Multi-center waste recovery scheduling method and device based on mixed sine and cosine algorithm

Abstract

The embodiment of the invention discloses a multi-center waste recovery scheduling method and device based on a mixed sine and cosine algorithm. According to one specific embodiment, the method comprises the steps that in response to parameter initialization completion, coding and decoding are carried out according to initialized parameters, an initial recycling line for a recycling center is generated, the initial recycling line is a one-way transport vehicle running line containing at least one to-be-recycled point, and the to-be-recycled point is a node to be subjected to waste recycling; constructing an initial solution set according to the initial recovery line and a chaotic mapping technology; processing is carried out according to the initial solution set and a hybrid evolution mechanism, a candidate solution set is obtained, and the hybrid evolution mechanism comprises a nonlinear parameter adjustment feature, a co-evolution feature and a random disturbance feature; and performing solution optimization on the candidate solution set through discrete domain search to obtain a final recovery line. The recovery scheduling algorithm of the embodiment has good performance.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the fields of computer technology, path planning, and waste recycling scheduling, specifically to a multi-center waste recycling scheduling method and apparatus based on a hybrid sine and cosine algorithm. Background Technology

[0002] In recent years, with continuous socio-economic development and ongoing urbanization, the amount of waste generated from production and daily life has been steadily increasing. Against this backdrop, the growing environmental awareness of residents and the increasing enforcement of environmental regulations highlight the economic value of waste recycling activities. Therefore, research on algorithms related to waste recycling scheduling is of paramount importance. Summary of the Invention

[0003] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0004] Some embodiments of this disclosure propose a multi-center waste recycling scheduling method and apparatus based on a hybrid sine and cosine algorithm to solve the technical problems mentioned in the background section above.

[0005] In a first aspect, some embodiments of this disclosure provide a multi-center waste recycling scheduling method based on a hybrid sine and cosine algorithm. The method includes: in response to parameter initialization, encoding and decoding based on the initialization parameters to generate an initial recycling route for the recycling center, wherein the initial recycling route is a unidirectional transport vehicle route containing at least one recycling point, and the recycling point is a node where waste is to be recycled; constructing an initial solution set based on the initial recycling route and chaotic mapping technology; processing the initial solution set and a hybrid evolutionary mechanism to obtain a candidate solution set, wherein the hybrid evolutionary mechanism includes: nonlinear parameter tuning features, co-evolutionary features, and random perturbation features; and optimizing the candidate solution set through discrete neighborhood search to obtain the final recycling route.

[0006] Secondly, some embodiments of this disclosure provide a multi-center waste recycling scheduling device based on a hybrid sine and cosine algorithm. The device includes: an encoding and decoding unit configured to encode and decode according to the initialization parameters in response to parameter initialization, generating an initial recycling route for a recycling center, wherein the initial recycling route is a unidirectional transport vehicle route containing at least one recycling point, and the recycling point is a node where waste is to be recycled; a construction unit configured to construct an initial solution set according to the initial recycling route and chaotic mapping technology; a processing unit configured to process the initial solution set according to a hybrid evolutionary mechanism to obtain a candidate solution set, wherein the hybrid evolutionary mechanism includes: nonlinear parameter tuning features, co-evolutionary features, and random perturbation features; and a solution optimization unit configured to optimize the candidate solution set through discrete neighborhood search to obtain the final recycling route.

[0007] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0008] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0009] The various embodiments of this disclosure have the following beneficial effects: Firstly, the multi-center waste recycling scheduling method based on a hybrid sine and cosine algorithm, as described in some embodiments of this disclosure, embeds a constructive rule generation problem scheduling scheme into the encoding. Secondly, it couples a chaotic mechanism to generate high-quality initial solutions. Furthermore, it constructs a hybrid individual evolution mechanism and a discrete neighborhood search method to enhance the convergence performance of the original algorithm. Finally, numerical experiments on the multi-center waste recycling scheduling problem are conducted, and the results verify that the recycling scheduling algorithm of this disclosure has good performance. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0011] Figure 1 This is a flowchart of some embodiments of the multi-center waste recycling scheduling method based on the hybrid sine and cosine algorithm according to the present disclosure;

[0012] Figure 2 This is a diagram illustrating the flipping process;

[0013] Figure 3 This is a diagram illustrating the insertion process;

