CN115456538A - Multi-vehicle multi-region urban cold chain low-carbon logistics distribution method - Google Patents
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Abstract
The invention provides a multi-vehicle type multi-region urban cold chain low-carbon logistics distribution method, which can be used for carrying out simulation on an urban cold chain logistics system by using simulation software, carrying out personnel and material allocation on each link in the whole system through the result, and providing corresponding optimization suggestions and schemes for the cold chain logistics links of logistics enterprises, thereby achieving the purpose of reducing the enterprise operation cost and realizing the transformation of the logistics enterprises to environment-friendly green logistics enterprises.
Description
Technical Field
The invention relates to the technical field of logistics distribution, in particular to a multi-vehicle multi-region urban cold chain low-carbon logistics distribution method.
Background
At present, with the continuous development of social economy, the quality of life of people is gradually improved, the requirement on fresh products is higher and higher, and the high requirement is provided for the cold chain distribution link of logistics enterprises. On the other hand, with the continuous development of cold-chain logistics and the continuous attention on environmental problems, how to realize green logistics low-carbon logistics in development is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a multi-vehicle multi-region urban cold chain low-carbon logistics distribution method, which is used for solving at least one technical problem in the prior art.
In order to solve the technical problem, the invention provides a multi-vehicle multi-region urban cold chain low-carbon logistics distribution method, which comprises the following steps:
s10, establishing an urban cold chain low-carbon logistics distribution path optimization model aiming at multiple vehicle types and multiple zones, wherein the optimization model comprises the following objective functions:
1) Total dispatch cost function:
2) Transportation cost function:
3) Refrigeration cost function:
wherein the heat load calculation formula of the ith vehicle type and the ith vehicle is as follows:
where ξ represents the degree of thermal insulation of the cold-chain logistics distribution vehicle;
S represents the heat conduction area of the distribution vehicle, the calculation method is the arithmetic square root of the product of the internal and external surface areas of the vehicle, and the calculation formula is
Wherein S w Representing the external surface area, S, of the refrigerated compartment of the vehicle n Representing the interior surface area of a vehicle refrigeration compartment;
T w representing an ambient temperature outside the vehicle;
T n indicating the temperature inside the refrigerated compartment;
the unit refrigeration cost of the delivery vehicle of the kth model is
4) The carbon tax cost function is:
s.t.
wherein, the first and the second end of the pipe are connected with each other,
the formula (1) is a minimum objective function of the total cost of cold-chain logistics distribution;
equations (2) and (3) represent the maximum payload and maximum capacity, respectively, that the transported goods cannot exceed the delivery vehicle's own specifications;
formula (4) shows that the delivery vehicle can not exceed the mileage limit of the vehicle in the transportation process;
equation (5) represents the delivery vehicle completing the delivery task leaving the customer site;
formula (6) represents that the delivery vehicles deliver in the order of the customer points defined by the established delivery plan;
formula (7) shows that the delivery vehicles can start delivery from any center and return to any delivery center after delivery tasks are completed (the number of the delivery centers can be adjusted according to actual conditions);
and, the decision variables are:
the above relevant parameters have the following meanings:
m-distribution center set, M = { M =1,2,3, ·, | M | };
n-customer point set, N = { N | N =1,2, ·, | N | };
k — a set of delivery vehicle types, K representing the kth model, K = { K | K =1,2, ·, | K | };
L k -a set of vehicles representing the k-th vehicle type, L = {1,2, ·, L k }，L k The number of vehicles of the kth vehicle type;
r-set of areas where customer points are located, R = { R | R =1,2, · ·, | R | }
l-the first vehicle of the kth vehicle type;
Q k -maximum load of delivery vehicle;
V k -maximum volume of delivery vehicle;
o-order quantity;
q j -the demand of client j, q j ＞0
v j Volume of item j, v j ＞0；
d ij -distance of client i to client j;
d k -mileage allowance of kth vehicle type;
i, j-node number,
v-vehicle speed;
C 1 -vehicle dispatch cost;
C a -a single dispatch cost of delivery vehicles;
C 2 -vehicle transportation costs;
C b -the transportation cost per unit distance of the delivery vehicle;
C 3 -the cost of refrigeration;
C 4 -carbon emission costs;
omega-diesel carbon dioxide emission coefficient;
lambda-unit distance oil consumption;
and S20, solving the model by using a hybrid intelligent optimization algorithm to obtain an optimal target solution meeting the constraint condition of the model and an optimized distribution path.
