CN109214571A - It is a kind of consider customer impression integrator for supplier selection method - Google Patents
It is a kind of consider customer impression integrator for supplier selection method Download PDFInfo
- Publication number
- CN109214571A CN109214571A CN201811032528.1A CN201811032528A CN109214571A CN 109214571 A CN109214571 A CN 109214571A CN 201811032528 A CN201811032528 A CN 201811032528A CN 109214571 A CN109214571 A CN 109214571A
- Authority
- CN
- China
- Prior art keywords
- cost
- individual
- duration
- supplier
- population
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010187 selection method Methods 0.000 title claims abstract description 14
- 238000010586 diagram Methods 0.000 claims abstract description 25
- 230000002068 genetic effect Effects 0.000 claims abstract description 15
- 230000032258 transport Effects 0.000 claims description 31
- 230000003044 adaptive effect Effects 0.000 claims description 16
- 239000011159 matrix material Substances 0.000 claims description 9
- 230000035772 mutation Effects 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 7
- 230000006978 adaptation Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 230000001934 delay Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 description 13
- 238000011160 research Methods 0.000 description 7
- 238000011161 development Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 206010063659 Aversion Diseases 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000002040 relaxant effect Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 230000003236 psychic effect Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000001568 sexual effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Biophysics (AREA)
- Development Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Genetics & Genomics (AREA)
- Evolutionary Computation (AREA)
- Physiology (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of selection method of the integrator for supplier of consideration customer impression, it is characterised in that with following steps: S1, the cost and duration information for initializing each logistics service provider；S2, logistics network is abstracted into a complicated network topological diagram；S3, according to prospect theory, consider cost and the duration of customer requirement, and the Multiple Attribute Decision Model for considering customer risk attitude is established according to entire transport task；The cost and duration information of S4, the network topological diagram and each logistics service provider that are taken out according to logistics network, design consider that the self-adapted genetic algorithm of elitism strategy optimizes problem model；The optimal logistics service provider's selection strategy scheme of S5, output.The psychological factor that the present invention considers client influences, and two aspects of cost and time are comprehensively considered, true client's psychological feelings is more in line with, can select integrator and supplier's traffic program of customer satisfaction is more enabled to have practical guided significance.
Description
Technical field
The present invention relates to logistics fields, specifically utilize behavior economy and operational research Methods, are passing for integrator
A kind of choosing of the integrator for consideration customer impression that client proposes for rational people for supplier is thought in system decision
Selection method.
Background technique
With the accelerated development of nowadays global economic integration, the rise of e-commerce and the development of Logistics Market, market
Requirement to logistics service is more specific and comprehensive.Fourth-party logistics enterprise is helped by providing complete supply chain solution
Enterprise effectively integrates resource to reduce cost, increases benefit.Fourth-party logistics enterprise of China still has just in the initial development stage
Many problem and shortage.There are a common hypotheses in early stage for logistics management and the research of decision, i.e. decision is to be based on
The optimization behavior of rational, and mediate in actual fortune and be usually expressed as bounded rationality.In actual operation, either
Operations Management work and technology, or theoretical Successful utilization are all highly dependent on the understanding of human behavior.In recent years, with behavior
Feature is that the research of the fourth-party logistics at visual angle is also seldom, and existing research achievement is also relatively simple, is mainly shown as research angle
Spend the problems such as single, method is single, the factor of consideration is single.Therefore, the fourth-party logistics for there is multiple decision-makers to participate in
For enterprise, by a variety of visual angles, a variety of methods, many factors are considered, study it comprehensively and operate potential limited reason in content
Sexual factor just seems particularly significant, and value is that it carries out us most of action problem for being related to people again
Understanding.
Summary of the invention
The problem of for existing research and limitation, the present invention propose a kind of integrator of consideration customer impression
For the selection method of supplier, Utilization prospects theory of the present invention considers that client has risk state for overall cost and duration
Selection method of the fourth-party logistics integrator of degree for TPL supplier.The present invention will play logistics management theory
To effective supplement and development function, play the role of to application, the development of behavior operational research theory perfect, is fourth-party logistics mould
Logistics Operation provides theory and method support under formula, and the sound development for fourth-party logistics in China has great importance.
