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CN112906959A - 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 PDF

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CN112906959A
CN112906959A CN202110165634.2A CN202110165634A CN112906959A CN 112906959 A CN112906959 A CN 112906959A CN 202110165634 A CN202110165634 A CN 202110165634A CN 112906959 A CN112906959 A CN 112906959A
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范雯娟
周琪琦
兰绍雯
邵凯宁
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Abstract

本发明提供一种考虑众包和自配送协同情形的路径优化方法和系统，涉及物流配送技术领域。本发明基于物流配送过程的运输成本、自配送成本、众包成本构建目标函数，然后对目标函数最小化以确定自配送的配送路线和众包配送的需求点，接着利用改进后的变邻域搜索算法与差分进化算法结合的混合算法进行寻优，获得最优的目标函数值fmin及其对应的xmin，并按照求解的最优结果优化物流配送过程。本发明在降低配送中心成本的同时，指导了配送中心配送方式的选择，解决了现有技术无法在考虑多方面影响因素时对物流配送进行优化的问题，实现了对物流配送进行整体优化的目的。

Figure 202110165634

The invention provides a path optimization method and system considering the collaborative situation of crowdsourcing and self-distribution, and relates to the technical field of logistics distribution. The invention constructs an objective function based on the transportation cost, self-distribution cost and crowdsourcing cost of the logistics distribution process, then minimizes the objective function to determine the distribution route of self-distribution and the demand point of crowdsourcing distribution, and then uses the improved variable neighborhood The hybrid algorithm combining the search algorithm and the differential evolution algorithm is used for optimization, and the optimal objective function value f min and its corresponding x min are obtained, and the logistics distribution process is optimized according to the optimal result of the solution. The present invention not only reduces the cost of the distribution center, but also guides the selection of the distribution mode of the distribution center, solves the problem that the prior art cannot optimize the logistics distribution when considering various influencing factors, and realizes the purpose of overall optimization of the logistics distribution. .

Figure 202110165634

Description

Path optimization method and system considering crowd-sourcing and self-distributing cooperation situation
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 logistics enterprise benefit increase 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 I1,x2,...,xnAnd generating an initial population pi ═ x1,x2,...,xn)；
S2, setting the maximum iteration number t of the variable neighborhood search algorithmmaxSetting 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) operation1，Q2And the maximum driving distance L of the electric vehiclemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2Cost of distribution parameter a for crowd-sourced and self-sourced distribution1，a2，b1，b2；
S3, calculating the value of the objective function f of each individual in the initial population pi, and acquiring the minimum objective function value fminAnd fminCorresponding individual xmin；
S4, judging t is less than or equal to tmaxWhether 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 x1Performing shaking (k) operation to obtain x'1Otherwise, directly to x1Performing shaking (k) operation to obtain x'1If 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'1Localsearch operation is carried out to obtain x ″)1Go to S7;
s7, judging f (x ″)1)＜f(x1) If yes, then x ″' is applied1Is assigned to x1And updating population pi and fminAnd xminOtherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm'1Operated on to give x ″)1And calculating f (x ″)1)；
S9, judgmentF is broken (x ″)1)＜f(x1) If yes, use x1Random substitution of x2，...，xnOf any of the above, and x ″ ", is1Is assigned to x1And updating population pi and fminAnd xminOtherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value fminAnd x corresponding theretomin。
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 x1,x2,...,xnThe n individuals are combined into an initial population pi ═ x1,x2,...,xn)。
Preferably, the objective function f can be expressed by the following formula:
f＝C1+C2+C3
Figure BDA0002937749870000031
Figure BDA0002937749870000032
Figure BDA0002937749870000033
Figure BDA0002937749870000034
Figure BDA0002937749870000035
the constraint conditions of the objective function f are as follows:
Figure BDA0002937749870000036
Figure BDA0002937749870000037
Q′i＞0,i∈I
Q′i-Xj+X′j＝Q′i
ti＜max(ti)
Figure BDA0002937749870000038
Figure BDA0002937749870000039
Figure BDA00029377498700000310
(T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M
(T1im+T2ik)×Yi 1≤1,i∈I,k∈K
y0jk＝yi0k,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c1Represents a delivery cost from a demand point of delivery; c2Represents the cost of transportation of the self-delivered goods; c3Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
Figure BDA0002937749870000041
representing the distribution cost of the self-distribution demand points in the demand points i;
Figure BDA0002937749870000042
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle1，Q2(ii) a Maximum driving distance L of electric automobilemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a1，a2，b1，b2；dijRepresenting the distance between customer points and the distance between the distribution center and the customer points; q'iIndicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y isi 1，Yi 2，T1im，T2ik，yijm，yijkAre all decision variables, Y i 11 means that customer point i is self-delivered by the delivery centre, otherwise Yi 1＝0；Y i 21 means that customer point i is delivered by crowdsourcing, otherwise, Yi 2＝0；T 1im1 means that customer point i is self-delivered by fuel vehicle m, otherwise T1im＝0；T 2ik1 means that customer point i is self-delivered by electric vehicle k, otherwise T2ik＝0；y ijm1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise yijm＝0；y ijk1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise yijk＝0；y0jm＝y0jmI 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 is0jm＝y0jmThe fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is0jk＝yi0kIndicating 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 x1Performing 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 a pairx1Performing 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 x1Performing 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, x 'in S6'1Localsearch operation is carried out to obtain x ″)1Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operationmaxAnd initializing y-0, l-1;
step 2: y is judged to be less than or equal tomaxIf 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'1Localsearch operation is carried out to obtain x ″)1And f (x ″)1)；
And 5: judgment of f (x ″)1)≤f(x′1) If yes, then x ″' is applied1Value to x'1And 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 output1And 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 I1,x2,...,xnAnd generating an initial population pi ═ x1,x2,...,xn)；
S2, setting the maximum iteration times of the variable neighborhood searching algorithmNumber tmaxSetting 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) operation1，Q2And the maximum driving distance L of the electric vehiclemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2Cost of distribution parameter a for crowd-sourced and self-sourced distribution1，a2，b1，b2；
S3, calculating the value of the objective function f of each individual in the initial population pi, and acquiring the minimum objective function value fminAnd fminCorresponding individual xmin；
S4, judging t is less than or equal to tmaxWhether 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 x1Performing shaking (k) operation to obtain x'1Otherwise, directly to x1Performing shaking (k) operation to obtain x'1If 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'1Localsearch operation is carried out to obtain x ″)1Go to S7;
s7, judging f (x ″)1)＜f(x1) If yes, then x ″' is applied1Is assigned to x1And updating population pi and fminAnd xminOtherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm'1Operated on to give x ″)1And calculating f (x ″)1)；
S9, judging f (x ″)1)＜f(x1) If yes, use x1Random substitution of x2，...，xnOf any of the above, and x ″ ", is1Is assigned to x1And updating population pi and fminAnd xminOtherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value fminAnd x corresponding theretomin；
An output module for outputting the optimal objective function value fminAnd x corresponding theretomin。
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 x1,x2,...,xnThe n individuals are combined into an initial population pi ═ x1,x2,...,xn)。
Preferably, when the processing module executes the steps S1-S10, the objective function f can be expressed by the following formula:
f＝C1+C2+C3
Figure BDA0002937749870000071
Figure BDA0002937749870000072
Figure BDA0002937749870000073
Figure BDA0002937749870000074
Figure BDA0002937749870000075
the constraint conditions of the objective function f are as follows:
Figure BDA0002937749870000076
Figure BDA0002937749870000077
Q′i＞0,i∈I
Q′i-Xj+X′j＝Q′i
ti＜max(ti)
Figure BDA0002937749870000078
Figure BDA0002937749870000079
Figure BDA00029377498700000710
(T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M
(T1im+T2ik)×Yi 1≤1,i∈I,k∈K
y0jk＝yi0k,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c1Represents a delivery cost from a demand point of delivery; c2Represents the cost of transportation of the self-delivered goods; c3Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
Figure BDA00029377498700000711
representing the distribution cost of the self-distribution demand points in the demand points i;
Figure BDA00029377498700000712
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle1，Q2(ii) a Maximum driving distance L of electric automobilemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a1，a2，b1，b2；dijRepresenting the distance between customer points and the distance between the distribution center and the customer points; q'iIndicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y isi 1，Yi 2，T1im，T2ik，yijm，yijkAre all decision variables, Yi 11 means that customer point i is self-delivered by the delivery centre, otherwise Yi 1＝0；Yi 21 means that customer point i is delivered by crowdsourcing, otherwise, Yi 2＝0；T1im1 means that customer point i is self-delivered by fuel vehicle m, otherwise T1im＝0；T2ik1 means that customer point i is self-delivered by electric vehicle k, otherwise T2ik＝0；yijm1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise yijm＝0；yijk1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise yijk＝0；y0jm＝y0jmI 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 is0jm＝y0jmThe fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is0jk＝yi0kIndicating 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 x1Performing 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 x1Performing a second neighborhood operation to obtain x'1(ii) a The second neighborhood operation represents: exchange two demand points of different vehicles at will withoutExchanging two points of crowdsourced distribution;
when k is 3, the scraping (3) operation represents the pair x1Performing 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 S6'1Performing local search operation to obtain x ″)1Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operationmaxAnd initializing y-0, l-1;
step 2: y is judged to be less than or equal tomaxIf 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'1Localsearch operation is carried out to obtain x ″)1And f (x ″)1)；
And 5: judgment of f (x ″)1)≤f(x′1) If yes, then x ″' is applied1Value to x'1And 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 output1And f (x ″)1) The value of (c).
(III) advantageous effects
The invention provides a path optimization method and system considering crowdsourcing and self-distribution 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 fminAnd x corresponding theretominAnd according to the solutionThe optimal result of (a) optimizes the logistics distribution process. 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.
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 below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings 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 according to an 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 a path optimization system considering crowdsourcing and self-distribution cooperation situations, solves the problem that logistics distribution cannot be optimized in consideration of various influence factors in the prior art, and achieves the purpose of overall optimization of 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 logistics distribution cannot be optimized in consideration of various influence factors in the prior art, the technical scheme is that a minimized objective function comprehensively considering transportation cost, self-distribution cost and crowdsourcing cost is established, then a distribution route of self-distribution and demand points of crowdsourcing distribution are determined, then an optimization process is solved by using a hybrid algorithm combining an improved variable neighborhood search algorithm and a differential evolution algorithm, and an optimal objective function value f is obtainedminAnd x corresponding theretominAnd 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 I1,x2,...,xnAnd generating an initial population pi ═ x1,x2,...,xn)；
S2, setting the maximum iteration number t of the variable neighborhood search algorithmmaxSetting 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) operation1，Q2And the maximum driving distance L of the electric vehiclemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2Cost of distribution parameter a for crowd-sourced and self-sourced distribution1，a2，b1，b2；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value fminAnd fminCorresponding individual xmin；
S4, judging t is less than or equal to tmaxWhether 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 x1Performing shaking (k) operation to obtain x'1Otherwise, directly to x1Performing shaking (k) operation to obtain x'1If 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'1Localsearch operation is carried out to obtain x ″)1Go to S7;
s7, judging f (x ″)1)＜f(x1) If yes, then x ″' is applied1Is assigned to x1And updating population pi and fminAnd xminOtherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm'1Operated on to give x ″)1And calculating f (x ″)1)；
S9, judging f (x ″)1)＜f(x1) If yes, use x1Random substitution of x2，...，xnOf any of the above, and x ″ ", is1Is assigned to x1And updating population pi and fminAnd xminOtherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value fminAnd x corresponding theretomin。
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 obtainedminAnd x corresponding theretominAnd 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 x1,x2,...,xnThe n individuals are grouped into an initial population pi ═ (x)1,x2,...,xn)
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 mode is that the objective function f can be expressed by the following formula:
f＝C1+C2+C3
Figure BDA0002937749870000121
Figure BDA0002937749870000122
Figure BDA0002937749870000123
Figure BDA0002937749870000124
Figure BDA0002937749870000125
the constraint conditions of the objective function f are as follows:
Figure BDA0002937749870000126
Figure BDA0002937749870000131
Q′i＞0,i∈I
Q′i-Xj+X′j＝Q′i
ti＜max(ti)
Figure BDA0002937749870000132
Figure BDA0002937749870000133
Figure BDA0002937749870000134
(T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M
(T1im+T2ik)×Yi 1≤1,i∈I,k∈K
y0jk＝yi0k,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c1Represents a delivery cost from a demand point of delivery; c2Represents the cost of transportation of the self-delivered goods; c3Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
Figure BDA0002937749870000135
representing the distribution cost of the self-distribution demand points in the demand points i;
Figure BDA0002937749870000136
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle1，Q2(ii) a Maximum driving distance Lmax of electric vehicle, cost parameter c of fuel vehicle and electric vehicle1，c2The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a1，a2，b1，b2；dijRepresenting the distance between customer points and the distance between the distribution center and the customer points; q'iIndicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y isi 1，Yi 2，T1im，T2ik，yijm，yijkAre all decision variables, Yi 11 means that customer point i is self-delivered by the delivery centre, otherwise Yi 1＝0；Yi 21 means that customer point i is delivered by crowdsourcing, otherwise, Yi 2＝0；T1im1 means that customer point i is self-delivered by fuel vehicle m, otherwise T1im＝0；T2ik1 means that customer point i is self-delivered by electric vehicle k, otherwise T2ik＝0；yijm1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise yijm＝0；yijk1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise yijk＝0；y0jm＝y0jmI 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 is0jm＝y0jmThe fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is0jk＝yi0kIndicating 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 x1Performing 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 x1Carry out the secondNeighborhood operation yields 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 x1Performing 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.
