CN105260785A - Logistic distribution vehicle path optimizing method based on improved Cuckoo algorithm - Google Patents
Logistic distribution vehicle path optimizing method based on improved Cuckoo algorithm Download PDFInfo
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Abstract
The invention relates to a logistic distribution vehicle path optimizing method based on an improved Cuckoo algorithm, which is characterized by comprising the following steps of 1, setting the parameters of the improved Cuckoo algorithm; 2, initializing population and calculating a fitness value, wherein the population adopts a uniform distribution function to generate size parasitic nests with an nd-dimensional searching space range of [1, carnumber], calculation of the fitness value comprises firstly adopting a 2-opt algorithm to locally optimize the path in each route and solving the path value of each optimized route by a segmented penalty function; 3, executing the levy migration operation of the parasitic nests; 4, executing the operation that the parasitic nests are detected; 5, executing the mutation operation of the parasitic nests; and 6, dynamically adjusting detection probabilities. According to the logistic distribution vehicle path optimizing method based on the improved Cuckoo algorithm, combination of the improved Cuckoo algorithm and the 2-opt algorithm is applied to solving the problem about the logistic distribution vehicle path, a novel available and effective solution is provided for solving the problem about the optimization of logistic vehicle distribution, and methods for solving the problem about optimizing the logistic distribution path are enriched.
Description
Technical field
The present invention relates to a kind of route optimizing method for logistic distribution vehicle, being specifically related to a kind of route optimizing method for logistic distribution vehicle based on improving cuckoo algorithm.
Background technology
Along with the develop rapidly of modern economy and network technology, material flow industry has become " Third profit source " of modern enterprise.The logistics total expenses of China in 2011, up to 8.5 trillion yuan, accounts for 17.8% of GDP.In the links of logistics, transport distribution cost accounts for about 60% of Logistics Total Cost, and too high logistics cost, constrains the development of national economy, also weakens the market competitiveness of enterprise simultaneously.Logistic distribution vehicle routing problem (VRP) is a link of most critical during logistics distribution is optimized.And logistic distribution vehicle routing problem is a typical NP-hard problem, proposed in nineteen fifty-nine by DantZig and Ramser, how to realize in main research logistics vehicles delivery process meeting customer need and other constraint condition (as: vehicle maximum load, vehicle is maximum forms distance etc.) under make the Optimum cost of vehicle delivery, as: shortest path, the targets such as expense is minimum.This problem has become the study hotspot problem in operational research and Combinatorial Optimization field.
In recent years, mainly concentrate on the various heuritic approach of employing to the research of logistics vehicles Distribution path problem to solve.Wang Tiejun etc., by the advantage in conjunction with the ergodicity of chaos and the rapidity of population, propose a kind of Chaos particle swarm optimization algorithm for Optimization of Physical Distribution Routing Problem; The improved adaptive GA-IAGA that official east etc. proposes solves the method for logistics distribution optimization problem, thus provides the effective way that solves associated optimization problem; Wu Yuechun proposes method TSP question particle cluster algorithm being applied to the optimization of logistics distribution routing problem; Wang Huadong, Li Wei etc. propose a kind of logistics distribution method for optimizing route of particle cluster algorithm.The application of traditional intelligence group algorithms such as genetic algorithm, particle cluster algorithm and ant group algorithm in logistics distribution optimization problem is all rested on to the research of logistics distribution optimization problem.But solving in Vehicle Routing, adopt single algorithm to be often easily absorbed in local optimum, cause optimization precision low.Cuckoo algorithm is Levy search pattern due to what adopt, its realize simple, need that parameters is few, low optimization accuracy and speed of convergence be all better than particle cluster algorithm and genetic algorithm.
Summary of the invention
Problem to be solved by this invention adopts a kind of method based on improving cuckoo algorithm realization logistic distribution vehicle routing problem.Its technical scheme is:
Based on a method for solving for the improvement Vehicle Routing of cuckoo algorithm, comprise the steps:
Step 1: the parameter arranging algorithm, arranges population scale size, the quantity nd of services client, and vehicle number carnumber needed for current solution problem, arranges the load CarrayCarCan of car, the probability of detection of bird egg in parasitic nest
, algorithm search spatial dimension [1, carnumber], population iterations Max_iter, iteration count N_iter=1;
Step 2: initialization population also calculates fitness value, adopts uniformly distributed function to produce size nd at random and ties up the parasitic nest that search volume scope is [1, carnumber], remember that i-th parasitic nest position is
, and (namely each parasitic nest ceil function is rounded
), to ensure that each client o'clock is served by a car, and calculate the fitness value of its correspondence
;
Step 3: the levy migration operation performing parasitic nest, adopts
update mode produces new parasitic nest position, and compares with the position before performing levy migration, and the good parasitic nest of chosen position remains into the next generation.
