CN112700185A - Logistics route planning method and system based on bionic intelligent optimization - Google Patents
Logistics route planning method and system based on bionic intelligent optimization Download PDFInfo
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Abstract
The invention discloses a logistics route planning method and a logistics route planning system based on bionic intelligent optimization, which comprises the steps of comparing route planning data of other logistics centers with existing route data in a logistics system of a target logistics center, determining all routes with route conflicts, mapping the routes with the route conflicts in a distribution service map to form a mapping distribution service map, embedding the mapping distribution service map into a trained logistics route planning model, and finally hiding the routes with the route conflicts in the logistics route planning model to obtain a new logistics route planning model; the invention can iteratively update the existing logistics route in the logistics system of the target logistics center to obtain a new optimal route to replace the existing route, can obtain the optimal route on the premise of reducing the competitive pressure, and can provide the optimal logistics distribution service for customers by the final distribution service map, thereby improving the comprehensive service capability of the target logistics center.
Description
Technical Field
The invention relates to the technical field of logistics route planning, in particular to a logistics route planning method and a logistics route planning system based on bionic intelligent optimization.
Background
Under the background of rapid development of economic society, the demand for goods circulation in different places or in local places is continuously increased, the logistics transportation gradually becomes an increasingly important ring, and the planning demand of corresponding logistics schemes is also increased. In order to deal with the problem, many intelligent logistics systems are already available in the market, and the powerful computing power of a computer is utilized to quickly provide an overall logistics planning scheme.
However, existing route planning schemes generally only involve route planning according to existing conditions, and are rarely efficient solutions in terms of the real-time dynamic route planning problem. When a plurality of cross service routes exist in a plurality of logistics centers, the transportation pressure of the routes is increased, so that a large amount of time is consumed on route congestion, how to quickly replace the old route with high congestion is how to improve the logistics service quality, and the logistics service cost is reduced. Therefore, the invention provides a logistics route planning method and a logistics route planning system based on bionic intelligent optimization, and aims to overcome the defects in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a logistics route planning method and a logistics route planning system based on bionic intelligent optimization, wherein the method comprises the steps of comparing the route planning data of other logistics centers with the existing route data in a logistics system of a target logistics center to determine all routes with route conflicts, mapping and marking the routes with the route conflicts with other logistics centers in a distribution service map to form a mapping distribution service map, embedding the mapping distribution service map into a trained logistics route planning model, finally hiding the marked routes with the route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model, so that iterative updating of the existing logistics routes in the logistics system of the target logistics center can be realized, and the new optimal routes can be obtained to replace the existing routes, the optimal route can be obtained on the premise of reducing the competitive pressure of the same row, and the formed final distribution service map can provide optimal logistics distribution service for the customers, so that the comprehensive service capacity of the target logistics center can be improved.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme:
the logistics route planning method based on bionic intelligent optimization comprises the following steps:
the method comprises the following steps: numbering all normally working logistics vehicles in a logistics system of a target logistics center, acquiring vehicle condition information of the logistics vehicles one by one according to the numbers to form vehicle condition factors in one-to-one correspondence with the numbers, and summarizing the vehicle condition factors to form a vehicle condition factor set;
step two: searching all other logistics centers with business similarity with the target logistics center in a single area, collecting path planning data of all other logistics centers, comparing the collected path planning data of all other logistics centers with the path data existing in a logistics system of the target logistics center, determining all routes with route conflicts, and identifying and counting the routes;
step three: acquiring a distribution service map of a target logistics center, and mapping and marking a route which conflicts with other logistics centers in the distribution service map to form a mapping distribution service map;
step four: collecting all key variables in the logistics route planning, collecting the key variables and vehicle condition factor sets in the optimal solution state from a key variable database as a common training sample, and carrying out normalization processing on the key variables and the vehicle condition factor sets in the common training sample;
step five: building a logistics route planning model, training the model by using a training common training sample after normalization processing, then embedding a mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
step six: calculating a new route in a new logistics route planning model by using a bionic intelligent ant colony optimization algorithm, sequencing the new route in a descending order according to the number of pheromones on the new route, and finally performing usability verification on the sequenced new route to remove invalid routes to obtain an optimal route;
step seven: the optimal route is remapped to a distribution service map of a target logistics center to form an optimal distribution service map, then the route which has route conflict with other logistics centers is specifically analyzed, the congestion probability of the route is analyzed, route sequencing is carried out according to the congestion probability, the old route with high congestion probability is replaced by the optimal route, and a final distribution service map is formed.
The further improvement lies in that: when the vehicle condition information of the logistics vehicle is collected in the first step, the method specifically comprises the following steps: the method comprises the steps of collecting comprehensive driving technology evaluation information of a vehicle driver, purchasing time of the vehicle, whether the vehicle finishes a maintenance plan regularly or not, road accident rate in the historical working process of the vehicle driver and the driving mileage of the vehicle.
