CN110533238A - Harmful influence vehicle path planning method under two type fuzzy enviroments - Google Patents

Harmful influence vehicle path planning method under two type fuzzy enviroments Download PDF

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CN110533238A
CN110533238A CN201910776617.5A CN201910776617A CN110533238A CN 110533238 A CN110533238 A CN 110533238A CN 201910776617 A CN201910776617 A CN 201910776617A CN 110533238 A CN110533238 A CN 110533238A
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蒋鹏
门金坤
许欢
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Hangzhou Electronic Science and Technology University
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Abstract

The invention discloses a kind of harmful influence vehicle path planning methods under two type fuzzy enviroments.Step of the present invention is the uncertainty for risk in transit, defines harmful influence haulage vehicle path planning model.Target is the transit route of determining risk minimization.Due to the mobility of personnel, the present invention introduces two type fuzzy variables on the basis of Traditional Transportation risk model, constructs Chance-constrained Model and its corresponding deterministic type of equal value according to confidence level method.For model characteristics, a kind of simulated annealing is devised.The SAA of proposition reaches global optimal solution it is therefore possible to jump out the optimal solution of this part to receive a solution than current solution difference with certain probability.The method of the present invention has the characteristics that open, flexibility and computation complexity are low.

Description

Harmful influence vehicle path planning method under two type fuzzy enviroments
Technical field
The invention belongs to harmful influence risk management fields, are related to automatic technology, more particularly to a kind of two patterns paste The chance constrained programming method of harmful influence Vehicle routing problem under environment.
Background technique
With the rapid industrial development in our country, harmful influence has become essential important composition portion in industrial production Point.It is any to use relevant activity to it due to the special nature of harmful influence, all along with huge risk.It is transported in harmful influence During defeated, the probability of harmful influence leakage accident caused by ordinary traffic accident is high, and such harmful influence shipping accident can cause Extensive casualties, environmental degradation and property loss.
Due to industrial development need, harmful influence risk in transit be it is unavoidable, can only be arranged by a series of risk managements It applies reduction accident probability and damage sequence, harmful influence Transport route planning is one of main risk in transit management measure.It is many Harmful influence transportation problem is considered as a certain problem by traditional method, has ignored the uncertainty of risk in transit, thus away from From practical application, there is also very big gaps.In addition, being asked using exact algorithms such as branch and bound method, cutting plane algorithm and PILP Dimension disaster is easy to produce when solving such Large-scale Optimization Problems, therefore, harmful influence Transport route planning is extremely difficult.
Summary of the invention
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, the smallest fleet's transit route of risk in transit is determined.
Since uncertainty will lead to the significant difference of risk in transit, the technical scheme is that transporting road in harmful influence Two type fuzzy variables are introduced in diameter plan model, by confidence level method, propose that a kind of chance constrained programming method will not known Property model conversion be its deterministic type of equal value.According to model characteristics, a kind of simulated annealing Solve problems are designed, are finally established The chance constrained programming method of harmful influence Vehicle routing problem under two type fuzzy enviroments.