The technological means that the present invention uses is as follows:
It is a kind of consider customer impression integrator for supplier selection method, have following steps:
The cost and duration information of S1, each logistics service provider of initialization；
S2, logistics network is abstracted into a complicated network topological diagram；
S3, according to prospect theory, consider cost and the duration of customer requirement, and establish according to entire transport task and consider visitor
The Multiple Attribute Decision Model of family attitudes toward risk；
The cost and duration information of S4, the network topological diagram and each logistics service provider that are taken out according to logistics network, if
Meter considers that the self-adapted genetic algorithm of elitism strategy optimizes problem model；
The optimal logistics service provider's selection strategy scheme of S5, output.
For the shortest limit time T of some traffic programSWith longest duration TLMeet following formula:
bijBe using network topological diagram interior joint i as starting point to the number of edges (i.e. the number of logistics service provider) between node j,It is node i, k-th of supplier is selected to transport shortest limit time between j, it is correspondingFor longest duration, xijkFor whether in node
K-th of supplier, y are chosen between i, jjWhether to select j as transhipment node, TjThe duration is transported for j node；
For the transportation cost C of some traffic programRMeet following formula:
CijkFor node i, the cost for selecting k-th of supplier to transport between j, CjThe duration is transported for j node.
Specific step is as follows by the step S3:
S31, it is directed to the customer requirement duration, modeled as follows:
T indicates the actual construction time of some traffic program, it is assumed that in [TS,TL] be uniformly distributed, TmFor customer requirement duration, E (u
(t)) value of utility of client's duration is required for consideration, α and β are the attitudes toward risk coefficient of cost function in prospect theory model, and λ is
Simultaneously α=β=0.88, λ=2.25 are arranged according to prospect theory in loss aversion coefficient；
S32, it is directed to customer requirement cost, modeled as follows:
C indicates some traffic program actual cost, CmFor customer requirement cost, E (u (c)) is to consider customer requirement cost
Value of utility, P be construction delay probability, C' be schedule delays punishment cost,
S33, comprehensively consider customer requirement cost and customer requirement duration, and established according to entire transport task and consider client
The Multiple Attribute Decision Model of attitudes toward risk:
Max VR=λ1E(u(t))+λ2E(u(c))
y1=yn=1
VRFor the total utility of some traffic program, objective function maximizes VR, λ1It is client to the attention coefficient of duration,
λ2Pay attention to coefficient for cost, and has λ1+λ2=1, it is an access from origin-to-destination, third that the first two, which constrains guarantee scheme,
A constraint condition indicates that shipping point of origin and terminal must be selected.
Specific step is as follows by the step S4:
S41, enable consider elitism strategy self-adapted genetic algorithm population at individual be network topological diagram in from starting point to end
One communication path of point, obtains the Supplier Selection Scheme of a complete transport task；
Network topological diagram is expressed as to the form of adjacency matrix, the corresponding position of matrix be two nodes of network topological diagram it
Between number of edges (i.e. supplier's number.), the element for successively taking out triangle on matrix forms one group of integer sequence, this sequence can reflect
All connections of entire complicated figure out；
It by the integer of each position multiplied by the random number in [0,1] section, and rounds up, obtained new sequence represents one
The simple graph extracted out at random from complicated figure, each position, which represents, to be had selected some supplier between two nodes and is responsible for this road
Section transport task recycles dijkstra's algorithm to acquire for this simple graph compared with shortest path, as the adaptive of consideration elitism strategy
Answer the individual of genetic algorithm；
S42, the population at individual number according to as defined in the self-adapted genetic algorithm for considering elitism strategy and step S41 are generated just
Beginning population brings all schemes in fitness function into, i.e., in objective function, acquire fitness it is optimal as elite individual,
It is saved；
S43, due to there is a situation where to drag phase and cost insufficient in the Supplier Selection Scheme of transport task, final utility value
The case where will appear negative, therefore selection individual link of the invention takes championship method: from the supply of all transport tasks
Randomly select 80% scheme in quotient's selection scheme, selection wherein best individual judge whether to meet adaptive intersection it is general
Rate；
S44, if satisfied, then intersected, and save, be unsatisfactory for, then return step S43 reselects individual；
S45, it repeats S44 more times, obtains the N-1 population to be made a variation for intersecting individual and elite individual composition, elite is added
Individual avoids the loss of outstanding gene, and wherein N is individual number in initial population；
S46, select wait the individual in the population that makes a variation, if meet adaptive mutation probability and this individual be not essence
English individual, then make a variation, obtain new individual, otherwise do not make a variation, obtain not variation individual；
All new individuals, all not variation individuals and elite individual form new population；
Adaptive purpose is to enhance the global convergence of algorithm in order to avoid generating Premature Convergence, accelerate the receipts of algorithm
Hold back speed.