In addition, in order to select an optimal solution from the adjacent solution space of the current solution as the current solution until reaching the local optimal solution, a preferred processing manner is to perform "x" pairs in S6'1Performing local search operation to obtain x ″)1The method comprises the following steps:
step 1: inputting the maximum iteration number y of the localsearch operationmaxAnd initializing y-0, l-1;
step 2: y is judged to be less than or equal tomaxIf 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'1Localsearch operation is carried out to obtain x ″)1And f (x ″)1)；
And 5: judgment of f (x ″)1)≤f(x′1) If yes, then x ″' is applied1Value to x'1And 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 output1And 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 vehicle number M and logistics distribution demand point I1,x2,...,xnAnd generating an initial population pi ═ x1,x2,...,xn)。
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 x1,x2,...,x10Generating an initial population pi ═ x1,x2,...,x10). Let x be1＝[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 algorithmmaxSetting 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) operation1，Q2And the maximum driving distance L of the electric vehiclemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2Cost of distribution parameter a for crowd-sourced and self-sourced distribution1，a2，b1，b2。
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 fminAnd fminCorresponding individual xmin。
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 vehicle1). 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＝C1+C2+C3
Figure BDA0002937749870000161
Figure BDA0002937749870000162
Figure BDA0002937749870000163
Figure BDA0002937749870000164
Figure BDA0002937749870000165
the constraint of the function f can be expressed as:
Figure BDA0002937749870000166
Figure BDA0002937749870000167
Q′i＞0,i∈I
Q′i-Xj+X′j＝Q′i
ti＜max(ti)
Figure BDA0002937749870000168
Figure BDA0002937749870000169
Figure BDA00029377498700001610
(T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M
(T1im+T2ik)×Yi 1≤1,i∈I,k∈K
y0jk＝yi0k,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c1Represents a delivery cost from a demand point of delivery; c2Represents the cost of transportation of the self-delivered goods; c3Representing a crowdsourcing cost of crowdsourcing the distribution of goods;
Figure BDA00029377498700001611
representing the distribution cost of the self-distribution demand points in the demand points i;
Figure BDA00029377498700001612
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle1，Q2(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. C1Represents the transportation cost per unit distance of the electric vehicle, c2The parameters of transportation cost per unit distance, distribution cargo cost of crowd-sourced distribution and self-distributed distribution of the fuel vehicle are a1Representing the base cost of self-distribution of the individual goods, a2Representing the base cost of crowd-sourced distribution of individual goods, b1Representing the cost of self-dispensing in excess of the basis weight, b2Representing the cost of crowd-sourced distribution over basis weight, dijRepresenting the distance between customer points and the distance between the distribution center and the customer points; q3Representing the base cost in time of charge, i.e. weight not exceeding Q3The distribution cost of the single cargo is a1(a2)；Q′iIndicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y isi 1，Yi 2，T1im，T2ik，yijm，yijkAre all decision variables, Yi 11 means that customer point i is self-delivered by the delivery centre, otherwise Yi 1＝0；Yi 21 means that customer point i is delivered by crowdsourcing, otherwise, Yi 2＝0；T1im1 representsThe customer point i is self-delivered by the fuel vehicle m, otherwise T1im＝0；T2ik1 means that customer point i is self-delivered by electric vehicle k, otherwise T2ik＝0；yijm1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise yijm＝0；yijk1 means that when there is an electric vehicle k passing through points i to j from the time of i to j delivery, otherwise yijk＝0；y0jm＝y0jmI 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 is0jk＝yi0kIndicating 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,
Figure BDA0002937749870000171
indicating that the total cargo delivered by the fuel vehicle cannot exceed the maximum load capacity of the fuel vehicle during self-delivery;
Figure BDA0002937749870000172
indicating that the total cargo of the electric automobile cannot exceed the maximum loading capacity of the electric automobile during self-distribution;
Q′ithe 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-Xj+X′j＝Q′ii 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;
ti＜max(ti) Indicating that the time to reach demand point i cannot exceed the latest time for the cargo to arrive;
Figure BDA0002937749870000181
indicating that waiting is needed if the time for reaching the demand point i is less than the earliest starting time;
Figure BDA0002937749870000182
indicating that the maximum travel distance of the electric vehicle cannot exceed the maximum travel distance of the electric vehicle at the time of self-distribution;
Figure BDA0002937749870000183
each self-distribution demand point i is represented, and only one vehicle enters the demand point i or one vehicle starts from the demand point i;
(T1im+T2ik)×Yi 1+Y i 21, 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;
(T1im+T2ik)×Yi 1the 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;
y0jm＝y0jmi 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;
y0jk＝yi0ki 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 tmaxAnd 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 x1Performing shaking (k) operation to obtain x'1Otherwise, directly to x1Performing shaking (k) operation to obtain x'1If 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'1Performing 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 x1Performing a second neighborhood operation to obtain x'1(ii) a The second neighborhood operation represents: exchange two demand points of different vehicles at will andtwo points of crowdsourced distribution are not swapped, as shown in FIG. 2 d;
when k is 3, the scraping (3) operation represents the pair x1Performing 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 crowdsourced distribution cannot be inserted between two crowdsourced distribution points, as shown in fig. 2 e.
S6, p 'x'1Localsearch operation is carried out to obtain x ″)1Then, S7 is performed.
To x'1Localsearch operation is carried out to obtain x ″)1Referring to fig. 3, the specific process is as follows:
step 1: inputting the maximum iteration number y of the localsearch operationmaxAnd initializing y-0, l-1;
step 2: y is judged to be less than or equal tomaxIf 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'1Localsearch operation is carried out to obtain x ″)1And f (x ″)1)；
And 5: judgment of f (x ″)1)≤f(x′1) If yes, then x ″' is applied1Value to x'1And 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 output1And f (x ″)1) The value of (c).
S7, judging f (x ″)1)＜f(x1) If yes, then x ″' is applied1Is assigned to x1And updating population pi and fminAnd xminOtherwise, let k be k +1 and R be R +1, return to S4.
S8, pair x 'by utilizing differential evolution algorithm'1Operated on to give x ″)1And calculating f (x ″)1)。
Randomly taking x out of population IIj，xzPerforming a mutation operation, i.e. v1＝x′1+F*(xj-xz) V obtained1And x1Performing a crossover operation to obtain x ″)1Then f (x ″') is calculated1). 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 II2＝[1,2,4,8,5,6,0,10,9,3,7](i.e., j ═ 2), x3＝[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:
v1＝x′1+F*(x2-x3)
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 v1＝[1,5,2,4,3,7,0,6,10,9,8]Then, the exchange operation is performed.
