CN117010670B - Intelligent logistics distribution system and method - Google Patents
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Abstract
The invention relates to the technical field of logistics scheduling, and particularly discloses an intelligent logistics distribution system and method. The method comprises the steps of firstly obtaining weather information in a preset time period in the future, then obtaining bearing capacity of a target delivery station, a plurality of target delivery cargos and delivery time requirement information of each target delivery cargos, and then obtaining a daily cargo scheduling plan of the target delivery station in the preset time period in the future and delivering the cargos based on a genetic algorithm according to the information. Compared with the prior art, the optimal dispatching scheme for dispatching the transfer center to the delivery station every day is obtained through the genetic algorithm, the delivery station can store and deliver the optimal quantity of the goods under the severe weather condition by combining the weather information, the problem of improper storage caused by excessive goods is avoided, damage of the severe weather to the goods is further reduced, and the timeliness of delivery can be guaranteed by combining the delivery time requirement information.
Description
Technical Field
The invention relates to the technical field of logistics scheduling, in particular to an intelligent logistics distribution system and method.
Background
In current logistics, goods are generally sent to a transfer center, then distributed and scheduled to each distribution station, and then distributed. However, such conventional logistics distribution systems have problems in practical operation, such as uneven levels of hardware facilities and environmental conditions of each distribution station, including the size of warehouse space and the perfection of facilities. At the same time, the supply requirements of different distribution stations are different. Therefore, when the amount of express delivery is excessive, some distribution stations may face the problem of insufficient storage space, and excessive goods are generally accumulated outdoors.
Under severe weather conditions, such as storm, storm snow and the like, losses can be caused to express delivery accumulated outdoors, and even goods stored indoors can be influenced under extremely severe weather conditions. For example, severe weather conditions can cause cargo to become wet, damaged, or even lost, causing numerous problems and losses to logistics businesses and consumers. At the same time, bad weather also increases the likelihood of damage to the goods during distribution.
Therefore, a novel logistics distribution system is urgently needed, distribution scheduling can be performed on the basis of considering weather conditions, and safety and timeliness of goods are improved.
Disclosure of Invention
The invention aims to provide an intelligent logistics distribution system and method, which solve the following technical problems:
How to consider the weather condition to carry out distribution scheduling, and improve the safety and timeliness of goods.
The aim of the invention can be achieved by the following technical scheme:
An intelligent logistics distribution system comprising:
The weather information acquisition module is used for acquiring weather information in a future preset time period;
the distribution information acquisition module is used for acquiring the bearing capacity of the target distribution station, a plurality of target distribution cargos and distribution time requirement information of each target distribution cargos;
the scheduling plan design module is used for obtaining a daily goods scheduling plan of the target delivery station in a future preset time period based on a genetic algorithm according to weather information in the future preset time period, the bearing capacity of the target delivery station and the delivery time requirement information of each target delivery goods;
And the dispatching plan execution module is used for dispatching the target dispatching goods to the target dispatching station according to the daily goods dispatching plan in the preset time period in the future and completing the dispatching.
An intelligent logistics distribution method, comprising:
acquiring weather information in a preset time period in the future;
Acquiring bearing capacity of a target delivery station, a plurality of target delivery cargoes and delivery time requirement information of each target delivery cargoes;
According to weather information in a future preset time period, bearing capacity of the target delivery station and delivery time requirement information of each target delivery goods, acquiring a daily goods scheduling plan of the target delivery station in the future preset time period based on a genetic algorithm;
And dispatching the target delivery goods to the target delivery station according to a daily goods dispatching plan in a preset time period in the future and completing delivery.