S47, selected again from new population the optimal scheme of fitness save as elite individual；
S48, judge whether to reach termination the number of iterations, not up to then return step S43 replaces all fortune with new population
The Supplier Selection Scheme of defeated task continues subsequent operation；Stop if meeting arrival and terminating the number of iterations, by step S47
Obtained elite individual is returned as optimal supplier selection scheme.
Adaptive crossover mutation PcWith adaptive mutation probability PmMeet following formula:
fmaxFor maximum adaptation value, f in populationavgFor the average fitness value of every generation group population, f' is to be intersected two
Biggish fitness value in individual, f are the fitness value for the individual to be made a variation；
Wherein, Pc1=0.9, Pc2=0.6, Pm1=0.1, Pm2=0.01.
Logistics network is abstracted as a complicated multimeshed network topological diagram by the present invention, is generated in consideration actual shipment task
The cost and duration of cost and transport duration and customer requirement, consideration visitor is established in conjunction with the attitudes toward risk analysis of prospect theory
The Multiple Attribute Decision Model of family attitudes toward risk is going to select in network topological diagram to go to undertake transport between node two-by-two according to model
The supplier of task selects the optimal transportation route for being best suitable for client cost and duration demand, completes multiple attribute decision making (MADM).This hair
The bright derivation algorithm used for model solution --- the elitism strategy self-adapted genetic algorithm of-insertion dijkstra's algorithm, first
By integer coding technology, complicated multigraph is reduced to simple graph, then find out more excellent transportation route with dijkstra's algorithm, made
It for the individual of genetic algorithm, then considers further that under the strategy that elite retains, adaptation mechanism is added in genetic algorithm, so that calculating
Faster, low optimization accuracy is more accurate for the convergence rate of method, finally acquires each section supplier selecting party for being best suitable for customer demand
Case completes decision.The psychological factor that the present invention considers client influences, and carries out synthesis for two aspects of cost and time and examines
Consider, is more in line with true client's psychological feelings, the supplier's traffic program for more enabling customer satisfaction can be selected integrator
There is practical guided significance.
The present invention can be widely popularized in fields such as logistics based on the above reasons.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Selection of the Fig. 1 for the integrator for considering customer's impression a kind of in a specific embodiment of the invention for supplier
Method solves flow chart.
Fig. 2 is network topological diagram (8 node) in a specific embodiment of the invention.
Fig. 3 is network topological diagram (16 node) in a specific embodiment of the invention.
Fig. 4 is network topological diagram (32 node) in a specific embodiment of the invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
As depicted in figs. 1 and 2, selection method of a kind of integrator considering customer's impression for supplier, feature
It is with following steps:
The cost and duration information of S1, each logistics service provider of initialization；
S2, logistics network is abstracted into a complicated network topological diagram；
S3, according to prospect theory, consider cost and the duration of customer requirement, and establish according to entire transport task and consider visitor
The Multiple Attribute Decision Model of family attitudes toward risk；
The cost and duration information of S4, the network topological diagram and each logistics service provider that are taken out according to logistics network, if
Meter considers that the self-adapted genetic algorithm of elitism strategy optimizes problem model；
The optimal logistics service provider's selection strategy scheme of S5, output.
