CN116384611A - Garbage transportation route optimization method and device based on quantum genetic algorithm - Google Patents
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Abstract
The invention relates to the technical field of route planning, in particular to a garbage transportation route optimization method and device based on a quantum genetic algorithm. The method comprises the following steps: acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and target data comprise vehicle data, garbage data, road data, weather data and population data; constructing an objective function and corresponding constraint conditions of a garbage transportation route with minimum shipping cost and minimum resident aversion index as targets based on the target data; and solving an objective function by utilizing a quantum genetic algorithm based on constraint conditions to obtain an optimal path of garbage transportation. According to the method and the device, the optimal receiving and transporting route which is compatible with economy and resident experience can be obtained.
Description
Technical Field
The invention relates to the technical field of route planning, in particular to a garbage transportation route optimization method and device based on a quantum genetic algorithm.
Background
Along with the acceleration of the urban process and the growth of population, social and environmental influences caused by urban household garbage are increasingly valued by people, and how to efficiently collect and transport the growing urban household garbage becomes a real problem to be solved.
In the related art, the urban household garbage collection and transportation route planning mainly considers the garbage collection and transportation cost, only focuses on economic benefits, but does not consider the influence of certain garbage collection points caused by unreasonable route planning in the collection and transportation process on residents due to traffic jam and odor emission.
Therefore, there is a need for a method and a device for optimizing a garbage transportation route based on a quantum genetic algorithm to solve the above technical problems.
Disclosure of Invention
The embodiment of the invention provides a garbage transportation route optimization method and device based on a quantum genetic algorithm, which can obtain an optimal collection route considering economy and resident experience.
In a first aspect, an embodiment of the present invention provides a method for optimizing a garbage transportation route based on a quantum genetic algorithm, including:
acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and the target data comprises vehicle data, garbage data, road data, weather data and population data;
constructing an objective function and corresponding constraint conditions of a garbage transportation route with minimum shipping cost and minimum resident aversion index as targets based on the target data;
and solving the objective function by utilizing a quantum genetic algorithm based on the constraint condition to obtain an optimal path of garbage transportation.
In some embodiments, the shipping costs include fixed costs and mileageCost of process, the cost of collection and transportation
The calculation formula of (2) is as follows:
in the method, in the process of the invention,
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij representing the distance from an i node to a j node, wherein n represents the number of nodes, and the nodes comprise the garbage transfer station, each garbage collection point and the target parking lot;Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In some embodiments, the resident aversion index is composed of a congestion aversion index and a garbage deposit aversion index, the resident aversion index
The calculation formula of (2) is as follows:
in the method, in the process of the invention,w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In some embodiments, the objective function is obtained by weighting a minimum shipping cost objective and a minimum residential aversion index objective, and the objective function is:
in the method, in the process of the invention,b 1 andb 2 the weight of the minimum object of the shipping cost and the minimum object of the aversion index of the residents are respectively;
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij representing the distance from an i node to a j node, wherein n represents the number of nodes, and the nodes comprise the garbage transfer station, each garbage collection point and the target parking lot;w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In some embodiments, the constraints are:
the load of the garbage truck at any node does not exceed the rated load;
the number of times that the garbage truck goes to any collecting point in a single journey is not more than 1 time;
the garbage cleaning time limit of any garbage collection point does not exceed a preset time limit;
the running time of the garbage truck from the i node to the j node does not exceed the preset time.
In some embodiments, the solving the objective function by using a quantum genetic algorithm to obtain an optimal route of garbage transportation includes:
s1, initializing a primary population, and randomly generating a plurality of chromosomes taking quantum bits as codes;
s2, measuring each individual in the population once to obtain a corresponding determination solution;
s3, taking the objective function as an fitness function, and carrying out fitness evaluation on each determined solution;
s4, recording the optimal individual and the corresponding fitness;
s5, judging whether the calculation process meets the end condition, if so, decoding to generate an optimal route, and if not, executing the step S6;
and S6, updating the current population by utilizing the quantum rotating gate, taking the updated population as a new population, and executing the step S2 until the ending condition is met.
