CN101673382A - Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution - Google Patents

Abstract

The invention relates to a combined optimization method for agricultural chain-operation logistics delivering and loading-distribution, belonging to the technical field of the combined optimization ofthe logistics delivering and loading-distribution. The technical scheme comprises: proposing a model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution and converting the model into the problem of the combined optimization of a single delivering center, a single variety and non-full-loading delivering and loading-distribution; designinga solution algorithm for the model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution based on the genetic algorithm principle and solving; and developing a visual vehicle delivering and loading-distribution scheduling management system according to an optimization algorithm and a GIS development platform. The proposed model can actually reflect the interaction of the agricultural loading process and the agricultural delivering process in the operation process of agricultural chain-operation logistics delivering enterprises and embodies that the agricultural loading scheme can determine the selection of agricultural delivering routes to a certain degree. The affection of the agricultural loading and the agricultural delivering on the cost of the agricultural chain-operation logistics delivering enterprises can be comprehensively considered, thereby effectively lowering the loading and delivering cost and the operating cost of agricultural logistics delivering enterprises.

Description

A kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution

Technical field

The invention belongs to logistics distribution prestowage combined optimization technique field, particularly a kind of means of agricultural production chain operation logistics logistics distribution prestowage combined optimization method.

Background technology

Chain operation is meant the some shops that manage similar commodity, use unified trading company, under the management of same general headquarters, takes centralized purchasing or authorizes mode such as the power of management, realizes a kind of modern goods circulation style of economies of scale.Means of agricultural production chain operation is exactly to introduce this advanced management style in means of agricultural production circulation, adopt the mode of joining and training to look for means of agricultural production chain operation person in villages and small towns, by the provide and deliver capital goods in the agricultural sector such as agricultural chemicals, chemical fertilizer, seed of various brands of general headquarters of corporate chains, then by the decentralized management of means of agricultural production supermarket, by main office's unified management.

The chain operation form has had successful development in the urban area, also obtained more achievement about the research of city chain operation logistics distribution theory and technology.According to incompletely statistics, China carries out nearly thousand families of the chain enterprise of the means of agricultural production at present, more than 30,000 of chain shops, and means of agricultural production chain operation is just grown out of nothing in China various places, is risen rapidly from less to more.But but very lag behind fast-developing practice needs at the agricultural material chain operation logistics Study on Theory at present.

Research of the present invention mainly is defined as the research of agricultural material chain operation logistics and prestowage theory and technology.Means of agricultural production logistics prestowage is actually vehicle route optimization problem (VRP with the dispensing combinatorial optimization problem, VehicleRouting Problem) subproblem, comprise consolidating the load line optimization, goods distribution and loading and delivery line optimization, and the integrated optimization of consolidating the load, goods distribution and loading and deliver goods.

The problem that consolidating the load, deliver goods dispensing path optimization will solve is the vehicle route optimization problem, and promptly vehicle is left for some dispensing tasks of finishing from home-delivery center, in order to improve the utilization factor of vehicle, can arrange a car to carry out several transport tasks.Promptly under special traffic condition and situation, how to arrange the route of vehicle, the feasible demand that had both satisfied each task, and make the total cost minimum.

The goods distribution and loading problem can be described as: a plurality of clients are arranged, and its kinds of goods volume and quality are all less than bicycle dress nominal load capacity and stowage.For improving vehicle loading efficient, reducing transportation cost, adopt which kind of assembly form that a plurality of clients' kinds of goods are contained on the same distribution vehicle, successively kinds of goods are sent to the client by a car by the path of certain appointment, make that simultaneously the vehicle number that loads kinds of goods is the least possible, the utilization factor maximum of vehicle perhaps makes the deliver goods profit maximum of vehicle.