For the shortest limit time T of some traffic programSWith longest duration TLMeet following formula:
bijBe using network topological diagram interior joint i as starting point to the number of edges (i.e. the number of logistics service provider) between node j,It is node i, k-th of supplier is selected to transport shortest limit time between j, it is correspondingFor longest duration, xijkWhether to save
K-th of supplier, y are chosen between point i, jjWhether to select j as transhipment node, TjThe duration is transported for j node；
For the transportation cost C of some traffic programRMeet following formula:
CijkFor node i, the cost for selecting k-th of supplier to transport between j, CjThe duration is transported for j node.
Specific step is as follows by the step S3:
S31, it is directed to the customer requirement duration, modeled as follows:
T indicates the actual construction time of some traffic program, it is assumed that in [TS,TL] be uniformly distributed, TmFor customer requirement duration, E (u
(t)) value of utility of client's duration is required for consideration, α and β are the attitudes toward risk coefficient of cost function in prospect theory model, and λ is
Simultaneously α=β=0.88, λ=2.25 are arranged according to prospect theory in loss aversion coefficient；
S32, it is directed to customer requirement cost, modeled as follows:
C indicates some traffic program actual cost, CmFor customer requirement cost, E (u (c)) is to consider customer requirement cost
Value of utility, P be construction delay probability, C' be schedule delays punishment cost,
S33, comprehensively consider customer requirement cost and customer requirement duration, and established according to entire transport task and consider client
The Multiple Attribute Decision Model of attitudes toward risk:
Max VR=λ1E(u(t))+λ2E(u(c))
y1=yn=1
VRFor the total utility of some traffic program, objective function maximizes VR, λ1It is client to the attention coefficient of duration,
λ2Pay attention to coefficient for cost, and has λ1+λ2=1.
Specific step is as follows by the step S4:
S41, the population at individual for enabling the self-adapted genetic algorithm of consideration elitism strategy are in network topological diagram from starting point (node
1) communication path for arriving terminal (node 8), as shown in Fig. 2, obtaining the Supplier Selection Scheme of a complete transport task；
Network topological diagram is expressed as to the form of adjacency matrix, the corresponding position of matrix be two nodes of network topological diagram it
Between number of edges (i.e. supplier's number.), the element for successively taking out triangle on matrix forms one group of integer sequence；
It by the integer of each position multiplied by the random number in [0,1] section, and rounds up, obtained new sequence represents one
The simple graph extracted out at random from complicated figure, each position, which represents, to be had selected some supplier between two nodes and is responsible for this road
Section transport task recycles dijkstra's algorithm to acquire for this simple graph compared with shortest path, as the adaptive of consideration elitism strategy
Answer the individual of genetic algorithm；
S42, the population at individual number according to as defined in the self-adapted genetic algorithm for considering elitism strategy and step S41 are generated just
Beginning population brings all schemes in fitness function into, i.e., in objective function, acquire fitness it is optimal as elite individual,
It is saved；
S43, due to there is a situation where to drag phase and cost insufficient in the Supplier Selection Scheme of transport task, final utility value
The case where will appear negative, therefore selection individual link of the invention takes championship method: from the supply of all transport tasks
Randomly select 80% scheme in quotient's selection scheme, selection wherein best individual judge whether to meet adaptive intersection it is general
Rate；
S44, if satisfied, then intersected, and save, be unsatisfactory for, then return step S43 reselects individual；
S45, it repeats S44 more times, obtains the N-1 population to be made a variation for intersecting individual and elite individual composition, wherein N is first
Individual number in beginning population；
S46, select wait the individual in the population that makes a variation, if meet adaptive mutation probability and this individual be not essence
English individual, then make a variation, obtain new individual, otherwise do not make a variation, obtain not variation individual；
All new individuals, all not variation individuals and elite individual form new population；
S47, selected again from new population the optimal scheme of fitness save as elite individual；
S48, judge whether to reach termination the number of iterations, not up to then return step S43 replaces all fortune with new population
The Supplier Selection Scheme of defeated task continues subsequent operation；Stop if meeting arrival and terminating the number of iterations, by step S47
Obtained elite individual is returned as optimal supplier selection scheme.