[0014] Figure 4 This is a schematic diagram of the structure of some embodiments of a multi-center waste recycling scheduling device based on a hybrid sine and cosine algorithm according to the present disclosure;

[0015] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0020] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0021] In addition, to aid understanding, the variables involved in this disclosure are uniformly explained and described, as shown in Table 1 below:

[0022] Table 1

[0023]

[0024]

[0025] In addition, in order to effectively construct the corresponding mathematical model, the following assumptions are made: (1) The location of each facility node and the distance between them are fixed and known. (2) The number of available transport vehicles at each recycling center is fixed and known. (3) The amount of waste at the recycling point is known. (4) Transport vehicles of the same specifications are used for waste recycling tasks. (5) The recycling route corresponding to the transportation process is a single-journey route, the first and last nodes of the route are both recycling centers, and the amount of waste transported is lower than the vehicle's loading capacity (rated load). (6) The amount of waste at each recycling point is less than the vehicle's load capacity, and each recycling point needs to be visited only once, while the waste at the recycling point cannot be split for transportation. Combining the variables and their meanings shown in Table 1, the VRP (Vehicle Routing Problem) problem involved in this disclosure can be modeled, and the specific problem model is shown in Table 2 below:

[0026] Table 2

[0027]

[0028]

[0029] The role of the model formulas in Table 2 is further explained below. Equation (1) is the optimization objective, representing minimizing the logistics intensity of the current transportation network. Equation (2) indicates that each recycling point needs to be served by only one transport vehicle once. Equation (3) represents the load constraint on the transport vehicles. Equation (4) is used to constrain the out-degree and in-degree values ​​of the transport vehicles at each facility node to be equal. Equation (5) is used to constrain the out-degree value of each transport vehicle at each central node (recycling center) to not exceed 1. Equation (6) is used to limit each transport vehicle to flow out (drive out) from at most one central node (recycling center). Equation (7) is used to limit the number of transport vehicles driving out of each central node to not exceed a specified threshold. Equation (8) is used to eliminate existing loops. Equation (9) is used for the correlation between variables, i.e., the correlation x kij and y ki Equation (10) is used to calculate the low-order value of the sum of the tare weight and load of the transport vehicle k on the recycling route from facility node i to facility node j. Equations (11) to (15) define the range of values ​​for the variables involved in the problem model.

[0030] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] refer to Figure 1 The flowchart 100 illustrates some embodiments of a multi-center waste recycling scheduling method based on a hybrid sine and cosine algorithm according to this disclosure. This multi-center waste recycling scheduling method based on a hybrid sine and cosine algorithm includes the following steps:

[0032] Step 101: In response to the completion of parameter initialization, the initial recycling line for the recycling center is generated by encoding and decoding according to the initialization parameters.

[0033] In some embodiments, the execution entity (e.g., a computing device) of the multi-center waste recycling scheduling method based on the hybrid sine and cosine algorithm can generate an initial recycling route for the recycling center by encoding and decoding according to the initialization parameters after the parameter initialization is completed.

[0034] The initial recycling route is a one-way transportation route that starts and ends at a recycling center and includes at least one collection point. A collection point is a node where waste is to be recycled. Transportation vehicles are vehicles used for transporting waste. A recycling center is a node used to collect waste collected from at least one collection point.

[0035] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0036] In some optional implementations of some embodiments, the execution entity, in response to the completion of parameter initialization, performs encoding and decoding based on the initialization parameters to generate an initial recycling line for the recycling center, including:

[0037] Step S1: Generate the initial array.

[0038] The initial array has the same length as the number of points to be recycled in the set of points to be recycled. The array values ​​in the initial array correspond one-to-one with the points to be recycled in the set of points to be recycled. The range of values ​​in the initial array is [0,1].

[0039] In practice, with a length of |N C The real-valued array (initial array) of |(total number of points to be recycled) represents the solution to the current problem. The values ​​in the initial array are set to the range [0,1]. The initial array is used to determine the priority order of the points to be recycled in the set.

[0040] Step S2: Map the array values ​​in the initial array in descending order to obtain the priority sequence.

[0041] The value mapping range is 1 to |N|. C|,N C This represents the set of points to be recycled.

[0042] Specifically, the smallest coded mapping value corresponds to recycling point 1; the second smallest coded mapping value corresponds to recycling point 2, and so on, thus generating a priority sequence corresponding to the set of recycling points. Among them, recycling points with higher priority indicate that waste will be recycled first.

