CN109726863A - A kind of material-flow method and system of multiple-objection optimization - Google Patents
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Abstract
The invention discloses a kind of material-flow method of multiple-objection optimization and system, method includes: setting logistics end parameter；Road net data is obtained from electronic map supplier；It obtains order data and generates basic dispatching task and constraint condition；Optimization aim is determined from preset optimization aim set；Road net data, constraint condition and optimization aim are handled based on saving algrithm or greedy algorithm to obtain the initial solution of distribution project；Based on optimization aim, evolution iteration initial solution is to obtain optimal solution.System is for executing method.The precondition of transport is determined by setting logistics end parameter, obtaining road net data by electronic map can reduce operating cost to obtain existing routing information, the constraint condition for limiting logistics scheme is formulated according to order data, basic dispatching task, the initial solution of distribution project is determined by saving algrithm or greedy algorithm, the initial solution that distribution project is handled by iterative evolution, can be sufficiently according to the suitable ogistics distribution schedule of various output with conditions.
Description
Technical field
The present invention relates to logistics technology, the material-flow method and system of especially a kind of multiple-objection optimization.
Background technique
Under the background of socio-economic development, urban inner and intercity cargo movement constantly increase, and logistics transportation exists
Play the role of during this considerable.In logistics progress, the allotment of order for goods and vehicle, the rule of transit route
Drawing all is the key factor for influencing conevying efficiency and cost.
In current logistics distribution process, dispatcher needs to cope with more and more Order splittings and route choosing；
Meanwhile the time window of picking being sent the demands such as the optimization aims such as constraint conditions and cost, time and route such as to limit also more
It is various.Under common situation, logistics distribution enterprise and there is the mechanism of a large amount of logistics distribution demands in carrying out the above-mentioned course of work
Or by the way of manual dispatching, working efficiency largely depends on the experience and subjective judgement of personnel, science judgement
Have with objective optimization to be reinforced.
Summary of the invention
The present invention is directed to solve one of critical issue in the related technology at least to a certain extent.For this purpose, of the invention
One purpose is to provide the material-flow method and system of a kind of multiple-objection optimization.
The technical scheme adopted by the invention is that:
A kind of material-flow method of multiple-objection optimization, comprising: S1, setting logistics end parameter, logistics end parameter includes storehouse
Storage point information, distribution point information dispense goods information and Carrier-information；S2, road is obtained from third-party electronic map supplier
Network data；S3, order data is obtained, basic dispatching task and constraint condition is generated according to the order data；S4, from preset
A kind of minimum optimization aim is determined in optimization aim set；S5, handled based on saving algrithm or greedy algorithm the road net data,
Basic dispatching task, constraint condition and optimization aim are to obtain the initial solution of distribution project, the initial unpacking of the distribution project
Include transit route, the selection of carrier, storage point loading and unloading detailed list of goods and time, distribution point loading and unloading detailed list of goods and time；
S6, the initial solution based on distribution project described in optimization aim iterative evolution are to obtain optimal solution.
Preferably, the road net data includes traffic route position, road physical geometric and historical traffic data, right
Answer, step S5 specifically include based on saving algrithm or greedy algorithm processing traffic route position, road physical geometric and
Historical traffic data dispenses task, constraint condition and optimization aim substantially to obtain the initial solution of distribution project.
Preferably, the order data includes storage point and mesh where cargo type, order requirements, quantity of goods, cargo
Standard configuration is made arrangements for his funeral a little, corresponding, and step S3 is specifically included: obtaining order data, the point of the storage according to belonging to cargo and target distribution point
Time window (time range of cargo handling limits) requires to generate time constraint condition；According to quantity of goods (in combined data library
Goods information measurement of cargo, goods weight can be calculated), spelling list (or singulated) feasibility of order requirements and Carrier-information
It generates and loads constraint condition, that is, which kind of carrier is needed just to be able to satisfy the loading of the cargo of corresponding demand；Parking for vehicle be not a little
The initial storage point of logistics distribution task or at the end of distribution point, whether starting point is returned to according to carrier after the completion of dispatching task
(or certain required location) generates return constraint condition, and label time constraint condition, loading constraint condition, return constraint condition are about
Beam condition.
Preferably, step S6 is specifically included: handling the distribution side based on genetic algorithm or annealing algorithm or ant group algorithm
The initial solution of case is to obtain optimal solution.
Preferably, the optimization aim includes: that route is most short, and logistics cost is minimum, carrier efficiency of loading highest and carrier
Usage quantity is minimum.