The present invention specifically includes the following steps:
Step 1: basic data is obtained, including haulage vehicle information, transport routes information, population distribution and harmful influence Information.
Step 2: building harmful influence vehicle transport path planning model.
Harmful influence Transport route planning model is defined in a complete digraph G=(N, L).N=0,1,2 ..., N } it is node collection in digraph, node 0 is storage node, and C={ 1,2 ..., n } is client node collection, qiIt is node i to dangerization The demand of product；L is the arc collection in digraph, if arcij∈ L is the arc of connecting node i and node j, dijFor arcijArc length； K={ k1, k2..., k|K|It is haulage vehicle collection, each car k ∈ K has fixed load capacity limitation Qk。
1. calculating risk in transit
According to " probability-consequence " frame, risk in transit is defined as the product of accident probability and damage sequence, any two section Risk in transit between point is as follows:
Rij=Pij×Csij, i, j ∈ N
In formula, RijIt is node i, the risk in transit between j ∈ N, PijIt is arc arcijOn accident probability；CsijIt is arc arcij Damage sequence on ∈ L.
2. introducing trapezoidal two type fuzzy variable of section
The density of population is set as a trapezoidal two type fuzzy variable of sectionIt is as follows:
In formula,WithFor two trapezoidal type fuzzy variables,Upper and lower layer subordinating degree function be respectively as follows:With It is upper The parameter of layer subordinating degree function,For the parameter of lower layer's subordinating degree function,WithRespectivelyWith's It is high.
Then, arc arcijOn risk in transit calculate it is as follows:
In formula,Risk in transit to introduce the node i after trapezoidal two type fuzzy variable of section, between j ∈ N.
3. harmful influence vehicle transport path planning model
Model decision variable is as follows:
Model objective function is as follows:
Model constraint is as follows:
Haulage vehicle must be eventually returned to storage node from storage node:
The demand of each customer will be satisfied, and can only be primary by service:
Vehicle cannot overload:
The haulage vehicle quantity used is no more than | K |:
4. Chance-Constrained Programming Model
According to trapezoidal two type fuzzy variable of sectionUpper and lower layer subordinating degree function, will be above-mentioned using confidence level method Model conversion is two Chance-Constrained Programming Models.
For upper layer subordinating degree function,Chance-Constrained Programming Model is such as Under:
For lower layer's subordinating degree function,Chance-Constrained Programming Model is such as Under:
In formula, ZUAnd ZLFor the objective function of Chance-Constrained Programming Model, αUAnd αLIt is predefined level of confidence；It is existing Note, Equivalent constraint is as follows:
In formula,WithIt is defined as follows:
The deterministic models of equal value of above-mentioned Chance-Constrained Programming Model are as follows:
It finally, can be by former harmful influence vehicle transport path planning model conversation are as follows:
Step 3: model solution
According to model characteristics, determined using the equivalence that simulated annealing solves harmful influence vehicle transport path planning model Type.Wherein simulated annealing is by an initial solution, new explanation is searched in the neighborhood of initial solution using neighbor operator. Metropolis criterion is used to judge whether new explanation can replace current solution.If current solution is better than optimal solution, using current solution Replace optimal solution.When reaching maximum internal number of iterations, Current Temperatures will decline according to predefined detemperature rate.Constantly repeat It states process and stops iteration standard until meeting.
