Method, device and equipment for planning collection and transportation of household garbage under fuzzy garbage generation amount

Abstract

The application discloses a kind of fuzzy garbage production under domestic waste clean-up planning method, device and equipment, the method includes: defining the fuzzy variable of the garbage quantity to be cleaned of each garbage generation point;Introduce fuzzy opportunity constraint based on credibility measure theory to handle the fuzziness of garbage production;The objective function of fuzzy opportunity constraint programming model is constructed to minimize the cost of cleaning and transporting, the negative effect of transfer station and the additional cost generated by cleaning and transporting failure;According to fuzzy variable, fuzzy opportunity constraint and objective function, construct fuzzy opportunity constraint model based on credibility measure theory;Based on the C-W saving algorithm of fuzzy simulation and the improved NSGA-II algorithm, the fuzzy opportunity constraint programming model is solved, and the transfer station position of each garbage generation point best garbage operation and cleaning and transporting car scheduling scheme are obtained.The application can plan the transfer station position of each garbage generation point best garbage operation and cleaning and transporting car scheduling scheme under the condition that garbage production is uncertain.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a household garbage collection and transportation planning method, device and equipment under fuzzy garbage generation quantity. BACKGROUND

[0002] Further research on the garbage collection and transportation network in the embodiment considers that the timeliness of garbage collection and transportation for different types of garbage is different, so that the garbage collection and transportation frequencies of different garbage points are different, thereby causing the problem that the garbage collection and transportation frequencies of different garbage points are inconsistent. The path planning problem hopes to reduce the cost and the driving distance of the garbage collection and transportation vehicle; however, the garbage site selection problem hopes to be farther away from the residential area, and the two goals pursued by the garbage collection and transportation scheme arrangement and the garbage transfer station site selection are in conflict. In reality, the garbage generation quantity of each garbage point before the garbage collection and transportation vehicle performs the garbage collection and transportation is uncertain. Therefore, how to obtain the transfer station position of the best garbage operation of each garbage generation point and the garbage collection and transportation vehicle scheduling scheme under the condition that the garbage generation quantity is uncertain, so as to realize the balance of the three of reducing the cost, reducing the driving distance of the garbage collection and transportation vehicle and the garbage site selection being farther away from the residential area, becomes a problem to be solved urgently. SUMMARY

[0003] The household garbage collection and transportation planning method, device and equipment under fuzzy garbage generation quantity provided by the embodiment of the present application can plan the transfer station position of the best garbage operation of each garbage generation point and the garbage collection and transportation vehicle scheduling scheme under the condition that the garbage generation quantity is uncertain, so as to realize the balance of the three of reducing the cost, reducing the driving distance of the garbage collection and transportation vehicle and the garbage site selection being farther away from the residential area.

[0004] An embodiment of the present application provides a household garbage collection and transportation planning method under fuzzy garbage generation quantity, comprising:

[0005] defining a fuzzy variable of the garbage generation quantity of each garbage generation point;

[0006] introducing a fuzzy chance constraint based on the credibility measure theory to process the fuzziness of the garbage generation quantity;

[0007] constructing an objective function of the fuzzy chance constraint planning model to minimize the collection and transportation cost, the negative effect of the transfer station and the additional cost generated by the collection and transportation failure;

[0008] constructing a fuzzy chance constraint model based on the credibility measure theory according to the fuzzy variable, the fuzzy chance constraint and the objective function;

[0009] solving the fuzzy chance constraint planning model based on the C-W saving algorithm and the improved NSGA-II algorithm based on fuzzy simulation to obtain the transfer station position of the best garbage operation of each garbage generation point and the garbage collection and transportation vehicle scheduling scheme.

[0010] As an improvement of the above scheme, a fuzzy variable of the amount of garbage to be collected at each garbage generation point is defined, including:

[0011] W{w|w=1,2,…,|W|} is a set of garbage types, there are |W| types of garbage in the collection system, representing the wth type of garbage;

[0012] E{e|e=1,2,…,N} is a set of garbage generation points;

[0013] D{d|d=1,2,…,M} is a set of transfer stations;

[0014] A{i,j|i,j=1,2,…,M+N} is the union of all points;

[0015] V{v|v=1,2,…,|V|} is a set of vehicles;

[0016] K{k|k=1,2,…,|K|} is a set of types of garbage collection vehicles, k represents the kth type of dedicated collection vehicle; kv represents a specific garbage collection vehicle;

[0017] S{s|s=1,2,…,s n} is a set of decision cycles;

[0018] d ij represents the distance between node i and node j;

[0019] t ij represents the travel time of the collection vehicle from node i to node j;

[0020] t kvi represents the time required for vehicle kv to arrive at node i;

[0021] C k represents the fixed cost of the kth type of dedicated collection vehicle;

[0022] C′ k represents the collection cost per unit distance of the kth type of dedicated collection vehicle;

[0023] cap ew represents the maximum capacity of the wth type of garbage at garbage generation point e;

[0024] δ w represents the timeliness constraint of the wth type of garbage;

[0025] π d represents the fixed cost of constructing a transfer station at a potential site D;

[0026] represents the amount of garbage type w generated at garbage generation point e in the Sth cycle;

[0027] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0028] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; kvw Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0029] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; i Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0030] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; i Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0031] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0032] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0033] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0034] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; t Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; t Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0035] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0036] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0037] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0038] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0039] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0040] Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle; Qs represents the amount of waste class w collected by the recycling vehicle kv at the waste point e in the Sth cycle;

[0041] min f1′=C′ w d′+αmax{[a i -(T i s +t′)],0}+βmax{[(T i s +t′)-b i ],0}。

[0042] As an improvement of the above scheme, the C-W saving algorithm based on fuzzy simulation and the improved NSGA-II algorithm are used to solve the fuzzy chance-constrained programming model, so as to obtain the optimal transfer station position of garbage operation and the garbage collection and transportation scheduling scheme of each garbage generation point, including:

[0043] All garbage generation points and transfer stations are connected in a loop, and each sub-loop is a round trip of 0-some garbage point-0.

[0044] Saving value calculation and merging: calculate the saving driving distance of any two sub-loops after merging, sort the saving values, select the two sub-loops with the largest saving value for merging, and stop until no further merging is possible, ensuring that each merging does not exceed the maximum capacity of the vehicle;

[0045] Multi-objective optimization: in the improved NSGA-II algorithm, the minimization of the total collection cost and the minimization of the negative effect of the garbage transfer station are considered as optimization objectives;

[0046] Fast non-dominated sorting: fast non-dominated sorting is performed on the population to distinguish different Pareto front solution sets;

[0047] Elite reservation strategy: the optimal solution set is selected and reserved by calculating the crowding degree of each solution, to ensure the diversity and uniformity of the solutions;

[0048] Genetic operation: new generations are generated through selection, crossover and mutation operations, and evolution is performed until the stopping condition is met;

[0049] Decision maker risk preference analysis: analyze the solutions of the model under different Cr* values, find the optimal Cr* value, and balance the cost and efficiency when the garbage generation amount is uncertain, to obtain the optimal transfer station location and garbage collection and transportation scheduling scheme.

[0050] Another embodiment of the present application provides a fuzzy garbage generation amount-based household garbage collection and transportation planning device, comprising:

[0051] A definition module is used to define the fuzzy variable of the to-be-collected garbage amount of each garbage generation point.

[0052] A constraint module is used to introduce a fuzzy chance constraint based on credibility measure theory to process the fuzziness of the garbage generation amount.

[0053] A first construction module has a target function for constructing a fuzzy chance-constrained programming model to minimize the collection cost, the negative effect of the transfer station, and the additional cost caused by the failure of collection;

[0054] A second construction module is used to construct a fuzzy chance-constrained model based on the credibility measure theory according to the fuzzy variable, the fuzzy chance constraint and the target function.