As a further scheme of the invention: according to weather information in a future preset time period, carrying capacity of the target delivery station and delivery time requirement information of each target delivery goods, a daily goods scheduling plan of the target delivery station in the future preset time period is obtained based on a genetic algorithm, and the method comprises the following steps:
establishing an initial population based on a preset chromosome structure;
optimizing the initial population based on a preset fitness function to obtain an optimal population;
According to the optimal individuals in the optimal population, a daily goods scheduling plan of the target delivery station in a future preset time period is obtained;
wherein, the preset chromosome structure is:
x=[x1,x2,x3...xi...xn-1,xn]；
Wherein x is a chromosome, i is the ith day in a future preset time period, n is the total number of days in the future preset time period, x i is a cargo dispatch plan vector of the ith day in the future preset time period, x ij represents the jth target cargo to be dispatched to the target dispatching station in the ith day in the future preset time period, m is the total number of target cargos to be dispatched to the target dispatching station in the ith day in the future preset time period, and m is smaller than a value representing the carrying capacity of the target dispatching station.
As a further scheme of the invention: the preset fitness function is as follows:
Wherein fit (x) represents fitness of chromosome x, sev (i) represents weather severity of the ith day in a preset time period in the future, the weather severity is obtained according to weather information, urg (x i) represents time urgency of a cargo scheduling plan of the ith day in the preset time period in the future, and the time urgency is obtained according to distribution time requirement information of each target cargo in the cargo scheduling plan.
As a further scheme of the invention: daily weather information includes various weather indicators for the day; the weather severity is obtained according to the following formula:
Wherein weain ik represents the kth weather indicator on the ith day, and α ik represents the weight coefficient corresponding to the kth weather indicator on the ith day.
As a further scheme of the invention: the distribution time requirement information comprises the remaining days of distribution requirement; the time urgency is obtained according to the following formula:
D (x ij) represents the remaining days of the delivery request corresponding to the j-th target delivery goods planned and scheduled to the target delivery station in the i-th day in the future preset time period.
As a further scheme of the invention: optimizing the initial population based on a preset fitness function to obtain an optimal population, including:
taking the initial population as a parent population;
calculating the fitness of each chromosome in the parent population based on a preset fitness function, and eliminating individuals in the parent population according to the calculation result to obtain a residual population;
mutating chromosomes in the residual population to obtain a offspring population;
And carrying out genetic optimization on the offspring population and the residual population which are the most parent population again until a termination condition is reached, and taking the offspring population and the residual population which are finally obtained as optimal populations.
As a further scheme of the invention: obtaining a offspring population from chromosomes in the mutated remaining population, comprising:
and exchanging the positions of the two cargo dispatch plan vectors in the target chromosome in the residual population to obtain the mutated target chromosome.
As a further scheme of the invention: obtaining a offspring population from chromosomes in the mutated remaining population, comprising:
And reallocating the target delivery cargos in the plurality of target cargo dispatch plan vectors based on the original target delivery cargos in the plurality of target cargo dispatch plan vectors in the residual population to obtain the mutated target chromosome.
The invention has the beneficial effects that:
The invention provides an intelligent logistics distribution system and method, which are characterized in that weather information in a preset time period in the future is firstly obtained, then the bearing capacity of a target distribution station, a plurality of target distribution cargos and distribution time requirement information of each target distribution cargos are obtained, then a daily cargo dispatching plan of the target distribution station in the preset time period in the future is obtained based on a genetic algorithm according to the weather information in the preset time period in the future, the bearing capacity of the target distribution station and the distribution time requirement information of each target distribution cargos, and then the target distribution cargos are dispatched to the target distribution station according to the daily cargo dispatching plan in the preset time period in the future and the distribution is completed. Compared with the prior art, the optimal dispatching scheme for dispatching the transfer center to the delivery station every day is obtained through the genetic algorithm, the delivery station can store and deliver the optimal quantity of the goods under the severe weather condition by combining the weather information, the problem of improper storage caused by excessive goods is avoided, damage of the severe weather to the goods is further reduced, and the timeliness of delivery can be guaranteed by combining the delivery time requirement information.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a system architecture diagram of the intelligent logistics distribution system of the present invention;
FIG. 2 is a method flow diagram of the intelligent logistics distribution method of the present invention;
fig. 3 is a flowchart of the method of step S203 in fig. 2.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Before describing particular embodiments, some terminology will be explained:
Genetic algorithm: genetic algorithm is an optimization algorithm, and inspiration comes from genetic and evolutionary principles in biology. It simulates the natural selection, crossover and mutation processes in evolution to search and optimize solutions to the problem.
