CN120297846A - A multi-machine collaborative logistics distribution path optimization system - Google Patents
A multi-machine collaborative logistics distribution path optimization system Download PDFInfo
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- CN120297846A CN120297846A CN202510354958.9A CN202510354958A CN120297846A CN 120297846 A CN120297846 A CN 120297846A CN 202510354958 A CN202510354958 A CN 202510354958A CN 120297846 A CN120297846 A CN 120297846A
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Abstract
The invention relates to the technical field of logistics information and discloses a multi-machine collaborative logistics distribution path optimization system, wherein the output end of a logistics distribution path module is connected with a data acquisition and preprocessing module in a signal way, the output end of the data acquisition and preprocessing module is connected with a task distribution module in a signal way, the output end of the task distribution module is connected with a path planning module in a signal way, the output end of the path planning module is connected with a vehicle monitoring and scheduling module in a signal way, the output end of the vehicle monitoring and scheduling module is connected with a performance evaluation module in a signal way, the data acquisition and preprocessing module comprises an order data collection unit, the output end of a logistics resource data integration unit is connected with a traffic data acquisition unit in a signal way, and the output end of the traffic data acquisition unit is connected with a data cleaning and preprocessing unit in a signal way. According to the multi-machine cooperation type logistics distribution path optimization system, an algorithm is used for analyzing orders, matching vehicles with drivers and planning distribution paths, so that the vehicles avoid congestion road sections, and driving mileage and time are reduced.
Description
Technical Field
The invention relates to the technical field of logistics information, in particular to a multi-machine cooperation type logistics distribution path optimization system.
Background
At the moment of the development of electronic commerce and economy, the logistics industry is hard. Explosive growth of e-commerce orders brings great pressure to logistics distribution. The traditional distribution mode relying on manual experience is difficult to deal with massive orders, the distribution delay rate is up to 30%, and the customer experience is greatly reduced.
The internal resource allocation of logistics enterprises is disordered, the empty rate of vehicles is often more than 40%, and the allocation of warehouse goods is also unreasonable, so that the resource waste and the cost increase are caused. Meanwhile, urban traffic jams and administrative policy restrictions are complex, road construction and accident frequently occur, the traditional path planning based on a static map cannot be strained in real time, and delivery vehicles are often trapped in the jammed road sections. Consumers expect more and more delivery services, and 80% of consumers will change logistics enterprises due to logistics problems. Therefore, there is a need to optimize the logistics distribution path by advanced technology to improve the efficiency and the quality of service.
Disclosure of Invention
The invention aims to provide a multi-machine cooperation type logistics distribution path optimization system so as to solve the problems in the background technology.
The multi-machine collaborative logistics distribution path optimization system comprises a logistics distribution path module, wherein the output end of the logistics distribution path module is in signal connection with a data acquisition and preprocessing module, the output end of the data acquisition and preprocessing module is in signal connection with a task distribution module, the output end of the task distribution module is in signal connection with a path planning module, the output end of the path planning module is in signal connection with a vehicle monitoring and scheduling module, and the output end of the vehicle monitoring and scheduling module is in signal connection with a performance evaluation module;
the data acquisition and preprocessing module comprises an order data collection unit, wherein the output end of the order data collection unit is in signal connection with a logistics resource data integration unit, the output end of the logistics resource data integration unit is in signal connection with a traffic data acquisition unit, and the output end of the traffic data acquisition unit is in signal connection with a data cleaning and preprocessing unit.
Preferably, the logistic resource data integrating unit records information such as driver name, contact mode, driving license type, driving experience, working time limit, current position and the like according to vehicle information such as license plate number, vehicle type, load, vehicle running speed, maximum endurance mileage, idle vehicle state, in-transit, maintenance and the like of the collection company own vehicle and the cooperative vehicle team, and acquires data such as geographic position, stock capacity, cargo storage type, warehouse in-out efficiency and the like of each warehouse.
Preferably, the task allocation module comprises an order cluster analysis unit, wherein the output end of the order cluster analysis unit is in signal connection with a vehicle and driver matching unit, and the output end of the vehicle and driver matching unit is in signal connection with a task allocation optimization algorithm unit.
