CN1750028A - A kind of particle group optimizing method of vehicle dispatching problem - Google Patents
A kind of particle group optimizing method of vehicle dispatching problem Download PDFInfo
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- CN1750028A CN1750028A CNA2005100612329A CN200510061232A CN1750028A CN 1750028 A CN1750028 A CN 1750028A CN A2005100612329 A CNA2005100612329 A CN A2005100612329A CN 200510061232 A CN200510061232 A CN 200510061232A CN 1750028 A CN1750028 A CN 1750028A
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Abstract
A kind of particle group optimizing method of vehicle dispatching problem by some preanalysis and the pre-service to this vehicle dispatching problem, is simplified actual vehicle path degree problem solving.Proposed the real coding mode and represented the scheduling scheme of vehicle dispatching problem, found the solution with the particle cluster algorithm method.For the VRP problem that L client arranged, use the state of the real number vector representation particle of L dimension.For each dimension of vector, its integral part is represented the vehicle at place, and integral part is identical, is illustrated in same the car.Fraction part is illustrated in the order of providing and delivering in the vehicle.The invention provides that a kind of simple to operate, computing velocity is fast, the intelligent optimization method of the vehicle scheduling being convenient to adjust.
Description
(1) technical field
The present invention relates to a kind of particle group optimizing method of vehicle dispatching problem.
(2) background technology
Vehicle scheduling is a gordian technique of implementing the socialization logistics, optimize route or travel by vehicle, promote enterprise competitiveness, reduce enterprise cost, solve problems such as urban traffic congestion, energy shortage, atmospheric pollution, the realization traffic is being unified at the interior of efficient, resource, environment and values each side, guarantees the sustainable development of material flow industry.But on the more meanings of the employed vehicle scheduling software of present most of enterprise is a management software, lack the function of distributing rationally to resource, also main dependence has the managerial personnel that enrich practical experience and dispatcher by means of the manual work mode that meeting, phone, form etc. manage and dispatch, and obviously can not satisfy the needs of allegro modern production and market cut-throat competition.Along with computing machine and computer network are introduced enterprise, the optimization vehicle scheduling that grows up based on information integration is risen.
VRP (Vehicle Routing Problem is vehicle scheduling) problem is commonly defined as: to a series of loading points and (or) clearance point, organize suitable driving route, make vehicle in an orderly manner by them, satisfying under certain constraint condition (as goods demand, traffic volume, friendship delivery availability, vehicle capacity restriction, distance travelled restriction, time restriction etc.), reach certain target (the shortest as distance, expense is minimum, the time as far as possible less, use the vehicle number few as far as possible etc.).
The factor that influences vehicle dispatching problem is a lot, has under the normal condition: the traffic of road, and client's demand, the arrival time of requirement, the load-carrying of vehicle, driver's situation, the expense etc. of travelling, these all are so-called constraint condition.Some constraint condition must satisfy, as the arrival time of customer requirement, and the load-carrying of car etc., and some reaches certain satisfaction and gets final product, as the expense of travelling etc.These constrain in and can be used as the certainty factor consideration when dispatching.And for the traffic of road, improper situations such as the change of customer demand all are unforeseeable in advance, mostly consider as uncertain factor when carrying out vehicle scheduling.
Vehicle dispatching problem is unusual complicated problems.Normally multiple constraint, multiple goal, uncertain optimization problem at random.The calculated amount of solution procedure is exponential increase with the scale of problem, has been proved to be np complete problem.Studies show that much the optimum solution of seeking vehicle dispatching problem is very difficult, the derivation algorithm that engineering significance is arranged most is a target of abandoning seeking optimum solution, then attempts to search out in reasonable, the limited time approximate, useful separating.
