Path planning method based on automobile part dispatching logistics

Abstract

The invention discloses a route planning method based on automobile part dispatching logistics, which comprises the following steps: importing basic data of station data, automobile part information, vehicle parameters, stacking rules, route information and appliance parameters, and constructing an algorithm input system; setting a plurality of core rules, and constructing a path planning algorithm model based on the core rules; predicting future cargo volume, and setting a path optimization target value to define an algorithm output direction; and carrying out traversal calculation on the model, screening an optimal path scheme meeting the constraint of the path model, generating a visual map route, and guiding transportation execution. According to the scheme, aiming at the characteristic that automobile parts are dispatched to logistics, the empty driving rate and resource waste are reduced through iterative calculation, the adaptability to complex scenes is enhanced, the total driving mileage is shortened, the vehicle utilization rate is improved, and meanwhile, the order response timeliness requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path planning, in particular to a path planning method based on automobile parts distribution logistics. BACKGROUND

[0002] Under the background of intensified competition in the automobile industry, enterprises urgently need to break through the industry bottleneck through core competitiveness and cost advantage. Optimization of the logistics system, especially the in-plant logistics link, is the key to improving the efficiency of the supply chain for the main machine factory. In-plant logistics involves the accurate supply of tens of thousands of parts, directly affecting production continuity and cost control, and its complexity and strategic value are increasingly prominent. The core of this mode is to scientifically plan the pickup route by combining intelligent path algorithms with actual production needs and dynamic data, and to achieve efficient allocation of logistics resources. Its value lies in resource intensification, cost controllability and production collaboration, etc. Integrating scattered supplier resources to reduce empty rate, fixed schedule and compliance path to reduce violation risk, multiple frequency delivery and inventory matching to ensure production line continuous operation. SUMMARY

[0003] In view of the above technical problems, the present application provides a path planning method based on automobile parts distribution logistics.

[0004] The present application is realized by adopting the following technical scheme: A path planning method based on automobile parts distribution logistics, comprising the following steps: Step S1: import site data, automobile part information, vehicle parameters, stacking rules, route information and instrument parameter basic data, and construct an algorithm input system; Step S2: set multiple core rules, and build a path planning algorithm model based on the core rules; Step S3: predict future freight volume, set path optimization target value and determine algorithm output direction; Step S4: the model performs traversal calculation, selects the optimal path scheme that meets the path model constraints, generates a visual map route and guides transportation execution.

[0005] Specifically, the site data in step S1 includes supplier code and address coordinates; the automobile part information includes variety, packaging form, model, quantity, weight relationship, unloading port and supply route; the vehicle parameters include car size, load capacity and unloading method; the stacking rules are combinable loading conditions; the route information includes distribution route number, subordinate stations and distance; and the instrument parameters include length, width, height and stacking layer limit.

[0006] Specifically, the core rules in step S2 include average freight volume based on historical and short-term data, map, light and heavy stacking rules, loading and unloading sequence rules, time window, unloading port flow and path flow.

[0007] Specifically, the path planning algorithm model is represented as follows: ; in, This represents the collection of all vehicles. Represents the set of all rounds. Represents the set of all lines; This represents the vehicle cost, which consists of the cost per kilometer and the vehicle usage cost. Indicates the line round Mileage Indicates vehicle The price per kilometer Indicates vehicle Fixed costs; This refers to penalties for the volume of water wasted by vehicles, among which... Indicates vehicle The total volume Indicates the shipment at the station in round Total volume of the vehicle loaded, This represents the penalty cost for wasting a unit of water volume in a vehicle. ; This represents the target weight, i.e., the penalty weight for wasting vehicle water volume; This represents the cost per round, i.e., the cost per vehicle. Execution rounds Fixed costs; This represents the route cost, i.e., the vehicle cost. Execution route Fixed costs.

[0008] Specifically, the path optimization target values ​​in step S3 include the shortest total mileage, the highest load factor, and the best timeliness.

[0009] Specifically, the traversal calculation of the model in step S4 includes the following sub-steps: Step S41: Input order information, master data and various parameters, and obtain an initial solution based on route groups and historical experience; Step S42: Enter the SA framework and determine the initial temperature T based on the initial solution; Step S43: Based on large neighborhood search, optimize the process by using deletion, insertion, swap operators, breaking solutions, and reshaping solutions; Step S44: Select a solution based on its performance, and simultaneously reduce the temperature T; Step S45: iterative calculation, if the temperature T>0.5 or the iteration number of the performance value maintaining unchanged is less than 100 times, return to step S43 for iterative calculation; Step S46: obtaining a solution, correcting the obtained solution according to the optimization of the order and the loading sequence to obtain a corrected solution.

