CN116452097A - Urban distribution path decision-making method considering consumable materials under cold chain logistics - Google Patents

Urban distribution path decision-making method considering consumable materials under cold chain logistics Download PDF

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CN116452097A
CN116452097A CN202310222716.5A CN202310222716A CN116452097A CN 116452097 A CN116452097 A CN 116452097A CN 202310222716 A CN202310222716 A CN 202310222716A CN 116452097 A CN116452097 A CN 116452097A
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cost
distribution path
cold chain
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refrigerant
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丁溢
李泳臻
舒嘉
曹长新
董炯
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Southeast University
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Abstract

The invention belongs to the technical field of logistics path optimization, and discloses a city distribution path decision method considering consumable materials under cold chain logistics, which comprises the following steps: and constructing a cost function comprising transportation cost, refrigerant cost and packaging cost, and constructing a mixed integer planning model with the minimum total distribution cost as a target. And (3) performing linear conversion on the nonlinear expression in the model and the constraint, providing an accurate solving algorithm aiming at the small-scale problem, and designing a heuristic algorithm for solving the larger-scale problem. The validity of the modeled model and the validity of the algorithm are verified through the analysis of the calculation example.

Description

Urban distribution path decision-making method considering consumable materials under cold chain logistics
Technical Field
The invention belongs to the technical field of logistics path optimization, and particularly relates to an urban distribution path decision method considering consumable materials under cold-chain logistics.
Background
In recent years, with the continuous improvement of urban and rural resident income level in China, consumers tend to pay more attention to freshness of seafood, meat, time fruits, vegetables and other products with short shelf life and perishable products. With the continuous expansion of the market scale of fresh electric suppliers, a large amount of order support is also obtained by the cold chain logistics closely related to the fresh electric suppliers, and the cold chain logistics industry enters a high-speed development period. Although the cold chain logistics is kept to develop rapidly, the most outstanding is that the cost of the cold chain logistics is high, especially in the distribution process, about 3 hundred million foam boxes, 10 hundred million cold chain consumables such as ice bags and the like are produced in China each year, the waste is increased by 30% of cost loss, and however, few inventions consider cost reduction and synergy from the consumable point of view. Therefore, the invention provides a city distribution path decision method considering consumable materials under cold chain logistics.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for deciding an urban distribution path by considering consumable materials under cold chain logistics, which constructs a cost function comprising transportation cost, refrigerant cost and packaging cost and establishes a path optimization model with the aim of minimum total cost.
The aim of the invention can be achieved by the following technical scheme:
a city distribution path decision method considering consumable materials under cold chain logistics comprises the following steps:
constructing a cost function comprising transportation cost, refrigerant cost and packaging cost, and constructing a mixed integer planning model by taking the minimum total distribution cost as a target;
a solving algorithm aiming at a small-scale distribution path decision problem example is provided;
designing a solving algorithm of a large-scale distribution path decision problem calculation example;
and verifying the rationality of the model and the effectiveness of the algorithm through the calculation example analysis.
Preferably, the transportation cost is mainly related to a travel distance of the vehicle, and the transportation cost co-generated during distribution may be expressed as:
wherein c ij Is the transport cost of arc (i, j), x ijk ∈{0,1} |V|×|V|×|K| Indicating whether the vehicle passes through the arc (i, j).
Preferably, the cost of the refrigerant is mainly determined by the consumption of the refrigerant quality, and can be expressed as
Wherein C is u Is the price of the refrigerant, m iupo The product is dispensed to the ith customer point by adopting the mass z of the cooling medium u required by the ith cooling medium, the p-th insulation can material and the o-th packaging specification iupo ∈{0,1} |V|×|U|×|P|×|O| Indicating whether the ith customer dispenses the product by adopting the ith refrigerant, the p-th insulation can material and the o-th packaging specification.
Preferably, the packaging cost can be expressed as
Wherein C is po Is the price of the heat insulation material and the package specification.
