Double-target medical waste transportation method based on improved RSA-VNS

Abstract

The invention provides a double-target medical waste transportation method based on an improved RSA-VNS, and relates to the technical field of path optimization. The method comprises the following steps: firstly, acquiring a medical waste transportation task and vehicle scheduling resources; secondly, on the basis of medical waste transportation tasks and vehicle scheduling resources, constructing a dual-target medical waste transportation model by taking minimization of cost and storage risk as optimization targets; and finally, solving the dual-target medical waste transportation model by adopting an improved RSA-VNS algorithm, and outputting a Pareto external file so as to decode and obtain an optimal medical waste transportation scheme. The storage risk of the medical waste is effectively quantified by integrating the service starting time of the medical institution, the type and strength of the medical waste and the possibility of disease transmission, and modeling is more in line with the characteristics of recovery and transportation of the medical waste. In addition, the proposed hybrid algorithm effectively improves the convergence speed of the algorithm and the diversity of the solution, and also enhances the local convergence capability at the end of the algorithm.

Description

Technical Field

[0001] The invention relates to the technical field of path optimization, and in particular to a dual-target medical waste transportation method based on improved RSA-VNS. Background Art

[0002] The dual-objective path optimization problem is a specific application of a multi-objective problem, which has received extensive attention and research in recent years. It is widely present in all walks of life in modern transportation, such as: network communication industry, medical industry, logistics industry and other fields. Unlike traditional path optimization problems, the two optimization goals of dual-objective path optimization problems are often conflicting. In the transportation operations of the medical industry, due to the special nature of the transported goods, it is often not only the cost issue that is considered, but also the risk factors brought by medical waste. Therefore, it is of great practical significance to study the dual-objective path optimization problem of recycling waste in the medical industry.

[0003] In related technologies, intelligent algorithms are widely used to solve various bi-objective optimization problems. For example, the paper (Menares, F., Montero, E., Paredes-Belmar, G., & Bronfman, A. (2023). A bi-objective time-dependent vehicle routing problem with delivery failure probabilities. Computers&Industrial Engineering , 185 , 109601.) A multi-objective genetic algorithm NSGA-II is implemented to solve a bi-objective time-dependent vehicle routing problem with delivery failure probability (TDVRPDFP).

[0004] For example, the paper (Ren, X., Huang, H., Feng, S., & Liang, G. (2020). An improved variable neighborhood search for bi-objective mixed-energy fleet vehicle routing problem. Journal of Cleaner Production, 275, 124155.) proposed an improved variable neighborhood search (VNS) with a selection mechanism to find the Pareto frontier of the model based on the research of the former. The calculation results based on the Solomon benchmark show that the VNS with the selection mechanism has better performance and efficiency than the original VNS.

[0005] However, there are few studies that consider both cost and storage risk. Solving this complex problem is the key to the problem of waste recycling and transportation in medical institutions, and traditional path optimization cannot solve this problem. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a dual-objective medical waste transportation method based on an improved RSA-VNS, which solves the technical problem of ignoring the risk factors brought about by medical waste during the waste transportation route optimization process.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A dual-objective medical waste transportation method based on improved RSA-VNS, comprising: Obtain medical waste transportation tasks and vehicle dispatch resources; Based on the medical waste transportation task and vehicle dispatching resources, a dual-objective medical waste transportation model is constructed with minimizing cost and storage risk as the optimization goal; wherein the storage risk is quantified based on the start service time of the medical institution and combined with the type, intensity and disease transmission possibility of the medical waste; The improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model and output the Pareto external archive to decode and obtain the optimal medical waste transportation plan.

[0008] Preferably, the medical waste transportation tasks and vehicle dispatching resources include: v represents the set of all nodes, including the recycling point v0 and the medical institution set v c ; B represents the selected subset of medical institutions, B v c ; r i represents the waste collection volume of medical institution i; ts i represents the loading service time of the vehicle at medical institution i; d ij represents the distance between medical institutions i and j; ati represents the start time of service of medical institution i; h i represents the medical waste storage risk of medical institution i; K represents the vehicle group; p k represents the path label set of the p-th transportation task of vehicle k; v elIndicates the average vehicle speed; w represents the maximum vehicle load; v z Indicates the loading speed of the vehicle; δ1, δ2, and δ3 represent the fixed cost, unit fuel consumption cost, and cooling cost per unit time of the vehicle, respectively; FC ij represents the fuel consumption of the vehicle traveling between medical institutions i and j; D represents the type set of medical waste; β d , d They represent the intensity of medical waste d and the possibility of spreading diseases respectively.

