles have the same specifications and travel at a constant speed;

[0112] (2) All mobile battery exchange vehicles have the same specifications and can carry up to 5 batteries for battery exchange service in a single trip. They maintain a constant speed during the trip and do not require energy replenishment;

[0113] (3) Electric medical waste transfer vehicles have a maximum waste processing capacity. If the capacity is exceeded, the vehicle must go to a medical waste treatment center.

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

[0115] (5) The energy consumption of electric medical waste transfer vehicles is proportional to the weight of the waste carried.

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

[0117] In this example, we analyze the coordinated scheduling of an electric medical waste transfer vehicle (EMWTV) and a mobile battery swap vehicle (BSV). We input information about 10 medical institutions, including their geographic coordinates, daily medical waste generation, service time windows, and the exposed population in their area.

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

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

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

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

[0122] Here, 0 represents the medical waste treatment center, numbers 1-10 represent the medical institution codes, EMWTV1 and EMWTV2 represent two electric medical waste transfer vehicles, and BSV-1 represents a mobile battery swap vehicle. BSV-1's service path is [0,2,0], indicating that the mobile battery swap vehicle (BSV) traveled from the medical waste treatment center to medical institution No. 2, swapped batteries for EMWTV2, and then returned to the medical waste treatment center. EMWTV1 maintained sufficient charge throughout the entire process and did not require battery swapping. This optimal solution comprehensively optimizes scheduling costs and potential infection risks during medical waste transfer, while meeting vehicle load limits, power limits, and time window constraints. This provides a valuable reference for practical medical waste transfer scheduling.

[0123] Please refer to Figure 4-Figure 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 in 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 reverse the order of the subpath [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 reversing.

[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, exchange the nodes at these two positions, and get a new solution P new For example, in the path [0,2,3,4,6,7,0], the positions of nodes 3 and 6 are swapped to become [0,2,6,4,3,7,0].

[0126] N3: Path segment Or-opt operation; please refer to Figure 7, 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, intercept the subpath [i, j] between these two positions and randomly insert it into other positions of the path, so as to obtain a new solution P new ; For example, [8,5,10] intercepted from the path [0,8,5,10,9,1,0] is inserted between nodes 9 and 1 to obtain the new path [0,9,8,5,10,1,0].

[0127] N4: Path subsequence shift operation; please refer to Figure 8 , when P best When the number of paths is greater than 1, two paths are randomly selected. From the first path, when the number of nodes in the path is greater than 3, two positions i and j are randomly selected from the non-starting and non-ending parts, and the subsequence [i, j] between these two positions is intercepted and moved to a different position on the second path to obtain a new solution P. new ; For example, select the subsequence [8] in path 2: [0,8,5,10,9,1,0] and move it to path 1: [0,2,3,4,6,7,0] to obtain the 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, two paths are randomly selected. When the number of nodes in both paths is greater than 3, position i is randomly selected from the non-starting and non-endpoint part of the first path, and position j is randomly selected from the non-starting and non-endpoint part of the second path. The part before i of the first path is connected with the part after j of the second path to form a new path, replace the first path and delete the second path to obtain the new solution P. new For example, [0,8,5,10,9] 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 jump-connected to form a new path [0,8,5,10,9,2,0], and the remaining path segments are also jump-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, select random exchange operation and operate according to N2 rule;

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

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

[0133] When k=5, the path jump connection operation is selected and the operation is performed 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 feasibility tested in combination with the mutation operation. 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 the solution of firefly j j As the corresponding comprehensive score;

[0136] S520, traverse other fireflies j' (j≠j'), and calculate the comprehensive score f of other fireflies j' j′ If f j′ <f j , then it is determined that firefly j′ is brighter than firefly j;

[0137] S530, after determining the firefly j′, assume that the partial path constructed by the current firefly is The paths of other fireflies in the population are Using the defined path difference metric function Use the sum of the path lengths to approximate the path difference r corresponding to fireflies j and j′ jj′ ; Calculate the attraction β of the current firefly to other fireflies based on the preset attraction formula;

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

[0139] β=β0exp(-γr jj′ )

[0140] Among them, β0 is the initial attraction and is used to determine the degree of attraction between fireflies when the distance is 0. γ is the light intensity absorption coefficient and is used to characterize the decay rate of attraction as the distance increases.

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

[0142] S550, perform mutation operation on the updated path, and select the path according to the probability P of mutation operation. k Select mutation type N k ,k=1,2,3,4,5, further adjust the path;

[0143] S560: Perform feasibility checks on the new EMWTV and BSV paths to check whether the paths meet the vehicle load limit, power limit, and time window constraints. If so, use it as the updated position of firefly j. If not, try to mutate again or retain the original position.