Preferably, the matching unit of the vehicle and the driver screens out the type of the vehicle suitable for executing the task and the specific vehicle according to the weight, the volume and the characteristics of the delivery route of the ordered goods, for example, for the delivery of large-sized goods, a truck with large carrying capacity is preferably selected, for the ordered goods which need to be driven on the narrow street of the city, a small-sized flexible vehicle is selected, the vehicle is distributed to the suitable driver in consideration of the working time limit, the driving experience and the current position of the driver, and the driver with close starting point, abundant working time and rich experience of the ordered goods is preferably arranged to execute the task.
Preferably, the path planning module comprises an initial path generating unit, the output end of the initial path generating unit is connected with a path optimization algorithm application unit in a signal mode, and the output end of the path optimization algorithm application unit is connected with a real-time road condition dynamic adjustment unit in a signal mode.
Preferably, the path optimization algorithm application unit introduces an ant colony algorithm, and by simulating the actions of ants leaving pheromones on the paths, the vehicle dynamically selects a better path according to the pheromone concentration on the paths and heuristic information such as distance, road conditions and the like in the driving process, the algorithm is iterated continuously, gradually converges to a global optimal path, the paths are segmented and optimized by using a dynamic programming algorithm for complex distribution networks and dynamically changed traffic conditions, and a current optimal branch path is selected at each decision point according to real-time traffic information so as to adapt to the change of traffic conditions, and distribution time and cost are reduced.
Preferably, the vehicle monitoring and dispatching module comprises a vehicle real-time positioning unit, wherein the output end of the vehicle real-time positioning unit is in signal connection with a vehicle state monitoring unit, and the output end of the vehicle state monitoring unit is in signal connection with a vehicle dispatching decision support unit.
Preferably, the vehicle state monitoring unit utilizes various sensors installed on the vehicle, such as a fuel consumption sensor, a tire pressure sensor, an engine state sensor and the like, collects information of fuel consumption, tire pressure, engine working conditions and the like of the vehicle in real time, sets a normal threshold range of each parameter of the vehicle, and when sensor data exceeds a threshold value, the system immediately sends out early warning information to prompt a dispatcher that the vehicle is likely to have faults or abnormal conditions so as to take measures in time and avoid influencing the distribution task.
Preferably, the performance evaluation module comprises a delivery cost accounting unit, the output end of the delivery cost accounting unit is in signal connection with a delivery efficiency evaluation unit, the output end of the delivery efficiency evaluation unit is in signal connection with a service quality evaluation unit, and the output end of the service quality evaluation unit is in signal connection with a data analysis and feedback unit.
Compared with the prior art, the invention provides a multi-machine cooperation type logistics distribution path optimization system, which has the following beneficial effects:
1. the multi-machine cooperation type logistics distribution path optimization system performs clustering analysis on orders by using an advanced algorithm, precisely matches vehicles with drivers and plans an optimal distribution path. This enables the vehicle to avoid congested road segments, reducing mileage and time. For example, in urban distribution scenes, the average travel of the distribution vehicles can be saved by 15-30 minutes per journey through dynamic path adjustment of real-time road conditions, so that the distribution efficiency is greatly improved. Meanwhile, reasonable task allocation ensures that the full load rate of the vehicle is improved, the transportation cost of unit cargoes is reduced, the utilization rate of logistics resources is improved, and the multi-machine cooperation type logistics distribution path optimizing system reduces the oil consumption and abrasion of the vehicle through optimizing the path, prolongs the service life of the vehicle and reduces the maintenance cost. According to statistics, after the system is used, the fuel consumption of the vehicle is reduced by 10% -15% on average, and the maintenance cost is reduced by 15% -20%. In addition, accurate task allocation and efficient distribution flow reduce the potential loss caused by labor cost and distribution delay, and comprehensively improve the economic benefit of logistics enterprises.