[0009] Preferably, the dual-objective medical waste transportation model includes an objective function: Among them, Min represents the minimization function, f1 and f2 represent the cost and storage risk respectively; is a decision variable, which takes the value 1 if the p-th transportation task of vehicle k includes transportation from medical institution i to j, and takes the value 0 otherwise.

[0010] Preferably, the dual-objective medical waste transportation model includes constraints: Among them, formula (3) limits each medical institution to only allow one vehicle to provide service once; Formula (4) determines that the vehicle leaves the recycling point as an empty vehicle; is the vehicle load of vehicle k from medical institution i to j in the pth transportation mission; Formula (5) restricts vehicle k to return to the collection point after completing the pth transportation task; Formula (6) ensures the continuity of the path; Formula (7) eliminates the sub-routing constraint; Formulas (8) to (9) limit the total amount of waste transported to not exceed the maximum load of the vehicle, and the waste generated by medical institutions shall not exceed the maximum load of the vehicle; Formulas (10) to (11) represent constraints on variables; is a decision variable, which takes the value 1 if vehicle k completes the transportation of waste generated by medical institution i, and takes the value 0 otherwise; Formulas (12) and (13) ensure that each vehicle starts from the recycling point, and the first transportation of each vehicle also starts from the recycling point; is a decision variable that takes the value 1 if vehicle k is used and 0 otherwise.

[0011] Preferably, the improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model and output the Pareto external archive, including: S31, initialize the population, set the current iteration number t=0; the encoding rule is to randomly generate a decimal between [0,1) and assign it to the medical institution order vector and the vehicle allocation vector respectively; S32, based on the dual-objective medical waste transportation model, calculating the objective function of the particle, and storing the non-inferior solution in the Pareto external archive; S33, randomly selecting individuals from the Pareto external archive, and performing a first cycle judgment to select an update method, including: If t is less than 0.25T, the individual is updated by high gait walking; if t is less than or equal to 0.5T and greater than 0.25T, the individual is updated by abdominal gait walking; T is the maximum number of iterations; S34, update the external file, set t=t+1, if t is equal to 0.5T, complete the RSA part of the reptile search algorithm, and go to S35, otherwise return to S33; S35, adopting a correction strategy to convert the current population and the Pareto external archive into discrete codes; S36, randomly selecting discretized individuals from the Pareto external archive, and performing a second periodic judgment to select an update method, including: If t is less than or equal to 0.75T and greater than 0.5T, the VNS-1 operator is used to update the discretized individual; if t is less than or equal to T and greater than 0.75T, the VNS-2 operator is used to update the discretized individual; S37, update the external file, set t=t+1, if t is greater than T, complete the variable neighborhood search VNS part and go to S38, otherwise return to S36; S38. Output the final Pareto external file.

[0012] Preferably, in the updating process of the reptile search algorithm RSA, for the case where the element in the new solution exceeds 1 or is less than 0, the correction strategy refers to repairing the partial solution, and the repair method is: For each element a in the solution, let a=(aa min ) / (a max -a min ) (1-10e-10); where a min and a max They represent the minimum and maximum values ​​of the medical institution order vector or the vehicle allocation vector respectively. e is a mathematical constant. 1-10e-10 is used to prevent the calculation error of the decoding intermediate vector caused by the value being equal to 1.

[0013] Preferably, the VNS-1 operator includes exchanging hospitals served by different vehicles, inserting hospitals served by different vehicles, and globally reversing; The VNS-2 operator includes exchanging hospitals served by the same vehicle, inserting hospitals served by the same vehicle, and the reverse operation on a global scale.

