CN106779570A - A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm - Google Patents

A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm Download PDF

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CN106779570A
CN106779570A CN201710141416.9A CN201710141416A CN106779570A CN 106779570 A CN106779570 A CN 106779570A CN 201710141416 A CN201710141416 A CN 201710141416A CN 106779570 A CN106779570 A CN 106779570A
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vehicle
parameter
cold chain
cost
chain logistics
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田力威
蔡之钰
夏建桥
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Zhenjiang Kangfei Automobile Manufacturing Co Ltd
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Zhenjiang Kangfei Automobile Manufacturing Co Ltd
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    • 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
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    • 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
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    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm, for easy to be corrupt and with time window the characteristic of cold chain product, goods damage coefficient is introduced in general VRPTW models, it is up to optimization aim with minimum being spent on time with delivery service of distribution cost, multiple constraints such as vehicle capacity, weak rock mass, fuzzy running time are considered simultaneously, establish Cold Chain Logistics multiple target vehicle routing optimization model, while solution to model using improved adaptive GA-IAGA, it is also adopted by the non-dominated sorted genetic algorithm with elitism strategy and model is solved.

Description

A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm
Technical field
The present invention relates to a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm.
Background technology
Cold chain product generally has an easily corrupt and characteristic with time window in currently available technology, Cold Chain Logistics transport with Distribution cost is minimum and up to optimization aim is spent in delivery service on time, while considering vehicle capacity, weak rock mass, fuzzy traveling Multiple constraints such as time, set up a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm and are increasingly closed by people Note.
The content of the invention
It is excellent it is an object of the invention to provide a kind of intelligent Cold Chain Logistics path multiple target to overcome the defect of prior art Change algorithm, degree sets up multiple objective function on time while considering distribution cost and delivery service, optimizes solution.From Cold Chain Logistics The angle of home-delivery center, with the minimum optimization aim of distribution cost.
The present invention solves technical problem and adopts the following technical scheme that：
A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm, comprises the following steps：
Step 1, the fixed cost parameter for determining vehicle, the fixed cost parameter of the vehicle include the fixed folding of vehicle It is old, go out car loss；
Step 2, the running cost parameter for determining vehicle, the fixed cost parameter of the vehicle include oil consumption, maintenance Expense；
Step 3, determine goods damage cost parameter, the goods damage cost parameter be cold chain product in delivery process due to rotten The loss that corruption is produced；
Step 4, determine punishment cost parameter, the punishment cost is that the punishment cost advanceed to up to client's point is wait The loss of cold chain product in time；
Step 5, the function using running time, set up delivery assembly this minimum target algorithm：
Step 6, set up delivery assembly this minimum target algorithm：
Step 7, determine distribution vehicle distance parameter, i.e.,
Step 8, determine service vehicle parameter, the service vehicle parameter is the vehicle for providing delivery service no more than total Vehicle number, i.e.,
Step 9, the customer quantity parameter for determining each car service, the customer quantity parameter of each car service have such as Lower scope：Client's number of each car service is no more than total client's number, i.e.,
Step 10, determine every dispensed amounts parameter of circuit, the dispensed amounts parameter of every circuit has following scope： Every the dispensed amounts of circuit are no more than vehicle dead weight, i.e.,
Step 11, determination arrive and depart from the vehicle parameter of each client, the vehicle phase for arriving and departing from each client Parameter has following scope：Every the dispensed amounts of circuit are no more than vehicle dead weight, i.e.,
Step 12, by vehicle, temporal continuity Characteristics are introduced into the function of time between two client's points, are obtained：
Step 13, the associated expression of step 7-12 is introduced in step 5-6, obtains then Cold Chain Logistics multiple target VRPTM Optimized models are：
Step 14, the parameter in step 1-4 is substituting in step 13.
Above-mentioned a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm, it is characterised in that the fixed cost parameter is： ∑v∈Vgv。
A kind of above-mentioned intelligent Cold Chain Logistics path multi-objective optimization algorithm, it is characterised in that the total traveling of the vehicle into This parameter is：
A kind of above-mentioned intelligent Cold Chain Logistics path multi-objective optimization algorithm, it is characterised in that total goods damage cost parameter For：
A kind of above-mentioned intelligent Cold Chain Logistics path multi-objective optimization algorithm, it is characterised in that the punishment cost parameter bag Include and advance to up to punishment cost and postpone punishment cost, described advanceing to up to punishment cost is：It is described Postponing punishment cost is
Compared with the prior art, beneficial effects of the present invention are embodied in：
For easy to be corrupt and with time window the characteristic of cold chain product, goods damage coefficient is introduced in general VRPTW models, It is up to optimization aim with minimum being spent on time with delivery service of distribution cost, while considering vehicle capacity, weak rock mass, obscuring row Multiple constraints such as time are sailed, Cold Chain Logistics multiple target vehicle routing optimization model is established, using improved adaptive GA-IAGA to model While solution, it is also adopted by the non-dominated sorted genetic algorithm with elitism strategy and model is solved.
Specific embodiment
Embodiment
A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm, from the angle of Cold Chain Logistics home-delivery center, to dispense into This minimum optimization aim.
The fixed cost of vehicle mainly includes the depreciation of fixed assets of vehicle, goes out car lossization etc., it is assumed that gvRepresent vehicle v Fixed cost, then total fixed cost be：∑v∈VgvDeng, it is assumed that gvThe fixed cost of vehicle v is represented, then total fixed cost For：∑v∈Vgv。
The running cost of vehicle is vehicle expense produced in the process of moving, mainly including oil consumption, maintenance etc., one As think that running cost can rise with the increase of distance, be the function on running time, then the total running cost of vehicle For：
Cold chain product can produce rotten corruption, this partial loss to be referred to as goods damage cost in delivery process.It is false in this project If cold chain the product corrupt running time and vehicle that occur only with vehicle is relevant in the service time of client's point, then total goods damage into Originally it is：
For the dispatching of cold chain product, the cold chain product up within the punishment cost as stand-by period of client's point is advanceed to Loss, that is, advanceing to the punishment cost for reaching is：Delay to reach the pin that can influence client's point next step Activity is sold, that is, postponing punishment cost is：
Total punishment cost is：
Finally, this minimum goal expression of delivery assembly is in constructed model：
From the angle of cold chain product client, up to optimization aim is spent on time with delivery service.
In the Cold Chain Logistics vehicle problem with time window, if distribution vehicle is advanceed to up to client's point, need to wait, Until client starts receiving service, if the time that distribution vehicle is reached has exceeded the time window of client, need to pay certain Rejection penalty, the punctuality of dispatching largely decides the satisfaction of client.The expression of the punctuality of delivery service Formula is：
In order to the integrality and validity of model are, it is necessary to do following constraint：
Each distribution vehicle be all by home-delivery center, eventually pass back to home-delivery center.I.e.：
The vehicle for providing delivery service is no more than total vehicle number.I.e.：
Client's number of each car service is no more than total client's number.I.e.：
Every the dispensed amounts of circuit are no more than vehicle dead weight.I.e.：
The vehicle for arriving and departing from each client is identical.I.e.：
Vehicle is continuous on the time between two client's points.I.e.：
Then Cold Chain Logistics multiple target VRPTW Optimized models are
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto, Any one skilled in the art the invention discloses technical scope in, the change or replacement that can be readily occurred in, Should all cover within the scope of the present invention.Therefore, protection scope of the present invention should be with the protection domain of claims It is defined.

