CN112906959B - Path optimization method and system considering crowd-sourcing and self-distributing cooperation situation - Google Patents
Path optimization method and system considering crowd-sourcing and self-distributing cooperation situation Download PDFInfo
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Abstract
The invention provides a path optimization method and system considering crowdsourcing and self-distribution cooperation situations, and relates to the technical field of logistics distribution. The method comprises the steps of constructing a target function based on transportation cost, self-distribution cost and crowdsourcing cost in a logistics distribution process, minimizing the target function to determine a distribution route of self-distribution and demand points of crowdsourcing distribution, and optimizing by utilizing a hybrid algorithm combining an improved variable neighborhood search algorithm and a differential evolution algorithm to obtain an optimal objective function value f min And x corresponding thereto min And optimizing the logistics distribution process according to the solved optimal result. The invention guides the selection of the distribution mode of the distribution center while reducing the cost of the distribution center, solves the problem that the logistics distribution cannot be optimized when considering various influence factors in the prior art, and realizes the purpose of integrally optimizing the logistics distribution.
Description
Technical Field
The invention relates to the technical field of logistics distribution, in particular to a path optimization method and system considering crowdsourcing and self-distribution cooperation situations.
Background
The rapid development of the e-commerce industry not only needs to meet the requirements of users on the quality of goods, but also puts new demands on the quality of logistics distribution. If the logistics distribution is unreasonable, the problems of accumulation and disordered placement of goods at the distribution point can be caused, and the user experience is reduced even if the logistics distribution is unreasonable, so that goods can be returned. However, in the logistics distribution, the quality of the logistics distribution is affected by selecting which distribution method (crowd-sourced distribution or self-distributed distribution), when to pick up or deliver goods, how to meet the requirement of the special demand point on the distribution time, and which type of vehicle to select for the logistics distribution.
At present, the research on logistics distribution mainly focuses on the research on the problem of multi-vehicle-type paths integrating taking and delivering goods at the same time with a time window or the research on the problem of crowdsourcing logistics to a crowdsourcing platform to achieve the increase of logistics enterprise benefits and the like. However, these studies only consider some of the logistics distribution influencing factors, some consider not the crowdsourcing problem when considering the pickup and delivery problem of the vehicle, and cannot consider the pickup and delivery integration, the time limitation, the limitation of the vehicle and the like at the same time when considering the crowdsourcing problem; in addition, the existing algorithm for solving the optimization problem has the problems of easy falling into local optimization, low solving speed and the like.
Therefore, the prior art has the problem that logistics distribution cannot be optimized when various influence factors are considered.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a path optimization method and a path optimization system considering crowdsourcing and self-distribution cooperation situations, and solves the problem that logistics distribution cannot be optimized when various influence factors are considered in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention first proposes a path optimization method considering a crowd-sourcing and self-provisioning coordination scenario, the method comprising:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi＝(x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum loading weight Q of the vehicle, wherein the initial iteration number t is 1, the R is 0, the initial value k in the shaking (k) operation is 1 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of the objective function f of each individual in the initial population pi, and acquiring the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max Whether the judgment is true or not is judged, if not, S10 is carried out, otherwise, S5 is carried out;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing shaking (k) operation to obtain x' 1 If t is t +1, it is determined whether R is equal to or less than 3, and if yes, S6 is performed; if not, go to S8;
s6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Go to S7;
s7, judging f (x ″) 1 )＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Operated on to give x ″) 1 And calculating f (x ″) 1 )；
S9, judging f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n Of any of the above, and x ″ ", is 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, let k equalk +1, R ═ 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min 。
Preferably, the S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and recording n individuals obtained from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are combined into an initial population pi ═ x 1 ,x 2 ,...,x n )。
Preferably, the objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint conditions of the objective function f are as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X′ j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 Represents a delivery cost from a demand point of delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance L of electric automobile max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Crowd-sourced distributionAnd the self-delivered delivery cost parameter is a 1 ，a 2 ，b 1 ，b 2 ；d ij Representing the distance between customer points and the distance between the distribution center and the customer points; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery center, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that when i to j are self-distributed, the fuel vehicle m passes through points i to j, otherwise y ijm ＝0；y ijk 1 means that when i to j self-delivery is the presence of an electric vehicle k passing through points i to j, otherwise y ijk ＝0；y 0jm ＝y 0jm The fuel vehicle M starts from a distribution center and finally returns to the distribution center; y is 0jm ＝y 0jm The fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k is going to go from the distribution center and finally goes back to the distribution center.
