CN109214571B - Method for selecting suppliers from integrators considering customer feeling - Google Patents

Method for selecting suppliers from integrators considering customer feeling Download PDF

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CN109214571B
CN109214571B CN201811032528.1A CN201811032528A CN109214571B CN 109214571 B CN109214571 B CN 109214571B CN 201811032528 A CN201811032528 A CN 201811032528A CN 109214571 B CN109214571 B CN 109214571B
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赵亚风
卢福强
王雷震
高孟影
陈伟东
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Abstract

The invention provides a method for selecting suppliers from an integrator considering customer side feelings, which is characterized by comprising the following steps: s1, initializing cost and construction period information of each logistics provider; s2, abstracting the logistics network into a complex network topological graph; s3, according to the prospect theory, considering the cost and the construction period required by the client, and establishing a multi-attribute decision-making model considering the risk attitude of the client according to the whole transportation task; s4, according to the network topological diagram abstracted by the logistics network and the cost and the construction period information of each logistics provider, designing a self-adaptive genetic algorithm considering the elite strategy to carry out optimization solution on the problem model; and S5, outputting the optimal logistics supplier selection strategy scheme. The invention considers the psychological factor influence of customers, comprehensively considers two aspects of cost and time, better accords with the real psychological feeling of customers, and has practical guiding significance for an integrator to select a supplier transportation scheme which is more satisfied with the customers.

Description

Method for selecting suppliers from integrators considering customer feeling
Technical Field
The invention relates to the field of logistics, in particular to a selection method of an integrator for suppliers, which is provided by considering customer feeling in the traditional decision making of the integrator, and aims to provide a method for the integrator to consider the customer as a completely rational person by using a behavior economics and operation research method.
Background
With the acceleration of the global economic integration, the rise of electronic commerce and the development of the logistics market, the demand of the market on the logistics service is more comprehensive and specific. The fourth logistics enterprise provides a complete supply chain solution to help the enterprise to effectively integrate resources, so that the cost is reduced, and the benefit is increased. In the preliminary development stage of the fourth logistics enterprise in China, many problems and defects still exist. Early research on logistics management and decision making has a common premise that decision making is based on fully rational optimization behavior, whereas in actual operation people often behave in a limited sense. In actual operation, neither operation management efforts and techniques, nor successful application of theory, are highly dependent on an understanding of human behavior. In recent years, the fourth logistics with behavior characteristics as a view point is rarely researched, and the existing research results are relatively single, and mainly show problems of single research angle, single method, single considered factor and the like. Therefore, for the fourth logistics enterprise with a plurality of decision-making subjects participating, through a plurality of perspectives, a plurality of methods and consideration of a plurality of factors, the comprehensive research of potential rationality factors in the operation content thereof is very important, and the value thereof is that the fourth logistics enterprise can re-know most of the operation problems related to people.
Disclosure of Invention
Aiming at the problems and limitations in the existing research, the invention provides a method for selecting suppliers by an integrator considering the feeling of a customer, and the invention utilizes a prospect theory to consider the method for selecting third-party logistics suppliers by a fourth-party logistics integrator with risk attitude of the customer on the comprehensive cost and the construction period. The invention has effective supplement and development effects on the logistics management theory, has perfect effects on the application and development of the behavior and operation research theory, provides theoretical and method support for logistics operation in the fourth logistics mode, and has important significance on the healthy development of the fourth logistics in China.
The technical means adopted by the invention are as follows:
a method for integrator to supplier selection taking into account customer perception, having the steps of:
s1, initializing cost and construction period information of each logistics provider;
s2, abstracting the logistics network into a complex network topological graph;
s3, according to the prospect theory, considering the cost and the construction period required by the client, and establishing a multi-attribute decision-making model considering the risk attitude of the client according to the whole transportation task;
s4, according to the network topological diagram abstracted by the logistics network and the cost and the construction period information of each logistics provider, designing a self-adaptive genetic algorithm considering the elite strategy to carry out optimization solution on the problem model;
and S5, outputting the optimal logistics supplier selection strategy scheme.