With regard to present Research, research is both at home and abroad often isolated analysis with vehicle route optimization problem and goods distribution and loading problem.When analyzing the vehicle route optimization problem, the optimization of less consideration vehicle loading utilization factor also or analyzing vehicle cargo is loaded when optimizing, and ignores the optimal path of vehicle delivery.Even if when considering that simultaneously cargo loading and vehicle route are optimized, also divide primary and secondary to consider that first prestowage is provided and delivered or provided and delivered prestowage more earlier often.

The major defect that exists: optimize earlier and load model, when vehicle route was optimized in the back, after the goods of vehicle loading was determined, the client that distribution vehicle need be visited just decided simultaneously.Under the prerequisite that logistics prestowage optimal case has been determined, cause the travelled by vehicle shortest path probably or optimal case that the spatial dimension of separating is is not necessarily provided and delivered, cause the increase of transportation cost and the waste of resource.Optimize the vehicle delivery path in the ban, when the vehicle loading scheme was determined in the back, after the optimal case of logistics distribution was determined, the client that distribution vehicle need be visited just decided simultaneously, and which goods by which platform vehicle is loaded and also just decides accordingly.Under the prerequisite that the logistics distribution optimal case has been determined, the prestowage scheme that causes logistics probably is the optimal case of prestowage not necessarily, causes the waste of logistics transport power.

Under the guidance of traditional vehicle route prioritization scheme, if loglstics enterprise is the shortest according to only pursuing distribution route, the most rational cargo loading distribution project carries out corporate operation, can be because the minimizing of dispensing distance, reduce the maintenance cost of oil consumption, vehicle, may save a part of operation cost.Also ignored simultaneously nominal load capacity and the volume that utilizes vehicle to greatest extent, this may cause having wasted the lifting capacity and the cargo space of a lot of vehicles, the direct problem of bringing is exactly that distribution vehicle increases, the loglstics enterprise distribution cost increases, caused the increase of vehicle flowrate on the road indirectly, increased blocking up of road, increased the weight of the influence of exhaust emissions environment.Otherwise, if loglstics enterprise carries out corporate operation according to the cargo loading distribution project of only pursuing vehicle loading utilization factor maximum, can increase the vehicle utilization factor, but may cause vehicle in delivery process, to walk repetition, lengthy and tedious route, increase oil consumption and operation cost.Indirectly, the influence that also can cause congestion in road, exhaust emissions to increase the weight of.

Summary of the invention

The present invention is directed in the agricultural material chain operation logistics center operation process, during vehicle scheduling, can not isolate the problem that vehicle cargo is loaded and vehicle delivery path optimization is selected, consider the two defective for overcoming in current logistics prestowage and the dispensing combinatorial optimization problem the first prestowage primary and secondary of prestowage score again of providing and delivering again or provide and deliver earlier, the present invention proposes a kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution, it is characterized in that, may further comprise the steps:

(1) objective function of proposition agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model:, determine the target of means of agricultural production chain operation dispensing prestowage combinatorial optimization problem according to means of agricultural production chain operation dispensing characteristics; Think that the objective function of dispensing prestowage Combinatorial Optimization scheme is polynary, routing problem is minimized to rationalize with the prestowage scheme incorporate total cost and minimize in the object module, with distribution cost and prestowage cost as the optimization aim function;

(2) abstract is banishd to provide and deliver and is carried the Combinatorial Optimization work flow, thereby optimizes total cost according to the selection in cargo distribution expense adjustment dispensing path;

(3) the agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model is proposed: think that dispensing prestowage combinatorial optimization problem can be converted into single home-delivery center, single variety, undercapacity dispensing prestowage combinatorial optimization problem, as follows with this prestowage Combinatorial Optimization Model that proposes to provide and deliver:

$\min z=\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\{k\·{\mathrm{FC}}_k\}+\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}\{d_{\mathrm{ij}}\×x_{\mathrm{ijk}}\×{\mathrm{VC}}_k\}$

s.t.

