CN107609720A - It is a kind of that vehicle dispatching method is dispensed based on the concrete of genetic algorithm and hill-climbing algorithm - Google Patents
It is a kind of that vehicle dispatching method is dispensed based on the concrete of genetic algorithm and hill-climbing algorithm Download PDFInfo
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Abstract
The invention discloses a kind of optimization method for solving concrete dispatching vehicle dispatching problem.For concrete vehicle dispatching problem, time window penalty mechanism is added, founding mathematical models, using the genetic algorithm solving model based on genetic algorithm and hill-climbing algorithm, finally draw the optimal vehicle scheduling scheme of the model.The optimization method combines the genetic algorithm in intelligent algorithm with hill-climbing algorithm, take full advantage of the stronger ability of searching optimum of genetic algorithm and the stronger local search ability of hill-climbing algorithm, hill climbing maneuver is added in genetic algorithmic procedures, convergence of algorithm speed is effectively improved and searches for the ability of optimal solution.
Description
Technical field
The present invention relates to a kind of concrete to dispense vehicle dispatching method, more particularly to a kind of based on genetic algorithm and calculation of climbing the mountain
The concrete dispatching vehicle dispatching method of method.
Background technology
Concrete is one of world today's dosage maximum, the widest construction material of purposes.Currently, construction building site
Concrete supply mainly has two ways：The dispatching of the commerical ready-mixed concrete factory of live mixing and specialty.Two kinds of methods of supplying respectively have model
Enclose.The dispatching of commerical ready-mixed concrete is to be stirred by commerical ready-mixed concrete factory according to construction site demand according to the proportioning being pre-designed
Afterwards, load concrete premixing car and transport construction site to.
If being unable to the time of making the product of each concrete premixing car of reasonable arrangement and going out car order, commerical ready-mixed concrete is just influenced whether
Factory and the interests of construction site both sides.The dispatching of concrete just become in commerical ready-mixed concrete industry supply and marketing link one it is non-
An often prominent ring.
The dispatching problem of concrete is the typical vehicle dispatching problem with time window (VRPTW), as intelligent optimization is calculated
Application of the method in terms of optimization problem is constantly expanded, and increasing domestic and foreign scholars are solved using the genetic algorithm of artificial intelligence
Certainly VRPTW problems, achieve good effect.But genetic algorithm also shows some inferior positions, as local search ability is weak, convergence
Speed is slow.
Can it is desirable to there is a kind of new algorithm for the concrete dispatching vehicle dispatching problem for solving having time window to use up
The disadvantage mentioned above of genetic algorithm can be overcome.
The content of the invention
Present disclosure is to propose a kind of to dispense vehicle scheduling side based on the concrete of genetic algorithm and hill-climbing algorithm
Method, operated by adding hill-climbing algorithm in traditional operatings of genetic algorithm step, organically by the good overall situation of genetic algorithm
The search capability local search ability good with hill-climbing algorithm combines, to improve the local search ability of algorithm and convergence speed
Degree.
In order to realize above-mentioned purpose, present invention employs following technical scheme：
A kind of to dispense vehicle dispatching method based on the concrete of genetic algorithm and hill-climbing algorithm, this method includes following step
Suddenly：
S1 is encoded:Each concrete vehicle scheduling scheme is all subjected to corresponding coding, one in each population
The corresponding coding of individual；
S2 initializes population:N number of coding corresponding to N number of feasible scheduling scheme is randomly generated, forms an initial population；
S3 carries out adaptive value evaluation to each individual, and adaptive value is used for judging each individual quality；
S4 selection operations：Selected after adaptive value evaluation is carried out to the individual in every generation population using roulette wheel method；
S5 crossover operations：It is individual according to probability P to being selected by S4CCarry out crossover operation and obtain middle offspring individual,
Method therefor of the present invention is intersected using double point of contacts carries out crossover operation.
S6 mutation operations：It is individual according to probability P to being selected by S4mCarry out mutation operation and obtain middle offspring individual；
S7 hill climbing maneuvers：The middle offspring individual obtained to S5, S6 carries out hill climbing maneuver, selects to each middle filial generation
The optimum individual that body progress hill climbing maneuver obtains is as offspring individual；
The offspring individual that S8 obtains to S7 carries out adaptive value evaluation, if optimum individual adaptive value has reached requirement, or algorithm
Reach default iterations, then algorithm terminates；Otherwise step S3 is gone to, continues the computing of algorithm；
S9 algorithms export optimal solution, optimal solution be in population adaptive value highest individual, namely optimal concrete fortune
Defeated vehicle scheduling scheme；
The object function that value function is defined as in the required solution mathematical modeling of the algorithm is adapted in the step：
Solution mathematical modeling is required by the algorithm：
Mathematicization constraints is:
PT≤TA, jk≤Bj (III)
Nj·aj≤KjL j=1,2 ... .m (V)
STjk+1-STjk≥BT (VI)
|STjk-ST(j+1)k|≥BT (VIII)
Formula (I), (II) represent to solve target totle drilling cost, including concrete mixing plant waits unload time cost, Yi Jiwei
Meet punishment cost caused by soft time-constrain.
Formula (III) represents that concrete distribution vehicle arrival time must be no earlier than building site cut-in time, but must expire
The rigid time windows constraints B of footj, i.e., arrival time must not delay project progress the latest.
Formula (IV) represents that each building site all has concrete demand, and all construction sites need dispensing vehicle time sum big
In being available for distribution vehicle quantity.
Formula (V) represents that each building site concrete overall supplies is not less than aggregate demand.
Formula (VI) represents that building site j front and rear transport vehicle interval time twice is more than loading time.
Formula (VII) represents that the concrete dispatching of each each train number in building site implements dispatching by a car.
Formula (VIII) represents that the kth time distribution time interval in different building sites is not less than loading time BT.
It is equal with total train number number in all building sites that formula (IX) represents that all vehicles always send number with charge free.
Each symbol implication is in the formula：
(1) constant parameter
(2) variable parameter
Incorporation time window is used in the model, is expressed as (Aj,Bj),AjWhen being expressed as construction site can receive to service soft
Between window constrain, distribution vehicle be later than reach construction site then can be by economic punishment.BjRepresent that construction site can receive dispatching
Latest time, be rigid time windows constraints, avoid being later than arrival, be later than BjThen construction site selection is sent to reject.
Preferably, the algorithm realizes hill climbing maneuver using gene exchange operator, and its concrete operation method is:1. in individual
Two genes of middle random selection, and exchange their position；2. judge whether its adaptive value increases after Inter-genic spacer, if adaptive value
Increase, then with the former individual of individual substitution after transposition；3. repeat 1., 2., untill the exchange times for reaching certain.
The present invention combines the genetic algorithm in intelligent algorithm and hill-climbing algorithm, efficiently utilizes hereditary calculation
The ability of searching optimum of method and the local search ability of hill-climbing algorithm, a kind of Revised genetic algorithum is developed to solve concrete
Distribution vehicle scheduling problem.
Brief description of the drawings
Fig. 1 is the method flow block diagram of the present invention.
Fig. 2 is dispatches a car sequentially and timetable with the concrete distribution vehicle for example that is solved of the present invention.
Fig. 3 is the result for example that is solved respectively with traditional genetic algorithm and inventive algorithm.
Embodiment
The embodiment that the invention will now be described in detail with reference to the accompanying drawings, its as part of this specification, passes through
Embodiment illustrates the principle of the present invention, other side of the invention, feature and its will a little be become by the detailed description
It is very clear.In the accompanying drawing of institute's reference, same or analogous part is represented using identical drawing reference numeral in different figures.
To make the present invention easier to understand, the specific embodiment of the present invention is further illustrated below.
Certain concrete mixing plant related data is as shown in table 1, the mixing plant and the distance such as table 2 of four construction sites in periphery
Shown, the related data such as the concrete demands of four construction sites, dispatching train number, time constraint condition is as shown in table 3, coagulation
Native stand-by period cost coefficient s=2.5, mixing plant and the unit interval punishment cost coefficient p=8 of each construction site agreement, are mixed
It is the morning 8 that solidifying soil mixing station, which starts distribution time,:30, it is the morning 9 that each construction site, which can receive the prefabricated components time,:00.
Examination arranges optimal vehicle deploying order and the time of departure under current constraints.
Table 1
Table 2
Table 3
Characterized in that, the solution concrete mixer scheduling model is as follows：
Mathematicization constraints is:
PT≤TA, jk≤Bj (III)
Nj·aj≤KjL j=1,2 ... .m (V)
STjk+1-STjk≥BT (VI)
|STjk-ST(j+1)k|≥BT (VIII)
Specific steps：
Coding：Each concrete vehicle scheduling scheme is all subjected to corresponding coding, in each population one by one
The corresponding coding of body, the present invention are encoded using Real-valued, and each gene is made up of three real numbers, and three real numbers are each
The dispensing appliance commerical ready-mixed concrete factory in model corresponding to representing, construction site, demand train number.
