CN109948855A - A kind of isomery harmful influence Transport route planning method with time window - Google Patents
A kind of isomery harmful influence Transport route planning method with time window Download PDFInfo
- Publication number
- CN109948855A CN109948855A CN201910222496.XA CN201910222496A CN109948855A CN 109948855 A CN109948855 A CN 109948855A CN 201910222496 A CN201910222496 A CN 201910222496A CN 109948855 A CN109948855 A CN 109948855A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- population
- harmful influence
- time window
- node
- 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
Classifications
-
- 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
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Traffic Control Systems (AREA)
Abstract
The isomery harmful influence Transport route planning method with time window that the invention discloses a kind of.The present invention is by fully considering haulage vehicle type, transport load-carrying, transport routes information, population distribution and harmful influence information; it proposes a kind of dynamic load risk in transit assessment models for considering type of vehicle and down time, constructs the Model for Multi-Objective Optimization of the isomery harmful influence Transport route planning with time window.According to model characteristics, a kind of mixing multi-objective Evolutionary Algorithm Solve problems based on change neighborhood search are designed, the isomery harmful influence vehicle path planning method with time window is finally established.Present invention combination harmful influence transportation characterization is on the basis of traditional vehicle path planning method; consider the risk factors of harmful influence transport; construct the isomery harmful influence transportation route Model for Multi-Objective Optimization with time window closer to harmful influence transport actual conditions, final design one solves the model based on becoming neighborhood and search more mixing multi-objective Algorithms.
Description
Technical field
The invention belongs to harmful influence risk management fields, are related to automatic technology, more particularly to a kind of band time window
Isomery harmful influence Transport route planning method.
Background technique
Harmful influence transport is the important component of China's chemical industry, with the expansion of chemical industry scale, dangerous material fortune
The defeated ratio for accounting for entire transport service is higher and higher.It is any to use relevant activity, all companion to it due to the special nature of harmful influence
With huge risk.
In harmful influence transportational process, ordinary traffic accident causes the probability of harmful influence leakage accident high, such dangerization
Product shipping accident can cause extensive casualties, environmental degradation and property loss.Since industrial development needs, harmful influence transport
Risk be it is unavoidable, can only pass through a series of risk management measures reduces accident probability and damage sequence, harmful influence transport
Path planning is one of main risk in transit management measure.
Many traditional harmful influence Transport route planning methods are transported only with single vehicle and are not accounted for user
Time window demand, thus there is also very big gaps apart from practical application, in addition, using linear weighting function by multiple optimizations
Targeted integration is difficult to evaluate each optimization aim importance when being single-object problem carries out tax power, and branch and bound method is cut
The exact algorithms such as planar process and PILP are easy to produce dimension disaster, therefore, dangerization when solving such Large-scale Optimization Problems
Product Transport route planning is extremely difficult.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of isomery harmful influence vehicle path planning side with time window
Method.
It is an object of the present invention to meet all given constraints for some problems in harmful influence Transport route planning
Under the background of condition, determine that the scale of isomery transport column and route transport specific harmful influence to one group from a storage center
Client with time window minimizes total transport cost, risk in transit and average traffic redundancy.
The technical scheme is that by fully considering haulage vehicle type, transport load-carrying, transport routes information, population
Distribution and harmful influence information propose a kind of dynamic load risk in transit assessment models for considering type of vehicle and down time, structure
Build the Model for Multi-Objective Optimization of the isomery harmful influence Transport route planning with time window.According to model characteristics, designs one kind and be based on
The mixing multi-objective Evolutionary Algorithm Solve problems for becoming neighborhood search, finally establish the isomery harmful influence vehicle route with time window
Planing method.
Beneficial effects of the present invention: base of the present invention combination harmful influence transportation characterization in traditional vehicle path planning method
On plinth, the risk factors of harmful influence transport are considered, construct the isomery with time window closer to harmful influence transport actual conditions
Harmful influence transportation route Model for Multi-Objective Optimization, final design one are solved based on becoming neighborhood and search more mixing multi-objective Algorithms
The model；The present invention has the characteristics that open, flexibility and computation complexity are low.
Detailed description of the invention
Fig. 1 is algorithm flow chart；
Fig. 2 is Pareto ordering strategy schematic diagram；
Fig. 3 is route switching crossover operator schematic diagram.
Specific embodiment
Below in conjunction with attached drawing, the invention will be further described.
The present invention the following steps are included:
Step 1: basic data is obtained, including haulage vehicle information, transport routes information, population distribution and harmful influence
Information.
Step 2: the Model for Multi-Objective Optimization of isomery harmful influence Transport route planning of the building with time window.
Model for Multi-Objective Optimization is defined in a complete digraph G=(N, L) by the present invention.N={ 0,1,2 ..., n }
It is the node collection in digraph, node 0 is storage node, and C={ 1,2 ..., n } is client node collection, and L is the arc in digraph
Collection.qi, i ∈ C is the non-negative demand of client i, [Tai, Tbi], i ∈ C is client's time window, TaiRepresent client's acceptable service
Earliest time, TbiRepresent the latest time of client's acceptable service.