Adaptive crossover mutation PcWith adaptive mutation probability PmMeet following formula:
fmaxFor maximum adaptation value, f in populationavgFor the average fitness value of every generation group population, f' is to be intersected two
Biggish fitness value in individual, f are the fitness value for the individual to be made a variation；
Wherein, Pc1=0.9, Pc2=0.6, Pm1=0.1, Pm2=0.01.
For the duration T of customer requirementmWith cost CmAnd the weight λ of two attributes of objective function1, λ2It is adjusted,
The different Supplier Selection Schemes under observation different parameters combination condition are gone, the verifying present invention is making consideration customer risk attitude
Validity when rear Supplier Selection Scheme.
The corresponding optimal supplier selecting party that different parameters combine lower transport task is exported and recorded according to adjusting parameter
Case is analyzed for the result obtained.
Analysis of experimental results
The present invention is directed to such as Fig. 2, the transport task of tri- kinds of different scales of Fig. 3, Fig. 4 respectively and is analyzed, and adjustment client wants
Hope for success this CmAnd customer requirement duration TmFrom strict to relaxing, C is worked as in the Supplier Selection Scheme for going observation to obtain, discoverymOr
TmIt is required that it is excessively stringent, when most of scheme is all unable to satisfy customer requirement, show that effectiveness is negative, customer can be at this time
Loss state is being adjusted to before just reaching and meet customer requirement, and the image change of value of utility is that curvature is larger, that is, is embodied
The attitude of the people loss aversion that can be shown and risk-seeking when loss in prospect theory.When continuing to adjust in experiment
When relaxing two and requiring parameter, the actual construction time of selected scheme and actual cost will be under the premise of meeting customer requirement, root
It goes to change respectively according to the duration and cost of scheme selected by customer requirement equilibrium, the value of utility for reaching objective function is optimal, i.e., every time
The change of customer requirement standard, scheme also can adjust selection to visitor in the range of the duration for meeting customer requirement, cost therewith
The highest scheme of family psychic gratification degree.And as what is required continues to relax, the growth curve of value of utility can be slowly slowed by,
Just embody in prospect theory that people is when income state this moment, with the slowly increase of income, satisfaction degree meeting at heart
It slowly cuts down, that is, the attitude of risk averse is presented.In the weight coefficient λ of adjustment duration and cost1, λ2When, scheme also can be with
λ1Increase, scheme can be automatically regulated to be that the transport task duration is shorter, cost mutually higher scheme than before；Corresponding λ2Increase,
Scheme can also be automatically regulated to be transport task, and cost is relatively low, the scheme that the duration is mutually grown partially than before.
These results also show novel supplier selection method proposed by the present invention in the transport of a variety of different scales
Task can effectively help integrator that can go to accomplish to comprehensively consider after customer requirement and true psychological feelings for transport task
Scheme carries out trade-off decision and corresponding adjustment, and the satisfaction for promoting client is really gone for enterprise of fourth-party logistics integrator
There is the directive significance of reality.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (5)
1. a kind of integrator for considering customer's impression is for the selection method of supplier, it is characterised in that have following steps:
The cost and duration information of S1, each logistics service provider of initialization；
S2, logistics network is abstracted into a complicated network topological diagram；
S3, according to prospect theory, consider cost and the duration of customer requirement, and establish according to entire transport task and consider client's wind
The Multiple Attribute Decision Model of dangerous attitude；
S4, the cost of the network topological diagram taken out according to logistics network and each logistics service provider and duration information, design are examined
The self-adapted genetic algorithm for considering elitism strategy optimizes problem model；
The optimal logistics service provider's selection strategy scheme of S5, output.