There is the people to adopt methods such as genetic algorithm, particle cluster algorithm, saving method and scanning method in the existing vehicle dispatching method.Wherein, the existing coded system of particle cluster algorithm has real coding, binary coding, integer coding.Finding the solution aspect this class combinatorial optimization problem of vehicle dispatching problem, the advantage of using the integer coding mode is to be easy to decoding, and it is convenient to calculate fitness, shortcoming is that calculated amount is bigger, result of calculation is poor, and population is easily sunk into Local Extremum, and precocious convergent situation is more serious.Use the integer coding mode that the characteristics of particle cluster algorithm itself are not brought into play.Current use real coding scheme is found the solution vehicle dispatching problem, and the vector table that is to use a 2L to tie up is shown with L client's VRP problem.The 2L dimension state of the correspondence of each particle is divided into 2 dimensions, and the vehicle of each client's correspondence of one-dimensional representation makes the dispensing order of each client of one-dimensional representation in corresponding vehicle.The dimension that this kind method for expressing is represented is higher, and to each dimension, when decoding, all needs to round, ordering, and the operation more complicated, computing velocity is slower, when illegal solution occurring, adjusts also relatively difficulty.
(3) summary of the invention
Slow for the complicated operation that overcomes existing vehicle dispatching method, computing velocity, as to adjust difficulty deficiency, the invention provides a kind of simple to operate, computing velocity fast, the particle group optimizing method of the vehicle dispatching problem being convenient to adjust.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of particle group optimizing method of vehicle dispatching problem, described method mainly may further comprise the steps:
(1), from order ticket, obtain distribution information, described distribution information comprises: the arrival time of the total quantity of the goods of customer name, customer demand, general assembly (TW), cumulative volume, unloading address, requirement;
(2), from committee's customer name of obtaining of waybill, from address database, inquire client's address information, comprise client's specific address, the distance between the client;
(3), set the parameter of particle cluster algorithm, described parameter comprises population scale, iterations, described population scale is represented the quantity of initial distribution project, iterations is illustrated in the searching times in numerous distribution project spaces;
(4), above-mentioned distribution information, client's address information is read in the particle cluster algorithm;
(5), according to client's number, calculate needed vehicle number, each distribution project is encoded, coded system adopts the coded system based on the real number vector:
For the distribution project that L client arranged, the real number vector X that uses L to tie up represents the state of particle; For each dimension of vector, its integral part is represented the vehicle at place, and is identical as integral part, is illustrated in same the car; Fraction part is illustrated in the order of providing and delivering in the vehicle, according to size order;
(6), use decoding algorithm to decode:
(6.1), carry out [X] operation for each dimension of the state of particle;
(6.2) divide into groups according to [X] value, form client's grouping;
(6.3) in grouping, particle is carried out { X} operation;
(6.4) according to { size of X} is arranged, and forms client's access order;
(7), adjust between circuit and the order in the circuit, and the distribution project that generates is optimized again;
(8), according to distribution cost numerical procedure, calculate all clients' of visit line length or time or the expense of visiting all clients, the fitness of particle is defined as the inverse of cost;
(9), the fitness of particle relatively, find out the particle that fitness is the highest in the population and preserve, each particle and the fitness that calculates before self are relatively preserved self best fitness simultaneously;
(10), the distribution project of particle representative is adjusted, carry out the renewal of particle state according to following formula (1):
In the following formula, Xi=(x
I1, x
I2... x
ID) expression i particle state, each particle represents that one of the D dimension space is separated Vi=(v
I1, v
I2... v
ID) represent each particle's velocity vector, and Vi satisfies: Vi≤maximal rate V
MaxP
iRepresent the optimum state that each particle lives through, P
gThe optimum state that expression colony lives through, c
1Be inertia weight, c
2, c
3It is acceleration constant.The state of particle is represented different implications in concrete application, in an application of the invention, the distribution project of the state representation vehicle scheduling of particle, promptly the client is by that car dispensing and the order of client in dispensing.Particle's velocity is represented the difference between distribution project, the difference of promptly same client place vehicle, the order difference of client in dispensing.