$\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{y}_{\mathrm{ik}}=1,i=1,\·\·\·,n$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{j0k}=\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{0\mathrm{jk}}=1,k\∈K$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i\×y_{\mathrm{ik}}\≤G_k,k\∈K$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{v}_i\×y_{\mathrm{ik}}\≤V_k,k\∈K$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},j=\mathrm{0,1},\·\·\·,n;k\∈K$

$\underset{j=0}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},i=\mathrm{0,1},\·\·\·,n;k\∈K$

Parameter declaration: Z is a total cost; K is spendable distribution vehicle set; N provides shops's number of delivery service for needs; FC _kIt is the fixed cost of k car; d _IjActual range for distribution vehicle from the i of shops to the j of shops; VC _kIt is the unit kilometer cost of k car; g _iWeight for the demand goods of the i of shops; G _kIt is the dead weight capacity of k car; v _iVolume for the demand goods of the i of shops; V _kIt is the volume of k car;

Variable declaration: x _IjkK car of=1 expression directly drives towards the j of shops from the i of shops, otherwise x _Ijk=0; y _Ik=1 expression i of shops is provided and delivered by k car, otherwise y _Ik=0;

(4) utilize that genetic algorithm has that the global optimization ability is strong, strong robustness, highly versatile, efficient optimization performance, based on the derivation algorithm of principle of genetic algorithm design agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model, described algorithm may further comprise the steps:

(41) coding: structure satisfies the chromosome of constraint condition;

(42) produce initial population at random: the group chromosome when initial population is the search beginning;

(43) calculate each chromosomal fitness: fitness is unique index of reflection chromosome quality, genetic algorithm to seek the chromosome of fitness maximum;

(44) use selection, intersection and mutation operator to produce sub-group;

(45) stop cycling condition: if satisfy the condition of convergence or fixed number of iterations then stops, then change (43) if do not satisfy condition and carry out evolutionary process again, evolutionary process just produces the colony of a new generation each time, and individual represented separating by evolving finally reaches optimum solution in the colony;

(5) design is based on the vehicle scheduling management system of optimized Algorithm:

With the GIS MapInfo that develops software is development platform, make full use of on this basis its with developing instrument MapBasic and object oriented programming languages such as C#, carry out applied software development in conjunction with the Combinatorial Optimization Model algorithm, obtain being used for the means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system of means of agricultural production chain operation dispensing prestowage Combinatorial Optimization management.

The polynary target of the means of agricultural production chain operation dispensing prestowage combinatorial optimization problem in the described step (1) comprises: the consumption minimum most effective or that distribution cost is minimum, the dispensing mileage is the shortest, punctuality is the highest, comprehensive cost is minimum, dispensing is worked of providing and delivering.

Described means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system is made up of information management, optimized Algorithm, 3 parts of management map: information management has realized importing, typing, inquiry, modification and the delete function to information; Optimized Algorithm realizes the dispensing path computing based on genetic algorithm; Management map comprises that the path of browsing of electronic chart and operation result shows.

The design of the database of described means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system is divided into two parts: the design of map data base and home-delivery center's information of vehicles and shops's demand information database design, wherein, map data base is the basic data of map display module, and map data base and home-delivery center's information of vehicles and shops's demand information database are the basic datas of optimized Algorithm.

Described map data base comprises the position of home-delivery center and position two parts of shops; Home-delivery center's information comprises: home-delivery center's numbering, home-delivery center's title, type of vehicle and all types of own vehicle number, load-carryings; Shops's demand information comprises: shops's numbering, shops's title and demand.

Beneficial effect of the present invention is: the model of proposition can truly reflect in the agricultural material chain operation logistics corporate operation process, connecting each other in means of agricultural production loading and the means of agricultural production delivery process embodies the selection that means of agricultural production loading pattern is determining means of agricultural production dispensing path to a certain extent.Taking all factors into consideration the means of agricultural production loads and the influence of means of agricultural production dispensing to the agricultural material chain operation logistics enterprise cost, overcome only single consideration optimization and planning and the resource brought and the waste of fund effectively reduce means of agricultural production logistics distribution enterprise to load distribution cost in a certain respect; Overcome in the means of agricultural production logistics distribution enterprise operation process, by virtue of experience the problem of operation management effectively reduces the logistics distribution enterprise operating cost.