2. selection method according to claim 1, it is characterised in that: for the shortest limit time T of some traffic programSMost
Long duration TLMeet following formula:
bijBe using network topological diagram interior joint i as starting point to the number of edges between node j,It is node i, k-th of confession is selected between j
Quotient is answered to transport shortest limit time, it is correspondingFor longest duration, xijkWhether to choose k-th of supplier, y between node i, jj
Whether to select j as transhipment node, TjThe duration is transported for j node；
For the transportation cost C of some traffic programRMeet following formula:
CijkFor node i, the cost for selecting k-th of supplier to transport between j, CjThe duration is transported for j node.
3. selection method according to claim 2, it is characterised in that: specific step is as follows by the step S3:
S31, it is directed to the customer requirement duration, modeled as follows:
T indicates the actual construction time of some traffic program, it is assumed that in [TS,TL] be uniformly distributed, TmFor customer requirement duration, E (u (t))
To consider to require the value of utility of client's duration, α and β are the attitudes toward risk coefficient of cost function in prospect theory model, and λ is loss
Detest coefficient and α=β=0.88, λ=2.25 are arranged according to prospect theory；
S32, it is directed to customer requirement cost, modeled as follows:
C indicates some traffic program actual cost, CmFor customer requirement cost, E (u (c)) is the effectiveness for considering customer requirement cost
Value, P are construction delay probability, and C' is the punishment cost of schedule delays,
S33, comprehensively consider customer requirement cost and customer requirement duration, and established according to entire transport task and consider customer risk
The Multiple Attribute Decision Model of attitude:
MaxVR=λ1E(u(t))+λ2E(u(c))
y1=yn=1
VRFor the total utility of some traffic program, objective function maximizes VR, λ1It is client to the attention coefficient of duration, λ2For at
This attention coefficient, and have λ1+λ2=1.
4. selection method according to claim 3, it is characterised in that: specific step is as follows by the step S4:
S41, the population at individual for enabling the self-adapted genetic algorithm of consideration elitism strategy are in network topological diagram from origin-to-destination
One communication path obtains the Supplier Selection Scheme of a complete transport task；
Network topological diagram is expressed as to the form of adjacency matrix, the corresponding position of matrix is between two nodes of network topological diagram
Number of edges, the element for successively taking out triangle on matrix form one group of integer sequence；
By the integer of each position multiplied by the random number in [0,1] section, and round up, obtained new sequence represent one from
The simple graph extracted out at random in complicated figure, each position represent had selected between two nodes some supplier be responsible for this section fortune
Defeated task recycles dijkstra's algorithm to acquire for this simple graph compared with shortest path, as the adaptive something lost for considering elitism strategy
The individual of propagation algorithm；
S42, the population at individual number according to as defined in the self-adapted genetic algorithm for considering elitism strategy and step S41 generate initial kind
Group, all schemes are brought into fitness function, i.e., in objective function, acquire optimal, the progress individual as elite of fitness
It saves；
S43, the scheme that 80% is randomly selected from the Supplier Selection Scheme of all transport tasks select wherein best
Body judges whether to meet adaptive crossover probability；
S44, if satisfied, then intersected, and save, be unsatisfactory for, then return step S43 reselects individual；
S45, it repeats S44 more times, obtains the N-1 population to be made a variation for intersecting individual and elite individual composition, wherein N is initial kind
Individual number in group；
S46, it selects wait the individual in the population that makes a variation, if meeting adaptive mutation probability and this individual is not elite
Body then makes a variation, and obtains new individual, does not otherwise make a variation, and obtains not variation individual；
All new individuals, all not variation individuals and elite individual form new population；
S47, selected again from new population the optimal scheme of fitness save as elite individual；
S48, judge whether to reach termination the number of iterations, not up to then return step S43 is appointed with new population instead of all transports
The Supplier Selection Scheme of business continues subsequent operation；Stop if meeting arrival and terminating the number of iterations, step S47 is obtained
Elite individual as optimal supplier selection scheme return.