If transport column is made of K kind vehicle, Qk, k ∈ K is the maximum load of vehicle k, fck, the fixation of k ∈ Ks vehicle k
Use cost, Vk, k ∈ K is the usable vehicle number of vehicle k, ARk, k ∈ K is the car accident rate of vehicle k.arcij∈ L is represented
Node i, the transportation route between j ∈ N, alijFor node i, transportation range between j ∈ N, tijFor node i, between j ∈ N
Haulage time,K ∈ K is vehicle that vehicle is k from node i ∈ N to the non-negative transportation cost of unit distance of node j ∈ N,
pdijPopulation of exposure density for node i, between j ∈ N.
For closing to reality traffic-operating period, current invention assumes that each vehicle have unified transportation cost of unit distance with
Unlimited available vehicle, that is,K ∈ K and Vk=∞, k ∈ K.In addition, in order to calculate the risk in transit present invention
It is assumed that the density of population and leakage accident probability on each arc are uniform.Model constraint are as follows: each haulage vehicle all must
Storage node must be eventually returned to from storage node；Each customer can only be primary by service in time window, does not allow point
Cut delivery；Haulage vehicle allows in earliest service time TaiIt reaches before, in this case, it must wait until time Tai
Just start to supply for client after arrival；Vehicle does not allow to overload.
Optimization object function is defined as the three-dimensional vector of a multiple target, Minimize (Z)=[Z by the present invention1, Z2,
Z3], wherein Z1It is the objective function always spent for minimizing transport；Z2It is the objective function for minimizing risk in transit；
Z3It is the objective function for minimizing average traffic redundancy.Optimized model needs two groups of variables, wherein SivkIt is first group of change
Amount is defined as the vehicle v ∈ V that type is k ∈ KkReach the time of correspondent node i ∈ C；Second group of variable is decision variable, fixed
Justice is supply of material sequence of the vehicle to correspondent node:
In formula,For decision variable i, j ∈ N；k∈K；V < Vk。
Objective function Z1:
Objective function Z2:
In formula,For the vehicle v ∈ V that type is k ∈ KkIn arc arcijCause the general of harmful influence leakage accident on ∈ L
Rate；CsijFor arc arcijLeakage accident consequence on ∈ L is expressed as in impacted number in one kilometer of spot；For
Calculate the probability of harmful influence accident, the present invention is by corresponding car accident rate, conditional probability, the arc length, vehicle of given accident
Load and waiting time merge as follows:
In formula, PRijFor arc arcijThe conditional probability of accident is given on ∈ L；β and α is established according to harmful influence type
Typical coefficient；For the vehicle v ∈ V that type is k ∈ KkIn arc arcijLoad on ∈ L；It is the vehicle v ∈ V that type is k ∈ KkIn the waiting time of node j ∈ N；Vehicle
Parked state accident rate be lower than driving condition, so τ is a compensation coefficient less than 1.
Objective function Z3:
The common optimization aim of conventional belt time window Vehicle routing problem first is that optimization vehicle fleet size.However, right
In the unlimited band time window isomery Vehicle routing problem of vehicle fleet size, average traffic redundancy is more suitable, because of the optimization mesh
Mark contains the reduction of vehicle fleet size, but not limited to this.
In formula,It is the vehicle v ∈ V that type is k ∈ KkRedundancy journey
Degree；It is all quantity using vehicle.
Model constraint are as follows:
It is primary that above formula indicates that each customer's point can only be accessed by haulage vehicle.
Above formula indicates that all vehicles disapprove overload.
Upper three formula indicates that all vehicles must return to storage from storage node 0 after accessing several non-duplicate clients
Node 0.
Above formula indicates that each client must be met in time window predetermined, and vehicle is allowed to reach in advance,
But do not allow to postpone.
Step 3: model solution is designed based on the mixing multi-objective Evolutionary Algorithm for becoming neighborhood search.
The present invention follows the frame of mixing multi-objective Evolutionary Algorithm, proposes a kind of multiple target solved in harmful influence transport
The mixing multi-objective Evolutionary Algorithm based on change neighborhood search with time window isomery Vehicle routing problem.The algorithm integration
For the special evolutionary operator before the two stages of initial population building to insertion algorithm, for optimizing different target and it is used for office
The change neighborhood search meta-heuristic of portion's search exploitation.Algorithm flow is shown in Fig. 1.
1. initial population constructs
In order to meet the requirement and specification of model constructed by step 2, the present invention before tradition to insertion algorithm on the basis of
It devised before two stages to insertion.Various types of vehicles are all unlimited available.Therefore, in the first stage, using fixation
A group of planes relaxes model constraint.In order to service client as much as possible, a fixed group of planes is formed using the maximum vehicle of load capacity.Band
Time window isomery Vehicle routing problem has been converted to band time window Vehicle routing problem.