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 uncertainty of risk in transit is considered, construct and endanger under the two type fuzzy enviroments closer to harmful influence transport actual conditions The chance constrained programming method of change product Vehicle routing problem, the present invention have open, flexibility and computation complexity The features such as low.
Detailed description of the invention
Fig. 1 is algorithm flow chart；
Fig. 2 is initial solution organigram；
Fig. 3 is Swap operator schematic diagram；
Fig. 4 is Reversion operator schematic diagram；
Fig. 5 is Insertion operator schematic diagram.
Specific embodiment
Below in conjunction with attached drawing, the invention will be further described.
The method of the present invention is specifically:
Step 1: basic data is obtained, including haulage vehicle information, transport routes information, population distribution and harmful influence Information.
Step 2: building harmful influence vehicle transport path planning model.
Harmful influence Transport route planning model is defined in a complete digraph G=(N, L) by the present invention.N=0, 1,2 ..., n } it is node collection in digraph, node 0 is storage node, and C={ 1,2 ..., n } is client node collection, qi, i ∈ C is Demand of the node i to harmful influence；L is the arc collection in digraph, arcij∈ L is node i, the transportation route between j, dijFor arcijArc length；K={ k1, k2..., k|K|It is haulage vehicle collection, each car k ∈ K has fixed load capacity limitation Qk.Mould Type constraint is as follows:
Haulage vehicle must be eventually returned to storage node from storage node；
The demand of each customer will be satisfied；
Each customer can only be primary by service；
Vehicle cannot overload；
The haulage vehicle quantity used is no more than | K |.
2-1, risk in transit is calculated
According to " probability-consequence " frame, risk in transit is defined as the product of accident probability and damage sequence, any two section Risk in transit between point is as follows:
Rij=Pij×Csij, i, j ∈ N
In formula, RijIt is node i, the risk in transit between j ∈ N, PijIt is arc arcijAccident probability on ∈ L；CsijIt is arc arcijDamage sequence on ∈ L.
Arc arcijAccident probability P on ∈ LijCalculation method is as follows:
Pij=ARij×Prij×dij, i, j ∈ N
In formula, ARijIt is arc arcijAccident rate on ∈ L；PrijIt is arc arcijHarmful influence leakage accident probability on ∈ L.
Arc arcijDamage sequence severity calculation method on ∈ L is as follows:
In formula, pdijIt is the density of population on spot periphery；It is accident impact radius, is typically set to 1 kilometer.
2-2, two type fuzzy variables are introduced
Due to the mobility of population, the density of population is set as a trapezoidal two types fuzzy variable by the present inventionIt is as follows:
In formula,WithFor two trapezoidal type fuzzy variables,Upper and lower layer subordinating degree function be respectively as follows:With It is upper The parameter of layer subordinating degree function,For the parameter of lower layer's subordinating degree function,WithRespectivelyWith Height.
Therefore, arc arcijRisk in transit on ∈ L calculates as follows:
2-3, harmful influence vehicle transport path planning model
Model decision variable is as follows:
Model objective function is as follows:
Model constraint is as follows:
Haulage vehicle must be eventually returned to storage node from storage node
The demand of each customer will be satisfied, and can only be primary by service
Vehicle cannot overload；
The haulage vehicle quantity used is no more than | K |
∑k∈K∑j∈Cx0jk≤|K|
2-4, Chance-Constrained Programming Model
According to two type fuzzy variable of sectionBoundary subordinating degree function, this work use confidence level method will be above-mentioned Model conversion is 2 Chance-Constrained Programming Models.
For upper layer subordinating degree function,Chance-Constrained Programming Model is such as Under:
For lower layer's subordinating degree function,Chance-Constrained Programming Model is such as Under:
In formula, αUAnd αLIt is predefined level of confidence；Now remember, Equivalent constraint is as follows:
In formula,WithIt is defined as follows:
The deterministic models of equal value of above-mentioned Chance-constrained Model are as follows:
It finally, can be by former harmful influence vehicle transport path planning model conversation are as follows:
Step 3: model solution
According to model characteristics, the invention proposes an effective simulated annealings to solve harmful influence vehicle transport path The deterministic type of equal value of plan model.Algorithm flow chart such as Fig. 1.The simulated annealing proposed is by an initial solution, adopt New explanation is searched in the neighborhood of initial solution with neighbor operator.Metropolis criterion is used to judge whether new explanation can replace currently Solution.If current solution is better than optimal solution, optimal solution is replaced using current solution.When reaching maximum internal number of iterations, Current Temperatures It will decline according to predefined detemperature rate.It can be repeated the above process after algorithm until meeting and stop iteration standard.
1. initial solution constructs
Initial solution is generated by a random sequence.The construction of initial solution is illustrated with a specific example.Such as Fig. 2 institute Show, the transport column of three vehicles needs to service 15 clients, that is, K={ k1, k2, k3, N={ 0,1,2 ..., 15 } and C=1, 2 ..., 15 }.One group of random sequence are as follows: [12,1,5,11,16,2,10,4,9,3,8,17,6,13,14,7,15].Now with stochastic ordering Initial solution is constituted for breakpoint greater than the number of customer quantity in column.
2. objective function
In order to accelerate convergence speed of the algorithm, it is as follows that the present invention introduces penalty factor in objective function:
In formula, δkIndicate vehicle k overload degree；Pf is all overload of vehicle degree.
Objective function with penalty factor is as follows:
Z*=Zr (1+pc*pf)
In formula, pc is penalty coefficient, and pc is bigger, and algorithm is smaller to the tolerance of infeasible solution.
3. neighbor operator
According to model characteristics, present invention employs three kinds of neighbor operators to generate new explanation.
Swap operator: random exchange solves two digital positions in corresponding sequence, as shown in figure 3, solution χ=0, 12,1,5,11,0 }, { 0,2,10,4,9,3,8,0 }, { 0,6,13,14,7,15,0 } } in ' 4 ' and ' 7 ' position calculated by Swap Son exchanges, then new explanation χnew={ { 0,12,1,5,11,0 }, { 0,2,10,0 }, { 0,9,3,8,4,6,13,14,7,15,0 } }.
Reversion operator: the random areas in converse solution corresponding sequence, as shown in figure 4, solution χ=0,12,1,5, 11,0 }, { 0,2,10,4,9,3,8,0 }, { 0,6,13,14,7,15,0 } } [4,9,3,8,17] region quilt in corresponding sequence Reversion operator is converse, then new explanation χnew={ 0,12,1,5,11,0 }, { 0,2,10,0 }, 0,8,3,9,4,6,13,14, 7,15,0 } }.
Insertion operator: the number solved in corresponding sequence is inserted into another position, such as Fig. 5 at random, solves χ ' 4 ' in the corresponding sequence of={ { 0,12,1,5,11,0 }, { 0,2,10,4,9,3,8,0 }, { 0,6,13,14,7,15,0 } } are weighed It is newly inserted into behind ' 17 ', then χnew={ 0,12,1,5,11,0 }, { 0,2,10,9,3,8,0 }, 0,4,6,13,14,7, 15,0 } }.
4. Metropolis criterion
Metropolis criterion is for determining that neighbor operator is applied to new explanation χ caused by current solution χnewIt is whether replaceable Current solution, note Δ are the objective function increment between current solution and new explanation, Δ=Z*(χnew)-Z*(χ).For minimization problem, Δ < 0 shows new explanation χnewBetter than current solution χ, then using the current solution of new explanation replacement.If Δ > 0, the probability that current solution is replaced For exp (- (Δ/T)), T is algorithm Current Temperatures.