(11), some particles of selection at random carry out interlace operation, the distribution project that each particle is represented is adjusted;
(12), repeat (6)～(11), possible distribution project is searched for, reach predetermined iterations after, the distribution project that the output particle cluster algorithm searches out.
Further, in described (7), use saving method, nearest neighbor algorithm or Or-Opt method to adjust between circuit and the order in the circuit.
Principle of work of the present invention is: particle cluster algorithm is to equal a kind of evolutionary computing that nineteen ninety-five proposes by Kennedy and Eberhart.Its core concept is the simulation to biological social behavior.Its initial imagination is the process of simulation flock of birds predation, and in research process, the optimization that is applied to variety of issue has obtained good result.Suppose bevy the predation, found food for wherein one, then some other bird can be followed this bird and be flown to the food place, and makes some can remove to seek better food source.In the whole process of predation, bird can utilize the experience of self and the information of colony to seek food.Particle cluster algorithm takes a hint from this behavior of flock of birds, and uses it for finding the solution of optimization problem.In particle cluster algorithm, each problem separate a bird that all is counted as in the search volume, we are called " particle ".The state quality of particle is used by the adaptive value of optimised problem decision and is represented.Each particle has a speed to represent the distance and the direction of particle flight.Particle is followed current optimal particle and is searched in solution space.When finding the solution a problem, adopt certain coding method to be encoded into particle this problem, carry out interative computation according to the mechanism of particle cluster algorithm then.
Find the solution the feasibility analysis of vehicle dispatching problem: vehicle dispatching problem is a np problem, uses conventional methods and finds the solution, and has that to find the solution problem scale little, the problem such as of low quality of separating.Particle cluster algorithm is a kind of novel bionic intelligence optimized Algorithm.It is fast to have the speed of finding the solution, and the quality of separating is high a bit.Population is in Neural Network Optimization, and successful Application has all been obtained in fields such as electric parameter design optimization.Finding the solution traveling salesman problem, NP aspects such as Task Distribution problem have also obtained good result.
The present invention proposes a kind of coded system based on the real number vector, and the key that the method is different from existing coding method is under the prerequisite that does not increase dimension, and the ordering of the client in vehicle and the vehicle line is come out in coded representation.For the VRP problem that L client arranged, use the state of the real number vector representation particle of L dimension.For each dimension of vector, its integral part is represented the vehicle at place, and integral part is identical, is illustrated in same the car.Fraction part is illustrated in the order of providing and delivering in the vehicle.
Beneficial effect of the present invention mainly shows: 1, simple to operate; , 2, be convenient to adjust; 3, computing velocity is fast.
(4) description of drawings
Fig. 1 is the process flow diagram of the particle group optimizing method of vehicle scheduling.
Fig. 2 is the system construction drawing of vehicle scheduling.
(5) embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1, a kind of particle group optimizing method of vehicle dispatching problem may further comprise the steps:
The first step: obtain distribution information from committee's waybill.These information comprise: customer name, and the total quantity of customer demand goods, general assembly (TW), cumulative volume, the unloading time that needs, is estimated at the arrival time of requirement in the unloading address.
Second step: according to the customer name that obtains from committee's waybill, from database, inquire client's address information, comprise client's specific address, the distance between the client (perhaps working time).
The 3rd step: algorithm parameter, the parameter of particle cluster algorithm comprises population scale, what initial distribution projects representative herein has.Iterations is illustrated in numerous distribution project spaces, the search that does not stop, and the number of times of search, according to client's number, the client is many more, and the number of times of algorithm iteration is many more, and population scale is big more.
The 4th step: above-mentioned described data message is read in the algorithm.
The 5th step: according to client's number, the needed vehicle number of pre-estimation is encoded according to the coded system of this paper design.So-called coding, and a distribution project is expressed with particle algorithm mode to understand.