[0043] Step S3: Based on the priority sequence, capture the first un-recyclable point in the set of un-recyclable points and use it as the starting station of the initial recycling route.

[0044] Step S4: Based on the number of transport vehicles owned by the recycling center and with the shortest distance as a constraint, determine the recycling center corresponding to the initial recycling route.

[0045] In practice, the number of transport vehicles assigned for waste collection should be less than the number of transport vehicles owned by the recycling center. Secondly, when determining the recycling centers corresponding to the initial recycling routes, the selection of recycling centers should be based on the shortest distance.

[0046] Step S5: Starting from the priority corresponding to the starting station, select the recycling points corresponding to the priorities in the priority sequence in ascending order, add the selected recycling points to the initial recycling line, and cut off the line according to the vehicle loading capacity of the transport vehicle to obtain the initial recycling line.

[0047] As an example, assume there are recycling centers 1 and 2 and 11 recycling points (recycling point 1, recycling point 2, recycling point 3, recycling point 4, recycling point 5, recycling point 6, recycling point 7, recycling point 8, recycling point 9, recycling point 10, recycling point 11). The initial array can be [0.07, 0.09, 0.42, 0.48, 0.49, 0.75, 0.85, 0.17, 0.11, 0.22, 0.24]. The corresponding priority sequence can be [1, 2, 7, 8, 9, 10, 11, 4, 3, 5, 6]. Recycling point 1 corresponds to the array value "0.07" and priority "1". Recycling point 2 corresponds to the array value "0.09" and priority "2". Recycling point 3 corresponds to the array value "0.42" and priority "7". Recycling point 4 corresponds to the array value "0.48" and priority "8". Point 5 corresponds to array value "0.49" and priority "9". Point 6 corresponds to array value "0.75" and priority "10". Point 7 corresponds to array value "0.85" and priority "11". Point 8 corresponds to array value "0.17" and priority "4". Point 9 corresponds to array value "0.11" and priority "3". Point 10 corresponds to array value "0.22" and priority "5". Point 11 corresponds to array value "0.24" and priority "6". Based on this, five initial recycling routes can be planned: Initial Recycling Route 1, Initial Recycling Route 2, Initial Recycling Route 3, Initial Recycling Route 4, and Initial Recycling Route 5. Initial Recycling Route 1 includes: Recycling Center 1, Point 1 to be recycled, and Point 2 to be recycled. Initial Recycling Route 2 includes: Recycling Center 2, Point 8 to be recycled, and Point 7 to be recycled. Initial Recycling Route 3 includes: Recycling Center 2, Point 10 to be recycled, and Point 9 to be recycled. The initial recycling route 4 includes: recycling center 1, recycling point 11, recycling point 4 and recycling point 3.

[0048] Furthermore, the encoding and decoding method in step 101 comprehensively considers factors such as vehicle loading capacity constraints, the number of available vehicles at each recycling center, and departure rules from nearby centers when constructing routes, efficiently simplifying the multiple complex constraints and thus well adapting to the solution of the current problem. However, extreme cases still exist, where all transport vehicles corresponding to the recycling centers have been assigned to their respective initial recycling routes for waste collection (i.e., there are no idle transport vehicles), but there are still unassigned transport vehicles at some recycling points. In this case, it may lead to the problem of virtual departure from the nearest recycling center. Considering the constraint of the number of available transport vehicles at each recycling center, a fusion penalty function is set to evaluate the solution generated by each encoding. The specific fusion penalty function is shown in the following formula:

[0049]

[0050] Where f represents the target value of the current solution, F represents the function value of the fusion penalty function, and the accumulation term... This indicates the degree of violation of the load constraints of the transport vehicle, and λ represents the penalty coefficient, which is a positive number and has a relatively large value.

[0051] Step 102: Construct an initial solution set based on the initial recycling route and chaotic mapping technique.

[0052] In some embodiments, the aforementioned execution entity may construct an initial solution set based on the initial recycling line and chaotic mapping techniques.

[0053] In practice, chaotic mapping techniques are integrated into the initialization stage to construct high-quality initial solution sets. Due to the randomness and ergodicity of chaotic mapping, this is significant for improving the diversity of the obtained initial solution sets. Furthermore, the selected Fuch chaotic mapping method exhibits excellent performance characteristics such as ergodic balance and fast convergence speed.