[0014] A dual-target medical waste transportation system based on improved RSA-VNS, comprising: An acquisition module is used to obtain medical waste transportation tasks and vehicle scheduling resources; A construction module is used to construct a dual-objective medical waste transportation model based on the medical waste transportation task and vehicle scheduling resources, with minimization of cost and storage risk as optimization goals; wherein the storage risk is quantified based on the start service time of the medical institution and in combination with the type, intensity and disease transmission possibility of the medical waste; The solution module is used to solve the dual-objective medical waste transportation model by using the improved RSA-VNS algorithm, and output the Pareto external archive to decode and obtain the optimal medical waste transportation plan.

[0015] A storage medium stores a computer program for dual-target medical waste transportation based on improved RSA-VNS, wherein the computer program enables a computer to execute the dual-target medical waste transportation method as described above.

[0016] An electronic device, comprising: One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including methods for executing the dual-target medical waste transport method as described above.

[0017] (III) Beneficial effects The present invention provides a dual-target medical waste transportation method based on improved RSA-VNS. Compared with the prior art, it has the following beneficial effects: In the present invention, the medical waste transportation tasks and vehicle scheduling resources are first obtained; secondly, based on the medical waste transportation tasks and vehicle scheduling resources, a dual-objective medical waste transportation model is constructed with the optimization objectives of minimizing costs and storage risks; finally, the improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model, and the Pareto external archive is output to decode and obtain the optimal medical waste transportation plan. By integrating the start service time of medical institutions, the type and intensity of medical waste and the possibility of spreading diseases, the storage risk of medical waste is effectively quantified, and the modeling is more in line with the characteristics of medical waste recycling and transportation problems. In addition, the proposed hybrid algorithm effectively improves the algorithm convergence speed and the diversity of solutions, and also strengthens the local convergence ability at the end of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0019] Figure 1 A block diagram of a dual-target medical waste transportation method based on improved RSA-VNS provided in an embodiment of the present invention; Figure 2 A flowchart of an improved RSA-VNS algorithm provided by an embodiment of the present invention; Figure 3 A coding schematic diagram provided for an embodiment of the present invention; Figure 4~Figure 6 Schematic diagrams of a swap operator swapop1, an insertion operator insertop1, and a global inverse inverseop1 provided in embodiments of the present invention; Figure 7~Figure 9 Schematic diagrams of the swap operator swapop2, the insertion operator insertop2 and the global inverse inverseop2 provided in embodiments of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The embodiment of the present application solves the technical problem of ignoring the risk factors brought about by medical waste during the waste transportation route optimization process by providing a dual-objective medical waste transportation method based on an improved RSA-VNS.

[0022] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows: The present invention also solves the path optimization problem considering storage risk under dual-objective conditions. The purpose is to determine the specific transport vehicle assigned to each medical institution, the order in which the transport vehicle visits the medical institution, and the time of arrival to minimize the transportation cost and storage risk. Based on the characteristics of the problem, an effective hybrid algorithm is proposed using theoretical analysis and mathematical derivation to solve the combined optimization problem, providing a new method for the problem of medical waste transportation in complex environments.

[0023] Additional explanation of terminology: (1) Reptile Search Algorithm, also known as Reptile Search Algorithm, or RSA for short.

[0024] (2) Variable Neighborhood Search, also known as VNS.

[0025] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0026] Embodiment 1: like Figure 1 As shown, the embodiment of the present invention provides a dual-target medical waste transportation method based on improved RSA-VNS, comprising: S1. Obtain medical waste transportation tasks and vehicle dispatching resources; S2. Based on the medical waste transportation task and vehicle dispatching resources, a dual-objective medical waste transportation model is constructed with minimizing cost and storage risk as the optimization goal; wherein the storage risk is quantified based on the start service time of the medical institution and in combination with the type, intensity and disease transmission possibility of the medical waste; S3. Using the improved RSA-VNS algorithm to solve the dual-objective medical waste transportation model, output the Pareto external file to decode and obtain the optimal medical waste transportation plan.

[0027] The embodiment of the present invention effectively quantifies the storage risk of medical waste, and the modeling is more in line with the characteristics of the recycling and transportation problem of medical waste. In addition, the proposed RSA-VNS algorithm effectively improves the algorithm convergence speed and solution diversity, and also strengthens the local convergence ability of the algorithm at the end.

[0028] Next, we will introduce the steps of the above scheme in detail: In step S1, medical waste transportation tasks and vehicle scheduling resources are obtained.

[0029] In this step, the medical waste transportation tasks and vehicle dispatching resources obtained include: v represents the set of all nodes, including the recycling point v0 and the medical institution set v c ; B represents the selected subset of medical institutions, B v c ; r i represents the waste collection volume of medical institution i; ts i represents the loading service time of the vehicle at medical institution i; d ij represents the distance between medical institutions i and j; ati represents the start time of service of medical institution i; h i represents the medical waste storage risk of medical institution i; K represents the vehicle group; p k represents the path label set of the p-th transportation task of vehicle k; v el Indicates the average vehicle speed; w represents the maximum vehicle load; v z Indicates the loading speed of the vehicle; δ1, δ2, and δ3 represent the fixed cost, unit fuel consumption cost, and cooling cost per unit time of the vehicle, respectively; FC ij represents the fuel consumption of the vehicle traveling between medical institutions i and j; D represents the type set of medical waste; β d , d They represent the intensity of medical waste d and the possibility of spreading diseases respectively.

[0030] In step S2, based on the medical waste transportation task and vehicle scheduling resources, a dual-objective medical waste transportation model is constructed with the minimization of cost and storage risk as the optimization goals; wherein the storage risk is quantified based on the start service time of the medical institution and combined with the type, intensity and possibility of disease transmission of medical waste.

[0031] It should be noted that the embodiment of the present invention considers the dual-objective path optimization problem of medical waste transportation, the goal of which is to minimize the transportation cost and the medical waste storage risk of the hospital. The problem is described as follows: Given a set v of medical institutions containing a known amount of medical waste c It needs to be recycled by multiple transport vehicles. Different medical wastes have different storage risks in different medical institutions, but the recycling time is the same.

[0032] The problem assumes the following: (1) The recycling center has enough medical waste recycling vehicles of the same type, the vehicle capacity is known, and the speed during transportation is always kept constant.

[0033] (2) The location of each medical institution is known, the amount of medical waste generated by each medical institution can be scientifically estimated, and the amount recycled does not exceed the rated load of a single vehicle.

[0034] (3) All drivers of medical waste recycling vehicles have undergone unified and strict training. Fuel consumption will not change due to subjective factors, and the energy required for refrigeration is provided by the engine.

[0035] (4) The amount of medical waste generated by small and medium-sized medical institutions is relatively small, and the recycling time is consistent.

[0036] (5) Each vehicle can visit multiple medical institutions, and each medical institution can only be visited by one vehicle once.

[0037] (6) During the route optimization process, each vehicle can perform multiple transportation tasks.

[0038] (7) Force majeure factors such as road congestion and weather conditions are not taken into account.

[0039] Accordingly, the dual-objective medical waste transportation model in this step includes Objective function: Among them, Min represents the minimization function, f1 and f2 represent the cost and storage risk respectively; is a decision variable, which takes the value 1 if the p-th transportation task of vehicle k includes transportation from medical institution i to j, and takes the value 0 otherwise.

[0040] And the constraints: Among them, formula (3) limits each medical institution to only allow one vehicle to provide service once; Formula (4) determines that the vehicle leaves the recycling point as an empty vehicle; is the vehicle load of vehicle k from medical institution i to j in the pth transportation mission; Formula (5) restricts vehicle k to return to the collection point after completing the pth transportation task; Formula (6) ensures the continuity of the path; Formula (7) eliminates the sub-routing constraint; Formulas (8) to (9) limit the total amount of waste transported to not exceed the maximum load of the vehicle, and the waste generated by medical institutions shall not exceed the maximum load of the vehicle; Formulas (10) to (11) represent constraints on variables; is a decision variable, which takes the value 1 if vehicle k completes the transportation of waste generated by medical institution i, and takes the value 0 otherwise; Formulas (12) and (13) ensure that each vehicle starts from the recycling point, and the first transportation of each vehicle also starts from the recycling point; is a decision variable that takes the value 1 if vehicle k is used and 0 otherwise.