[0010] Specifically, the step S4 path model constraint includes: Time planning constraint: planning the departure time of each vehicle, the waiting time at each station, and the entry and exit time; Station route constraint: arranging single-vehicle pick-up and delivery tasks under the constraints of loading and unloading rules, route station quantity limit, and station working time limit; Multi-round loading: splicing according to the single-vehicle information generated by optimization, single-vehicle recycling, and completing multi-vehicle tasks; Intersection constraint: adjusting the multi-round plan to make the vehicle transportation task meet the intersection receiving and sending flow limit of each warehouse; Transit warehouse rule: scattered parts can be collected by the transit warehouse and sent to the destination.

[0011] The beneficial effects of the present application are that the present application is based on the differentiated path planning algorithm of the cycle picking and the multi-constraint rule engine, calculates the lean logistics execution scheme of the differentiated path planning, and has the following advantages: Dynamic order-driven flexible path generation: different from static rules, the present scheme realizes differentiated path generation through order driving, meets the personalized needs of high-frequency small-batch logistics, and realizes flexible adaptation of path planning through real-time order data input and combination of rule engine dynamic path generation, breaking through the limitations of traditional fixed rules or artificial experience.

[0012] (2) Multi-constraint rule engine and data deduplication mechanism: comprehensive time window, load limit, vehicle type and other multi-constraint conditions are used to build a rule engine to ensure the compliance and efficiency of path calculation. The deduplication algorithm (such as order merging and path node optimization) is used to avoid repeated calculation and improve the efficiency of algorithm execution.

[0013] (3) Global optimization and local balance traversal calculation: taking the factory production rhythm, logistics network foundation and other "big rules" as the premise, the stability of the overall delivery logic is ensured. Through traversal calculation, the personalized needs of order priority and delivery time limit are dynamically balanced, and the shortest total path and the highest accumulation rate of the collection scheme are output.

[0014] (4) Lean and scene adaptability improvement: According to the characteristics of automobile parts distribution logistics, the air travel rate and resource waste are reduced through iterative calculation, and the adaptability to complex scenes is enhanced. Compared with artificial experience or traditional rules, the scheme shortens the total travel mileage, improves the vehicle utilization rate, and meets the order response time requirement at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0016] Fig. 1 The flow chart of the path planning method based on automobile parts distribution logistics in the embodiment of the present application is shown in the figure. Fig. 2 The architecture diagram of the path planning method based on automobile parts distribution logistics in the embodiment of the present application is shown in the figure. Fig. 3 The flow chart of the path planning algorithm model solution in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0018] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0019] The following will be described in combination with the drawings Figs. 1-3 Some embodiments of the present application will be described in detail. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0020] The present application proposes a path planning method based on automobile parts distribution logistics. In a preferred embodiment, the steps are as shown in the figure, including: Fig. 1 ​Step one, basic data import, including site data (supplier code), coordinate information (supplier address coordinates), part data (supplier corresponding part variety and packaging form, part model, part quantity, weight relationship, unloading port, supply route), vehicle data (vehicle compartment internal length, width and height dimensions, GB 1589 download weight permission, unloading method), stacking scheme (rules for combinable loading), route data (route number, route affiliated supplier site information, site distance information), and tool data (tool length, width and height information, maximum number of stackable layers), to build an algorithm input system; Step two: path planning algorithm model, based on 11 core rules such as average cargo volume (historical / short-term data), Gaode map civilian version map, light and heavy stacking rules, loading / unloading sequence rules, time window (factory production cycle), unloading port flow, path flow, etc., to build a path model.

[0021] Step three: set the future cargo volume prediction and the path optimization target value (such as the shortest total mileage, the highest stowage rate, the optimal time efficiency, etc.), and clearly define the algorithm output direction.

[0022] Step four: model calculation and scheme output, filter the optimal path scheme that meets the path model constraints, generate a visual map route and guide the transportation execution.

[0023] In this embodiment, the path planning algorithm model is represented as: ; wherein, represents a set of all vehicles, represents a set of all rounds, represents a set of all routes; represents the vehicle cost composed of the kilometer unit price cost and the vehicle use cost, wherein, represents the driving mileage of the route round , represents the kilometer unit price of the vehicle , represents the fixed cost of the vehicle ; represents the penalty term composed of the wasted water volume of the vehicle, wherein, represents the total volume of the vehicle , represents the total volume of the site cargo loaded in the round , represents the penalty cost of the wasted water volume of the vehicle per unit, ; represents the target weight, i.e. the vehicle water volume waste cost penalty weight; represents the round cost, i.e. the fixed cost of the vehicle performing a round . represents the route cost, i.e. the fixed cost of the vehicle performing a route .