Further, in step S20, a hybrid algorithm combining the genetic algorithm, the ant colony algorithm, and the adaptive algorithm is used to solve the model.
The three algorithms are adopted to mutually make up for the defects of the three algorithms, so that the circulating trap can be effectively jumped out, and the calculation efficiency is improved.
The main disadvantage of the genetic algorithm is that the blind search phenomenon is caused by the fact that feedback information in a system is not fully utilized in the search process; meanwhile, a local optimal solution trap is easy to be trapped in the solving process, so that the efficiency of solving the optimal solution is too low. The ant colony algorithm has the defects that the concentration of pheromones at the initial searching stage is low, and the iteration speed of the algorithm is influenced. And solving the model by adopting a fusion algorithm, wherein the algorithm is suitable for path optimization models of other low-carbon angles.
Further, step S20 includes the steps of:
s21, generating initial path pheromone distribution by using a genetic algorithm to help, and reducing the influence of the initial pheromone on the ant colony algorithm solution;
setting a search space as two dimensions, and dividing a path space by adopting a raster method;
and then determining a path coding mode and a fitness function, generating an initial population, and selecting the population by adopting a genetic algorithm through flow operations of selection, intersection and mutation genetic operators, so as to obtain an initial path of the distributed pheromone meeting the conditions.
S22, the ant colony algorithm stage is mainly used for receiving a group of solutions generated by the genetic algorithm to initialize the distribution of the environment pheromone and calculating a fusion genetic operator with self-adaptive algorithm convergence rate, so that the solution approaching to the global optimum is solved.
Further, in step S21, the grid method divides the space into a coordinate method or a number method.
Further, in step S21, dividing the space into a coordinate method by using a grid method specifically includes:
s211, establishing a rectangular coordinate system on the grids, wherein the upper left corner is used as an origin, the horizontal right direction is the positive direction of an x axis, the vertical downward direction is the positive direction of a y axis, and any grid can be represented by coordinates (x, y);
s212, generating an initial population, and in order to make a path coding rule simpler and more convenient, adopting a method of fixing an abscissa and generating a random number of 0 to (M-1) by using a rand function to determine a chromosome gene, namely determining the chromosome position through the abscissa and determining the chromosome gene through the ordinate;
for the path selection problem, since the starting point of the path is already determined, the genes of the chromosome beginning and ending are already determined, namely the ordinate of the starting point is determined;
for the scale of the initial population, the larger the number of the initial population, the higher the diversity of the individuals, and the better the result of the algorithm in the global search, and the less possibility of falling into the local optimal solution.
However, if the number is too large, the calculation amount of the algorithm increases, the algorithm efficiency is reduced, and the number of the population is finally determined to be n =20 to 200 by investigating data and experiments.
S213, calculating the fitness, and dividing the TSP feasible solution by depending on a 'split routes' function to obtain a vehicle driving route and the required vehicle number, and calculating the driving distance by using a 'calDistance' function.
S214 determining genetic operator
And (4) selecting an operator, namely selecting the chromosome with higher fitness and determining how many filial generations are generated for subsequent genetic operator operation.
