CN104240054A - Implementation method of logistics vehicle dispatching based on particle swarms - Google Patents
Implementation method of logistics vehicle dispatching based on particle swarms Download PDFInfo
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Abstract
The invention relates to an implementation method of logistics vehicle dispatching based on particle swarms. The method is characterized by comprising the following steps that 01. particle initialization is carried out; 02. parameter factors are set; 03. the coding mode of a real number vector is used for solving a vehicle dispatching problem, wherein each particle corresponds to two-dimension vectors X and Xpso, X represents a position vector, and Xpso represents a dispatching point corresponding to the vector X; 04. particle speed and a vehicle number are adjusted automatically; and 05. a Mapreduce model is introduced. A basic particle swarm algorithm is improved and is combined with MapReduce parallel computing under cloud computing, the algorithm is used in a vehicle dispatching problem, and reasonable dispatching of vehicles is achieved.
Description
Technical field
The present invention is by having carried out basic particle group algorithm improving and combining with the MapReduce parallel computation under cloud computing, and applied to vehicle dispatching problem (Vehicle Routing Problem, VRP), in, vehicle management and control technical field is belonged to.
Background technology
Along with the fast development of economic globalization and the network information technology, material flow industry be described as " accelerator " of economic development, " lubricant " of industrial structure transformation and modern enterprise " third party's profit source ", material flow industry is day by day remarkable on the impact of economic activity.And vehicle scheduling is as the core link of logistics distribution, rational vehicle scheduling can simplify dispensing program, increase materials flow economy benefit, to realize logistics scientific.
Vehicle dispatching problem refers to there is a series of point of receiving a little or deliver, find suitable traffic route, vehicle is made to carry out orderly access to each dispensing point, in the condition that meets certain constraint as vehicle restriction, the restriction of vehicle maximum load, time window restriction etc., reach required target as: expense is minimum, shortest path, expend time in the shortest etc.
Particle cluster algorithm is a kind of random search algorithm based on group collaboration grown up by simulation flock of birds foraging behavior, can be used in solving various combinatorial optimization problem and comprises VRP.
Summary of the invention
The object of this invention is to provide a kind of implementation method of the vehicles dispatching system based on population, the rational management of vehicle can be realized.
The present invention adopts following scheme to realize: a kind of implementation method of the vehicles dispatching system based on population, is characterized in that comprising the following steps:
Step S01: for each particle, integer k between random selecting 0 ~ n, wherein n is the number of dispensing point, obtain the initial position of all the other distances of respectively providing and delivering between point to dispensing point k as current particle, in population, initial population optimal location is using each dispensing point to the distance of home-delivery center as particle colony history optimal location;
Step S02: the setting carrying out parameter factors, comprises inertia weight w and is set to adjustable factors: w=0.9-r*0.4/d; Studying factors c2 is also set to adjustable factors: c
2=2.0*r/d; Wherein r is current iteration number of times, and d is total iterations;
Step S03: adopt the coded system of real number vector to solve vehicle dispatching problem; The wherein corresponding two-dimensional vector X and Xpso of each particle, wherein X represents position vector, and Xpso represents the dispensing point that position vector X is corresponding;
Step S04: the speed of particle adopts the self-regulation factor, the optimum speed of current particle group is searched in certain interval range, when drawing optimum solution by comparing, whether vehicle all uses, if all do not used, then the vehicle that when next round is run, schedulable vehicle just changes into when finding optimum solution so far uses number;
Step S05: the improve PSO algorithm based on Mapreduce: become mutually independently data block to carry out Map operation Segmentation of Data Set by Mapreduce model, the calculating of group optimal solution is realized by Reduce, each iteration of particle is upgraded and completes with a Mapreduce.