[0055] a solving module configured to solve the fuzzy chance-constrained programming model based on the fuzzy simulation C-W saving algorithm and the improved NSGA-II algorithm, to obtain the optimal transfer station position of garbage collection and transportation for each garbage generation point and a garbage collection and transportation vehicle scheduling scheme.

[0056] As an improvement of the above scheme, the defining module is specifically configured to define the following parameters:

[0057] W{w|w=1,2,…,|W|} is a set of garbage types, there are |W| types of garbage in the garbage collection and transportation system, and represents the wth type of garbage;

[0058] E{e|e=1,2,…,N} is a set of garbage generation points;

[0059] D{d|d=1,2,…,M} is a set of transfer stations;

[0060] A{i,j|i,j=1,2,…,M+N} is a set of all points;

[0061] V{v|v=1,2,…,|V|} is a set of vehicles;

[0062] K{k|k=1,2,…,|K|} is a set of types of garbage collection vehicles, and k represents the kth type of dedicated garbage collection vehicle; and kv represents a specific garbage collection vehicle;

[0063] S{s|s=1,2,…,s n} is a set of decision cycles;

[0064] d ij represents the distance between node i and node j;

[0065] t ij represents the travel time of the garbage collection and transportation vehicle from node i to node j;

[0066] t kvi represents the time required for the vehicle kv to arrive at node i;

[0067] C k represents the fixed cost of the kth type of dedicated garbage collection vehicle;

[0068] C′ k represents the garbage collection and transportation cost per unit distance of the kth type of dedicated garbage collection vehicle;

[0069] cap ew represents the maximum capacity of the wth type of garbage at the garbage generation point e;

[0070] δ w represents the timeliness constraint of the wth type of garbage;

[0071] πd This represents the fixed cost of constructing a transit station at potential location D;

[0072] This represents the amount of waste type w generated at waste generation point e in the Sth cycle;

[0073] This indicates the amount of type w waste collected by recycling vehicle kv at waste point e during cycle S;

[0074] Q kvw This indicates the cargo compartment capacity of vehicle kv for loading type w waste;

[0075] a i Let represent the earliest allowed service time to start at node i, where i∈A;

[0076] b i Let represent the latest allowed service time to start at node i, where i∈A;

[0077] Let represent the time it takes for the waste collection vehicle to reach node i in the Sth cycle, where i∈A;

[0078] α represents the waiting cost incurred if the vehicle arrives earlier;

[0079] β represents the penalty cost incurred if the vehicle arrives late;

[0080] U = (u1, u2, ..., u) t ), u t ∈E represents the planned waste collection route of the waste collection vehicle;

[0081] t′ represents the additional travel time caused by the failure of the collection;

[0082] d′ represents the additional travel distance resulting from the failure of waste collection;

[0083]

[0084] As an improvement to the above scheme, the objective function is as follows:

[0085]

[0086] minf1′=C′ w d′+αmax{[a i -(T i s +t')],0}+βmax{[(T i s +t′)-b i ],0}.

[0087] As an improvement of the above scheme, the solving module is specifically used for:

[0088] All garbage generation points and transfer stations are connected in a head-to-tail manner to form an initial sub-loop set, and each sub-loop is a round trip of 0-some garbage point-0;

[0089] Saving value calculation and merging: calculate the saving driving distance after merging any two sub-loops, sort the saving values, and select the two sub-loops with the largest saving value to merge, until no further merging is possible, to ensure that each merging does not exceed the maximum capacity of the vehicle;

[0090] Multi-objective optimization: in the improved NSGA-II algorithm, the minimization of the total collection cost and the minimization of the negative effect of the garbage transfer station are considered as optimization objectives at the same time;

[0091] Fast non-dominated sorting: fast non-dominated sorting is performed on the population to distinguish different Pareto front solution sets;

[0092] Elite reservation strategy: the optimal solution set is selected and reserved by calculating the crowding degree of each solution, to ensure the diversity and uniform distribution of the solutions;

[0093] Genetic operation: new generations of populations are generated through selection, crossover and mutation operations, and continuously evolve until the stopping condition is met;

[0094] Decision maker risk preference analysis: analyze the solutions of the model under different Cr* values, find the optimal Cr* value, and balance the cost and efficiency when the garbage generation amount is uncertain, to obtain the optimal transfer station location and collection vehicle scheduling scheme.

[0095] Another embodiment of the present application provides a household garbage collection planning device under fuzzy garbage generation, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the household garbage collection planning method under fuzzy garbage generation as described in the above embodiment of the application when executing the computer program.

[0096] Another embodiment of the present application provides a storage medium, which comprises a stored computer program, wherein the computer program controls the device where the computer readable storage medium is located to execute the household garbage collection planning method under fuzzy garbage generation as described in the above embodiment of the application when the computer program is running.

[0097] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0098] By defining the to-be-cleaned garbage amount of each garbage generation point as a fuzzy variable, the uncertainty and variability are processed through fuzzy mathematics, so that the model can better reflect the real-world scenario; by introducing a fuzzy opportunity constraint based on credibility measure theory, the uncertainty of the garbage generation amount can be more flexibly processed, ensuring that the cleaning task can be successfully completed to a certain extent even in the face of fluctuations in the garbage amount during garbage cleaning, while reducing resource waste caused by uncertainty; and the objective function constructed comprehensively considers the cleaning cost, the negative effect of the transfer station on the surrounding environment, and the additional cost increased due to cleaning failure, which helps to achieve the best balance between cost-effectiveness and environmental impact; then, based on the fuzzy variable, the fuzzy opportunity constraint and the objective function, a fuzzy opportunity constraint model is established, which can be used to quantify and optimize the entire garbage cleaning process, ensuring that reasonable decisions can be made under uncertain conditions; finally, the C-W saving algorithm based on fuzzy simulation and the improved NSGA-II algorithm are used to solve the model, the C-W saving algorithm is mainly used for local optimization, and by simulating the failure conditions in the actual cleaning process, the cleaning path is rescheduled to reduce the additional cost; and the improved NSGA-II algorithm is used to solve the multi-objective optimization problem, and through the means of fast non-dominated sorting, elite retention strategy and the like, the optimal transfer station location and cleaning vehicle scheduling scheme are ensured. From the above analysis, the embodiment of the present application creatively integrates fuzzy mathematics and optimization algorithm in view of the uncertainty of the amount of household garbage, describes the garbage amount variation range by constructing a fuzzy variable, introduces a fuzzy opportunity constraint based on credibility measure to manage cleaning risks, and designs a multi-objective model that comprehensively considers cost, environmental impact and operation efficiency. By combining the C-W saving algorithm with the improved NSGA-II algorithm, the problem of transfer station location and vehicle scheduling for household garbage cleaning under fuzzy garbage generation is solved, so as to realize efficient, low-cost and environmentally friendly garbage cleaning planning. Therefore, the embodiment of the present application can plan the optimal transfer station location and cleaning vehicle scheduling scheme for each garbage generation point under the condition of uncertain garbage generation, so as to realize the balance of reducing cost, reducing the driving distance of the cleaning vehicle and the distance between the garbage location and the residential area. BRIEF DESCRIPTION OF DRAWINGS

[0099] Figure 1 is a flowchart of a garbage cleaning planning method under fuzzy garbage generation provided by an embodiment of the present application;

[0100] Figure 2 is a flowchart of a C-W saving algorithm provided by an embodiment of the present application;

[0101] Figure 3 is a flowchart of a hybrid algorithm of the C-W saving algorithm based on fuzzy simulation and the improved NSGA-II algorithm provided by an embodiment of the present application;