Description of drawings

Fig. 1 is a logistics distribution prestowage Combinatorial Optimization operation process chart;

Fig. 2 is the genetic algorithm process flow diagram.

Embodiment

The invention provides a kind of means of agricultural production chain operation dispensing prestowage combined optimization method, the present invention will be further described below by description of drawings and embodiment.

Fig. 1 is a logistics distribution prestowage Combinatorial Optimization operation process chart.The agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model is based on following hypothesis:

(1) vehicle number of vehicle all is limited, but total vehicle number can guarantee to finish all business.

The position of (2) single home-delivery center, and known each shops and the demand of required article.

(3) all demand points can only be by a car service once, and service finishes the back vehicle will return home-delivery center.

The objective function of model comprises the fixed cost of prestowage scheme and the distance variable cost of the used vehicle of distribution project.The fixed cost of the used vehicle of wherein prestowage scheme is the vehicle of used participation dispensing and the summation of amassing of the fixed cost of this kind vehicle, and its expression formula is:

FC wherein _kVehicle according to k car is decided, and used vehicle number k also is a variable, is determining the variation of each scheme fixed cost.Because fixed cost shared proportion in whole cost is bigger, therefore, one of target of optimization is exactly that as far as possible the suitable distribution vehicle of selecting for use guarantees higher rate of loading or plot ratio, reduces the prestowage fixed cost accordingly.

The distance that the variable cost of the used vehicle of distribution project is travelled for all distribution vehicle and the summation of the product of every kilometer variable cost, its function expression is as follows:

For different distribution projects, VC _kBe changeless, the milimeter number that distribution vehicle is travelled changes, and is exactly to reduce the milimeter number that distribution vehicle is travelled so want to reduce the variable cost of used vehicle, the shortest route of trying one's best away.If VC _kAll be 1, then target becomes the distance of travelling.Two partial objectives for are combined promptly form the general objective function $\min z=\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\{k\·{\mathrm{FC}}_k\}+\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\underset{i=0}{\overset{N}{\mathrm{\Σ}}}\underset{j=0}{\overset{N}{\mathrm{\Σ}}}\{d_{\mathrm{ij}}\×x_{\mathrm{ijk}}\×{\mathrm{VC}}_k\}.$

The implication of constraint function is: $\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{y}_{\mathrm{ik}}=1,i=1,\·\·\·,n$ Represent that each shops has and can only carry out delivery service by a car; $\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{j0k}=\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{0\mathrm{jk}}=1,k\∈K$ After finishing the work, the expression distribution vehicle will get back to home-delivery center; $\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i\×y_{\mathrm{ik}}\≤G_k,k\∈K$ The general assembly (TW) of lade is no more than the payload capacity of vehicle in the vehicle that expression is used; $\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{v}_i\×y_{\mathrm{ik}}\≤V_k,k\∈K$ The cumulative volume of lade is no more than the useful volume of vehicle in the vehicle that expression is used; $\underset{i=0}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},j=\mathrm{0,1},\·\·\·,n;k\∈K$ Even the j of shops is by vehicle k dispensing, and then this car must arrive the j of shops from the i of shops; $\underset{j=0}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},i=\mathrm{0,1},\·\·\·,n;k\∈K$ Expression is if the i of shops is provided and delivered by vehicle k, and then this car must arrive the next j of shops after having sent the i of shops.