[0054] In some optional implementations of certain embodiments, the aforementioned execution entity constructs an initial solution set based on the initial recycling line and chaotic mapping technique, including:

[0055] Step S1: Generate a random variable μ, while simultaneously making i ← 1.

[0056] Where i represents the index, and μ takes values ​​in the range of (0,1).

[0057] Step S2: Make d←1.

[0058] Where d∈{1,2,…,D}, and D represents the length of the initial array.

[0059] Step S3: In response to μ being 0, reset μ to a random value within the range (0,1], and make μ←cos(1 / μ) 2 ).

[0060] Wherein, cos(1 / μ 2 The range of ) is [-1, 1].

[0061] Step S4: Based on the array value x of the i-th population solution at dimension d in the initial population... d and make

[0062] in, This represents the minimum value in the initial array. μ represents the maximum value in the initial array. i+1 Let μ represent the (i+1)th random variable. The initial population is determined based on the initial recycling line.

[0063] Step S5: Make d←d+1, in response to d≤D, set x=(x1,…,x d ,...) is determined as the i-th initial solution in the initial solution set, and the process jumps to step S3.

[0064] Step S6: In response to d>D, i←i+1, and in response to i≤nPop, jump to step S2.

[0065] Wherein, nPop represents the population size.

[0066] Step 103: Process the initial solution set and the hybrid evolution mechanism to obtain the candidate solution set.

[0067] In some embodiments, the aforementioned execution entity can process the solution set according to the initial solution set and the hybrid evolution mechanism to obtain a candidate solution set.

[0068] Among them, the hybrid evolutionary mechanism includes: nonlinear parameter tuning characteristics, co-evolutionary characteristics, and random perturbation characteristics.

[0069] In practice, the conventional SCA (Sine Cosine Algorithm) algorithm solves the optimization problem by simulating the sine and cosine changes in mathematical operations. This algorithm has the characteristics of random search and adaptive parameter adjustment, and its performance is particularly outstanding in global exploration and local development. However, the individual evolution mechanism corresponding to the SCA algorithm has the following shortcomings: (1) It adjusts its adaptive parameter r1 in a linear manner. Studies have shown that adjusting r1 nonlinearly is beneficial for the algorithm to explore the spatial domain of the solution more fully; (2) It guides the population evolution with the current best solution. All solutions evolve in the same direction, which easily leads to local optima; (3) It only integrates the gene information of the current best solution into the update process of candidate solutions. As the iterative search progresses, the gene diversity of the entire population is poor. Based on this, nonlinear parameter tuning features, co-evolutionary features, and random perturbation features are introduced on the basis of the SCA algorithm.

[0070] Optionally, the nonlinear parameter tuning characteristics are controlled by an adaptive parameter r1, which is characterized by the following formula:

[0071]

[0072] Where 'a' represents a constant, with a default value of 3, 'g' represents the current iteration number, and 'G' represents the total number of iterations.

[0073] Optionally, the co-evolutionary feature is controlled by the top three high-quality solutions corresponding to the gray wolf optimization algorithm, and the above-mentioned random perturbation feature is controlled by random perturbation with a 50% probability.

[0074] In practice, the Grey Wolf Optimizer (GWO) algorithm incorporates the hierarchical structure of grey wolf packs and constructs an optimization process by simulating hunting behavior, exhibiting superior performance. Its core design philosophy lies in using multiple high-quality solutions to guide population evolution. Specifically, leveraging the GWO algorithm's concept, population evolution is guided by the top three high-quality solutions in the current population (α wolf, β wolf, and δ wolf, respectively). The representation of these three high-quality solutions is shown in Table 3 below.

[0075] Table 3

[0076]

[0077] Where, x d Let r1, r2, r3, and r4 represent the encoded values ​​of the solution vector x in the d-th dimension, and r1, r2, r3, and r4 represent the adaptive parameters for iterations 1 to 4, respectively. This represents the high-quality solution corresponding to wolf α. This represents the high-quality solution corresponding to β wolf. Let δ represent the high-quality solution corresponding to the wolf. In particular, equation (20) represents the solution obtained by using the top-ranked high-quality solution in the current population. The candidate solution positions are updated; equations (21) and (22) have the same effect. Equation (23) indicates that the average of the three high-quality solutions is taken as the final value.