[0102] Figure 4 is a structural schematic view of a household garbage collection and transportation planning device under a fuzzy garbage generation amount according to an embodiment of the present application. DETAILED DESCRIPTION

[0103] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0104] Reference is made to Figure 1 is a flowchart of a household garbage collection and transportation planning method under a fuzzy garbage generation amount according to an embodiment of the present application. The household garbage collection and transportation planning method under the fuzzy garbage generation amount comprises steps S10 to S14:

[0105] S10, defining a fuzzy variable of a garbage amount to be collected and transported at each garbage generation point;

[0106] S11, introducing a fuzzy chance constraint based on a credibility measure theory to process the fuzziness of the garbage generation amount;

[0107] S12, constructing an objective function of the fuzzy chance constraint planning model to minimize a collection and transportation cost, a transfer station negative effect and an additional cost generated due to a collection and transportation failure;

[0108] S13, constructing a fuzzy chance constraint model based on the credibility measure theory according to the fuzzy variable, the fuzzy chance constraint and the objective function;

[0109] S14, solving the fuzzy chance constraint planning model based on a fuzzy simulation C-W savings algorithm and an improved NSGA-II algorithm to obtain a transfer station position of optimal garbage transportation at each garbage generation point and a collection and transportation vehicle scheduling scheme.

[0110] The embodiment of the present application defines the to-be-cleaned garbage amount of each garbage generation point as a fuzzy variable, so that the uncertainty and variability are processed through fuzzy mathematics, so that the model can better reflect the real world scenario; by introducing a fuzzy opportunity constraint based on credibility measure theory, the uncertainty of the garbage generation amount can be more flexibly processed, ensuring that the cleaning and transportation process can be successfully completed to a certain extent even in the face of fluctuations in the amount of garbage, while reducing resource waste caused by uncertainty; and the objective function constructed comprehensively considers the cleaning and transportation cost, the negative effect of the transfer station on the surrounding environment, and the additional cost increased due to the failure of cleaning and transportation, which helps to achieve the best balance between cost-effectiveness and environmental impact; then, based on the fuzzy variable, the fuzzy opportunity constraint and the objective function, a fuzzy opportunity constraint model is established, so that the entire process of garbage cleaning and transportation can be quantified and optimized, ensuring that reasonable decisions can be made under uncertain conditions; finally, the C-W saving algorithm based on fuzzy simulation and the improved NSGA-II algorithm are used to solve the model, the C-W saving algorithm is mainly used for local optimization, and the cleaning and transportation path is rescheduled to reduce the additional cost by simulating the failure in the actual cleaning and transportation process; and the improved NSGA-II algorithm is used to solve the multi-objective optimization problem, and through the means of fast non-dominated sorting, elite preservation strategy and the like, the optimal transfer station location and cleaning and transportation vehicle scheduling scheme are ensured. From the above analysis, the embodiment of the present application creatively integrates fuzzy mathematics and optimization algorithm in view of the uncertainty of the amount of household garbage, describes the garbage amount variation range by constructing fuzzy variables, introduces a fuzzy opportunity constraint based on credibility measure to manage the cleaning and transportation risk, and designs a multi-objective model that comprehensively considers cost, environmental impact and operation efficiency. By combining the C-W saving algorithm with the improved NSGA-II algorithm, the problem of transfer station location and vehicle scheduling for household garbage cleaning and transportation under fuzzy garbage generation is solved, so as to realize efficient, low-cost and environmentally friendly garbage cleaning and transportation planning. Therefore, the embodiment of the present application can plan the best transfer station location and cleaning and transportation vehicle scheduling scheme for each garbage generation point under the condition of uncertain garbage generation, so as to realize the balance of reducing cost, reducing the driving distance of the cleaning and transportation vehicle and reducing the distance between the garbage location and the residential area.

[0111] In the recycling operation, special vehicles for each type of garbage are used as cleaning and transportation tools, and special garbage cleaning and transportation vehicles can only clean and transport a specified type of garbage. The cleaning and transportation process includes S={1,2,…,s n decision cycles, and two cleaning and transportation time constraints need to be met in each cycle, one is to meet the timeliness constraint δ w of w types of garbage in the system, and the other is to meet the maximum capacity constraint cap ewWhen the amount of a certain type of garbage reaches the maximum capacity of the garbage generation point, it needs to be collected and transported in time. In the site selection problem of the garbage transfer station, because the garbage transfer station mainly concentrates on the treatment of recycled garbage, residents will prefer that the transfer station is far away from the residential area and is not suitable to be built near the residential area. Therefore, in addition to considering the transportation cost, the site selection of the transfer station also needs to consider the negative effects on the nearby residents.

[0112] In addition, in order to make the established model more in line with the actual situation, the following assumptions can be made:

[0113] 1) The coordinates and service time window of each garbage point are known, and the amount of garbage generated is uncertain. The amount of each type of garbage generated at each garbage generation point does not exceed the maximum carrying capacity of the garbage collection vehicle of that type;

[0114] 2) The transfer station has a certain number of vehicles;

[0115] 3) The garbage truck makes a judgment when it arrives at the garbage generation point. When the amount of the corresponding type of garbage can meet the loading capacity of the garbage collection vehicle, the garbage at the point will be collected and transported (such as a special garbage collection vehicle for recyclable garbage, which needs the amount of recyclable garbage at the garbage point to be less than or equal to the loading capacity of the vehicle), otherwise the garbage point will be skipped and the garbage will be collected and transported by other garbage collection vehicles;

[0116] 4) Different types of garbage at the same garbage point can be collected and transported by multiple garbage collection vehicles;

[0117] 5) The vehicle is not affected by traffic conditions;

[0118] 6) The location of the garbage transfer station must be selected from the candidate points, and the construction cost of the garbage transfer station needs to be considered;

[0119] 7) The special vehicle is a single vehicle cabin and can only transport a specific type of garbage;

[0120] 8) Each garbage generation point is an undirected arc, that is, the vehicle can go from garbage generation point i to garbage generation point j, or from garbage collection point j to garbage collection point i;

[0121] 9) The garbage collection vehicle can start collecting garbage at the residential area without waiting;

[0122] 10) Different types of garbage are transported by special garbage collection vehicles and cannot be mixed;

[0123] 11) The garbage collection time is at the end of each period, that is, the accumulated garbage and the garbage generated on the same day are collected and transported together.

[0124] As an improvement of the above scheme, the fuzzy variables of the amount of garbage to be collected at each garbage generation point are defined, including:

[0125] W {w | w = 1, 2, …, |W|} is a set of garbage types, there are |W| types of garbage in the collection system, representing the wth type of garbage;

[0126] E {e | e = 1, 2, …, N} is a set of garbage generation points;

[0127] D {d | d = 1, 2, …, M} is a set of transfer stations;

[0128] A {i, j | i, j = 1, 2, …, M+N} is the union of all points;

[0129] V {v | v = 1, 2, …, |V|} is a set of vehicles;

[0130] K {k | k = 1, 2, …, |K|} is a set of garbage collection vehicle types, k represents the kth type of dedicated collection vehicle; kv represents a specific garbage collection vehicle;

[0131] S {s | s = 1, 2, …, s n} is a set of decision cycles;

[0132] d ij represents the distance between node i and node j;

[0133] t ij represents the travel time of the collection vehicle from node i to node j;

[0134] t kvi represents the time required for vehicle kv to arrive at node i;

[0135] C k represents the fixed cost of the kth type of dedicated collection vehicle;

[0136] C′ k represents the collection cost per unit distance of the kth type of dedicated collection vehicle;