In a second aspect, an embodiment of the present invention further provides a garbage transportation route optimization device based on a quantum genetic algorithm, including:
the acquisition module is used for acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and the target data comprises vehicle data, garbage data, road data, weather data and population data;
the construction module is used for constructing an objective function and corresponding constraint conditions of the garbage transportation route with minimum shipping cost and minimum resident aversion index as targets based on the target data;
and the solving module is used for solving the objective function by utilizing a quantum genetic algorithm based on the constraint condition to obtain an optimal path of garbage transportation.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of the present specification is implemented.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method according to any of the embodiments of the present specification.
The embodiment of the invention provides a garbage transportation route optimization method and device based on a quantum genetic algorithm. The method comprises the steps of firstly determining a target area of route planning, wherein a plurality of garbage collection points and a transfer station exist in the target area, and garbage at each garbage collection point is transported to the transfer station by garbage trucks in a target parking lot. And then, acquiring target data of the target area, wherein the target data comprises historical data and real-time data, and the garbage generation speed of each garbage collection point, the weather condition and road conditions in the transportation process, including the traffic flow and the traffic flow, and the like can be predicted through the target data. Based on the target data, an objective function with minimum transportation cost and minimum aversion index of residents is constructed, so that when a transportation route is planned, not only economic benefits are considered, but also influence on life of the residents is reduced to the minimum, and resident experience is improved and complaint rate is reduced under the condition that the transportation cost meets the requirement. In addition, the objective function is solved through the quantum genetic algorithm, so that the speed and accuracy of route planning can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a garbage transportation route optimization method based on a quantum genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;
fig. 3 is a block diagram of a garbage transportation route optimizing device based on a quantum genetic algorithm according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
In the prior art, garbage at each garbage collection point is transported to a garbage transfer station by a garbage truck, and then is transported to a centralized processing center for centralized processing by a large-scale special garbage truck. At present, in the process of selecting a garbage transportation route, a transportation company mainly considers the transportation cost of the transportation company, so as to achieve the minimum collection and transportation cost. However, the household garbage has a plurality of hazards, on one hand, the household garbage can be rotten and mildewed after being piled for a long time, a large amount of harmful gas is released, dust and fine particles fly with wind, and the surrounding atmosphere is harmed. On the other hand, although the existing garbage truck has certain tightness, along with the increase of the service life of the truck and improper operation of a driver, the problem of leakage still exists in the transportation process, so that harmful substances are emitted, and the influence is caused to pedestrians in the transportation process. For the above reasons, when a vehicle selects a route with the minimum transportation cost as a target, inconvenience is often caused to the life of residents, for example, in order to shorten the course, a route with a relatively short distance but a relatively large flow of people is travelled, so that the probability of people inhaling unhealthy gas is increased; or the garbage amount of some garbage collection points is small, and the vehicle can choose to avoid the garbage collection points, so that the garbage of the part is not cleaned timely, and inconvenience is caused to the life of nearby residents. According to statistics of complaint centers, the complaint rate caused by the problem of garbage collection and transportation is increased year by year in recent years, and therefore, the problem that garbage collection and transportation cannot only consider economic problems is solved, people are required to use the garbage collection and transportation, comprehensive planning is combined with demands of residents, and under the condition that transportation cost is guaranteed to meet the demands, resident experience is improved, and the complaint rate is reduced.
Based on the above problems, the inventor proposes that the minimum shipping cost and the minimum index of dislike of residents can be used as comprehensive targets, an objective function of the garbage transportation route is constructed based on the comprehensive targets, and then the objective function is solved through a quantum genetic algorithm, so that an optimal route capable of balancing the transportation cost and the resident experience is obtained.
Referring to fig. 1, an embodiment of the present invention provides a garbage transportation route optimization method based on a quantum genetic algorithm, including:
102, constructing an objective function and corresponding constraint conditions of a garbage transportation route with minimum shipping cost and minimum index of dislike of residents as targets based on the target data;
and 104, solving the objective function by utilizing a quantum genetic algorithm based on the constraint condition to obtain an optimal path of garbage transportation.