Preferably, the scraping (k) operation in S5 includes:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: two demand points of the same vehicle are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the shaking (3) operation represents x 1 Go on to the thirdSeed neighborhood operation yields x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j-th bit, and a point of crowdsourcing distribution cannot be inserted between two points of crowdsourcing distribution.
Preferably, the component S6 is p 'to x' 1 Localsearch operation is carried out to obtain x ″) 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operation max And initializing y-0, l-1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing a step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 Localsearch was carried out to give x ″' 1 And f (x ″) 1 )；
And 5: determine f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Value to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
In a second aspect, the present invention further provides a path optimization system considering a crowd-sourcing and self-provisioning collaborative scenario, the system comprising:
a processing module for performing the steps of:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum load capacity Q of the vehicle, where the initial iteration number t is 1 and the initialization R is 0, and the initial value k is 1 in the shaking (k) operation 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of an objective function f of each individual in the initial population pi, and acquiring the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max Whether the judgment is true or not is judged, if not, S10 is carried out, otherwise, S5 is carried out;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing shaking (k) operation to obtain x' 1 If t is t +1, determining whether R is equal to or less than 3, and if so, performing S6; if not, go to S8;
s6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Go to S7;
s7, determining f (x ″) 1 )＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Operated on to give x ″) 1 And f (x ″) is calculated 1 )；
S9, judging f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n And (c) the value of any of (A) and (B), and (c) the sum of 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min ；
An output module for outputting the optimal objective function value f min And x corresponding thereto min 。
Preferably, when the processing module executes S1, the S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and recording n individuals obtained from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are combined into an initial population pi ═ x 1 ,x 2 ,...,x n )。
Preferably, when the processing module executes the steps S1-S10, the objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint conditions of the objective function f are as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X′ j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 Represents a delivery cost from a demand point of delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance L of electric automobile max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a 1 ，a 2 ，b 1 ，b 2 ；d ij Indicating the distance between customer sites and the distribution center and customer sitesThe distance between them; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery center, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise y ijm ＝0；y ijk 1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise y ijk ＝0；y 0jm ＝y 0jm The fuel vehicle M starts from a distribution center and finally returns to the distribution center; y is 0jm ＝y 0jm The fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k is going to go from the distribution center and finally goes back to the distribution center.
Preferably, when the processing module executes S5, the scraping (k) operation in S5 includes:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: two demand points of the same vehicle are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will, inserted into the j bit and cannot be crowdsourcedThe point of delivery is inserted between the two points of crowd-sourced delivery.
Preferably, the processing module, when executing S6, pairs x 'in S6' 1 Performing local search operation to obtain x ″) 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operation max And initializing y-0, l-1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing the step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 Localsearch was carried out to give x ″' 1 And f (x ″) 1 )；
And 5: judgment of f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Assigned to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
(III) advantageous effects
The invention provides a path optimization method and system considering crowd-sourcing and self-distributing cooperation situations. Compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of constructing a target function based on transportation cost, self-distribution cost and crowdsourcing cost in a logistics distribution process, minimizing the target function to determine a distribution route of self-distribution and demand points of crowdsourcing distribution, and optimizing by utilizing a hybrid algorithm combining an improved variable neighborhood search algorithm and a differential evolution algorithm to obtain an optimal objective function value f min And x corresponding thereto min And optimizing the logistics distribution process according to the solved optimal result. The invention guides the selection of the distribution mode of the distribution center while reducing the cost of the distribution center, solves the problem that the prior art can not optimize the logistics distribution when considering various influence factors, and realizes the aim of integrally optimizing the logistics distributionIn (1).