Preferably, the model is expressed as:
the constraint conditions are as follows:
m iupo ＝f(ΔT 1 ,L u ,S o ,d o ,β p )t i (11)
wherein q is i Is the demand of customer point i, t i Is the time when the vehicle arrives at the ith customer, t ij Is the transit time of arc (i, j), a i Is the earliest arrival time of node i, b i Is the latest arrival time of the node s i Service time of customer point i, deltaT 1 Is the temperature difference between the inside and outside of the transport package, L u Is the latent heat of phase change of the u-th refrigerant, S o Surface area of package No. o, d o Is the wall thickness of the packaging specification of the o type, M po The maximum package load is beta when the p-th insulation can material and the o-th package specification are adopted p The heat conductivity coefficient of the p-th insulation can material is that M is an infinite number.
Preferably, the model is linearly transformed with the nonlinear expression in the constraint:
step 0: for t i ·z io Linearization, which after linearization translates into the following two expressions:
step 1: for x ijk ·t i ·z io After linearization, the conversion is to the following two expressions:
preferably, the small-scale delivery path decision problem calculation example adopts an optimization solver to accurately solve.
Preferably, the large-scale distribution path decision problem calculation example is solved by adopting a genetic algorithm, and the genetic algorithm comprises the following steps:
step 0: setting parameters including population size N pop Maximum algebra Gen, crossover probability P c Probability of variation P m Iterative tag gen=1;
step 1: dividing the chromosome into two parts, wherein each part adopts a natural number coding mode, the first part adopts the vehicle access sequence, namely the coding is an arrangement of N positive integers, wherein each positive integer represents a customer, the second part adopts the packaging specification adopted by each customer and also consists of N positive integers, and each positive integer is one of |o| packaging specifications;
step 2: designing a fitness function as the total distribution cost corresponding to each chromosome;
step 3: random generation bar N pop Chromosome as initial population;
step 4:
step 4.1: a tournament strategy is adopted, and the operation steps of the strategy are as follows: randomly taking out 2 individuals from the population each time, comparing the fitness values of the individuals, selecting the individuals with higher fitness values to enter the offspring population, and repeating the operation until the new population reaches the original population scale;
step 4.2: the chromosome has two parts, and each part adopts different crossing modes: the first part is crossed by an OX operator; the second part adopts a two-point crossing strategy;
step 4.3: the two parts of the chromosome respectively adopt different mutation methods, and the first part adopts a 2-opt operator; the second part randomly re-generates new package specifications within the feasible package specifications for each customer point;
step 5: if the maximum algebra is reached, terminating the iterative process and outputting a result; otherwise, go to step 2.
According to a further aspect of the invention, the invention proposes an apparatus comprising:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform a method of urban distribution path decision making under a cold chain logistics considering consumables as set forth in any one of the preceding claims.
According to a further aspect of the present invention, a computer readable storage medium storing a computer program which when executed by a processor implements a method for determining a city delivery path taking into account consumables under a cold chain logistics as defined in any one of the above is provided.
The invention has the beneficial effects that:
the invention starts from the view of considering consumable materials, combines the cold chain consumable materials into a vehicle path planning model, aims at optimizing comprehensive cost, builds a cold chain city distribution path planning model considering consumable materials, solves small-scale examples by utilizing an optimizing solver in a numerical experiment, solves larger-scale examples by designing a genetic algorithm, analyzes the solving results of the two algorithms, and verifies the rationality of the modeling type and the effectiveness of the algorithm by analyzing the examples.
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 description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.
FIG. 1 is a flow chart of the structure of the present invention;
FIG. 2 is a convergence diagram of a genetic algorithm of the present invention;
FIG. 3 is a path layout diagram of the present invention in view of cold chain consumables;
fig. 4 is a conventional path planning diagram.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an urban path distribution decision method considering consumable materials under cold chain logistics. The study solved the problem using Genetic Algorithm (GA). According to the flow of fig. 1, the technical scheme of the invention comprises the following steps:
1. description of problems and mathematical modeling
From a cold-chain logistics distribution centre L 0 Is responsible for providing a plurality of customers { L ] 1 ,L 2 ,L 3 ,...,L n The method comprises the steps of (1) delivering fresh products, taking 'refrigerants and heat preservation boxes' as heat preservation materials, taking a normal-temperature truck as a transport means, knowing the demand and the geographic position of each customer, and each customer has a specific delivery time window, wherein during delivery, the corresponding refrigerants, heat preservation box materials and box body specifications are required to be selected for each customer, the upper load limit of the selected heat preservation boxes cannot be exceeded, all vehicles return to a delivery center after delivery is completed, under the conditions of meeting the demands of the customers, time window constraints, vehicle load constraints and heat preservation box load constraints, comprehensively considering the transportation cost, the refrigerant cost and the packaging cost in the delivery process, constructing a cold chain city delivery path planning model with optimal comprehensive cost, and obtaining a vehicle delivery scheme with optimal total cost under the constraints, namely the quantity of delivery vehicles and the routes of serving the customers, and the choices of the refrigerants, the heat preservation box materials and the box body specifications at the corresponding customers.