[0137] cap ew represents the maximum capacity of the garbage generation point e for w types of garbage;

[0138] δ w represents the timeliness constraint of the wth type of garbage;

[0139] π d represents the fixed cost of building a transfer station at a potential site D;

[0140] represents the generation amount of garbage type w at garbage generation point e in the Sth cycle;

[0141] represents the amount of w type garbage collected at garbage point e using garbage collection vehicle kv in the Sth cycle;

[0142] Q kvw This indicates the cargo compartment capacity of vehicle kv for loading type w waste;

[0143] a i Let represent the earliest allowed service time to start at node i, where i∈A;

[0144] b i Let represent the latest allowed service time to start at node i, where i∈A;

[0145] Let represent the time it takes for the waste collection vehicle to reach node i in the Sth cycle, where i∈A;

[0146] α represents the waiting cost incurred if the vehicle arrives earlier;

[0147] β represents the penalty cost incurred if the vehicle arrives late;

[0148] U = (u1, u2, ..., u) t ), u t ∈E represents the planned waste collection route of the waste collection vehicle;

[0149] t′ represents the additional travel time caused by the failure of the collection;

[0150] d′ represents the additional travel distance resulting from the failure of waste collection;

[0151]

[0152]

[0153] In one embodiment, the amount of waste generated at each waste generation point e (e∈E) is represented by a triangular fuzzy number. To indicate, among which

[0154] If the waste generated at the point of origin has already been cleared in cycle s-1, then after a certain waste collection vehicle has cleared m customer points, the amount of type w waste loaded on the vehicle in cycle s is:

[0155]

[0156] If the waste generated at this point was not cleared in the previous x cycles, then after a certain waste collection vehicle has cleared m customer points, the amount of type w waste loaded on the vehicle in the s-th cycle will be:

[0157]

[0158] The vehicle's remaining collection capacity for type w waste is:

[0159]

[0160] Since the demand of the garbage generation point is a triangular fuzzy number The remaining cleaning capacity of the vehicle Qm = (Qm1, Qm2, Qm3) is also a triangular fuzzy number, where Qm1≤ Qm2≤ Qm3, and

[0161]

[0162] Since the amount of garbage generated is fuzzy, it is difficult to determine whether the cleaning vehicle has enough cleaning capacity to continue serving the next garbage point after cleaning the garbage of m garbage points. However, it is clear that the stronger the remaining cleaning capacity of the cleaning vehicle, the less garbage the next node generates, and the greater the opportunity for the cleaning vehicle to serve the next node. This embodiment determines the vehicle routing-location problem under fuzzy garbage generation by adding a fuzzy opportunity constraint. According to the actual situation of garbage cleaning, the decision maker's preference value is set in advance Then the relationship between the amount of w-class garbage of the next garbage generation point m+1 and the remaining cleaning capacity of the cleaning vehicle after serving m customers is a fuzzy constraint.

[0163] A fuzzy event with a possibility of 1 may not occur; a fuzzy event with a necessity of 0 may also occur. However, if the credibility of a fuzzy event is 1, the event will definitely occur; in other words, if the credibility of a fuzzy event is 0, the event will definitely not occur. In fuzzy theory, the credibility measure is better than the possibility and necessity measures, so this embodiment uses the credibility measure as a measure to obtain more accurate results. The credibility measure of the amount of garbage of the next garbage generation point being less than the remaining cleaning capacity of the cleaning vehicle is represented as:

[0164]

[0165] where C r ∈ [0, 1]. When the amount of garbage of the next garbage generation point is smaller and the remaining cleaning capacity of the cleaning vehicle is larger, C r is larger, and the credibility of the cleaning vehicle meeting the fuzzy amount of garbage of the next garbage generation point is higher; C r＝ 1 indicates that the cleaning vehicle definitely has the ability to clean the garbage generated by the next node; C r＝ 0 indicates that the cleaning vehicle definitely does not have the ability to clean the garbage generated by the next node and needs other cleaning vehicles to clean the node. In the fuzzy opportunity constraint , the decision maker's subjective risk preference coefficient It is a quantitative indicator for decision-makers to choose whether to collect waste from a certain waste-generating point in an ambiguous environment. For "risk-seekers" who hope to make full use of the remaining collection capacity of waste collection trucks and are willing to risk that the trucks will not be able to meet the needs of the next waste-generating point, they will generally consider... The C value is set lower; conversely, in order to make it more likely that the remaining collection capacity of the collection truck can be used to collect the garbage at the next node, "risk-averse" individuals will generally set the C value higher. This expresses a radical attitude, regardless of the credibility measure C. r Regardless of the value, the garbage truck is required to proceed to the next garbage collection point to perform the collection task. This indicates a conservative approach: the garbage truck will only proceed to the next garbage collection point to perform its collection task if its remaining collection capacity is greater than or equal to the amount of garbage at that point.

[0166] For the pre-set decision-maker's subjective risk preference coefficient The reliability of the statement that the amount of garbage at the next garbage collection point is less than the remaining collection capacity of the garbage truck is C. r In the route planning of waste collection vehicles, when When the time comes, arrange for a garbage truck to proceed to the next garbage collection point for collection; when When a node is abandoned, the garbage truck continues to collect garbage from subsequent points, and a new truck is dispatched to collect garbage from that node. This process is repeated until all garbage collection points are covered, resulting in a feasible garbage truck route plan. However, in fuzzy reliability theory, C... r This differs from probability in probability theory, that is, when Even if all garbage collection points in the planned route of the garbage truck meet the requirements... While constraints exist, the amount of garbage at certain collection points may still exceed the remaining collection capacity of the collection trucks during actual operation. This creates failure points, requiring the trucks to return to the garbage transfer station to unload the garbage before returning empty to the failure point to complete the remaining collection task. This results in additional travel routes and potentially incurs time window penalties. However, due to the ambiguity of garbage generation, the exact location, frequency, and resulting additional travel time and distance of failure points in the planned route are unknown. Therefore, when evaluating planned route options for collection trucks, both the cost of the collection route and the additional travel routes caused by failure points must be considered. Thus, in addressing the issue of collection truck routing with uncertain demand, it is necessary to determine the most suitable subjective risk preference coefficient for decision-makers. This minimizes the total cost of waste removal.

[0167] After setting the conditions and defining the parameters above, the objective function of the fuzzy chance-constrained programming model is constructed as follows:

[0168]

[0169] minf1′=C′ w d′+αmax{[a i -(T i s +t′)],0}+βmax{[(T i s +t')-b i ],0}(4-8)

[0170] Equation (4-6) represents the objective function of minimizing costs when clearing according to the expected route plan; Equation (4-7) represents minimizing the negative effects of the transfer station on the surrounding area, where θ and r are parameters that reflect the degree of influence of the corresponding coefficients on utility; Equation (4-8) represents the objective function of minimizing the additional transportation costs and additional time window costs incurred when clearing fails.