The technical scheme adopted by the invention is that:
A kind of logistics system of multiple-objection optimization, comprising: parameter input module, for logistics end parameter, the object to be arranged
Flowing end parameter includes storage point information, and distribution point information dispenses goods information and Carrier-information；Guiding module is used for from third
The electronic map supplier of side obtains road net data；Order input module, for obtaining order data, according to the order data
Generate basic dispatching task and constraint condition；Optimum choice module, for determining minimum one from preset optimization aim set
Kind optimization aim；Processing module, for based on saving algrithm or greedy algorithm handle the road net data, substantially dispense task,
To obtain the initial solution of distribution project, the initial solution of the distribution project includes transit route, carries for constraint condition and optimization aim
The selection of tool is stored in a warehouse point loading and unloading detailed list of goods and time, distribution point loading and unloading detailed list of goods and time；Optimization module is used for
Initial solution based on distribution project described in optimization aim iterative evolution is to obtain optimal solution.
Preferably, the road net data includes traffic route position, road physical geometric and historical traffic data, right
Answer, step S5 specifically include based on saving algrithm or greedy algorithm processing traffic route position, road physical geometric and
Historical traffic data dispenses task, constraint condition and optimization aim substantially to obtain the initial solution of distribution project.
Preferably where, the order data includes cargo type, order requirements, quantity of goods, cargo storage point and
Target dispenses terminal, corresponding, and order input module is specifically used for obtaining order data, the point of the storage according to belonging to cargo and target
Distribution point time window (time range of cargo handling limits) requires to generate time constraint condition；According to quantity of goods (in conjunction with number
Measurement of cargo, goods weight can be calculated according to the goods information in library), spelling list (or singulated) feasibility and load of order requirements
Have information and generate loading constraint condition, that is, which kind of carrier is needed just to be able to satisfy the loading of the cargo of corresponding demand；Vehicle is parked
Point be not logistics distribution task initial storage point or at the end of distribution point, whether returned according to carrier after the completion of dispatching task
Starting point (or certain required location) generates return constraint condition, and label time constraint condition loads constraint condition, return constraint item
Part is constraint condition.
Preferably, the optimization module is specifically used for being based on matching described in genetic algorithm or annealing algorithm or ant group algorithm processing
Send the initial solution of scheme to obtain optimal solution.
Preferably, the optimization aim includes: that route is most short, and logistics cost is minimum, carrier efficiency of loading highest and carrier
Usage quantity is minimum.
The beneficial effects of the present invention are:
The present invention determines the precondition of transport by setting logistics end parameter, obtains road net data by electronic map
It can reduce operating cost to obtain existing routing information, formulated according to order data, basic dispatching task for limiting
The constraint condition of logistics scheme is determined the initial solution of distribution project by saving algrithm or greedy algorithm, passes through iterative evolution
The initial solution for handling distribution project, can be sufficiently according to the suitable ogistics distribution schedule of various output with conditions.
Detailed description of the invention
Fig. 1 is a kind of schematic diagram of the material-flow method of multiple-objection optimization of the invention；
Fig. 2 is the schematic diagram of spatial relationship of the invention；
Fig. 3 is a kind of schematic diagram of the logistics system of multiple-objection optimization of the invention.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.
Embodiment 1
The present embodiment provides a kind of material-flow method of multiple-objection optimization as shown in Figure 1, S1, setting logistics end parameter, institutes
Stating logistics end parameter includes storage point information, and distribution point information dispenses goods information and Carrier-information；S2, from third-party electricity
Sub- map supply quotient obtains road net data；S3, order data is obtained, basic dispatching task peace treaty is generated according to the order data
Beam condition；S4, a kind of minimum optimization aim is determined from preset optimization aim set；S5, it is calculated based on saving algrithm or greed
Method handles the road net data, dispenses task, constraint condition and optimization aim substantially to obtain the initial solution of distribution project, described
The initial solution of distribution project includes transit route, the selection of carrier, storage point loading and unloading detailed list of goods and time, distribution point handling
Carry detailed list of goods and time；S6, the initial solution based on distribution project described in optimization aim iterative evolution are to obtain optimal solution.