Shortest time limit T for a certain transportation schemeSAnd the longest construction period TLThe following formula is satisfied:
Figure BDA0001790100980000021
Figure BDA0001790100980000022
bijthe number of edges from the node i to the node j in the network topology (i.e. the number of logistics providers) is used,
Figure BDA0001790100980000023
selecting the k-th supplier among the nodes i, j for the shortest transportation period, and corresponding
Figure BDA0001790100980000024
For the longest period of time, xijkTo choose the k-th vendor, y, between nodes i, jjWhether or not toSelect j as a transit node, TjTransferring the construction period for the j node;
transportation cost C for a certain transportation schemeRThe following formula is satisfied:
Figure BDA0001790100980000025
Cijkcost of selecting the k-th supplier shipment between nodes i, j, CjAnd transferring the construction period for the j node.
The specific steps of step S3 are as follows:
s31, aiming at the client requirement construction period, modeling is carried out as follows:
Figure BDA0001790100980000031
t represents the actual duration of a certain transportation scheme, assuming that TS,TL]Uniformly distributed, TmRequesting a construction period for a client, wherein E (u (t)) is a utility value considering the construction period of the client, alpha and beta are risk attitude coefficients of a value function in a foreground theoretical model, lambda is a loss aversion coefficient, and alpha and beta are set to be 0.88 and lambda is set to be 2.25 according to the foreground theory;
s32, aiming at the cost required by the customer, modeling is carried out as follows:
Figure BDA0001790100980000032
c denotes the actual cost of a certain transportation scheme, CmFor customer demand cost, E (u (C)) is utility value considering customer demand cost, P is project delay probability, C' is penalty cost for delay project,
Figure BDA0001790100980000033
s33, comprehensively considering the client requirement cost and the client requirement construction period, and establishing a multi-attribute decision model considering the client risk attitude according to the whole transportation task:
Max VR＝λ1E(u(t))+λ2E(u(c))
Figure BDA0001790100980000034
Figure BDA0001790100980000035
y1＝yn＝1
VRfor the total utility of a certain transportation scheme, the objective function is to maximize VR，λ1Factor, lambda, for the customer's attention to the construction period2Importance coefficient for cost and has lambda1+λ2The first two constraints guarantee that a path from start to end is followed, and the third constraint indicates that the start and end of the transport must be selected.
The specific steps of step S4 are as follows:
s41, enabling population individuals of the adaptive genetic algorithm considering the elite strategy to be a communication path from a starting point to an end point in the network topological graph, and obtaining a supplier selection scheme of a complete transportation task;
representing the network topological graph in the form of an adjacent matrix, wherein the corresponding position of the matrix is the number of edges (namely the number of suppliers) between two nodes of the network topological graph, and sequentially taking out the elements of triangles on the matrix to form a group of integer sequences which can reflect all connection conditions of the whole complex graph;
multiplying the integer of each position by the random number of the [0,1] interval, and rounding upwards to obtain a new sequence representing a simple graph randomly extracted from the complex graph, wherein each position represents that a certain supplier is selected between two nodes to take charge of the road transportation task, and then a Dijkstra algorithm is utilized to obtain a better path aiming at the simple graph to be used as an individual of an adaptive genetic algorithm considering the elite strategy;
s42, generating an initial population according to the population individual number specified by the adaptive genetic algorithm considering the elite strategy and the step S41, bringing all schemes into a fitness function, namely an objective function, obtaining and storing an elite individual with optimal fitness;
s43, because the supplier selection scheme of the transportation task has the condition of delay and insufficient cost, and the final utility value has the condition of negative number, the individual selection link of the invention adopts the championship method: randomly extracting 80% of schemes from all the supplier selection schemes of the transportation tasks, and selecting the best individual to judge whether the self-adaptive cross probability is met;
s44, if yes, performing intersection and storing, and if not, returning to the step S43 to reselect the individual;
s45, repeating S44 for multiple times to obtain a population to be mutated consisting of N-1 crossed individuals and elite individuals, and adding the elite individuals to avoid loss of excellent genes, wherein N is the number of individuals in the initial population;
s46, selecting individuals in the population to be mutated, if the adaptive mutation probability is met and the individuals are not elite individuals, mutating to obtain new individuals, otherwise, not mutating to obtain unchanged individuals;
all new individuals, all invariant individuals and elite individuals form a new population;
the self-adaptation aims to avoid premature convergence, enhance the overall convergence of the algorithm and accelerate the convergence speed of the algorithm.