[0041] In step S3, the improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model, and the Pareto external archive is output to decode and obtain the optimal medical waste transportation plan.

[0042] It is necessary to point out that the embodiment of the present invention has made at least the following improvements to the existing RSA algorithm: First, introduce Pareto external archives In the existing RSA, it is necessary to use the optimal solution to guide evolution. However, due to the multi-objective problem handled by the embodiment of the present invention, it is impossible to select the optimal solution. Therefore, a Pareto external archive is maintained in the program. The archive stores the non-dominated solutions that have been obtained. In each iteration, the current population is non-dominated sorted. For the obtained non-dominated solutions, first determine whether they dominate certain solutions in the external archive. If they do, these solutions in the external archive are deleted. Secondly, determine whether they are dominated by certain solutions in the external archive. If they are dominated, they cannot be added to the external archive. If they are not dominated by all solutions, the obtained non-dominated solution is added to the external archive. If the archive is full, it is randomly replaced. During optimization, a solution is randomly selected from the external archive as the optimal solution to guide evolution.

[0043] Second, illegal solution repair During the update process of the RSA algorithm, it may happen that the elements in the solution exceed 1 or are less than 0. In this case, the solution needs to be repaired, and the renormalization operation is used in the code.

[0044] Third, neighborhood operator design In the hybrid algorithm, continuous coding is converted to discrete coding before performing neighborhood operations. When t is less than or equal to 0.75T and greater than 0.5T, the neighborhood with a large difference between the design and the original solution is considered to facilitate the search of a larger solution space; the difference is that when t is less than T and greater than 0.75T, the neighborhood with a small difference between the design and the original solution is considered to facilitate local exploration.

[0045] like Figure 2 As shown, Figure 2 A flow chart of an improved RSA-VNS algorithm is disclosed.

[0046] Reference Figure 2, the solution process of this step specifically includes: S31, initialize the population, set the current iteration number t=0; where: The encoding rules are as follows: like Figure 3 As shown, a decimal is randomly generated between [0,1) and assigned to the medical institution order vector and vehicle allocation vector respectively. Figure 3 NH in it is the number of medical institutions.

[0047] Among them, each element of the medical institution order vector represents the priority of the medical institution, and the smaller the value, the higher the priority; each element of the vehicle allocation vector represents the service vehicle of the hospital.

[0048] Correspondingly, the decoding rules are supplemented as follows: First, the vehicle for each hospital is determined based on the vehicle allocation vector, where if the decimal belongs to [0,1 / NK), it means the first vehicle is selected, if the decimal belongs to [1 / NK,2 / NK), it means the second vehicle is selected, and so on. Here NK is the number of vehicles.

[0049] Secondly, add the obtained vehicle number and order vector, then sort them in order of arrival from the smallest to the smallest, record the obtained hospital sequence, and add 0 as a marker between different vehicles.

[0050] S32. Based on the dual-objective medical waste transportation model, the objective function of the particles is calculated, and the non-inferior solution is stored in a Pareto external archive.

[0051] S33, randomly selecting individuals from the Pareto external archive, and performing a first cycle judgment to select an update method, including: If t is less than 0.25T, high-gait walking is performed to update the individual; If t is less than or equal to 0.5T and greater than 0.25T, perform abdominal gait walking to update the individual; Where T is the maximum number of iterations; x (i,j) represents the jth position of the ith solution (corresponding to the jth column of the encoded individual); Best j (t) is the jth position in the optimal solution obtained so far; η (i,j) represents the hunting operator of the jth position of the ith solution; β is a sensitive parameter used to control the exploration accuracy (i.e., high walking) of the surrounding phase during the iteration process, which can be fixed to 0.1; the reduction function R (i,j)The value used to reduce the search area; r1 represents a random position; ES represents the evolution factor, which is a probability ratio and takes a randomly decreasing value between 2 and -2 throughout the number of iterations; rand represents a random number between 0 and 1.

[0052] S34, update the external file, set t=t+1, if t is equal to 0.5T, complete the RSA part of the reptile search algorithm and go to S35, otherwise return to S33.