When adopting the genetic algorithm for solving problem, need do following work:

1, chromosomal structural design

The present invention adopts the method for natural number coding to chromosome.Chromosome (the i that to weave into a length be N+m-1 ₁, i ₂... i _s, 0, i _j..., i _k, 0 ..., 0, i _p..., i _q), there be non-0 natural number N in this chromosome, 0 number is m-1.Wherein, natural number i _jRepresent j shops, 0 expression home-delivery center.In the coding each section non-0 sequence of natural numbers represents that a car provides the traveling route scheme of delivery service.The head and the tail of sequence of natural numbers are 0, and the expression vehicle is from home-delivery center, and get back to home-delivery center's (0 of chromosomal initial and end place omitting for the sake of simplicity) at last.Thus, total m-1 0 is divided into the m section with chromosome coding, forms m subpath, represents to finish all transport tasks by m car.Such chromosome coding can be interpreted as: first car is from home-delivery center 0, through i ₁, i ₂... i _sAfter the shops, get back to home-delivery center, form subpath 1; The 2nd car is also from home-delivery center, by way of i _j... i _kBehind the client, get back to home-delivery center, form subpath 2; M car finished all transport tasks successively from home-delivery center, constitutes the m single sub path.

For example: 3 cars of chromosome 12304506789 expressions are finished an arrangement path scheme of the transport task of 9 shops: the distribution project traveling path of vehicle 1 is a subpath 1: home-delivery center, shops 1, shops 2, shops 3, home-delivery center; The distribution project traveling path of vehicle 2 is a subpath 2: home-delivery center, shops 4, shops 5, home-delivery center; The distribution project traveling path of vehicle 3 is a subpath 3: home-delivery center, shops 6, shops 7, shops 8, shops 9, home-delivery center.

In order to arrange route, an estimation need be arranged the vehicle number of required use, can pre-estimate the needed vehicle number of finishing the work $m=[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i/\mathrm{\αG}]+1.$ Wherein, [] expression is not more than the maximum integer of bracket inner digital, g _iThe goods demand of the expression i of shops, α is a parameter, 0＜α＜1 is to the complicacy degree of entrucking (or unloading) and retrain what estimation, in general, dress (unloading) car is complicated more, constraint condition is many more, and the α value is more little, represents that the car loading that a car can hold is few more, can adjust the size of α in the reality by man-machine conversation, G represents the max cap. of vehicle.

2, the initialization of genetic group

In order to make algorithm convergence to global optimum, can guarantee counting yield again simultaneously, in example of the present invention, regulation population size value is 100.Initial population adopts random approach to produce, and constitutes a chromosome that satisfies the problem needs, repeats this process, satisfies the chromosome of population size number until generation.

3, the chromosome fitness is determined

Fitness function is transformed by objective function and obtains.In this problem, capacity-constrained can use following conversion that it is become the part of objective function: $\min Z=\underset{i=0}{\overset{N}{\mathrm{\Σ}}}\underset{j=0}{\overset{N}{\mathrm{\Σ}}}\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}{x}_{\mathrm{ijk}}+M\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\max (\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i{y}_{\mathrm{ik}}-G_k,0),$ M in the formula is a penalty coefficient, can make the chromosomal target function value that does not meet constraint condition very big, reduce this chromosomal fitness thus, make the fitness of feasible solution be far longer than the fitness of non-feasible solution, and excluded gradually along with the increase of evolutionary generation.For capacity-constrained is satisfied in strictness, should there be M to be tending towards ∞.But consider the inconvenience of Computer Processing, the desirable suitably big positive number of M.

Generally, fitness function requires non-negative, thus with objective function by conversion f _i=min z/z _iBe converted into fitness function.Wherein: f _iBe the chromosomal fitness of i bar, min z is an optimum chromosomal desired value in the current colony, Z _iIt is the chromosomal desired value of i bar.

4, genetic group renewal process

The genetic group renewal process is exactly to select the big individuality of fitness the colony from current separating, and intersects, makes a variation, and generates the new process of separating colony.Concrete operation method is as follows:

Select: in conjunction with the system of selection of competition (Stochastic Tournament) at random, each a pair of individuality of picked at random allows it be at war with then, high selected of fitness, so repeatedly, till being full.