In genetic algorithms, the solution to the problem is represented as a set of codes, either numerical or symbolic, called genes or chromosomes. These chromosomes create new solutions by mimicking the crossover and mutation of genes, similar to the genetic mutation of organisms. Then, through the selection operation, competition is carried out according to the fitness evaluation of each solution, and a better solution has higher probability to be selected, and then the next generation is entered.
By continually iterating the process of interleaving, mutation and selection, the genetic algorithm is able to search and optimize solutions to the problem in solution space, gradually converging towards better solutions. It is widely used in the fields of combination optimization, function optimization, machine learning, artificial intelligence, etc.
It is understood that the terminology related to genetic algorithms, such as chromosomes, populations, etc., are well known in the art and will not be described in detail herein.
Referring to fig. 1, the present invention is an intelligent logistics distribution system 100, comprising:
A weather information acquisition module 110, configured to acquire weather information in a future preset time period;
a delivery information obtaining module 120, configured to obtain a carrying capacity of the target delivery station, a plurality of target delivered cargos, and delivery time requirement information of each target delivered cargos;
the dispatch plan design module 130 is configured to obtain a daily cargo dispatch plan of the target delivery station in a future preset time period based on a genetic algorithm according to weather information in the future preset time period, the bearing capacity of the target delivery station, and the delivery time requirement information of each target delivery cargo;
the dispatch plan execution module 140 is configured to dispatch the target delivery goods to the target delivery station and complete delivery according to a daily goods dispatch plan within a preset time period in the future.
Compared with the prior art, the optimal dispatching scheme for dispatching the transfer center to the delivery station every day is obtained through the genetic algorithm, the delivery station can store and deliver the optimal quantity of the goods under the severe weather condition by combining the weather information, the problem of improper storage caused by excessive goods is avoided, damage of the severe weather to the goods is further reduced, and the timeliness of delivery can be guaranteed by combining the delivery time requirement information.
The system can flexibly adjust the distribution strategy according to the weather conditions of the places where the distribution stations are located. Under the condition of bad weather, corresponding early warning and scheduling measures can be carried out, and the goods are prevented from being detained outdoors and damaged.
The invention also provides an intelligent logistics distribution method, which is shown in the figure 2, and comprises the following steps:
S201, acquiring weather information in a preset time period in the future;
S202, acquiring bearing capacity of a target delivery station, a plurality of target delivery cargoes and delivery time requirement information of each target delivery cargoes;
s203, according to weather information in a preset time period in the future, bearing capacity of the target delivery station and delivery time requirement information of each target delivery, acquiring a daily delivery schedule of the target delivery station in the preset time period in the future based on a genetic algorithm;
S204, dispatching the target delivery goods to the target delivery station according to a daily goods dispatching plan in a preset time period in the future and completing delivery.
Specifically, in combination with fig. 3, in a preferred embodiment, step S203, according to weather information, the carrying capacity of the target delivery station, and the delivery time requirement information of each target delivery, obtains a daily delivery schedule of the target delivery station in a future preset time period based on a genetic algorithm, which specifically includes:
s301, establishing an initial population based on a preset chromosome structure;
S302, optimizing an initial population based on a preset fitness function to obtain an optimal population;
s303, according to the optimal individuals in the optimal population, acquiring a daily cargo scheduling plan of the target delivery station in a future preset time period;
wherein, the preset chromosome structure is:
x=[x1,x2,x3...xi...xn-1,xn]；
Wherein x is a chromosome, i is the ith day in a future preset time period, n is the total number of days in the future preset time period, x i is a cargo dispatch plan vector of the ith day in the future preset time period, x ij represents the jth target cargo to be dispatched to the target dispatching station in the ith day in the future preset time period, m is the total number of target cargos to be dispatched to the target dispatching station in the ith day in the future preset time period, and m is smaller than a value representing the carrying capacity of the target dispatching station.