Above formula is for defining the initial client's point of each paths, al0iFor node i to the distance of storage node 0；anglei
It is the polar coordinates angle of node i；CsFor also not visited client node collection.Current path has selected initial client's point, forward direction
Insertion is from CsOne customer of middle selection, the customer are minimized in the case where not time-to-violation and capacity-constrained in current routing
Insertion totle drilling cost between each edge.When current routing is for none feasible insertion position of unappropriated client, build
Stand a new routing.When all clients are assigned, the first stage terminates.It is average using the smallest vehicle in second stage
Degree of redundancy fleet replaces a current fixed group of planes, that is, the haulage vehicle type k of each routing will make the redundancy journey of the vehicle
Spend VRvkIt is minimum.Time window Vehicle routing problem is converted into band time window isomery Vehicle routing problem again.
2. Pareto sorts
After completing initial population building, population level is divided using Pareto ranking method.Pareto ranking method uses pa
Tired support dominance relation compares the superiority and inferiority in population between individual.Assuming that population is P, Pareto ranking method needs to calculate every in population
Two parameter n of individual p ∈ PpAnd Sp, npIt is the number of individuals that individual p is dominated in population, SpIt is that individual p is dominated in population
Individual collections.After traversing all individuals in population, all np=0 individual will be divided into the first layer P of population1, for P1
Interior individual l ∈ P1, the individual collections dominated are Sl, traverse SlIn individual m ∈ Sl, execute nm=nm- 1, all nm=0
Individual will be divided into the second layer P of population2, and so on, until entire population is layered.The layering P of serial number 11It is non-
Dominate layer, P1Interior all individuals are the Pareto optimal solution of current population.As shown in Fig. 2, assigning the solution of order 1 prior to assigning order 2
Solution, that is, assignment order be 1 solution remaining solution is superior in any optimization aim.Therefore, the solution for distributing to the 1st grade is non-branch
Match.
3. evolutionary operator
It selects individual to carry out cross and variation operation in current population by binary system prize algorithm, generates new population,
It is layered the smaller individual of serial number, the probability for being selected progress cross and variation operation is bigger.
Since standard evolutionary operator cannot be guaranteed to generate feasible subsolution, it is poor to may cause performance, is searched based on neighborhood is become
The mixing multi-objective Evolutionary Algorithm of rope uses route switching crossover operator and mutation operator is eliminated in routing.
Route switching crossover operator is for designed by optimization aim 1 and optimization aim 2, as shown in figure 3, the operator is permitted
Perhaps other individuals in the good route in a chromosome or gene order and population are shared.Gene order trip to be exchanged
Row cost it is smaller and with time window perfect matching.Optimization aim 3 reflects the average unit cost of unit transport, indicates, the optimization
Target can be averaged by fully phasing out specific route or cancelling client and dispose the smaller vehicle of fixed cost to reduce
Vehicle redundancy.Therefore, excellent to improve using routing elimination mutation operator based on the mixing multi-objective Evolutionary Algorithm for becoming neighborhood search
Change target 3.
4. local search is developed
In order to further improve the individual in population, at random from the layering P of serial number 11Interior one non-domination solution of selection is made
Local search is carried out to become the initial solution of neighborhood search (VNS) meta-heuristic algorithm.The basic thought of VNS is by searching for
Change neighbour structure set in journey systematically to expand search range, obtain locally optimal solution, is then based on this part again
Optimal solution changes neighbour structure set, expands search range, finds the process of another locally optimal solution.So-called " neighborhood " is
Refer to the set of all solutions adjacent with initial solution obtained using a certain method (such as Exchange, Or-opt).VNS is one
Meta-heuristic algorithm of the kind based on local search, performance is particularly splendid when for solving np hard problem, big especially suitable for solving
Scale issue.General meta-heuristic algorithm, such as tabu search algorithm, climbing method and simulated annealing are using single
Neighbour structure scan for.And VNS is to convert neighbour structure according to certain mechanism from an initial solution, is constantly increased
Search range makes algorithm jump out local optimum, to obtain globally optimal solution.
VNS meta-heuristic algorithm mainly includes initialization, shake, local search and mobile four-stage.Algorithm initialization
Stage definitions one initial solution x and one group of neighbour structure Nμ, μ=1,2 ..., μmax.The stage is shaken in algorithm, from initial solution x
The μ neighborhood in randomly choose a solution x '.The algorithm local search stage uses and resets position operator (Relocate) from x '
Generate a new solution x ".If x, " dominating x, x " will replace x to become new initial solution, conversely, algorithm is used in mobile phase
New neighbour structure NμIt repeats the above process.If all neighbour structures cannot all make initial solution be further improved, VNS member
Heuritic approach stops concussion process.
