CN1790398A - Particle swarm optimization method for open vehicle routing problem with time windows - Google Patents
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Abstract
The invention discloses a particle group optimum method of open type car dispatching problem with time window, which is characterized by the following: the calculation quantity of solution course displays exponential increasing along the problem scale, which is testified as NP complete problem; the method adapts the improved cheapest insert algorithm to optimize the car customer sequence, which improves the calculation speed and algorithm precision.
Description
(1) technical field
The present invention relates to the particle group optimizing method of the open vehicle dispatching problem of free window.
(2) background technology
Vehicle scheduling is a gordian technique of implementing the socialization logistics, optimize route or travel by vehicle, promote enterprise competitiveness, reduce enterprise cost, solve problems such as urban traffic congestion, energy shortage, atmospheric pollution, the realization traffic is being unified at the interior of efficient, resource, environment and values each side, guarantees the sustainable development of material flow industry.But on the more meanings of the employed vehicle scheduling software of present most of enterprise is a management software, lack the function of distributing rationally to resource, also main dependence has the managerial personnel that enrich practical experience and dispatcher by means of the manual work mode that meeting, phone, form etc. manage and dispatch, and obviously can not satisfy the needs of allegro modern production and market cut-throat competition.Along with computing machine and computer network are introduced enterprise, the optimization vehicle scheduling that grows up based on information integration is risen.
The open vehicle dispatching problem OVRPTW (Open Vehicle Routing Problemwith Time Windows) of free window is defined as: in the logistics service network, the position of known customer and home-delivery center, satisfying vehicle maximum load, customer demand (goods demand, the time of delivery demand) under the prerequisite, the design vehicle path, the dispensing client, it is minimum to reach vehicle, targets such as distance is the shortest, and the time is minimum.Vehicle does not need to return home-delivery center after having visited all clients.
The mathematical model of the open vehicle dispatching problem of free window is as follows: suppose that each car still gets back to virtual home-delivery center, the distance between client and home-delivery center is 0, and c
I0=0 (i=1,2 ... L).We provide three subscript mathematical models of open vehicle route problem herein: supposition home-delivery center can use K (k=1,2 at most ... K) car is to individual L (i=1,2 ... L) client transports dispensing, and i=0 represents the warehouse.Each vehicle load is b
k(k=1,2 ... K), each client's demand is d
i(i=1,2 ... L), client i is c to the transportation cost of client j
Ij(can be distance, time, expense etc.).Be defined as follows variable:
t
j≥t
i+s
i+t
ij-T*(1-x
ijk) (7)
t
j＝max{E
j，t
i+s
i+t
ij} (8)
t
j＜L
j (9)
x
Ijk=0 or 1 i, j, k (10)
y
Ik=0 or 1 i, k (11)
Constraint (2) guarantees the ability constraint of each car.Constraint (3) guarantees that each client is serviced.Constraint (4) (5) guarantee that the client is only by a car visit.Sub-loop is eliminated in constraint (6).Constraint (7)-(9) have guaranteed that the client is serviced in time window, the span of (10) (11) expression variable.
The open vehicle dispatching problem of free window has many examples in actual life, from saving cost consideration, used vehicle is not own vehicle under a lot of situations as logistics company, but public vehicles, can save logistics cost on the one hand, order on the one hand can the integrating society resource.In this case, vehicle does not need to return home-delivery center.Similarly situation also appears in the shuttle bus service of company, school.In the open vehicle dispatching problem of free window, each bar circuit all is path, Hamilton (Hamiltonian Path), and is hamiltonian cycle (Hamiltonian Cycle) in vehicle dispatching problem (VRP).Though it is a lot of to find the solution the algorithm of VRP, can not directly find the solution OVRPTW.
The method of open vehicle route problem of finding the solution free window at present is fewer, mainly uses tabu search algorithm and method such as contiguous heuritic approach.Tabu search algorithm is stronger to the dependence of initial solution, and the field search operators design in the algorithm is complicated, though algorithm can obtain reasonable separating, single computing time is long.The most contiguous heuritic approach realizes simple, calculate rapidly, but gained result and optimal value has bigger gap.