The chromosome structure can intuitively show the dispatching plan of goods dispatched to the dispatching station by the transfer center every day, and the method can be improved in interpretation. It will be appreciated that in the chromosome x described above, the number of the total number m of the target delivery cargos in each of the cargo dispatch plan vectors x i may be different.
Further, in a preferred embodiment, the predetermined fitness function is:
Wherein fit (x) represents fitness of chromosome x, sev (i) represents weather severity of the ith day in a preset time period in the future, the weather severity is obtained according to weather information, urg (x i) represents time urgency of a cargo scheduling plan of the ith day in the preset time period in the future, and the time urgency is obtained according to distribution time requirement information of each target cargo in the cargo scheduling plan.
The weather severity and the time emergency in the fitness function are calculated by adopting a nonlinear calculation mode, and the higher the fitness value is, the more excellent the scheduling scheme corresponding to chromosome x is represented, and the less cargo loss is caused. By adding one to the denominator, even if the input weather severity and time urgency are small, a certain influence can be maintained. This allows for a more accurate capture of the impact of weather severity and urgency on compliance.
On the other hand, the value of the fitness calculated by the function may lie between 0 and 1, which makes it possible to use it as an index for ranking, scalar evaluation and comparison. This may be more conveniently used for business decisions.
Specifically, in a preferred embodiment the daily weather information includes a plurality of weather indicators for the day; the weather severity is obtained according to the following formula:
Wherein weain ik represents the kth weather indicator on the ith day, and α ik represents the weight coefficient corresponding to the kth weather indicator on the ith day.
The weather indicator in the weather information refers to indicator data describing weather conditions, and generally includes the following contents:
Temperature: may be degrees celsius or degrees fahrenheit.
Humidity: indicating the water vapor content of the air.
Wind power: the strength of wind is expressed, typically in meters per second, kilometers per hour, or miles per hour.
Precipitation amount: indicating the amount of precipitation per unit time and may be millimeters, centimeters or inches.
Thunderstorm conditions: indicating whether there is lightning activity.
Visibility: represents the range of visible objects in air, typically in meters or kilometers.
In the embodiment, a plurality of weather indexes are expressed by one weather severity, so that complex description is simplified, and calculation and understanding are easy. It can be understood that the specific method for calculating the weather severity according to the weather information in practice can be flexibly set according to specific requirements.
In a preferred embodiment, the delivery time requirement information includes a number of days remaining for delivery requirement; the time urgency is obtained according to the following formula:
D (x ij) represents the remaining days of the delivery request corresponding to the j-th target delivery goods planned and scheduled to the target delivery station in the i-th day in the future preset time period.
It can be understood that the actual delivery time requirement information may be the latest delivery date, and the like, and how to calculate the time urgency according to the delivery time requirement information in practice may be flexibly set according to specific needs.
In a preferred embodiment, the step S302 optimizes the initial population based on a preset fitness function to obtain an optimal population, which specifically includes:
taking the initial population as a parent population;
calculating the fitness of each chromosome in the parent population based on a preset fitness function, and eliminating individuals in the parent population according to the calculation result to obtain a residual population;
mutating chromosomes in the residual population to obtain a offspring population;
And carrying out genetic optimization on the offspring population and the residual population which are the most parent population again until a termination condition is reached, and taking the offspring population and the residual population which are finally obtained as optimal populations.
Because of the limitation of the chromosome structure in this embodiment, when the offspring population is obtained by inheriting, if a new chromosome is obtained by adopting a crossover mode, a problem of cargo error may occur, for example, the same target cargo may occur in two cargo scheduling plans of different days, so that the crossover mode is abandoned, and only a mutation mode is adopted to carry out genetic iteration, so as to ensure the accuracy of data.
Specifically, in a preferred embodiment, the chromosomes in the remaining population are mutated to yield a population of offspring, specifically comprising:
and exchanging the positions of the two cargo dispatch plan vectors in the target chromosome in the residual population to obtain the mutated target chromosome.