Intersect: adopt part mate bracketing method (Partially Matched Crossover, PMX).Suppose to have two parent A, B, crossover location is " | ", A=12|4576|389, and B=21|5437|869,

Earlier the order of 4576 among the A is composed preceding 4 positions of giving filial generation A1, compares with 4576 one by one with the element among the B 21547869 then, if identical then put need not, as if inequality, just place it in the follow-up location of daughter A1 successively.With first element among the B is example, and 2 and 4567 these four elements are all inequality, then place 7 with 2 after, become daughter A ₁The 5th element.In view of the above, thus obtain daughter A ₁Be 456521389, similarly, the order of 5437 among the B composed to filial generation B ₁Preceding 4 positions, compare with 5437 one by one with the element among the A then, if identical then put need not, if inequality, just it is placed on daughter B in proper order ₁Follow-up location, thereby can obtain daughter B ₁Be 543712689.Two chromosomes that produce when two individualities that this step is intersected are selected from back according to class PMX method, produce two new filial generations and carry out the parent of mutation operation as next step.

Variation: adopt at random repeatedly swap mode, decide rapid two the new chromosomes that produce of previous step whether to carry out mutation operation according to certain variation probability.For example: it is 125473698 that a chromosome F is arranged, and exchanges the switch 3 and 7 that operation produces two appointments at random, and then the 3rd element 5 and the 7th element 6 are exchanged, and obtain new chromosome 126473598.

Update mode between heredity Dai Qun: in the process that colony upgrades, adopt update mode between chlamydate generation, when whenever carrying out the renewal of generation population, from initial population, reject the chromosome of minimum fitness, and replace the higher chromosome of fitness.

The process flow diagram of genetic algorithm is seen accompanying drawing 2.

Specify with simple example, it is 8 that dispensing is counted, and type of vehicle is 2,3 cars of every model, and 1 type appearance of vehicle amount is that 7,2 type appearance of vehicle amounts are 8.Each cost coefficient of general objective is: go out car fixed cost coefficient 100,120}; The distance costs coefficient be 0.65,0.95}; The overload penalty coefficient is 10000.

Table 1 dispensing point shops demand

Shops	?1	??2	??3	??4	??5	??6	??7	??8
									Demand	?1	??2	??1	??2	??1	??4	??2	??2

Operating range between table 2 home-delivery center and each shops

Distance	??0	??1	??2	??3	??4	??5	??6	??7	??8
										??0	??0	??4	??6	??7.5	??9	??20	??10	??16	??8
??1	??4	??0	??6.5	??4	??10	??5	??7.5	??11	??10
										??2	??6	??6.5	??0	??7.5	??10	??10	??7.5	??7.5	??7.5
??3	??7.5	??4	??7.5	??0	??10	??5	??9	??9	??15
										??4	??9	??10	??10	??10	??0	??10	??7.5	??7.5	??10
??5	??20	??5	??10	??5	??10	??0	??7	??7	??7.5
										??6	??10	??7.5	??7.5	??9	??7.5	??7	??0	??7	??10
??7	??16	??11	??7.5	??9	??7.5	??9	??7	??0	??10
										??8	??8	??10	??7.5	??15	??10	??7.5	??10	??10	??0

The correlation parameter of genetic algorithm is: population scale 100, maximum evolution number of times 500, crossover probability 0.9, variation probability 0.02.Vehicle number is calculated as: [15/ (α 8)]+1, and α gets 0.95, and calculating vehicle number is 2.