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, it is obvious that the drawings in the following description are only 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 a path optimization method considering a crowd-sourcing and self-provisioning coordination scenario in an embodiment of the present invention;
FIG. 2 is a diagram illustrating neighborhood operations in an embodiment of the present invention;
FIG. 3 is a schematic view of the localsearch operation in the embodiment of the present invention;
fig. 4 is a schematic diagram of a differential evolution algorithm in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. 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 embodiment of the application provides a path optimization method and system considering crowd-sourcing and self-distribution cooperation situations, solves the problem that logistics distribution cannot be optimized when multi-aspect influence factors are considered in the prior art, and achieves the purpose of integrally optimizing the logistics distribution.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in order to solve the problem that the logistics distribution cannot be optimized in consideration of various influence factors in the prior art, the technical scheme firstly establishes a minimum objective function comprehensively considering transportation cost, self-distribution cost and crowdsourcing cost, and thenThen, a self-distribution route and crowdsourcing distribution demand point are determined, then, the optimization process is solved by utilizing a hybrid algorithm combining an improved variable neighborhood search algorithm and a differential evolution algorithm, and an optimal objective function value f is obtained min And x corresponding thereto min And finally, optimizing the logistics distribution process according to the solving result.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
in a first aspect, the present invention first discloses a path optimization method considering crowdsourcing and self-distribution coordination situations, the method comprising:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum loading weight Q of the vehicle, wherein the initial iteration number t is 1, the R is 0, the initial value k in the shaking (k) operation is 1 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max Whether the determination is true or not, if not, performing S10, otherwise, performing S5;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing shaking (k) operation to obtain x' 1 If t is t +1, determining whether R is equal to or less than 3, and if so, performing S6; if not, the conditionThen proceed to S8;
s6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Go to S7;
s7, determining f (x ″) 1 )＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Operated on to give x ″) 1 And calculating f (x ″) 1 )；
S9, judging f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n Of any of the above, and x ″ ", is 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, let k be k +1, R be 0 and return to S4;
s10, finishing algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min 。
Therefore, in the embodiment, the objective function is constructed based on the transportation cost, the self-distribution cost and the crowd-sourcing cost in the logistics distribution process, then the objective function is minimized to determine the distribution route of self-distribution and the demand point of crowd-sourcing distribution, and then the optimization is performed by using the hybrid algorithm of the improved variable neighborhood search algorithm and the differential evolution algorithm, so that the optimal objective function value f is obtained min And x corresponding thereto min And optimizing the logistics distribution process according to the solved optimal result. The invention guides the selection of the distribution mode of the distribution center while reducing the cost of the distribution center, solves the problem that the prior art can not optimize the logistics distribution when considering various influence factors, and realizes the aim of integrally optimizing the logistics distribution.
In the above method according to an embodiment of the present invention, in order to ensure the difference of each individual in the generated initial population, when the initial population is generated, a preferable processing manner in the step S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and recording n individuals obtained from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are grouped into an initial population pi ═ (x) 1 ,x 2 ,...,x n )
In addition, in order to ensure the lowest total cost of the whole logistics distribution process under the condition of comprehensively considering the influence of various influencing factors on the logistics distribution process, a preferred processing method is that the objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint conditions of the objective function f are as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X′ j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 A delivery cost representing a demand point from delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of the demand point i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance Lmax of electric vehicle, cost parameter c of fuel vehicle and electric vehicle 1 ，c 2 The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a 1 ，a 2 ，b 1 ，b 2 ；d ij Representing the distance between customer pointsAnd the distance between the distribution center and the customer site; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery centre, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that when i to j are self-distributed, the fuel vehicle m passes through points i to j, otherwise y ijm ＝0；y ijk 1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise y ijk ＝0；y 0jm ＝y 0jm I belongs to I, j belongs to I, and M belongs to M, which indicates that the fuel vehicle M starts from the distribution center and finally returns to the distribution center; y is 0jm ＝y 0jm The fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k is going to go from the distribution center and finally goes back to the distribution center.
In the foregoing method according to the embodiment of the present invention, in order to prevent the local optimal solution from being trapped and generate a new feasible solution, a preferred processing manner is that the shaking (k) operation in S5 includes:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: exchanging two demand points of the same vehicle at will without exchanging two crowdsourcing distribution points;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j-th bit, and a point of crowdsourcing distribution cannot be inserted between two points of crowdsourcing distribution.