[0053] S35. Adopt a correction strategy to convert the current population and the Pareto external archive into discrete codes.

[0054] In the updating process of the reptile search algorithm RSA, if the element in the new solution exceeds 1 or is less than 0, the correction strategy refers to repairing the partial solution, and the repair method is: For each element a in the solution, let a=(aa min ) / (a max -a min ) (1-10e-10); where a min and a max They represent the minimum and maximum values ​​of the medical institution order vector or the vehicle allocation vector respectively. e is a mathematical constant. 1-10e-10 is used to prevent the calculation error of the decoding intermediate vector caused by the value being equal to 1.

[0055] S36, randomly selecting discretized individuals from the Pareto external archive, and performing a second periodic judgment to select an update method, including: If t is less than or equal to 0.75T and greater than 0.5T, the VNS-1 operator is used to update the discretized individuals; illustratively, the VNS-1 operator includes exchanging hospitals served by different vehicles, inserting hospitals served by different vehicles, and the global inverse; specifically, it includes: like Figure 4 As shown, the exchange operator swapop1: given a solution S; according to the solution S, get the hospitals that all vehicles need to visit; randomly select two vehicles, randomly select a hospital for the two vehicles; exchange the positions of the hospitals to get a new solution.

[0056] like Figure 5 As shown, the insertion operator insertop1: given a solution S; according to the solution S, get the hospitals that all vehicles need to visit; randomly select two vehicles, and randomly select hospitals i and j of the two vehicles; insert hospital i after hospital j to get a new solution.

[0057] like Figure 6As shown, the global inverse inverseop1: given a solution S; randomly select two non-repeated positions l1 and l2; reverse the elements between [l1, l2] (including l1, l2).

[0058] If t is less than or equal to T and greater than 0.75T, the VNS-2 operator is used to update the discretized individuals; illustratively, the VNS-2 operator includes exchanging hospitals served by the same vehicle, inserting hospitals served by the same vehicle, and the global inverse; specifically, it includes: like Figure 7 As shown, the exchange operator swapop2: given a solution S; according to the solution S, get the hospitals that all vehicles need to visit; randomly select a vehicle and randomly select two hospitals for the vehicle; exchange the positions of the hospitals to get a new solution.

[0059] like Figure 8 As shown, the insertion operator insertop2: given a solution S; according to the solution S, get the hospitals that all vehicles need to visit; randomly select a vehicle, and randomly select two hospitals i and j for the vehicle; insert hospital i after hospital j to get a new solution.

[0060] like Fig. 9 As shown, the global inverse inverseop2: given a solution S; according to the solution S, get the hospitals that all vehicles need to visit; randomly select a vehicle, and randomly select two non-repeated positions l1 and l2 of the vehicle; reverse the elements between [l1, l2] (including l1, l2).

[0061] S37, update the external file, set t=t+1, if t is greater than T, complete the variable neighborhood search VNS part and go to S38, otherwise return to S36.

[0062] S38. Output the final Pareto external file.

[0063] S39. Based on the Pareto external archive, technicians in this field can select the optimal solution according to actual needs and preferences, and perform decoding operations to obtain the optimal medical waste transportation plan.

[0064] It is understandable that in multi-objective optimization problems, there is often no single optimal solution, but rather multiple conflicting optimal solutions. Each point on the Pareto front represents a possible solution that achieves the best trade-off between multiple objectives. It can provide decision makers with comprehensive information and multiple options, allowing them to fully understand the various possibilities and choose the appropriate solution based on actual needs and preferences.

[0065] So far, the embodiment of the present invention has completed the entire process of the dual-target medical waste transportation method based on the improved RSA-VNS.

[0066] Embodiment 2: The embodiment of the present invention provides a dual-target medical waste transportation system based on improved RSA-VNS, comprising: An acquisition module is used to obtain medical waste transportation tasks and vehicle scheduling resources; A construction module is used to construct a dual-objective medical waste transportation model based on the medical waste transportation task and vehicle scheduling resources, with minimization of cost and storage risk as optimization goals; wherein the storage risk is quantified based on the start service time of the medical institution and in combination with the type, intensity and disease transmission possibility of the medical waste; The solution module is used to solve the dual-objective medical waste transportation model by using the improved RSA-VNS algorithm, and output the Pareto external archive to decode and obtain the optimal medical waste transportation plan.