The crossover operator is a process of randomly combining two individuals in the parent with probability P to generate a new individual, and is a guarantee of global search;
operator crossing is carried out by adopting single-point crossing of randomly selected crossing points, namely, chromosome crossing positions are randomly selected, and the corresponding positions of the parents chromosomes are exchanged;
if the two father chromosomes are selected as:
chromosoma1＝02130
chromosoma2＝04521
the randomly chosen hybridization position is the second point, and then the new chromosomes after crossing are:
chromosoma1＝02530
chromosoma2＝04121
the higher the probability of crossover, the faster new individuals are introduced into the population, and the stronger the global searchability, but the probability of loss of the genetic structure of the dominant individual will also increase. Conversely, a low cross probability will make the search stop short. Typically P =0.6 to 1.0.
Mutation operator is the process of introducing new genes and is also a guarantee that the algorithm converges to local optimum, and the mutation operator is used as an auxiliary operator to cooperate with a crossover operator;
two new individuals are generated by adopting a binary variation method, namely, two new individuals are generated through binary variation operation, and each gene in the new individuals is respectively the same or different of the corresponding gene value of the original chromosome.
The method changes the traditional variation mode, effectively overcomes premature convergence, and improves the optimization speed of the genetic algorithm.
S215 Algorithm end conditions
And stopping the algorithm from entering the ant colony algorithm of the next stage when the evolution times reach the preset iteration maximum value.
Preferably, the selection operators are roulette, tournament, sort, etc.
In order to ensure the global searchability of the algorithm, the application preferably adopts a tournament selection method as a method for selecting an operator. The method randomly selects N chromosomes from a parent generation, then selects the chromosome with the highest fitness, and repeats the steps until reaching the specified number of offspring.
Further, step S22 specifically includes:
s221 setting pheromone
And the ant colony algorithm pheromone is set as the maximum value, so that the search range is enlarged, and the algorithm is prevented from being premature. Meanwhile, the pheromone concentration obtained by the genetic algorithm in the previous stage is added as the initial pheromone concentration of the final ant colony algorithm;
s222 pheromone update
Adopting an ant circle model with the strongest global property, namely, only ants with the shortest path in the iteration carry out pheromone updating;
s223 convergence rate parameter
Although the convergence rate of the genetic and ant colony fusion algorithm is improved to a great extent, the later ant colony algorithm is easy to fall into a local optimal solution trap due to the fact that the convergence rate is too high. For the situation, an algorithm based on the self-adaptive fusion of the convergence rate parameters of the ant colony algorithm is adopted. The convergence rate parameter is the ratio of each round of algorithm optimization expressed by the path length of ants.
And defining a convergence speed threshold constant, and entering the genetic operator part by the algorithm when the parameter is greater than the threshold constant in each circulation.
S224 Algorithm end conditions
And whether the result meets the model objective function or not, namely whether the solution is the optimal solution of the target or not. If the conditions are met, the circulation is ended; if not, continuing iteration until a preset maximum iteration number is met.
By adopting the technical scheme, the invention has the following beneficial effects:
according to the multi-vehicle-type multi-region urban cold chain low-carbon logistics distribution method, simulation software can be used for carrying out simulation on an urban cold chain logistics system, personnel and materials are distributed to all links in the whole system according to the result, corresponding optimization suggestions and schemes are provided for cold chain logistics links of logistics enterprises, the purpose of reducing enterprise operation cost is achieved, and the logistics enterprises are transformed to environment-friendly green logistics enterprises.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a grid diagram of coordinates in a coordinate method according to an embodiment of the present invention;
FIG. 3 is a grid diagram of an initial population generated in the coordinate method in the embodiment of the present invention;
fig. 4 is an algorithm flowchart of an ant colony algorithm stage according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be further explained with reference to specific embodiments.
As shown in fig. 1, in this embodiment, the characteristics of a city are combined, the current city mostly meets the requirement of vehicle regional driving, and an urban cold chain low-carbon logistics distribution path optimization model for multiple types and multiple regions of the city is established by combining the characteristics of a cold chain logistics distribution link on the basis of multi-type and multi-region urban logistics distribution.