Furthermore, in order to select a best solution as the current solution from the solution space adjacent to the current solution until reaching the local best solution, a preferred way to process is to x 'in the S6' 1 Performing local search operation to obtain x ″) 1 The method comprises the following steps:
step 1: inputting the maximum iteration number y of the localsearch operation max And initializing y-0, l-1;
step 2: judging y is less than or equal to y max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and 3, step 3: judging whether l is less than or equal to 3, if so, performing a step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 Localsearch operation is carried out to obtain x ″) 1 And f (x ″) 1 )；
And 5: judgment of f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Value to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
The following describes a specific implementation process of the present invention by taking the demand point I as 10, the logistics distribution vehicle as a fuel vehicle, and the number M of vehicles as 2 as an example.
And S1, generating an initial population. Obtaining n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )。
Assuming that 10 demand points and 2 fuel vehicles are provided, firstly, randomly sequencing the 10 demand points, and then inserting 0 into the demand points to obtain an initial solution, namely n individuals x 1 ,x 2 ,...,x 10 Generating an initial population pi ═ (x) 1 ,x 2 ,...,x 10 ). Let us assume that x 1 ＝[1,2,3,4,5,6,0,7,8,9,10]Then the path of the first vehicle at this time is [1,2,3,4,5,6 ]]The path of the second vehicle is [7,8,9,10 ]]。
And S2, setting parameters. Setting maximum iteration times t of variable neighborhood search algorithm max Setting the maximum load capacity Q of the vehicle, where the initial iteration number t is 1 and the initialization R is 0, and the initial value k is 1 in the shaking (k) operation 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 。
And S3, determining a distribution path and calculating cost. Calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value f min And f min Corresponding individual x min 。
An actual travel path of the vehicle is determined. Determining an actual travel path and cost f (x) of the vehicle based on the payload limit and the time limit of the vehicle 1 ). Referring to fig. 2, in vehicle one, the travel paths 1,2,3 are taken as self-distribution and 4,5,6 are taken as crowd-sourced distribution, as shown in fig. 2 a; in vehicle two, the travel paths 7,8 are used as self-distribution and 9,10 are used as crowd-sourced distribution, as shown in fig. 2 b. And calculating the cost f (x) 1 ). When calculating the cost, calculating according to the following formula:
f＝C 1 +C 2 +C 3
the constraint of the function f can be expressed as:
Q′ i ＞0,i∈I
Q′ i -X j +X′ j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 Indicating self-dispensingDistribution cost of demand points; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a The maximum driving distance Lmax of the electric automobile, and the cost parameters of the fuel vehicle and the electric automobile are as follows: c. C 1 Represents the transportation cost per unit distance of the electric vehicle, c 2 The parameters of transportation cost per unit distance, distribution cargo cost of crowd-sourced distribution and self-distributed distribution of the fuel vehicle are a 1 Representing the base cost of self-distribution of the individual goods, a 2 Representing the base cost of crowd-sourced distribution of individual goods, b 1 Representing the cost of self-dispensing in excess of the basis weight, b 2 Representing the cost of crowd-sourced distribution over basis weight, d ij Representing the distance between customer points and the distance between the distribution center and the customer points; q 3 Representing the base cost in time of charge, i.e. weight not exceeding Q 3 The distribution cost of the single cargo is a 1 (a 2 )；Q′ i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery centre, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise y ijm ＝0；y ijk When 1 denotes i to j self-deliveryWhen the electric vehicle k passes through the points i to j, otherwise y ijk ＝0；y 0jm ＝y 0jm I belongs to I, j belongs to I, and M belongs to M, which indicates that the fuel vehicle M starts from the distribution center and finally returns to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k is going to go from the distribution center and finally goes back to the distribution center. In particular, the amount of the solvent to be used,
Q′ i the I belongs to the I and represents that the residual capacity of the vehicle passing through the I point is not less than 0;
Q′ i -X j +X′ j ＝Q′ i i is connected with j to indicate that the residual capacity leaving the point i is equal to the residual capacity leaving the point j plus the delivered cargo amount of the point i minus the pickup amount;
t i ＜max(t i ) Indicating that the time to reach demand point i cannot exceed the latest time for the cargo to arrive;
(T 1im +T 2ik )×Y i 1 +Y i 2 1, I belongs to I, M belongs to M and represents that the demand point can only be self-distributed or can only be crowd-sourced;
(T 1im +T 2ik )×Y i 1 the number of the demand points I is less than or equal to 1, I belongs to I, and K belongs to K and represents that the demand points I of self distribution are transported by only one vehicle;
y 0jm ＝y 0jm i belongs to I, j belongs to I, M belongs to M and indicates that the fuel vehicle M starts from the distribution center o and finally returns to the distribution center;
y 0jk ＝y i0k i e I, j e I, K e K indicates that the electric vehicle K departs from the distribution center o and finally returns to the distribution center.