Claims (5)

1. a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm, it is characterised in that comprise the following steps：
Step 1, the fixed cost parameter for determining vehicle, the fixed cost parameter of the vehicle include the depreciation of fixed assets of vehicle, go out Car is lost；
Step 2, the running cost parameter for determining vehicle, the fixed cost parameter of the vehicle include oil consumption, maintenance cost；
Step 3, determine goods damage cost parameter, the goods damage cost parameter be cold chain product in delivery process due to rotten corruption The loss of generation；
Step 4, determine punishment cost parameter, the punishment cost is to advance to the punishment cost as stand-by period up to client's point The loss of interior cold chain product；
Step 5, the function using running time, set up delivery assembly this minimum target algorithm：
Step 6, set up delivery assembly this minimum target algorithm：
min B = Σ v ∈ V Σ i ∈ I { m a x { a i - at i v , 0 } + m a x { at i v - b j } , 0 }
Step 7, determine distribution vehicle distance parameter, i.e.,
Step 8, determine service vehicle parameter, the service vehicle parameter is that the vehicle for providing delivery service is no more than total vehicle Number, i.e.,
Step 9, the customer quantity parameter for determining each car service, the customer quantity parameter of each car service have following model Enclose：Client's number of each car service is no more than total client's number, i.e.,
Step 10, determine every dispensed amounts parameter of circuit, the dispensed amounts parameter of every circuit has following scope：Every The dispensed amounts of circuit are no more than vehicle dead weight, i.e.,
Step 11, determination arrive and depart from the vehicle parameter of each client, the vehicle coherent for arriving and departing from each client Number has following scope：Every the dispensed amounts of circuit are no more than vehicle dead weight, i.e.,
Step 12, by vehicle, temporal continuity Characteristics are introduced into the function of time between two client's points, are obtained：
Step 13, the associated expression of step 7-12 is introduced in step 5-6, obtains then Cold Chain Logistics multiple target VRPTW excellent Changing model is：
min B = Σ v ∈ V Σ i ∈ I { m a x { a i - at i v , 0 } + m a x { at i v - b j } , 0 }
Step 14, the parameter in step 1-4 is substituting in step 13.
2. a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm as claimed in claim 1, it is characterised in that the fixation Cost parameter is：∑v∈Vgv。
3. a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm as claimed in claim 1, it is characterised in that the vehicle Total running cost parameter is：
4. a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm as claimed in claim 1, it is characterised in that total goods Damaging cost parameter is：
5. a kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm as claimed in claim 1, it is characterised in that the punishment cost Parameter includes advanceing to up to punishment cost and postpones punishment cost, and described advanceing to up to punishment cost is： It is described delay punishment cost be
CN201710141416.9A 2017-03-10 2017-03-10 A kind of intelligent Cold Chain Logistics path multi-objective optimization algorithm Pending CN106779570A (en)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108510227A (en) * 2018-03-23 2018-09-07 东华大学 A kind of real-time planning system of vehicle-mounted logistics distribution based on machine learning
CN108985677A (en) * 2018-06-11 2018-12-11 华东理工大学 The multiple batches of fresh agricultural products Distribution path optimization method of multi items
CN109978213A (en) * 2017-12-28 2019-07-05 北京京东尚科信息技术有限公司 A kind of task path planning method and device
CN112085271A (en) * 2020-09-08 2020-12-15 东南大学 Crowdsourcing mode-based traditional industry cluster goods collection path optimization method
WO2021164390A1 (en) * 2020-02-21 2021-08-26 北京京东振世信息技术有限公司 Route determination method and appparatus for cold chain distribution, server and storage medium
CN116167680A (en) * 2023-04-26 2023-05-26 成都运荔枝科技有限公司 Intelligent flow control method for cold chain system
CN116579685A (en) * 2023-04-23 2023-08-11 中国石油大学(北京) Finished oil logistics optimization method, system, medium and equipment based on multiparty cooperation
CN117541146A (en) * 2023-11-22 2024-02-09 四川信特农牧科技有限公司 Logistics path planning method

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Publication number Priority date Publication date Assignee Title
CN103699982A (en) * 2013-12-26 2014-04-02 浙江工业大学 Logistics distribution control method with soft time windows
CN105787596A (en) * 2016-02-29 2016-07-20 泰华智慧产业集团股份有限公司 Emergency logistic route optimizing method based on improved ant colony algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103699982A (en) * 2013-12-26 2014-04-02 浙江工业大学 Logistics distribution control method with soft time windows
CN105787596A (en) * 2016-02-29 2016-07-20 泰华智慧产业集团股份有限公司 Emergency logistic route optimizing method based on improved ant colony algorithm

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109978213A (en) * 2017-12-28 2019-07-05 北京京东尚科信息技术有限公司 A kind of task path planning method and device
CN108510227A (en) * 2018-03-23 2018-09-07 东华大学 A kind of real-time planning system of vehicle-mounted logistics distribution based on machine learning
CN108985677A (en) * 2018-06-11 2018-12-11 华东理工大学 The multiple batches of fresh agricultural products Distribution path optimization method of multi items
CN108985677B (en) * 2018-06-11 2022-07-08 华东理工大学 Method for optimizing distribution path of multiple varieties of fresh agricultural products in multiple batches
WO2021164390A1 (en) * 2020-02-21 2021-08-26 北京京东振世信息技术有限公司 Route determination method and appparatus for cold chain distribution, server and storage medium
CN112085271A (en) * 2020-09-08 2020-12-15 东南大学 Crowdsourcing mode-based traditional industry cluster goods collection path optimization method
CN112085271B (en) * 2020-09-08 2022-03-11 东南大学 Crowdsourcing mode-based traditional industry cluster goods collection path optimization method
CN116579685A (en) * 2023-04-23 2023-08-11 中国石油大学(北京) Finished oil logistics optimization method, system, medium and equipment based on multiparty cooperation
CN116579685B (en) * 2023-04-23 2024-01-12 中国石油大学(北京) Finished oil logistics optimization method, system, medium and equipment based on multiparty cooperation
CN116167680A (en) * 2023-04-26 2023-05-26 成都运荔枝科技有限公司 Intelligent flow control method for cold chain system
CN117541146A (en) * 2023-11-22 2024-02-09 四川信特农牧科技有限公司 Logistics path planning method
CN117541146B (en) * 2023-11-22 2024-06-21 四川信特农牧科技有限公司 Logistics path planning method

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Application publication date: 20170531