1.1 model hypothesis
Condition a: all delivery vehicles are normal temperature trucks, the heat preservation mode in delivery is coolant and heat preservation box, and the delivered products are single products and fresh products;
condition b: the product demand of all customers, the geographic position and the time window are known, and the distance between the customers is Euclidean distance, so that the triangle inequality is satisfied;
condition c: the environment temperature outside the incubator is kept unchanged in the transportation process, and when the quality of the refrigerant accords with a certain functional relation, the temperature inside the incubator is kept unchanged, and in addition, the product is assumed to be at a proper temperature, so that the cost of goods loss caused by the quality loss is negligible;
condition d: the heat generated by the products in the incubator due to respiration is ignored;
1.2 model parameter variables
V= {0,1,2, …, n, n+1}, representing a set of distribution centers and customer nodes, where 0 and n+1 both represent distribution centers;
k= {1,2,3,., K }, representing a set of vehicles;
u= {1,2,3,..u }, representing a refrigerant set;
p= {1,2,3., P }, representing a set of insulation materials;
o= {1,2,3,., O }, set of packaging specifications;
a: the set of all arcs in the graph satisfies the condition
c ij : the transportation cost of arc (i, j);
t ij : the transit time of arc (i, j); the unit is s;
q i : the product demand quality of the customer point i is in kg;
a i : the earliest arrival time of the node i is expressed as s;
b i : the latest arrival time of the node i is in s;
s i : the service time of the customer point i is s;
Q k : the maximum load of the kth vehicle is in kg;
L u : the phase change latent heat of the u-th refrigerant is J/kg;
S o : surface area of package No. o, unit is m 2 ；
d o : the wall thickness of the packaging specification of the o type is expressed as m;
β p : the heat conductivity coefficient of the p-th insulation can material is W/(m.K);
M po : the maximum packing load is kg when the p-th insulation can material and the o-th packing specification are adopted;
ΔT 1 : the temperature difference between the inside and outside of the transport package is K;
m iupo : the product is dispensed to the ith customer point by adopting the mass of the cooling medium u required by the ith heat insulation box material and the ith packaging specification in kg;
x ijk ∈{0,1} |V|×|V|×|K| : indicating whether the vehicle k walks through an arc (i, j);
t i : the time the vehicle arrives at the ith customer;
z iupo ∈{0,1} |V|×|U|×|P|×|O| : and (3) whether the ith customer distributes the product by adopting a ith refrigerant, a p-th insulation can material and an o-th packaging specification.
1.3 transportation costs
The transportation cost is mainly the fuel consumption cost of the vehicle in the delivery process and mainly related to the driving distance of the vehicle, and the transportation cost generated in the delivery process can be expressed as:
1.4 cost of refrigerant
Unlike the method of refrigerating a refrigerated truck by consuming a refrigerant to maintain the temperature of the product, in the delivery mode of "normal temperature truck+refrigerant+incubator" adopted herein, in order to maintain the temperature of the product, sufficient refrigerant must be added to the incubator to keep the temperature, so that the cost of the refrigerant is mainly dependent on the consumption of the refrigerant mass, which can be expressed as:
1.5 packaging costs
Under the delivery mode of normal temperature truck, refrigerant and insulation can, the cost of the insulation can also need to be considered, can be expressed as:
1.6 modeling
The constraint conditions are that,
m io ＝f(ΔT 1 ,L u ,S o ,d o ,β p )t i (14)
wherein equation (4) represents that the sum of the cost of the objective functions of the model is minimum; equation (5) indicates that each vehicle must exit the distribution center; equation (6) indicates that the vehicle arriving at each customer point must leave; equation (7) indicates that all vehicles must be returned to the distribution center; equation (8) indicates that the needs of each customer point must be satisfied; equation (9) indicates that the upper load limit of each vehicle cannot be exceeded; equation (10) represents the magnitude relation of the start service time between served adjacent nodes, and is a loop cancellation constraint; equation (11) indicates that the time window constraint of each node is satisfied; formula (12) indicates that each customer node can select only one combination for insulation and packaging; the formula (13) indicates that the load limit of the incubator cannot be violated; formula (14) represents a relational expression of the mass of the refrigerant, the temperature difference between the inside and outside of the incubator, the latent heat of the refrigerant, the heat insulation coefficient of the material of the incubator, the surface area of the incubator, the wall thickness of the incubator and the total distribution time; the expression (15-17) represents the value constraint of three decision variables.