Initialize population:100 codings corresponding to 100 feasible scheduling schemes are randomly generated, form an initial kind
Group；
Adaptive value evaluation is carried out to each individual, adaptive value is used for judging each individual quality；Deacclimatizing value is：
Selection operation：Selected after adaptive value evaluation is carried out to the individual in every generation population using roulette wheel method；
Crossover operation：Crossover operation is carried out according to 0.9 crossover probability to the individual by selection operation and obtains middle son
Generation individual, intersected using double point of contacts and carry out crossover operation；
Variation：To the individual by selection operation, according to 0.02 mutation probability, optionally some genes invert on chromosome
Its place value carries out mutation operation and obtains middle offspring individual；
Hill climbing maneuver：Hill climbing maneuver is carried out to the middle offspring individual for intersecting, being obtained after mutation operation, using Inter-genic spacer
Operator realizes hill climbing maneuver, and its concrete operation method is:1. randomly choosing two genes in individual, and exchange their position
Put:2. judge whether its adaptive value increases after Inter-genic spacer, if adaptive value increases, with the former individual of individual substitution after transposition；
3. repeat 1., 2. 100 times.The optimum individual for selecting to obtain each middle offspring individual progress hill climbing maneuver is as filial generation
Body；
Adaptive value evaluation is carried out to offspring individual obtained above, if optimum individual adaptive value has reached requirement, or algorithm
Reach default iterations 1000 times, then algorithm terminates；Otherwise selection operation is gone to, continues the computing of algorithm；
Export optimal solution.It is as shown in Figure 2 to obtain optimal distribution project.
It is last it is to be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.To the greatest extent
Pipe is explained in detail with reference to preferred embodiments to the present invention, it will be understood by those within the art that, can be to the present invention
Technical scheme modify or replace on an equal basis, without departing from the spirit and scope of technical solution of the present invention.
Claims (5)
1. a kind of concrete vehicle dispatching method based on genetic algorithm and hill-climbing algorithm, it is characterised in that this method includes following
The step of
S1 is encoded:Each concrete vehicle scheduling scheme is all subjected to corresponding coding, the individual in each population
A corresponding coding；
S2 initializes population:N number of coding corresponding to N number of feasible scheduling scheme is randomly generated, forms an initial population；
S3 carries out adaptive value evaluation to each individual, and adaptive value is used for judging each individual quality；
S4 selection operations：Selected after adaptive value evaluation is carried out to the individual in every generation population using roulette wheel method；
S5 crossover operations：It is individual according to crossing-over rate P to being selected by S4CCarry out crossover operation and obtain middle offspring individual, this hair
Bright method therefor is intersected using double point of contacts carries out crossover operation；
S6 mutation operations：It is individual according to aberration rate P to being selected by S4mCarry out mutation operation and obtain middle offspring individual；
S7 hill climbing maneuvers：The middle offspring individual obtained to S5, S6 carries out hill climbing maneuver, selects to enter each middle offspring individual
The optimum individual that row hill climbing maneuver obtains is as offspring individual；
The offspring individual that S8 obtains to S7 carries out adaptive value evaluation, if optimum individual adaptive value has reached requirement, or algorithm reaches
Default iterations, then algorithm terminate；Otherwise step S3 is gone to, continues the computing of algorithm；
S9 algorithms export optimal solution, and optimal solution is adaptive value highest individual, namely optimal vehicle scheduling scheme in population.
2. a kind of concrete mixer dispatching method based on genetic algorithm and hill-climbing algorithm according to claim 1,
It is defined as solving the object function in mathematical modeling required by the algorithm it is characterized in that value function will be adapted in the step S3：
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3. a kind of concrete mixer dispatching method based on genetic algorithm and hill-climbing algorithm according to claim 2,
It is characterized in that the required solution mathematical modeling of the algorithm is
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PT≤Tα, jk≤Bj (III)
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Formula (I), (II) are represented to solve target totle drilling cost, including concrete mixing plant is waited unloading and time cost and do not met
Punishment cost caused by soft time-constrain；
Formula (III) represents that concrete distribution vehicle arrival time must be no earlier than building site cut-in time, but must is fulfilled for hard
Property time windows constraints Bj, i.e., arrival time must not delay project progress the latest；
Formula (IV) represents that each building site all has a concrete demand, and all construction sites need dispensing vehicle time sum to be greater than can
For distribution vehicle quantity；
Formula (V) represents that each building site concrete overall supplies is not less than aggregate demand；
Formula (VI) represents that building site j front and rear transport vehicle interval time twice is more than loading time；
Formula (VII) represents that the concrete dispatching of each each train number in building site implements dispatching by a car；
Formula (VIII) represents that the kth time distribution time interval in different building sites is not less than loading time BT；
It is equal with total train number number in all building sites that formula (IX) represents that all vehicles always send number with charge free,
The concrete meaning of symbol is as follows in above-mentioned each formula：
Constant parameter
Variable parameter
4. a kind of concrete mixer dispatching method based on genetic algorithm and hill-climbing algorithm according to claim 1,
It is characterized in that in the step S7, hill climbing maneuver is realized using gene exchange operator, its concrete operation method is：1. in individual
Two genes of middle random selection, and exchange their position；2. judge whether its adaptive value increases after Inter-genic spacer, if adaptive value
Increase, then with the former individual of individual substitution after transposition；3. repeat 1., 2., untill the exchange times for reaching certain.