(3) summary of the invention
For the inharmonic deficiency of algorithm complexity, computing velocity and arithmetic accuracy of the open vehicle dispatching method that overcomes existing free window, the invention provides the particle group optimizing method of open vehicle dispatching problem that a kind of algorithm possesses the free window of computing velocity faster and higher arithmetic accuracy simply, simultaneously.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of particle group optimizing method of open vehicle dispatching problem of free window, described method mainly may further comprise the steps:
(1), from order ticket, obtain distribution information, described distribution information comprises: the arrival time of the total quantity of the goods of customer name, customer demand, general assembly (TW), cumulative volume, unloading address, requirement;
(2), from committee's customer name of obtaining of waybill, from address database, inquire client's address information, comprise client's specific address, the distance between the client;
(3), set the parameter of particle cluster algorithm, described parameter comprises population scale, iterations, described population scale is represented the quantity of initial distribution project, iterations is illustrated in the searching times in numerous distribution project spaces;
(4), above-mentioned distribution information, client's address information is read in the particle cluster algorithm;
(5), according to client's number, calculate needed vehicle number, each distribution project is encoded;
(6), use decoding algorithm to decode;
(7), use the improved cheap order that heuritic approach (Cheapest Insert Algorithm) is optimized client in the vehicle of inserting;
(7.1), set up improved cheap heuritic approach (the Cheapest Insert Algorithm) model that inserts: the expense that client u inserts between client i and the j can be calculated according to the definition of following formula:
c
1(i，u，j)＝d
iu+d
uj-d
ij (12)
c
2(i，u，j)＝b
ju-b
j (13)
c
3＝T
u-T (14)
c
4(i，u，j)＝α
1c
1(i，u，j)+α
2c
2(i，u，j)+α
3(i，u，j)?(15)
In the following formula, d
Ij(i, j ∈ 1,2 ... n) between two clients of expression for example, b
i(i ∈ 1,2 ... n) be illustrated in time that begins to serve of this client, T represents the stand-by period that circuit is total; c
1(i, u, after j) u is inserted in expression, the increment of distance; c
2(i, u after j) u is inserted in expression, arrive j client's time retardation amount; c
3After u is inserted in expression, the recruitment of the stand-by period that circuit is total; c
4(i, u j) are illustrated in the expense of inserting u between client i and the j, are c
1, c
2, c
3Weighted sum;
(7.2) if all clients have distributed to vehicle, then algorithm stops; Otherwise select circuit of the current beginning service time of client's initialization the latest.
(7.2), for current unappropriated each client, be inserted into each feasible location in the current circuit, calculate the insertion expense according to (4) formula, choose value minimum in all values, this client is inserted corresponding position;
(7.3), repeat the second step process, satisfy the constraint of vehicle or do not have the client to insert up to this circuit, then change the first step;
(8), according to distribution cost numerical procedure, calculate all clients' of visit line length or time or the expense of visiting all clients, the fitness of particle is defined as the inverse of cost;
(9), the fitness of particle relatively, find out the particle that fitness is the highest in the population and preserve, each particle and the fitness that calculates before self are relatively preserved self best fitness simultaneously;
(10), the distribution project of particle representative is adjusted, carry out the renewal of particle state according to following formula (16):
In the following formula, Xi=(x
I1, x
I2... x
ID) expression i particle state, each particle represents that one of the D dimension space is separated Vi=(v
I1, v
I2... v
ID) represent each particle's velocity vector, and Vi satisfies: Vi≤maximal rate V
MaxP
iRepresent the optimum state that each particle lives through, P
gThe optimum state that expression colony lives through, c
1Be inertia weight, c
2, c
3It is acceleration constant.The state of particle is represented different implications in concrete application, in an application of the invention, the distribution project of the state representation vehicle scheduling of particle, promptly the client is by that car dispensing and the order of client in dispensing.Particle's velocity is represented the difference between distribution project, the difference of promptly same client place vehicle, the order difference of client in dispensing.
(11), some particles of selection at random carry out interlace operation, the distribution project that each particle is represented is adjusted;
(12), repeat (6)～(11), possible distribution project is searched for, reach predetermined iterations after, the distribution project that the output particle cluster algorithm searches out.
Further, in described (7), after all clients have distributed to vehicle, re-use again the usage quantity that insertion algorithm (Re-Insert) is optimized vehicle, the step of insertion algorithm is again:
The first step: find out the minimum circuit of client in all circuits, with this line delete;
Second step: for the client of each unallocated vehicle, each position in all circuits that its trial insertion is later if can insert, then is inserted into;
The 3rd step:, then calculate and finish if all unappropriated clients distribute; Otherwise, use the improved cheap newly-generated circuit of heuritic approach that inserts.