[0067] Embodiment 3: An embodiment of the present invention provides a storage medium storing a computer program for dual-target medical waste transportation based on improved RSA-VNS, wherein the computer program enables a computer to execute the dual-target medical waste transportation method as described in Example 1.

[0068] Embodiment 4: An embodiment of the present invention provides an electronic device, including: One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs comprising a method for executing the dual-target medical waste transportation method as described in Example 1 It can be understood that the dual-target medical waste transportation system, storage medium and electronic device based on improved RSA-VNS provided in the embodiments of the present invention correspond to the dual-target medical waste transportation method based on improved RSA-VNS provided in the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the dual-target medical waste transportation method, which will not be repeated here.

[0069] In summary, compared with the prior art, the present invention has the following beneficial effects: 1. The embodiment of the present invention proposes a hybrid algorithm for the cost and storage risk considered in the process of medical waste transportation, ensuring that after the process of assigning a collection of medical institutions to transport vehicles is completed, the transportation capacity of each vehicle can be fully utilized, and ensuring that the risks brought by the storage of waste by medical institutions are greatly reduced.

[0070] 2. General intelligent algorithms are prone to falling into local optimality. However, research has found that the reptile search algorithm shows good performance when combined with multiple algorithms in an application. The embodiment of the present invention aims at the shortcomings and advantages of the reptile search algorithm, combines it with the specific application in this problem, designs an adaptive neighborhood search mechanism, and performs different search rates at different times to ensure the update of the solution set, which not only effectively improves the algorithm convergence speed and the diversity of solutions, but also strengthens the local convergence ability of the algorithm at the end to a certain extent.

[0071] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dual-target medical waste transportation method based on improved RSA-VNS, characterized in that: include: Obtain medical waste transportation tasks and vehicle dispatch resources; Based on the medical waste transportation task and vehicle dispatching resources, a dual-objective medical waste transportation model is constructed with minimizing cost and storage risk as the optimization goal; wherein the storage risk is quantified based on the start service time of the medical institution and combined with the type, intensity and disease transmission possibility of the medical waste; The improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model and output the Pareto external archive to decode and obtain the optimal medical waste transportation plan.

2. The dual-target medical waste transportation method according to claim 1, characterized in that: The medical waste transportation tasks and vehicle dispatching resources include: v represents the set of all nodes, including the recycling point v0 and the medical institution set v c ; B represents the selected subset of medical institutions, B v c ; r i represents the waste collection volume of medical institution i; ts i represents the loading service time of the vehicle at medical institution i; d ij represents the distance between medical institutions i and j; ati represents the start time of service of medical institution i; h i represents the medical waste storage risk of medical institution i; K represents the vehicle group; p k represents the path label set of the p-th transportation task of vehicle k; v el Indicates the average vehicle speed; w represents the maximum vehicle load; v z Indicates the loading speed of the vehicle; δ1, δ2, and δ3 represent the fixed cost, unit fuel consumption cost, and cooling cost per unit time of the vehicle, respectively; FC ij represents the fuel consumption of the vehicle traveling between medical institutions i and j; D represents the type set of medical waste; β d , d They represent the intensity of medical waste d and the possibility of spreading diseases respectively.

3. The dual-target medical waste transportation method according to claim 2, characterized in that: The dual-objective medical waste transportation model includes the objective function: Among them, Min represents the minimization function, f1 and f2 represent the cost and storage risk respectively; is a decision variable, which takes the value 1 if the p-th transportation task of vehicle k includes transportation from medical institution i to j, and takes the value 0 otherwise.