Wherein, logistics end parameter is to manually enter, and is also possible to import by Excel table；Storage point and distribution point (position
Set) information is place longitude and latitude data, pass through the location point in longitude and latitude Data Matching to electronic map；Goods information includes goods
Object ID (corresponding cargo type), the monomer weight of cargo, (weight and volume is calculated by cargo data by formula monomer volume
To) and characteristics such as frangible, moisture-sensitive, be initial input to system (i.e. for executing the carrier of method, including calculator,
The structures such as server, external structure, network) inside representative cargo feature master data；Order data includes cargo ID
And quantity, system need automatic weight, volume and the other limitations for calculating order cargo, characterization cargo during transportation
The transport resource of occupancy；Information of vehicles (i.e. Carrier-information) includes loading capacity, container volume, type of vehicle (large-scale, medium-sized), table
The transport capacity of its offer is provided and travels the limitation that may be subject on road；Road net data is by network map provider
(third party) open interface, system is directly accessed, when the traveling time for needing to calculate certain a certain distance, the length in specific section
Degree and the traffic passage situation at corresponding moment are accessed and are calculated as external initial parametric data；According to order data it is known that
Which cargo will be transported to where, pass through stored goods information (information such as type, quantity including cargo, and the essence of cargo
It is the saved cargo of storage point) it is known that the corresponding cargo of storage point, storage point of the part order to demand transport cargo
There are specific descriptions with distribution point, part order then only has specific requirement to the distribution point of cargo, may be selected to be stored with dispatching demand
Any storage point of cargo；The point of the storage according to belonging to cargo and target distribution point time window (time range of cargo handling limits)
It is required that generating time constraint condition；According to quantity of goods, (measurement of cargo, goods can be calculated in the goods information in combined data library
Object weight), spelling list (or singulated) feasibility of order requirements and Carrier-information generate and load constraint condition, that is, which kind of carrier needed
Just it is able to satisfy the loading of the cargo of corresponding demand；Vehicle park a little be not logistics distribution task initial storage point or at the end of
Distribution point, according to carrier after the completion of dispatching task whether return starting point (or certain required location) generate return constraint condition；
The road net data, dispatching task, constraint condition and optimization aim are handled based on saving algrithm or greedy algorithm to obtain logistics
Initial solution handles the logistics initial solution based on iterative evolution to obtain optimal solution.
So-called greedy algorithm refers to: always making when to problem solving and is currently appearing to be best selection.Namely
It says, is not taken in from total optimization, what is made is only locally optimal solution in some sense.Greedy algorithm is not solid
The key of fixed algorithm frame, algorithm design is the selection of Greedy strategy.
Saving algrithm is the heuritic approach for solving haulage vehicle number uncertain problem, also known as saving algrithm or section
Provisional constitution can optimize running distance with parallel mode and serial mode；Saving algrithm core concept is successively by transportation problem
In two circuits merge into a circuit, the amplitude for every time reducing total transportation range after merging is maximum, until reaching one
When the loading of vehicle is restricted, then carry out the optimization of the loading of next vehicle；The particular content of optimization needs to meet path excellent
Change formula: S (Oi+Oj+ij)+C > S (Oi)+S (iO)+S (Oj)+S (jO)+2C, wherein S is mileage cost, and C is vehicle use
Cost, O, i, j are place (spatial relationship as shown in Figure 2), i.e., can the cost that inspection Fig. 2 b situation uses be lower than Fig. 2 a feelings
The cost that condition uses；The reduction for only meeting distance/cost, just will do it the execution of saving algrithm, that is, Fig. 2 a to Fig. 2 b occurs
The case where.
Time window consider, storage point loading and distribution point delivery all must in the time range of order requirements, and
It cannot be beyond the service time range of storage point and distribution point；
It loads limitation to consider, no more than vehicle maximum loading, more than the optimization for carrying out next vehicle；
Route segmentation is limited to double constraints, a string of routes (individual) with time window and loading, if next point is discontented
Sufficient distribution point time window or vehicle loading, that is, disconnect, and means one vehicle delivery point of increase.
After obtaining initial solution, more algorithms (genetic algorithm, annealing algorithm and ant group algorithm etc.) in algorithm pond are utilized
It is iterated, attempts to find globally optimal solution.This process is according to the complicated and time consumption of problem, therefore application system can be set herein
The upper limit solves the time, can also be with manual abort iterative process.
For different target problems, during iteration chooses optimal solution, the iterative algorithm preferentially selected can not yet
Together.For example, it is contemplated that can empirically consider preferentially to be iterated calculating using genetic algorithm when Optimum cost, it in this way can be bigger
Guarantee in degree obtained solution be globally optimal solution probability it is higher.