The above process exchanges two-day scheduling plans to realize variation.
Further, in a preferred embodiment, the mutating chromosomes in the remaining population to obtain a offspring population further comprises:
And reallocating the target delivery cargos in the plurality of target cargo dispatch plan vectors based on the original target delivery cargos in the plurality of target cargo dispatch plan vectors in the residual population to obtain the mutated target chromosome.
The above process redistributes the schedule plan for a few days to realize variation.
The invention provides an intelligent logistics distribution system and method, which are characterized in that weather information in a preset time period in the future is firstly obtained, then the bearing capacity of a target distribution station, a plurality of target distribution cargos and distribution time requirement information of each target distribution cargos are obtained, then a daily cargo dispatching plan of the target distribution station in the preset time period in the future is obtained based on a genetic algorithm according to the weather information in the preset time period in the future, the bearing capacity of the target distribution station and the distribution time requirement information of each target distribution cargos, and then the target distribution cargos are dispatched to the target distribution station according to the daily cargo dispatching plan in the preset time period in the future and the distribution is completed. Compared with the prior art, the optimal dispatching scheme for dispatching the transfer center to the delivery station every day is obtained through the genetic algorithm, the delivery station can store and deliver the optimal quantity of the goods under the severe weather condition by combining the weather information, the problem of improper storage caused by excessive goods is avoided, damage of the severe weather to the goods is further reduced, and the timeliness of delivery can be guaranteed by combining the delivery time requirement information.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (5)
1. An intelligent logistics distribution system, comprising:
The weather information acquisition module is used for acquiring weather information in a future preset time period;
the distribution information acquisition module is used for acquiring the bearing capacity of the target distribution station, a plurality of target distribution cargos and distribution time requirement information of each target distribution cargos;
the scheduling plan design module is used for obtaining a daily goods scheduling plan of the target delivery station in a future preset time period based on a genetic algorithm according to weather information in the future preset time period, the bearing capacity of the target delivery station and the delivery time requirement information of each target delivery goods;
The dispatching plan execution module is used for dispatching the target dispatching goods to the target dispatching station according to a daily goods dispatching plan in a preset time period in the future and completing the dispatching;
The method for obtaining the daily goods scheduling plan of the target delivery station in the future preset time period based on the genetic algorithm according to weather information in the future preset time period, the bearing capacity of the target delivery station and the delivery time requirement information of each target delivery goods comprises the following steps:
establishing an initial population based on a preset chromosome structure;
optimizing the initial population based on a preset fitness function to obtain an optimal population;
According to the optimal individuals in the optimal population, a daily goods scheduling plan of the target delivery station in a future preset time period is obtained;
wherein, the preset chromosome structure is:
x=[x1,x2,x3...xi...xn-1,xn ]；
xi=[xi1,xi2,xi3...xij...xi(m-1),xim ] T；
Wherein x is a chromosome, i is the ith day in a future preset time period, n is the total number of days in the future preset time period, x i is a cargo scheduling plan vector of the ith day in the future preset time period, x ij represents the jth target cargo to be scheduled to the target delivery station in the ith day in the future preset time period, m is the total number of target cargo to be scheduled to the target delivery station in the ith day in the future preset time period, and m is smaller than a value representing the carrying capacity of the target delivery station;
the preset fitness function is as follows:
；
wherein fit (x) represents fitness of chromosome x, sev (i) represents weather severity of the ith day in a preset time period in the future, the weather severity is obtained according to weather information, urg (x i) represents time urgency of a cargo scheduling plan of the ith day in the preset time period in the future, and the time urgency is obtained according to distribution time requirement information of each target cargo in the cargo scheduling plan;
daily weather information includes various weather indicators for the day; the weather severity is obtained according to the following formula:
；
Wherein weain ik represents the kth weather indicator on the ith day, and α ik represents a weight coefficient corresponding to the kth weather indicator on the ith day;
the distribution time requirement information comprises the remaining days of distribution requirement; the time urgency is obtained according to the following formula:
；
D (x ij) represents the remaining days of the delivery request corresponding to the j-th target delivery goods planned and scheduled to the target delivery station in the i-th day in the future preset time period.