Figure BDA0002937749870000201
Then x' is obtained1＝[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(x1) If yes, use x1Random substitution of x2，...，xnOf any of the above, and x ″ ", is1Is assigned to x1And updating population pi and fminAnd xminOtherwise, 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 fminAnd x corresponding theretomin。
After the algorithm execution is finished, the optimal objective function value f is outputminAnd x corresponding theretominA 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 I1,x2,...,xnAnd generating an initial population pi ═ x1,x2,...,xn)；
S2, setting the maximum iteration number t of the variable neighborhood search algorithmmaxSetting 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) operation1，Q2And the maximum driving distance L of the electric vehiclemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2Cost of distribution parameter a for crowd-sourced and self-sourced distribution1，a2，b1，b2；
S3, calculating the value of the objective function f of each individual in the initial population II, and obtaining the minimum objective function value fminAnd fminCorresponding individual xmin；
S4, judging t is less than or equal to tmaxWhether 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 x1Performing shaking (k) operation to obtain x'1Otherwise, directly to x1Performing shaking (k) operation to obtain x'1If 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'1Localsearch operation is carried out to obtain x ″)1Go to S7;
s7, judging f (x ″)1)＜f(x1) If yes, then x ″' is applied1Is assigned to x1And updating population pi and fminAnd xminOtherwise, making k equal to k +1 and R equal to R +1, and returning to S4;
s8, pair x 'by utilizing differential evolution algorithm'1Operated on to give x ″)1And calculating f (x ″)1)；
S9, judging f (x ″)1)＜f(x1) If yes, use x1Random substitution of x2，...，xnOf any of the above, and x ″ ", is1Is assigned to x1And updating population pi and fminAnd xminOtherwise, let k be k +1, R be 0 and return to S4;
s10, finishing the algorithm execution and outputting the optimal objective function value fminAnd x corresponding theretomin；
An output module for outputting the optimal objective function value fminAnd x corresponding theretomin。
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 x1,x2,...,xnThe n individuals are grouped into an initial population pi ═ (x)1,x2,...,xn)。
Preferably, when the processing module executes the steps S1-S10, the objective function f can be expressed by the following formula:
f＝C1+C2+C3
Figure BDA0002937749870000221
Figure BDA0002937749870000222
Figure BDA0002937749870000223
Figure BDA0002937749870000224
Figure BDA0002937749870000225
the constraint conditions of the objective function f are as follows:
Figure BDA0002937749870000226
Figure BDA0002937749870000227
Q′i＞0,i∈I
Q′i-Xj+X′j＝Q′i
ti＜max(ti)
Figure BDA0002937749870000228
Figure BDA0002937749870000229
Figure BDA00029377498700002210
(T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M
(T1im+T2ik)×Yi 1≤1,i∈I,k∈K
y0jk＝yi0k,i∈I,j∈I,k∈K
wherein f represents the total cost in the logistics distribution process; c1Represents a delivery cost from a demand point of delivery; c2Represents the cost of transportation of the self-delivered goods;C3representing a crowdsourcing cost of crowdsourcing the distribution of goods;
Figure BDA0002937749870000231
representing the distribution cost of the self-distribution demand points in the demand points i;
Figure BDA0002937749870000232
representing the crowdsourcing distribution cost of demand points i; maximum load capacity Q of vehicle1，Q2(ii) a Maximum driving distance L of electric automobilemaxCost parameter c for fuel-oil vehicles and electric vehicles1，c2The cost parameters of the goods delivered by crowdsourcing and self-delivering are respectively a1，a2，b1，b2；dijRepresenting the distance between customer points and the distance between the distribution center and the customer points; q'iIndicating the remaining capacity of the vehicle after the vehicle has provided service to customer i; y isi 1，Yi 2，T1im，T2ik，yijm，yijkAre all decision variables, Yi 11 means that customer point i is self-delivered by the delivery centre, otherwise Yi 1＝0；Yi 21 means that customer point i is delivered by crowdsourcing, otherwise, Yi 2＝0；T1im1 means that customer point i is self-delivered by fuel vehicle m, otherwise T1im＝0；T2ik1 means that customer point i is self-delivered by electric vehicle k, otherwise T2ik＝0；yijm1 means that the fuel vehicle m passes through the points i to j when i to j are self-distributed, otherwise yijm＝0；yijk1 means that when i to j self-delivery is such that there is an electric vehicle k passing through points i to j, otherwise yijk＝0；y0jm＝y0jmI 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 is0jm＝y0jmThe fuel vehicle m is shown to be sent out from the distribution center and finally returned to the distribution center; y is0jk＝yi0kIndicating 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 x1Performing 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 x1Performing 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 x1Performing 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 S6'1Performing local search operation to obtain x ″)1Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step 1: inputting the maximum iteration number y of the localsearch operationmaxAnd initializing y-0, l-1;
step 2: y is judged to be less than or equal tomaxIf 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'1Localsearch operation is carried out to obtain x ″)1And f (x ″)1)；