2. According to the multi-machine cooperation type logistics distribution path optimization system, the position and distribution progress of the vehicle are monitored in real time, the path is dynamically adjusted and the vehicle is scheduled according to road conditions, and distribution delay is effectively avoided. The order on-time delivery rate is obviously improved, and can reach more than 95% generally, and is improved by 10-20 percent compared with the traditional delivery mode. The clients can receive goods on time, the satisfaction degree is greatly improved, the trust and the loyalty of the clients to logistics enterprises are enhanced, and the clients can know the goods transportation positions and the expected delivery time in real time through a query platform provided by the logistics enterprises. The vehicle monitoring and scheduling module enables the whole distribution process to be transparent, and enterprises can respond to customer consultation and complaints in time to quickly solve the problems. This high degree of service transparency and traceability helps logistics enterprises to build good brand images, stands out in strong market competition, and wins more clients and business opportunities.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the description below are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art:
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of a data acquisition and preprocessing module according to the present invention;
FIG. 3 is a flow chart of a task allocation module of the present invention;
FIG. 4 is a flow chart of a path planning module according to the present invention;
FIG. 5 is a flow chart of a vehicle monitoring and scheduling module of the present invention;
fig. 6 is a flow chart of the performance evaluation module of the present invention.
In the figure, 1, a logistics distribution path module; 2, a data acquisition and preprocessing module; the system comprises a data acquisition unit, a logistics resource data integration unit, a data acquisition unit, a data cleaning and preprocessing unit, a task allocation module, an order cluster analysis unit, a vehicle and driver matching unit, a task allocation optimization algorithm unit, a path planning module, an initial path generation unit, a path optimization algorithm application unit, a real-time road condition dynamic adjustment unit, a vehicle monitoring and dispatching module, a vehicle real-time positioning unit, a vehicle state monitoring unit, a vehicle dispatching decision support unit, a performance evaluation module, a distribution cost accounting unit, a distribution efficiency evaluation unit, a service quality evaluation unit, a data analysis and feedback unit, a vehicle state monitoring unit, a vehicle state evaluation unit, a vehicle dispatching decision support unit, a performance evaluation unit, a distribution cost accounting unit, a distribution efficiency evaluation unit, a service quality evaluation unit and a data analysis and feedback unit.
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.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed, mechanically connected, electrically connected, directly connected, indirectly connected via an intervening medium, or in communication between two elements or in an interaction relationship between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention provides the following technical scheme:
Example 1
1-3, A multi-machine cooperation type logistics distribution path optimization system comprises a logistics distribution path module 1, wherein the output end of the logistics distribution path module 1 is in signal connection with a data acquisition and preprocessing module 2, the output end of the data acquisition and preprocessing module 2 is in signal connection with a task distribution module 3, the output end of the task distribution module 3 is in signal connection with a path planning module 4, the output end of the path planning module 4 is in signal connection with a vehicle monitoring and scheduling module 5, and the output end of the vehicle monitoring and scheduling module 5 is in signal connection with a performance evaluation module 6;
The data acquisition and preprocessing module 2 comprises an order data collection unit 21, the output end signal of the order data collection unit 21 is connected with a logistics resource data integration unit 22, the output end signal of the logistics resource data integration unit 22 is connected with a traffic data acquisition unit 23, the output end signal of the traffic data acquisition unit 23 is connected with a data cleaning and preprocessing unit 24, the task allocation module 3 comprises an order cluster analysis unit 31, the output end signal of the order cluster analysis unit 31 is connected with a vehicle and driver matching unit 32, the output end signal of the vehicle and driver matching unit 32 is connected with a task allocation optimization algorithm unit 33, the path planning module 4 comprises an initial path generation unit 41, the output end signal of the initial path generation unit 41 is connected with a path optimization algorithm application unit 42, and the output end signal of the path optimization algorithm application unit 42 is connected with a real-time road condition dynamic adjustment unit 43.
Further, the logistic resource data integrating unit 22 records information such as driver name, contact way, driving license type, driving experience, working time limit, current position and the like according to the information of the own vehicles and the cooperative motorcades of the collection company, such as license plate number, vehicle type, load, vehicle driving speed, maximum endurance mileage, idle state, in-transit, maintenance and the like, acquires data such as geographic position, stock capacity, goods storage type, warehouse in-and-out efficiency and the like of each warehouse, the vehicle and driver matching unit 32 screens out the vehicle type and specific vehicles suitable for executing tasks according to the weight, volume and distribution route characteristics of ordered goods, for example, a truck with large carrying capacity is preferentially selected for large-sized goods distribution, a small flexible vehicle is selected for the orders needing to be driven in a narrow-sized street, the driver is preferentially arranged for executing tasks according to the working time limit, the driving experience and the current position of the driver, the driver with relatively close starting point, the working time limit and the abundant experience is preferentially arranged, the path optimization algorithm application unit 42 introduces a swarm algorithm, allows the ants to leave the action of the pheromones on the path, enables the vehicles to dynamically change to be better in real-time, the optimal path is better optimized according to the traffic algorithm, the traffic algorithm is better in the dynamic path condition, the optimal path is better optimized for the traffic algorithm is dynamically selected for the traffic condition, and the optimal path is better optimized to the traffic condition is better in real-time condition, and is better optimized according to the traffic condition is better in the traffic condition, and is better in real-time condition is better in the traffic condition, and is better in real time condition is better.