After obtaining initial solution, system can keep in the optimal solution of existing acquisition；It is obtained every time in iterative process
Xie Junhui is compared with temporary optimal solution, if acquired results become temporary optimal solution, otherwise temporarily better than temporary optimal solution
Optimal solution is deposited to remain unchanged；When iterative process terminates or north stops manually, keeps in optimal solution and exported as final result.
Output result information includes: the car number (belonging to Carrier-information) of selection, the transit route of each car and time
It limits, in the type of merchandize and quantity (optimal solution, i.e. logistics distribution scheme) of each parking node handling goods.
Embodiment 2
The purpose of the present embodiment is that explaining preferred embodiment.
Logistics end parameter includes the field description list of storage dispensing point position data, information of vehicles and dispatching cargo,
Storage point and distribution point position data are usually the location expression to them, are then automatically converted to corresponding coordinate by system and retouch
It states, is stored after consigner confirms, each storage point or distribution point have unique position encoded；Haulage vehicle information includes vehicle
Load-carrying, storage volume, travel speed and height of car, each car have unique vehicle ID；Dispense the field description of cargo
List is the complete or collected works that possible transport cargo, and the attribute description of every kind of cargo includes unique cargo ID, Description of Goods, cargo class
Type, measurement unit, Unit Weight (heavy cargo) or unit volume (low density cargo).
The information of dispatching order includes the storage point number of picking, the number of the distribution point of delivery, the every kind of goods that need to be dispensed
The ID and quantity of object, total number of packages of cargo, total weight and total volume；It meanwhile further including loading and unloading the time window of goods and floating accordingly
Dynamic time range.In addition, during this, the problem of haulage vehicle (i.e. carrier) will also cope with reversed order, reply is from dispatching
Point picking is loaded back into storage point.
For the programs ultimately generated, there are four the optional optimization aims of aspect: (from the aspect of transport) route
It is most short, (considering from managing, cost is minimum) Optimum cost, (i.e. carrier charging ratio, the purpose is in vehicle for vehicle loading rate highest
Meet logistics demand in insufficient situation) and using vehicle fleet size minimum (carrier usage quantity is minimum, and the purpose is to deal with needs
The client of primary or few unloading, also for the situation of the vehicle deficiency for the cargo transport being likely to occur).Route is most short to refer to all vehicles
Traveling total kilometrage it is most short；Optimum cost then considers the unit cost of vehicle transport, freight total amount, line length and road
The combined influence of bridge expense etc.；The charging ratio of vehicle is the total measurement (volume) for occupying volume and all call vehicle of entire cargo
Ratio (or considering the ratio of the gross weight of entire cargo and the airlift of rolling stock, the two takes high level)；It is at least using vehicle
Consider that the vehicle for completing order dispatching required by task calling is minimum.
Should having certain boundary, (there may be do not have for the data (order data, road net data, logistics end parameter) of input
Have the case where solution), a variety of strategies are exactly to scan under a variety of desired selected objective targets simultaneously, it is desired to be able to obtain one
A feasible solution or one group of feasible solution (diversity for enriching the input parameter of subsequent evolution algorithm), so as to subsequent evolution algorithm into
Row iteration optimization.
Embodiment 3
The present embodiment provides a kind of logistics systems of multiple-objection optimization as shown in Figure 3, including a kind of multiple-objection optimization
Logistics system, comprising: parameter input module 1, for logistics end parameter to be arranged, logistics end parameter includes storage point information,
Distribution point information dispenses goods information and Carrier-information；Guiding module 2, for being obtained from third-party electronic map supplier
Road net data；Order input module 3 generates basic dispatching task peace treaty according to the order data for obtaining order data
Beam condition；Optimum choice module 4, for determining a kind of minimum optimization aim from preset optimization aim set；Processing module
5, for handling the road net data based on saving algrithm or greedy algorithm, dispensing task, constraint condition and optimization aim substantially
To obtain the initial solution of distribution project, the initial solution of the distribution project includes transit route, the selection of carrier, storage point handling
Carry detailed list of goods and time, distribution point loading and unloading detailed list of goods and time；Optimization module 6, for based on optimization aim iteration into
Change the initial solution of the distribution project to obtain optimal solution.
It is to be illustrated to preferable implementation of the invention, but the invention is not limited to the implementation above
Example, those skilled in the art can also make various equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all included in the scope defined by the claims of the present application.