S4, judging t is less than or equal to t max And (4) whether the condition is satisfied, if not, performing S10, otherwise, performing S5.
S5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing a scraping (k) operation to obtain x' 1 If t is t +1, determining whether R is equal to or less than 3, and if so, performing S6; if not, S8 is performed.
When k is 1, the scraping (1) operation represents x' 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: two demand points of the same vehicle are exchanged at will and two points of crowd-sourced distribution are not exchanged, as shown in fig. 2 c;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points for different vehicles are exchanged at will and two points for crowd-sourced distribution are not exchanged, as shown in fig. 2 d;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j bit, and crowdsourcing cannot be achievedThe point of distribution is inserted between the points of two crowd-sourced distributions as shown in fig. 2 e.
S6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Then, S7 is performed.
To x' 1 Localsearch operation is carried out to obtain x ″) 1 Referring to fig. 3, the specific process is as follows:
step 1: inputting the maximum iteration number y of the localsearch operation max And initializing y-0, l-1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing a step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 Localsearch operation is carried out to obtain x ″) 1 And f (x ″) 1 )；
And 5: judgment of f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Value to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
S7, judging f (x ″) 1 )＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, let k be k +1 and R be R +1, return to S4.
S8, pair x 'by utilizing differential evolution algorithm' 1 Is operated to give x ″) 1 And f (x ″) is calculated 1 )。
Randomly taking x out of population II j ，x z Performing a mutation operation, i.e. v 1 ＝x′ 1 +F*(x j -x z ) V obtained 1 And x 1 Performing a crossover operation to obtain x ″) 1 Then f (x ″') is calculated 1 ). For example, referring to FIG. 4, assume x' 1 ＝[1,2,3,4,5,6,0,7,8,9,10]Randomly selecting two individuals, such as x, in the population II 2 ＝[1,2,4,8,5,6,0,10,9,3,7](i.e., j ═ 2), x 3 ＝[1,4,6,8,9,10,0,2,5,3,7](i.e., z-3), a differential evolution algorithm (DE) operation is performed according to the following formula:
v 1 ＝x′ 1 +F*(x 2 -x 3 )
the resulting solution is [1,1,2,4,3,4,0,11,10,9,10]At this time, the demand point appearing twice is removed to generate a new solution v 1 ＝[1,5,2,4,3,7,0,6,10,9,8]Then, the exchange operation is performed.
Then x' is obtained 1 ＝[1,2,3,4,3,7,0,6,10,9,8]。
S9, comparing and iterating the cost of the operation of the DE. Judgment of f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n Of any of the above, and x ″ ", is 1 Is assigned to x 1 And updating populations pi and f min And x min Otherwise, let k be k +1 and R be 0 and return to S4.
S10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min 。
After the algorithm execution is finished, according to the output optimal objective function value f min And x corresponding thereto min A logistics distribution scheme is determined and the logistics distribution process is performed according to the scheme.
Thus, the whole process of the path optimization method considering the crowdsourcing and self-distribution cooperation situation is completed.