Step 4: perform parasitic nest and be found operation, produce random number
if,
, disturbance is carried out to this parasitic nest and produces new parasitic nest, and the position corresponding with before disturbance compares, the good parasitic nest of retention position.
Step 5: perform parasitic nest mutation operation, adopts
mutation operation is carried out to parasitic nest.
Step 6: dynamic conditioning probability of detection, adopts
the parasitic nest of dynamic conditioning is found probability
, interim
represent the probability of detection carrying out the t time iteration and be;
,
be respectively maximum probability of detection and minimum probability of detection;
for maximum iteration time;
for current iteration number of times.The method guarantees the initial stage at algorithm, due to distant from optimal value of individuality, needs larger position to change speed; In the later stage of algorithm, because most of disaggregation is around optimal location, less position is needed to change speed.
Step 7: the optimal location and the fitness thereof that retain each search, judge whether Search Results meets the requirements, if meet
, then go to Step8, otherwise go to Step3.
Step 8: export optimum parasitic nest position and corresponding fitness value thereof, obtain optimum logistic distribution vehicle route scheme.
Further, the calculation procedure of fitness value is as follows:
Step 1: determine vehicle required for any one logistics distribution scheme x by roadindex=unique (x), determines vehicle number car_index by [row, car_index]=size (roadindex), arranges fitness value
, the optimum Distribution path total_road of Current protocols;
Step 2: determine client's point that each car is served by subRout=find (Zx==roadindex (i)), and the Distribution path subRout forming that Current vehicle formed according to dispensing numbering size;
Step 3: adopt 2-opt algorithm to be optimized to every bar Distribution path subRout, thus obtain the optimal path road of single vehicle dispensing, and record this vehicle delivery path order: total_road (subRout)=road;
Step 4: calculate Current vehicle optimal path path length that road experiences;
Step 5: the customer demand sum sum_q calculating Current vehicle optimal path road process;
Step 6: adopt segmentation penalty function method punishment load to exceed the maximum load of Current vehicle, if punishment amount is: fp;
Step 7: the fitness value calculating Current vehicle optimal path road is:
,
;
Step 8: judge
if satisfy condition, then go to step 2, otherwise go to step 9;
Step 9: the fitness value of output scheme x
, and the optimum Distribution path total_road. of Current protocols
This patent adopts the cuckoo algorithmic rule vehicle improved to send with charge free and divide into groups, and then adopts 2-opt algorithm to be optimized to obtain an optimum dispensing circuit in every group to every bar circuit.
This patent provides a kind of feasible and effective solution for solving logistics vehicles dispensing optimization problem, has enriched the method for Optimization of Physical Distribution Routing Problem.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of this Algorithm for Solving Vehicle Routing;
Fig. 2 is the process flow diagram solving the corresponding fitness value of each logistics distribution scheme.
Embodiment
Do to set forth in detail further to technical scheme of the present invention below by accompanying drawing and instantiation, but protection scope of the present invention is not limited to this.
The implementation method solved based on the Vehicle Routing improving cuckoo algorithm of the present embodiment.Refer to Fig. 1, Fig. 1 is the schematic flow sheet of this algorithm, comprises the following steps:
1. the parameter of algorithm is set.Arrange population scale size, the quantity nd of services client, vehicle number carnumber needed for current solution problem, arranges the load CarrayCarCan of car.In basic cuckoo algorithm, in parasitic nest, bird egg is found probability and is usually set to a constant, thus the parasitic nest causing algorithm to perform basic cuckoo algorithm be found operation time, no matter be in more excellent position or the parasitic nest of poor position all can be replaced with equal probabilities, if
value arranges too small, then poor in searching process solution convergence is comparatively slow, if
value arranges excessive, and the solution of more excellent position is more difficult converges on optimum solution, and therefore the present invention adopts a kind of dynamic discovery mechanism:
, interim
, algorithm search spatial dimension [1, carnumber], population iterations Max_iter, iteration count t=1;
2. initialization population calculate fitness value.Calculate between home-delivery center and each customer demand point and transport cost, in this example with the spacing of each point for target, the false code of its account form is as follows:
fori=1:nd
forj=1:nd
cost(i,j)=sqrt((xy(i,1)-xy(j,1))^2+(xy(i,2)-xy(j,2))^2);
end
end
Wherein xy is the coordinate of each point.