[0171] Other constraints of the fuzzy chance-constrained programming model are as follows:

[0172]

[0173]

[0174]

[0175] wherein, formula (4-9) is that the credibility of the garbage point generated garbage quantity not exceeding the maximum capacity of the garbage point is higher than the preset confidence level; formula (4-10) indicates that the vehicle cabin capacity of the w type garbage is greater than the garbage quantity of the garbage generation point, so that the garbage can be removed; formula (4-11) indicates that when the garbage point is determined by the garbage removal vehicle, the garbage quantity of the point is not less than the maximum load of the garbage removal vehicle, and the credibility is greater than the preset confidence level; formula (4-12) is a garbage removal quantity constraint; formula (4-13) is a garbage removal time constraint; formula (4-14) is that each type of garbage of each garbage generation point can be removed by only one vehicle; formula (4-15) ensures that the vehicle reaches the garbage point, so that the garbage of the garbage point can be removed; formula (4-16) ensures that the construction of the garbage transfer station does not overlap; formula (4-17) prohibits the garbage removal vehicle from traveling between the transfer stations; formula (4-18) indicates that the garbage removal vehicle is prohibited from leaving after entering the garbage generation point; formula (4-19) indicates that a garbage source can be accessed multiple times; formula (4-20) ensures that each garbage removal vehicle can be matched with only one garbage transfer station; formula (4-21) ensures that the garbage removal vehicle departs from and returns to the same transfer station; formula (4-22) ensures that each garbage removal vehicle departs from the transfer station; formulas (4-23)-(4-27) are 0-1 variable constraints; formula (4-28) indicates that the time when the vehicle k reaches each node meets the time window requirement of each node; and formula (4-29) indicates that the time when the vehicle reaches the next customer j is the sum of the time when the vehicle reaches the previous customer i and the vehicle travel time from the previous customer i to the next customer j, and the vehicle operation is a continuous process.

[0176] In one embodiment, since the garbage removal vehicle cannot determine the garbage quantity generated by each garbage point before garbage removal, the accurate garbage quantity can be known only when the garbage removal vehicle reaches the garbage generation point for garbage removal. In this embodiment, the failure point is determined by simulating the garbage quantity generated by the garbage generation point, and then the actual garbage removal path is simulated to determine the additional garbage removal cost caused by the failure of the garbage removal vehicle, and the specific process is as follows:

[0177] Step 1: A “actual” garbage quantity to be removed is randomly generated for each garbage generation point e, and the “actual” garbage quantity to be removed is generated by using a simulation method, and the specific steps are as follows: ① a number g is randomly extracted in , and the membership degree μ(g) of g is calculated; ② a number θ is randomly extracted in [0, 1]; ③ μ(g) and θ are compared, if μ(g)≥θ, g is the actual garbage quantity of the garbage generation point; otherwise, the above steps are repeated; ④ the above steps are repeated until the simulation “actual” garbage quantity to be removed of all garbage generation points is generated.

[0178] Step2: According to the "actual" amount of waste to be treated generated in Step1, evaluate the additional transportation costs and time loss costs caused by "failure".

[0179] Step3: Repeat Step1, Step2 M times.

[0180] Step4: Calculate the average value through M times of simulation to estimate the additional transportation costs and time loss costs caused by the "failure" path.

[0181] As an improvement of the above scheme, the C-W saving algorithm based on fuzzy simulation and improved NSGA-II algorithm are used to solve the fuzzy chance-constrained programming model to obtain the optimal transfer station location and collection and transportation vehicle scheduling scheme for each waste generation point, including:

[0182] Connect all waste generation points and transfer stations end to end to form an initial set of sub-loops, and each sub-loop is a round trip of 0-some waste point-0;

[0183] Saving value calculation and merging: calculate the saving driving distance after merging any two sub-loops, sort the saving values, select the two sub-loops with the largest saving value to merge, and stop until no further merging is possible, ensuring that each merging does not exceed the maximum capacity of the vehicle;

[0184] Multi-objective optimization: in the improved NSGA-II algorithm, the minimization of total collection cost and the minimization of negative effect of waste transfer station are considered as optimization objectives;

[0185] Fast non-dominated sorting: perform fast non-dominated sorting on the population to distinguish different Pareto front solution sets;

[0186] Elite reservation strategy: select the optimal solution set for reservation by calculating the crowding degree of each solution to ensure the diversity and uniformity of the solution distribution;

[0187] Genetic operation: generate a new generation of population through selection, crossover and mutation operations, and continuously evolve until the stopping condition is met;

[0188] Decision maker risk preference analysis: analyze the solutions of the model under different Cr* values to find the optimal Cr* value to balance the cost and efficiency when the amount of waste generated is uncertain, and obtain the optimal transfer station location and collection and transportation vehicle scheduling scheme.

[0189] In this embodiment, specifically:

[0190] In the use of NSGA-II to solve the model, a pre-optimization scheme is generated, but in the pre-optimization scheme, even if the remaining collection capacity of the collection vehicle meets the reliability of all garbage generation points, in practice, due to the large deviation between the accumulated garbage generation point and the remaining collection capacity of the collection vehicle, the collection vehicle cannot perform collection when it reaches the garbage point, at this time the collection vehicle cannot meet the requirements of the garbage point, which is called a failure point. After encountering a failure point, the collection vehicle returns to the transfer station, and the failure point and subsequent garbage points on this route (all garbage points to be collected after the failure point on the same route) cannot continue to complete the collection, at this time the garbage generation point rescheduling strategy needs to be executed to adjust, and the strategy is to recombine and calculate a new collection route for all failure points and subsequent garbage points on the route in the pre-optimization scheme according to the global optimization idea. The steps of the garbage generation point rescheduling strategy are as follows:

[0191] Step 1: Identify failure points and subsequent points. First, use fuzzy simulation to simulate each collection route in the pre-optimization scheme, identify failure points and subsequent points, and because the model established in this embodiment allows the vehicle to visit the garbage generation point multiple times, the vehicle that cannot complete the collection at one time first uses the remaining collection capacity to collect part of the garbage, at this time the garbage point has not been completely collected, and then the collection vehicle returns to the garbage transfer station to end the collection task of this route. A set of garbage points to be rescheduled E' = {1, 2, …, e'} is established, E' ∈ E, the elements in the set are all failure points and subsequent garbage points in the pre-scheme, because the collection vehicle has visited the failure point, the amount of garbage to be collected in E' is known, but the amount of garbage to be collected in the subsequent garbage point is still unknown. If E' is an empty set, it means that the pre-optimization scheme is reasonable, and the collection vehicle can completely collect the garbage amount of each garbage point in the route, so there is no need to perform the rescheduling operation; otherwise, Step 2 is executed.

[0192] Step 2: Garbage generation point rescheduling execution. In the rescheduling strategy, the same fuzzy demand vehicle routing problem is solved, but because the rescheduling strategy is based on the pre-optimization scheme, before encountering the failure point, the route in the pre-optimization scheme is feasible, that is, a part of the garbage generation point has been completed. Therefore, the problem solved in the rescheduling strategy is relatively small compared to the problem model solved in the pre-optimization scheme, so this embodiment introduces the principle of the saving algorithm to design an insertion algorithm to reschedule the garbage points in E'. In the pre-optimization scheme, the decision maker's subjective risk preference coefficient The increase in demand requires that the garbage collection and transportation needs of the garbage points be met at one time, and failure should be avoided as much as possible, so there are The steps are the same as in the pre-optimization stage, and the fuzzy opportunity constraint must also be met in the rescheduling However, if there is still a possibility of failure, at which point the algorithm uses the failure point return strategy, and the route after rescheduling optimization is simulated using fuzzy simulation.

[0193] For the number of times the garbage truck is used, the rescheduling optimization strategy has three paths, so the number of times the garbage truck is used is three, while the failure point return strategy has four paths, so the number of times the garbage truck is used is four (the vehicle departs from the transfer station, which is considered as use), and it is clear that the rescheduling optimization strategy is less than the failure point return strategy in terms of the number of times the garbage truck is used, and the vehicle fixed cost of the rescheduling optimization strategy is lower.

[0194] For the path cost, the path cost c a of the garbage truck planning scheme is 135, the path cost c b of the garbage failure point return strategy is 215, and the path cost c c of the rescheduling optimization strategy is 200, and it is clear that c b >c c , that is, after rescheduling adjustment, the path cost can be effectively reduced.