Based on the coded system of real number vector, the key that the method is different from above-mentioned method is under the prerequisite that does not increase dimension, and the ordering of the client in vehicle and the vehicle line is come out in coded representation.For the VRP problem that L client arranged, use the state of the real number vector representation particle of L dimension.For each dimension of vector, its integral part is represented the vehicle at place, and integral part is identical, is illustrated in same the car.Fraction part is illustrated in the order of providing and delivering in the vehicle.
Definition: [X] represent x round numbers part, and { X} represents X is got fraction part, and the corresponding client of the expression that each particle integral part is identical serves on a car, and the order of serving is represented in the arrangement of fraction part.
The process of decoding:
(1), carries out [X] operation for each dimension of the state of particle.
(2) divide into groups according to [X] value, form client's grouping.
(3) in grouping, particle is carried out { X} operation.
(4) according to { size of X} is arranged, and forms client's access order.
Suppose 7 clients' VRP problem, the vehicle number that needs is 3, and problem adopts this kind coded system coding with distribution project hereto, obtains following result:
Customer number: 1234567
X： 4.1 1.86 1.53 1.12 1.24 3.29 3.05
The path of separating of this group coding correspondence is:
Article one, circuit: 0-1-0
Second circuit: 0-4-5-3-2-0
Three-line: 0-7-6-0
This method for expressing, dimension is suitable with client's number, and it is simple to operate to carry out the particle state renewal, can bring into play the intrinsic advantage of particle cluster algorithm.
The 6th step: be client's grouping and arrangement according to above-mentioned coding/decoding method with the state decode of particle, reaches the scheme of vehicle scheduling.
The 7th step: use Or-Opt, nearest neighbor algorithm, methods such as saving method are adjusted between circuit and the order in the circuit, and the distribution project that generates is optimized again.
The 8th step: then according to distribution cost numerical procedure, and can be the line length or the time of calculating all clients of visit, also can be all clients' of visit expense etc.The fitness of particle is defined as the inverse of cost.
The 9th step: compare the fitness of particle, find out the particle that fitness is the highest in the population and preserve, the fitness of each particle and calculating before self is relatively preserved self best fitness simultaneously.The purpose of this operation is to preserve the optimum distribution project that optimum distribution project that current algorithm searches and each particle search arrive
The tenth step: carry out the renewal of particle state according to formula mentioned above (10.1), the process of this renewal is exactly the adjustment process to the distribution project of particle representative.And allow the distribution project of all particle representatives all draw close to the distribution project of at present known optimum.
The 11 step: some particles of selection at random carry out interlace operation, and this operates expression, and the distribution project that each particle is represented is adjusted.Can produce more outstanding distribution project thus.
The 12 step: repeat the 6th and go on foot the process in 11 steps, and possible distribution project is searched for, reach predetermined iterations, then withdraw from from algorithm.
The 13 step: the distribution project that the output particle cluster algorithm searches out shows to carry single form.
With reference to Fig. 2, use the Vehicular intelligent dispatching system of the method realization of present embodiment, mainly comprise: Back ground Information subsystem, intelligent algorithm subsystem.
The sub-infosystem in described basis comprises:
(1), client's coordinate information
This function can allow the user add, revise, inquire about client's coordinate, and these operations all are based on electronic chart, and the client can point out client's position by mouse, and coordinate values can be revised automatically, shows.
(2), the distance between the client (time)
This function provides inquiry, interpolation, the modification of distance (time) between the client.These operations all are based on electronic chart, make things convenient for the user to check.
(3), the complexity of client's dispensing
Inquiry, interpolation, modification that this function provides the client to provide and deliver complexity.Complexity is represented with words such as popular words " difficulty ", " easily ", is user-friendly to.
(4), the parameter information of algorithm
This function provides the demonstration of various algorithm parameters, revises.
Described intelligent algorithm subsystem comprises:
(1), the minimum scheduling of vehicle
The target of optimizing for these task needs of sending a car vehicle is minimum, move circuit the shortest (also can be targets such as cost is minimum, and oil consumption is minimum) according to the difference of the information that provides in the Back ground Information; This scheduling has following function:
(1.1), intelligent algorithm scheduling
Adopt vehicle scheduling scheme of the present invention to carry out vehicle scheduling, show distribution project, prepare for next step generates committee's waybill.The process of scheduling as shown in Figure 1, the description of method is slightly.