[0144] In the embodiments of this application, it is understood that the position of each firefly in the population is updated based on the firefly algorithm's attraction mechanism. Darker fireflies will move toward brighter ones, with the distance of movement determined by attraction. During this position update, this application incorporates mutation operations to adjust the solution. After the position update, the newly generated solution undergoes a rigorous feasibility check to fully verify that it meets all established conditions, such as vehicle load limits, power limits, and time window constraints, to ensure the solution's validity.

[0145] In some embodiments, the present application provides a coordinated dispatching system 600 for an electric medical waste transfer vehicle and a mobile battery exchange vehicle, such as Figure 2 As shown, the collaborative scheduling system 600 may include the following modules:

[0146] Acquisition module 610 is used to obtain customer data and initialize algorithm parameters, setting the maximum number of iterations t max and the initial number of iterations t = 1 to control the iterative process of the algorithm;

[0147] A construction module 620 is used to construct an initial feasible solution based on the firefly algorithm and the nearest neighbor method to represent the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV;

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

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

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

[0151] The judgment optimization module 660 is used to set t=t+1 and judge t≥tmax Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g best , otherwise continue iterative optimization.

[0152] According to an embodiment of the present application, any multiple modules among the acquisition module 610, the construction module 620, the calculation and selection module 630, the first update module 640, the second update module 650, and the judgment and optimization module 660 can be combined into a single module for implementation, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module.

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

[0154] In some embodiments, the present application provides an electronic device, the structural diagram of the electronic device is as follows Figure 10 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), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0157] The memory 720 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 720 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 720 may include removable or non-removable (or fixed) media. Where appropriate, the memory 720 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 720 is a non-volatile solid-state memory.

[0158] The memory 720 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 720 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) 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 one of the above-mentioned embodiments of the method for coordinated dispatch of an electric medical waste transfer vehicle and a mobile battery exchange vehicle.

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

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

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

[0162] Bus 700 includes hardware, software or both, and the parts of online data flow metering equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 700 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0163] In addition, in conjunction with the coordinated scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle in the above-mentioned embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the coordinated scheduling methods for an electric medical waste transfer vehicle and a mobile battery exchange vehicle in the above-mentioned embodiments.

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

[0165] 1. In research on the issue of electric medical waste transfer vehicles, this application introduces a novel strategy for charging with mobile battery swap vehicles, providing rapid battery replacement services for electric medical waste transfer vehicles (EMWTVs). This strategy effectively improves the flexibility and efficiency of vehicle battery replenishment during transfers, reduces transfer delays caused by insufficient power, and helps medical waste reach treatment locations more promptly. This provides a new solution for medical waste transfer and improves transfer efficiency to a certain extent.

[0166] 2. This application fully considers multiple optimization objectives during the transfer process, integrating cost, infection risk, and load-dependent energy consumption into unified considerations. Through precise cost accounting and control measures, operating costs during the transfer process are reduced; scientific route planning and scheduling methods are used to avoid densely populated areas as much as possible to reduce infection risks; based on research on load-dependent energy consumption, vehicle routes and load distribution are rationally planned to reduce energy consumption, thereby achieving 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 transfer vehicles. This algorithm, relying on its unique search mechanism, strikes a balance between minimizing total cost and minimizing infection risk, and can quickly generate high-quality path planning and scheduling solutions under complex transfer conditions. Through in-depth optimization of the algorithm and incorporating it into the actual needs of transfers, the dynamic coordination of path optimization and mobile charging addresses issues such as short driving range, strict time windows, and high infection risk, further reducing transfer costs and making transfer solutions more scientific and reasonable.

Claims

1. A coordinated dispatching method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle, characterized in that: include: Obtain customer data and initialize algorithm parameters, setting the maximum number of iterations t max and the initial number of iterations t = 1 to control the iterative process of the algorithm; Based on the firefly algorithm and the nearest neighbor method, an initial feasible solution is constructed to represent the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV. Calculate the total cost F and infection risk I of each firefly path in the population and get the comprehensive objective function value f(x i ), and select the firefly path with the smallest objective function value as the current global optimal solution g best ; Select a mutation operation N according to the weight and selection probability of the mutation operation k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution. Update the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, and adjust and test the feasibility of the solution in combination with the mutation operation during the position update process; Let t = t + 1, and determine t ≥ t max Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g bxst , otherwise continue iterative optimization.