S47, reselecting a scheme with the optimal fitness from the new population and storing the scheme as an elite individual;
s48, judging whether the number of times of terminating iteration is reached, if not, returning to the step S43, and continuing to perform subsequent operations by replacing all supplier selection schemes of the transportation tasks with the new population; and stopping if the number of the termination iterations is met, and returning the elite individual obtained in the step S47 as an optimal supplier selection scheme.
Adaptive cross probability PcAnd an adaptive mutation probability PmThe following formula is satisfied:
Figure BDA0001790100980000051
Figure BDA0001790100980000052
fmaxis the maximum fitness value in the population, favgF' is the larger fitness value of the two individuals to be crossed, and f is the fitness value of the individual to be mutated;
wherein, Pc1＝0.9,Pc2＝0.6,Pm1＝0.1,Pm2＝0.01。
The invention abstracts a logistics network into a complex multi-network topological graph, considers the cost and the transportation period generated in the actual transportation task and the cost and the period required by a customer, combines risk attitude analysis of a prospect theory to establish a multi-attribute decision model considering the risk attitude of the customer, selects a supplier between every two nodes to bear the transportation task in the network topological graph according to the model, selects an optimal transportation path which best meets the requirements of the customer cost and the period, and completes multi-attribute decision. Aiming at a solving algorithm adopted by model solving, namely an elite strategy adaptive genetic algorithm embedded with a Dijkstra algorithm, the method simplifies a complex multiple graph into a simple graph by an integer coding technology, then calculates a better transportation path by the Dijkstra algorithm to be used as an individual of the genetic algorithm, and then adds an adaptive mechanism into the genetic algorithm under the consideration of an elite reservation strategy, so that the convergence speed of the algorithm is higher, the optimization precision is more accurate, and finally, a selection scheme of each path section supplier which best meets the requirements of customers is calculated to complete decision making. The invention considers the psychological factor influence of customers, comprehensively considers two aspects of cost and time, better accords with the real psychological feeling of customers, and has practical guiding significance for an integrator to select a supplier transportation scheme which is more satisfied with the customers.
Based on the reason, the invention can be widely popularized in the fields of logistics and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for integrator-to-supplier selection in consideration of customer experience in accordance with an embodiment of the present invention.
Fig. 2 is a network topology diagram (8 nodes) in an embodiment of the present invention.
Fig. 3 is a network topology diagram (16 nodes) in an embodiment of the present invention.
Fig. 4 is a network topology diagram (32 nodes) in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1 and 2, a method for selecting suppliers from integrators considering customer experience is characterized by the steps of:
s1, initializing cost and construction period information of each logistics provider;
s2, abstracting the logistics network into a complex network topological graph;
s3, according to the prospect theory, considering the cost and the construction period required by the client, and establishing a multi-attribute decision-making model considering the risk attitude of the client according to the whole transportation task;
s4, according to the network topological diagram abstracted by the logistics network and the cost and the construction period information of each logistics provider, designing a self-adaptive genetic algorithm considering the elite strategy to carry out optimization solution on the problem model;
and S5, outputting the optimal logistics supplier selection strategy scheme.
Shortest time limit T for a certain transportation schemeSAnd the longest construction period TLThe following formula is satisfied:
Figure BDA0001790100980000071
Figure BDA0001790100980000072
bijthe number of edges from the node i to the node j in the network topology (i.e. the number of logistics providers) is used,
Figure BDA0001790100980000073
selecting the k-th supplier among the nodes i, j for the shortest transportation period, and corresponding
Figure BDA0001790100980000074
For the longest period of time, xijkTo select between nodes i, jThe k-th supplier of (C), yjTo select j as a transit node, TjTransferring the construction period for the j node;
transportation cost C for a certain transportation schemeRThe following formula is satisfied:
Figure BDA0001790100980000075
Cijkcost of selecting the k-th supplier shipment between nodes i, j, CjAnd transferring the construction period for the j node.