Example 2:
in a second aspect, the present invention also provides a path optimization system considering a crowd-sourced and self-served collaborative scenario, the system comprising:
a processing module for performing the steps of:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum load capacity Q of the vehicle, where the initial iteration number t is 1 and the initialization R is 0, and the initial value k is 1 in the shaking (k) operation 1 ，Q 2 And maximum driving distance L of electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max Whether the determination is true or not, if not, performing S10, otherwise, performing S5;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing a scraping (k) operation to obtain x' 1 If t is t +1, determining whether R is equal to or less than 3, and if so, performing S6; if not, go to S8;
s6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Performing S7;
s7, judging f (x ″) 1 )＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Is operated to give x ″) 1 And calculating f (x ″) 1 )；
S9, judging f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n Of any of the above, and x ″ ", is 1 Assigned to x 1 And update the speciesGroup pi and f min And x min Otherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min ；
An output module for outputting the optimal objective function value f min And x corresponding thereto min 。
Preferably, when the processing module executes S1, the S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and recording n individuals obtained from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are combined into an initial population pi ═ (x) 1 ,x 2 ,...,x n )。
Preferably, when the processing module executes the steps S1-S10, the objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint condition of the objective function f is as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X′ j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 Represents a delivery cost from a demand point of delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance L of electric automobile max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a 1 ，a 2 ，b 1 ，b 2 ；d ij Representing the distance between customer points and the distance between the distribution center and the customer points; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery centre, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise y ijm ＝0；y ijk 1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise y ijk ＝0；y 0jm ＝y 0jm I belongs to I, j belongs to I, and M belongs to M, which indicates that the fuel vehicle M starts from the distribution center and finally returns to the distribution center; y is 0jm ＝y 0jm The fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k departs from the distribution center and finally returns to the distribution center.
Preferably, when the processing module executes S5, the scraping (k) operation in S5 includes:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: exchanging two demand points of the same vehicle at will without exchanging two crowdsourcing distribution points;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j-th bit, and a point of crowdsourcing distribution cannot be inserted between two points of crowdsourcing distribution.
Preferably, the processing module, when executing S6, pairs x 'in S6' 1 Performing local search operation to obtain x ″) 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operation max And initializing y-0, l-1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing a step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 Localsearch operation is carried out to obtain x ″) 1 And f (x ″) 1 )；
And 5: judgment of f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Value to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
It is to be understood that the path optimization system considering the cooperative situations of crowdsourcing and self-distribution provided in the embodiment of the present invention corresponds to the path optimization method considering the cooperative situations of crowdsourcing and self-distribution, and for the explanation, examples, and beneficial effects of the related contents, reference may be made to corresponding contents in the path optimization method considering the cooperative situations of crowdsourcing and self-distribution, and details are not described here again.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the method comprises the steps of constructing an objective function based on transportation cost, self-distribution cost and crowdsourcing cost in the logistics distribution process, minimizing the objective function to determine a distribution route of self-distribution and demand points of crowdsourcing distribution, optimizing by using a hybrid algorithm of an improved variable neighborhood search algorithm and a differential evolution algorithm, and obtaining an optimal objective function value f min And x corresponding thereto min And optimizing the logistics distribution process according to the solved optimal result. The invention guides the selection of the distribution mode of the distribution center while reducing the cost of the distribution center, solves the problem that the prior art can not optimize the logistics distribution when considering various influence factors, and realizes the aim of integrally optimizing the logistics distribution;