[0078] In practice, random perturbation is controlled with a 50% probability. Specifically, an individual x is randomly selected from the current population. rand The current solution is updated through the individual evolution mechanism of the SCA algorithm, which can be expressed by the following formula:

[0079]

[0080] in, Represents individual x rand The encoded value in dimension d.

[0081] Analysis revealed that the improved algorithm fully combines the advantages of the GWO algorithm, while also enhancing its performance by introducing adaptive parameters and random perturbations. Specifically, it has the following effects:

[0082] (1) Adjust the adaptive parameter r1 in a sinusoidal manner so that the search process can explore the solution space of the current problem more fully;

[0083] (2) Integrating multiple high-quality solutions guides population evolution, preventing the population from evolving in a single direction and enhancing the algorithm's ability to escape local optima;

[0084] (3) Integrating the genes of random solutions into the solution update process enhances the gene diversity of the population in the later stages of iteration.

[0085] Step 104: Optimize the candidate solution set through discrete domain search to obtain the final recovery route.

[0086] In some embodiments, the aforementioned execution entity can optimize the candidate solution set through discrete domain search to obtain the final recovery route.

[0087] In some optional implementations of certain embodiments, the aforementioned execution entity optimizes the candidate solution set through discrete neighborhood search to obtain the final recovery route, including:

[0088] Step S1: Based on the variable neighborhood descent search, generate the current solution and neighborhood solutions through discrete mutation operation according to the candidate solution set.

[0089] Step S2: Optimize the solution based on the current solution and the surrounding solution to obtain the final recycling route.

[0090] In practice, hybrid evolutionary mechanisms utilize the differences between the encoded values ​​of different candidate solutions to generate offspring solutions. However, as the population evolves iteratively, the positional deviations between different individuals gradually decrease, making the aforementioned principle prone to failure. Meanwhile, the current problem employs real-sequence mapping and rule-based path construction methods to generate the final scheduling scheme, effectively perturbing the current solution by changing the relative magnitudes of the encoded values. Based on this, to enhance the algorithm's mining performance, this paper constructs a discrete neighborhood search mechanism and embeds it into the algorithm. Specifically, using a variable neighborhood descent search framework, discrete mutation operations are used to generate neighborhood solutions for the current solution, thereby exploring more possible high-quality solution spaces. This paper utilizes two mutation methods—flipping and insertion—to generate neighborhood solutions for the current solution.

[0091] Specifically, flipping refers to generating two integer values ​​d1 and d2 using a random method based on the encoding length D, requiring 1≤d1≤d2≤D, and reversing the real number encoding between positions d1 and d2 to generate a new encoding scheme.

[0092] As an example, see Figure 2The diagram illustrates the flipping process, where the 7th to 11th array values ​​in the initial array are flipped. Specifically, the reversal position d1 is 7, and the reversal position d2 is 11. This means that the five array values ​​[0.11, 0.17, 0.85, 0.22, 0.24] within the initial array [0.07, 0.09, 0.42, 0.48, 049, 0.75, 0.11, 0.17, 0.85, 0.22, 0.24] are symmetrically flipped. After the flip, the initial array becomes [0.07, 0.09, 0.42, 0.48, 049, 0.75, 0.24, 0.22, 0.85, 0.17, 0.11]. The corresponding priority sequence also changes, updating to [1, 2, 7, 8, 9, 10, 6, 5, 11, 4, 3]. The corresponding recycling routes at this time include: (Recycling) Route 1, (Recycling) Route 2, (Recycling) Route 3, (Recycling) Route 4, and (Recycling) Route 5. Among them, (Recycling) Route 1 includes: Recycling Center 1, Recycling Point 1, and Recycling Point 2. (Recycling) Route 2 includes: Recycling Center 2, Recycling Point 8, and Recycling Point 7. (Recycling) Route 3 includes: Recycling Center 2, Recycling Point 10, and Recycling Point 9. (Recycling) Route 4 includes: Recycling Center 1, Recycling Point 4, and Recycling Point 3. (Recycling) Route 5 includes: Recycling Center 1, Recycling Point 6, Recycling Point 5, and Recycling Point 11. Figure 2 The number after the "|" indicates the amount of waste to be recycled at the recycling point.

[0093] Specifically, the insertion operation refers to generating two integer values ​​d1 and d2 using a random method based on the length D, requiring 1≤d1≤D and 1≤d2≤D, and inserting the code at position d1 before position d2 to generate a new encoding scheme.