2. The coordinated dispatching method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle according to claim 1, characterized in that: The customer data includes the number N of medical institutions, the location information of each medical institution (x i ,y i ), service demand D i , service time window (e i ,l i ) and the exposed population value E ei ; The algorithm parameters include the maximum load C of the electric medical waste transfer vehicle EMWTV, the maximum battery capacity Q, the lower limit coefficient ρ, the maximum number of batteries U loaded by the mobile battery swap vehicle BSV n , Fixed cost parameter C of electric medical waste transfer vehicle EMWTV K 、Fixed cost parameter C of mobile battery swap vehicle BSV B , variable cost parameter C of electric medical waste transfer vehicle EMWTV k Variable cost parameter C of mobile battery swap vehicle BSV b , Battery replacement cost parameter C s , weighting factor α used to balance cost and infection risk, disease infection probability I ij , the driving speed v of the electric medical waste transfer vehicle EMWTV k and the driving speed v of the mobile battery swap vehicle BSV b .

3. The coordinated dispatching method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle according to claim 1, characterized in that: The firefly algorithm and the nearest neighbor method are used to construct an initial feasible solution to characterize the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV, including: 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 position of EMWTV to 0 to represent the medical waste treatment center, set the load w = 0, and the power Construct a candidate set S = {1, 2, ..., N}, where N is the number of medical institutions excluding medical waste treatment centers; p opsize represents the population size; Based on the optimized nearest neighbor method, the nodes in the candidate set S are screened to simultaneously meet the load condition, power condition and time window condition; when the load condition, power condition and time window condition are all met, the node j is added to the initial path P of the electric medical waste transfer vehicle EMWTV corresponding to Firefly z. z Meanwhile, the relevant information of Electric Medical Waste Transfer Vehicle EMWTV will be updated; In the case that the load condition is not met, that is, w+D j >C, then stop building the current path, D j is the service demand of node j; If the power condition is not met, When , the cost of dispatching the mobile battery swap vehicle BSV and the cost of dispatching the new electric medical waste transfer vehicle EMWTV are calculated and compared; if the cost of dispatching the mobile battery swap vehicle BSV is lower, a battery replacement request is sent to the mobile battery swap vehicle BSV in advance, and the power information is updated after the battery replacement service is accepted. is the power of the electric medical waste transfer vehicle EMWTV when it leaves node j, and node j is removed from the candidate set S and the path construction continues; otherwise, the current path construction is stopped; If the time window condition is not met, calculate the time window penalty cost and the cost of dispatching a new electric medical waste transport vehicle EMWTV, comparing the two; where p j is the time window penalty cost coefficient, is the decision variable, Indicates that the electric medical waste transfer vehicle k does not violate the time window of node j, otherwise If the time window penalty cost is low, continue to build the path and record the penalty cost; otherwise, stop the current path construction; If node j joins the initial path P of the electric medical waste transfer vehicle EMWTV z Then update the time B when the electric medical waste transfer 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 They represent the service time of the electric medical waste transfer vehicle EMWTV at node j and the service time required to perform a battery replacement service respectively; When there are no available nodes in the candidate set S, add the starting point 0 to the initial path P z , filter the path set, remove invalid paths that only contain starting points, and retain those that satisfy |P z |>2, the obtained path set is determined to be the initial feasible solution of EMWTV constructed by Firefly z; Based on the received battery swap request and the constructed EMWTV initial feasible solution, the node whose battery power is below the threshold and needs battery replacement is determined to be the service node of the mobile battery swap vehicle (BSV), and the mobile battery swap vehicle (BSV) is ensured to meet the time condition; When node j joins the initial path P′ of the mobile battery swap vehicle BSV, determine the start time of the mobile battery swap vehicle BSV battery swap service And update the time when the mobile battery swap car BSV leaves node j Then continue to determine whether the next node can be added to the initial path P′ of the mobile battery swap vehicle BSV; When there are no available nodes in the candidate set S, the starting point 0 is added to P′, the path set is screened, invalid paths containing only the starting point are removed, and paths satisfying |P′|>2 are retained to obtain the BSV initial feasible solution.