Adopt uniformly distributed function to produce size nd at random and tie up the parasitic nest that search volume scope is [1, carnumber], remember that i-th parasitic nest position is
, and (namely each parasitic nest ceil function is rounded
); To ensure that each client o'clock is served by a car, and calculate the fitness value of its correspondence
;
3. the levy migration operation of parasitic nest is performed.Adopt
update mode produces new parasitic nest position, due to Levy distribution integral contrast difficulty, adopts Mantegana algorithm to realize of equal value calculating, and compares with the position before execution levy migration, and the good parasitic nest of chosen position remains into the next generation.
4. perform parasitic nest and be found operation.Produce random number
if,
, disturbance is carried out to this parasitic nest and produces new parasitic nest, and the position corresponding with before disturbance compares, the good parasitic nest of retention position.
5. parasitic nest mutation operation is performed.Adopt
mutation operation is carried out to parasitic nest.
6. dynamic conditioning probability of detection.Adopt
the parasitic nest of dynamic conditioning is found probability
, interim
represent the probability of detection carrying out the N_iter time iteration and be;
,
be respectively maximum probability of detection and minimum probability of detection;
for maximum iteration time;
for current iteration number of times.The method guarantees the initial stage at algorithm, due to distant from optimal value of individuality, needs larger position to change speed; In the later stage of algorithm, because most of disaggregation is around optimal location, less position is needed to change speed.
7. retain optimal location and the fitness thereof of each search, what fitness value was low is more excellent logistic distribution vehicle route scheme.Judge whether Search Results meets the requirements, if meet
, then go to 8., otherwise go to 3..
8. export optimum parasitic nest position and corresponding fitness value thereof, obtain optimum logistic distribution vehicle route scheme.
[0020] the involved fitness value in above solution procedure
solve and refer to Fig. 2, Fig. 2 is the schematic flow sheet that fitness value solves, and comprises the following steps:
Step 1: determine vehicle required for any one logistics distribution scheme x by roadindex=unique (x), fitness value is set
, the optimum Distribution path total_road of Current protocols;
Step 2: determine client's point that i-th car is served by subRout=find (Zx==roadindex (i)), and the Distribution path subRout forming that Current vehicle formed according to dispensing numbering size;
Step 3: adopt 2-opt algorithm to be optimized to every bar Distribution path subRout, thus obtain the optimal path road of i-th car dispensing, and record this vehicle delivery path order: total_road (subRout)=road;
Step 4: calculate Current vehicle optimal path path length that road experiences;
Step 5: the customer demand sum sum_q calculating Current vehicle optimal path road process;
Step 6: adopt segmentation penalty function method punishment load to exceed the maximum load of Current vehicle, if punishment amount is: fp, concrete account form is as follows:
represent that solution runs counter to degree to constraint condition, adopt following formulae discovery:
represent strength of punishment, adopt following formulae discovery:
for multistage mapping function, run counter to degree according to difference and determine different punishment dynamics, its payment method adopts following formulae discovery:
Step 7: the fitness value calculating Current vehicle optimal path road is:
,
;
Step 8: judge whether it is last car, if satisfy condition, then go to step 2, otherwise go to step 9;
Step 9: the fitness value of output scheme x
, and the optimum Distribution path total_road of Current protocols.
Claims (2)
1., based on the route optimizing method for logistic distribution vehicle improving cuckoo algorithm, it is characterized in that, comprise the following steps:
Solution procedure:
Step 1: the parameter arranging algorithm, arranges population scale size, the quantity nd of services client, and vehicle number carnumber needed for current solution problem, arranges the load CarrayCarCan of car, the probability of detection of bird egg in parasitic nest
, algorithm search spatial dimension [1, carnumber], population iterations Max_iter, iteration count N_iter=1;
Step 2: initialization population also calculates fitness value, adopts uniformly distributed function to produce size nd at random and ties up the parasitic nest that search volume scope is [1, carnumber], remember that i-th parasitic nest position is
, and (namely each parasitic nest ceil function is rounded
), to ensure that each client o'clock is served by a car, and calculate the fitness value of its correspondence
;
Step 3: the levy migration operation performing parasitic nest, adopts
update mode produces new parasitic nest position, and compares with the position before performing levy migration, and the good parasitic nest of chosen position remains into the next generation;
Step 4: perform parasitic nest and be found operation, produce random number
if,
, disturbance is carried out to this parasitic nest and produces new parasitic nest, and the position corresponding with before disturbance compares, the good parasitic nest of retention position;
Step 5: perform parasitic nest mutation operation, adopts
mutation operation is carried out to parasitic nest;
Step 6: dynamic conditioning probability of detection, adopts
the parasitic nest of dynamic conditioning is found probability
, interim
represent the probability of detection carrying out the t time iteration and be;
,
be respectively maximum probability of detection and minimum probability of detection;
for maximum iteration time;
for current iteration number of times; The method guarantees the initial stage at algorithm, due to distant from optimal value of individuality, needs larger position to change speed; In the later stage of algorithm, because most of disaggregation is around optimal location, less position is needed to change speed;
Step 7: the optimal location and the fitness thereof that retain each search, judge whether Search Results meets the requirements, if meet
, then go to Step8, otherwise go to Step3;
Step 8: export optimum parasitic nest position and corresponding fitness value thereof, obtain optimum logistic distribution vehicle route scheme.