The concrete model is as follows:
and (3) related parameters:
m-distribution center set, M = { M | M =1,2,3, · |, | M | };
n — customer point set, N = { N | N =1,2, · ·, | N | };
k — a set of delivery vehicle types, K representing the kth model, K = { K | K =1,2, ·, | K | };
L k -a set of vehicles representing the k-th vehicle type, L = {1,2, ·, L k }，L k The number of vehicles of the kth vehicle type;
r-set of areas where the customer points are located, R = { R | R =1,2, · |, | R | }
l-the first vehicle of the kth vehicle type;
Q k -maximum load of delivery vehicle;
V k -maximum volume of delivery vehicle;
o-order quantity;
q j -the demand, q, of customer j j ＞0
v j Volume of cargo j, v j ＞0；
d ij -distance of client i to client j;
d k -mileage allowance of kth vehicle type;
i, j-node number,
v-vehicle speed;
C 1 -vehicle dispatch cost;
C a -a single dispatch cost of delivery vehicles;
C 2 -vehicle transportation costs;
C b -the transportation cost per distance of the delivery vehicle;
C 3 -the cost of refrigeration;
C 4 -carbon emission costs;
omega-diesel carbon dioxide emission coefficient;
lambda-unit distance oil consumption.
The objective function includes:
1. total dispatch cost:
2. transportation cost:
3. refrigeration cost:
the heat load calculation formula of the ith vehicle type is as follows:
(xi represents the degree of thermal insulation of the cold-chain logistics distribution vehicle,
is the thermal conductivity of the vehicle, and represents the heat transferred per unit area of the distribution vehicle in unit time under the environment of unit temperature difference, and the unit is kcal/(h × m) 2 * C), S represents the heat conduction area of the distribution vehicle, the calculation method is the arithmetic square root of the product of the internal and external surface areas of the vehicle, and the calculation formula is
Wherein S w Denotes the external surface area of the vehicle' S refrigerated compartment, S n Representing the interior surface area of the vehicle's refrigeration compartment. T is a unit of w Indicating the ambient temperature, T, outside the vehicle n Indicating the temperature inside the refrigerated compartment. After determining the thermal conductivity of the vehicle, assume that the delivery vehicle of the kth model has a unit cooling cost of
The calculation method for the vehicle refrigeration cost can be further obtained as follows:
4. carbon tax cost:
s.t.
the decision variables include:
equation (1) represents the objective function, i.e., the total cost of cold-chain logistics distribution is minimized.
Equations (2) and (3) represent that the transported goods cannot exceed the maximum payload and maximum capacity specified by the delivery vehicle itself, respectively;
formula (4) shows that the delivery vehicle can not exceed the mileage limit of the vehicle in the transportation process;
equation (5) represents the delivery vehicle completing the delivery task leaving the customer site;
formula (6) represents that the delivery vehicles deliver in the order of the customer points defined by the established delivery plan; formula (7) shows that the delivery vehicles can start delivery from any center and return to any delivery center after delivery tasks are completed (the number of delivery centers can be adjusted according to actual conditions); equations (8) - (10) are decision variables.
2) And solving the established model by using a hybrid intelligent optimization algorithm.
According to the method, a hybrid algorithm combining a genetic algorithm, an ant colony algorithm and an adaptive algorithm is selected to solve the model, the three algorithms mutually make up for self defects, a circular trap is skipped, and finally an optimal target solution meeting the constraint condition of the model and an optimized distribution path are obtained.
Algorithm design:
genetic algorithms and ant colony algorithms are used as intelligent algorithms widely applied to various fields nowadays, and advantages and disadvantages coexist.
The main disadvantage of the genetic algorithm is that the blind search phenomenon is caused by the fact that feedback information in the system is not fully utilized in the search process; meanwhile, a local optimal solution trap is easy to be trapped in the solving process, so that the efficiency of solving the optimal solution is too low.