The specific steps of step S3 are as follows:
s31, aiming at the client requirement construction period, modeling is carried out as follows:
Figure BDA0001790100980000076
t represents the actual duration of a certain transportation scheme, assuming that TS,TL]Uniformly distributed, TmRequesting a construction period for a client, wherein E (u (t)) is a utility value considering the construction period of the client, alpha and beta are risk attitude coefficients of a value function in a foreground theoretical model, lambda is a loss aversion coefficient, and alpha and beta are set to be 0.88 and lambda is set to be 2.25 according to the foreground theory;
s32, aiming at the cost required by the customer, modeling is carried out as follows:
Figure BDA0001790100980000081
c denotes the actual cost of a certain transportation scheme, CmFor customer demand cost, E (u (C)) is utility value considering customer demand cost, P is project delay probability, C' is penalty cost for delay project,
Figure BDA0001790100980000082
s33, comprehensively considering the client requirement cost and the client requirement construction period, and establishing a multi-attribute decision model considering the client risk attitude according to the whole transportation task:
Max VR＝λ1E(u(t))+λ2E(u(c))
Figure BDA0001790100980000083
Figure BDA0001790100980000084
y1＝yn＝1
VRfor the total utility of a certain transportation scheme, the objective function is to maximize VR，λ1Factor, lambda, for the customer's attention to the construction period2Importance coefficient for cost and has lambda1+λ2＝1。
The specific steps of step S4 are as follows:
s41, enabling population individuals of the adaptive genetic algorithm considering the elite strategy to be a communication path from a starting point (node 1) to a terminal point (node 8) in the network topological graph, and obtaining a supplier selection scheme of a complete transportation task as shown in FIG. 2;
representing the network topological graph in the form of an adjacent matrix, wherein the corresponding position of the matrix is the number of edges (namely the number of suppliers) between two nodes of the network topological graph, and sequentially taking out the elements of triangles on the matrix to form a group of integer sequences;
multiplying the integer of each position by the random number of the [0,1] interval, and rounding upwards to obtain a new sequence representing a simple graph randomly extracted from the complex graph, wherein each position represents that a certain supplier is selected between two nodes to take charge of the road transportation task, and then a Dijkstra algorithm is utilized to obtain a better path aiming at the simple graph to be used as an individual of an adaptive genetic algorithm considering the elite strategy;
s42, generating an initial population according to the population individual number specified by the adaptive genetic algorithm considering the elite strategy and the step S41, bringing all schemes into a fitness function, namely an objective function, obtaining and storing an elite individual with optimal fitness;
s43, because the supplier selection scheme of the transportation task has the condition of delay and insufficient cost, and the final utility value has the condition of negative number, the individual selection link of the invention adopts the championship method: randomly extracting 80% of schemes from all the supplier selection schemes of the transportation tasks, and selecting the best individual to judge whether the self-adaptive cross probability is met;
s44, if yes, performing intersection and storing, and if not, returning to the step S43 to reselect the individual;
s45, repeating S44 for multiple times to obtain a population to be varied, which consists of N-1 crossed individuals and elite individuals, wherein N is the number of individuals in the initial population;
s46, selecting individuals in the population to be mutated, if the adaptive mutation probability is met and the individuals are not elite individuals, mutating to obtain new individuals, otherwise, not mutating to obtain unchanged individuals;
all new individuals, all invariant individuals and elite individuals form a new population;
s47, reselecting a scheme with the optimal fitness from the new population and storing the scheme as an elite individual;
s48, judging whether the number of times of terminating iteration is reached, if not, returning to the step S43, and continuing to perform subsequent operations by replacing all supplier selection schemes of the transportation tasks with the new population; and stopping if the number of the termination iterations is met, and returning the elite individual obtained in the step S47 as an optimal supplier selection scheme.