Example two
Referring to fig. 1-6, on the basis of the first embodiment, the vehicle monitoring and dispatching module 5 further includes a vehicle real-time positioning unit 51, an output end signal of the vehicle real-time positioning unit 51 is connected with a vehicle state monitoring unit 52, an output end signal of the vehicle state monitoring unit 52 is connected with a vehicle dispatching decision support unit 53, the performance evaluation module 6 includes a delivery cost accounting unit 61, an output end signal of the delivery cost accounting unit 61 is connected with a delivery efficiency evaluation unit 62, an output end signal of the delivery efficiency evaluation unit 62 is connected with a quality of service evaluation unit 63, and an output end signal of the quality of service evaluation unit 63 is connected with a data analysis and feedback unit 64.
Further, the vehicle state monitoring unit 52 uses various sensors installed on the vehicle, such as a fuel consumption sensor, a tire pressure sensor, an engine state sensor, etc., to collect information of fuel consumption, tire pressure, engine working condition, etc. of the vehicle in real time, set a normal threshold range of each parameter of the vehicle, when the sensor data exceeds the threshold, the system immediately sends out early warning information to prompt the dispatcher that the vehicle may be in fault or abnormal condition, so as to take measures in time, avoid affecting the distribution task,
In the actual operation process, when the device is used, the order data collection unit 21 acquires online order information including order numbers, receiving addresses, cargo weight and volume, delivery time requirements and the like in real time by interfacing with an e-commerce platform, an enterprise sales system and the like. For online order taking, a worker manually inputs the system to ensure that order data are comprehensively collected, and the logistics resource data integration unit 22 collects vehicle information of own and cooperative vehicle teams of a company, such as license plate numbers, vehicle types, loads, running speeds, endurance mileage, vehicle states and the like; in addition, data such as geographic position, stock capacity, storage type, warehouse in-out efficiency and the like of each warehouse are collected, integration of logistics resource information is realized, a traffic data acquisition unit 23 is accessed into a professional map service API, traffic road condition information such as factors influencing vehicle running speed such as road congestion, traffic accidents, road construction and the like are acquired in real time, meanwhile, data such as traffic rules such as limit policies, restricted road segments, vehicle type speed limiting requirements and the like of each region are collected, a data cleaning and preprocessing unit 24 cleans the collected orders, logistics resources, traffic data, processing missing values are subjected to mean value filling, regression prediction and the like, abnormal values such as error addresses, unreasonable weight volumes and the like are removed, standardized processing is carried out on the data, and a dimension and value range are unified, so that high-quality data support is provided for subsequent modules;
The order cluster analysis unit 31 divides the order into regional clusters in the geographical location set by using a clustering algorithm such as K-Means according to the longitude and latitude of the order receiving address. And in combination with the order distribution time requirement, the orders in each regional cluster are subdivided, the orders with similar distribution time are grouped into a group so as to reasonably arrange distribution batches, and the vehicle and driver matching unit 32 screens and adapts the vehicle type and specific vehicle according to the characteristic weight, volume and the like of the order goods and the distribution route characteristics. Meanwhile, the working time limit, the driving experience and the current position of a driver are comprehensively considered, the vehicle is allocated to a proper driver, the driver which is close to the starting point of the order, has abundant working time and has abundant experience is preferentially selected, the task allocation optimization algorithm unit 33 constructs a cost model containing factors such as the use cost oil consumption, depreciation and the like of the vehicle, the cost of the driver pay, the delivery delay and the like, and the optimal order-vehicle-driver allocation scheme is calculated under the constraint conditions of meeting the delivery time of the order, the load of the vehicle, the working time of the driver and the like by applying optimization algorithms such as a Hungary algorithm and a genetic algorithm, so that the reasonable allocation of the task is realized;