The ant colony algorithm has the defects that the concentration of pheromones at the initial searching stage is low, and the iteration speed of the algorithm is influenced.
In order to overcome the defects of the algorithm, the fusion algorithm is adopted to solve the model, and meanwhile, the algorithm is suitable for path optimization models of other low-carbon angles.
1. And the genetic algorithm is utilized to help generate initial path pheromone distribution, and the influence of the initial pheromone on the ant colony algorithm solving is reduced.
The search space is two-dimensional, the space division method is many, and the most common grid method is adopted in the fusion algorithm to divide the path space. Then, a path coding mode and a fitness function are determined. After the initial population is generated, selecting the population by adopting a genetic algorithm through the flow operations of selection, intersection and mutation genetic operators, and then obtaining an initial path of the distributed pheromone meeting the conditions;
(1) The grid method is divided into a coordinate method and a sequence number method, and the coordinate method is adopted for space division for convenience of subsequent genetic operator calculation.
(1.1) coordinate method: a rectangular coordinate system is established on grids, the upper left corner serves as an origin, the horizontal right side is the positive direction of an x axis, the vertical downward side is the positive direction of a y axis, any grid can be represented by coordinates (x, y), and the m x n coordinate grid division method is shown in figure 2.
(1.2) generating an initial population: referring to fig. 2, in order to make the path coding rule simpler, a method of fixing the abscissa and generating a random number of 0 to (M-1) using a rand function to determine a chromosomal gene is used, i.e., the chromosomal position is determined by the abscissa and the chromosomal gene is determined by the ordinate. Also for the routing problem since the starting point of the route has already been determined, the genes at the beginning and end of the chromosome are already determined, i.e. the ordinate of the starting point is determined. The method for generating the initial population is shown in FIG. 3 below.
For the scale of the initial population, the larger the number of the initial population, the higher the diversity of the individuals, and the better the result of the algorithm in the global search, and the less likely to fall into the local optimal solution. However, if the number is too large, the calculation amount of the algorithm is increased, the algorithm efficiency is reduced, and the population number is finally determined to be n = 20-200 by researching data and experiments.
(1.3) the fitness calculation depends on a 'split routes' function to divide TSP feasible solution to obtain a vehicle driving route and the required vehicle number, and the 'calDistance' function calculates the driving distance.
(1.4) determining genetic operators
And (4) selecting an operator, namely selecting the chromosome with higher fitness and determining how many filial generations are generated for subsequent genetic operator operation. Common selection operators include roulette, tournament, and sort. In order to ensure the global searchability of the algorithm, a tournament selection method is adopted as a method for selecting an operator. The method randomly selects N chromosomes from a parent generation, then selects the chromosome with the highest fitness, and repeats the steps until reaching the specified number of offspring.
And (4) a crossover operator, namely crossover is a process of randomly combining two individuals in a parent with probability P to generate a new individual, and is a guarantee of global search. And (3) carrying out operator crossing by adopting single-point crossing of randomly selected crossing points, namely randomly selecting chromosome crossing positions and exchanging the corresponding positions of the parent chromosomes. If the two father chromosomes are selected as:
chromosoma1＝02130
chromosoma2＝04521
the randomly chosen hybridization position is the second point, and the new chromosome after crossing is:
chromosoma1＝02530
chromosoma2＝04121
the higher the probability of crossover, the faster new individuals are introduced into the population, and the stronger the global searchability, but the probability of loss of the genetic structure of the dominant individual will also increase. Conversely, a low cross probability will make the search stop short. Typically P =0.6 to 1.0.
Mutation operator is the process of introducing new genes and is also a guarantee that the algorithm converges to local optimum, and the mutation operator is used as an auxiliary operator to be matched with a crossover operator. Two new individuals are generated by adopting a binary variation method, namely, two new individuals are generated through binary variation operation, and each gene in the new individuals is respectively the same or different of the corresponding gene value of the original chromosome. The method changes the traditional variation mode, effectively overcomes premature convergence, and improves the optimization speed of the genetic algorithm.