Further again, in described (5), coded system adopts the coded system based on the real number vector: for the distribution project that L client arranged, the real number vector X that uses L to tie up represents the state of particle; For each dimension of vector, its integral part is represented the vehicle at place, and is identical as integral part, is illustrated in same the car; Fraction part is illustrated in the order of providing and delivering in the vehicle, according to size order;
In described (6), the process of decoding is:
(6.1), carry out [X] operation for each dimension of the state of particle;
(6.2) divide into groups according to [X] value, form client's grouping;
(6.3) in grouping, particle is carried out { X} operation;
(6.4) according to { size of X} is arranged, and forms client's access order.
Principle of work of the present invention is: particle cluster algorithm is to equal a kind of evolutionary computing that nineteen ninety-five proposes by Kennedy and Eberhart.Its core concept is the simulation to biological social behavior.Its initial imagination is the process of simulation flock of birds predation, and in research process, the optimization that is applied to variety of issue has obtained good result.Suppose bevy the predation, found food for wherein one, then some other bird can be followed this bird and be flown to the food place, and makes some can remove to seek better food source.In the whole process of predation, bird can utilize the experience of self and the information of colony to seek food.Particle cluster algorithm takes a hint from this behavior of flock of birds, and uses it for finding the solution of optimization problem.In particle cluster algorithm, each problem separate a bird that all is counted as in the search volume, we are called " particle ".The state quality of particle is used by the adaptive value of optimised problem decision and is represented.Each particle has a speed to represent the distance and the direction of particle flight.Particle is followed current optimal particle and is searched in solution space.When finding the solution a problem, adopt certain coding method to be encoded into particle this problem, carry out interative computation according to the mechanism of particle cluster algorithm then.
Find the solution the feasibility analysis of the open vehicle dispatching problem of free window: the open vehicle dispatching problem of free window is a np problem, uses conventional methods and finds the solution, and has that to find the solution problem scale little, the problem such as of low quality of separating.Particle cluster algorithm is a kind of novel bionic intelligence optimized Algorithm.It is fast to have the speed of finding the solution, and the quality of separating is high a bit.Population is in Neural Network Optimization, and successful Application has all been obtained in fields such as electric parameter design optimization.Finding the solution traveling salesman problem, NP aspects such as Task Distribution problem have also obtained good result.
The open vehicle dispatching problem of free window is unusual complicated problems.Normally multiple constraint, multiple goal, uncertain optimization problem at random.The calculated amount of solution procedure is exponential increase with the scale of problem, has been proved to be np complete problem.Studies show that much the optimum solution of seeking vehicle dispatching problem is very difficult, the derivation algorithm that engineering significance is arranged most is a target of abandoning seeking optimum solution, then attempts to search out in reasonable, the limited time approximate, useful separating.
The present invention proposes a kind of coded system based on the real number vector, and the key that the method is different from existing coding method is under the prerequisite that does not increase dimension, and the ordering of the client in vehicle and the vehicle line is come out in coded representation.For the OVRPTW problem that L client arranged, use the state of the real number vector representation particle of L dimension.For each dimension of vector, its integral part is represented the vehicle at place, and integral part is identical, is illustrated in same the car.Fraction part is illustrated in the order of providing and delivering in the vehicle.
Beneficial effect of the present invention mainly shows: 1, algorithm is simple; 2, possess computing velocity and higher arithmetic accuracy faster simultaneously; 3, in reasonable, the limited time, search out useful separating.
(4) description of drawings
Fig. 1 is the process flow diagram of particle group optimizing method of the open vehicle scheduling of free window.
Fig. 2 is the system construction drawing of the open vehicle scheduling of free window.
(5) embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1, a kind of particle group optimizing method of open vehicle dispatching problem of free window may further comprise the steps:
The first step: obtain distribution information from committee's waybill.These information comprise: customer name, and the total quantity of customer demand goods, general assembly (TW), cumulative volume, the unloading time that needs, is estimated at the arrival time of requirement in the unloading address.
Second step: according to the customer name that obtains from committee's waybill, from database, inquire client's address information, comprise client's specific address, the distance between the client (perhaps working time).
The 3rd step: algorithm parameter, the parameter of particle cluster algorithm comprises population scale, what initial distribution projects representative herein has.Iterations is illustrated in numerous distribution project spaces, the search that does not stop, and the number of times of search, according to client's number, the client is many more, and the number of times of algorithm iteration is many more, and population scale is big more.
The 4th step: above-mentioned described data message is read in the algorithm.
The 5th step: according to client's number, the needed vehicle number of pre-estimation is encoded according to the coded system of this paper design.So-called coding, and a distribution project is expressed with particle algorithm mode to understand.
Based on the coded system of real number vector, the key that the method is different from above-mentioned method is under the prerequisite that does not increase dimension, and the ordering of the client in vehicle and the vehicle line is come out in coded representation.For the OVRPTW problem that L client arranged, use the state of the real number vector representation particle of L dimension.For each dimension of vector, its integral part is represented the vehicle at place, and integral part is identical, is illustrated in same the car.Fraction part is illustrated in the order of providing and delivering in the vehicle.
Definition: [X] represent x round numbers part, and { X} represents X is got fraction part, and the corresponding client of the expression that each particle integral part is identical serves on a car, and the order of serving is represented in the arrangement of fraction part.
The process of decoding:
(1), carries out [X] operation for each dimension of the state of particle.
(2) divide into groups according to [X] value, form client's grouping.
(3) in grouping, particle is carried out { X} operation.
(4) according to { size of X} is arranged, and forms client's access order.
Suppose 7 clients' OVRPTW problem, the vehicle number that needs is 3, and problem adopts this kind coded system coding with distribution project hereto, obtains following result:
Customer number: 1234567
X： 4.1 1.86 1.53 1.12 1.24 3.29 3.05
The path of separating of this group coding correspondence is:
Article one, circuit: 0-1
Second circuit: 0-4-5-3-2
Three-line: 0-7-6
This method for expressing, dimension is suitable with client's number, and it is simple to operate to carry out the particle state renewal, can bring into play the intrinsic advantage of particle cluster algorithm.
The 6th step: be client's grouping and arrangement according to above-mentioned coding/decoding method with the state decode of particle, reaches the scheme of vehicle scheduling.
The 7th step: uses improved cheap insert heuritic approach and again method such as insertion algorithm adjust between circuit and the order in the circuit, and the distribution project of generation is optimized again:
(7.1), set up improved cheap heuritic approach (the Cheapest Insert Algorithm) model that inserts, the expense that client u inserts between client i and the j can be calculated according to the definition of following formula:
c
1(i，u，j)＝d
iu+d
uj-d
ij (12)
c
2(i，u，j)＝b
ju-b
j (13)
c
3＝T
u-T (14)
c
4(i，u，j)＝α
1c
1(i，u，j)+α
2c
2(i，u，j)+α
3(i，u，j) (15)
In the following formula, d
Ij(i, j ∈ 1,2 ... n) between two clients of expression for example, b
i(i ∈ 1,2 ... n) be illustrated in time that begins to serve of this client, T represents the stand-by period that circuit is total; c
1(i, u, after j) u is inserted in expression, the increment of distance; c
2(i, u after j) u is inserted in expression, arrive j client's time retardation amount; c
3After u is inserted in expression, the recruitment of the stand-by period that circuit is total; c
4(i, u j) are illustrated in the expense of inserting u between client i and the j, are c
1, c
2, c
3Weighted sum.
(7.2) if all clients have distributed to vehicle, then algorithm stops; Otherwise select circuit of the current beginning service time of client's initialization the latest.
(7.2), for current unappropriated each client, be inserted into each feasible location in the current circuit, calculate the insertion expense according to (4) formula, choose value minimum in all values, this client is inserted corresponding position;
(7.3), repeat the second step process, satisfy the constraint of vehicle or do not have the client to insert up to this circuit, then change the first step;
For the few circuit of client, after all clients have distributed to vehicle, re-use again the usage quantity that insertion algorithm (Re-Insert) is optimized vehicle, the step of insertion algorithm is again:
(1), finds out the minimum circuit of client in all circuits, with this line delete;
(2), for the client of each unallocated vehicle, it is attempted inserting each position in all later circuits, if can insert, then be inserted into;
(3), then calculate and finish if all unappropriated clients distribute; Otherwise, use the improved cheap newly-generated circuit of heuritic approach that inserts.
The 8th step: then according to distribution cost numerical procedure, and can be the line length or the time of calculating all clients of visit, also can be all clients' of visit expense etc.The fitness of particle is defined as the inverse of cost.
The 9th step: compare the fitness of particle, find out the particle that fitness is the highest in the population and preserve, the fitness of each particle and calculating before self is relatively preserved self best fitness simultaneously.The purpose of this operation is to preserve the optimum distribution project that optimum distribution project that current algorithm searches and each particle search arrive
The tenth step: carry out the renewal of particle state according to formula mentioned above (16), the process of this renewal is exactly the adjustment process to the distribution project of particle representative.And allow the distribution project of all particle representatives all draw close to the distribution project of at present known optimum.
The 11 step: some particles of selection at random carry out interlace operation, and this operates expression, and the distribution project that each particle is represented is adjusted.Can produce more outstanding distribution project thus.
The 12 step: repeat the 6th and go on foot the process in 11 steps, and possible distribution project is searched for, reach predetermined iterations, then withdraw from from algorithm.
The 13 step: the distribution project that the output particle cluster algorithm searches out shows to carry single form.
With reference to Fig. 2, use the Vehicular intelligent dispatching system that this method realizes, mainly comprise: Back ground Information subsystem, intelligent algorithm subsystem.
The sub-infosystem in described basis comprises:
(1), client's coordinate information
This function can allow the user add, revise, inquire about client's coordinate, and these operations all are based on electronic chart, and the client can point out client's position by mouse, and coordinate values can be revised automatically, shows.
(2), the distance between the client (time)
This function provides inquiry, interpolation, the modification of distance (time) between the client.These operations all are based on electronic chart, make things convenient for the user to check.
(3), the complexity of client's dispensing
Inquiry, interpolation, modification that this function provides the client to provide and deliver complexity.Complexity is represented with words such as popular words " difficulty ", " easily ", is user-friendly to.
(4), the parameter information of algorithm
This function provides the demonstration of various algorithm parameters, revises.
Described intelligent algorithm subsystem comprises:
(1), the open vehicle scheduling of free window
The target of optimizing for these task needs of sending a car vehicle is minimum, move circuit the shortest (also can be targets such as cost is minimum, and oil consumption is minimum) according to the difference of the information that provides in the Back ground Information; Emphasize the time of customer demand in the constraint condition, vehicle did not return the parking lot after dispensing was finished.This scheduling has following function:
(1.1), intelligent algorithm scheduling
Adopt vehicle scheduling scheme of the present invention to carry out the open vehicle scheduling of free window, show distribution project, prepare for next step generates committee's waybill.The process of scheduling as shown in Figure 1, the description of method is slightly.
(1.2), generate the acknowledgement of consignment list
It is single that satisfied scheduling result is generated acknowledgement of consignment.The single numbering of acknowledgement of consignment produced automatically according to the date, and the single information of acknowledgement of consignment can be checked, revised deletion.
(1.3), the graphical demonstration
Adopt the result of graphic method display scheduling.The lines of using different colours on map are represented the circuit of different car dispensings.Can express dispensing client's position and title on the map.The simulation dolly can be according to the line access client of algorithm arrangement.
(1.4), convergence map
Convergence map that the algorithm that draws this time calculates provides a criterion judging that this operation result is whether outstanding to the user.
(2), the minimum model scheduling of vehicle
This model is to return under the situation of home-delivery center at vehicle, and the target of optimization needs to such an extent that vehicle is minimum for this task of sending a car, move circuit the shortest (also can be targets such as cost is minimum, and oil consumption is minimum) according to the difference of the information that provides in the Back ground Information.Function is identical with the open vehicle scheduling model of the free window of above-mentioned (1).
(3), punctual arrival dispatched
This model is that vehicle returns under the situation in the dispensing, and the target of optimization is at first to be to satisfy the temporal requirement of client, and guarantees to provide and deliver in the time that customer requirement reaches.Next is to wish that used vehicle number is minimum, the distance of vehicle ' the shortest (also can be targets such as cost is minimum, and oil consumption is minimum, according to the difference of the information that provides in the Back ground Information).Function is identical with the open vehicle scheduling model of above-mentioned (1) free window.
Claims (3)
1, a kind of particle group optimizing method of open vehicle dispatching problem of free window, described method mainly may further comprise the steps:
(1), from order ticket, obtain distribution information, described distribution information comprises: the arrival time of the total quantity of the goods of customer name, customer demand, general assembly (TW), cumulative volume, unloading address, requirement;
(2), from committee's customer name of obtaining of waybill, from address database, inquire client's address information, comprise client's specific address, the distance between the client;
(3), set the parameter of particle cluster algorithm, described parameter comprises population scale, iterations, described population scale is represented the quantity of initial distribution project, iterations is illustrated in the searching times in numerous distribution project spaces;
(4), above-mentioned distribution information, client's address information is read in the particle cluster algorithm;
(5), according to client's number, calculate needed vehicle number, each distribution project is encoded;
(6), use decoding algorithm to decode;
(7), use the improved cheap order that heuritic approach (Cheapest Insert Algorithm) is optimized client in the vehicle of inserting;
(7.1), set up improved cheap heuritic approach (the Cheapest Insert Algorithm) model that inserts, the expense that client u inserts between client i and the j can be calculated according to the definition of following formula:
c
1(i，u，j)＝d
iu+d
uj-d
ij (12)
c
2(i，u，j)＝b
ju-b
j (13)
c
3＝T
u-T (14)
c
4(i，u，j)＝α
1c
1(i，u，j)+α
2c
2(i，u，j)+α
3(i，u，j) (15)
In the following formula, d
Ij(i, j ∈ 1,2 ... n) between two clients of expression for example, b
i(i ∈ 1,2 ... n) be illustrated in time that begins to serve of this client, T represents the stand-by period that circuit is total; c
1(i, u, after j) u is inserted in expression, the increment of distance; c
2(i, u after j) u is inserted in expression, arrive j client's time retardation amount; c
3After u is inserted in expression, the recruitment of the stand-by period that circuit is total; c
4(i, u j) are illustrated in the expense of inserting u between client i and the j, are c
1, c
2, c
3Weighted sum;
(7.2) if all clients have distributed to vehicle, then algorithm stops; Otherwise select circuit of the current beginning service time of client's initialization the latest.
(7.2), for current unappropriated each client, be inserted into each feasible location in the current circuit, calculate the insertion expense according to (4) formula, choose value minimum in all values, this client is inserted corresponding position;
(7.3), repeat the second step process, satisfy the constraint of vehicle or do not have the client to insert up to this circuit, then change the first step;
(8), according to distribution cost numerical procedure, calculate all clients' of visit line length or time or the expense of visiting all clients, the fitness of particle is defined as the inverse of cost;
(9), the fitness of particle relatively, find out the particle that fitness is the highest in the population and preserve, each particle and the fitness that calculates before self are relatively preserved self best fitness simultaneously;
(10), the distribution project of particle representative is adjusted, carry out the renewal of particle state according to following formula (16):
In the following formula, Xi=(x
I1, x
I2... x
ID) expression i particle state, each particle represents that one of the D dimension space is separated Vi=(v
I1, v
I2... v
ID) represent each particle's velocity vector, and Vi satisfies: Vi≤maximal rate V
MaxP
iRepresent the optimum state that each particle lives through, P
gThe optimum state that expression colony lives through, c
1Be inertia weight, c
2, c
3It is acceleration constant;
(11), some particles of selection at random carry out interlace operation, the distribution project that each particle is represented is adjusted;
(12), repeat (6)～(11), possible distribution project is searched for, reach predetermined iterations after, the distribution project that the output particle cluster algorithm searches out.
2, the particle group optimizing method of the open vehicle dispatching problem of a kind of free window as claimed in claim 1, it is characterized in that: in described (7), after all clients have distributed to vehicle, re-use again insertion algorithm (Re-Insert) and optimize the usage quantity of vehicle, the step of insertion algorithm is again:
The first step: find out the minimum circuit of client in all circuits, with this line delete;
Second step: for the client of each unallocated vehicle, each position in all circuits that its trial insertion is later if can insert, then is inserted into;
The 3rd step:, then calculate and finish if all unappropriated clients distribute; Otherwise, use the improved cheap newly-generated circuit of heuritic approach that inserts.
3, the particle group optimizing method of the open vehicle dispatching problem of a kind of free window as claimed in claim 1 or 2 is characterized in that: in described (5), coded system adopts the coded system based on the real number vector:
For the distribution project that L client arranged, the real number vector X that uses L to tie up represents the state of particle; For each dimension of vector, its integral part is represented the vehicle at place, and is identical as integral part, is illustrated in same the car; Fraction part is illustrated in the order of providing and delivering in the vehicle, according to size order;
In described (6), the process of decoding is:
(6.1), carry out [X] operation for each dimension of the state of particle;
(6.2) divide into groups according to [X] value, form client's grouping;
(6.3) in grouping, particle is carried out { X} operation;
(6.4) according to { size of X} is arranged, and forms client's access order.
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