And 5: judgment of f (x ″)1)≤f(x′1) If yes, then x ″' is applied1Value to x'1And 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 output1And 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 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 fminAnd x corresponding theretominAnd 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, so that the aim of optimizing logistics distribution when the influence factors in various aspects are considered 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 will 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 (10)

1.一种考虑众包和自配送协同情形的路径优化方法，其特征在于，所述方法包括：1. a path optimization method considering crowdsourcing and self-distribution collaborative situation, is characterized in that, described method comprises: S1、基于车辆数M和物流配送需求点I获取n个个体x1,x2,...,xn，并生成初始种群П＝(x1,x2,...,xn)；S1. Obtain n individuals x 1 , x 2 ,..., x n based on the number of vehicles M and the logistics distribution demand point I, and generate an initial population П=(x 1 , x 2 ,..., x n ); S2、设定变邻域搜索算法的最大迭代次数tmax，初始迭代次数t＝1，并初始化R＝0，shaking(k)操作中初始值k＝1，设置车辆的最大载重量Q1，Q2，和电动汽车最大的行驶距离Lmax，燃油车和电动汽车的成本参数c1，c2，众包配送和自配送的配货成本参数a1，a2，b1，b2；S2. Set the maximum number of iterations t max of the variable neighborhood search algorithm, the initial number of iterations t=1, and initialize R=0, the initial value k=1 in the shaking(k) operation, and set the maximum load of the vehicle Q 1 , Q 2 , and the maximum travel distance L max of electric vehicles, cost parameters c 1 , c 2 of fuel vehicles and electric vehicles, and distribution cost parameters a 1 , a 2 , b 1 , b 2 of crowdsourcing and self-distribution; S3、计算初始种群П中每个个体的目标函数f的值，并获取最小的目标函数值fmin以及fmin对应的个体xmin；S3, calculate the value of the objective function f of each individual in the initial population П, and obtain the minimum objective function value f min and the individual x min corresponding to f min ; S4、判断t≤tmax是否成立，若不成立，则进行S10，否则进行S5；S4, determine whether t≤t max is established, if not, go to S10, otherwise go to S5; S5、判断k≤3是否成立，若不成立，则令k＝1，并对x1进行shaking(k)操作得到x′1，否则直接对x1进行shaking(k)操作得到x′1，并令t＝t+1，判断R≤3是否成立，若成立则进行S6；若不成立，则进行S8；S5. Determine whether k≤3 is established, if not, set k=1, and perform the shaking(k) operation on x 1 to obtain x′ 1 , otherwise directly perform the shaking(k) operation on x 1 to obtain x′ 1 , and Let t=t+1, judge whether R≤3 is established, if so, go to S6; if not, go to S8; S6、对x′1进行local search操作得到x″1，进行S7；S6, perform a local search operation on x′ 1 to obtain x″ 1 , and perform S7; S7、判断f(x″1)＜f(x1)是否成立，若成立，则将x″1赋值给x1，并更新种群∏以及fmin和xmin，否则令k＝k+1，R＝R+1，返回S4；S7. Determine whether f(x″ 1 )<f(x 1 ) holds, if so, assign x″ 1 to x 1 , and update the population ∏ and f min and x min , otherwise set k=k+1, R=R+1, return to S4; S8、利用差分进化算法对x′1进行操作得到x″1，并计算f(x″1)；S8. Use the differential evolution algorithm to operate x′ 1 to obtain x″ 1 , and calculate f(x″ 1 ); S9、判断f(x″1)＜f(x1)是否成立，S9. Determine whether f(x″ 1 )<f(x 1 ) is established, 若成立，则用x1随机替换x2，...，xn中任意个体的值，并将x″1赋值给x1，并更新种群∏以及fmin和xmin，If it is established, then randomly replace the value of any individual in x 2 , . 否则令k＝k+1，R＝0并返回S4；Otherwise, set k=k+1, R=0 and return to S4; S10、算法执行结束并输出最优的目标函数值fmin及其对应的xmin。S10, the algorithm execution ends and the optimal objective function value f min and its corresponding x min are output. 2.如权利要求1所述的方法，其特征在于，所述S1包括：2. The method of claim 1, wherein the S1 comprises: 选定车辆数M和物流配送需求点I；Select the number of vehicles M and the logistics distribution demand point I; 随机对需求点I进行编码排序，并将M-1个0随机插入序列中获取n个个体记为x1,x2,...,xn，将这n个个体组成初始种群∏＝(x1,x2,...,xn)。Randomly code and sort the demand point I, and randomly insert M-1 0s into the sequence to obtain n individuals and record them as x 1 , x 2 ,..., x n , and form these n individuals into the initial population ∏=( x 1 ,x 2 ,...,x n ). 3.如权利要求1所述的方法，其特征在于，所述目标函数f可用如下公式表示：3. The method of claim 1, wherein the objective function f can be represented by the following formula: f＝C1+C2+C3 f=C 1 +C 2 +C 3
Figure FDA0002937749860000021
Figure FDA0002937749860000021
Figure FDA0002937749860000022
Figure FDA0002937749860000022
Figure FDA0002937749860000023
Figure FDA0002937749860000023
Figure FDA0002937749860000024
Figure FDA0002937749860000024
Figure FDA0002937749860000025
Figure FDA0002937749860000025
 所述目标函数f的约束条件为：The constraints of the objective function f are:
Figure FDA0002937749860000026
Figure FDA0002937749860000026
Figure FDA0002937749860000027
Figure FDA0002937749860000027
 Q′i＞0,i∈IQ′ i > 0, i∈I Q′i-Xj+X′j＝Q′i Q' i -X j +X' j =Q' i ti＜max(ti)t i <max(t i )
Figure FDA0002937749860000028
Figure FDA0002937749860000028
Figure FDA0002937749860000029
Figure FDA0002937749860000029
Figure FDA0002937749860000031
Figure FDA0002937749860000031
 (T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M(T 1im +T 2ik )×Y i 1 +Y i 2 =1,i∈I,m∈M (T1im+T2ik)×Yi 1≤1,i∈I,k∈K(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K y0jk＝yi0k,i∈I,j∈I,k∈Ky 0jk =y i0k ,i∈I,j∈I,k∈K 其中，f表示物流配送过程中的总成本；C1表示自配送的需求点的配送成本；C2表示自配送货物的运输成本；C3表示众包配送货物的众包成本；
Figure FDA0002937749860000032
表示需求点i中自配送需求点的配送成本；
Figure FDA0002937749860000033
表示需求点i的众包配送成本；车辆的最大载重量Q1，Q2；电动汽车最大的行驶距离Lmax，燃油车和电动汽车的成本参数c1，c2，众包配送和自配送的配送货成本参数分别为a1，a2，b1，b2；dij表示客户点之间的距离以及配送中心与顾客点之间的距离；Q′i表示车辆为顾客i提供完服务之后的剩余容量；Yi 1，Yi 2，T1im，T2ik，yijm，yijk均为决策变量，Yi 1＝1表示客户点i由配送中心自配送，否则Yi 1＝0；Yi 2＝1表示客户点i由众包配送，否则，Yi 2＝0；T1im＝1表示客户点i由燃油车m自配送，否则T1im＝0；T2ik＝1表示客户点i由电动车k自配送，否则T2ik＝0；yijm＝1表示i到j自配送时存在燃油车m经过点i至j，否则yijm＝0；yijk＝1表示i到j自配送是存在电动汽车k经过点i至j时，否则yijk＝0；y0jm＝y0jm,i∈I,j∈I,m∈M表示燃油车m从配送中心出发，最终要回到配送中心；y0jm＝y0jm表示燃油车m从配送中心出发最终要回到配送中心；y0jk＝yi0k表示电动汽车k从配送中心出发最终要回到配送中心。Among them, f represents the total cost in the process of logistics distribution; C1 represents the distribution cost of self-delivery demand points; C2 represents the transportation cost of self - delivery goods; C3 represents the crowdsourcing cost of crowdsourcing delivery goods;
Figure FDA0002937749860000032
Represents the delivery cost of self-delivery demand point in demand point i;
Figure FDA0002937749860000033