[0024] In the embodiment, the traversal calculation of the model is as shown in Fig. 3 , including: inputting order information, master data and various parameters, obtaining an initial solution based on a route group and historical experience; entering an SA framework, determining an initial temperature T according to the initial solution; based on a large neighborhood search, performing an optimization process through a deletion, insertion, exchange operator, destroyed solution and remodeled solution; selecting a solution according to the performance of the solution, while reducing the temperature T; iterative calculation, if the temperature T>0.5 or the iteration number of the performance value maintaining unchanged is less than 100 times, returning to step S43 for iterative calculation; obtaining a solution, correcting the obtained solution according to the optimized pickup order and loading order, and obtaining a corrected solution.

[0025] In the embodiment, the path planning algorithm model proposed in the scheme supports multiple modes, and according to the customer requirements for algorithm results, the path planning can be performed according to a route or a region. The requirements and constraints of the path model include: time planning constraints: planning the departure time of each vehicle, the waiting time at each station, and the entering and leaving station time; station route constraint: under the constraints of loading and unloading rules, path station quantity limit and station working time limit, arranging single vehicle round trip tasks; multi-round loading: splicing according to the single vehicle information generated by optimization, single vehicle cyclic use, and completing multi-vehicle tasks; crossing constraint: adjusting the multi-round plan to make the vehicle transportation task meet the crossing receiving and sending flow limit of each warehouse; transit warehouse rule: scattered parts can be collected by the transit warehouse and sent to the destination.

[0026] In a specific embodiment, the architecture of the path planning algorithm model proposed in the scheme is as shown in Fig. 2As shown, through route arrangement, vehicle and cargo matching, multi-objective optimization route planning engine, the basic data management module in the algorithm contains the basic data constraints: (1) supplier, (2) host factory, (3) intersection information, (4) unloading port, (5) off-site time distance matrix (6) Intra-factory time distance matrix (7) static route, (8) packaging, (9) part location, (10) maximum number of merged chains, etc.

[0027] In this embodiment, a path planning model is constructed, and a path planning algorithm engine for a circulating goods pickup scenario is built, which includes average goods quantity import, map import, single vehicle maximum loading rate, time difference rule between two chains, waiting redundancy time between route sites, mileage + vehicle number optimization rule, one round of turnover time rule, vehicle availability and unloading conflict constraint rule, and minimum vehicle number rule.

[0028] The planning target value includes: (1) vehicle loading rate upper limit, (2) same route supplier number upper limit (3) same route unloading port upper limit (4) turnover time upper limit, (5) maximum chain number (6) basic waiting time (7) Single supplier waiting time (8) average daily working time.

[0029] The path planning task management module calculates through the path planning algorithm model to meet the model requirements and output the optimal logistics delivery path under the operation conditions. It includes route name, vehicle type, loading rate, cargo volume, distance, turnover time, vehicle number, pickup frequency, and return empty information, etc. The map software is integrated to output the map route guidance.

[0030] This method is based on the differentiated path planning algorithm of the circulating goods pickup and multi-constraint rule engine, and calculates the lean logistics execution scheme of the differentiated path planning, including: (1) Dynamic order-driven flexible path generation: Different from static rules, this scheme realizes differentiated path generation through order-driven, meeting the personalized needs of high-frequency small-batch logistics delivery. Through real-time order data input, combined with rule engine dynamic path generation, it breaks through the limitations of traditional fixed rules or manual experience, and realizes flexible adaptation of path planning.

[0031] (2) Multi-constraint rule engine and data deduplication mechanism: Comprehensive time window, load limit, vehicle type and other multi-constraint conditions, build rule engine to ensure the compliance and efficiency of path calculation. Adopting deduplication algorithm (such as order merging, path node optimization), avoiding repeated calculation, improving algorithm execution efficiency.

[0032] (3) Global optimization and local balance traversal calculation: Based on the "big rules" such as factory production rhythm and logistics network foundation, the stability of the overall delivery logic is ensured. Through traversal calculation, the personalized needs such as order priority and delivery time limit are dynamically balanced, and the collection scheme with the shortest total path and the highest accumulation rate is output.

[0033] (4) Lean and scenario adaptability improvement: According to the characteristics of automobile parts delivery logistics, through iterative calculation, the empty running rate and resource waste are reduced, and the adaptability to complex scenarios is enhanced. Compared with artificial experience or traditional rules, this scheme shortens the total driving distance, improves the vehicle utilization rate, and meets the order response time limit.

[0034] This scheme automatically generates paths through intelligent algorithms, combines real-time traffic and constraint conditions, relies on multi-frequency, small batch, and timed operation, plans logistics routes through future freight volume, improves in-plant unloading port turnover efficiency, reduces resource investment, improves calculation efficiency and scheme quality; According to the collection route, it circulates, reflects the small batch and multi-frequency arrival, and the in-plant inventory is set to meet the interval time between two cycles of arrival, which can greatly reduce the in-plant inventory, while planning the number and frequency of in-plant trucks to ensure that the transportation time limit and production rhythm match. Reduce costs; integrate real-time data and dynamic path planning technology, execute cycle collection according to time window, compatible with peak period coefficient, realize multi-vehicle collaborative scheduling, predict risks in advance and flexibly adjust paths.