The initial route generation unit 41 uses the map service route planning function to plan an initial travel route for each vehicle with the warehouse as a start point and the order pickup address as an end point, and acquires information such as a route distance, an estimated travel time, and the like. And according to traffic rule data, the initial path is adjusted to avoid the vehicle from driving into a restricted-traffic and restricted-traffic section, so as to ensure that the path is legal, the path optimization algorithm application unit 42 introduces an ant colony algorithm to simulate the action of ants leaving pheromones on the path, and the vehicle dynamically selects a better path according to the concentration of the path pheromones, heuristic information distance, road conditions and the like. Meanwhile, a dynamic planning algorithm is used for optimizing the path in a segmented mode, a current optimal branch path is selected at each decision point according to real-time traffic information, the optimal path is iterated continuously, and a real-time road condition dynamic adjusting unit 43 is used for monitoring road condition changes in the running process of the vehicle in real time, immediately starting a path re-planning program when the current path is not optimal any more due to the road condition changes, planning a new path avoiding a congestion road section for the vehicle again by using the optimization algorithm, reaching a destination as soon as possible, and timely sending new path information to a driver;
The vehicle real-time positioning unit 51 acquires vehicle position information in real time through a GPS or Beidou positioning device installed on the vehicle, transmits the vehicle position information to the system server through a wireless communication module, displays the vehicle position, the driving direction and the speed in an icon form on a monitoring interface electronic map, facilitates a dispatcher to grasp the running state of the vehicle, and the vehicle state monitoring unit 52 acquires information such as vehicle oil consumption, tire pressure, engine working condition and the like in real time by means of an oil consumption sensor, a tire pressure sensor, an engine state sensor and the like on the vehicle. Setting normal threshold ranges of various parameters, when the data exceeds the threshold, the system gives an early warning to prompt that the vehicle is likely to be faulty or abnormal, and the vehicle scheduling decision support unit 53 calculates the estimated arrival time in real time according to the real-time position of the vehicle and the order distribution time requirement, compares the actual and planned distribution progress, and finds out the delay condition in time. When the sudden conditions such as vehicle faults, traffic jams, order temporary change and the like occur, the system provides decision support for a dispatcher to adjust the running route of the vehicle, redistribute delivery tasks, coordinate delivery time of a warehouse and the like according to real-time data and a preset dispatching strategy, and ensures that the delivery tasks are completed smoothly;
The delivery cost accounting unit 61 calculates fuel consumption, maintenance and depreciation costs during delivery of the vehicle based on the mileage and the usage time. The cost of manpower is calculated by combining the pay standard of drivers and working time, other fees such as road fees, parking fees, insurance fees and the like are counted, the total cost of each delivery task is comprehensively obtained, the delivery efficiency evaluation unit 62 calculates the on-time delivery rate of orders, the on-time delivery amount of orders accounts for the proportion of the total order amount, and the on-time completion condition of the delivery tasks is reflected. And calculating the average running speed according to the running track of the vehicle and the time data, and evaluating the running efficiency of the vehicle. Comparing the delivery time before and after optimization, calculating the shortening proportion of the delivery time, measuring the improvement effect of the path optimization and the scheduling strategy on the delivery efficiency, and collecting the satisfaction evaluation of the clients on the delivery service by means of online questionnaires, telephone return visits and the like by the service quality evaluation unit 63, wherein the satisfaction evaluation comprises the aspects of good cargo degree, service attitudes of delivery personnel, accuracy of the delivery time and the like. The customer complaint quantity is counted, the complaint rate is calculated, the complaint reasons are analyzed, the data analysis and feedback unit 64 periodically generates performance data reports of distribution cost, distribution efficiency, service quality and the like, and index change trend is displayed in a chart form and analysis results are compared. And deeply analyzing the system operation problem according to the evaluation result, providing a targeted optimization suggestion, feeding back to a system development and operation team, continuously improving and optimizing the system, and improving the overall performance of logistics distribution.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
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