[0195] By comparing the results of the use of the garbage truck and the path cost, it is shown that the rescheduling optimization strategy can better adjust the scheme, reduce the number of times the garbage truck is used, and reduce the vehicle fixed cost and path cost.

[0196] Since the rescheduling problem is a smaller fuzzy demand vehicle routing problem compared to the original problem, the C-W saving algorithm is used in this embodiment to execute the rescheduling optimization strategy. The C-W saving algorithm can optimize the vehicle travel distance through threading and parallelism. With the addition of time window constraints and vehicle capacity constraints to the problem, the C-W saving algorithm focuses on solving the VRP problem with uncertain number of distribution vehicles.

[0197] The basic principle of the C-W saving algorithm is to gradually combine two routes in the route planning problem into one, and each combination maximizes the reduction of vehicle travel distance. Only when a vehicle is fully loaded will the route of the next vehicle be planned. When merging the routes, the greedy idea is used, that is, the two routes with the largest saving value are combined without exceeding the maximum limit of vehicle capacity. When any two routes cannot be combined anymore, the calculation is ended.

[0198] The design principle is to use the triangle side length theorem - the sum of two sides is greater than the third side. The garbage truck is required to start from the garbage transfer station and go to two garbage generating points for garbage collection, and is required to return to the garbage transfer station after completing the garbage collection, and the amount of garbage to be collected at the two garbage generating points is not greater than the maximum load limit of the garbage truck. Respectively, the total travel distance is: L1=2a+2b. The garbage generating point 1 and the garbage generating point 2 are combined in the same route, and then the same garbage truck is used for garbage collection, and the total travel distance is: L2=a+b+c. By comparing the two methods of separate collection and combined collection, the distribution mileage saving value ΔL is: ΔL=L1-L2=2a+2b-(a+c+b)=a+b-c. Since the sum of two sides of a triangle is greater than the third side, ΔL>0, that is, the more customer points in a route, the more mileage can be saved, and the shortest travel distance can be achieved.

[0199] Therefore, the C-W saving algorithm solving steps can be obtained, and the algorithm flow is as shown in Figure 2

[0200] Step 1: Connect all garbage generating points and garbage transfer stations end to end, and the line after connection is: the garbage truck starts from the garbage transfer station, goes to a garbage generating point to complete the garbage collection, and then returns to the garbage transfer station. If there are e garbage generating points, then e 0-1-0, 0-2-0, …, 0-e-0 sub-loops can be connected to generate.

[0201] Step 2: Merge any two sub-loops to generate a new path. After merging, the total distance of all loops will definitely decrease, calculate the reduced travel distance and fill it into the saving value table.

[0202] Step 3: Sort the saving values from high to low in the saving value table.

[0203] Step 4: Merge the two sub-loops with the highest saving value. If the merged path exceeds the maximum capacity of the garbage truck, then merge the second or lower ranked two sub-loops. The merged path is, for example, 0-1-2-0, 0-3-4-0.

[0204] Step 5: Update the garbage truck path table, delete the merged sub-loops from the table, and add the newly synthesized path to the table.

[0205] Step 6: Repeat the above steps until any two routes are merged, which will violate the garbage truck capacity constraint, i.e. stop.

[0206] ​A hybrid algorithm combining a fuzzy simulation-based CW saving algorithm and an improved NSGA-II algorithm is presented. For fuzzy variables, a fuzzy simulation method and a rescheduling strategy are embedded. The process is as follows: Figure 3 As shown:

[0207] Step 1: Initialize the population using the optimal point set strategy, perform a fast non-dominated sort of the population, and generate the first generation population through three basic operations: selection, crossover, and mutation.

[0208] Step 2: Perform fuzzy simulation on each individual in the newly generated population, calculate the target value, and then, following the steps of the elite preservation strategy, merge the parent and offspring into a new population. Perform fast non-dominated sorting on the synthesized population and calculate the crowding degree of each individual in order to determine the optimal new parent population based on the non-dominated relationship and dynamic crowding degree.

[0209] Step 3: Perform selection, crossover, and mutation operations on the new parent population from Step 2 to generate the offspring population;

[0210] Step 4: When the maximum number of iterations is reached, output the Pareto optimal solution; otherwise, set Gen = Gen + 1 and return to Step 2 to loop again.

[0211] Step 5: Detect the output solution. If there is a failure point, use the CW algorithm to perform a rescheduling strategy on the output solution. Similarly, use fuzzy simulation on the rescheduled solution and compare it with the original solution. If the rescheduled solution is better than the original solution, output the result; otherwise, re-execute the rescheduling strategy.

[0212] Due to the decision-maker's subjective risk preference coefficient This has a significant impact on the site selection of garbage transfer stations and the planning of garbage collection vehicles. Therefore, this embodiment mainly solves different... The results of the model under different values ​​are used to analyze their impact on the objective function, and in The optimal solution is obtained by solving the problem. The constructed fuzzy chance-constrained programming model includes a decision-maker's subjective risk preference coefficient C, which ranges from [0,1]. Solving for different... The Pareto optimal solution set under the given values.

[0213] along with As the value of increases, the total cost gradually decreases because... The greater, the greater the travel distance of the removal scheme is expected, the smaller the additional travel distance caused by failure is, and thus the removal cost changes accordingly with the travel distance. With the change of the removal route, the location of the garbage transfer station is also affected, and the cost reduction of the removal vehicle caused by removal failure can be compensated to the location of the garbage transfer station, so that the selected transfer station is appropriately farther away from the garbage generation point. When the removal path planning under the fuzzy garbage generation amount is performed, the decision maker's subjective risk preference coefficient is 0.7, the optimal Pareto frontier can be obtained, that is, the total cost and negative effect value are minimum, and the value 0.7 indicates that when the credibility of the garbage generation point to the removal vehicle is 0.7, the removal vehicle can provide removal service for the garbage generation point.

[0214] Referring to Figure 4 , the structure diagram of a life garbage removal planning device under a fuzzy garbage generation amount provided by an embodiment of the present application is shown. The life garbage removal planning device under the fuzzy garbage generation amount comprises:

[0215] A definition module 10 is configured to define a fuzzy variable of the garbage generation amount of each garbage generation point.

[0216] A constraint module 11 is configured to introduce a fuzzy chance constraint based on the credibility measure theory to process the fuzziness of the garbage generation amount.

[0217] A first construction module 12 is configured to construct a target function of the fuzzy chance constraint programming model to minimize the removal cost, the transfer station negative effect, and the additional cost caused by removal failure.

[0218] A second construction module 13 is configured to construct a fuzzy chance constraint model based on the credibility measure theory according to the fuzzy variable, the fuzzy chance constraint, and the target function.

[0219] A solution module 14 is configured to solve the fuzzy chance constraint programming model based on the fuzzy simulation C-W saving algorithm and the improved NSGA-II algorithm to obtain the optimal garbage transfer station position of each garbage generation point and the removal vehicle scheduling scheme.