2. Solving method
Because the vehicle path problem belongs to the combination optimization problem, the general accurate algorithm is difficult to solve. The present invention is therefore focused on efficient heuristic algorithms that have many successful applications in this field. In order to verify the rationality of the modeling type and the effectiveness of the genetic algorithm, firstly, an optimization solver is utilized to accurately solve small-scale examples, secondly, the genetic algorithm is designed to solve larger-scale examples, and then, two solving results are analyzed and compared.
2.1 accurate solution
Since there are some nonlinear expressions in the model, such as objective function (4), constraints (9), (12), there are all expressions similar to x ijk ·t i ·z io ，t i ·z io The situation where the decision variables are multiplied. Thus, the nonlinear term is first linearized before solving.
Step 0: first for t i ·z io Linearization, which after linearization translates into the following two expressions:
step 1: on the basis, x ijk ·t i ·z io After linearization, the conversion is to the following two expressions:
2.2 genetic Algorithm solution
The invention uses genetic algorithms to solve large-scale problems, as it is one of the most successful meta-heuristic algorithms to solve the combinatorial optimization problem, with the ability to explore other areas of the feasible space and to avoid local optimizations as much as possible.
In genetic algorithms, each chromosome represents one solution to the problem, the quality of which is represented by fitness. In the present invention, integer codes are used to represent chromosomes. Each chromosome is composed of several integers like genes. The genetic algorithm is implemented as follows:
step 0 (initialization): setting parameters including population size N pop Maximum algebra Gen, crossover probability P c Probability of variation P m The label gen=1 for the iteration.
Step 1 (encoding): the chromosome is divided into two parts, each part adopts a natural number coding mode, the first part adopts the vehicle access sequence, namely the coding is an arrangement of N positive integers, wherein each positive integer represents a customer, the second part adopts the packaging specification adopted by each customer and also consists of N positive integers, each positive integer is one of |o| packaging specifications,
step 2 (calculation of fitness function): since the objective function constructed herein is the lowest overall cost, the present invention designs the fitness function as the total cost of the decision scheme for each chromosome.
Step 3 (population initialization): random generation of stripes N herein pop Chromosome is used as the initial population.
Step 4 (new population generation):
step 4.1 (selection): a tournament strategy is adopted, and the operation steps of the strategy are as follows: 2 individuals are randomly taken out of the population each time, the fitness values of the individuals are compared, the individuals with higher fitness values are selected to enter the offspring population, and the operation is repeated until the new population size reaches the original population size.
Step 4.2 (crossover): the chromosome has two parts in total, and each part adopts different crossing modes. For a first portion of the chromosome, crossing with an OX operator; for the second part of the chromosome, a two-point crossing strategy is used.
Step 4.3 (mutation): two parts of the chromosome adopt different mutation methods respectively, and for the first part, a 2-opt operator is adopted; the second part, for each customer point, re-generates a new package specification randomly within its viable package specifications.
Step 5 (stop iteration): if the maximum algebra is reached, the iterative process is terminated and the result is output. Otherwise, go to step 2.