5. algorithm iteration
2. 4. repetition is arrived, until meeting algorithm greatest iteration number.Finally, based on the mixing multi-target evolution for becoming neighborhood search
Algorithm provides one group of Pareto optimal solution for meeting difference preference.
Claims (1)
1. a kind of isomery harmful influence Transport route planning method with time window, it is characterised in that method includes the following steps:
Step 1: obtaining basic data, including haulage vehicle information, transport routes information, population distribution and harmful influence information；
Step 2: the Model for Multi-Objective Optimization of isomery harmful influence Transport route planning of the building with time window；
Model for Multi-Objective Optimization is defined in a complete digraph G=(N, L)；N={ 0,1,2 ..., n } is in digraph
Node collection, node 0 are storage nodes, and C={ 1,2 ..., n } is client node collection, and L is the arc collection in digraph；qiIt is client i
Non- negative demand, i ∈ C, [Tai,Tbi] it is client's time window, TaiRepresent the earliest time of client's acceptable service, TbiRepresent visitor
The latest time of family acceptable service；
If transport column is made of K kind vehicle, QkIt is the maximum load of vehicle k, k ∈ K, fckIt is that the fixed of vehicle k uses cost,
VkIt is the usable vehicle number of vehicle k, ARkIt is the car accident rate of vehicle k；arcijRepresent node i, the transportation route between j,
alijFor node i, transportation range between j, tijFor node i, haulage time between j,It is vehicle that vehicle is k from section
The non-negative transportation cost of unit distance of point i to node j, pdijPopulation of exposure density for node i, between j；
It is assumed that each vehicle has unified transportation cost of unit distance and unlimited available vehicle, that is,
And Vk=∞；Assume that the density of population and leakage accident probability on each arc are uniform simultaneously；Model constraint are as follows: each
Haulage vehicle must all be eventually returned to storage node from storage node；Each customer can only be in time window by service one
It is secondary, do not allow to divide and deliver；Haulage vehicle allows in earliest service time TaiIt reaches before, in this case, it is necessary
Wait until time TaiJust start after arrival as client's supply of material；Vehicle does not allow to overload；
Optimization object function is defined as to the three-dimensional vector of a multiple target, Minimize (Z)=[Z1,Z2,Z3], wherein Z1It is
The objective function always spent for minimizing transport；Z2It is the objective function for minimizing risk in transit；Z3It is for minimizing
The objective function of average traffic redundancy；Optimized model needs two groups of variables, wherein SivkIt is first group of variable, being defined as type is
The vehicle v of k reaches the time of correspondent node i；Second group of variable is decision variable, and it is suitable to the supply of material of correspondent node to be defined as vehicle
Sequence:
In formula,For decision variable i, j ∈ N；k∈K；v<Vk；
Objective function Z1:
Objective function Z2:
In formula,For type be k vehicle v in arc arcijThe upper probability for causing harmful influence leakage accident；CsijFor arc arcijOn
Leakage accident consequence is expressed as in impacted number in one kilometer of spot；In order to calculate the general of harmful influence accident
Rate merges conditional probability, arc length, vehicular load and the waiting time of corresponding car accident rate, given accident as follows:
In formula, PRijFor arc arcijThe conditional probability of upper given accident；β and α is the standard system established according to harmful influence type
Number；For type be k vehicle v in arc arcijOn load；wtjvkBe type be k vehicle v in the waiting of node j
Between；
Objective function Z3:
In formula,It is the degree of redundancy for the vehicle v that type is k；It is all quantity using vehicle；
Model constraint are as follows:
It is primary that above formula indicates that each customer's point can only be accessed by haulage vehicle；
Above formula indicates that all vehicles disapprove overload；
Upper three formula indicates that all vehicles must return to storage node from storage node 0 after accessing several non-duplicate clients
0；
Above formula indicates that each client must be met in time window predetermined, allows vehicle to reach in advance, but not
Allow to postpone；
Step 3: model solution is designed based on the mixing multi-objective Evolutionary Algorithm for becoming neighborhood search；
1. initial population constructs
In order to meet the requirement and specification of model constructed by step 2, two ranks are devised on the basis of before tradition to insertion algorithm
Duan Qianxiang insertion；In the first stage, model constraint is relaxed using a fixed group of planes；In order to service client as much as possible, use