In the embodiment of the invention, the garbage collection and transportation takes a specific area as a research object, a target area of route planning is firstly determined, a plurality of garbage collection points and a transfer station exist in the target area, and garbage at each garbage collection point is transported to the transfer station by garbage trucks in a target parking lot. And then acquiring target data of the target area, wherein the target data comprises historical data and real-time data, and the garbage generation speed of each garbage collection point, the weather condition in the transportation process, road conditions (such as human flow and vehicle flow) and the like can be predicted through the target data. Based on the target data, an objective function with minimum transportation cost and minimum aversion index of residents is constructed, so that when a transportation route is planned, not only economic benefits are considered, but also influence on life of the residents is reduced to the minimum, and resident experience is improved and complaint rate is reduced under the condition that the transportation cost meets the requirement. In addition, the objective function is solved through the quantum genetic algorithm, so that the speed and accuracy of route planning can be improved.
The manner in which the individual steps shown in fig. 1 are performed is described below.
Firstly, aiming at step 100, acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and target data comprise vehicle data, garbage data, road data, weather data and population data.
In this step, the target area is based on the actual operating area, which may be, for example, a county administrative area in which the refuse at all refuse collection points is transported to a specific transfer station by refuse trucks at a certain target yard. After the target area is determined, the data related to the target area can be acquired through the related department. For example, the type of the refuse vehicle, the service life, the maximum load, the fuel consumption, etc. may be acquired from a target yard, different periods of time may be acquired from a traffic department, traffic congestion states of different road segments, average traffic flows, etc., historical refuse types and refuse generation speeds of each refuse collection point may be acquired from an environmental protection department, real-time weather conditions may be acquired from a weather department, and population distribution conditions of each refuse collection point may be acquired from a demographic bureau, etc. Further, by the map locating function, the positions of the refuse transfer station, the target yard, and each refuse collection point are known.
Then, for step 102, based on the target data, an objective function and corresponding constraint conditions of the refuse transportation route targeting the minimum shipping cost and minimum resident aversion index are constructed.
In this step, the shipping costs are mainly composed of fixed costs and mileage costs. The fixed costs are mainly composed of wages of drivers and staff. The mileage cost mainly consists of oil cost, maintenance cost and depreciation cost, and the vehicle type and service life of the determined garbage truck are known, so that the corresponding oil cost, maintenance cost and depreciation cost are also predictable, and the unit distance cost of the garbage truck can be converted according to the cost.
In some embodiments, shipping costs
The calculation formula of (2) is as follows:
in the method, in the process of the invention,
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij the distance from the i node to the j node is represented, n represents the number of nodes, and the nodes comprise a garbage transfer station, each garbage collection point and a target parking lot;Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In this implementation, although there are multiple garbage collection points in the target area, the garbage truck does not approach each garbage collection point in a single trip, but selects the best collection point in combination with its own load carrying capacity, distances between nodes, population data, and the like. The passing collection points are different, the running distance of the garbage truck is also different, and the generated unit distance cost is also different. Therefore, the shipping costs vary from shipping route to shipping route, and route optimization is to find the route that minimizes shipping costs.
However, with the increasing demands of people for quality of life, garbage collection and transportation cannot be aimed at only minimizing economic cost, and the influence of the collection and transportation process on residents is considered at the same time. For example, the minimum of the resident aversion index is taken as the route optimization target.
In some embodiments, the resident aversion index is composed of a congestion aversion index and a refuse accumulation aversion index, the resident aversion index
The calculation formula of (2) is as follows:
in the method, in the process of the invention,w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In this embodiment of the present invention, in one embodiment,w ij is determined based on the average traffic flow between the i node and the j node, the average traffic flow being determined based on historical traffic data. Dividing the average traffic into a plurality of grades according to the historical traffic data, wherein different grades correspond to different weights, and determining according to the inclusion relation between the average traffic between the i node and the j node and each gradew ij Is a numerical value of (2). For example, the average people flow has 5 grades, the average people flow of grade A is more than 200 people/min, and the weight is 0.8; the average flow of people in class B is 150-200 people/min, and the weight is 0.7; the average flow of people in class C is 100-150 people/min, and the weight is 0.6; the average flow of people in class D is 50-100 people/min, and the weight is givenThe weight is 0.4; the average people flow rate of class E is 0-50 people/min, and the weight is 0.2. Then, when the average people flow from the 1 node to the 2 node at the same moment is 80 people/min according to the historical traffic data, the value of the average people flow is within the D-level intervalw 12 =0.4; the average people flow from the node 2 to the node 4 is 220 people/min, and the value of the average people flow is within the class A intervalw 24 =0.8; and so on, the weight occupied by the index of dislike of residents between any two nodes can be determined. It should be noted that, the average traffic volume is related to the time period, and the user may call the average traffic volume in the corresponding time period according to the actual time period of receiving and transporting the garbage truck.