In an embodiment of the present invention, the set-up procedure solving middle infeasible solution of described step S03 is:
(1) the average load-carrying of each car: when rolling stock all uses, on average the load-carrying averageCap of each car;
(2) preliminary election vehicle: suppose the restriction not having number of vehicles, identical for integral part in position vector X, the dispensing corresponding to it puts vectorial Xpso by same car service, calculates the load-carrying preCap of required number of vehicles and each car respectively;
(3) Transporting Arrangement: adjustment preliminary election vehicle, first judges whether the load-carrying realCap [k1] of the vehicle k1 of actual arrangement is greater than averageCap, if be greater than, then choose next vehicle k1+1 in addition; Otherwise, judge that the load-carrying preCap [k2] of current preliminary election vehicle k2 adds whether realCap [k1] is greater than the maximum load of vehicle successively, if be less than, then the dispensing point that current preliminary election vehicle k2 serves is added in vehicle k1, same judgement is done to next preliminary election vehicle until all preliminary election vehicle all judges to terminate; Otherwise choose vehicle k1+1 in addition.
In an embodiment of the present invention, the concrete steps of the described improve PSO algorithm based on Mapreduce are: first, and initialization population and each particle personal best particle and colony's optimal location are also preserved on the local computer in the form of a file; Then the data in file are read, the Map stage completes the calculating of valuation functions value and the renewal of particle state, intermediate result that Map has operated is preserved on the local computer, the reduce stage complete particle colony history optimal location renewal and in result writing in files.
The present invention by having carried out basic particle group algorithm improving and combining with the MapReduce parallel computation under cloud computing, and is applied in vehicle dispatching problem, achieves the rational management of vehicle.
Accompanying drawing explanation
Fig. 1 is adjustment infeasible solution schematic flow sheet.
Fig. 2 is vehicle self-adjusting process flow diagram schematic diagram.
Fig. 3 is the improve PSO algorithm schematic flow sheet based on Mapreduce.
Embodiment
For enabling above-mentioned purpose of the present invention, feature and advantage more become apparent, and are described in detail the specific embodiment of the present invention below in conjunction with accompanying drawing.
Set forth detail in the following description so that fully understand the present invention.But the present invention can be different from alternate manner described here to implement with multiple, those skilled in the art can when without prejudice to doing similar popularization when intension of the present invention.Therefore the present invention is not by the restriction of following public embodiment.
The implementation method of the vehicles dispatching system based on population of the present embodiment comprises the following steps:
1. particle initialization
In basic particle group algorithm, population initial position is random selecting, does not associate with solving of practical problems.And in order to make particle cluster algorithm follow practical problems to be associated and better solve vehicle dispatching problem, in the present invention, the determination of particle initial position is: for each particle, integer k (wherein n is the number of dispensing point) between random selecting 0 ~ n, obtain the initial position of all the other distances of respectively providing and delivering between point to dispensing point k as current particle, in population, initial population optimal location is using each distance of point to home-delivery center of providing and delivering as particle colony history optimal location.The population scale of particle gets 5 times of dispensing point number.
2. the setting of parameter factors
In basic particle group algorithm, the parameter of particle rapidity more in new formula, generally chooses inertia weight w=1 and Studying factors c
2=2, but consider that basic particle group algorithm is easily absorbed in local optimum, and inertia weight w mainly particle keep the inertia of displacement state, be used for balanced algorithm local and global search, be conducive to when w takes large values exploring frontier, time less, be conducive to detailed search current region.Therefore when iteration is initial, particle should expand region of search, carries out global search, makes region of search converge on a certain regional area, and along with the increase of iterations, particle should carry out subrange search, is conducive to finding optimum solution, prevents from being absorbed in local optimum.So inertia weight w is set to adjustable factors in the present invention: w=0.9-r*0.4/d.
Because Studying factors c2 is that particle learns to colony's history optimal location, particle starts the search phase, the advantage of colony's history optimal location occupied by group optimal solution is not also clearly, therefore make the particle incipient stage lower to the proportion of colony's history optimal location study by optimum configurations, and search for the later stage, because the proportion of colony's history optimal location in search globally optimal solution is larger, therefore the history optimal location study of particle multidirectional colony can be made, prevent from being absorbed in local optimum, be conducive to finding optimum solution.So in the present invention, c2 is also set to adjustable factors: c
2=2.0*r/d wherein r is current iteration number of times, and d is total iterations.