Adaptive cross probability PcAnd an adaptive mutation probability PmThe following formula is satisfied:
Figure BDA0001790100980000091
Figure BDA0001790100980000092
fmaxas in the populationMaximum adaptation value, favgF' is the larger fitness value of the two individuals to be crossed, and f is the fitness value of the individual to be mutated;
wherein, Pc1＝0.9,Pc2＝0.6,Pm1＝0.1,Pm2＝0.01。
Construction period T for customer requirementsmAnd cost CmAnd the weight λ of the two attributes of the objective function1，λ2And adjusting to observe different supplier selection schemes under different parameter combination conditions, and verifying the effectiveness of the method in making the supplier selection scheme considering the risk attitude of the customer.
And outputting and recording corresponding optimal supplier selection schemes of the transportation tasks under different parameter combinations according to the adjustment parameters, and analyzing the obtained results.
Analysis of Experimental results
The invention analyzes the transportation tasks with three different scales as shown in figure 2, figure 3 and figure 4 respectively, and adjusts the cost C required by the customermAnd customer requested period TmFrom strict to relaxed, to observe the resulting supplier selection, it was found that when CmOr TmThe requirement is too strict, when most of schemes cannot meet the requirement of a client, the obtained utility is negative, the client is in a loss state at the moment, and the image change of the utility value is larger in curvature before the utility value is adjusted to just meet the requirement of the client, namely the attitude of loss aversion and risk pursuit, which can be shown by people in the foreground theory when the people lose, is reflected. When two requirement parameters are continuously adjusted and relaxed in the experiment, the actual construction period and the actual cost of the selected scheme respectively change according to the customer requirement and the construction period and the cost of the selected scheme under the premise of meeting the customer requirement, so that the utility value of the objective function is optimal, namely, the scheme is adjusted and selected to the scheme with the highest customer psychological satisfaction degree within the range of the construction period and the cost meeting the customer requirement every time the customer requirement standard is changed. And with the continuous relaxation of the requirements, the growth curve of the utility value slowly becomes slow, and the prospect theory is embodied at the momentWhen the Chinese people are in the income state, along with the slow increase of the income, the satisfaction degree in mind can be slowly reduced, namely the attitude of risk avoiding is presented. Weighting factor lambda in adjusting construction period and cost1，λ2The scheme will also follow λ1The scheme can be automatically adjusted to a scheme with shorter transportation task period and higher cost than the previous scheme; corresponding lambda2The scheme can be automatically adjusted to a scheme with lower transportation task cost and longer construction period than before.
The results also show that the novel supplier selection method provided by the invention can effectively help an integrator to make selection decision and corresponding adjustment on a transportation task scheme after comprehensively considering customer requirements and real psychological feelings in a plurality of transportation tasks of different scales, and has practical guiding significance for truly improving the customer satisfaction degree of a fourth logistics integration enterprise.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. A method for integrator to supplier selection taking into account customer perception, characterized by the steps of:
s1, initializing cost and construction period information of each logistics provider;
s2, abstracting the logistics network into a complex network topological graph;
s3, according to the prospect theory, considering the cost and the construction period required by the client, and establishing a multi-attribute decision-making model considering the risk attitude of the client according to the whole transportation task;
s4, according to the network topological diagram abstracted by the logistics network and the cost and the construction period information of each logistics provider, designing a self-adaptive genetic algorithm considering the elite strategy to carry out optimization solution on the problem model;
s5, outputting an optimal logistics supplier selection strategy scheme;
the specific steps of step S3 are as follows:
s31, aiming at the client requirement construction period, modeling is carried out as follows:
Figure FDA0003275918850000011
t represents the actual duration of a certain transportation scheme, assuming that TS,TL]Uniformly distributed, TmRequesting a construction period for a client, wherein E (u (t)) is a utility value considering the construction period of the client, alpha and beta are risk attitude coefficients of a value function in a foreground theoretical model, lambda is a loss aversion coefficient, and alpha and beta are set to be 0.88 and lambda is set to be 2.25 according to the foreground theory;
s32, aiming at the cost required by the customer, modeling is carried out as follows:
Figure FDA0003275918850000012
c denotes the actual cost of a certain transportation scheme, CmFor customer demand cost, E (u (C)) is utility value considering customer demand cost, P is project delay probability, C' is penalty cost for delay project,
Figure FDA0003275918850000021
s33, comprehensively considering the client requirement cost and the client requirement construction period, and establishing a multi-attribute decision model considering the client risk attitude according to the whole transportation task:
Max VR＝λ1E(u(t))+λ2E(u(c))
s.t.