Represents the crowdsourced distribution cost of demand point i; the maximum load weight of vehicles Q 1 , Q 2 ; the maximum travel distance L max of electric vehicles, the cost parameters c 1 , c 2 of fuel vehicles and electric vehicles, crowdsourced distribution and self-distribution The delivery cost parameters of , respectively, are a 1 , a 2 , b 1 , b 2 ; d ij represents the distance between customer points and the distance between the distribution center and the customer point; Q′ i represents that the vehicle has completed the service for customer i The remaining capacity after that; Yi 1 , Yi 2 , T 1im , T 2ik , y ijm , y ijk are decision variables, Yi 1 = 1 indicates that customer point i is delivered by the distribution center, otherwise Yi 1 =0 ;Y i 2 =1 indicates that customer point i is distributed by crowdsourcing, otherwise, Yi 2 =0; T 1im =1 indicates that customer point i is self-distributed by fuel vehicle m, otherwise T 1im =0; T 2ik =1 indicates that customer Point i is self-distributed by electric vehicle k, otherwise T 2ik =0; y ijm =1 means that there is a fuel vehicle m passing through point i to j when i to j self-distribution, otherwise y ijm =0; y ijk =1 means i to j Self-distribution is when there is an electric vehicle k passing through points i to j, otherwise y ijk = 0; y 0jm = y 0jm , i∈I, j∈I, m∈M means that the fuel vehicle m departs from the distribution center and eventually returns to Distribution center; y 0jm = y 0jm indicates that fuel vehicle m will eventually return to the distribution center from the distribution center; y 0jk = y i0k indicates that electric vehicle k will eventually return to the distribution center from the distribution center. 4.如权利要求1所述的方法，其特征在于，所述S5中shaking(k)操作包括：4. The method of claim 1, wherein the shaking(k) operation in the S5 comprises: 当k＝1时，shaking(1)操作表示对x1进行第一种邻域操作得到x′1；所述第一种邻域操作表示：随意交换同一辆车的两个需求点且不交换众包配送的两个点；When k=1, the shaking(1) operation means that the first neighborhood operation is performed on x 1 to obtain x′ 1 ; the first neighborhood operation means that the two demand points of the same vehicle are exchanged at will and not exchanged Two points of crowdsourcing delivery; 当k＝2时，shaking(2)操作表示对x1进行第二种邻域操作得到x′1；所述第二种邻域操作表示：随意交换不同辆车的两个需求点且不交换众包配送的两个点；When k=2, the shaking(2) operation means performing the second neighborhood operation on x 1 to obtain x′ 1 ; the second neighborhood operation means: exchange two demand points of different vehicles at will without exchanging Two points of crowdsourcing delivery; 当k＝3时，shaking(3)操作表示对x1进行第三种邻域操作得到x′1；所述第三种邻域操作表示：随意取出一个需求点i，将其插到第j位，且不能将众包配送的点插到两个众包配送的点之间。When k=3, the shaking(3) operation means that x 1 is subjected to the third neighborhood operation to obtain x′ 1 ; the third neighborhood operation means: randomly take out a demand point i and insert it into the jth position, and the point of crowdsourced delivery cannot be inserted between two points of crowdsourced delivery. 5.如权利要求1所述的方法，其特征在于，所述S6中对x′1进行local search操作得到x″1包括，5. The method of claim 1, wherein in the S6, performing a local search operation on x' 1 to obtain x'' 1 comprises, 步骤1：输入local search操作的最大迭代次数ymax以及初始化y＝0,l＝1；Step 1: Enter the maximum number of iterations y max of the local search operation and initialize y=0, l=1; 步骤2：判断y≤ymax是否成立，若成立则进行步骤3，此时y＝y+1；否则进行步骤6；Step 2: Determine whether y≤y max is established, if so, go to step 3, at this time y=y+1; otherwise, go to step 6; 步骤3：判断l≤3是否成立，若成立则进行步骤4，否则令l＝1，返回步骤2；Step 3: judge whether l≤3 is established, if so, go to step 4, otherwise set l=1, and return to step 2; 步骤4：对x′1进行local search操作，得到x″1和f(x″1)；Step 4: Perform a local search operation on x′ 1 to obtain x″ 1 and f(x″ 1 ); 步骤5：判断f(x″1)≤f(x′1)是否成立，若成立，则将x″1赋值给x′1,且令l＝1，否则令l＝l+1；返回步骤2；Step 5: Determine whether f(x″ 1 )≤f(x′ 1 ) is established, if so, assign x″ 1 to x′ 1 , and let l=1, otherwise let l=l+1; return to step 2; 步骤6：流程结束，并输出x″1和f(x″1)的值。Step 6: The process ends, and the values of x″ 1 and f(x″ 1 ) are output. 6.一种考虑众包和自配送协同情形的路径优化系统，其特征在于，所述系统包括：6. A route optimization system considering the collaborative situation of crowdsourcing and self-distribution, wherein the system comprises: 处理模块，用于执行以下步骤：A processing module that performs the following steps: S1、基于车辆数M和物流配送需求点I获取n个个体x1,x2,...,xn，并生成初始种群∏＝(x1,x2,...,xn)；S1. Obtain n individuals x 1 , x 2 ,..., x n based on the number of vehicles M and the logistics distribution demand point I, and generate an initial population ∏=(x 1 , x 2 ,..., x n ); S2、设定变邻域搜索算法的最大迭代次数tmax，初始迭代次数t＝1，并初始化R＝0，shaking(k)操作中初始值k＝1，设置车辆的最大载重量Q1，Q2，和电动汽车最大的行驶距离Lmax，燃油车和电动汽车的成本参数c1，c2，众包配送和自配送的配货成本参数a1，a2，b1，b2；S2. Set the maximum number of iterations t max of the variable neighborhood search algorithm, the initial number of iterations t=1, and initialize R=0, the initial value k=1 in the shaking(k) operation, and set the maximum load of the vehicle Q 1 , Q 2 , and the maximum travel distance L max of electric vehicles, cost parameters c 1 , c 2 of fuel vehicles and electric vehicles, and distribution cost parameters a 1 , a 2 , b 1 , b 2 of crowdsourcing and self-distribution; S3、计算初始种群Π中每个个体的目标函数f的值，并获取最小的目标函数值fmin以及fmin对应的个体xmin；S3, calculate the value of the objective function f of each individual in the initial population Π, and obtain the minimum objective function value f min and the individual x min corresponding to f min ; S4、判断t≤tmax是否成立，若不成立，则进行S10，否则进行S5；S4, determine whether t≤t max is established, if not, go to S10, otherwise go to S5; S5、判断k≤3是否成立，若不成立，则令k＝1，并对x1进行shaking(k)操作得到x′1，否则直接对x1进行shaking(k)操作得到x′1，并令t＝t+1，判断R≤3是否成立，若成立则进行S6；若不成立，则进行S8；S5. Determine whether k≤3 is established, if not, set k=1, and perform the shaking(k) operation on x 1 to obtain x′ 1 , otherwise directly perform the shaking(k) operation on x 1 to obtain x′ 1 , and Let t=t+1, judge whether R≤3 is established, if so, go to S6; if not, go to S8; S6、对x′1进行local search操作得到x″1，进行S7；S6, perform a local search operation on x′ 1 to obtain x″ 1 , and perform S7; S7、判断f(x″1)＜f(x1)是否成立，若成立，则将x″1赋值给x1，并更新种群Π以及fmin和xmin，否则令k＝k+1，R＝R+1，返回S4；S7. Determine whether f(x″ 1 )<f(x 1 ) is established, if so, assign x″ 1 to x 1 , and update the population Π and f min and x min , otherwise set k=k+1, R=R+1, return to S4; S8、利用差分进化算法对x′1进行操作得到x″1，并计算f(x″1)；S8. Use the differential evolution algorithm to operate x′ 1 to obtain x″ 1 , and calculate f(x″ 1 ); S9、判断f(x″1)＜f(x1)是否成立，若成立，则用x1随机替换x2，...