4. The coordinated dispatching method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle according to claim 3, characterized in that: The total cost F includes vehicle fixed cost, transportation cost, battery replacement cost and time window penalty cost. The infection risk I is determined by the exposed population value E. i 、The medical waste load at that time was w i and infection probability E eij Joint decision-making; The load condition satisfies the expression: w i +D j ≤C, where w i is the load of the electric medical waste transfer vehicle EMWTV when it leaves node i, D j is the service demand of node j, C is the maximum load of the electric medical waste transfer vehicle EMWTV; The power condition satisfies the expression: ρ is the lower limit of the battery power of the electric medical waste transfer vehicle EMWTV, is the amount of electricity 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., e j ≤A j j ≤l j , e j The earliest start time of the service, j The latest end time of the service; A j and B j are the arrival and departure times of the electric medical waste transfer vehicle EMWTV at node j, respectively.​ 5. The coordinated dispatching method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle according to claim 4, characterized in that: The updating of the relevant information of the electric medical waste transfer vehicle EMWTV includes: updating the load information, updating the power information and updating the time information; the updating of the load information includes: updating the vehicle load w j =w i +D j , D j is the service demand of node j; The updating of power information includes: according to 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 , and then update the power Where h and ω are the energy consumption per unit distance of the electric medical waste transfer vehicle EMWTV when it is unloaded and the additional energy consumption per unit distance per unit load, respectively; w i is the vehicle load of the electric medical waste transfer vehicle EMWTV when it leaves node i, d ij is the distance between node i and node j; and They represent the power of the electric medical waste transfer vehicle EMWTV when it leaves node i and arrives at node j; The updating time information includes: calculating the driving time of the electric medical waste transfer vehicle EMWTV from the current customer node i to the next customer node j Among them, v k is the driving speed of the electric medical waste transfer vehicle EMWTV, and updates the time when the electric medical waste transfer vehicle EMWTV arrives at node j.

6. The coordinated dispatching method of an electric medical waste transfer vehicle and a mobile battery exchange vehicle according to claim 1, characterized in that: The mutation operation N is selected according to the weight and selection probability of the mutation operation. k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution, including: For the current global optimal solution g best Perform variable neighborhood search VNS; A local search is performed in the solution space through five preset neighborhood operations to find a better path solution; the five neighborhood operations include path reversal operation, random exchange 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 optimal solution g is updated. best ; The weights of different mutation operations are dynamically adjusted according to the quality of the mutation results. If the mutation result is excellent, the weight of the mutation operation is increased; if the mutation result is poor, the weight of the mutation operation is reduced.

7. The coordinated dispatching method of an electric medical waste transfer vehicle and a mobile battery exchange vehicle according to claim 1, characterized in that: The firefly algorithm updates the position of each firefly in the population based on its attraction mechanism, and during the position update process, combines mutation operations to adjust the solution and perform feasibility tests, including: For each firefly j in the population, calculate the objective function value f corresponding to the solution of firefly j j As the corresponding comprehensive score; Traverse other fireflies j′ (j≠j′) and calculate the comprehensive score f of other fireflies j′ j′ If f j′ <f j , then it is determined that firefly j′ is brighter than firefly j; After determining firefly j′, assume that the partial path constructed by the current firefly is The paths of other fireflies in the population are Using the defined path difference metric function Use the sum of the path lengths to approximate the path difference r corresponding to fireflies j and j′ jj′ ; Calculate the attraction β of the current firefly to other fireflies based on the preset attraction formula; For the EMWTV path solutions corresponding to fireflies j and j′, traverse their subpaths and generate a random number r∈[0,1] for each node position i in the subpath. If r<β, select a node from the subpath of firefly j to add to the new path. If r≥β, select a node from the subpath of firefly j′ to add to the new path to generate a new EMWTV path. Perform mutation operation on the updated path, according to the probability P of being selected by the mutation operation k Select mutation type N k ,k=1,2,3,4,5, further adjust the path; A feasibility test is performed on the new EMWTV path and BSV path to check whether the path meets the vehicle load limit, power limit, and time window constraints; if so, it is used as the updated position of firefly j; if not, an attempt is made to mutate again or retain the original position.

8. A coordinated dispatching system for electric medical waste transfer vehicles and mobile battery exchange vehicles, characterized in that: include: The acquisition module is used to obtain customer data and initialize algorithm parameters, setting the maximum number of iterations t max and the initial number of iterations t = 1 to control the iterative process of the algorithm; A construction module for constructing an initial feasible solution based on the firefly algorithm and the nearest neighbor method to represent the initial path of the electric medical waste transfer vehicle EMWTV and the initial path of the mobile battery swap vehicle BSV; The calculation selection module is used to calculate the total cost F and infection risk I of each firefly path in the population, and obtain the comprehensive objective function value f(x i ), and select the firefly path with the smallest objective function value as the current global optimal solution g best ; The first update module is used to select a mutation operation N according to the weight and selection probability of the mutation operation k Generate a neighborhood solution. If the solution is feasible, calculate the corresponding objective function value and compare it with the current global optimal solution to update the global optimal solution. 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 in the process of position update, it combines the mutation operation to adjust the solution and test its feasibility; Judgment optimization module, used to set t=t+1 and judge t≥t max Is it satisfied? If so, the algorithm terminates and outputs the currently found global optimal solution g best , otherwise continue iterative optimization.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the method for collaboratively scheduling an electric medical waste transfer vehicle and a mobile battery exchange vehicle as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the coordinated scheduling method of the electric medical waste transfer vehicle and the mobile battery exchange vehicle as described in any one of claims 1 to 7 is implemented.

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