The present invention performs a computational experiment to evaluate the effectiveness of the proposed model and algorithm. The distribution networks used for the experiments share a fixed distribution center (L 0 ,L n+1 ) There are 100 customer demand points (L 1 ,L 2 ,...,L 100 ) The relevant data for each node is shown in table a.1 (partial data). The maximum capacity of each vehicle in the distribution network is 9, the freight rate per unit freight distance is 1, the optional packaging specification is six, and the related data are shown in table 2. In addition, the refrigerant in the distribution process adopts ice cubes with the latent heat of 336J/kg and the unit cost of 8, the heat insulation material adopts a polystyrene heat insulation box with the heat conductivity coefficient of 0.08W/(m.K), and in addition, the temperature required by the cold chain product is kept at 9 ℃ and the environmental temperature is 25 ℃, namely delta T 1 ＝16℃。
Table a.1 information about each node
TABLE A.2 information about different packaging specifications
The calculation is completed on a computer with 64-bit Windows 10 operating system, 1.8GHz CPU and 8G memory. The smaller-scale examples are solved by using an optimization solver, and the first 5, 10, 15 and 20 customer points in the table A.1 are accurately solved respectively to obtain the number of vehicles used, each cost, consumable cost ratio and solving time under each network scale, wherein the table is shown in table 4.1.
TABLE 4.1 solving results at different network scales
Based on the proposed genetic algorithm, the small-scale network is solved, the solving result is compared with the solving result of the optimization solver, the performance of the algorithm and the convergence of the genetic algorithm are tested, each network is operated for 20 times on a scale, and the optimal solution is taken as the final solving result. On this basis, the larger-scale network is solved by adopting the first 30, 60, 80 and 100 customers, so as to obtain the vehicle use quantity, solving time, various costs and consumable cost ratio under each network scale, as shown in table 4.2.
TABLE 4.2 genetic algorithm solution results for Large Scale networks
As shown in Table 4.3 and FIG. 2, the result of the genetic algorithm is within 2% error compared to the optimal solution solved by the optimization solver and can converge to a better solution at a faster rate.
TABLE 4.3 genetic algorithm Performance test
The invention considers the path planning of the cold chain consumable and the conventional path planning, and as can be seen from table 4.4, when the network scale is smaller, the distribution time is shorter, and the required refrigerant is not enough to exceed the capacity limit of the vehicle on the basis of distributing the product, so that the distribution lines are consistent, and the transportation cost under the optimal condition is the same. As the network scale increases, the delivery time becomes longer, and on the optimal delivery path of the conventional VRP, the maximum load of the vehicle is insufficient to accommodate the refrigerant required for the path, and thus it is necessary to increase the number of vehicles, re-route the route, and thus increase the transportation cost. The distribution network is plotted with 20 customer nodes before and after the cold chain consumables are considered, as shown in fig. 2 and 3. The invention also analyzes the sensitivity of the latent heat parameter of the refrigerant to 0.2,0.5,1.0,2.0,5.0 times of the original latent heat parameter, the result is shown in table 4.5, alpha represents the multiple, and when the sensitivity is analyzed, the maximum load of the vehicle is set to 20 to ensure the feasibility of the result. It can be seen that when the latent heat of the refrigerant is smaller, more refrigerant must be placed to ensure the lower temperature of the product, on one hand, the refrigerant cost is remarkably increased and the required specification of the incubator is enlarged, on the other hand, a large amount of vehicle capacity is occupied, so that more vehicles have to be called to meet the demands of customers during distribution, and when the latent heat of the refrigerant is increased to a certain extent, the quality of the refrigerant maintaining a certain temperature is negligible relative to the load of the vehicle, so that only the refrigerant cost is changed.
TABLE 4.4 Cold-chain consumable VRP vs conventional VRP
TABLE 4.5 sensitivity analysis of refrigerant latent heat
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing embodiments, which have been described in the foregoing description merely illustrates the principles of the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined in the appended claims.

Claims (10)

1. The urban distribution path decision-making method considering consumable materials under cold chain logistics is characterized by comprising the following steps of:
constructing a cost function comprising transportation cost, refrigerant cost and packaging cost, and constructing a mixed integer planning model by taking the minimum total distribution cost as a target;
a solving algorithm aiming at a small-scale distribution path decision problem example is provided;
designing a solving algorithm of a large-scale distribution path decision problem calculation example;
and verifying the rationality of the model and the effectiveness of the algorithm through the calculation example analysis.
2. The method for determining a city delivery path taking into account consumables under a cold chain logistics according to claim 1, wherein the transportation cost is mainly related to a driving distance of a vehicle, and the transportation cost generated during the delivery process can be expressed as:
wherein c ij Is the transport cost of arc (i, j), x ijk ∈{0,1} |V|×|V|×|K| Indicating whether the vehicle passes through the arc (i, j).