Renewal due to particle position depends on the renewal of particle rapidity, and weight coefficient w and Studying factors c2 is depended in the renewal of particle rapidity to a great extent, therefore the adjustment increasing parameter factors on the selection basis of step particle initial position 1. can make particle position upgrade more pragmatize and towards the snap of optimum solution target, prevents particle search process to be absorbed in local optimum.
3. coded system
The coded system of real number vector is adopted in the present invention.The corresponding two-dimensional vector X and Xpso of each particle, wherein X represents position vector, and be the positional information storing corresponding particle on step basis 2., Xpso represents the dispensing point that position vector X is corresponding.
Adopt this coded system directly can utilize the more new formula of particle cluster algorithm and not need to redefine speed renewal rewards theory, and only need carry out a minor sort and floor operation to position vector X when decoding, conveniently particle state is adjusted.The corresponding dispensing point that in position vector X, integral part is identical is by same car service, and fraction part represents the service order of dispensing point in corresponding vehicle.
Example 1: suppose there is 1 home-delivery center, 8 dispensing points, it is as shown in table 1 that vectorial Xpso is put in the dispensing of position vector X and correspondence:
The position vector of table 1 dispensing point and correspondence thereof
First position vector X is sorted during decoding, by dispensing identical for integral part point by same car service, then selects service order by the size of fraction part, therefore can show that the vehicle route of this coded representation is as shown in table 2:
Table 2 real coding rear vehicle arranges and service order
| Vehicle | 1 | 2 | 3 | 4 | 5 |
| Dispensing order | 0→2→8→0 | 0→3→1→0 | 0→6→0 | 0→7→4→0 | 0→5→0 |
If only have 3 cars can to provide and deliver to the dispensing point in example 1, obviously can find that simple employing real number vector coding mode solves easily because the restriction of number of vehicles causes drawing a large amount of infeasible solutions to vehicle dispatching problem, therefore need to adjust real number vector coding mode, make most of infeasible solution transfer feasible solution to, strengthen the validity of algorithm.
Refer to Fig. 1, Fig. 1 is the schematic flow sheet of infeasible solution adjustment, and the step of adjustment infeasible solution is:
(1) the average load-carrying of each car: when rolling stock all uses, on average the load-carrying averageCap of each car;
(2) preliminary election vehicle: suppose the restriction not having number of vehicles, identical for integral part in position vector X, the dispensing corresponding to it puts vectorial Xpso by same car service, calculates the load-carrying preCap of required number of vehicles and each car respectively;
(3) Transporting Arrangement: adjustment preliminary election vehicle, first judges whether the load-carrying realCap [k1] of the vehicle k1 of actual arrangement is greater than averageCap, if be greater than, then choose next vehicle k1+1 in addition.Otherwise, judge that the load-carrying preCap [k2] of current preliminary election vehicle k2 adds whether realCap [k1] is greater than the maximum load of vehicle successively, if be less than, then the dispensing point that current preliminary election vehicle k2 serves is added in vehicle k1, same judgement is done to next preliminary election vehicle until all preliminary election vehicle all judges to terminate; Otherwise choose vehicle k1+1 in addition.
If only have 3 cars to serve dispensing point, the maximum load of each car is 8, and the demand of each dispensing point is d=[3 312121 2], according to averageCap=5 known in the present invention, according in the present invention, infeasible solution is adjusted, then show that feasible solution is for shown in table 3:
The adjustment rear vehicle arrangement of table 3 infeasible solution and service order
| Vehicle | 1 | 2 | 3 |
| Dispensing order | 0→2→8→0 | 0→3→1→6→0 | 0→7→4→5→0 |
4. particle rapidity and number of vehicles self-adjusting
In basic particle group algorithm, the maximal rate of population is set to a constant usually, and choosing of particle maximal rate is related to the searching of population for optimum solution.When colony's speed is excessive, particle easily because moment flying speed excessive and crosses optimum solution, what cause finally finding is not optimum solution; And speed too small time, particle easily stays for a long time in zonule, be difficult to open up frontier, find optimum solution.In the present invention, in order to better find optimum solution, prevent population due to speed excessive or too small and be absorbed in local optimum, the maximal rate of particle adopts the self-regulation factor, in certain interval range, search for the optimum speed of current particle group, prevent from being absorbed in local optimum.