(1.5) Algorithm termination Condition
And stopping the algorithm from entering the ant colony algorithm of the next stage when the evolution times reach the preset iteration maximum value.
2. The main task of the ant colony algorithm phase is to accept a set of solutions generated by genetic algorithms to initialize the distribution of the context pheromones. Meanwhile, calculating a fusion genetic operator with self-adaptive convergence rate of the algorithm, thereby solving the convergence rate approaching to the global optimal solution.
(2.1) setting pheromones
The ant colony algorithm pheromone is set to be the maximum value, the search range is enlarged, and the algorithm is prevented from being premature. And meanwhile, the pheromone concentration obtained by the genetic algorithm in the previous stage is added on the basis to be used as the initial pheromone concentration of the final ant colony algorithm.
(2.2) pheromone update
And (4) adopting an ant circle model with the strongest global property, namely, only the ant with the shortest path in the iteration carries out pheromone updating.
(2.3) convergence Rate parameter
Although the convergence rate of the genetic and ant colony fusion algorithm is improved to a great extent, the later ant colony algorithm is easy to fall into a local optimal solution trap due to the fact that the convergence rate is too high. Aiming at the situation, an algorithm based on the self-adaptive fusion of the convergence rate parameters of the ant colony algorithm is adopted. The convergence rate parameter is the ratio of each round of algorithm optimization expressed by the path length of ants.
And defining a convergence speed threshold constant, and entering the genetic operator part by the algorithm when the parameter is greater than the threshold constant in each circulation.
(2.4) Algorithm end Condition
Referring to fig. 4, whether the result satisfies the model objective function, i.e., whether the solution is the target optimal solution, is shown. If the condition is met, the circulation is ended; if not, continuing the iteration until the preset maximum iteration number is met.
Aiming at the characteristics of urban logistics, a multi-vehicle type multi-region urban low-carbon logistics distribution path selection method is provided. The method comprehensively considers the benefits of modern logistics enterprises and the concept of environmental protection, takes the cold-chain logistics distribution of modern cities as an application object, refers to the carbon emission economic measures which are carried out in other developed countries, quantifies the carbon emission amount with the difficulty in determining the accurate value in the logistics process into the carbon emission cost required to be borne by the enterprises through a carbon tax policy by combining with the vehicle dispatching cost, the transportation cost and the refrigeration cost in China as the optimization targets of the established model, and achieves the lowest transportation cost of the logistics enterprises. Meanwhile, the reduction of carbon emission cost reflects the reduction of carbon emission from the side, so that the purposes of energy conservation, emission reduction and green logistics are achieved, and the social responsibility of logistics enterprises is reflected.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A multi-vehicle multi-region urban cold chain low-carbon logistics distribution method is characterized by comprising the following steps:
s10, establishing an urban cold chain low-carbon logistics distribution path optimization model aiming at multiple vehicle types and multiple zones, wherein the optimization model comprises the following objective functions:
1) Total dispatch cost function:
2) Transportation cost function:
3) Refrigeration cost function:
wherein the heat load calculation formula of the ith vehicle type and the ith vehicle is as follows:
wherein ξ represents the degree of thermal insulation of the cold-chain logistics distribution vehicle;
S represents the heat conduction area of the distribution vehicle, the calculation method is the arithmetic square root of the product of the internal and external surface areas of the vehicle, and the calculation formula is
Wherein S w Denotes the external surface area of the vehicle' S refrigerated compartment, S n Showing the interior of a vehicle's refrigerated compartmentA surface area;
T w representing an ambient temperature outside the vehicle;
T n indicating the temperature inside the refrigerated compartment;
the unit refrigeration cost of the delivery vehicle of the kth model is
4) The carbon tax cost function is:
s.t.