The further improvement lies in that: and when all key variables in the logistics route planning are collected in the fourth step, each key variable is obtained by collecting a plurality of variables, then performing weight removal, loss removal and standardization, and then averaging.
The further improvement lies in that: and sixthly, when the usability of the sorted new routes is checked, the method also comprises the steps of firstly carrying out road condition integration analysis on the new routes and preferentially selecting the new routes with good road conditions.
The logistics route planning system based on bionic intelligent optimization comprises a vehicle condition acquisition module, a regional path comprehensive data acquisition module, a data information processing module, a key variable data acquisition module, a model construction module, an algorithm execution module and a logistics route decision module;
the vehicle condition acquisition module is used for acquiring vehicle condition information of the logistics vehicles and then summarizing the acquired vehicle condition factors to form a vehicle condition factor set;
the regional path comprehensive data acquisition module is used for acquiring all other logistics center path data with path conflict with the existing path data in the logistics system of the target logistics center in a single region, and identifying and counting the paths with repeated conflict;
the data information processing module is used for mapping and marking the route which conflicts with the route of other logistics centers in a distribution service map to form a mapping distribution service map;
the key variable data acquisition module is used for collecting all key variables in the logistics route planning, then constructing a training sample and carrying out normalization processing on the training sample;
the model building module is used for building a logistics route planning model, training the model, embedding the mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
the algorithm execution module is used for calculating a new route in the new logistics route planning model by using a bionic intelligent ant colony optimization algorithm to obtain an optimal route;
the logistics route decision module is used for remapping the optimal route to a distribution service map of a target logistics center to form an optimal distribution service map, specifically analyzing routes having route conflicts with other logistics centers, and replacing old routes with high congestion probability with the optimal route to form a final distribution service map.
The further improvement lies in that: the key variable data acquisition module also comprises a real-time route information receiving module, and the real-time route information receiving module is used for receiving real-time information on the route returned by a vehicle driver on the existing route data, and constructing a training sample by taking the collected real-time information of the route as a secondary key variable together with the key variable.
The further improvement lies in that: the logistics route planning system further comprises a standby algorithm pool module, wherein the standby algorithm pool module is used for storing standby algorithms, specifically comprises a firefly algorithm, a genetic algorithm, a tabu algorithm, a simulated annealing algorithm and a particle swarm algorithm, and is used for selecting any one of the standby algorithms to carry out calculation on the auxiliary algorithm execution module when the algorithm execution module is overloaded.
The invention has the beneficial effects that: the method of the invention trains the model by taking the vehicle condition factors of the logistics vehicle as the vehicle condition factor set, the key variables and the secondary key variables as the common training sample, can improve the comprehensiveness and scientificity of the model training result, the robustness of the trained model is high, all routes with route conflicts are determined by comparing the route planning data of other logistics centers with the existing route data in the logistics system of the target logistics center, the routes with route conflicts with other logistics centers are mapped and marked in the distribution service map to form the mapping distribution service map, the mapping distribution service map is embedded into the trained logistics route planning model, and finally the marked routes with route conflicts with other logistics centers are hidden in the logistics route planning model to obtain a new logistics route planning model, the method can realize iterative updating of the existing logistics route in the logistics system of the target logistics center, obtain a new optimal route to replace the existing route, obtain the optimal route on the premise of reducing the competition pressure of the same row, and form a final distribution service map which can provide the optimal logistics distribution service for customers, thereby improving the comprehensive service capability of the target logistics center.