[0094] As an example, see Figure 3The diagram illustrates the insertion process, where d1 is 10 and d2 is 3. The initial array can be [0.07, 0.09, 0.42, 0.48, 049, 0.75, 0.11, 0.17, 0.85, 0.22, 0.24]. Therefore, inserting the array value "0.22" before the array value "0.42" results in the reversed initial array [0.07, 0.09, 0.22, 0.42, 0.48, 049, 0.75, 0.11, 0.17, 0.85, 0.24]. The corresponding priority sequence will also change, updating to [1, 2, 5, 7, 8, 9, 10, 3, 4, 11, 6]. The corresponding recycling lines at this point include: (Recycling) Line 1, (Recycling) Line 2, (Recycling) Line 3, (Recycling) Line 4, and (Recycling) Line 5. The recycling route 1 includes: recycling center 1, collection point 1, and collection point 2. Recycling route 2 includes: recycling center 2, collection point 5, collection point 7, and collection point 8. Recycling route 3 includes: recycling center 2, collection point 9, and collection point 10. Recycling route 4 includes: recycling center 1, collection point 3, collection point 4, and collection point 11. Recycling route 5 includes: recycling center 1 and collection point 6. Figure 3 The number after the "|" indicates the amount of waste to be recycled at the recycling point.

[0095] Based on the aforementioned neighborhood structure, given the candidate solution x to be improved and the number of iterations L for the variable neighborhood descent search, and defining the neighborhood sequence as <exchange, flip>, the steps of the discrete neighborhood search are summarized as follows:

[0096] Step S1: Make k←1, then proceed to step S2.

[0097] Step S2: Make l ← 1 and x * ←x, proceed to step S3.

[0098] Where l is the number of iterations.

[0099] Step S3: If k=1, proceed to step S4; if k=2, proceed to step S5; otherwise, proceed to step S9.

[0100] Step S4: Randomly generate integer variables d1 and d2, requiring 1≤d1≤d2≤D. Reverse the encoding of x between positions d1 and d2 to generate x′, and go to step S6.

[0101] Step S5: Randomly generate integer variables d1 and d2, requiring 1≤d1≤d2≤D. Swap the encodings of solution x on d1 and d2 to generate x′, and go to step S6.

[0102] Step S6: The decoding result in response to x′ is better than x. *The decoding result of x * ←x′; then proceed to step S7.

[0103] Step S7: Make l←l+1. If l≤L, go to step S3; otherwise, go to step S8.

[0104] Step S8: If the solution x * The decoding result of x is better than the decoding result of x, so let x←x * And k←1. Then, proceed to step S2.

[0105] Step S9: Output the improved solution x.

[0106] In practice, the improved solution x is used as a basis to adjust the corresponding priority sequence, and the final recycling line is obtained based on the priority sequence and the generation process corresponding to the initial recycling line.

[0107] Simulation Experiment

[0108] To verify the performance of the method disclosed in this paper in solving the multi-center waste recycling scheduling problem, numerical simulation experiments were conducted, with MATLAB 2018a selected as the platform.

[0109] (1) Instance parameters

[0110] In one area, a plan is underway to recycle routes to 40 nodes (facility nodes). Table 4 below provides the parameters corresponding to the recycling points:

[0111] Table 4

[0112]

[0113]

[0114] Next, please refer to Table 5 below for the parameters corresponding to the recycling center:

[0115] Table 5

[0116]

[0117]

[0118] (2) Analysis of experimental results

[0119] Simulation experiments were conducted based on Tables 4 and 5 above. The SCA algorithm and the CFWA (Cooperative Firefly Algorithm) algorithm were selected for comparison. The effectiveness of the proposed algorithm was verified by comparing the SCA algorithm with that of the proposed algorithm. The competitiveness of the proposed algorithm with scheduling algorithms in the same field was verified by comparing the CFWA algorithm with that of the proposed algorithm.

[0120] The parameters for the numerical experiment were set as follows: individual size 20, iteration count 600, the number of iterations for the discrete neighborhood search method in the HSCA algorithm 10, and other parameters for the CFWA algorithm set according to the corresponding literature. To balance efficiency and fairness, the SCA algorithm, CFWA algorithm, and the algorithm disclosed herein were all run 20 times.