(1.2), generate the acknowledgement of consignment list
It is single that satisfied scheduling result is generated acknowledgement of consignment.The single numbering of acknowledgement of consignment produced automatically according to the date, and the single information of acknowledgement of consignment can be checked, revised deletion.
(1.3), the graphical demonstration
Adopt the result of graphic method display scheduling.The lines of using different colours on map are represented the circuit of different car dispensings.Can express dispensing client's position and title on the map.The simulation dolly can be according to the line access client of algorithm arrangement.
(1.4), convergence map
Convergence map that the algorithm that draws this time calculates provides a criterion judging that this operation result is whether outstanding to the user.
(2), punctual arrival dispatched
The target of the punctual row of arrival car is at first to be to satisfy the temporal requirement of client, and guarantees to provide and deliver in the time that customer requirement reaches.Next is to wish that used vehicle number is minimum, the distance of vehicle ' the shortest (also can be targets such as cost is minimum, and oil consumption is minimum, according to the difference of the information that provides in the Back ground Information).Function is identical with the minimum model of the vehicle of above-mentioned (1).
(3), comprehensive intelligent algorithm row car
This function provides the integrated dispatch algorithm of multiple algorithm, and each scheduling result provides four kinds of row's car schemes, and the user can select suitable scheme as required.Every kind of scheme can be checked in detail, can see graphical demonstration result.It is single that the scheme of choosing can be produced acknowledgement of consignment.Function is identical with the minimum model of the vehicle of above-mentioned (1).
Claims (4)
1, a kind of particle group optimizing method of vehicle dispatching problem, described method mainly may further comprise the steps:
(1), from order ticket, obtain distribution information, described distribution information comprises: the arrival time of the total quantity of the goods of customer name, customer demand, general assembly (TW), cumulative volume, unloading address, requirement;
(2), from committee's customer name of obtaining of waybill, from address database, inquire client's address information, comprise client's specific address, the distance between the client;
(3), set the parameter of particle cluster algorithm, described parameter comprises population scale, iterations, described population scale is represented the quantity of initial distribution project, iterations is illustrated in the searching times in numerous distribution project spaces;
(4), above-mentioned distribution information, client's address information is read in the particle cluster algorithm;
(5), according to client's number, calculate needed vehicle number, each distribution project is encoded, coded system adopts the coded system based on the real number vector:
For the distribution project that L client arranged, the real number vector X that uses L to tie up represents the state of particle; For each dimension of vector, its integral part is represented the vehicle at place, and is identical as integral part, is illustrated in same the car; Fraction part is illustrated in the order of providing and delivering in the vehicle, according to size order;
(6), use decoding algorithm to decode:
(6.1), carry out [X] operation for each dimension of the state of particle;
(6.2) divide into groups according to [X] value, form client's grouping;
(6.3) in grouping, particle is carried out { X} operation;
(6.4) according to { size of X} is arranged, and forms client's access order;
(7), adjust between circuit and the order in the circuit, and the distribution project that generates is optimized again;
(8), according to distribution cost numerical procedure, calculate all clients' of visit line length or time or the expense of visiting all clients, the fitness of particle is defined as the inverse of cost;
(9), the fitness of particle relatively, find out the particle that fitness is the highest in the population and preserve, each particle and the fitness that calculates before self are relatively preserved self best fitness simultaneously;
(10), the distribution project of particle representative is adjusted, carry out the renewal of particle state according to following formula, formula following (1):
In the following formula, Xi=(x
I1, x
I2... x
ID) expression i particle state, each particle represents that one of the D dimension space is separated Vi=(v
I1, v
I2... v
ID) represent each particle's velocity vector, and Vi satisfies: Vi≤maximal rate V
MaxP
iRepresent the optimum state that each particle lives through, P
gThe optimum state that expression colony lives through, c
1Be inertia weight, c
2, c
3It is acceleration constant;
(11), some particles of selection at random carry out interlace operation, the distribution project that each particle is represented is adjusted;
(12), repeat (6)～(11), possible distribution project is searched for, reach predetermined iterations after, the distribution project that the output particle cluster algorithm searches out.