The maximum vehicle of load capacity forms a fixed group of planes；Band time window isomery Vehicle routing problem has been converted to band time window
Vehicle routing problem；
Above formula is for defining the initial client's point of each paths, al0iFor node i to the distance of storage node 0；angleiIt is section
The polar coordinates angle of point i；CsFor also not visited client node collection；Current path has selected initial client's point, forward direction insertion
Method is from CsOne customer of middle selection, the customer minimize in current routing every in the case where not time-to-violation and capacity-constrained
Insertion totle drilling cost between side；When current routing is for none feasible insertion position of unappropriated client, one is established
A new routing；When all clients are assigned, the first stage terminates；In second stage, it is averaged redundancy using the smallest vehicle
Degree fleet replaces a current fixed group of planes, that is, the haulage vehicle type k of each routing will make the degree of redundancy of the vehicle
VRvkIt is minimum；Time window Vehicle routing problem is converted into band time window isomery Vehicle routing problem again；
2. Pareto sorts
After completing initial population building, population level is divided using Pareto ranking method；Pareto ranking method uses Pareto
Dominance relation compares the superiority and inferiority in population between individual；Assuming that population is P, Pareto ranking method needs to calculate each in population
Two parameter n of individual p ∈ PpAnd Sp, npIt is the number of individuals that individual p is dominated in population, SpIt is the individual that individual p is dominated in population
Set；After traversing all individuals in population, all np=0 individual will be divided into the first layer P of population1, for P1Interior
Individual l ∈ P1, the individual collections dominated are Sl, traverse SlIn individual m ∈ Sl, execute nm=nm- 1, all nm=0
Body will be divided into the second layer P of population2, and so on, until entire population is layered；The layering P of serial number 11It is non-dominant
Layer, P1Interior all individuals are the Pareto optimal solution of current population；
3. evolutionary operator
It selects individual to carry out cross and variation operation in current population by binary system prize algorithm, generates new population, be layered
The smaller individual of serial number, the probability for being selected progress cross and variation operation are bigger；
Since standard evolutionary operator cannot be guaranteed to generate feasible subsolution, it may cause that performance is poor, based on becoming neighborhood search
It mixes multi-objective Evolutionary Algorithm and uses route switching crossover operator and routing elimination mutation operator；Route switching crossover operator is
For optimization aim Z1With optimization aim Z2Designed, which allows good route or gene sequence in a chromosome
Column and other individuals in population are shared；Gene order travel cost to be exchanged it is smaller and with time window perfect matching；It is excellent
Change target Z3The average unit cost of unit transport is reflected, which can be by fully phasing out specific route or cancellation
Client simultaneously disposes the smaller vehicle of fixed cost to reduce average traffic redundancy；Therefore, based on the more mesh of mixing for becoming neighborhood search
Mark evolution algorithm eliminates mutation operator using routing to improve optimization aim Z3；
4. local search is developed
In order to further improve the individual in population, at random from the layering P of serial number 11It is interior to select a non-domination solution adjacent as change
The initial solution of domain search meta-heuristic algorithm carries out local search；The basic thought for becoming neighborhood search is by search process
Change neighbour structure set systematically to expand search range, obtain locally optimal solution, is then based on this local optimum again
Solution changes neighbour structure set, expands search range, finds the process of another locally optimal solution；
Becoming neighborhood search meta-heuristic algorithm mainly includes initialization, shake, local search and mobile four-stage；Algorithm is initial
Change stage definitions one initial solution x and one group of neighbour structure Nμ, μ=1,2 ..., μmax；The stage is shaken in algorithm, from initial solution
A solution x ' is randomly choosed in the μ neighborhood of x；The algorithm local search stage is new from the middle generation of x ' one using position operator is reset
Solution x "；If x, " dominating x, x " will replace x to become new initial solution, conversely, algorithm uses new neighborhood knot in mobile phase
Structure NμIt repeats the above process；If all neighbour structures cannot all make initial solution be further improved, VNS meta-heuristic algorithm
Stop concussion process；
5. algorithm iteration