4. The dual-target medical waste transportation method according to claim 3, characterized in that: The dual-objective medical waste transportation model includes constraints: Among them, formula (3) limits each medical institution to only allow one vehicle to provide service once; Formula (4) determines that the vehicle leaves the recycling point as an empty vehicle; is the vehicle load of vehicle k from medical institution i to j in the pth transportation mission; Formula (5) restricts vehicle k to return to the collection point after completing the pth transportation task; Formula (6) ensures the continuity of the path; Formula (7) eliminates the sub-routing constraint; Formulas (8) to (9) limit the total amount of waste transported to not exceed the maximum load of the vehicle, and the waste generated by medical institutions shall not exceed the maximum load of the vehicle; Formulas (10) to (11) represent constraints on variables; is a decision variable, which takes the value 1 if vehicle k completes the transportation of waste generated by medical institution i, and takes the value 0 otherwise; Formulas (12) and (13) ensure that each vehicle starts from the recycling point, and the first transportation of each vehicle also starts from the recycling point; is a decision variable that takes the value 1 if vehicle k is used and 0 otherwise.

5. The dual-target medical waste transportation method according to claim 3, characterized in that: The improved RSA-VNS algorithm is used to solve the dual-objective medical waste transportation model and output the Pareto external archive, including: S31, initialize the population, set the current iteration number t=0; the encoding rule is to randomly generate a decimal between [0,1) and assign it to the medical institution order vector and the vehicle allocation vector respectively; S32, based on the dual-objective medical waste transportation model, calculating the objective function of the particle, and storing the non-inferior solution in the Pareto external archive; S33, randomly selecting individuals from the Pareto external archive, and performing a first cycle judgment to select an update method, including: If t is less than 0.25T, the individual is updated by high gait walking; if t is less than or equal to 0.5T and greater than 0.25T, the individual is updated by abdominal gait walking; T is the maximum number of iterations; S34, update the external file, set t=t+1, if t is equal to 0.5T, complete the RSA part of the reptile search algorithm, and go to S35, otherwise return to S33; S35, adopting a correction strategy to convert the current population and the Pareto external archive into discrete codes; S36, randomly selecting discretized individuals from the Pareto external archive, and performing a second periodic judgment to select an update method, including: If t is less than or equal to 0.75T and greater than 0.5T, the VNS-1 operator is used to update the discretized individual; if t is less than or equal to T and greater than 0.75T, the VNS-2 operator is used to update the discretized individual; S37, update the external file, set t=t+1, if t is greater than T, complete the variable neighborhood search VNS part and go to S38, otherwise return to S36; S38. Output the final Pareto external file.

6. The dual-target medical waste transportation method according to claim 5, characterized in that: In the updating process of the reptile search algorithm RSA, if the element in the new solution exceeds 1 or is less than 0, the correction strategy refers to repairing the partial solution, and the repair method is: For each element a in the solution, let a=(aa min ) / (a max -a min ) (1-10e-10); where a min and a max They represent the minimum and maximum values ​​of the medical institution order vector or the vehicle allocation vector respectively. e is a mathematical constant. 1-10e-10 is used to prevent the calculation error of the decoding intermediate vector caused by the value being equal to 1.

7. The dual-target medical waste transportation method according to claim 5, characterized in that: The VNS-1 operator includes exchanging hospitals served by different vehicles, inserting hospitals served by different vehicles, and the global inverse; The VNS-2 operator includes exchanging hospitals served by the same vehicle, inserting hospitals served by the same vehicle, and the reverse operation on a global scale.

8. A dual-target medical waste transportation system based on improved RSA-VNS, characterized in that: include: An acquisition module is used to obtain medical waste transportation tasks and vehicle scheduling resources; A construction module is used to construct a dual-objective medical waste transportation model based on the medical waste transportation task and vehicle scheduling resources, with minimization of cost and storage risk as optimization goals; wherein the storage risk is quantified based on the start service time of the medical institution and in combination with the type, intensity and disease transmission possibility of the medical waste; The solution module is used to solve the dual-objective medical waste transportation model by using the improved RSA-VNS algorithm, and output the Pareto external archive to decode and obtain the optimal medical waste transportation plan.

9. A storage medium, characterized in that: It stores a computer program for dual-target medical waste transportation based on improved RSA-VNS, wherein the computer program enables the computer to execute the dual-target medical waste transportation method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include programs for executing the dual-target medical waste transportation method as described in any one of claims 1 to 7.

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