5. a kind of concrete mixer dispatching method based on genetic algorithm and hill-climbing algorithm according to claim 3,
It is characterized in that using incorporation time window in the model, (A is expressed asj,Bj),AjService can be received by being expressed as construction site
Weak rock mass constrains, and distribution vehicle is later than arrival construction site then can be by economic punishment, BjRepresent that construction site can receive
The latest time of dispatching, it is rigid time windows constraints, avoids being later than arrival, be later than BjThen construction site selection is sent to reject.
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| CN112668248A (en) * | 2021-01-20 | 2021-04-16 | 中国建筑土木建设有限公司 | Method and system for scheduling optimization calculation theoretical model of concrete transport vehicle |
| CN113469473A (en) * | 2021-09-06 | 2021-10-01 | 华南理工大学 | Same-city distribution route planning method considering reverse logistics |
| CN114742329A (en) * | 2022-06-13 | 2022-07-12 | 武汉大学 | Improved urban waterlogging vehicle risk-avoiding path genetic planning method |
| WO2023050946A1 (en) * | 2021-09-28 | 2023-04-06 | 湖南三一智能控制设备有限公司 | Scheduling method and apparatus for engineering transportation vehicle, engineering transportation vehicle and electronic device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023426A (en) * | 2015-05-18 | 2015-11-04 | 南京邮电大学 | Urban public transport route selection method |
| US20160203419A1 (en) * | 2013-03-09 | 2016-07-14 | Bigwood Technology, Inc. | Metaheuristic-guided trust-tech methods for global unconstrained optimization |
| CN105976030A (en) * | 2016-03-15 | 2016-09-28 | 武汉宝钢华中贸易有限公司 | Multi-agent-based platform scheduling intelligent sorting model structure |
-
2017
- 2017-11-01 CN CN201711059858.5A patent/CN107609720A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160203419A1 (en) * | 2013-03-09 | 2016-07-14 | Bigwood Technology, Inc. | Metaheuristic-guided trust-tech methods for global unconstrained optimization |
| CN105023426A (en) * | 2015-05-18 | 2015-11-04 | 南京邮电大学 | Urban public transport route selection method |
| CN105976030A (en) * | 2016-03-15 | 2016-09-28 | 武汉宝钢华中贸易有限公司 | Multi-agent-based platform scheduling intelligent sorting model structure |
Non-Patent Citations (2)
| Title |
|---|
| 吴海兵等: "混合遗传算法在物流配送路径优化问题中的应用", 《2006系统仿真技术及其应用学术交流会论文集》 * |
| 李萍萍: "装配式 PC 构件配送成本优化研究", 《道客巴巴 HTTP://M.DOC88.COM/MIP-5724920791379.HTML》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919688A (en) * | 2019-03-29 | 2019-06-21 | 杭州电子科技大学 | A kind of electronic cigarette product line planing method considering the market factor |
| CN109919688B (en) * | 2019-03-29 | 2021-03-05 | 杭州电子科技大学 | Electronic cigarette product line planning method considering market factors |
| CN112580865A (en) * | 2020-12-15 | 2021-03-30 | 北京工商大学 | Mixed genetic algorithm-based takeout delivery path optimization method |
| CN112668248A (en) * | 2021-01-20 | 2021-04-16 | 中国建筑土木建设有限公司 | Method and system for scheduling optimization calculation theoretical model of concrete transport vehicle |
| CN112668248B (en) * | 2021-01-20 | 2023-12-26 | 中国建筑土木建设有限公司 | Method and system for dispatching optimization calculation theoretical model of concrete truck |
| CN113469473A (en) * | 2021-09-06 | 2021-10-01 | 华南理工大学 | Same-city distribution route planning method considering reverse logistics |
| CN113469473B (en) * | 2021-09-06 | 2022-01-04 | 华南理工大学 | Same-city distribution route planning method considering reverse logistics |
| WO2023050946A1 (en) * | 2021-09-28 | 2023-04-06 | 湖南三一智能控制设备有限公司 | Scheduling method and apparatus for engineering transportation vehicle, engineering transportation vehicle and electronic device |
| CN114742329A (en) * | 2022-06-13 | 2022-07-12 | 武汉大学 | Improved urban waterlogging vehicle risk-avoiding path genetic planning method |
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