2. 4. repetition is arrived, until meeting algorithm greatest iteration number；Finally, based on the mixing multi-objective Evolutionary Algorithm for becoming neighborhood search
Provide one group of Pareto optimal solution for meeting difference preference.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910222496.XA CN109948855B (en) | 2019-03-22 | 2019-03-22 | Heterogeneous hazardous chemical transportation path planning method with time window |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910222496.XA CN109948855B (en) | 2019-03-22 | 2019-03-22 | Heterogeneous hazardous chemical transportation path planning method with time window |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109948855A true CN109948855A (en) | 2019-06-28 |
| CN109948855B CN109948855B (en) | 2021-08-31 |
Family
ID=67011367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910222496.XA Active CN109948855B (en) | 2019-03-22 | 2019-03-22 | Heterogeneous hazardous chemical transportation path planning method with time window |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109948855B (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110516938A (en) * | 2019-08-14 | 2019-11-29 | 天津市市政工程设计研究院 | A kind of region boycott road demarcation method based on heuritic approach |
| CN110533238A (en) * | 2019-08-22 | 2019-12-03 | 杭州电子科技大学 | Harmful influence vehicle path planning method under two type fuzzy enviroments |
| CN110689307A (en) * | 2019-10-10 | 2020-01-14 | 中国科学技术大学 | Method and system for optimizing article delivery time |
| CN111091329A (en) * | 2019-12-18 | 2020-05-01 | 北京化工大学 | Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances |
| CN111144647A (en) * | 2019-12-25 | 2020-05-12 | 华院数据技术(上海)有限公司 | General vehicle path planning method and system based on large-scale neighborhood search algorithm |
| CN111144568A (en) * | 2019-12-19 | 2020-05-12 | 华南理工大学 | Multi-target urban logistics distribution path planning method |
| CN111260129A (en) * | 2020-01-15 | 2020-06-09 | 深圳大学 | Multi-yard vehicle path planning method and device, computer equipment and storage medium |
| CN111428929A (en) * | 2020-03-23 | 2020-07-17 | 复旦大学 | Path planning system with two-dimensional loading and unloading and time window limitation |
| CN111461447A (en) * | 2020-04-09 | 2020-07-28 | 南方科技大学 | Path planning method and device, electronic equipment and storage medium |
| CN111798172A (en) * | 2020-05-26 | 2020-10-20 | 嘉兴亚航信息技术有限公司 | Dangerous chemical transportation method based on idea of horse racing |
| CN112686458A (en) * | 2021-01-05 | 2021-04-20 | 昆明理工大学 | Optimized scheduling method for multi-vehicle fleet cargo delivery process |
| CN112766614A (en) * | 2021-03-05 | 2021-05-07 | 重庆邮电大学 | Dynamic vehicle path optimization method based on two-stage heuristic algorithm |
| CN112949177A (en) * | 2021-02-26 | 2021-06-11 | 国网北京市电力公司 | Multi-optimization target weighting method and system in construction of comprehensive energy system |
| CN113077106A (en) * | 2021-04-16 | 2021-07-06 | 北京京东振世信息技术有限公司 | Time window-based article transportation method and device |
| CN113393665A (en) * | 2021-05-12 | 2021-09-14 | 杭州电子科技大学 | Planning method for dangerous goods transportation path under uncertain time-varying road network |
| CN113435675A (en) * | 2020-03-23 | 2021-09-24 | 上海浦东建筑设计研究院有限公司 | Road grading method based on mobile hazardous chemical transportation and application thereof |
| CN113570112A (en) * | 2021-06-30 | 2021-10-29 | 南京理工大学 | Optimization algorithm for solving cooperative vehicle path problem with time window |
| CN113780676A (en) * | 2021-09-23 | 2021-12-10 | 河南科技大学 | Method for optimizing distribution path of bottled liquefied gas vehicle |
| CN113919772A (en) * | 2021-09-26 | 2022-01-11 | 山东师范大学 | Time-varying vehicle path planning method and system with time window |
| CN114091722A (en) * | 2021-10-09 | 2022-02-25 | 山东师范大学 | Vehicle route optimization method and system based on hybrid tabu search |
| CN114612049A (en) * | 2022-05-11 | 2022-06-10 | 弥费实业(上海)有限公司 | Path generation method and device, computer equipment and storage medium |
| CN116358594A (en) * | 2023-06-01 | 2023-06-30 | 北京京东乾石科技有限公司 | Vehicle path planning method and device |
| CN117078141A (en) * | 2023-09-08 | 2023-11-17 | 兰州交通大学 | Multi-vehicle type dangerous goods fully-loaded delivery vehicle scheduling method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651043A (en) * | 2016-12-28 | 2017-05-10 | 中山大学 | Intelligent algorithm for solving a multi-objective MDVRPTW (Multi-Depot Vehicle Routing Problem with Time Window) |
| CN107451693A (en) * | 2017-08-02 | 2017-12-08 | 南京工业大学 | Multipoint and multi-target dangerous chemical transport path optimization method |
| US20180129985A1 (en) * | 2016-11-07 | 2018-05-10 | International Business Machines Corporation | Time window selection for vehicle routing problem |
| US20180181894A1 (en) * | 2016-12-02 | 2018-06-28 | Gary Michael Schneider | System and method for developing multi-objective production plans for prouction agriculture |