，xn中任意个体的值，并将x″1赋值给x1，并更新种群∏以及fmin和xmin，否则令k＝k+1，R＝0并返回S4；S9. Determine whether f(x″ 1 )<f(x 1 ) holds, and if so, randomly replace the value of any individual in x 2 , . . . , x n with x 1 , and assign x″ 1 to x 1 , and update the population ∏ and f min and x min , otherwise set k=k+1, R=0 and return to S4; S10、算法执行结束并输出最优的目标函数值fmin及其对应的xmin；S10, the algorithm execution ends and the optimal objective function value f min and its corresponding x min are output; 输出模块，用于输出最优的目标函数值fmin及其对应的xmin。The output module is used to output the optimal objective function value f min and its corresponding x min . 7.如权利要求6所述系统，其特征在于，所述处理模块在执行S1时，所述S1包括：7. The system according to claim 6, wherein when the processing module executes S1, the S1 comprises: 选定车辆数M和物流配送需求点I；Select the number of vehicles M and logistics distribution demand point I; 随机对需求点I进行编码排序，并将M-1个0随机插入序列中获取n个个体记为x1,x2,...,xn，将这n个个体组成初始种群∏＝(x1,x2,...,xn)。Randomly code and sort the demand point I, and randomly insert M-1 0s into the sequence to obtain n individuals and record them as x 1 , x 2 ,..., x n , and form these n individuals into the initial population ∏=( x 1 ,x 2 ,...,x n ). 8.如权利要求6所述系统，其特征在于，所述处理模块在执行所述S1-S10时，所述目标函数f可用如下公式表示：8. The system according to claim 6, wherein, when the processing module executes the S1-S10, the objective function f can be represented by the following formula: f＝C1+C2+C3 f=C 1 +C 2 +C 3
Figure FDA0002937749860000061
Figure FDA0002937749860000061
Figure FDA0002937749860000062
Figure FDA0002937749860000062
Figure FDA00029377498600000610
Figure FDA00029377498600000610
Figure FDA0002937749860000063
Figure FDA0002937749860000063
Figure FDA0002937749860000064
Figure FDA0002937749860000064
 所述目标函数f的约束条件为：The constraints of the objective function f are:
Figure FDA0002937749860000065
Figure FDA0002937749860000065
Figure FDA0002937749860000066
Figure FDA0002937749860000066
 Q′i＞0,i∈IQ′ i > 0, i∈I Q′i-Xj+X′j＝Q′i Q' i -X j +X' j =Q' i ti＜max(ti)t i <max(t i )
Figure FDA0002937749860000067
Figure FDA0002937749860000067
Figure FDA0002937749860000068
Figure FDA0002937749860000068
Figure FDA0002937749860000069
Figure FDA0002937749860000069
 (T1im+T2ik)×Yi 1+Yi 2＝1,i∈I,m∈M(T 1im +T 2ik )×Y i 1 +Y i 2 =1,i∈I,m∈M (T1im+T2ik)×Yi 1≤1,i∈I,k∈K(T 1im +T 2ik )×Y i 1 ≤1,i∈I,k∈K y0jk＝yi0k,i∈I,j∈I,k∈Ky 0jk =y i0k ,i∈I,j∈I,k∈K 其中，f表示物流配送过程中的总成本；C1表示自配送的需求点的配送成本；C2表示自配送货物的运输成本；C3表示众包配送货物的众包成本；
Figure FDA0002937749860000071
表示需求点i中自配送需求点的配送成本；
Figure FDA0002937749860000072
表示需求点i的众包配送成本；车辆的最大载重量Q1，Q2；电动汽车最大的行驶距离Lmax，燃油车和电动汽车的成本参数c1，c2，众包配送和自配送的配送货成本参数分别为a1，a2，b1，b2；dij表示客户点之间的距离以及配送中心与顾客点之间的距离；Q′i表示车辆为顾客i提供完服务之后的剩余容量；Yi 1，Yi 2，T1im，T2ik，yijm，yijk均为决策变量，Yi 1＝1表示客户点i由配送中心自配送，否则Yi 1＝0；Yi 2＝1表示客户点i由众包配送，否则，Yi 2＝0；T1im＝1表示客户点i由燃油车m自配送，否则T1im＝0；T2ik＝1表示客户点i由电动车k自配送，否则T2ik＝0；yijm＝1表示i到j自配送时存在燃油车m经过点i至j，否则yijm＝0；yijk＝1表示i到j自配送是存在电动汽车k经过点i至j时，否则yijk＝0；y0jm＝y0jm,i∈I,j∈I,m∈M表示燃油车m从配送中心出发，最终要回到配送中心；y0jm＝y0jm表示燃油车m从配送中心出发最终要回到配送中心；y0jk＝yi0k表示电动汽车k从配送中心出发最终要回到配送中心。Among them, f represents the total cost in the process of logistics distribution; C1 represents the distribution cost of self-delivery demand points; C2 represents the transportation cost of self - delivery goods; C3 represents the crowdsourcing cost of crowdsourcing delivery goods;
Figure FDA0002937749860000071
Represents the delivery cost of self-delivery demand point in demand point i;
Figure FDA0002937749860000072
Represents the crowdsourced distribution cost of demand point i; the maximum load weight of vehicles Q 1 , Q 2 ; the maximum travel distance L max of electric vehicles, the cost parameters c 1 , c 2 of fuel vehicles and electric vehicles, crowdsourced distribution and self-distribution The delivery cost parameters of , respectively, are a 1 , a 2 , b 1 , b 2 ; d ij represents the distance between customer points and the distance between the distribution center and the customer point; Q′ i represents the vehicle has completed the service for customer i Remaining capacity after; Yi 1 , Yi 2 , T 1im , T 2ik , y ijm , y ijk are decision variables, Yi 1 = 1 indicates that customer point i is delivered by the distribution center, otherwise Yi 1 =0 ;Y i 2 =1 indicates that customer point i is distributed by crowdsourcing, otherwise, Yi 2 =0; T 1im =1 indicates that customer point i is self-distributed by fuel vehicle m, otherwise T 1im =0; T 2ik =1 indicates that customer Point i is self-distributed by electric vehicle k, otherwise T 2ik = 0; y ijm =1 means that there is a fuel vehicle m passing through point i to j when i to j self-distribution, otherwise y ijm =0; y ijk =1 means i to j Self-distribution is when there is an electric vehicle k passing through points i to j, otherwise y ijk = 0; y 0jm = y 0jm , i∈I, j∈I, m∈M means that the fuel vehicle m starts from the distribution center and eventually returns to Distribution center; y 0jm = y 0jm means that the fuel vehicle m starts from the distribution center and eventually returns to the distribution center; y 0jk = y i0k means that the electric vehicle k starts from the distribution center and finally returns to the distribution center. 9.