2. An intelligent logistics distribution method is characterized by comprising the following steps:
acquiring weather information in a preset time period in the future;
Acquiring bearing capacity of a target delivery station, a plurality of target delivery cargoes and delivery time requirement information of each target delivery cargoes;
According to weather information in a future preset time period, bearing capacity of the target delivery station and delivery time requirement information of each target delivery goods, acquiring a daily goods scheduling plan of the target delivery station in the future preset time period based on a genetic algorithm;
dispatching the target delivery goods to the target delivery station according to a daily goods dispatching plan in a preset time period in the future and completing delivery;
The method for obtaining the daily goods scheduling plan of the target delivery station in the future preset time period based on the genetic algorithm according to weather information in the future preset time period, the bearing capacity of the target delivery station and the delivery time requirement information of each target delivery goods comprises the following steps:
establishing an initial population based on a preset chromosome structure;
optimizing the initial population based on a preset fitness function to obtain an optimal population;
According to the optimal individuals in the optimal population, a daily goods scheduling plan of the target delivery station in a future preset time period is obtained;
wherein, the preset chromosome structure is:
x=[x1,x2,x3...xi...xn-1,xn ]；
xi=[xi1,xi2,xi3...xij...xi(m-1),xim ] T；
Wherein x is a chromosome, i is the ith day in a future preset time period, n is the total number of days in the future preset time period, x i is a cargo scheduling plan vector of the ith day in the future preset time period, x ij represents the jth target cargo to be scheduled to the target delivery station in the ith day in the future preset time period, m is the total number of target cargo to be scheduled to the target delivery station in the ith day in the future preset time period, and m is smaller than a value representing the carrying capacity of the target delivery station;
the preset fitness function is as follows:
；
wherein fit (x) represents fitness of chromosome x, sev (i) represents weather severity of the ith day in a preset time period in the future, the weather severity is obtained according to weather information, urg (x i) represents time urgency of a cargo scheduling plan of the ith day in the preset time period in the future, and the time urgency is obtained according to distribution time requirement information of each target cargo in the cargo scheduling plan;
daily weather information includes various weather indicators for the day; the weather severity is obtained according to the following formula:
;
Wherein weain ik represents the kth weather indicator on the ith day, and α ik represents a weight coefficient corresponding to the kth weather indicator on the ith day;
the distribution time requirement information comprises the remaining days of distribution requirement; the time urgency is obtained according to the following formula:
;
D (x ij) represents the remaining days of the delivery request corresponding to the j-th target delivery goods planned and scheduled to the target delivery station in the i-th day in the future preset time period.
3. The intelligent logistics distribution method of claim 2, wherein optimizing the initial population based on the predetermined fitness function to obtain an optimal population comprises:
taking the initial population as a parent population;
calculating the fitness of each chromosome in the parent population based on a preset fitness function, and eliminating individuals in the parent population according to the calculation result to obtain a residual population;
mutating chromosomes in the residual population to obtain a offspring population;
And carrying out genetic optimization on the offspring population and the residual population which are the most parent population again until a termination condition is reached, and taking the offspring population and the residual population which are finally obtained as optimal populations.
4. The intelligent logistics distribution method of claim 3, wherein mutating the chromosomes in the remaining population to obtain a offspring population, comprising:
and exchanging the positions of the two cargo dispatch plan vectors in the target chromosome in the residual population to obtain the mutated target chromosome.
5. The intelligent logistics distribution method of claim 3, wherein mutating the chromosomes in the remaining population to obtain a offspring population, comprising:
And reallocating the target delivery cargos in the plurality of target cargo dispatch plan vectors based on the original target delivery cargos in the plurality of target cargo dispatch plan vectors in the residual population to obtain the mutated target chromosome.
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| CN202311285799.9A CN117010670B (en) | 2023-10-07 | 2023-10-07 | Intelligent logistics distribution system and method |
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