Traffic aversion indexT ij Is determined based on an average congestion time between the i node and the j node, the average congestion time being determined based on historical traffic data. Dividing the average congestion time into a plurality of grades according to the historical traffic data, wherein different grades correspond to different weights, and determining according to the average congestion time between the i node and the j node and the inclusion relation of each gradeT ij Is a numerical value of (2). For example, the average congestion time is 5 grades, the average congestion time of grade A is more than 8 minutes, and the weight is 0.8; the average congestion time of the class B is 6-8 minutes, and the weight is 0.7; the average congestion time of the class C is 4-6 minutes, and the weight is 0.6; the average congestion time of the grade D is 2-4 minutes, and the weight is 0.4; the average congestion time of the E class is 0-2 minutes, and the weight is 0.2. Then, when it is determined from the historical traffic data that the average congestion time from node 1 to node 2 at the same time is 6 minutes, the value thereof is within the B-class intervalT 12 =0.7; the average congestion time from node 2 to node 4 is 1 minute, and the value of the congestion time is within the E-class intervalT 24 =0.2; and so on, the weight occupied by the index of dislike of residents between any two nodes can be determined. It should be noted that, the average congestion time is related to the time period, and the user may call the average congestion time of the corresponding time period according to the actual time period of receiving and transporting the garbage truck.
Garbage accumulation aversion indexS j Is determined based on the cleaning effect of the garbage, and the more the residual amount of the garbage isThe longer the cleaning time period, the greater the pile-up aversion index. The user may determine a corresponding pile-up aversion index for each garbage collection point based on historical data for each garbage collection point.
In addition, in the case of the optical fiber,y j the weight of (2) is related to the location of the garbage collection point, the farther from the crowd gathering,y j the smaller the weight of (c), the larger the opposite.
In some embodiments, the objective function is obtained by weighting the minimum objective of shipping cost and the minimum objective of the index of aversion to residents, and the objective function is:
in the method, in the process of the invention,b 1 andb 2 the weight of the minimum object of the shipping cost and the minimum object of the aversion index of the residents are respectively;
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij the distance from the i node to the j node is represented, n represents the number of nodes, and the nodes comprise a garbage transfer station, each garbage collection point and a target parking lot;w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In this embodimentIn the process, the liquid crystal display device comprises a liquid crystal display device,b 1 andb 2 the value of (2) is determined based on the comprehensive benefit and social benefit of the shipping company, and the profit threshold of the shipping company is ensured when the weights of the benefit and the social benefit are determined. In addition, in the case of the optical fiber,b 1 andb 2 the value of (2) is related to seasons, for example, the temperature in summer is higher, the process of mildew and decay of garbage is faster, the odor emitted by garbage accumulation and the odor emitted during transportation are heavier, and the garbage should be taken into considerationb 2 The value of (2) is increased appropriately. The temperature in winter is low, the mildew speed of the garbage is low, and the smell is small, so that the garbage can be properly reducedb 2 Is a weight of (2). In practical application, the user can adjust the weight between the two targets according to weather conditions, the operation condition of the carrier and social factors.
It should be further noted that, the garbage truck is provided with a real-time communication system, based on the communication system, the garbage truck to be optimized can communicate with other garbage trucks in the target area, and can acquire traffic data (including traffic flow, road conditions, etc.), garbage data (including positions of garbage collection points and garbage amount), weather data, etc. in the target area in real time. The user can adjust each parameter in the objective function according to the data acquired in real time, and feeds back the calculated optimal route to the driver in real time.
In some embodiments, when solving the objective function, a corresponding constraint condition needs to be formulated, including:
the load of the garbage truck at any node does not exceed the rated load;
the number of times that the garbage truck goes to any collecting point in a single journey is not more than 1 time;
the garbage cleaning time limit of any garbage collection point does not exceed a preset time limit;
the running time of the garbage truck from the i node to the j node does not exceed the preset time.