2, the particle group optimizing method of a kind of vehicle dispatching problem as claimed in claim 1 is characterized in that: in described (7), use the saving method to adjust between circuit and the order in the circuit.
3, the particle group optimizing method of a kind of vehicle dispatching problem as claimed in claim 1 is characterized in that: in described (7), use nearest neighbor algorithm to adjust between circuit and the order in the circuit.
4, the particle group optimizing method of a kind of vehicle dispatching problem as claimed in claim 1 is characterized in that: in described (7), use the Or-Opt method to adjust between circuit and the order in the circuit.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101837591A (en) * | 2010-03-12 | 2010-09-22 | 西安电子科技大学 | Robot path planning method based on two cooperative competition particle swarms and Ferguson spline |
| CN101056074B (en) * | 2007-05-18 | 2010-11-10 | 吉林大学 | An ultrasonic motor control method based on the immunity particle cluster algorithm |
| CN102254082A (en) * | 2011-04-22 | 2011-11-23 | 中科怡海高新技术发展江苏股份公司 | Real-time intelligent vehicle and boat scheduling method |
| CN103049805A (en) * | 2013-01-18 | 2013-04-17 | 中国测绘科学研究院 | Vehicle route optimization method with time window constraint based on improved particle swarm optimization (PSO) |
| CN104240054A (en) * | 2014-08-13 | 2014-12-24 | 福州大学 | Implementation method of logistics vehicle dispatching based on particle swarms |
| CN104700251A (en) * | 2015-03-16 | 2015-06-10 | 华南师范大学 | Maximum-minimum ant colony optimization method and maximum-minimum ant colony optimization system for solving vehicle scheduling problem |
| 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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| CN101837591A (en) * | 2010-03-12 | 2010-09-22 | 西安电子科技大学 | Robot path planning method based on two cooperative competition particle swarms and Ferguson spline |
| CN102254082A (en) * | 2011-04-22 | 2011-11-23 | 中科怡海高新技术发展江苏股份公司 | Real-time intelligent vehicle and boat scheduling method |
| CN102254082B (en) * | 2011-04-22 | 2013-09-18 | 中科怡海高新技术发展江苏股份公司 | Real-time intelligent vehicle and boat scheduling method |
| CN103049805A (en) * | 2013-01-18 | 2013-04-17 | 中国测绘科学研究院 | Vehicle route optimization method with time window constraint based on improved particle swarm optimization (PSO) |
| CN104240054A (en) * | 2014-08-13 | 2014-12-24 | 福州大学 | Implementation method of logistics vehicle dispatching based on particle swarms |
| CN104700251A (en) * | 2015-03-16 | 2015-06-10 | 华南师范大学 | Maximum-minimum ant colony optimization method and maximum-minimum ant colony optimization system for solving vehicle scheduling problem |
| CN104700251B (en) * | 2015-03-16 | 2018-02-02 | 华南师范大学 | The improvement minimax ant colony optimization method and system of a kind of vehicle dispatching problem |
| CN106489161A (en) * | 2015-06-15 | 2017-03-08 | 每续株式会社 | Process the method server that delivery information confirms working |
| 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 |
| CN110428161A (en) * | 2019-07-25 | 2019-11-08 | 北京航空航天大学 | A kind of unmanned mine car cloud intelligent dispatching method based on end edge cloud framework |
| CN112950091A (en) * | 2021-04-19 | 2021-06-11 | 深圳市汉德网络科技有限公司 | Vehicle scheduling method, device and storage medium |
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