[0121] The Relative Percentage Deviation (RPD) index was selected for evaluation, and the calculation formula for the RPD index is shown below:

[0122]

[0123] Here, represents the index value of the deviation index, and represents the optimal target value obtained, corresponding to the target values ​​of the optimal, average, and worst cases, respectively.

[0124] Specifically, the optimization results of the three algorithms are detailed in Table 6 below:

[0125] Table 6

[0126]

[0127]

[0128] In addition, the optimal recovery routes for the three algorithms are detailed in Table 7 below:

[0129] Table 7

[0130]

[0131]

[0132] Based on the experimental results, the following conclusions can be drawn: First, based on Tables 4, 5, 6 and 7, the effectiveness of the SCA algorithm, the CFWA algorithm and the algorithm of this disclosure can be proved, that is, the algorithm can handle various constraints of the multi-center recycling scheduling problem and find high-quality solutions. At the same time, it is verified that the encoding and decoding method and penalty term processing method proposed in this disclosure can effectively adapt to the model and algorithm.

[0133] As shown in Table 6, the optimization results demonstrate that the proposed algorithm achieves superior performance in the optimal, mean, and worst-case scenarios. For the optimal solution, the RPD value obtained by the proposed algorithm is 5.08% and 4.43% higher than that of the SCA and CFWA algorithms, respectively. For the mean solution, the RPD value obtained by the proposed algorithm is 7.71%, which is 3.33% and 1.47% lower than that of the SCA and CFWA algorithms, respectively. For the worst-case solution, the RPD value obtained by the proposed algorithm is 11.08%, while the corresponding values ​​for the SCA and CFWA algorithms are 13.98% and 12.46%, respectively. In summary, the above example studies verify the effectiveness of the current mathematical model and demonstrate that the proposed algorithm has good optimization performance.

[0134] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a multi-center waste recycling scheduling device based on a hybrid sine and cosine algorithm. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this multi-center waste recycling scheduling device based on a hybrid sine and cosine algorithm can be specifically applied to various electronic devices.

[0135] like Figure 4 As shown, a multi-center waste recycling scheduling device 400 based on a hybrid sine and cosine algorithm in some embodiments includes: an encoding / decoding unit 401, a construction unit 402, a processing unit 403, and a solution optimization unit 404. The encoding / decoding unit 401 is configured to encode and decode according to the initialization parameters after parameter initialization is completed, generating an initial recycling route for the recycling center. The initial recycling route is a unidirectional transport vehicle route containing at least one recycling point, where each recycling point is a node where waste is to be recycled. The construction unit 402 is configured to construct an initial solution set based on the initial recycling route and chaotic mapping technology. The processing unit 403 is configured to process the initial solution set and a hybrid evolutionary mechanism to obtain a candidate solution set. The hybrid evolutionary mechanism includes: nonlinear parameter tuning features, co-evolutionary features, and random perturbation features. The solution optimization unit 404 is configured to optimize the candidate solution set through discrete neighborhood search to obtain the final recycling route.

[0136] It is understandable that the units described in the multi-center waste recycling scheduling device 400 based on the hybrid sine and cosine algorithm are similar to the reference units. Figure 1 The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the multi-center waste recycling scheduling device 400 based on the hybrid sine and cosine algorithm and the units contained therein, and will not be repeated here.

[0137] The following is for reference. Figure 5It illustrates a schematic diagram of the structure of an electronic device (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0138] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0139] In one embodiment, the processor is configured to run a computer program stored in a memory to perform the following steps: in response to parameter initialization, encoding and decoding are performed according to the initialization parameters to generate an initial recycling route for the recycling center, wherein the initial recycling route is a unidirectional transport vehicle route containing at least one recycling point, and the recycling point is a node where waste is to be recycled; an initial solution set is constructed based on the initial recycling route and chaotic mapping technology; a candidate solution set is obtained by processing the initial solution set and a hybrid evolutionary mechanism, wherein the hybrid evolutionary mechanism includes: nonlinear parameter tuning features, co-evolutionary features, and random perturbation features; and the candidate solution set is optimized through discrete neighborhood search to obtain the final recycling route.