2. when the objective function is constructed, the distribution cost and the transportation cost of the distribution center, the selection condition of self-distribution and crowdsourcing distribution, the combination condition of goods taking and delivery, the time limit of special demand points, the selection of the types of distributed goods and the like are comprehensively considered, and finally a distribution scheme with the lowest total cost is found out, so that the aim of optimizing logistics distribution when considering influence factors in various aspects is fulfilled;
3. in the process of optimizing the distribution scheme with the lowest total logistics distribution cost, the variable neighborhood search algorithm and the differential evolution algorithm are combined by combining the characteristics of specific problems in the logistics distribution process according to the defects and advantages of the variable neighborhood search algorithm and the differential evolution algorithm, so that not only is the solving precision of the algorithm effectively improved, but also the convergence speed of the algorithm is greatly improved.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for path optimization that considers crowd-sourced and self-provisioned cooperative scenarios, the method comprising:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum load capacity Q of the vehicle, where the initial iteration number t is 1 and the initialization R is 0, and the initial value k is 1 in the shaking (k) operation 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max If it is not true, proceedS10, otherwise, carrying out S5;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing shaking (k) operation to obtain x' 1 If t is t +1, it is determined whether R is equal to or less than 3, and if yes, S6 is performed; if not, go to S8;
s6, p 'x' 1 Localsearch operation is carried out to obtain x ″) 1 Go to S7;
s7, judgment f (x' 1 )＜f(x 1 ) If yes, x 'is formed' 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Was operated to give x' 1 And calculating f (x' 1 )；
S9, judgment f (x' 1 )＜f(x 1 ) Whether or not the above-mentioned conditions are satisfied,
if true, use x 1 Random substitution of x 2 ，...，x n And x 'to any of' 1 Is assigned to x 1 And updating population pi and f min And x min ，
Otherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min ；
The objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint conditions of the objective function f are as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X' j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 A delivery cost representing a demand point from delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance L of electric automobile max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a 1 ，a 2 ，b 1 ，b 2 ；d ij Representing the distance between customer points and the distance between the distribution center and the customer points; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery centre, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise y ijm ＝0；y ijk 1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise y ijk ＝0；y 0jm ＝y i0m The fuel vehicle M starts from a distribution center and finally returns to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k is going to go from the distribution center and finally goes back to the distribution center.
2. The method of claim 1, wherein the S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and marking n individuals acquired from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are grouped into an initial population pi ═ (x) 1 ,x 2 ,...,x n )。
3. The method of claim 1, wherein the shaking (k) operation in S5 comprises:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: exchanging two demand points of the same vehicle at will without exchanging two crowdsourcing distribution points;
when k is 2, the shaking (2) operation represents x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j-th bit, and a point of crowdsourcing distribution cannot be inserted between two points of crowdsourcing distribution.
4. The process of claim 1, wherein x 'is the pair in S6' 1 Localsearch operation is carried out to obtain x ″) 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the local search operation max And initiallyChanging y to 0 and l to 1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing a step 4, otherwise, making l equal to 1, and returning to the step 2;
and 4, step 4: to x' 1 A local search operation is performed to obtain x ″' 1 And f (x ″) 1 )；
And 5: judgment of f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Assigned to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
5. A path optimization system that considers crowd-sourced and self-provisioned collaborative scenarios, the system comprising:
a processing module for performing the steps of:
s1, acquiring n individuals x based on the number M of vehicles and the logistics distribution demand point I 1 ,x 2 ,...,x n And generating an initial population pi ═ x 1 ,x 2 ,...,x n )；
S2, setting the maximum iteration number t of the variable neighborhood search algorithm max Setting the maximum load capacity Q of the vehicle, where the initial iteration number t is 1 and the initialization R is 0, and the initial value k is 1 in the shaking (k) operation 1 ，Q 2 And the maximum driving distance L of the electric vehicle max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 Cost of distribution parameter a for crowd-sourced and self-sourced distribution 1 ，a 2 ，b 1 ，b 2 ；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value f min And f min Corresponding individual x min ；