And solving the objective function based on the constraint condition to obtain an optimal route meeting the requirement. Of course, the user can also add more constraints according to the requirements, for example, the load of the garbage truck cannot be lower than the minimum threshold value, and different results can be obtained based on different constraints.
Finally, for step 104, solving the objective function by using the quantum genetic algorithm based on the constraint condition to obtain an optimal route of garbage transportation, including:
s1, initializing a primary population, and randomly generating a plurality of chromosomes taking quantum bits as codes;
s2, measuring each individual in the population once to obtain a corresponding determination solution;
s3, taking the objective function as an fitness function, and carrying out fitness evaluation on each determined solution;
s4, recording the optimal individual and the corresponding fitness;
s5, judging whether the calculation process meets the end condition, if so, decoding to generate an optimal route, and if not, executing the step S6;
and S6, updating the current population by utilizing the quantum rotating gate, taking the updated population as a new population, and executing the step S2 until the ending condition is met.
In this embodiment, the objective function is solved by the quantum genetic algorithm, so that the speed and accuracy of route planning can be improved.
It should be noted that, after the mathematical model is determined, how to establish the primary population and how to update and solve the primary population by using the quantum genetic algorithm are common general knowledge of those skilled in the art, and will not be described in detail herein.
The above embodiment is to start the garbage truck from the target parking lot, pass through a plurality of garbage collection points and garbage transfer stations, and then return to the target parking lot as a whole for route planning. And the influence of the change of the state of the garbage truck and the difference of road conditions on the route is not considered.
Thus, in some embodiments, the transport route of the target area may also be divided into a plurality of sub-phases, each sub-phase being performed:
adjusting the weight of each target in the target function corresponding to the current sub-stage based on the target data;
and solving an objective function of the current sub-stage by utilizing a quantum genetic algorithm to obtain an optimal sub-route of the current sub-stage. And finally, combining each optimal sub-route to obtain an optimal route of the target area.
In some embodiments, the number of sub-stages is three, and the method for dividing the transportation route of the target area into three sub-stages is as follows:
dividing a route from the target yard to any garbage collection point into a first stage;
dividing a route between any two garbage collection points and a route from any one garbage collection point to the garbage transfer station into a second stage;
and dividing the route from the garbage transfer station to the target parking lot into a third stage.
In the first stage, the garbage truck usually goes from the target parking lot to the garbage collection point in an empty state, and no garbage exists in the garbage truck, so harmful gas is basically not emitted, the influence on residents is relatively small, and at the moment, economic indexes and the garbage amount of each garbage point are mainly considered. Thus, this stage can be appropriately increasedb 1 Weight of (2) is reducedb 2 Is a weight of (2).
In the second stage, part of garbage is loaded in the garbage truck, at the moment, the influence of odor emitted in the transportation process on residents needs to be determined by combining the tightness of the garbage truck, the type of garbage, road conditions and the like, and the longer the congestion time is, the larger the influence on pedestrians passing through the route is, at the moment, the larger the average traffic volume interval needs to be avoided, and some economic indexes are properly reduced. Thus, this stage can be suitably reducedb 1 To increase the weight of (2)b 2 Is a weight of (2).
In the third stage, the garbage truck has completely unloaded the transported garbage to the garbage transfer station, is in an empty state, has small smell and does not need to consider the garbage amount of each garbage collection point any more, and can return to the target parking lot as soon as possible at this time as a main consideration factor. Thus, this stage can beb 1 The weight of (2) is set to be maximum.
The embodiment can effectively improve the rationality of route planning by optimizing the route section.
And finally, combining each optimal sub-route to obtain an optimal route of the target area. The specific method comprises the following steps: and taking the end node of the first sub-stage as the starting point of the second sub-stage, taking the end node of the second sub-stage as the starting point of the third sub-stage, and the like, so as to obtain the optimal route.
It should be noted that the primary population of each sub-phase is all possible routes that can connect the start node and the end node in that sub-phase. In addition, the method for solving the objective function of the current sub-stage by utilizing the quantum genetic algorithm to obtain the optimal sub-route of the current sub-stage is the same as the method for solving the whole-course objective function by utilizing the quantum genetic algorithm, and the detailed description is omitted here.