It is as shown in table 3 to carry out independent experiment 10 times:

Table 3 test findings

Sequence number	Route scheme	Total path	Load [vehicle arrangement]	Charging ratio	Distribution cost and prestowage cost	Total cost
							??1	??274801356	??69	?8[2]7[1]	?100％	??39*0.95+30*0.65+100*1+120*1	??276.55

??2	??164027358	??71	??7[1]8[2]	?100％	??28*0.65+43*0.95+120*1+100*1	??279.05
							??3	??135820476	??67.5	??7[1]8[2]	?100％	??34*0.65+33.5*0.95+120*1+100*1	??273.925
??4	??847206531	??69	??8[2]7[1]	?100％	??39*0.95+30*0.65+100*1+120*1	??276.55
							??5	??357480162	??70	??8[2]7[1]	?100％	??45*0.95+25*0.65+120*1+100*1	??279.00
??6	??862047531	??70	??8[2]7[1]	?100％	??31.5*0.95+38.5*0.65+120*1+100*1	??274.95
							??7	??135780264	??68	??7[1]8[2]	?100％	??38*0.65+30*0.95+120*1+100*1	??273.20
??8	??823510674	??70.5	??7[1]8[2]	?100％	??37*0.65+33.5*0.95+120*1+100*1	??276.875
							??9	??674085312	??70.5	??8[2]7[1]	?100％	??33.5*0.95+37*0.65+120*1+100*1	??276.875
??10	??136408572	??69.5	??8[2]7[1]	?100％	??33.5*0.95+36*0.65+120*1+100*1	??276.225

Can find out by operation result:

To delivery assembly influence originally, should not choose the dispensing path of the 3rd experiment if do not consider the prestowage scheme, its single distribution project is 0-1-3-5-8-2-0,0-4-7-6-0; Total cost is 273.925.

To delivery assembly influence originally, should choose the dispensing path of the 7th experiment if consider the prestowage scheme, its single distribution project is 0-1-3-5-7-8-0,0-2-6-4-0; Total cost is 273.20.

The result shows, it is effective and feasible that prestowage dispensing Optimization Model is compared independent consideration dispensing path optimization.

Based on above-mentioned theory and model, with the GIS MapInfo that develops software is development platform, make full use of on this basis its with developing instrument MapBasic and object oriented programming languages such as C#, carry out applied software development in conjunction with the Combinatorial Optimization Model algorithm, obtain being used for the means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system of means of agricultural production chain operation dispensing prestowage Combinatorial Optimization management.

Claims (5)

1. a combined optimization method for agricultural chain-operation logistics delivering and loading-distribution is characterized in that, may further comprise the steps:

(1) objective function of proposition agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model:, determine the target of means of agricultural production chain operation dispensing prestowage combinatorial optimization problem according to means of agricultural production chain operation dispensing characteristics; Think that the objective function of dispensing prestowage Combinatorial Optimization scheme is polynary, routing problem is minimized to rationalize with the prestowage scheme incorporate total cost and minimize in the object module, with distribution cost and prestowage cost as the optimization aim function;

(2) abstract is banishd to provide and deliver and is carried the Combinatorial Optimization work flow, thereby optimizes total cost according to the selection in cargo distribution expense adjustment dispensing path;

(3) the agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model is proposed: think that dispensing prestowage combinatorial optimization problem can be converted into single home-delivery center, single variety, undercapacity dispensing prestowage combinatorial optimization problem, as follows with this prestowage Combinatorial Optimization Model that proposes to provide and deliver:

$\min z=\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\{k\·{\mathrm{FC}}_k\}+\underset{k=1}{\overset{K}{\mathrm{\Σ}}}\underset{i=0}{\overset{N}{\mathrm{\Σ}}}\underset{j=0}{\overset{N}{\mathrm{\Σ}}}\{d_{\mathrm{ij}}\×x_{\mathrm{ijk}}\×{\mathrm{VC}}_k\}$

s.t.