When schedulable number of vehicles is more, in the present invention, improve PSO algorithm all uses to make vehicle as far as possible, and causes most of vehicle to be in zero load (dead weight capacity is less) state, does not thus find optimum solution.Therefore, consider that the present invention improves further to used improve PSO algorithm, introduces the thinking of self-adjusting vehicle in order to make vehicle fully loaded and obtain optimum solution as far as possible above.When drawing optimum solution by comparing, whether vehicle all uses, if all do not used, then when next round is run, schedulable vehicle just changes vehicle when finding optimum solution so far into and uses number, the self-adjusting process flow diagram of vehicle as shown in Figure 2:
5. the introducing of Mapreduce model
Due to when Solve problems scale is comparatively huge, required computing time will exponentially increase, by finding that the state updating of each particle in population is independent of each other to the research of improve PSO algorithm, only with the individual history optimal location of this particle and colony's history optimal location relevant, therefore can consider the renewal process of each particle of parallel processing.The Mapreduce model simultaneously associated under hadoop is applicable to the parallel processing processing large-scale data, independently data block is become mutually to carry out Map operation Segmentation of Data Set by Mapreduce model, the calculating of group optimal solution is realized by Reduce, completes so each iteration of particle is upgraded in the present invention with a Mapreduce.
Concrete steps based on the improve PSO algorithm of Mapreduce are: first, and initialization population and each particle personal best particle and colony's optimal location are also preserved on the local computer in the form of a file; Then the data in file are read, the Map stage completes the calculating of valuation functions value and the renewal of particle state, intermediate result that Map has operated is preserved on the local computer, the reduce stage complete particle colony history optimal location renewal and in result writing in files.Process flow diagram as shown in Figure 3.
By to the debugging of code and analysis, when can show that improve PSO algorithm runs herein, part the most consuming time is in the valuation functions value calculating each particle, therefore the solution procedure parallelization to Particle evaluations functional value.In this paper Mapreduce model, the major function of Map function calculates the valuation functions value of each particle and upgrades the state of each particle, and the major function of Reduce finds group optimal solution and upgrades colony's history optimal location.
Wherein, the false code of Map function and Reduce function is as shown in table 4:
The false code of table 4 Map and Reduce function
In the false code of the Map function of table 4, type and the implication thereof of Map input parameter <key1, value1> are as follows: key1 is IntWritable type, represent the particle when pre-treatment; Value1 is Text type, wherein contains the number of times of iteration, the position of particle, speed, personal best particle, colony's history optimal location, individual history optimum evaluation functional value, colony's history optimum evaluation functional value.Map output parameter <key2, the input parameter <key3 of Reduce function in key2, value2 and Fig. 1-3 in value2>, the type of value3> and output parameter <key4, value4> and the implication of representative all follow the same of Map function input parameters.
Owing to completing at every turn, MapReduce is complete all can generate an output file afterwards, and output file is the file automatically generated when running Reduce process, cannot deposit hereof in advance, therefore need to process output file, otherwise when iteration 1000 times, just may produce 1000 output files.Because consider when carrying out Mapreduce operation at every turn, all need to call a Reduce when process of Map function and operate the file exported, therefore can input file erase after completing a Mapreduce, and then the content replication of output file to input, delete output file.Realize the file that the limit working procedure edge contract last time runs generation.
Although the present invention with preferred embodiment openly as above; but it is not for limiting the present invention; any those skilled in the art without departing from the spirit and scope of the present invention; the Method and Technology content of above-mentioned announcement can be utilized to make possible variation and amendment to technical solution of the present invention; therefore; every content not departing from technical solution of the present invention; the any simple modification done above embodiment according to technical spirit of the present invention, equivalent variations and modification, all belong to the protection domain of technical solution of the present invention.The foregoing is only preferred embodiment of the present invention, all equalizations done according to the present patent application the scope of the claims change and modify, and all should belong to covering scope of the present invention.