| WO2018152206A1 (en) * | 2017-02-14 | 2018-08-23 | United Parcel Service Of America, Inc. | Dangerous goods shipping management systems |
| CN109086914A (en) * | 2018-07-12 | 2018-12-25 | 杭州电子科技大学 | Harmful influence vehicle path planning modeling method based on dynamic domino risk |
-
2019
- 2019-03-22 CN CN201910222496.XA patent/CN109948855B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180129985A1 (en) * | 2016-11-07 | 2018-05-10 | International Business Machines Corporation | Time window selection for vehicle routing problem |
| US20180181894A1 (en) * | 2016-12-02 | 2018-06-28 | Gary Michael Schneider | System and method for developing multi-objective production plans for prouction agriculture |
| CN106651043A (en) * | 2016-12-28 | 2017-05-10 | 中山大学 | Intelligent algorithm for solving a multi-objective MDVRPTW (Multi-Depot Vehicle Routing Problem with Time Window) |
| WO2018152206A1 (en) * | 2017-02-14 | 2018-08-23 | United Parcel Service Of America, Inc. | Dangerous goods shipping management systems |
| CN107451693A (en) * | 2017-08-02 | 2017-12-08 | 南京工业大学 | Multipoint and multi-target dangerous chemical transport path optimization method |
| CN109086914A (en) * | 2018-07-12 | 2018-12-25 | 杭州电子科技大学 | Harmful influence vehicle path planning modeling method based on dynamic domino risk |
Non-Patent Citations (2)
| Title |
|---|
| ROJEE PRADHANANGA ETC: ""Bi-objective decision support system for routing and scheduling of hazardous materials"", 《SOCIO-ECONOMIC PLANNING SCIENCES》 * |
| 宋晓宇等: ""一种求解带时间窗车辆路径问题的混合差分进化算法"", 《计算机科学》 * |
Cited By (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110516938B (en) * | 2019-08-14 | 2023-09-05 | 天津市政工程设计研究总院有限公司 | Regional goods exclusion road demarcating method based on heuristic algorithm |
| CN110516938A (en) * | 2019-08-14 | 2019-11-29 | 天津市市政工程设计研究院 | A kind of region boycott road demarcation method based on heuritic approach |
| CN110533238A (en) * | 2019-08-22 | 2019-12-03 | 杭州电子科技大学 | Harmful influence vehicle path planning method under two type fuzzy enviroments |
| CN110533238B (en) * | 2019-08-22 | 2022-08-26 | 杭州电子科技大学 | Method for planning paths of dangerous chemical vehicles in two-type fuzzy environment |
| CN110689307A (en) * | 2019-10-10 | 2020-01-14 | 中国科学技术大学 | Method and system for optimizing article delivery time |
| CN110689307B (en) * | 2019-10-10 | 2022-09-09 | 中国科学技术大学 | Method and system for optimizing article delivery time |
| CN111091329A (en) * | 2019-12-18 | 2020-05-01 | 北京化工大学 | Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances |
| CN111091329B (en) * | 2019-12-18 | 2022-12-16 | 北京化工大学 | Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemicals |
| CN111144568A (en) * | 2019-12-19 | 2020-05-12 | 华南理工大学 | Multi-target urban logistics distribution path planning method |
| CN111144568B (en) * | 2019-12-19 | 2023-06-20 | 华南理工大学 | Multi-target city logistics distribution path planning method |
| CN111144647A (en) * | 2019-12-25 | 2020-05-12 | 华院数据技术(上海)有限公司 | General vehicle path planning method and system based on large-scale neighborhood search algorithm |
| CN111260129B (en) * | 2020-01-15 | 2023-04-07 | 深圳大学 | Multi-yard vehicle path planning method and device, computer equipment and storage medium |
| CN111260129A (en) * | 2020-01-15 | 2020-06-09 | 深圳大学 | Multi-yard vehicle path planning method and device, computer equipment and storage medium |
| CN113435675B (en) * | 2020-03-23 | 2024-04-16 | 上海浦东建筑设计研究院有限公司 | Road grading method based on mobile dangerous chemical transportation and application thereof |
| CN113435675A (en) * | 2020-03-23 | 2021-09-24 | 上海浦东建筑设计研究院有限公司 | Road grading method based on mobile hazardous chemical transportation and application thereof |
| CN111428929A (en) * | 2020-03-23 | 2020-07-17 | 复旦大学 | Path planning system with two-dimensional loading and unloading and time window limitation |
| CN111461447A (en) * | 2020-04-09 | 2020-07-28 | 南方科技大学 | Path planning method and device, electronic equipment and storage medium |
| CN111461447B (en) * | 2020-04-09 | 2023-04-18 | 南方科技大学 | Path planning method and device, electronic equipment and storage medium |
| CN111798172A (en) * | 2020-05-26 | 2020-10-20 | 嘉兴亚航信息技术有限公司 | Dangerous chemical transportation method based on idea of horse racing |
| CN111798172B (en) * | 2020-05-26 | 2023-07-18 | 嘉兴亚航信息技术有限公司 | Dangerous chemical transportation method based on field-contraindicated horse racing concept |
| CN112686458B (en) * | 2021-01-05 | 2023-03-07 | 昆明理工大学 | Optimized dispatching method for multi-vehicle fleet delivery process |
| CN112686458A (en) * | 2021-01-05 | 2021-04-20 | 昆明理工大学 | Optimized scheduling method for multi-vehicle fleet cargo delivery process |
| CN112949177B (en) * | 2021-02-26 | 2024-03-08 | 国网北京市电力公司 | Multi-optimization target weighting method and system in comprehensive energy system construction |
| CN112949177A (en) * | 2021-02-26 | 2021-06-11 | 国网北京市电力公司 | Multi-optimization target weighting method and system in construction of comprehensive energy system |
| CN112766614A (en) * | 2021-03-05 | 2021-05-07 | 重庆邮电大学 | Dynamic vehicle path optimization method based on two-stage heuristic algorithm |