S4, judging t is less than or equal to t max If the determination is not true, the process proceeds to S10,otherwise, performing S5;
s5, judging whether k is less than or equal to 3, if not, making k equal to 1, and comparing x 1 Performing shaking (k) operation to obtain x' 1 Otherwise, directly to x 1 Performing shaking (k) operation to obtain x' 1 If t is t +1, determining whether R is equal to or less than 3, and if so, performing S6; if not, go to S8;
s6, p 'x' 1 Performing local search operation to obtain x ″) 1 Go to S7;
s7, determination f (x) 1 ”)＜f(x 1 ) If yes, then x ″' is applied 1 Is assigned to x 1 And updating population pi and f min And x min Otherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm' 1 Operated on to give x ″) 1 And calculating f (x ″) 1 )；
S9, judging f (x ″) 1 )＜f(x 1 ) If yes, use x 1 Random substitution of x 2 ，...，x n Of any of the above, and x ″ ", is 1 Assigned to x 1 And updating population pi and f min And x min Otherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value f min And x corresponding thereto min ；
An output module for outputting the optimal objective function value f min And x corresponding thereto min ；
The objective function f can be expressed by the following formula:
f＝C 1 +C 2 +C 3
the constraint conditions of the objective function f are as follows:
Q′ i ＞0,i∈I
Q′ i -X j +X' j ＝Q′ i
t i ＜max(t i )
(T 1im +T 2ik )×Y i 1 +Y i 2 ＝1,i∈I,m∈M
(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K
y 0jk ＝y i0k ,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c 1 Represents a delivery cost from a demand point of delivery; c 2 Represents the cost of transportation of the self-delivered goods; c 3 Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
representing the distribution cost of the self-distribution demand points in the demand points i;
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle 1 ，Q 2 (ii) a Maximum driving distance L of electric automobile max Cost parameter c for fuel-oil vehicles and electric vehicles 1 ，c 2 The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a 1 ，a 2 ，b 1 ，b 2 ；d ij Representing the distance between customer points and the distance between the distribution center and the customer points; q' i Indicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y is i 1 ，Y i 2 ，T 1im ，T 2ik ，y ijm ，y ijk Are all decision variables, Y i 1 1 means that customer point i is self-delivered by the delivery centre, otherwise Y i 1 ＝0；Y i 2 1 means that customer point i is delivered by crowdsourcing, otherwise, Y i 2 ＝0；T 1im 1 means that customer point i is self-delivered by fuel vehicle m, otherwise T 1im ＝0；T 2ik 1 means that customer point i is self-delivered by electric vehicle k, otherwise T 2ik ＝0；y ijm 1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise y ijm ＝0；y ijk 1 means i to j self-dispensing is the presence of electric vapourWhen the vehicle k passes through points i to j, otherwise y ijk ＝0；y 0jm ＝y i0m I belongs to I, j belongs to I, and M belongs to M, which indicates that the fuel vehicle M starts from the distribution center and finally returns to the distribution center; y is 0jk ＝y i0k Indicating that the electric vehicle k departs from the distribution center and finally returns to the distribution center.
6. The system of claim 5, wherein the processing module, when executing S1, the S1 includes:
selecting the number M of vehicles and a logistics distribution demand point I;
randomly coding and sequencing demand points I, and recording n individuals obtained from M-1 0 random insertion sequences as x 1 ,x 2 ,...,x n The n individuals are combined into an initial population pi ═ x 1 ,x 2 ,...,x n )。
7. The system of claim 5, wherein the processing module, when executing S5, shaking (k) operation in S5 comprises:
when k is 1, the scraping (1) operation represents x 1 Performing a first neighborhood operation to obtain x' 1 (ii) a The first neighborhood operation represents: exchanging two demand points of the same vehicle at will without exchanging two crowdsourcing distribution points;
when k is 2, the scraping (2) operation represents the pair x 1 Performing a second neighborhood operation to obtain x' 1 (ii) a The second neighborhood operation represents: two demand points of different vehicles are exchanged at will and two crowdsourced distribution points are not exchanged;
when k is 3, the scraping (3) operation represents the pair x 1 Performing a third neighborhood operation to obtain x' 1 (ii) a The third neighborhood operation represents: a demand point i is taken out at will and inserted into the j-th bit, and a point of crowdsourcing distribution cannot be inserted between two points of crowdsourcing distribution.
8. The system of claim 5, wherein the processing module, when executing S6, pairs x 'in S6' 1 Performing a localsearch operationObtaining x ″) 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the local search operation max And initializing y-0, l-1;
step 2: y is judged to be less than or equal to max If yes, performing step 3, wherein y is y + 1; otherwise, performing step 6;
and step 3: judging whether l is less than or equal to 3, if so, performing the step 4, otherwise, enabling l ＝ 1, returning to the step 2;
and 4, step 4: to x' 1 Performing local search operation to obtain x ″) 1 And f (x ″) 1 )；
And 5: determine f (x ″) 1 )≤f(x′ 1 ) If yes, then x ″' is applied 1 Value to x' 1 And let l equal to 1, otherwise let l equal to l + 1; returning to the step 2;
step 6: the flow ends and x ″' is output 1 And f (x ″) 1 ) The value of (c).
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