Figure FDA0003275918850000022
Figure FDA0003275918850000023
y1＝yn＝1
VRfor the total utility of a certain transportation scheme, the objective function is to maximize VR，λ1Factor, lambda, for the customer's attention to the construction period2Importance coefficient for cost and has lambda1+λ2＝1。
2. Selection method according to claim 1, characterized in that: shortest time limit T for a certain transportation schemeSAnd the longest construction period TLThe following formula is satisfied:
Figure FDA0003275918850000024
Figure FDA0003275918850000025
bijthe number of edges from node i to node j in the network topology graph is used as a starting point,
Figure FDA0003275918850000026
selecting the k-th supplier among the nodes i, j for the shortest transportation period, and corresponding
Figure FDA0003275918850000027
For the longest period of time, xijkTo choose the k-th vendor, y, between nodes i, jjTo select j as a transit node, TjTransferring the construction period for the j node;
transportation cost C for a certain transportation schemeRThe following formula is satisfied:
Figure FDA0003275918850000028
Cijkcost of selecting the k-th supplier shipment between nodes i, j, CjAnd transferring the construction period for the j node.
3. Selection method according to claim 1, characterized in that: the specific steps of step S4 are as follows:
s41, enabling population individuals of the adaptive genetic algorithm considering the elite strategy to be a communication path from a starting point to an end point in the network topological graph, and obtaining a supplier selection scheme of a complete transportation task;
representing the network topological graph in the form of an adjacent matrix, wherein the corresponding position of the matrix is the number of edges between two nodes of the network topological graph, and sequentially taking out the elements of a triangle on the matrix to form a group of integer sequences;
multiplying the integer of each position by the random number of the [0,1] interval, and rounding upwards to obtain a new sequence representing a simple graph randomly extracted from the complex graph, wherein each position represents that a certain supplier is selected between two nodes to take charge of the road transportation task, and then a Dijkstra algorithm is utilized to obtain a better path aiming at the simple graph to be used as an individual of an adaptive genetic algorithm considering the elite strategy;
s42, generating an initial population according to the population individual number specified by the adaptive genetic algorithm considering the elite strategy and the step S41, bringing all schemes into a fitness function, namely an objective function, obtaining and storing an elite individual with optimal fitness;
s43, randomly extracting 80% of schemes from all the supplier selection schemes of the transportation tasks, and selecting the best individual to judge whether the self-adaptive cross probability is met;
s44, if yes, performing intersection and storing, and if not, returning to the step S43 to reselect the individual;
s45, repeating S44 for multiple times to obtain a population to be varied, which consists of N-1 crossed individuals and elite individuals, wherein N is the number of individuals in the initial population;
s46, selecting individuals in the population to be mutated, if the adaptive mutation probability is met and the individuals are not elite individuals, mutating to obtain new individuals, otherwise, not mutating to obtain unchanged individuals;
all new individuals, all invariant individuals and elite individuals form a new population;
s47, reselecting a scheme with the optimal fitness from the new population and storing the scheme as an elite individual;
s48, judging whether the number of times of terminating iteration is reached, if not, returning to the step S43, and continuing to perform subsequent operations by replacing all supplier selection schemes of the transportation tasks with the new population; and stopping if the number of the termination iterations is met, and returning the elite individual obtained in the step S47 as an optimal supplier selection scheme.
4. Selection method according to claim 3, characterized in that: adaptive cross probability PcAnd an adaptive mutation probability PmThe following formula is satisfied:
Figure FDA0003275918850000041
Figure FDA0003275918850000042
fmaxis the maximum fitness value in the population, favgF' is the larger fitness value of the two individuals to be crossed, and f is the fitness value of the individual to be mutated;
wherein, Pc1＝0.9,Pc2＝0.6,Pm1＝0.1,Pm2＝0.01。
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