[0140] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0141] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0142] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0143] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A multi-center waste recycling scheduling method based on a hybrid sine and cosine algorithm, characterized in that, include: In response to the completion of parameter initialization, the initial recycling route for the recycling center is generated by encoding and decoding according to the initialization parameters. The initial recycling route is a one-way transportation vehicle route that includes at least one recycling point. The recycling point is a node where waste is to be recycled. Based on the initial recycling route and chaotic mapping technique, construct the initial solution set; The candidate solution set is obtained by processing the initial solution set and the hybrid evolution mechanism, which includes: nonlinear parameter tuning feature, co-evolution feature and random perturbation feature. The candidate solution set is optimized by discrete domain search to obtain the final recovery route.

2. The method according to claim 1, characterized in that, The response, upon completion of parameter initialization, involves encoding and decoding based on the initialization parameters to generate an initial recycling route for the recycling center, including: Generate an initial array, wherein the length of the initial array is the same as the number of points to be recycled in the set of points to be recycled, the array values ​​in the initial array correspond one-to-one with the points to be recycled in the set of points to be recycled, and the range of the array values ​​in the initial array is [0,1]. Following the ascending order of array values, value mapping is performed on the array values ​​in the initial array to obtain a priority sequence, where the mapping range is 1 to |N|. C |,N C Represents the set of points to be recycled; Based on the priority sequence, the first un-arranged recycling point in the set of recycling points is selected as the starting point of the initial recycling route; Based on the number of transport vehicles owned by the recycling center and constrained by the shortest distance, determine the recycling center corresponding to the initial recycling route; Starting from the priority of the starting station, select the recycling points corresponding to the priorities in the priority sequence in ascending order, add the selected recycling points to the initial recycling route, and cut off the route according to the vehicle loading capacity of the transport vehicle to obtain the initial recycling route.

3. The method according to claim 2, characterized in that, The construction of the initial solution set based on the initial recovery path and chaotic mapping technique includes: Step S1: Generate a random variable μ such that i←1, where i represents the index and μ takes values ​​in the range of (0,1]. Step S2: Make d←1, where d∈{1,2,…,D}, and D represents the length of the initial array; Step S3: In response to μ being 0, reset μ to a random value within the range (0,1], and make μ←cos(1 / μ) 2 ), where cos(1 / μ 2 The range of ) is [-1, 1]; Step S4: Based on the array value x of the i-th population solution at dimension d in the initial population... d , making in, This represents the minimum value in the initial array. μ represents the maximum value in the initial array. i+1 Let μ represent the (i+1)th random variable, and the initial population is determined based on the initial recycling route; Step S5: Make d←d+1, in response to d≤D, set x=(x1,…,x d ...) is determined as the i-th initial solution in the initial solution set, and the process jumps to step S3; Step S6: In response to d>D, i←i+1, and in response to i≤nPop, jump to step S2, where nPop represents the population size.

4. The method according to claim 3, characterized in that, The nonlinear parameter tuning characteristic is controlled by an adaptive parameter r1, which is characterized by the following formula: Where 'a' represents a constant, with a default value of 3, 'g' represents the current iteration number, and 'G' represents the total number of iterations.

5. The method according to claim 4, characterized in that, The co-evolutionary feature is controlled by the top three high-quality solutions corresponding to the Gray Wolf optimization algorithm, and the random perturbation feature is controlled by random perturbation with a 50% probability.

6. The method according to claim 5, characterized in that, The step of optimizing the candidate solution set through discrete neighborhood search to obtain the final recovery route includes: Based on the variable neighborhood descent search, the current solution and neighborhood solutions are generated through discrete mutation operations according to the candidate solution set; The solution is optimized based on the current solution and the solution in the domain to obtain the final recycling route.

7. A multi-center waste recycling scheduling device based on a hybrid sine and cosine algorithm, characterized in that, include: The encoding and decoding unit is configured to encode and decode according to the initialization parameters in response to the completion of parameter initialization, and generate an initial recycling route for the recycling center. The initial recycling route is a unidirectional transport vehicle route that includes at least one recycling point, and the recycling point is a node where waste is to be recycled. The building unit is configured to construct an initial solution set based on the initial recycling path and the chaotic mapping technique; The processing unit is configured to process the initial solution set and the hybrid evolution mechanism to obtain the candidate solution set, wherein the hybrid evolution mechanism includes: nonlinear parameter tuning features, co-evolutionary features and random perturbation features; The solution optimization unit is configured to optimize the candidate solution set through discrete neighborhood search to obtain the final recovery route.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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