Claims (10)
1. a kind of material-flow method of multiple-objection optimization characterized by comprising
S1, setting logistics end parameter, logistics end parameter include storage point information, distribution point information, dispatching goods information and
Carrier-information；
S2, road net data is obtained from third-party electronic map supplier；
S3, order data is obtained, basic dispatching task and constraint condition is generated according to the order data；
S4, a kind of minimum optimization aim is determined from preset optimization aim set；
S5, the road net data is handled based on saving algrithm or greedy algorithm, dispenses task, constraint condition and optimization aim substantially
To obtain the initial solution of distribution project, the initial solution of the distribution project includes transit route, the selection of carrier, storage point handling
Carry detailed list of goods and time, distribution point loading and unloading detailed list of goods and time；
S6, the initial solution based on distribution project described in optimization aim iterative evolution are to obtain optimal solution.
2. a kind of material-flow method of multiple-objection optimization according to claim 1, which is characterized in that the road net data includes
Traffic route position, road physical geometric and historical traffic data, corresponding, step S5 is specifically included based on saving algrithm
Or greedy algorithm handles traffic route position, road physical geometric and historical traffic data, dispenses task, constraint item substantially
Part and optimization aim are to obtain the initial solution of distribution project.
3. a kind of material-flow method of multiple-objection optimization according to claim 1, which is characterized in that the order data includes
Storage point and target dispense terminal where cargo type, order requirements, quantity of goods, cargo, and corresponding, step S3 is specifically wrapped
It includes:
Order data is obtained, requires to generate time constraint condition, root according to storage point and target distribution point time window where cargo
It is generated according to quantity of goods, order requirements and Carrier-information and loads constraint condition, whether need to go to according to carrier after the completion of dispatching
To generate return constraint condition, label time constraint condition loads constraint condition, return constraint condition as constraint item for preset
Part.
4. a kind of material-flow method of multiple-objection optimization according to claim 1, which is characterized in that step S6 is specifically included:
The initial solution of the distribution project is handled based on genetic algorithm or annealing algorithm or ant group algorithm to obtain optimal solution.
5. a kind of material-flow method of multiple-objection optimization according to claim 1, which is characterized in that the optimization aim packet
Include: route is most short, and logistics cost is minimum, and carrier efficiency of loading highest and carrier usage quantity are minimum.
6. a kind of logistics system of multiple-objection optimization characterized by comprising
Parameter input module, for logistics end parameter to be arranged, logistics end parameter includes storing in a warehouse to put information, distribution point information,
Dispense goods information and Carrier-information；
Guiding module, for obtaining road net data from third-party electronic map supplier；
Order input module generates basic dispatching task and constraint condition according to the order data for obtaining order data；
Optimum choice module, for determining a kind of minimum optimization aim from preset optimization aim set；
Processing module, for handling the road net data based on saving algrithm or greedy algorithm, dispensing task, constraint condition substantially
Obtain the initial solution of distribution project with optimization aim, the initial solution of the distribution project include transit route, carrier selection,
Storage point loading and unloading detailed list of goods and time, distribution point loading and unloading detailed list of goods and time；
Optimization module, for the initial solution based on distribution project described in optimization aim iterative evolution to obtain optimal solution.
7. a kind of logistics system of multiple-objection optimization according to claim 6, which is characterized in that the road net data includes
Traffic route position, road physical geometric and historical traffic data, corresponding, step S5 is specifically included based on saving algrithm
Or greedy algorithm handles traffic route position, road physical geometric and historical traffic data, dispenses task, constraint item substantially
Part and optimization aim are to obtain the initial solution of distribution project.
8. a kind of logistics system of multiple-objection optimization according to claim 6, which is characterized in that the order data includes
Storage point and target dispense terminal where cargo type, order requirements, quantity of goods, cargo, corresponding, order input module tool
Body generates starting point constraint condition for obtaining order data, according to cargo type and storage object information, according to quantity of goods and load
Have information and generate loading constraint condition, according to storage point, storage point information and distribution point generation parking space constraint where cargo
Condition, label starting point constraint condition load constraint condition, parking space constraint condition as constraint condition.
9. a kind of logistics system of multiple-objection optimization according to claim 6, which is characterized in that the optimization module is specific
For the initial solution of the distribution project being handled based on genetic algorithm or annealing algorithm or ant group algorithm to obtain optimal solution.
10. a kind of logistics system of multiple-objection optimization according to claim 6, which is characterized in that the optimization aim packet
Include: route is most short, and logistics cost is minimum, and carrier efficiency of loading highest and carrier usage quantity are minimum.
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