$\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{y}_{\mathrm{ik}}=1,i=1,...,n$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{j0k}=\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{x}_{0\mathrm{jk}}=1,k\∈K$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{g}_i\×y_{\mathrm{ik}}\≤G_k,k\∈K$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{v}_i\×y_{\mathrm{ik}}\≤V_k,k\∈K$

$\underset{i=0}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},j=\mathrm{0,1},...,n;k\∈K$

$\underset{j=0}{\overset{N}{\mathrm{\Σ}}}{x}_{\mathrm{ijk}}=y_{\mathrm{ik}},i=\mathrm{0,1},...,n;k\∈K$

Parameter declaration: Z is a total cost; K is spendable distribution vehicle set; N provides shops's number of delivery service for needs; FC _kIt is the fixed cost of k car; d _IjActual range for distribution vehicle from the i of shops to the j of shops; VC _kIt is the unit kilometer cost of k car; g _iWeight for the demand goods of the i of shops; G _kIt is the dead weight capacity of k car; v _iVolume for the demand goods of the i of shops; V _kIt is the volume of k car;

Variable declaration: x _IjkK car of=1 expression directly drives towards the j of shops from the i of shops, otherwise x _Ijk=0; y _Ik=1 expression i of shops is provided and delivered by k car, otherwise y _Ik=0;

(4) utilize that genetic algorithm has that the global optimization ability is strong, strong robustness, highly versatile, efficient optimization performance, based on the derivation algorithm of principle of genetic algorithm design agricultural chain-operation logistics delivering and loading-distribution Combinatorial Optimization Model, described algorithm may further comprise the steps:

(41) coding: structure satisfies the chromosome of constraint condition;

(42) produce initial population at random: the group chromosome when initial population is the search beginning;

(43) calculate each chromosomal fitness: fitness is unique index of reflection chromosome quality, genetic algorithm to seek the chromosome of fitness maximum;

(44) use selection, intersection and mutation operator to produce sub-group;

(45) stop cycling condition: if satisfy the condition of convergence or fixed number of iterations then stops, then change (43) if do not satisfy condition and carry out evolutionary process again, evolutionary process just produces the colony of a new generation each time, and individual represented separating by evolving finally reaches optimum solution in the colony;

(5) design is based on the vehicle scheduling management system of optimized Algorithm:

With the GIS MapInfo that develops software is development platform, make full use of on this basis its with developing instrument MapBasic and object oriented programming languages such as C#, carry out applied software development in conjunction with the Combinatorial Optimization Model algorithm, obtain being used for the means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system of means of agricultural production chain operation dispensing prestowage Combinatorial Optimization management.

2. a kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution according to claim 1, it is characterized in that the polynary target of the means of agricultural production chain operation dispensing prestowage combinatorial optimization problem in the described step (1) comprises: the consumption minimum most effective or that distribution cost is minimum, the dispensing mileage is the shortest, punctuality is the highest, comprehensive cost is minimum, dispensing is worked of providing and delivering.

3. a kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution according to claim 1, it is characterized in that described means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system is made up of information management, optimized Algorithm, 3 parts of management map: information management has realized importing, typing, inquiry, modification and the delete function to information; Optimized Algorithm realizes the dispensing path computing based on genetic algorithm; Management map comprises that the path of browsing of electronic chart and operation result shows.

4. a kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution according to claim 1, it is characterized in that, the design of the database of described means of agricultural production chain operation dispensing prestowage Combinatorial Optimization opportunity GIS visualized management system is divided into two parts: the design of map data base and home-delivery center's information of vehicles and shops's demand information database design, wherein, map data base is the basic data of map display module, and map data base and home-delivery center's information of vehicles and shops's demand information database are the basic datas of optimized Algorithm.

5. a kind of combined optimization method for agricultural chain-operation logistics delivering and loading-distribution according to claim 4 is characterized in that, described map data base comprises the position of home-delivery center and position two parts of shops; Home-delivery center's information comprises: home-delivery center's numbering, home-delivery center's title, type of vehicle and all types of own vehicle number, load-carryings; Shops's demand information comprises: shops's numbering, shops's title and demand.

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