As shown in fig. 2 and 3, the embodiment of the invention provides a garbage transportation route optimizing device based on a quantum genetic algorithm. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 2, a hardware architecture diagram of an electronic device where a garbage transportation route optimization device based on a quantum genetic algorithm is located according to an embodiment of the present invention is shown, where in addition to a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 2, the electronic device where the device is located in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a packet, and so on. Taking a software implementation as an example, as shown in fig. 3, the device in a logic sense is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located and running the computer program. The garbage transportation route optimizing device based on the quantum genetic algorithm provided by the embodiment comprises:
an acquisition module 300, configured to acquire target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and target data comprise vehicle data, garbage data, road data, weather data and population data;
a construction module 302, configured to construct an objective function and a corresponding constraint condition of a refuse transportation route targeting minimum shipping cost and minimum index of aversion to residents based on the objective data;
and the solving module 304 is configured to solve the objective function by using a quantum genetic algorithm based on the constraint condition, so as to obtain an optimal path for garbage transportation.
In an embodiment of the present invention, the obtaining module 300 may be used to perform the step 100 in the above method embodiment, the constructing module 302 may be used to perform the step 102 in the above method embodiment, and the solving module 304 may be used to perform the step 104 in the above method embodiment.
In some implementations, the shipping costs in the build module 302 include fixed costs and mileage costs, shipping costs
The calculation formula of (2) is as follows:
in the method, in the process of the invention,
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij the distance from the i node to the j node is represented, n represents the number of nodes, and the nodes comprise a garbage transfer station, each garbage collection point and a target parking lot;Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
The resident aversion index in the construction block 302 is composed of a congestion aversion index and a garbage accumulation aversion index, the resident aversion index
The calculation formula of (2) is as follows:
in the method, in the process of the invention,w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
The objective function constructed by the construction module 302 is obtained by weighting and calculating the minimum objective of the shipping cost and the minimum objective of the aversion index of the residents, and the objective function is as follows:
in the method, in the process of the invention,b 1 andb 2 the weight of the minimum object of the shipping cost and the minimum object of the aversion index of the residents are respectively;
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij the distance from the i node to the j node is represented, n represents the number of nodes, and the nodes comprise a garbage transfer station, each garbage collection point and a target parking lot;w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
In some implementations, the constraints in the build module 302 are:
the load of the garbage truck at any node does not exceed the rated load;
the number of times that the garbage truck goes to any collecting point in a single journey is not more than 1 time;
the garbage cleaning time limit of any garbage collection point does not exceed a preset time limit;
the running time of the garbage truck from the i node to the j node does not exceed the preset time.
In some implementations, the solution module 304 is configured to perform the following operations:
s1, initializing a primary population, and randomly generating a plurality of chromosomes taking quantum bits as codes;
s2, measuring each individual in the population once to obtain a corresponding determination solution;
s3, taking the objective function as an fitness function, and carrying out fitness evaluation on each determined solution;
s4, recording the optimal individual and the corresponding fitness;
s5, judging whether the calculation process meets the end condition, if so, decoding to generate an optimal route, and if not, executing the step S6;
and S6, updating the current population by utilizing the quantum rotating gate, taking the updated population as a new population, and executing the step S2 until the ending condition is met.
It will be appreciated that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation of a refuse transportation route optimizing apparatus based on a quantum genetic algorithm. In other embodiments of the invention, a refuse transportation route optimization device based on a quantum genetic algorithm may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
The embodiment of the invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the garbage transportation route optimization method based on the quantum genetic algorithm in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium is stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the garbage transportation route optimization method based on the quantum genetic algorithm in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of additional identical elements in a process, method, article or apparatus that comprises the element.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The garbage transportation route optimization method based on the quantum genetic algorithm is characterized by comprising the following steps of:
acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and the target data comprises vehicle data, garbage data, road data, weather data and population data;
constructing an objective function and corresponding constraint conditions of a garbage transportation route with minimum shipping cost and minimum resident aversion index as targets based on the target data;
and solving the objective function by utilizing a quantum genetic algorithm based on the constraint condition to obtain an optimal path of garbage transportation.