[0220] The embodiment of the present application defines the to-be-cleaned garbage amount of each garbage generation point as a fuzzy variable, so that the model can better reflect the real world situation through fuzzy mathematical processing of uncertainty and variability; the fuzzy opportunity constraint based on the credibility measure theory is introduced, so that the uncertainty of the garbage generation amount can be more flexibly processed, and the smooth completion of the cleaning task can be ensured to a certain extent even in the face of fluctuations in the garbage amount during garbage cleaning, while reducing the waste of resources caused by uncertainty; and the objective function constructed comprehensively considers the cleaning cost, the negative effect of the transfer station on the surrounding environment, and the additional cost increased due to cleaning failure, and such multi-dimensional consideration helps to achieve the best balance between cost-effectiveness and environmental impact; then, based on the fuzzy variable, the fuzzy opportunity constraint and the objective function, a fuzzy opportunity constraint model is established, so that the whole process of garbage cleaning can be quantified and optimized, and reasonable decisions can be made under uncertainty; finally, the C-W saving algorithm based on fuzzy simulation and the improved NSGA-II algorithm are used to solve the model, the C-W saving algorithm is mainly used for local optimization, and the cleaning path is rescheduled to reduce the additional cost by simulating the failure in the actual cleaning process; and the improved NSGA-II algorithm is used to solve the multi-objective optimization problem, and the optimal transfer station location and cleaning vehicle scheduling scheme are ensured by means of fast non-dominated sorting, elite retention strategy and other means. From the above analysis, the embodiment of the present application creatively integrates fuzzy mathematics and optimization algorithm in view of the uncertainty of the amount of household garbage, describes the garbage amount variation range by constructing fuzzy variables, introduces the fuzzy opportunity constraint based on credibility measure to manage the cleaning risk, and designs a multi-objective model that comprehensively considers cost, environmental impact and operation efficiency. By combining the C-W saving algorithm with the improved NSGA-II algorithm, the transfer station location and vehicle scheduling problem of household garbage cleaning under fuzzy garbage generation amount is solved, so as to realize efficient, low-cost and environmentally friendly garbage cleaning planning. Therefore, the embodiment of the present application can plan the optimal transfer station location and cleaning vehicle scheduling scheme of each garbage generation point under the condition of uncertain garbage generation amount, so as to realize the balance of reducing cost, reducing cleaning vehicle driving distance and garbage location distance from the residential area.

[0221] As an improvement of the above scheme, the definition module is specifically used to define the following parameters:

[0222] W{w|w=1,2,…,|W|} is a set of garbage types, there are |W| types of garbage in the cleaning system, representing the wth type of garbage;

[0223] E{e|e=1,2,…,N} is a set of garbage generation points;

[0224] D{d|d=1,2,…,M} is a set of transfer stations;

[0225] A {i, j | i, j = 1, 2,..., M + N} is the set of all nodes;

[0226] V {v | v = 1, 2,..., |V|} is the set of vehicles;

[0227] K {k | k = 1, 2,..., |K|} is the set of types of garbage collection vehicles, k represents the kth type of dedicated collection vehicle; kv represents a specific garbage collection vehicle;

[0228] S {s | s = 1, 2,..., s n} is the set of decision periods;

[0229] d ij represents the distance between node i and node j;

[0230] t ij represents the travel time of the collection vehicle from node i to node j;

[0231] t kvi represents the time required for vehicle kv to arrive at node i;

[0232] C k represents the fixed cost of the kth type of dedicated collection vehicle;

[0233] C′ k represents the per-unit distance collection cost of the kth type of dedicated collection vehicle;

[0234] cap ew represents the maximum capacity of w types of garbage at the garbage generation point e;

[0235] δ w represents the timeliness constraint of the wth type of garbage;

[0236] π d represents the fixed cost of constructing a transfer station at the potential site D;

[0237] represents the amount of w type of garbage generated at the garbage generation point e in the Sth period;

[0238] represents the amount of w type of garbage collected at the garbage point e using the garbage collection vehicle kv in the Sth period;

[0239] Q kvw represents the vehicle cabin capacity of vehicle kv for loading w type of garbage;

[0240] a i represents the earliest service time allowed to start at node i, where i ∈ A;

[0241] b irepresents the latest service time allowed to start at node i, where i∈A;

[0242] represents the time of the Sth cycle of the truck driving to node i, where i∈A;

[0243] α represents the waiting cost generated if the vehicle arrives in advance;

[0244] β represents the penalty cost generated if the vehicle arrives late;

[0245] U=(u1,u2,…,u t ), u t ∈E represents the truck's planned collection path;

[0246] t' represents the additional driving time generated by the collection failure;

[0247] d' represents the additional driving distance generated by the collection failure;

[0248]

[0249] As an improvement of the above scheme, the objective function is as follows:

[0250]

[0251] minf1′=C′ w d′+αmax[a i -(T i s +t′)],0}+βmax{[(T i s +t′)-b i ],0}。

[0252] As an improvement of the above scheme, the solving module is specifically configured to:

[0253] Connect all garbage generation points and transfer stations in a head-to-tail manner to form an initial set of sub-loops, and each sub-loop is a round trip of 0-some garbage point-0;

[0254] Saving value calculation and merging: calculate the saving driving distance after merging any two sub-loops, sort the saving values, select the two sub-loops with the largest saving value to merge, and stop until no further merging is possible, ensuring that each merging does not exceed the maximum capacity of the vehicle;

[0255] Multi-objective optimization: in the improved NSGA-II algorithm, the minimization of the total collection cost and the minimization of the negative effect of the garbage transfer station are considered as optimization objectives at the same time;

[0256] Fast non-dominated sorting: fast non-dominated sorting is performed on the population to distinguish different Pareto front solution sets;

[0257] Elite preservation strategy: by calculating the crowding degree of each solution, the optimal solution set is selected for preservation to ensure the diversity and uniformity of the solution distribution;

[0258] Genetic operation: new generation population is generated through selection, crossover and mutation operations, and evolution is continuously carried out until the stop condition is met;

[0259] Decision maker risk preference analysis: analyze the solutions of the model under different Cr* values, find the optimal Cr* value, and balance the cost and efficiency when the garbage generation amount is uncertain, to obtain the optimal transfer station site selection and collection and transportation vehicle scheduling scheme.

[0260] An embodiment of the present application provides a household garbage collection and transportation planning device under fuzzy garbage generation. The household garbage collection and transportation planning device under fuzzy garbage generation of the embodiment comprises a processor, a memory and a computer program stored in the memory and executable on the processor, for example, a household garbage collection and transportation planning program under fuzzy garbage generation. When the processor executes the computer program, the steps in each of the household garbage collection and transportation planning methods under fuzzy garbage generation embodiments are implemented. Alternatively, the processor executes the computer program to implement the functions of each module / unit in each of the device embodiments.

[0261] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the household garbage collection and transportation planning device under fuzzy garbage generation.

[0262] The household garbage collection and transportation planning device under fuzzy garbage generation can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The household garbage collection and transportation planning device under fuzzy garbage generation can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the schematic diagram is only an example of the household garbage collection and transportation planning device under fuzzy garbage generation, and does not constitute a limitation on the household garbage collection and transportation planning device under fuzzy garbage generation, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the household garbage collection and transportation planning device under fuzzy garbage generation can also include an input / output device, a network access device, a bus, etc.

[0263] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor. The processor is a control center of the household garbage collection planning device under the fuzzy garbage generation amount, and connects various parts of the household garbage collection planning device under the fuzzy garbage generation amount through various interfaces and lines.