[0035] For the foregoing embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the application is not limited by the order of the described actions, because according to the application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily required by the application.

[0036] In the above embodiments, the basic principles and main features of the application and the advantages of the application are described. Those skilled in the art should understand that the application is not limited by the above embodiments, and the above embodiments and the description in the specification are only to illustrate the principles of the application. Without departing from the spirit and scope of the application, modifications and changes made by those skilled in the art do not deviate from the spirit and scope of the application, and should be within the protection scope of the claims of the application.

Claims

1. A route planning method based on automotive parts dispatch logistics, characterized in that, Includes the following steps: Step S1: Import basic data such as site data, automotive parts information, vehicle parameters, stacking rules, route information, and equipment parameters to construct the algorithm input system; Step S2: Set multiple core rules and build a path planning algorithm model based on the core rules; Step S3: Predict future cargo volume, set path optimization target value, and clarify the direction of algorithm output; Step S4: The model performs traversal calculations, selects the optimal route scheme that satisfies the path model constraints, generates a visual map route, and guides transportation execution.

2. The route planning method based on automotive parts delivery logistics as described in claim 1, characterized in that, In step S1, the site data includes supplier code and address coordinates; the automotive parts information includes variety, packaging form, model, quantity, weight relationship, unloading port and supply route; vehicle parameters include cargo box size, load limit and unloading method; stacking rules are combinable loading conditions; route information includes dispatch route number, subordinate stations and distance; and equipment parameters include length, width and height and stacking layer limit.

3. The route planning method based on automotive parts delivery logistics as described in claim 1, characterized in that, The core rules of step S2 include average cargo volume based on historical and short-term data, map, light and heavy stacking rules, loading and unloading sequence rules, time window, unloading port flow and path flow.

4. The route planning method based on automotive parts delivery logistics as described in claim 3, characterized in that, The path planning algorithm model is represented as follows: ； in, This represents the collection of all vehicles. Represents the set of all rounds. Represents the set of all lines; This represents the vehicle cost, which consists of the cost per kilometer and the vehicle usage cost. Indicates the line Rounds Mileage Indicates vehicle The price per kilometer Indicates vehicle Fixed costs; This refers to penalties for the volume of water wasted by vehicles, among which... Indicates vehicle The total volume Indicates the shipment at the station in round Total volume of the vehicle loaded, This represents the penalty cost for wasting a unit of water volume in a vehicle. ; This represents the target weight, i.e., the penalty weight for wasting vehicle water volume; This represents the cost per round, i.e., the cost per vehicle. Execution rounds Fixed costs; This represents the route cost, i.e., the vehicle cost. Execution route Fixed costs.

5. The route planning method based on automotive parts delivery logistics as described in claim 4, characterized in that, The path optimization objectives in step S3 include the shortest total mileage, the highest load factor, and the best timeliness.

6. The route planning method based on automotive parts delivery logistics as described in claim 5, characterized in that, The traversal calculation of the model in step S4 includes the following sub-steps: Step S41: Input order information, master data and various parameters, and obtain an initial solution based on route groups and historical experience; Step S42: Enter the SA framework and determine the initial temperature T based on the initial solution; Step S43: Based on large neighborhood search, optimize the process by using deletion, insertion, swap operators, breaking solutions, and reshaping solutions; Step S44: Select a solution based on its performance, and simultaneously reduce the temperature T; Step S45: Iterative calculation. If the temperature T>0.5 or the number of iterations in which the performance value remains unchanged is less than 100, then return to step S43 to perform iterative calculation. Step S46: Obtain the solution. Correct the obtained solution based on the optimized pickup and loading order to obtain the corrected solution.

7. The route planning method based on automotive parts delivery logistics as described in claim 6, characterized in that, The path model constraints in step S4 include: Time planning constraints: Plan the departure time of each vehicle, the waiting time at each station, and the arrival and departure times at each station; Station route constraints: Under the constraints of loading and unloading rules, the number of stations along the route, and the working time of each station, arrange the loading and unloading tasks for a single vehicle trip; Multi-round loading: Based on the optimized single-trip vehicle information, the data is spliced ​​together, and a single vehicle is used cyclically to complete multiple vehicle tasks; Level crossing constraints: Adjust multi-round plans to ensure that vehicle transportation tasks meet the level crossing flow restrictions of each warehouse; Transit warehouse rules: Loose items can be collected at the transit warehouse and then sent to their destination.

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