2. The method of claim 1, wherein the shipping costs include a fixed cost and a mileage cost, the shipping costs
The calculation formula of (2) is as follows:
in the method, in the process of the invention,
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij representing the distance from an i node to a j node, wherein n represents the number of nodes, and the nodes comprise the garbage transfer station, each garbage collection point and the target parking lot;Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
3. The method according to claim 2, wherein the resident aversion index is composed of a congestion aversion index and a refuse accumulation aversion index, the resident aversion index
The calculation formula of (2) is as follows:
in the method, in the process of the invention,w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
4. A method according to claim 3, wherein the objective function is obtained by weighting a minimum shipping cost objective and a minimum residential aversion index objective, and the objective function is:
in the method, in the process of the invention,b 1 andb 2 the weight of the minimum object of the shipping cost and the minimum object of the aversion index of the residents are respectively;
for the fixed cost of garbage truck, < >>
Indicating the cost per unit distance of the garbage truck,d ij representing the distance from an i node to a j node, wherein n represents the number of nodes, and the nodes comprise the garbage transfer station, each garbage collection point and the target parking lot;w ij representing the weight occupied by the dislike index of residents from the i node to the j node, wherein the value of the weight is related to the average flow of people from the i node to the j node;T ij a traffic jam aversion index from the i node to the j node;S j is the garbage accumulation aversion index of the j node,T ij ∈（0,1），S j ∈（0,1）；y j the weight occupied by the j-node garbage collection aversion index is represented,y j ∈（0,1）；Z ij e {0,1}, where,Z ij =1 indicates that the garbage truck moves from node i to node j,Z ij =0 indicates that the garbage truck has not moved from node i to node j.
5. The method of claim 4, wherein the constraints are:
the load of the garbage truck at any node does not exceed the rated load;
the number of times that the garbage truck goes to any garbage collection point in a single journey is not more than 1 time;
the garbage cleaning time limit of any garbage collection point does not exceed a preset time limit;
the running time of the garbage truck from the i node to the j node does not exceed the preset time.
6. The method of claim 1, wherein the solving the objective function using a quantum genetic algorithm results in an optimal route for garbage transportation, comprising:
s1, initializing a primary population, and randomly generating a plurality of chromosomes taking quantum bits as codes;
s2, measuring each individual in the population once to obtain a corresponding determination solution;
s3, taking the objective function as an fitness function, and carrying out fitness evaluation on each determined solution;
s4, recording the optimal individual and the corresponding fitness;
s5, judging whether the calculation process meets the end condition, if so, decoding to generate an optimal route, and if not, executing the step S6;
and S6, updating the current population by utilizing the quantum rotating gate, taking the updated population as a new population, and executing the step S2 until the ending condition is met.
7. The utility model provides a rubbish transportation route optimizing device based on quantum genetic algorithm which characterized in that includes:
the acquisition module is used for acquiring target data of a target area; the target area comprises a garbage transfer station and a plurality of garbage collection points, garbage at each garbage collection point is transported to the garbage transfer station by garbage trucks in a target parking lot, and the target data comprises vehicle data, garbage data, road data, weather data and population data;
the construction module is used for constructing an objective function and corresponding constraint conditions of the garbage transportation route with minimum shipping cost and minimum resident aversion index as targets based on the target data;
and the solving module is used for solving the objective function by utilizing a quantum genetic algorithm based on the constraint condition to obtain an optimal path of garbage transportation.
8. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the method according to any of claims 1-6.
9. A storage medium having stored thereon a computer program, which, when executed in a computer, causes the computer to perform the method of any of claims 1-6.
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| CN116127857A (en) * | 2023-04-12 | 2023-05-16 | 中国科学院地理科学与资源研究所 | Classification-oriented household garbage collection and transportation path multi-objective optimization method and system |
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| CN113650993A (en) * | 2021-08-17 | 2021-11-16 | 北京航空航天大学 | Intelligent garbage disposal method based on optimized path |
| CN116127857A (en) * | 2023-04-12 | 2023-05-16 | 中国科学院地理科学与资源研究所 | Classification-oriented household garbage collection and transportation path multi-objective optimization method and system |
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