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FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system frame in the second embodiment of the present invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
Example one
As shown in fig. 1, the embodiment provides a logistics route planning method based on bionic intelligent optimization, which includes the following steps:
the method comprises the following steps: all normally working logistics vehicles in the logistics system of the target logistics center are numbered, and vehicle condition information of the logistics vehicles is collected one by one according to the numbers, and the logistics vehicle information management method specifically comprises the following steps: collecting comprehensive driving technology evaluation information of a vehicle driver, purchasing time of the vehicle, whether the vehicle regularly completes a maintenance plan, road accident occurrence rate and driving mileage of the vehicle in the historical working process of the vehicle driver to form vehicle condition factors which have one-to-one correspondence with the serial numbers, and summarizing the vehicle condition factors to form a vehicle condition factor set;
step two: searching all other logistics centers with business similarity with the target logistics center in a single area, collecting path planning data of all other logistics centers, comparing the collected path planning data of all other logistics centers with the path data existing in a logistics system of the target logistics center, determining all routes with route conflicts, and identifying and counting the routes;
step three: acquiring a distribution service map of a target logistics center, and mapping and marking a route which conflicts with other logistics centers in the distribution service map to form a mapping distribution service map;
step four: collecting all key variables in the logistics route planning, wherein each key variable is obtained by performing weight removal, loss removal and standardization after collecting a plurality of variables and then averaging, collecting the key variables and vehicle condition factor sets under the optimal solution state from a key variable database as a common training sample, and performing normalization processing on the key variables and the vehicle condition factor sets in the common training sample;
step five: building a logistics route planning model, training the model by using a training common training sample after normalization processing, then embedding a mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
step six: calculating a new route in a new logistics route planning model by using a bionic intelligent ant colony optimization algorithm, sequencing the new routes in a descending order according to the number of pheromones on the new routes, performing usability verification on the sequenced new routes, performing road condition integration analysis on the new routes, preferentially selecting the new routes with good road conditions, and finally performing usability verification on the sequenced new routes to remove invalid routes to obtain an optimal route;
step seven: the optimal route is remapped to a distribution service map of a target logistics center to form an optimal distribution service map, then the route which has route conflict with other logistics centers is specifically analyzed, the congestion probability of the route is analyzed, route sequencing is carried out according to the congestion probability, the old route with high congestion probability is replaced by the optimal route, and a final distribution service map is formed.
Example two
According to fig. 2, the logistics route planning system based on bionic intelligent optimization provided by the embodiment comprises a vehicle condition acquisition module, a regional path comprehensive data acquisition module, a data information processing module, a key variable data acquisition module, a model construction module, an algorithm execution module and a logistics route decision module;
the vehicle condition acquisition module is used for acquiring vehicle condition information of the logistics vehicles and then summarizing the acquired vehicle condition factors to form a vehicle condition factor set;
the regional path comprehensive data acquisition module is used for acquiring all other logistics center path data with path conflict with the existing path data in the logistics system of the target logistics center in a single region, and identifying and counting the paths with repeated conflict;
the data information processing module is used for mapping and marking the route which conflicts with the route of other logistics centers in a distribution service map to form a mapping distribution service map;
the key variable data acquisition module is used for collecting all key variables in the logistics route planning, then constructing a training sample and carrying out normalization processing on the training sample;
the model building module is used for building a logistics route planning model, training the model, embedding the mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
the algorithm execution module is used for calculating a new route in the new logistics route planning model by using a bionic intelligent ant colony optimization algorithm to obtain an optimal route;
the logistics route decision module is used for remapping the optimal route to a distribution service map of a target logistics center to form an optimal distribution service map, specifically analyzing routes having route conflicts with other logistics centers, and replacing old routes with high congestion probability with the optimal route to form a final distribution service map.
The key variable data acquisition module also comprises a real-time route information receiving module, and the real-time route information receiving module is used for receiving real-time information on the route returned by a vehicle driver on the existing route data, and constructing a training sample by taking the collected real-time information of the route as a secondary key variable together with the key variable.
The logistics route planning system further comprises a standby algorithm pool module, wherein the standby algorithm pool module is used for storing standby algorithms, specifically comprises a firefly algorithm, a genetic algorithm, a tabu algorithm, a simulated annealing algorithm and a particle swarm algorithm, and is used for selecting any one of the standby algorithms to carry out calculation on the auxiliary algorithm execution module when the algorithm execution module is overloaded.
The method of the invention trains the model by taking the vehicle condition factors of the logistics vehicle as the vehicle condition factor set, the key variables and the secondary key variables as the common training sample, can improve the comprehensiveness and scientificity of the model training result, the robustness of the trained model is high, all routes with route conflicts are determined by comparing the route planning data of other logistics centers with the existing route data in the logistics system of the target logistics center, the routes with route conflicts with other logistics centers are mapped and marked in the distribution service map to form the mapping distribution service map, the mapping distribution service map is embedded into the trained logistics route planning model, and finally the marked routes with route conflicts with other logistics centers are hidden in the logistics route planning model to obtain a new logistics route planning model, the method can realize iterative updating of the existing logistics route in the logistics system of the target logistics center, obtain a new optimal route to replace the existing route, obtain the optimal route on the premise of reducing the competition pressure of the same row, and form a final distribution service map which can provide the optimal logistics distribution service for customers, thereby improving the comprehensive service capability of the target logistics center.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A logistics route planning method based on bionic intelligent optimization is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: numbering all normally working logistics vehicles in a logistics system of a target logistics center, acquiring vehicle condition information of the logistics vehicles one by one according to the numbers to form vehicle condition factors in one-to-one correspondence with the numbers, and summarizing the vehicle condition factors to form a vehicle condition factor set;
step two: searching all other logistics centers with business similarity with the target logistics center in a single area, collecting path planning data of all other logistics centers, comparing the collected path planning data of all other logistics centers with the path data existing in a logistics system of the target logistics center, determining all routes with route conflicts, and identifying and counting the routes;
step three: acquiring a distribution service map of a target logistics center, and mapping and marking a route which conflicts with other logistics centers in the distribution service map to form a mapping distribution service map;
step four: collecting all key variables in the logistics route planning, collecting the key variables and vehicle condition factor sets in the optimal solution state from a key variable database as a common training sample, and carrying out normalization processing on the key variables and the vehicle condition factor sets in the common training sample;
step five: building a logistics route planning model, training the model by using a training common training sample after normalization processing, then embedding a mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
step six: calculating a new route in a new logistics route planning model by using a bionic intelligent ant colony optimization algorithm, sequencing the new route in a descending order according to the number of pheromones on the new route, and finally performing usability verification on the sequenced new route to remove invalid routes to obtain an optimal route;
step seven: the optimal route is remapped to a distribution service map of a target logistics center to form an optimal distribution service map, then the route which has route conflict with other logistics centers is specifically analyzed, the congestion probability of the route is analyzed, route sequencing is carried out according to the congestion probability, the old route with high congestion probability is replaced by the optimal route, and a final distribution service map is formed.