wherein the content of the first and second substances,
formula (1) is a minimum objective function of the total cost of cold-chain logistics distribution;
equations (2) and (3) represent the maximum payload and maximum capacity, respectively, that the transported goods cannot exceed the delivery vehicle's own specifications;
formula (4) shows that the distribution vehicle cannot exceed the mileage limit of the vehicle during transportation;
equation (5) represents that the delivery vehicle leaves the customer site after completing the delivery task;
formula (6) represents that the delivery vehicles deliver in the order of the customer points defined by the established delivery plan;
formula (7) shows that the delivery vehicles can start delivery from any center and return to any delivery center after the delivery task is completed (the number of the delivery centers can be adjusted according to actual conditions);
and, the decision variables are:
the above relevant parameters have the following meanings:
m-distribution center set, M = { M | M =1,2,3, \8230 |, | M | };
n-a set of customer points, N = { N | N =1,2, \8230 |, | N | };
k-delivery vehicle type set, K denotes the kth vehicle model, K = { K | K =1,2, \8230 |, | K | };
L k -indicating the kth vehicleSet of vehicles of type, L = {1,2, \8230;, L k }，L k The number of vehicles of the kth vehicle type;
r-set of areas where the customer points are located, R = { R | R =1,2, \8230 |, | R | }
l-the first vehicle of the kth vehicle type;
Q k -distributing the maximum load of the vehicle;
V k -distributing the maximum capacity of the vehicle;
o-order quantity;
q j -the demand of client j, q j ＞0
v j Volume of cargo j, v j ＞0；
d ij -distance of client i to client j;
d k -mileage allowance of kth vehicle type;
i, j-node number,
v-vehicle speed;
C 1 -vehicle dispatch cost;
C a -a single dispatch cost of delivery vehicles;
C 2 -vehicle transportation costs;
C b -the transportation cost per unit distance of the delivery vehicle;
C 3 -the cost of refrigeration;
C 4 -carbon emission costs;
omega-diesel carbon dioxide emission coefficient;
lambda-unit distance oil consumption;
and S20, solving the model by using a hybrid intelligent optimization algorithm to obtain an optimal target solution meeting the constraint condition of the model and an optimized distribution path.
2. The multi-vehicle multi-region urban cold chain low-carbon logistics distribution method according to claim 1, wherein in step S20, a hybrid algorithm combining three algorithms of a genetic algorithm, an ant colony algorithm and an adaptive algorithm is used for solving the model.
3. The multi-vehicle multi-region urban cold chain low-carbon logistics distribution method according to claim 2, wherein the step S20 comprises the following steps:
s21, generating initial path pheromone distribution by using a genetic algorithm to help, and reducing the influence of the initial pheromone on the ant colony algorithm solution;
setting a search space as two dimensions, and dividing a path space by adopting a raster method;
and then determining a path coding mode and a fitness function, generating an initial population, and selecting the population by adopting a genetic algorithm through flow operations of selection, intersection and mutation genetic operators, so as to obtain an initial path of the distributed pheromone meeting the conditions.
S22, the main task of the ant colony algorithm stage is to receive a group of solutions generated by the genetic algorithm to initialize the distribution of the environment pheromone, and simultaneously calculate the fusion genetic operator with the algorithm convergence rate self-adaptive, thereby solving the solution approaching to the global optimum.
4. The multi-vehicle multi-region urban cold chain low-carbon logistics distribution method according to claim 3, wherein in step S21, the grid method divides the space into a coordinate method or a serial number method.