2. a kind of route optimizing method for logistic distribution vehicle based on improving cuckoo algorithm according to claim 1, it is characterized in that, the calculation procedure of involved fitness value is as follows:
Step 1: determine vehicle required for any one logistics distribution scheme x by roadindex=unique (x), determines vehicle number car_index by [row, car_index]=size (roadindex), arranges fitness value
, the optimum Distribution path total_road of Current protocols;
Step 2: determine client's point that each car is served by subRout=find (Zx==roadindex (i)), and the Distribution path subRout forming that Current vehicle formed according to dispensing numbering size;
Step 3: adopt 2-opt algorithm to be optimized to every bar Distribution path subRout, thus obtain the optimal path road of single vehicle dispensing, and record this vehicle delivery path order: total_road (subRout)=road;
Step 4: calculate Current vehicle optimal path path length that road experiences;
Step 5: the customer demand sum sum_q calculating Current vehicle optimal path road process;
Step 6: adopt segmentation penalty function method punishment load to exceed the maximum load of Current vehicle, if punishment amount is: fp;
Step 7: the fitness value calculating Current vehicle optimal path road is:
,
;
Step 8: judge
if satisfy condition, then go to step 2, otherwise go to step 9;
Step 9: the fitness value of output scheme x
, and the optimum Distribution path total_road of Current protocols.
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|---|---|---|---|---|
| CN105975342A (en) * | 2016-04-29 | 2016-09-28 | 广东工业大学 | Improved cuckoo search algorithm based cloud computing task scheduling method and system |
| CN106127295A (en) * | 2016-06-21 | 2016-11-16 | 湘潭大学 | A kind of Optimal Design of Pressure Vessel method based on self adaptation cuckoo Yu fireworks hybrid algorithm |
| CN106651086A (en) * | 2016-09-07 | 2017-05-10 | 河南科技学院 | Automated stereoscopic warehouse scheduling method considering assembling process |
| CN107909228A (en) * | 2017-12-23 | 2018-04-13 | 深圳大学 | Based on mould because of the dynamic vehicle shipping and receiving paths planning method and device of calculating |
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| CN108921338A (en) * | 2018-06-20 | 2018-11-30 | 广东工业大学 | A kind of more vehicle shop logistics transportation dispatching methods |
| CN110334853A (en) * | 2019-06-10 | 2019-10-15 | 福建工程学院 | A kind of imitative nature body optimization method of logistics distribution center Warehouse Location |
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| CN111178596A (en) * | 2019-12-12 | 2020-05-19 | 浙江浙大网新国际软件技术服务有限公司 | Logistics distribution route planning method and device and storage medium |
| CN112232602A (en) * | 2020-11-19 | 2021-01-15 | 湘潭大学 | Logistics distribution path optimization method and system for large-scale nodes |
| CN116502473A (en) * | 2023-06-27 | 2023-07-28 | 中科航迈数控软件(深圳)有限公司 | Wire harness electromagnetic compatibility optimization method and device, electronic equipment and storage medium |
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| CN105975342A (en) * | 2016-04-29 | 2016-09-28 | 广东工业大学 | Improved cuckoo search algorithm based cloud computing task scheduling method and system |
| CN106127295A (en) * | 2016-06-21 | 2016-11-16 | 湘潭大学 | A kind of Optimal Design of Pressure Vessel method based on self adaptation cuckoo Yu fireworks hybrid algorithm |
| CN106651086A (en) * | 2016-09-07 | 2017-05-10 | 河南科技学院 | Automated stereoscopic warehouse scheduling method considering assembling process |
| CN107909228A (en) * | 2017-12-23 | 2018-04-13 | 深圳大学 | Based on mould because of the dynamic vehicle shipping and receiving paths planning method and device of calculating |
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| CN108921338A (en) * | 2018-06-20 | 2018-11-30 | 广东工业大学 | A kind of more vehicle shop logistics transportation dispatching methods |
| CN110334853A (en) * | 2019-06-10 | 2019-10-15 | 福建工程学院 | A kind of imitative nature body optimization method of logistics distribution center Warehouse Location |
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| CN111178730A (en) * | 2019-12-24 | 2020-05-19 | 中国航空工业集团公司西安飞机设计研究所 | Method and device for planning supply of oiling machine |
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