3. The method for determining city distribution path considering consumable materials under cold chain logistics according to claim 2, wherein the cost of the refrigerant is mainly dependent on the consumption of the refrigerant quality, and can be expressed as
Wherein C is u Is the price of the refrigerant, m iupo The product is dispensed to the ith customer point by adopting the mass z of the cooling medium u required by the ith cooling medium, the p-th insulation can material and the o-th packaging specification iupo ∈{0,1} |V|×|U|×|P|×|O| Indicating whether the ith customer dispenses the product by adopting the ith refrigerant, the p-th insulation can material and the o-th packaging specification.
4. A method for determining a city delivery path taking into account consumable materials under a cold chain stream according to claim 3, wherein said packaging cost is expressed as
Wherein C is po Is the price of the heat insulation material and the package specification.
5. The method for determining a city distribution path taking into account consumables under a cold chain logistics according to claim 4, wherein the model is expressed as:
the constraint conditions are as follows:
m iupo ＝f(ΔT 1 ,L u ,S o ,d o ,β p )t i (11)
wherein q is i Is the demand of customer point i, t i Is the time when the vehicle arrives at the ith customer, t ij Is the transit time of arc (i, j), a i Is the earliest arrival time of node i, b i Is the latest arrival time of the node s i Service time of customer point i, deltaT 1 Is the temperature difference between the inside and outside of the transport package, L u Is the latent heat of phase change of the u-th refrigerant, S o Surface area of package No. o, d o Is the wall thickness of the packaging specification of the o type, M po The packaging maximum load is beta when the p-th packaging material and the o-th packaging specification are adopted p The heat conductivity coefficient of the p-th insulation can material is that M is an infinite number.
6. The urban distribution path decision method considering consumables under cold-chain logistics according to claim 5, wherein the linear transformation of the model and the nonlinear expression in the constraint condition is as follows:
step 0: for t i ·z io Linearization, after which the conversion into the following two expressions is performed:
step 1: for x ijk ·t i ·z io After linearization, the conversion is to the following two expressions:
7. the urban distribution path decision-making method considering consumable materials under cold chain logistics according to claim 1, wherein the small-scale distribution path decision-making problem calculation example adopts an optimization solver to accurately solve.
8. The urban distribution path decision method considering consumable materials under cold chain logistics according to claim 1, wherein the large-scale distribution path decision problem calculation example is solved by adopting a genetic algorithm, and the genetic algorithm comprises the following steps:
step 0: setting parameters including population size N pop Maximum algebra Gen, crossover probability P c Probability of variation P m Iterative tag gen=1;
step 1: dividing the chromosome into two parts, wherein each part adopts a natural number coding mode, the first part adopts the vehicle access sequence, namely the coding is an arrangement of N positive integers, wherein each positive integer represents a customer, the second part adopts the packaging specification adopted by each customer and also consists of N positive integers, and each positive integer is one of |o| packaging specifications;
step 2: designing a fitness function as the total distribution cost corresponding to each chromosome;
step 3: random generation bar N pop Chromosome as initial population;
step 4:
step 4.1: a tournament strategy is adopted, and the operation steps of the strategy are as follows: randomly taking out 2 individuals from the population each time, comparing the fitness values of the individuals, selecting the individuals with higher fitness values to enter the offspring population, and repeating the operation until the new population reaches the original population scale;
step 4.2: the chromosome has two parts, and each part adopts different crossing modes: the first part is crossed by an OX operator; the second part adopts a two-point crossing strategy;
step 4.3: the two parts of the chromosome respectively adopt different mutation methods, and the first part adopts a 2-opt operator; the second part randomly re-generates new package specifications within the feasible package specifications for each customer point;
step 5: if the maximum algebra is reached, terminating the iterative process and outputting a result; otherwise, go to step 2.
9. An apparatus is proposed, the apparatus comprising:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform a method of urban distribution path decision making under-cold-chain logistics considering consumables as claimed in any one of claims 1-8.
10. A computer readable storage medium storing a computer program, which when executed by a processor implements a method for determining urban distribution paths taking into account consumables under cold chain logistics according to any one of claims 1-8.
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