2. The logistics route planning method based on bionic intelligent optimization of claim 1, wherein the logistics route planning method comprises the following steps: when the vehicle condition information of the logistics vehicle is collected in the first step, the method specifically comprises the following steps: the method comprises the steps of collecting comprehensive driving technology evaluation information of a vehicle driver, purchasing time of the vehicle, whether the vehicle finishes a maintenance plan regularly or not, road accident rate in the historical working process of the vehicle driver and the driving mileage of the vehicle.
3. The logistics route planning method based on bionic intelligent optimization of claim 1, wherein the logistics route planning method comprises the following steps: and when all key variables in the logistics route planning are collected in the fourth step, each key variable is obtained by collecting a plurality of variables, then performing weight removal, loss removal and standardization, and then averaging.
4. The logistics route planning method based on bionic intelligent optimization of claim 1, wherein the logistics route planning method comprises the following steps: and sixthly, when the usability of the sorted new routes is checked, the method also comprises the steps of firstly carrying out road condition integration analysis on the new routes and preferentially selecting the new routes with good road conditions.
5. Logistics route planning system based on bionical intelligence is optimized, its characterized in that: the system comprises a vehicle condition acquisition module, a regional path comprehensive data acquisition module, a data information processing module, a key variable data acquisition module, a model construction module, an algorithm execution module and a logistics route decision module;
the vehicle condition acquisition module is used for acquiring vehicle condition information of the logistics vehicles and then summarizing the acquired vehicle condition factors to form a vehicle condition factor set;
the regional path comprehensive data acquisition module is used for acquiring all other logistics center path data with path conflict with the existing path data in the logistics system of the target logistics center in a single region, and identifying and counting the paths with repeated conflict;
the data information processing module is used for mapping and marking the route which conflicts with the route of other logistics centers in a distribution service map to form a mapping distribution service map;
the key variable data acquisition module is used for collecting all key variables in the logistics route planning, then constructing a training sample and carrying out normalization processing on the training sample;
the model building module is used for building a logistics route planning model, training the model, embedding the mapping distribution service map into the trained logistics route planning model, and finally hiding marked routes which have route conflicts with other logistics centers in the logistics route planning model to obtain a new logistics route planning model;
the algorithm execution module is used for calculating a new route in the new logistics route planning model by using a bionic intelligent ant colony optimization algorithm to obtain an optimal route;
the logistics route decision module is used for remapping the optimal route to a distribution service map of a target logistics center to form an optimal distribution service map, specifically analyzing routes having route conflicts with other logistics centers, and replacing old routes with high congestion probability with the optimal route to form a final distribution service map.
6. The logistics route planning system based on bionic intelligent optimization of claim 5, wherein: the key variable data acquisition module also comprises a real-time route information receiving module, and the real-time route information receiving module is used for receiving real-time information on the route returned by a vehicle driver on the existing route data, and constructing a training sample by taking the collected real-time information of the route as a secondary key variable together with the key variable.
7. The logistics route planning system based on bionic intelligent optimization of claim 5, wherein: the logistics route planning system further comprises a standby algorithm pool module, wherein the standby algorithm pool module is used for storing standby algorithms, specifically comprises a firefly algorithm, a genetic algorithm, a tabu algorithm, a simulated annealing algorithm and a particle swarm algorithm, and is used for selecting any one of the standby algorithms to carry out calculation on the auxiliary algorithm execution module when the algorithm execution module is overloaded.
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