5. The multi-vehicle-type multi-region urban cold chain low-carbon logistics distribution method as claimed in claim 4, wherein in step S21, the grid method is used for dividing space into coordinate methods, and specifically comprises the following steps:
s211, establishing a rectangular coordinate system on the grids, wherein the upper left corner is used as an origin, the horizontal right direction is the positive direction of an x axis, the vertical downward direction is the positive direction of a y axis, and any grid can be represented by coordinates (x, y);
s212, generating an initial population, and in order to make a path coding rule simpler and more convenient, adopting a method of fixing an abscissa and generating a random number of 0 to (M-1) by using a rand function to determine a chromosome gene, namely determining the chromosome position through the abscissa and determining the chromosome gene through the ordinate;
s213, calculating the fitness, and dividing the TSP feasible solution by depending on a 'split routes' function to obtain a vehicle running route and the required vehicle number, wherein the 'calDistance' function calculates the running distance;
s214 determining genetic operator
Selecting an operator, namely selecting a chromosome with higher fitness, and determining the generation of offspring for subsequent genetic operator operation;
the crossover operator is a process of randomly combining two individuals in the parent with probability P to generate a new individual, and is a guarantee of global search;
adopting single-point crossing of randomly selected cross points to carry out operator crossing, namely randomly selecting chromosome crossing positions and exchanging the corresponding positions of the father chromosome;
mutation operator is a process of introducing new genes and is also a guarantee that the algorithm converges to local optimum, and the mutation operator is used as an auxiliary operator to cooperate with a crossover operator;
adopting a binary variation method, namely generating two new individuals through binary variation operation, wherein each gene in the new individuals respectively takes the identity or the difference of the corresponding gene value of the original chromosome;
s215 Algorithm end conditions
And stopping the algorithm from entering the ant colony algorithm of the next stage when the evolution times reach the preset iteration maximum value.
6. The multi-vehicle-type multi-region urban cold chain low-carbon logistics distribution method is characterized in that the selection operator comprises a roulette method, a tournament selection method or a sequencing selection method.
7. The multi-vehicle multi-region urban cold chain low-carbon logistics distribution method according to claim 3, wherein the step S22 specifically comprises:
s221 setting pheromone
And the ant colony algorithm pheromone is set as the maximum value, so that the search range is enlarged, and the algorithm is prevented from being premature. Meanwhile, the pheromone concentration obtained by the genetic algorithm in the previous stage is added on the basis to be used as the initial pheromone concentration of the final ant colony algorithm;
s222 pheromone update
Adopting an ant circle model with the strongest global property, namely, only ants with the shortest path in the iteration carry out pheromone updating;
s223 convergence rate parameter
Although the convergence rate of the genetic and ant colony fusion algorithm is improved to a great extent, the later ant colony algorithm is easy to fall into a local optimal solution trap due to the fact that the convergence rate is too high. For the situation, an algorithm based on the self-adaptive fusion of the convergence rate parameters of the ant colony algorithm is adopted. The convergence rate parameter represents the optimized ratio of each round of algorithm by the path length of ants;
defining a convergence speed threshold constant, and entering the genetic operator part by the algorithm when the parameter is greater than the threshold constant in each circulation;
s224 algorithm end condition
Whether the result meets a model objective function or not, namely whether the solution is the optimal solution of the target or not is judged; if the conditions are met, the circulation is ended; if not, continuing the iteration until the preset maximum iteration number is met.
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| CN117474429A (en) * | 2023-12-27 | 2024-01-30 | 国网浙江省电力有限公司金华供电公司 | Intelligent visual electric power material transportation method and platform with carbon emission measuring and calculating function |
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| CN117252496A (en) * | 2023-03-09 | 2023-12-19 | 江苏齐博冷链科技有限公司 | Regional intelligent logistics coordination system |
| CN117474429A (en) * | 2023-12-27 | 2024-01-30 | 国网浙江省电力有限公司金华供电公司 | Intelligent visual electric power material transportation method and platform with carbon emission measuring and calculating function |
| CN117474429B (en) * | 2023-12-27 | 2024-03-08 | 国网浙江省电力有限公司金华供电公司 | Intelligent visual electric power material transportation method and platform with carbon emission measuring and calculating function |
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