Claims (3)

1. the harmful influence vehicle path planning method under two type fuzzy enviroments, which is characterized in that this method specifically includes following step It is rapid:
Step 1: obtaining basic data, believe including haulage vehicle information, transport routes information, population distribution and harmful influence Breath；
Step 2: building harmful influence vehicle transport path planning model；
Harmful influence Transport route planning model is defined in a complete digraph G=(N, L)；If 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, qiIt is node i to harmful influence Demand；L is the arc collection in digraph, if arcij∈ L is d between connecting node i and the arc of node jijFor arcijArc It is long；K={ k1, k2..., k|K|It is haulage vehicle collection, each car k ∈ K has fixed load capacity limitation Qk；
1. calculating risk in transit
According to " probability-consequence " frame, risk in transit is defined as the product of accident probability and damage sequence, between any two node Risk in transit it is as follows:
Rij=Pij×Csij, i, j ∈ N
In formula, RijIt is node i, the risk in transit between j ∈ N, PijIt is arc arcijOn accident probability；CsijIt is arc arcijOn ∈ L Damage sequence；
2. introducing trapezoidal two type fuzzy variable of section
The density of population is set as a trapezoidal two type fuzzy variable of sectionIt is as follows:
In formula,WithFor two trapezoidal type fuzzy variables,Upper and lower layer subordinating degree function be respectively as follows:With It is upper The parameter of layer subordinating degree function,For the parameter of lower layer's subordinating degree function,WithRespectivelyWith's It is high；
Then, arc arcijOn risk in transit calculate it is as follows:
In formula,Risk in transit to introduce the node i after trapezoidal two type fuzzy variable of section, between j ∈ N；
3. harmful influence vehicle transport path planning model
Model decision variable is as follows:
Model objective function is as follows:
Model constraint is as follows:
Haulage vehicle must be eventually returned to storage node from storage node:
The demand of each customer will be satisfied, and can only be primary by service:
Vehicle cannot overload:
The haulage vehicle quantity used is no more than | K |:
∑k∈K∑j∈Cx0jk≤|K|
4. Chance-Constrained Programming Model
According to trapezoidal two type fuzzy variable of sectionUpper and lower layer subordinating degree function, using confidence level method by above-mentioned model Be converted to two Chance-Constrained Programming Models；
For upper layer subordinating degree function,Chance-Constrained Programming Model is as follows:
For lower layer's subordinating degree function,Chance-Constrained Programming Model is as follows:
In formula, ZUAnd ZLFor the objective function of Chance-Constrained Programming Model, αUAnd αLIt is predefined level of confidence；Now remember, Equivalent constraint is as follows:
In formula,WithIt is defined as follows:
The deterministic models of equal value of above-mentioned Chance-Constrained Programming Model are as follows:
It finally, can be by former harmful influence vehicle transport path planning model conversation are as follows:
Step 3: model solution
According to model characteristics, the deterministic type of equal value of harmful influence vehicle transport path planning model is solved using simulated annealing； Wherein simulated annealing is by an initial solution, new explanation is searched in the neighborhood of initial solution using neighbor operator； Metropolis criterion is used to judge whether new explanation can replace current solution；If current solution is better than optimal solution, using current solution Replace optimal solution；When reaching maximum internal number of iterations, Current Temperatures will decline according to predefined detemperature rate；Constantly repeat It states process and stops iteration standard until meeting.
2. the harmful influence vehicle path planning method under two types fuzzy enviroment according to claim 1, it is characterised in that: arc arcijOn accident probability PijIt calculates as follows:
Pij=ARij×Prij×dij
In formula, ARijIt is arc arcijOn accident rate；PrijIt is arc arcijOn harmful influence leakage accident probability.
3. the harmful influence vehicle path planning method under two types fuzzy enviroment according to claim 1, it is characterised in that: arc arcijDamage sequence Cs on ∈ LijIt calculates as follows:
In formula, pdijIt is the density of population on spot periphery；It is accident impact radius.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
CN113516323A (en) * 2021-09-15 2021-10-19 山东蓝湾新材料有限公司 Transportation path recommendation method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103761588A (en) * 2014-02-18 2014-04-30 张家港美核电子科技有限公司 Hazardous chemical substance transport scheduling method based on multi-target modeling optimization
CN104933474A (en) * 2015-05-24 2015-09-23 北京化工大学 Fuzzy bi-level optimization method for dangerous chemical transportation
DE102014215473A1 (en) * 2014-08-05 2016-02-11 Bayerische Motoren Werke Aktiengesellschaft Control device, vehicle and method
CN107451693A (en) * 2017-08-02 2017-12-08 南京工业大学 The harmful influence transportation route optimization method of multiple spot multiple target
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
CN109948855A (en) * 2019-03-22 2019-06-28 杭州电子科技大学 A kind of isomery harmful influence Transport route planning method with time window

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103761588A (en) * 2014-02-18 2014-04-30 张家港美核电子科技有限公司 Hazardous chemical substance transport scheduling method based on multi-target modeling optimization
DE102014215473A1 (en) * 2014-08-05 2016-02-11 Bayerische Motoren Werke Aktiengesellschaft Control device, vehicle and method
CN104933474A (en) * 2015-05-24 2015-09-23 北京化工大学 Fuzzy bi-level optimization method for dangerous chemical transportation
CN107451693A (en) * 2017-08-02 2017-12-08 南京工业大学 The harmful influence transportation route optimization method of multiple spot multiple target
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
CN109948855A (en) * 2019-03-22 2019-06-28 杭州电子科技大学 A kind of isomery harmful influence Transport route planning method with time window

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
CN113516323A (en) * 2021-09-15 2021-10-19 山东蓝湾新材料有限公司 Transportation path recommendation method
CN113516323B (en) * 2021-09-15 2021-11-30 山东蓝湾新材料有限公司 Transportation path recommendation method

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