Claims (3)
1., based on an implementation method for the vehicles dispatching system of population, it is characterized in that comprising the following steps:
Step S01: for each particle, integer k between random selecting 0 ~ n, wherein n is the number of dispensing point, obtain the initial position of all the other distances of respectively providing and delivering between point to dispensing point k as current particle, in population, initial population optimal location is using each dispensing point to the distance of home-delivery center as particle colony history optimal location;
Step S02: the setting carrying out parameter factors, comprises inertia weight w and is set to adjustable factors: w=0.9-r*0.4/d; Studying factors c2 is also set to adjustable factors: c
2=2.0*r/d; Wherein r is current iteration number of times, and d is total iterations;
Step S03: adopt the coded system of real number vector to solve vehicle dispatching problem; The wherein corresponding two-dimensional vector X and Xpso of each particle, wherein X represents position vector, and Xpso represents the dispensing point that position vector X is corresponding;
Step S04: the speed of particle adopts the self-regulation factor, the optimum speed of current particle group is searched in certain interval range, when drawing optimum solution by comparing, whether vehicle all uses, if all do not used, then the vehicle that when next round is run, schedulable vehicle just changes into when finding optimum solution so far uses number;
Step S05: the improve PSO algorithm based on Mapreduce: become mutually independently data block to carry out Map operation Segmentation of Data Set by Mapreduce model, the calculating of group optimal solution is realized by Reduce, each iteration of particle is upgraded and completes with a Mapreduce.
2. the implementation method of the vehicles dispatching system based on population according to claim 1, is characterized in that: the set-up procedure solving middle infeasible solution of described step S03 is:
(1) the average load-carrying of each car: when rolling stock all uses, on average the load-carrying averageCap of each car;
(2) preliminary election vehicle: suppose the restriction not having number of vehicles, identical for integral part in position vector X, the dispensing corresponding to it puts vectorial Xpso by same car service, calculates the load-carrying preCap of required number of vehicles and each car respectively;
(3) Transporting Arrangement: adjustment preliminary election vehicle, first judges whether the load-carrying realCap [k1] of the vehicle k1 of actual arrangement is greater than averageCap, if be greater than, then choose next vehicle k1+1 in addition; Otherwise, judge that the load-carrying preCap [k2] of current preliminary election vehicle k2 adds whether realCap [k1] is greater than the maximum load of vehicle successively, if be less than, then the dispensing point that current preliminary election vehicle k2 serves is added in vehicle k1, same judgement is done to next preliminary election vehicle until all preliminary election vehicle all judges to terminate; Otherwise choose vehicle k1+1 in addition.
3. the implementation method of the vehicles dispatching system based on population according to claim 1, it is characterized in that: the concrete steps of the described improve PSO algorithm based on Mapreduce are: first, initialization population and each particle personal best particle and colony's optimal location are also preserved on the local computer in the form of a file; Then the data in file are read, the Map stage completes the calculating of valuation functions value and the renewal of particle state, intermediate result that Map has operated is preserved on the local computer, the reduce stage complete particle colony history optimal location renewal and in result writing in files.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105868843A (en) * | 2016-03-22 | 2016-08-17 | 南京邮电大学 | Route planning method oriented to goods delivery |
| CN105868949A (en) * | 2016-03-31 | 2016-08-17 | 北京小度信息科技有限公司 | Logistics distribution scheduling method and apparatus |
| CN106203911A (en) * | 2016-07-07 | 2016-12-07 | 成都镜杰科技有限责任公司 | Intelligent logistics data managing method based on cloud computing |
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| CN107578199A (en) * | 2017-08-21 | 2018-01-12 | 南京航空航天大学 | A kind of method for solving two dimension and loading constraint logistics vehicle dispatching problem |
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| CN109816225A (en) * | 2019-01-11 | 2019-05-28 | 郑州嘉晨电器有限公司 | A kind of method for scheduling task based on fork truck cloud platform |
| CN111695667A (en) * | 2020-05-27 | 2020-09-22 | 江苏信息职业技术学院 | MapReduce-based distributed particle swarm clustering algorithm |
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