| CN112766614B (en) * | 2021-03-05 | 2023-09-15 | 重庆邮电大学 | Dynamic vehicle path optimization method based on two-stage heuristic algorithm |
| CN113077106A (en) * | 2021-04-16 | 2021-07-06 | 北京京东振世信息技术有限公司 | Time window-based article transportation method and device |
| CN113393665A (en) * | 2021-05-12 | 2021-09-14 | 杭州电子科技大学 | Planning method for dangerous goods transportation path under uncertain time-varying road network |
| CN113570112A (en) * | 2021-06-30 | 2021-10-29 | 南京理工大学 | Optimization algorithm for solving cooperative vehicle path problem with time window |
| CN113570112B (en) * | 2021-06-30 | 2024-04-16 | 南京理工大学 | Optimization algorithm for solving vehicle path problem with time window considering cooperation |
| CN113780676B (en) * | 2021-09-23 | 2023-06-23 | 河南科技大学 | Method for optimizing distribution path of bottled liquefied gas vehicle |
| CN113780676A (en) * | 2021-09-23 | 2021-12-10 | 河南科技大学 | Method for optimizing distribution path of bottled liquefied gas vehicle |
| CN113919772A (en) * | 2021-09-26 | 2022-01-11 | 山东师范大学 | Time-varying vehicle path planning method and system with time window |
| CN114091722A (en) * | 2021-10-09 | 2022-02-25 | 山东师范大学 | Vehicle route optimization method and system based on hybrid tabu search |
| CN114612049A (en) * | 2022-05-11 | 2022-06-10 | 弥费实业(上海)有限公司 | Path generation method and device, computer equipment and storage medium |
| CN116358594A (en) * | 2023-06-01 | 2023-06-30 | 北京京东乾石科技有限公司 | Vehicle path planning method and device |
| CN116358594B (en) * | 2023-06-01 | 2023-11-03 | 北京京东乾石科技有限公司 | Vehicle path planning method and device |
| CN117078141A (en) * | 2023-09-08 | 2023-11-17 | 兰州交通大学 | Multi-vehicle type dangerous goods fully-loaded delivery vehicle scheduling method |
| CN117078141B (en) * | 2023-09-08 | 2024-07-19 | 兰州交通大学 | Multi-vehicle type dangerous goods fully-loaded delivery vehicle scheduling method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109948855B (en) | 2021-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109948855A (en) | A kind of isomery harmful influence Transport route planning method with time window | |
| Barco et al. | Optimal routing and scheduling of charge for electric vehicles: A case study | |
| Pourghader Chobar et al. | A novel multi-objective model for hub location problem considering dynamic demand and environmental issues | |
| Li-ying et al. | Multiple charging station location-routing problem with time window of electric vehicle. | |
| CN112146673B (en) | Expressway multipoint collaborative rescue path planning method based on improved ant colony algorithm | |
| Huang et al. | Large scale real-time ridesharing with service guarantee on road networks | |
| Balseiro et al. | An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows | |
| CN109919365A (en) | A kind of electric vehicle paths planning method and system based on double decision searches | |
| Zhou et al. | Multi-objective optimization of electric vehicle routing problem with battery swap and mixed time windows | |
| Yousefikhoshbakht et al. | A mixed integer programming formulation for the heterogeneous fixed fleet open vehicle routing problem | |
| Ashouri et al. | A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery | |
| CN110322066A (en) | A kind of collaborative vehicle method for optimizing route based on shared carrier and shared warehouse | |
| CN113848970A (en) | Multi-target collaborative path planning method for vehicle and unmanned aerial vehicle | |
| CN115062868B (en) | Pre-polymerization type vehicle distribution path planning method and device | |
| Wallar et al. | Optimizing multi-class fleet compositions for shared mobility-as-a-service | |
| Minis et al. | Real-time management of vehicle breakdowns in urban freight distribution | |
| CN114611794A (en) | Vehicle-machine cooperative pick-and-place path optimization method and system based on sub-heuristic algorithm | |
| Huang et al. | Monte carlo tree search for dynamic bike repositioning in bike-sharing systems | |
| Noori et al. | High-level relay hybrid metaheuristic method for multi-depot vehicle routing problem with time windows | |
| Bouanane et al. | Multi-depots vehicle routing problem with simultaneous delivery and pickup and inventory restrictions: Formulation and resolution | |
| Alinaghian et al. | A bi-objective mathematical model for two-dimensional loading time-dependent vehicle routing problem | |
| Júnior et al. | A multiple ant colony system with random variable neighbourhood descent for the vehicle routing problem with time windows | |
| Wang et al. | Competitive decision algorithm for the split vehicle routing problem with simultaneous pickup and delivery and time windows | |
| KR101462337B1 (en) | Method and apparatus of scheduling electric vehicle relocation using genetic algorithms | |
| Oonsrikaw et al. | Modified ant colony optimization algorithm for multiple-vehicle traveling salesman problems |
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 |