[0264] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the household garbage collection planning device under the fuzzy garbage generation amount by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required for a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0265] The modules / units integrated with the household garbage collection and transportation planning device under the fuzzy garbage production amount can be stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, according to legislation and patent practice in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0266] It should be noted that the above-described device embodiments are only illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0267] The above is the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A household garbage collection planning method for a fuzzy garbage generation amount, characterized by, The application relates to a fuzzy chance-constrained programming model for garbage collection and transportation. The model includes: Defining fuzzy variables of garbage amounts at various garbage generation points; Introducing fuzzy chance constraints based on credibility measure theory to deal with the fuzziness of garbage amounts; Constructing an objective function of the fuzzy chance-constrained programming model to minimize collection and transportation costs, negative effects of transfer stations, and additional costs caused by collection and transportation failures; Based on the fuzzy variables, fuzzy chance constraints, and the objective function, a fuzzy chance-constrained programming model based on credibility measure theory is constructed; The model is solved by a fuzzy simulation-based C-W saving algorithm and an improved NSGA-II algorithm to obtain optimal transfer station locations for garbage collection and transportation at various garbage generation points and a garbage collection and transportation vehicle scheduling scheme. The fuzzy simulation-based C-W saving algorithm and the improved NSGA-II algorithm for solving the fuzzy chance-constrained programming model to obtain optimal transfer station locations for garbage collection and transportation at various garbage generation points and a garbage collection and transportation vehicle scheduling scheme include: Connecting all garbage generation points and transfer stations at the beginning and end to form an initial sub-loop set, and each sub-loop is a 0-some garbage point-0 round trip; Saving value calculation and merging: calculating the saving distance after merging any two sub-loops, sorting the saving values, selecting the two sub-loops with the largest saving value for merging, and stopping until no further merging is possible, ensuring that each merging does not exceed the maximum capacity of the vehicle; Multi-objective optimization: in the improved NSGA-II algorithm, minimizing the total collection and transportation cost and minimizing the negative effect of the garbage transfer station are simultaneously considered as optimization objectives; Scheduling strategy solving for fuzzy variables: Step 1: initialize the population by the elite set strategy, quickly non-dominantly sort the population, and generate the first generation population through selection, crossover, and mutation; Step 2: after calculating the target value by fuzzy simulation of each individual in the new population, the elite reservation strategy is used to first combine the parent and child populations into a new population, perform quick non-dominant sorting on the combined population, and calculate the crowding degree of each individual, so as to determine the best new parent population according to the non-dominant relationship and dynamic crowding degree; Step 3: perform selection, crossover, and mutation on the new parent population in Step 2 to generate a child population; Step 4: when the maximum number of iterations is reached, output the Pareto optimal solution; otherwise, let Gen = Gen + 1 and return to Step 2 for re-circulation; Step 5: detect the output scheme, and if there is a failure point, use the C-W algorithm to perform a rescheduling strategy on the output scheme, similarly use fuzzy simulation on the rescheduled scheme, and compare the rescheduled scheme with the original scheme; if the rescheduled scheme is better than the original scheme, the result is output; otherwise, the rescheduling strategy is re-executed; Decision maker risk preference analysis: analyze the solutions of the model under different Cr* values, find the optimal Cr* value, balance the cost and efficiency when the garbage amount is uncertain, and obtain the optimal transfer station location and garbage collection and transportation vehicle scheduling scheme. wherein the failure point is determined by fuzzy simulation of the amount of garbage to be collected at the garbage generation point, and then the actual collection path is simulated to determine the additional collection cost caused by the failure of the collection vehicle, including: Step 1: Randomly generate an "actual" amount of waste to be collected for each waste generation point e. This "actual" amount of waste to be collected is generated using a simulation method. The specific steps are as follows: ① In ① Randomly select a number g from the range [0,1] and calculate the membership degree μ(g) of g; ② Randomly select a number θ from the range [0,1]; ③ Compare the size of μ(g) and θ. If μ(g)≥θ, then g is the actual amount of garbage at the garbage generation point; otherwise, repeat the above steps; ④ Repeat the above steps until the simulated "actual" amount of garbage to be collected at all garbage generation points is generated. Step 2: According to the "actual" amount of garbage to be processed generated in Step 1, evaluate the additional transportation cost and time loss cost caused by "failure"; Step 3: Repeat Step 1, Step 2 M times; Step 4: Calculate the average value through M simulations to estimate the additional transportation cost and time loss cost that the "failure" path may cause; When it is necessary to recombine all failure points and subsequent garbage points in the pre-optimized scheme to calculate a new collection route, the following garbage generation point rescheduling strategy is executed for adjustment: Identify failure points and subsequent points: use fuzzy simulation to simulate each collection route in the pre-optimized scheme in turn to identify failure points and subsequent points, and the collection vehicle returns to the garbage transfer station to end the collection task of this route; a set of garbage points to be rescheduled E' = {1, 2, …, e'} is established, E' ∈ E, the elements in this set are all failure points and subsequent garbage points in the pre-scheme, the amount of garbage to be collected at the failure point is known, but the amount of garbage to be collected at the subsequent garbage point is unknown; if E' is an empty set, it means that the pre-optimized scheme is reasonable, and the collection vehicle can completely collect the garbage amount of each garbage point in the route, so there is no need to perform rescheduling operation; otherwise, execute the garbage generation point rescheduling step; Garbage point rescheduling: the principle of saving algorithm is introduced to design the insertion algorithm to reschedule the garbage points in E'. Because the pre-optimization scheme has planned the route once, if the decision maker's subjective risk preference coefficient is increased in the pre-optimization scheme, the planning has failed, and in the rescheduling, the decision maker's subjective risk preference coefficient is increased, which requires to meet the garbage point's cleaning and transportation demand at one time to meet the fuzzy opportunity constraints in the rescheduling If there is still a possibility of failure, the failure point return strategy is used at this time, and the route after rescheduling optimization is simulated using fuzzy simulation.

2. The household garbage collection planning method under the amount of fuzzy garbage generation as claimed in claim 1, wherein, Define fuzzy variables of the amount of garbage to be collected at each garbage generation point, including: W{w|w=1,2,…,|W|} is a set of garbage types, there are |W| types of garbage in the collection system, representing the wth type of garbage; E{e|e=1,2,…,N} is a set of garbage generation points; D{d|d=1,2,…,M} is a set of transfer stations; A{i,j|i,j=1,2,…,M+N} is the union of all points; V{v|v=1,2,…,|V|} is a set of vehicles; K{k|K=1,2,…,|K|} is a set of types of recycling garbage vehicles, k represents the kth type of special collection vehicle; kv represents a specific recycling garbage vehicle; S{s|s=1,2,…,sn} is a set of decision cycles; d ij denotes the distance between node i and node j; t ij represents the travel time of a cart from node i to node j; t kvi denotes the time needed for a vehicle k v to reach node i; C k Ck represents the fixed cost of the kth type of dedicated collection vehicle; C′ k represents the cost of the kth type of dedicated collection vehicle per unit distance; cap ew represents the maximum capacity of the garbage generation point e for w kinds of garbage; delta w δw represents the timeliness constraint for the wth waste; π d represents the fixed cost of building a transit station at potential site D; an amount of generation of the garbage type w indicating the garbage generation point e of the S-th cycle; represents the amount of waste of type w collected at the waste point e by the recycling vehicle kv in the s-th cycle; Q kvw represents the vehicle kv loading w type of garbage car capacity; a i denotes the earliest service time at which starting is allowed at node i, where i ∈ A; b i denotes the latest service time at which starting is allowed at node i, where i e A; denotes the time at which the s-th cycle of the transport vehicle is driven to the node i, wherein i e A; α represents the waiting cost generated if the vehicle arrives ahead of schedule; β represents the penalty cost generated if the vehicle arrives late; U = (u1, u2,..., u t ), u t ∈ E represents the planned collection path of the collection vehicle; t' represents the additional driving time caused by the failure of collection; d' represents the additional driving distance caused by the failure of collection; 3. The household garbage collection planning method under the amount of fuzzy garbage generation as claimed in claim 2, wherein, The objective function is as follows: minf1' = C w d' + a max{[a i - (T i s + t') - b i s + t') - b i ], 0}.

4. A waste collection and transportation planning device for fuzzy waste generation, characterized in that, The processor, the memory, and the computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program, the fuzzy garbage generation amount-based household garbage collection planning method of any one of claims 1-3 is implemented.

5. A computer readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the household garbage collection and transportation planning method under the fuzzy garbage production amount when the computer program runs.

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