5. selection method according to claim 4, it is characterised in that: adaptive crossover mutation PcIt is general with adaptive variation
Rate PmMeet following formula:
fmaxFor maximum adaptation value, f in populationavgFor the average fitness value of every generation group population, f' is to be intersected two
Biggish fitness value in body, f are the fitness value for the individual to be made a variation；
Wherein, Pc1=0.9, Pc2=0.6, Pm1=0.1, Pm2=0.01.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811032528.1A CN109214571B (en) | 2018-09-05 | 2018-09-05 | Method for selecting suppliers from integrators considering customer feeling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811032528.1A CN109214571B (en) | 2018-09-05 | 2018-09-05 | Method for selecting suppliers from integrators considering customer feeling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109214571A true CN109214571A (en) | 2019-01-15 |
| CN109214571B CN109214571B (en) | 2022-02-01 |
Family
ID=64987539
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811032528.1A Active CN109214571B (en) | 2018-09-05 | 2018-09-05 | Method for selecting suppliers from integrators considering customer feeling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109214571B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049841A (en) * | 2013-01-29 | 2013-04-17 | 河南理工大学 | Coevolution model of logistics service provider density |
-
2018
- 2018-09-05 CN CN201811032528.1A patent/CN109214571B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049841A (en) * | 2013-01-29 | 2013-04-17 | 河南理工大学 | Coevolution model of logistics service provider density |
Non-Patent Citations (4)
| Title |
|---|
| 乔佩利等: "考虑逆向物流第三方配送的选址路径问题研究", 《计算机工程与应用》 * |
| 任亮等: "考虑客户拖期厌恶行为的4PL路径优化问题", 《计算机集成制造系统》 * |
| 张广胜等: "考虑服务时效的物流服务供应链应急任务分配", 《计算机应用》 * |
| 张红: "基于多属性决策的第四方物流路径优化问题的研究", 《中国优秀硕士学位论文全文数据库·信息科技辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109214571B (en) | 2022-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111858009B (en) | Task scheduling method of mobile edge computing system based on migration and reinforcement learning | |
| WO2022021119A1 (en) | Method and system for fully autonomous waterborne transport scheduling between container terminals | |
| CN106297774B (en) | A kind of the distributed parallel training method and system of neural network acoustic model | |
| CN109919376A (en) | Multi-field model and multi-vehicle-type vehicle route dispatch control method | |
| CN101777990B (en) | Method for selecting multi-objective immune optimization multicast router path | |
| CN107220731A (en) | A kind of logistics distribution paths planning method | |
| CN112418497A (en) | Material distribution path optimization method for manufacturing Internet of things | |
| CN107678850A (en) | Repeater satellite method for scheduling task and device | |
| CN112631717A (en) | Network service function chain dynamic deployment system and method based on asynchronous reinforcement learning | |
| CN101616074B (en) | Multicast routing optimization method based on quantum evolution | |
| CN102299854B (en) | Opportunistic network environment-oriented multi-object routing decision making system | |
| CN107101645A (en) | A kind of paths planning method and path planning apparatus | |
| CN112996019B (en) | Terahertz frequency band distributed constellation access control method based on multi-objective optimization | |
| CN111917642B (en) | SDN intelligent routing data transmission method for distributed deep reinforcement learning | |
| CN109951391B (en) | Network path rapid calculation method based on multi-QoS constraint | |
| CN115032997B (en) | Fourth logistics transportation path planning method based on ant colony algorithm | |
| CN111885493B (en) | Micro-cloud deployment method based on improved cuckoo search algorithm | |
| CN104199820A (en) | Cloud platform MapReduce workflow scheduling optimizing method | |
| CN107276664A (en) | The empty net mapping method of mixing loaded based on thresholding formula | |
| CN109359771A (en) | A kind of line haul vehicle and goods matching algorithm based on big data | |
| CN109800911B (en) | Unified navigation method for delivery paths of multiple couriers | |
| CN109840625B (en) | Courier group path navigation method | |
| CN116185523A (en) | Task unloading and deployment method | |
| CN114611794A (en) | Vehicle-machine cooperative pick-and-place path optimization method and system based on sub-heuristic algorithm | |
| CN109214571A (en) | It is a kind of consider customer impression integrator for supplier selection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |