CN113033895A - Multi-source multi-point path planning method, equipment and storage medium - Google Patents
Multi-source multi-point path planning method, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-source multi-point path planning method, equipment and a storage medium, wherein the multi-source multi-point path planning method comprises the following steps: firstly, establishing a path planning objective function of a multi-source multi-point logistics distribution problem, and introducing the weight of a distributed product and the rated load capacity of a vehicle into the function; coding warehouses and receiving points on a distribution path in a natural number coding mode; generating an initial population by a modified random method; generating new individuals in a crossing way by using a method for simulating cell division, and carrying out operations such as selection, mutation and the like to update the population; iteratively calculating the fitness value of the optimal individual until an iteration condition is met to obtain the optimal individual; determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; and finally, outputting the distribution path according to the optimal individual. The advantages are that: the generation of the initial population is optimized, and the next generation is generated in a mode of simulating cell division, so that the optimization time is shortened; the problem of distribution of a single vehicle to multiple receiving points through multiple storehouses in actual transportation is well solved.
Description
Technical Field
The invention relates to a multi-source multi-point path planning method, equipment and a storage medium, and belongs to the technical field of logistics distribution, path planning and computer application.
Background
The path planning problem is a key to solve the logistics distribution process, but is a very complex NP-hard problem, and it is very necessary to find a practical and effective optimization algorithm. The path algorithm is researched more, and the research from the original accuracy traditional algorithm to the intelligent heuristic algorithm is not limited. The precise algorithm depends on strict mathematical logic thinking, takes the optimal solution as target guidance, is suitable for solving small-scale problems, and when the problems to be solved are more and more complex, the calculation amount of the algorithm can be exponentially increased, so that the solving efficiency is low or the limitation that the optimal solution cannot be obtained at all is caused. The heuristic algorithm is more flexible than the precise algorithm, and by establishing a reasonable solving model through analysis and reasoning on actual problems, mathematical models established from different analysis angles are different, which is reasonably existed in the heuristic algorithm. Classifying basic models of vehicle scheduling according to actual logistics application scenes, wherein the basic models comprise full load types and non-full load types, namely classifying according to the load condition of a vehicle, and the vehicle is full load type when the load capacity is consistent with the vehicle capacity, and is non-full load type when the load capacity is smaller than the vehicle capacity; according to the distribution time constraint, the system has a time window type and a common type, if the system is in an emergency, the system has a requirement on time, the system has a vehicle path problem with time window constraint with definite time requirement limitation, and otherwise, the system is in a non-time-window constraint type; the distribution is classified according to the number of distribution centers (warehouses), and single-source distribution and multi-source distribution are available.
The traditional path planning method is considered, the factors of vehicle load are not considered under the condition of multiple sources and multiple points, and the logistics distribution path planning requirement sensitive to the weight of cargos cannot be met in practical application.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a multi-source multi-point path planning method, equipment and a storage medium.
To solve the above technical problems, the present invention provides a multi-source multi-point path planning method,
acquiring warehouse and harvest point information;
determining a multi-source multi-point path planning objective function of the weight of introduced distribution products and the rated load capacity of the vehicle aiming at minimizing the cost according to the warehouse and the receiving point information;
coding warehouses and receiving points on a distribution path in a natural number coding mode; generating an initial population by a modified random method; generating new individuals in a crossing way by using a method for simulating cell division, and carrying out operations such as selection, mutation and the like to update the population; iteratively calculating the fitness value of the optimal individual until an iteration condition is met to obtain the optimal individual; determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; and finally, planning the optimal solution of the target function according to the multi-source multi-point path and outputting a distribution path.
Further, the multi-source multi-point path planning objective function is as follows:
where d (a, b) represents the distance between a and b, a and b representing the receiving point r or warehouse s; when the total weight of the cargoes is greater than the rated loading capacity of the vehicle, the distance of partial receiving points is counted in the path planning, wherein alpha is 1, and beta is 0; on the contrary, when the total weight of the goods which can be accommodated by the rated loading capacity meets the requirement, the receiving point distance is not required to be considered, only the distance between warehouses is considered, wherein alpha is 0, and beta is 1; r is a collection of receiving points,
respectively representing warehousesjAnd a warehouse sj+1Two receiving goods in betweenPoints are set, k belongs to R, m belongs to R, values of k and m between two warehouses are generated according to a fitness function, and the values of k and m are not equal; m is the number of warehouses, N is the number of receiving points, i is 1,2, …, N-1, j is 1,2, …, M-1, k is 1,2, …, N, M is 1,2, …, N;
the multi-source multi-point path planning objective function further comprises the following constraint conditions:
1) the total amount of different products sent to each receiving point does not exceed the total storage amount;
2) the load capacity of the vehicle does not exceed the rated load capacity of the vehicle type in the distribution process;
3) on the basis of meeting the conditions, each warehouse is regulated to pass through once based on a greedy strategy, and all products to be delivered are loaded as long as the warehouse has the products to be delivered;
4) each receiving point passes at least once, but not more than twice; the weight of the product required by the receiving point does not exceed the rated load of the vehicle, and the product is only passed once when the weight of the product exceeds the rated load, and the product is required twice when the weight of the product exceeds the rated load.
Further, the encoding process of the warehouse and the receiving point on the distribution path by using the natural number encoding method includes:
filling each point of the chromosome with natural numbers, wherein the number of each receiving point takes the positive value of the number and is used for representing the receiving point; each delivery point takes its negative value to indicate the delivery point.
Further, the generating of the initial population by the improved random method; generating new individuals in a crossing way by using a method for simulating cell division, and carrying out operations such as selection, mutation and the like to update the population; iteratively calculating the fitness value of the optimal individual until an iteration condition is met to obtain the optimal individual; determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; finally, the process of outputting the distribution path according to the optimal solution of the multi-source multi-point path planning objective function comprises the following steps:
(1) generating each individual in the initial population by using a random method;
(2) calculating the fitness of each individual in the initial population, wherein the fitness comprises the following steps: respectively generating a fitness function value when the vehicle arrives at a first receiving point from the warehouse and a fitness function value when the vehicle leaves the first receiving point and transfers to a next receiving point or the warehouse;
(3) a selecting operation comprising: sorting the individuals in the population according to fitness, applying an elite retention strategy, selecting 50% of the individuals with the highest fitness value to directly select into offspring, and eliminating the individuals with the minimum fitness;
(4) a crossover operation comprising: randomly selecting parent individuals according to a certain probability to carry out gene recombination, and merging the parent individuals into offspring;
(5) a mutation operation comprising: randomly selecting a certain parent, performing variation operation by using a mode of exchanging gene values corresponding to receiving points on the basis of a non-uniform variation mode, and merging the gene values into offspring;
(6) circularly executing the selection operation, the cross operation and the variation operation to form a filial generation population, calculating the fitness of the individuals in the population, and outputting the current optimal individual if the population reaches a specified genetic algebra or the number of times that the fitness value of the optimal individual is not improved reaches a preset value; if the termination condition is not reached, the step (3) is continued to be executed after the recording time t is t + 1;
(7) determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; and finally, planning the optimal solution of the target function according to the multi-source multi-point path and outputting a distribution path.
Further, the process of generating each individual in the initial population using a random method includes:
11) calculating a difference C between the total delivery amount of all receiving points and the rated load capacity of the automobile;
12) generating a random integer x, filling-x in position 0 of the array chm, indicating that it will go from warehouse x, and array chm indicating chromosome individuals in the population;
13) generating a random decimal y, and a probability
In contrast, if y ≦ psThe next pass is the warehouse if y > psThen, next onePassing through the receiving points, turning to step 16), wherein M is the number of warehouses, and N is the number of the receiving points;
14) when the next pass is judged to be a warehouse, judging whether the next pass is effective or available, and if so, adding an array chm;
15) when the next pass is judged not to be the warehouse, the random number is regenerated to be the next arrival receiving point; if the receiving point has been added with the array chm, the processing method is the same as the warehouse, and the value of the difference C is subtracted by the value required by the receiving point, after that, step 13) is executed to continue to search the warehouse or the receiving point which needs to pass next time;
16) when the current difference C is not greater than 0, an optimal path is found between the receiving points and is entered into the array chm.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method.
The invention achieves the following beneficial effects:
the invention optimizes the generation of the initial population, and generates the next generation by a mode of simulating cell division, thereby effectively avoiding the problems of premature convergence and the like in the traditional genetic algorithm and shortening the optimization time; the invention provides a better solution for the problem of distribution of a single vehicle to multiple receiving points through multiple storehouses in actual transportation.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of chromosome coding.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a multi-source multi-point path planning method includes the following steps:
(1) establishing multi-source multi-target path planning target function
Suppose there are M warehouses, and it is recorded as set S ═ S1,s2,...sM}; the product types stored in the warehouse are respectively h1,h2,h3…hn(ii) a There are N receiving points, denoted as set R ═ R1,r2,...rN}. Aiming at the distribution path optimization problem under the logistics distribution situation of multi-warehouse delivery and multi-client receiving, a cost minimization objective function is established:
where d (a, b) represents the distance between a and b, and a and b represent the receiving point r or warehouse s. When the total weight of the cargoes is greater than the rated loading capacity of the vehicle, the distance of partial receiving points is counted in the path planning, wherein alpha is 1, and beta is 0; on the contrary, when the total weight of the cargo which can be accommodated by the rated loading capacity is satisfied, the receiving point distance is not required to be considered, but only the distance between the warehouses is required to be considered, wherein alpha is 0, and beta is 1.
Respectively representing warehousesjAnd a warehouse sj+1The k and m values between the two warehouses are generated according to the fitness function and are not equal to each other, and the multi-point multi-source path planning problem is further constrained by the following conditions:
1) the total amount of different products sent to each receiving point can not exceed the total storage capacity of the products
Wherein b isp(j) To send the quantity of p-type products to the receiving point j, BpThe total storage capacity of the p-type products.
2) The loading capacity of the vehicle in the distribution process can not exceed the rated loading capacity of the vehicle type
Wherein: IR is the number of receiving points between warehouse j and warehouse j + 1. b (i) represents the weight of the cargo sent to the receiving point i, and Q is the rated load capacity of the vehicle.
3) On the basis of meeting the conditions, each warehouse is regulated to pass through once based on a greedy strategy, and all products to be delivered are loaded as long as the warehouse has the products to be delivered, so that the delivery cost is reduced.
4) Also for cost reduction, each delivery point is passed at least once, but more than twice. The process is only carried out once as long as the weight of the product required by the receiving point does not exceed the rated load of the vehicle; and if the rated load is exceeded, the load is allowed to be divided into two times to meet the product requirement of the load.
(2) Formulating chromosome coding rules
The method adopts natural numbers to fill each point of the chromosome, and the set R is { R ═ R1,r2,...rNThe number of each receiving point in the item is a positive value, and is used for representing the receiving point; set S ═ S1,s2,...sMEach delivery point takes its negative value to indicate the delivery point, and the symbol indicates the difference from the receiving point. The chromosomal codes are shown in FIG. 1.
(3) Population initialization
An initial population p (t) is generated using a stochastic method, assuming that the chromosome individuals in the population are represented as an array chm, the process is as follows.
1) Calculating the difference C between the total delivery amount of all receiving points and the rated load capacity of the automobile;
2) a random integer x is generated, where x ≦ M, and x is filled into location 0 of array chm, indicating that it will go from warehouse x.
3) Generating a random decimal y, and a probability
In contrast, if y ≦ psThe next pass is the warehouse. If y > psIf yes, the next passing is a receiving point, and the step 6) is executed;
4) when the warehouse is judged, if the warehouse is the last warehouse and the difference C is larger than 0, turning to the step 3); if the warehouse is the last warehouse and the difference C is not greater than 0, the automobile loading capacity is met, and step 6) is executed; if the last warehouse is not selected and the difference value C is not greater than 0, adding the rest warehouses into the array chm in sequence and executing the step 6); if not the last warehouse and the difference C is greater than 0, it is determined whether the random number has been added to the array chm, i.e., whether the receiving point has passed once, and if the random number has not been added to the array chm, the random number is added in reverse, the sum of the demand of each receiving point for the warehouse goods is calculated, and the sum is added to the difference C. If so, it is determined whether the neighbor number is added until an un-added bin is found and the add array is negated chm.
5) When the warehouse is judged not to be the warehouse, the random number is regenerated to be the next arrival receiving point. If the receiving point has been added to the array chm, the process is the same as warehouse and the value of the difference C is subtracted from the value required by the receiving point. And after the completion, executing the step 3) to continuously search a warehouse or a receiving point which needs to be passed next time.
6) At this stage, the current difference value C is not larger than 0, and then an optimal path is searched among the receiving points.
(4) A fitness function is generated.
The model establishment of the fitness function of the method is divided into the following two conditions:
1) the fitness function when a vehicle arrives at the first delivery point from the warehouse is as follows:
in the formula, N(s)j,hj) Representing a fitness value, M representing a threshold for demand, K representing a threshold for distance, Pn(hj) Indicating a delivery point n to a cargo hjThe required amount of (d)(s)j,pn) Representing warehouses sjTo the point of delivery pnThe distance of (c).
2) The fitness function when a vehicle leaves a first delivery point to go to the next delivery point (or warehouse) is as follows:
where C represents the difference between its total demand and the vehicle load, and T is a constant with a value greater than 1, for the purpose of improving the fitness.
(5) Selection operation
And (3) solving fitness values of individuals in the population and sequencing, selecting 50% of individuals with the highest fitness value, applying an elite retention strategy, directly incorporating the optimal individuals in the population into filial generations, wherein the optimal individuals can retain excellent genes of the optimal individuals and eliminate the individuals with the minimum fitness.
(6) Crossover operation
For the path planning problem, because each individual is composed of multiple receiving points and multiple bins, if the way of randomly selecting cross points and exchanging gene segments is adopted, the optimal gene segments of the individual can be damaged. Therefore, the method provides a method for simulating cell division to generate the next generation, copies all information of the parent individuals into the offspring individuals, and recombines the gene sequences when the offspring individuals are generated. The recombination rule comprises the following steps:
1) if only two warehouses are available, any one receiving point between the two warehouses is randomly selected, the adaptability value of the point to other receiving points is judged, the receiving point with the maximum adaptability value is found, and the receiving point is exchanged with the first receiving point behind the receiving point. Judging the size of the total fitness value after exchange and the total fitness value before exchange, and if the size of the total fitness value after exchange is smaller than the total fitness value before exchange, discarding the gene recombination; otherwise, marking the receiving point, and calculating the receiving point if the receiving point is randomly reached in the next recombination.
2) If the number of warehouses is greater than 2, repeating step 1) between each warehouse.
3) And judging the recombined positions of all receiving points after the last warehouse in the mode of the step 1).
(7) Mutation operation
The method carries out mutation operation by exchanging the gene value corresponding to the receiving point on the basis of the non-uniform mutation mode, and firstly generates the mutation probability P of the individual(n)mAnd the probability is compared with the set total variation probability PmMaking a comparison if P(n)m＜PmGenerating two random numbers for identifying and exchanging two receiving points; otherwise, the next individual is judged. The total variation probability is used in a staged manner, namely, the total variation probability is smaller and smaller as the generation number is larger, so that excellent individuals in the population can not be damaged in the last stage of population iteration.
(8) Terminate
And if the population reaches a specified genetic algebra or the number of times that the fitness value of the optimal individual is not improved reaches a preset value, terminating the method and outputting the current optimal individual. If the termination condition is not reached, the step (3) is switched to t +1 to continue execution.
Correspondingly, the invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the method when executing the computer program.
The invention accordingly also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A multi-source multi-point path planning method is characterized in that,
acquiring warehouse and harvest point information;
determining a multi-source multi-point path planning objective function of the weight of introduced distribution products and the rated load capacity of the vehicle aiming at minimizing the cost according to the warehouse and the receiving point information;
coding warehouses and receiving points on a distribution path in a natural number coding mode; generating an initial population by a modified random method; generating new individuals in a crossing way by using a method for simulating cell division, and carrying out operations such as selection, mutation and the like to update the population; iteratively calculating the fitness value of the optimal individual until an iteration condition is met to obtain the optimal individual; determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; and finally, planning the optimal solution of the target function according to the multi-source multi-point path and outputting a distribution path.
2. The multi-source multi-point path planning method according to claim 1, wherein the multi-source multi-point path planning objective function is:
wherein d (a, b) represents the distance between a and b, a and b representing the receiving point r or warehouse s; when the total weight of the cargoes is greater than the rated loading capacity of the vehicle, the distance of partial receiving points is counted in the path planning, wherein alpha is 1, and beta is 0; on the contrary, when the total weight of the goods which can be accommodated by the rated loading capacity meets the requirement, the receiving point distance is not required to be considered, only the distance between warehouses is considered, wherein alpha is 0, and beta is 1; r is a collection of receiving points,
respectively representing warehousesjAnd a warehouse sj+1K belongs to R, m belongs to R, and the values of k and m between the two warehouses are generated according to the fitness function and are not equal to each other; m is the number of warehouses, N is the number of receiving points, i is 1,2, …, N-1, j is 1,2, …, M-1, k is 1,2, …, N, M is 1,2, …, N;
the multi-source multi-point path planning objective function further comprises the following constraint conditions:
1) the total amount of different products sent to each receiving point does not exceed the total storage amount;
2) the load capacity of the vehicle does not exceed the rated load capacity of the vehicle type in the distribution process;
3) on the basis of meeting the conditions, each warehouse is regulated to pass through once based on a greedy strategy, and all products to be delivered are loaded as long as the warehouse has the products to be delivered;
4) each receiving point passes at least once, but not more than twice; the weight of the product required by the receiving point does not exceed the rated load of the vehicle, and the product is only passed once when the weight of the product exceeds the rated load, and the product is required twice when the weight of the product exceeds the rated load.
3. The multi-source multi-point path planning method according to claim 1, wherein the encoding of the warehouse and the receiving point on the distribution path in a natural number encoding manner comprises:
filling each point of the chromosome with natural numbers, wherein the number of each receiving point takes the positive value of the number and is used for representing the receiving point; each delivery point takes its negative value to indicate the delivery point.
4. The multi-source multipoint path planning method according to claim 1, wherein the initial population is generated by a modified stochastic method; generating new individuals in a crossing way by using a method for simulating cell division, and carrying out operations such as selection, mutation and the like to update the population; iteratively calculating the fitness value of the optimal individual until an iteration condition is met to obtain the optimal individual; determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; finally, the process of outputting the distribution path according to the optimal solution of the multi-source multi-point path planning objective function comprises the following steps:
(1) generating each individual in the initial population by using a random method;
(2) calculating the fitness of each individual in the initial population, wherein the fitness comprises the following steps: respectively generating a fitness function value when the vehicle arrives at a first receiving point from the warehouse and a fitness function value when the vehicle leaves the first receiving point and transfers to a next receiving point or the warehouse;
(3) a selecting operation comprising: sorting the individuals in the population according to fitness, applying an elite retention strategy, selecting 50% of the individuals with the highest fitness value to directly select into offspring, and eliminating the individuals with the minimum fitness;
(4) a crossover operation comprising: randomly selecting parent individuals according to a certain probability to carry out gene recombination, and merging the parent individuals into offspring;
(5) a mutation operation comprising: randomly selecting a certain parent, performing variation operation by using a mode of exchanging gene values corresponding to receiving points on the basis of a non-uniform variation mode, and merging the gene values into offspring;
(6) circularly executing the selection operation, the cross operation and the variation operation to form a filial generation population, calculating the fitness of the individuals in the population, and outputting the current optimal individual if the population reaches a specified genetic algebra or the number of times that the fitness value of the optimal individual is not improved reaches a preset value; if the termination condition is not reached, the step (3) is continued to be executed after the recording time t is t + 1;
(7) determining an optimal solution of a multi-source multi-point path planning target function according to the optimal individual; and finally, planning the optimal solution of the target function according to the multi-source multi-point path and outputting a distribution path.
5. The multi-source multipoint path planning method according to claim 4, wherein said process of generating individual individuals in an initial population using a stochastic method comprises:
11) calculating a difference C between the total delivery amount of all receiving points and the rated load capacity of the automobile;
12) generating a random integer x, filling-x in position 0 of the array chm, indicating that it will go from warehouse x, and array chm indicating chromosome individuals in the population;
13) generating a random decimal y, and a probability
In contrast, if y ≦ psThe next pass is the warehouse if y > psIf yes, the next passing is a receiving point, and the step 16) is repeated, wherein M is the number of warehouses, and N is the number of receiving points;
14) when the next pass is judged to be a warehouse, judging whether the next pass is effective or available, and if so, adding an array chm;
15) when the next pass is judged not to be the warehouse, the random number is regenerated to be the next arrival receiving point; if the receiving point has been added with the array chm, the processing method is the same as the warehouse, and the value of the difference C is subtracted by the value required by the receiving point, after that, step 13) is executed to continue to search the warehouse or the receiving point which needs to pass next time;
16) when the current difference C is not greater than 0, an optimal path is found between the receiving points and is entered into the array chm.
6. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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