如权利要求6所述系统，其特征在于，所述处理模块在执行S5时，所述S5中shaking(k)操作包括：9. The system of claim 6, wherein when the processing module executes S5, the shaking(k) operation in S5 comprises: 当k＝1时，shaking(1)操作表示对x1进行第一种邻域操作得到x′1；所述第一种邻域操作表示：随意交换同一辆车的两个需求点且不交换众包配送的两个点；When k=1, the shaking(1) operation means that the first neighborhood operation is performed on x 1 to obtain x′ 1 ; the first neighborhood operation means that the two demand points of the same vehicle are exchanged at will and not exchanged Two points of crowdsourcing delivery; 当k＝2时，shaking(2)操作表示对x1进行第二种邻域操作得到x′1；所述第二种邻域操作表示：随意交换不同辆车的两个需求点且不交换众包配送的两个点；When k=2, the shaking(2) operation means performing the second neighborhood operation on x 1 to obtain x′ 1 ; the second neighborhood operation means: exchange two demand points of different vehicles at will without exchanging Two points of crowdsourcing delivery; 当k＝3时，shaking(3)操作表示对x1进行第三种邻域操作得到x′1；所述第三种邻域操作表示：随意取出一个需求点i，将其插到第j位，且不能将众包配送的点插到两个众包配送的点之间。When k=3, the shaking(3) operation means performing a third neighborhood operation on x 1 to obtain x′ 1 ; the third neighborhood operation means: randomly taking out a demand point i and inserting it into the jth position, and the point of crowdsourced delivery cannot be inserted between two points of crowdsourced delivery. 10.如权利要求6所述系统，其特征在于，所述处理模块在执行S6时，所述S6中对x′1进行local search操作得到x″1包括，10. The system according to claim 6, wherein when the processing module executes S6, performing a local search operation on x' 1 in the S6 to obtain x'' 1 includes, 步骤1：输入local search操作的最大迭代次数ymax以及初始化y＝0,l＝1；Step 1: Enter the maximum number of iterations y max of the local search operation and initialize y=0, l=1; 步骤2：判断y≤ymax是否成立，若成立则进行步骤3，此时y＝y+1；否则进行步骤6；Step 2: Determine whether y≤y max is established, if so, go to step 3, at this time y=y+1; otherwise, go to step 6; 步骤3：判断l≤3是否成立，若成立则进行步骤4，否则令l＝1，返回步骤2；Step 3: judge whether l≤3 is established, if so, go to step 4, otherwise set l=1, and return to step 2; 步骤4：对x′1进行local search操作，得到x″1和f(x″1)；Step 4: Perform a local search operation on x′ 1 to obtain x″ 1 and f(x″ 1 ); 步骤5：判断f(x″1)≤f(x′1)是否成立，若成立，则将x″1赋值给x′1,且令l＝1，否则令l＝l+1；返回步骤2；Step 5: Determine whether f(x″ 1 )≤f(x′ 1 ) is established, if so, assign x″ 1 to x′ 1 , and let l=1, otherwise let l=l+1; return to step 2; 步骤6：流程结束，并输出x″1和f(x″1)的值。Step 6: The process ends, and the values of x″ 1 and f(x″ 1 ) are output.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344443A (en) * 2021-07-01 2021-09-03 广东富状元科技有限公司 Project process analysis and evaluation system and method based on big data
CN113962617A (en) * 2021-12-22 2022-01-21 华清科盛(北京)信息技术有限公司 Method and device for dispatching logistics in factory, electronic equipment and storage medium
CN114091722A (en) * 2021-10-09 2022-02-25 山东师范大学 A method and system for vehicle path optimization based on hybrid tabu search

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002297725A (en) * 2001-03-30 2002-10-11 Dainippon Printing Co Ltd System and program for delivery
CN107590603A (en) * 2017-09-11 2018-01-16 合肥工业大学 Based on the dispatching method and system for improving change neighborhood search and differential evolution algorithm
CN109376952A (en) * 2018-11-21 2019-02-22 深圳大学 A method and system for crowdsourcing logistics distribution path planning based on trajectory big data
CN110059934A (en) * 2019-03-27 2019-07-26 浙江工商大学 The method of fuel vehicle and the scheduling of new energy vehicle coperating distribution
CN111047087A (en) * 2019-09-18 2020-04-21 合肥工业大学 Intelligent optimization method and device for path under cooperation of unmanned aerial vehicle and vehicle
CN111461395A (en) * 2020-02-24 2020-07-28 合肥工业大学 Method and system for site selection of temporary distribution center
CN112053117A (en) * 2020-09-11 2020-12-08 东北大学 A route planning method and device for cooperative distribution

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002297725A (en) * 2001-03-30 2002-10-11 Dainippon Printing Co Ltd System and program for delivery
CN107590603A (en) * 2017-09-11 2018-01-16 合肥工业大学 Based on the dispatching method and system for improving change neighborhood search and differential evolution algorithm
CN109376952A (en) * 2018-11-21 2019-02-22 深圳大学 A method and system for crowdsourcing logistics distribution path planning based on trajectory big data
CN110059934A (en) * 2019-03-27 2019-07-26 浙江工商大学 The method of fuel vehicle and the scheduling of new energy vehicle coperating distribution
CN111047087A (en) * 2019-09-18 2020-04-21 合肥工业大学 Intelligent optimization method and device for path under cooperation of unmanned aerial vehicle and vehicle
CN111461395A (en) * 2020-02-24 2020-07-28 合肥工业大学 Method and system for site selection of temporary distribution center
CN112053117A (en) * 2020-09-11 2020-12-08 东北大学 A route planning method and device for cooperative distribution

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
RUI LIU ET AL.: "Task-Oriented Path Planning Algorithm Considering POIs and Dynamic Collaborative Targets Distribution", 《2018 SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF)》 *
双莎莎等: "基于互联网技术的最后一公里综合配送模式分析", 《技术与创新管理》 *
宋强: "基于改进差分变邻域算法的多行程车辆路径问题的研究", 《重庆交通大学学报(自然科学版)》 *
贾倩倩等: "快递"最后一公里"配送新模式", 《北京信息科技大学学报(自然科学版)》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344443A (en) * 2021-07-01 2021-09-03 广东富状元科技有限公司 Project process analysis and evaluation system and method based on big data
CN113344443B (en) * 2021-07-01 2022-02-08 广东富状元科技有限公司 Project process analysis and evaluation system and method based on big data
CN114091722A (en) * 2021-10-09 2022-02-25 山东师范大学 A method and system for vehicle path optimization based on hybrid tabu search
CN113962617A (en) * 2021-12-22 2022-01-21 华清科盛(北京)信息技术有限公司 Method and device for dispatching logistics in factory, electronic equipment and storage medium
CN113962617B (en) * 2021-12-22 2022-03-08 华清科盛(北京)信息技术有限公司 Method and device for dispatching logistics in factory, electronic equipment and storage medium

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